From cbc00281738bcb4b9016118f917a8ed599dd61bb Mon Sep 17 00:00:00 2001 From: Wentao Wu Date: Fri, 24 Nov 2023 17:32:41 +0800 Subject: [PATCH] k230 canmv docs release v0.2 --- .gitignore | 5 + .gitlab-ci.yml | 31 +- Makefile | 31 + _static/img/ch-flag.a61145a4.svg | 9 + _static/img/logo.2f7049e2.svg | 6 + _static/topbar.css | 176 + _templates/versions.html | 41 + conf.py | 75 + exts/sphinx_rtd_dark_mode/__init__.py | 27 + exts/sphinx_rtd_dark_mode/dark_mode_loader.py | 63 + .../static/dark_mode_css/custom.css | 77 + .../static/dark_mode_css/dark.css | 559 + .../static/dark_mode_css/general.css | 68 + .../static/dark_mode_js/default_dark.js | 13 + .../static/dark_mode_js/default_light.js | 13 + .../static/dark_mode_js/theme_switcher.js | 39 + index.rst | 16 + preprocess.py | 35 + requirements.txt | 32 + zh/00_hardware/.keep | 1 - zh/01_software/example/.keep | 1 - ...1\351\231\220\345\205\254\345\217\270.txt" | 8 - ...56\351\242\230\350\247\243\347\255\224.md" | 69 + 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zh/example/machine/pwm/pwm.md | 19 + zh/example/machine/spi/spi.md | 63 + zh/example/machine/timer/timer.md | 14 + zh/example/machine/wdt/wdt.md | 21 + zh/example/media/acodec.md | 145 + zh/example/media/audio.md | 125 + zh/example/media/camera.md | 132 + zh/example/media/display.md | 76 + zh/example/media/media.md | 131 + zh/example/media/mp4muxer.md | 63 + zh/example/media/player.md | 32 + zh/example/media/venc.md | 136 + ...3\345\206\214_\346\250\241\346\235\277.md" | 118 - ...77\347\224\250\350\257\264\346\230\216.md" | 0 ...77\347\224\250\350\257\264\346\230\216.md" | 8 + ...77\347\224\250\350\257\264\346\230\216.md" | 177 + ...77\347\224\250\350\257\264\346\230\216.md" | 0 zh/userguide/images/balena-Etcher-finish.jpg | Bin 0 -> 73170 bytes .../images/balena-Etcher-flash-from-file.jpg | Bin 0 -> 156630 bytes zh/userguide/images/balena-Etcher-flash.jpg | Bin 0 -> 44966 bytes .../images/balena-Etcher-select-target.jpg | Bin 0 -> 60778 bytes zh/userguide/images/canaan-cover.png | Bin 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create mode 100755 zh/userguide/images/ide-5.png create mode 100755 zh/userguide/images/ide.png create mode 100755 zh/userguide/images/ide_image.png create mode 100755 zh/userguide/images/linux-serial.jpg create mode 100755 zh/userguide/images/logo.png create mode 100755 zh/userguide/images/micropython-start.jpg create mode 100755 zh/userguide/images/openmv-ide-connect.png create mode 100755 zh/userguide/images/windows-serial.jpg create mode 100755 zh/userguide/userguide.rst diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e84257d --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +_build +venv +*.pyc +_templates/layout.html + diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index c49336f..8542529 100755 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -59,7 +59,10 @@ markdown-link-check: - uname -a - whoami - pwd - - find . -name \*.md -print0 | xargs -0 -n1 markdown-link-check -q + #- find . -name \*.md -print0 | xargs -0 -n1 markdown-link-check -q + - git fetch + - git diff origin/dev --name-only -z + - git diff origin/dev --name-only -z | (grep "\\.md$" || echo zh/CanMV-K230常见问题解答.md) | xargs markdown-link-check # new MR open markdownlint: @@ -76,7 +79,10 @@ markdownlint: - whoami - pwd #- markdownlint-cli2 *.md **/*.md **/**/*.md **/**/**/*.md **/**/**/**/*.md - - find . -name \*.md -print0 | xargs -0 markdownlint-cli2 + #- find . -name \*.md -print0 | xargs -0 markdownlint-cli2 + - git fetch + - git diff origin/dev --name-only -z + - git diff origin/dev --name-only -z | (grep "\\.md$" || echo zh/CanMV-K230常见问题解答.md) | xargs markdownlint-cli2 .setup_env: before_script: @@ -137,3 +143,24 @@ markdownlint: - echo "---push to github---" - git push --atomic --tags -u -f github main || git push --atomic --tags -u -f github main || git push --atomic --tags -u -f github main +build-web-docs: + stage: build + rules: + - if: $CI_PIPELINE_SOURCE == "push" && $CI_COMMIT_BRANCH == "dev" + image: ai.b-bug.org:5000/python:web-docs + tags: + - k230 + environment: + name: dev + url: https://ai.b-bug.org/k230/k230_canmv_docs/dev/ + script: + - git fetch + - git checkout dev + - git pull + - python3 preprocess.py + - git config --global user.email "auto@canaan-creative.com" + - git config --global user.name "GitLab CI" + - git commit "*.md" -m "remove cover" + - make mhtml + - rm -rf /data1/k230/k230_canmv_docs/* + - cp -r _build/* /data1/k230/k230_canmv_docs diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..3d6647d --- /dev/null +++ b/Makefile @@ -0,0 +1,31 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SPHINXMULTIVERSION ?= sphinx-multiversion +SOURCEDIR = . +BUILDDIR = _build +WEB_DOCS_BUILDER_USER ?= gitlab+deploy-token-8 +WEB_DOCS_BUILDER_TOKEN ?= _qsc99tPFsbcBhSbXH4S + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile _templates/layout.html + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +mhtml: _templates/layout.html + @$(SPHINXMULTIVERSION) "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +_templates/layout.html: + git clone --depth 1 https://$(WEB_DOCS_BUILDER_USER):$(WEB_DOCS_BUILDER_TOKEN)@g.a-bug.org/huangziyi/web-docs-builder.git + cp web-docs-builder/layout.html _templates/layout.html + rm -rf web-docs-builder diff --git a/_static/img/ch-flag.a61145a4.svg b/_static/img/ch-flag.a61145a4.svg new file mode 100644 index 0000000..9f340c6 --- /dev/null +++ b/_static/img/ch-flag.a61145a4.svg @@ -0,0 +1,9 @@ + + + + + + + + + diff --git a/_static/img/logo.2f7049e2.svg b/_static/img/logo.2f7049e2.svg new file mode 100644 index 0000000..6a6e0f5 --- /dev/null +++ b/_static/img/logo.2f7049e2.svg @@ -0,0 +1,6 @@ + + + + + + diff --git a/_static/topbar.css b/_static/topbar.css new file mode 100644 index 0000000..5c28c60 --- /dev/null +++ b/_static/topbar.css @@ -0,0 +1,176 @@ +/* Customize for top bar (testing and outdated docs)*/ + +:target { + scroll-margin-top: 50px; +} + +.wy-nav-side +{ + min-height: calc(100% - 50px); + top: 50px; +} + +.wy-nav-content-wrap +{ + margin-top: 50px !important; +} + +/* The navigation bar */ +nav.navbar { + z-index: 3; + overflow: hidden; + background-color: #333; + position: fixed; /* Set the navbar to fixed position */ + top: 0; /* Position the navbar at the top of the page */ + width: 100%; /* Full width */ +} + +/* Links inside the navbar */ +nav.navbar a { + float: left; + display: block; + color: rgba(255,255,255,0.5); + text-align: center; + padding: 14px 16px; + text-decoration: none; + text-wrap: nowrap; +} + +.jn_container-view { + width: 100%; + height: 50px; + background: #0d0d0d; + position: fixed; + left: 0; + top: 0; + z-index: 1501; +} + +.jn_menu-container { + width: 100%; + max-width: 1100px; + height: 50px; + align-items: center; + margin: 0 auto; + display: flex; + position: relative +} + +.jn_menu-container .logo { + width: 157px; + height: 28px; + background: url(img/logo.2f7049e2.svg) no-repeat 100%; + background-size: auto 100%; +} + +.jn_menu-container .menu-list { + display: flex; + height: 100%; + font-size: 16px; + color: rgba(255,255,255,0.4); + margin-left: 78px; + max-width: 506px; +} + +.menu-list .menu-item { + margin-right: 30px; + height: 100%; + cursor: pointer; + display: flex; + align-items: center; +} + +@media (max-width: 768px) { + .logo { + display: none; + } + + .menu-list .menu-item { + margin-right: 0; + } + + .jn_menu-container .menu-list { + margin-left: 0; + justify-content: space-around; + flex-grow: 1; + } + + nav.navbar a { + padding: 3px; + } +} + +.jn_menu-container .menu-item.active a { + color: #fff +} + +.jn_menu-container .menu-item:last-child { + margin-right: 0 +} + +.jn_menu-container .active, +.jn_menu-container .menu-item a:hover { + color: #fff +} + +.jn_menu-container .menu-lang { + position: absolute; + right: 0; + top: 50%; + font-size: 14px; + color: hsla(0, 0%, 100%, .5); + transform: translateY(-50%) +} + +.jn_menu-container .menu-lang .lang-txt { + height: 20px; + padding-right: 28px; + background: url(img/ch-flag.a61145a4.svg) no-repeat 100%; + background-size: auto 70%; + display: block; + cursor: pointer +} + +.menu-doc a { + color: #f2f2f2 !important; +} + +.model-list { + position: absolute; + top: 50px; + background: #0d0d0d; + display: none; + cursor: pointer; + flex-direction: column; +} + +.menu-doc:hover > .model-list { + display: flex; + align-items: center; +} + +.document-select { + padding: 10px !important; + color: #fff; + text-align: start !important; +} + +.document-select:hover { + background-color: #249ee8 !important; +} + +.expanded-menu { + position: relative; +} +.expanded-menu-container { + display: none; + flex-direction: column; + position: absolute; + top: 0; + right: -120px; + width: 120px; + background-color: #0d0d0d; +} +.expanded-menu:hover .expanded-menu-container { + display: flex; +} diff --git a/_templates/versions.html b/_templates/versions.html new file mode 100644 index 0000000..4f17cae --- /dev/null +++ b/_templates/versions.html @@ -0,0 +1,41 @@ +{%- if current_version %} +
+ + Versions + {{ current_version.name }} + + +
+ + {%- if versions.tags %} +
+
Tags
+ {%- for item in versions.tags %} +
{{ item.name }}
+ {%- endfor %} +
+ {%- endif %} + {%- if versions.branches %} +
+
Branches
+ {%- for item in versions.branches %} +
{{ item.name }}
+ {%- endfor %} +
+
+
External links
+
+ K210 CanMV +
+
+ CanMV Docs +
+
+ {%- endif %} +
+
+{%- endif %} diff --git a/conf.py b/conf.py new file mode 100644 index 0000000..08c76b7 --- /dev/null +++ b/conf.py @@ -0,0 +1,75 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +import sys, os +import sphinx_rtd_theme +import datetime + +sys.path.append(os.path.abspath('exts')) + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = os.getenv('PROJECT') or 'K230 CanMV' +copyright = str(datetime.datetime.now().year) + ' ' + (os.getenv('COPYRIGHT') or 'Canaan Inc.') +author = os.getenv('AUTHOR') or 'Canaan' +# release = '0.1' +root_doc = os.getenv('ROOT_DOC') or 'index' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + 'sphinx_copybutton', + 'myst_parser', + 'sphinx_rtd_dark_mode', + 'sphinx_multiversion' +] + +templates_path = ['_templates'] +exclude_patterns = [] + +language = 'zh_CN' + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +myst_heading_anchors = 4 +suppress_warnings = ["myst.header"] + +html_copy_source = True +html_show_sourcelink = False + +html_favicon = 'favicon.ico' + +# html_show_sphinx = False + +# html_theme = 'alabaster' +html_theme = "sphinx_rtd_theme" +html_static_path = ['_static'] + +# if want to add top nav for canann, enable this. +html_css_files = ['topbar.css'] + +default_dark_mode = True + +locale_dirs = ['locale'] + +html_theme_options = { + # 'analytics_id': 'G-XXXXXXXXXX', # Provided by Google in your dashboard + # 'analytics_anonymize_ip': False, + # 'logo_only': False, + 'display_version': True, + 'prev_next_buttons_location': 'bottom', + 'style_external_links': False, + 'vcs_pageview_mode': '', + # 'style_nav_header_background': '#2980B9', + # Toc options + 'collapse_navigation': True, + 'sticky_navigation': True, + 'navigation_depth': 7, + 'includehidden': True, + 'titles_only': False +} diff --git a/exts/sphinx_rtd_dark_mode/__init__.py b/exts/sphinx_rtd_dark_mode/__init__.py new file mode 100644 index 0000000..fd6f49b --- /dev/null +++ b/exts/sphinx_rtd_dark_mode/__init__.py @@ -0,0 +1,27 @@ +""" +Sphinx Read the Docs Dark Mode +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Allows for toggable dark mode on the Read the Docs +theme for Sphinx. +""" + +__title__ = "sphinx-rtd-dark-mode" +__description__ = "Dark mode for the Sphinx Read the Docs theme." +__author__ = "MrDogeBro" +__version__ = "1.2.4" +__license__ = "MIT" + +from sphinx_rtd_dark_mode.dark_mode_loader import DarkModeLoader + + +def setup(app): + app.add_config_value("default_dark_mode", True, "html") + + app.connect("config-inited", DarkModeLoader().configure) + + return { + "version": __version__, + "parallel_read_safe": True, + "parallel_write_safe": True, + } diff --git a/exts/sphinx_rtd_dark_mode/dark_mode_loader.py b/exts/sphinx_rtd_dark_mode/dark_mode_loader.py new file mode 100644 index 0000000..1a32e0c --- /dev/null +++ b/exts/sphinx_rtd_dark_mode/dark_mode_loader.py @@ -0,0 +1,63 @@ +from pathlib import Path + + +class DarkModeLoader: + def __init__(self): + self.config = None + + def configure(self, app, config): + self.config = config + + self.check_sphinx_theme() + + if not self.config.html_static_path: + self.config.html_static_path = [ + str(Path.joinpath(Path(__file__).resolve().parent, "static")) + ] + else: + self.config.html_static_path.append( + str(Path.joinpath(Path(__file__).resolve().parent, "static")) + ) + + if not self.config.default_dark_mode: + self.load_default_theme("light") + self.load_css() + return + + self.load_default_theme("dark") + self.load_css() + + def check_sphinx_theme(self): + if not self.config.html_theme == "sphinx_rtd_theme": + self.config.html_theme = "sphinx_rtd_theme" + + def load_default_theme(self, default_theme: str): + if not self.config.html_js_files: + self.config.html_js_files = [ + "dark_mode_js/default_{default_theme}.js".format( + default_theme=default_theme + ), + "dark_mode_js/theme_switcher.js", + ] + else: + self.config.html_js_files.append( + "dark_mode_js/default_{default_theme}.js".format( + default_theme=default_theme + ) + ) + self.config.html_js_files.append("dark_mode_js/theme_switcher.js") + + def load_css(self): + if "css_files" in self.config.html_context: + self.config.html_context["css_files"].append("_static/dark_mode_css/general.css") + self.config.html_context["css_files"].append("_static/dark_mode_css/dark.css") + return + + if not self.config.html_css_files: + self.config.html_css_files = [ + "dark_mode_css/general.css", + "dark_mode_css/dark.css", + ] + else: + self.config.html_css_files.append("dark_mode_css/general.css") + self.config.html_css_files.append("dark_mode_css/dark.css") diff --git a/exts/sphinx_rtd_dark_mode/static/dark_mode_css/custom.css b/exts/sphinx_rtd_dark_mode/static/dark_mode_css/custom.css new file mode 100644 index 0000000..989c2ad --- /dev/null +++ b/exts/sphinx_rtd_dark_mode/static/dark_mode_css/custom.css @@ -0,0 +1,77 @@ +.wy-side-nav-search input[type='text'] { + border-radius: 3px; +} + +input[type='color'], +input[type='date'], +input[type='datetime-local'], +input[type='datetime'], +input[type='email'], +input[type='month'], +input[type='number'], +input[type='password'], +input[type='search'], +input[type='tel'], +input[type='text'], +input[type='time'], +input[type='url'], +input[type='week'] { + box-shadow: none; +} + +.theme-switcher { + border-radius: 50%; + position: fixed; + right: 1.6em; + bottom: 1.4em; + z-index: 3; + border: none; + height: 2.2em; + width: 2.2em; + background-color: #fcfcfc; + font-size: 20px; + -webkit-box-shadow: 0px 3px 14px 4px rgba(0, 0, 0, 0.62); + box-shadow: 0px 3px 14px 4px rgba(0, 0, 0, 0.62); + color: #404040; + transition: all 0.3s ease-in-out; +} + +.wy-nav-content a, +.wy-nav-content a:visited { + color: #3091d1; +} + +body, +.wy-nav-content-wrap, +.wy-nav-content, +.section, +.highlight, +.rst-content div[class^='highlight'], +.wy-nav-content a, +.btn-neutral, +.btn, +footer, +.wy-nav-side, +.wy-menu-vertical li, +.wy-menu-vertical a, +.wy-side-nav-search .wy-dropdown, +.wy-side-nav-search a, +.wy-side-nav-search input, +html.writer-html4 .rst-content dl:not(.docutils) > dt, +html.writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + > dt, +.rst-content code, +.rst-content tt, +html.writer-html4 .rst-content dl:not(.docutils) dl:not(.field-list) > dt, +html.writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + dl:not(.field-list) + > dt, +code, +.rst-content code.xref, +.rst-content tt.xref { + transition: all 0.2s ease-in-out; +} diff --git a/exts/sphinx_rtd_dark_mode/static/dark_mode_css/dark.css b/exts/sphinx_rtd_dark_mode/static/dark_mode_css/dark.css new file mode 100644 index 0000000..6a47f69 --- /dev/null +++ b/exts/sphinx_rtd_dark_mode/static/dark_mode_css/dark.css @@ -0,0 +1,559 @@ +:root { + /* --dark-text-color: #c1c1c1; */ + --dark-text-color: #ff0000; + --dark-link-color: #249ee8; +} + +html[data-theme='dark'] body { + color: #bfbfbf; +} + +html[data-theme='dark'] .wy-nav-content-wrap { + /* background-color: #101010; */ + background-color: #353535; +} + +html[data-theme='dark'] .wy-nav-content { + /* background-color: #141414; */ + background-color: #393939; +} + +html[data-theme='dark'] .section { + color: var(--dark-text-color); +} + +html[data-theme='dark'] .highlight { + /* background-color: #17181c; */ + background-color: #000000; +} + +html[data-theme='dark'] .highlight .nn { + color: var(--dark-text-color); +} + +html[data-theme='dark'] .highlight .nb { + color: #8bb8df; +} + +html[data-theme='dark'] .highlight .kn, +html[data-theme='dark'] .highlight .kc, +html[data-theme='dark'] .highlight .k { + color: #41c2ea; +} + +html[data-theme='dark'] .highlight .s1, +html[data-theme='dark'] .highlight .s2 { + color: #b3e87f; +} + +html[data-theme='dark'] .highlight .nt { + color: #ccb350; +} + +html[data-theme='dark'] .highlight .c1 { + color: #27ae60; + font-style:normal !important; +} + +html[data-theme='dark'] .rst-content div[class^='highlight'] { + /* border-color: #1a1a1a; */ + border-color: #6e6e6e; +} + +html[data-theme='dark'] .rst-content div[class^='highlight'] pre { + font-size: 14px !important; +} + +html[data-theme='dark'] .wy-nav-content a, +html[data-theme='dark'] .wy-nav-content a:visited { + color: var(--dark-link-color); +} + +html[data-theme='dark'] .btn-neutral { + /* background-color: #17181c !important; */ + background-color: #363738 !important; + color: #c1c1c1 !important; +} + +html[data-theme='dark'] .btn-neutral:hover { + /* background-color: #101114 !important; */ + background-color: #353639 !important; +} + +/* +html[data-theme='dark'] .btn-neutral:visited { + color: #c1c1c1 !important; +} +*/ + +html[data-theme='dark'] .btn { + box-shadow: none; +} + +html[data-theme='dark'] footer { + color: #bdbdbd; +} + +html[data-theme='dark'] .wy-nav-side { + /* background-color: #0d0d0d; */ + background-color: #323232; +} + +html[data-theme='dark'] .wy-menu-vertical li.current { + /* background-color: #141414; */ + background-color: #393939; +} + +html[data-theme='dark'] .wy-menu-vertical li.current > a, +html[data-theme='dark'] .wy-menu-vertical li.on a { + /* background-color: #141415; */ + background-color: #39393A; + color: var(--dark-text-color); +} + +html[data-theme='dark'] .wy-menu-vertical li.toctree-l1.current > a, +html[data-theme='dark'] .wy-menu-vertical li.current a { + /* border-color: #0b0c0d; */ + border-color: #303132; +} + +html[data-theme='dark'] .wy-menu-vertical li.current a { + color: #bbb; +} + +html[data-theme='dark'] .wy-menu-vertical li.current a:hover { + background-color: #303030; +} + +html[data-theme='dark'] .wy-menu-vertical a:hover, +html[data-theme='dark'] .wy-menu-vertical li.current > a:hover, +html[data-theme='dark'] .wy-menu-vertical li.on a:hover { + /* background-color: #1e1e1e; */ + background-color: #434343; +} + +html[data-theme='dark'] .wy-menu-vertical li.toctree-l2.current > a, +html[data-theme='dark'] + .wy-menu-vertical + li.toctree-l2.current + li.toctree-l3 + > a { + /* background-color: #18181a; */ + background-color: #3D3D3F; +} + +html[data-theme='dark'] .wy-side-nav-search { + /* background-color: #0b152d; */ + /* background-color: #1b1e25; */ + background-color: #0d0d0d; +} + +html[data-theme='dark'] .wy-side-nav-search .wy-dropdown > a, +html[data-theme='dark'] .wy-side-nav-search > a { + color: #ddd; +} + +html[data-theme='dark'] .wy-side-nav-search input[type='text'] { + border-color: #111; + /* background-color: #141414; */ + background-color: #393939; + color: var(--dark-text-color); +} + +html[data-theme='dark'] .theme-switcher { + /* background-color: #0b0c0d; */ + background-color: #303132; + /* color: var(--dark-text-color); */ + color: #c1c1c1; +} + +html[data-theme='dark'].writer-html4 .rst-content dl:not(.docutils) > dt, +html[data-theme='dark'].writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + > dt { + background-color: #0b0b0b; + color: #007dce; + border-color: #282828; +} + +html[data-theme='dark'] .rst-content code, +html[data-theme='dark'] .rst-content tt { + color: var(--dark-text-color); +} + +html[data-theme='dark'].writer-html4 + .rst-content + dl:not(.docutils) + dl:not(.field-list) + > dt, +html[data-theme='dark'].writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + dl:not(.field-list) + > dt { + background-color: #0f0f0f; + color: #959595; + border-color: #2b2b2b; +} + +html[data-theme='dark'] .rst-content code, +html[data-theme='dark'] .rst-content tt, +html[data-theme='dark'] code { + /* background-color: #2d2d2d; */ + /* border-color: #1c1c1c; */ + background-color: #525252; + border-color: #414141; +} + +html[data-theme='dark'] .rst-content code.xref, +html[data-theme='dark'] .rst-content tt.xref, +html[data-theme='dark'] a .rst-content code, +html[data-theme='dark'] a .rst-content tt { + color: #cecece; +} + +html[data-theme='dark'] .rst-content .hint, +html[data-theme='dark'] .rst-content .important, +html[data-theme='dark'] .rst-content .tip, +html[data-theme='dark'] .rst-content .wy-alert-success.admonition, +html[data-theme='dark'] .rst-content .wy-alert-success.admonition-todo, +html[data-theme='dark'] .rst-content .wy-alert-success.attention, +html[data-theme='dark'] .rst-content .wy-alert-success.caution, +html[data-theme='dark'] .rst-content .wy-alert-success.danger, +html[data-theme='dark'] .rst-content .wy-alert-success.error, +html[data-theme='dark'] .rst-content .wy-alert-success.note, +html[data-theme='dark'] .rst-content .wy-alert-success.seealso, +html[data-theme='dark'] .rst-content .wy-alert-success.warning, +html[data-theme='dark'] .wy-alert.wy-alert-success { + background-color: #00392e; +} + +html[data-theme='dark'] .rst-content .hint .admonition-title, +html[data-theme='dark'] .rst-content .hint .wy-alert-title, +html[data-theme='dark'] .rst-content .important .admonition-title, +html[data-theme='dark'] .rst-content .important .wy-alert-title, +html[data-theme='dark'] .rst-content .tip .admonition-title, +html[data-theme='dark'] .rst-content .tip .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-success.admonition-todo + .admonition-title, +html[data-theme='dark'] + .rst-content + .wy-alert-success.admonition-todo + .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-success.admonition + .admonition-title, +html[data-theme='dark'] + .rst-content + .wy-alert-success.admonition + .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-success.attention + .admonition-title, +html[data-theme='dark'] + .rst-content + .wy-alert-success.attention + .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-success.caution + .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-success.caution .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-success.danger .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-success.danger .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-success.error .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-success.error .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-success.note .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-success.note .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-success.seealso + .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-success.seealso .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-success.warning + .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-success.warning .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert.wy-alert-success + .admonition-title, +html[data-theme='dark'] + .wy-alert.wy-alert-success + .rst-content + .admonition-title, +html[data-theme='dark'] .wy-alert.wy-alert-success .wy-alert-title { + background-color: #006a56; +} + +html[data-theme='dark'] .rst-content .note, +html[data-theme='dark'] .rst-content .seealso, +html[data-theme='dark'] .rst-content .wy-alert-info.admonition, +html[data-theme='dark'] .rst-content .wy-alert-info.admonition-todo, +html[data-theme='dark'] .rst-content .wy-alert-info.attention, +html[data-theme='dark'] .rst-content .wy-alert-info.caution, +html[data-theme='dark'] .rst-content .wy-alert-info.danger, +html[data-theme='dark'] .rst-content .wy-alert-info.error, +html[data-theme='dark'] .rst-content .wy-alert-info.hint, +html[data-theme='dark'] .rst-content .wy-alert-info.important, +html[data-theme='dark'] .rst-content .wy-alert-info.tip, +html[data-theme='dark'] .rst-content .wy-alert-info.warning, +html[data-theme='dark'] .wy-alert.wy-alert-info { + background-color: #002c4d; +} + +html[data-theme='dark'] .rst-content .note .admonition-title, +html[data-theme='dark'] .rst-content .note .wy-alert-title, +html[data-theme='dark'] .rst-content .seealso .admonition-title, +html[data-theme='dark'] .rst-content .seealso .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-info.admonition-todo + .admonition-title, +html[data-theme='dark'] + .rst-content + .wy-alert-info.admonition-todo + .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-info.admonition + .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-info.admonition .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-info.attention .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-info.attention .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-info.caution .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-info.caution .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-info.danger .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-info.danger .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-info.error .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-info.error .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-info.hint .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-info.hint .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-info.important .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-info.important .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-info.tip .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-info.tip .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-info.warning .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-info.warning .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert.wy-alert-info .admonition-title, +html[data-theme='dark'] .wy-alert.wy-alert-info .rst-content .admonition-title, +html[data-theme='dark'] .wy-alert.wy-alert-info .wy-alert-title { + background-color: #004a7b; +} + +html[data-theme='dark'] .rst-content .admonition-todo, +html[data-theme='dark'] .rst-content .attention, +html[data-theme='dark'] .rst-content .caution, +html[data-theme='dark'] .rst-content .warning, +html[data-theme='dark'] .rst-content .wy-alert-warning.admonition, +html[data-theme='dark'] .rst-content .wy-alert-warning.danger, +html[data-theme='dark'] .rst-content .wy-alert-warning.error, +html[data-theme='dark'] .rst-content .wy-alert-warning.hint, +html[data-theme='dark'] .rst-content .wy-alert-warning.important, +html[data-theme='dark'] .rst-content .wy-alert-warning.note, +html[data-theme='dark'] .rst-content .wy-alert-warning.seealso, +html[data-theme='dark'] .rst-content .wy-alert-warning.tip, +html[data-theme='dark'] .wy-alert.wy-alert-warning { + background-color: #533500; +} + +html[data-theme='dark'] .rst-content .admonition-todo .admonition-title, +html[data-theme='dark'] .rst-content .admonition-todo .wy-alert-title, +html[data-theme='dark'] .rst-content .attention .admonition-title, +html[data-theme='dark'] .rst-content .attention .wy-alert-title, +html[data-theme='dark'] .rst-content .caution .admonition-title, +html[data-theme='dark'] .rst-content .caution .wy-alert-title, +html[data-theme='dark'] .rst-content .warning .admonition-title, +html[data-theme='dark'] .rst-content .warning .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-warning.admonition + .admonition-title, +html[data-theme='dark'] + .rst-content + .wy-alert-warning.admonition + .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.danger .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.danger .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.error .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.error .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.hint .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.hint .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-warning.important + .admonition-title, +html[data-theme='dark'] + .rst-content + .wy-alert-warning.important + .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.note .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.note .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-warning.seealso + .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.seealso .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.tip .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-warning.tip .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert.wy-alert-warning + .admonition-title, +html[data-theme='dark'] + .wy-alert.wy-alert-warning + .rst-content + .admonition-title, +html[data-theme='dark'] .wy-alert.wy-alert-warning .wy-alert-title { + background-color: #803b00; +} + +html[data-theme='dark'] .rst-content .danger, +html[data-theme='dark'] .rst-content .error, +html[data-theme='dark'] .rst-content .wy-alert-danger.admonition, +html[data-theme='dark'] .rst-content .wy-alert-danger.admonition-todo, +html[data-theme='dark'] .rst-content .wy-alert-danger.attention, +html[data-theme='dark'] .rst-content .wy-alert-danger.caution, +html[data-theme='dark'] .rst-content .wy-alert-danger.hint, +html[data-theme='dark'] .rst-content .wy-alert-danger.important, +html[data-theme='dark'] .rst-content .wy-alert-danger.note, +html[data-theme='dark'] .rst-content .wy-alert-danger.seealso, +html[data-theme='dark'] .rst-content .wy-alert-danger.tip, +html[data-theme='dark'] .rst-content .wy-alert-danger.warning, +html[data-theme='dark'] .wy-alert.wy-alert-danger { + background-color: #82231a; +} + +html[data-theme='dark'] .rst-content .danger .admonition-title, +html[data-theme='dark'] .rst-content .danger .wy-alert-title, +html[data-theme='dark'] .rst-content .error .admonition-title, +html[data-theme='dark'] .rst-content .error .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-danger.admonition-todo + .admonition-title, +html[data-theme='dark'] + .rst-content + .wy-alert-danger.admonition-todo + .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-danger.admonition + .admonition-title, +html[data-theme='dark'] + .rst-content + .wy-alert-danger.admonition + .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-danger.attention + .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.attention .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.caution .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.caution .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.hint .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.hint .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert-danger.important + .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.important .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.note .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.note .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.seealso .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.seealso .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.tip .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.tip .wy-alert-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.warning .admonition-title, +html[data-theme='dark'] .rst-content .wy-alert-danger.warning .wy-alert-title, +html[data-theme='dark'] + .rst-content + .wy-alert.wy-alert-danger + .admonition-title, +html[data-theme='dark'] + .wy-alert.wy-alert-danger + .rst-content + .admonition-title, +html[data-theme='dark'] .wy-alert.wy-alert-danger .wy-alert-title { + background-color: #b9372b; +} + +html[data-theme='dark'] .wy-nav-top { + background-color: #0b152d; +} + +html[data-theme='dark'] .rst-content table.docutils thead, +html[data-theme='dark'] .rst-content table.field-list thead, +html[data-theme='dark'] .wy-table thead { + color: var(--dark-text-color); +} + +html[data-theme='dark'] + .rst-content + table.docutils:not(.field-list) + tr:nth-child(2n-1) + td, +html[data-theme='dark'] .wy-table-backed, +html[data-theme='dark'] html[data-theme='dark'] .wy-table-odd td, +html[data-theme='dark'] .wy-table-striped tr:nth-child(2n-1) td { + /* background-color: #181818; */ + background-color: #3D3D3D; +} + +html[data-theme='dark'] .rst-content table.docutils td, +html[data-theme='dark'] .wy-table-bordered-all td, +html[data-theme='dark'].writer-html5 .rst-content table.docutils th, +html[data-theme='dark'] .rst-content table.docutils, +html[data-theme='dark'] .wy-table-bordered-all { + border-color: #262626; +} + +html[data-theme='dark'] .rst-content table.docutils caption, +html[data-theme='dark'] .rst-content table.field-list caption, +html[data-theme='dark'] .wy-table caption { + color: var(--dark-text-color); +} + +html[data-theme='dark'] .wy-menu-vertical li.toctree-l3.current > a, +html[data-theme='dark'] + .wy-menu-vertical + li.toctree-l3.current + li.toctree-l4 + > a { + /* background-color: #18181a; */ + background-color: #3D3D3F; +} + +html[data-theme='dark'] + .wy-menu-vertical + li.toctree-l1 + > a.reference.internal:hover { + background-color: #303030; +} + +html[data-theme='dark'] + .wy-menu-vertical + li.toctree-l2 + > a.reference.internal:hover { + background-color: #313131; +} + +html[data-theme='dark'] + .wy-menu-vertical + li.toctree-l3 + > a.reference.internal:hover { + background-color: #323232; +} + +html[data-theme='dark'] + .wy-menu-vertical + li.toctree-l4 + > a.reference.internal:hover { + background-color: #333333; +} diff --git a/exts/sphinx_rtd_dark_mode/static/dark_mode_css/general.css b/exts/sphinx_rtd_dark_mode/static/dark_mode_css/general.css new file mode 100644 index 0000000..aa614f8 --- /dev/null +++ b/exts/sphinx_rtd_dark_mode/static/dark_mode_css/general.css @@ -0,0 +1,68 @@ +input[type='color'], +input[type='date'], +input[type='datetime-local'], +input[type='datetime'], +input[type='email'], +input[type='month'], +input[type='number'], +input[type='password'], +input[type='search'], +input[type='tel'], +input[type='text'], +input[type='time'], +input[type='url'], +input[type='week'] { + box-shadow: none; +} + +.theme-switcher { + border-radius: 50%; + position: fixed; + right: 1.6em; + bottom: 1.4em; + z-index: 3; + border: none; + height: 2.2em; + width: 2.2em; + background-color: #fcfcfc; + font-size: 20px; + -webkit-box-shadow: 0px 3px 14px 4px rgba(0, 0, 0, 0.62); + box-shadow: 0px 3px 14px 4px rgba(0, 0, 0, 0.62); + color: #404040; + transition: all 0.3s ease-in-out; +} + +body, +.wy-nav-content-wrap, +.wy-nav-content, +.section, +.highlight, +.rst-content div[class^='highlight'], +.wy-nav-content a, +.btn-neutral, +.btn, +footer, +.wy-nav-side, +.wy-menu-vertical li, +.wy-menu-vertical a, +.wy-side-nav-search .wy-dropdown, +.wy-side-nav-search a, +.wy-side-nav-search input, +html.writer-html4 .rst-content dl:not(.docutils) > dt, +html.writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + > dt, +.rst-content code, +.rst-content tt, +html.writer-html4 .rst-content dl:not(.docutils) dl:not(.field-list) > dt, +html.writer-html5 + .rst-content + dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) + dl:not(.field-list) + > dt, +code, +.rst-content code.xref, +.rst-content tt.xref { + transition: all 0.2s ease-in-out; +} diff --git a/exts/sphinx_rtd_dark_mode/static/dark_mode_js/default_dark.js b/exts/sphinx_rtd_dark_mode/static/dark_mode_js/default_dark.js new file mode 100644 index 0000000..ea63e07 --- /dev/null +++ b/exts/sphinx_rtd_dark_mode/static/dark_mode_js/default_dark.js @@ -0,0 +1,13 @@ +const loadTheme = () => { + let theme = localStorage.getItem('theme'); + + if (theme !== null) { + if (theme === 'dark') + document.documentElement.setAttribute('data-theme', 'dark'); + } else { + localStorage.setItem('theme', 'dark'); + document.documentElement.setAttribute('data-theme', 'dark'); + } +}; + +loadTheme(); diff --git a/exts/sphinx_rtd_dark_mode/static/dark_mode_js/default_light.js b/exts/sphinx_rtd_dark_mode/static/dark_mode_js/default_light.js new file mode 100644 index 0000000..2b19f92 --- /dev/null +++ b/exts/sphinx_rtd_dark_mode/static/dark_mode_js/default_light.js @@ -0,0 +1,13 @@ +const loadTheme = () => { + let theme = localStorage.getItem('theme'); + + if (theme !== null) { + if (theme === 'dark') + document.documentElement.setAttribute('data-theme', 'dark'); + } else { + localStorage.setItem('theme', 'light'); + document.documentElement.setAttribute('data-theme', 'light'); + } +}; + +loadTheme(); diff --git a/exts/sphinx_rtd_dark_mode/static/dark_mode_js/theme_switcher.js b/exts/sphinx_rtd_dark_mode/static/dark_mode_js/theme_switcher.js new file mode 100644 index 0000000..860bd5d --- /dev/null +++ b/exts/sphinx_rtd_dark_mode/static/dark_mode_js/theme_switcher.js @@ -0,0 +1,39 @@ +const createThemeSwitcher = () => { + let btn = document.createElement('BUTTON'); + btn.className = 'theme-switcher'; + btn.id = 'themeSwitcher'; + btn.innerHTML = + ''; + document.body.appendChild(btn); + + if (localStorage.getItem('theme') === 'dark') $('#themeMoon').hide(0); + else $('#themeSun').hide(0); +}; + +$(document).ready(() => { + createThemeSwitcher(); + $('#themeSwitcher').click(switchTheme); + + $('footer').html( + $('footer').html() + + 'Dark theme provided by MrDogeBro.' + ); +}); + +const switchTheme = () => { + if (localStorage.getItem('theme') === 'dark') { + localStorage.setItem('theme', 'light'); + document.documentElement.setAttribute('data-theme', 'light'); + + $('#themeSun').fadeOut(200, () => { + $('#themeMoon').fadeIn(200); + }); + } else { + localStorage.setItem('theme', 'dark'); + document.documentElement.setAttribute('data-theme', 'dark'); + + $('#themeMoon').fadeOut(200, () => { + $('#themeSun').fadeIn(200); + }); + } +}; diff --git a/index.rst b/index.rst new file mode 100644 index 0000000..4236081 --- /dev/null +++ b/index.rst @@ -0,0 +1,16 @@ +.. K230 documentation master file, created by + sphinx-quickstart on Thu Oct 12 15:18:20 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to K230 CanMV's documentation! +================================ + +.. toctree:: + :maxdepth: 2 + + zh/CanMV-K230快速入门指南.md + zh/userguide/userguide.rst + zh/example/example.rst + zh/api/api.rst + zh/CanMV-K230常见问题解答.md \ No newline at end of file diff --git a/preprocess.py b/preprocess.py new file mode 100755 index 0000000..2cd6bed --- /dev/null +++ b/preprocess.py @@ -0,0 +1,35 @@ +# 移除封面、免责声明等内容 + +import glob + +if __name__ == '__main__': + docs = glob.glob("**/*.md", recursive=True) + # docs = glob.glob("zh/02_applications/business_poc/K230_场景实战_智能门锁.md") + for d in docs: + with open(d, 'rb') as f: + text = f.read() + pos1 = text.find(b'![cover]') + if pos1 < 0: + print(f"{d} does not match") + continue + pos2 = text.find(b'[TOC]') + if pos2 < 0: + pos2 = text.find(b'[toc]') + if pos2 < 0: + pos3 = text.find(b'page-break-after') + if pos3 >= 0: + pos2 = text.find(b'page-break-after', pos3 + 16) + if pos2 >= 0: + pos2 += 31 + else: + pos2 = pos3 + 31 + else: + pos2 += 5 + else: + pos2 += 5 + if pos1 < 0 or pos2 < 0: + print(f"{d} does not match") + continue + with open(d, 'wb') as f: + f.write(text[:pos1]) + f.write(text[pos2:]) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..25e29e6 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,32 @@ +alabaster==0.7.13 +Babel==2.12.1 +certifi==2023.7.22 +charset-normalizer==3.2.0 +docutils==0.18.1 +idna==3.4 +imagesize==1.4.1 +importlib-metadata==6.8.0 +Jinja2==3.1.2 +markdown-it-py==2.2.0 +MarkupSafe==2.1.3 +mdit-py-plugins==0.3.5 +mdurl==0.1.2 +myst-parser==1.0.0 +packaging==23.1 +Pygments==2.16.1 +PyYAML==6.0.1 +requests==2.31.0 +snowballstemmer==2.2.0 +sphinx==5.3.0 +sphinx-copybutton==0.5.2 +sphinx-multiversion==0.2.4 +sphinx-rtd-theme==1.3.0 +sphinxcontrib-applehelp==1.0.7 +sphinxcontrib-devhelp==1.0.5 +sphinxcontrib-htmlhelp==2.0.4 +sphinxcontrib-jquery==4.1 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.6 +sphinxcontrib-serializinghtml==1.1.9 +urllib3==2.0.5 +zipp==3.17.0 diff --git a/zh/00_hardware/.keep b/zh/00_hardware/.keep deleted file mode 100644 index 8b13789..0000000 --- a/zh/00_hardware/.keep +++ /dev/null @@ -1 +0,0 @@ - diff --git a/zh/01_software/example/.keep b/zh/01_software/example/.keep deleted file mode 100644 index 8b13789..0000000 --- a/zh/01_software/example/.keep +++ /dev/null @@ -1 +0,0 @@ - diff --git "a/zh/02_other/\345\205\215\350\264\243\345\243\260\346\230\216_\345\214\227\344\272\254\345\230\211\346\245\240\346\215\267\346\200\235\344\277\241\346\201\257\346\212\200\346\234\257\346\234\211\351\231\220\345\205\254\345\217\270.txt" "b/zh/02_other/\345\205\215\350\264\243\345\243\260\346\230\216_\345\214\227\344\272\254\345\230\211\346\245\240\346\215\267\346\200\235\344\277\241\346\201\257\346\212\200\346\234\257\346\234\211\351\231\220\345\205\254\345\217\270.txt" deleted file mode 100644 index 28fdbd3..0000000 --- "a/zh/02_other/\345\205\215\350\264\243\345\243\260\346\230\216_\345\214\227\344\272\254\345\230\211\346\245\240\346\215\267\346\200\235\344\277\241\346\201\257\346\212\200\346\234\257\346\234\211\351\231\220\345\205\254\345\217\270.txt" +++ /dev/null @@ -1,8 +0,0 @@ -免责声明 - - 嘉楠提醒您，本文档所提供的交付件仅供客户开发参考。在使用本文件夹所提供的交付件前，请您务必仔细阅读并透彻理解本声明。您可以选择不使用本文件夹中的交付件，但如果您使用，您的使用行为将被视为对本声明全部内容的认可。 - 本文档所提供的交付件仍然是“按现状”提供，不附有任何种类的陈述与保证，在法律许可的最大范围内，嘉楠声明免除所有默示的陈述与保证，包括但不限于默示的有关适销、非侵权和适用于某种特定用途的陈述与保证。本清单中所提供的数据在不同的测试环境下可能存在一定的偏差，因使用本清单中所提供的数据而导致的所有设计、开发错误，或其他一切因使用本目录中所提供的数据而可能遭致的意外、疏忽、侵权及其造成的损失（包括但不限于直接的、间接的、后果性的或附带性的损失），其后果由您自行承担，嘉楠对其概不负责，亦不承担任何法律责任。 - - - 声明单位：北京嘉楠捷思信息技术有限公司 - 日 期：2023-5-31 diff --git "a/zh/CanMV-K230\345\270\270\350\247\201\351\227\256\351\242\230\350\247\243\347\255\224.md" "b/zh/CanMV-K230\345\270\270\350\247\201\351\227\256\351\242\230\350\247\243\347\255\224.md" new file mode 100755 index 0000000..d70b7d7 --- /dev/null +++ "b/zh/CanMV-K230\345\270\270\350\247\201\351\227\256\351\242\230\350\247\243\347\255\224.md" @@ -0,0 +1,69 @@ +# CanMV-K230 常见问题解答 + +![cover](images/canaan-cover.png) + +版权所有©2023北京嘉楠捷思信息技术有限公司 + +
+ +## 免责声明 + +您购买的产品、服务或特性等应受北京嘉楠捷思信息技术有限公司（“本公司”，下同）及其关联公司的商业合同和条款的约束，本文档中描述的全部或部分产品、服务或特性可能不在您的购买或使用范围之内。除非合同另有约定，本公司不对本文档的任何陈述、信息、内容的正确性、可靠性、完整性、适销性、符合特定目的和不侵权提供任何明示或默示的声明或保证。除非另有约定，本文档仅作为使用指导参考。 + +由于产品版本升级或其他原因，本文档内容将可能在未经任何通知的情况下，不定期进行更新或修改。 + +## 商标声明 + +![logo](images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 + +**版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** +非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 + +
+ +## 目录 + +[TOC] + +## 前言 + +### 概述 + +本文档主要介绍了K230 SDK V0.9.0 版本发布相关的内容，包括当前版本支持的硬件、功能、使用限制等。 + +### 读者对象 + +本文档（本指南）主要适用于以下人员： + +- 技术支持工程师 +- 软件开发工程师 + +### 缩略词定义 + +| 简称 | 说明 | +|--------------------|--------------------------------------------------------| +| K230 USIP LP3 EVB | USIP封装形式的K230芯片搭载LPDDR3的开发板 | +| VENC | Video Encoder，视频编码模块 | +| VDEC | Video Decoder，视频解码模块 | +| VICAP | Video Input Capture，图像输入采集模块 | +| VO | Video Output，视频输出模块 | +| AI | Audio Input，音频输入模块 | +| AO | Audio Output，音频输出模块 | +| AENC | Audio Encoder，音频编码模块 | +| ADEC | Audio Decoder，音频解码模块 | +| NonAI-2D | 2D图形加速处理模块，支持OSD叠加，画框，CSC变换等功能。 | +| MCM | Multi Camera Management ,多摄像头管理 | + +### 修订记录 + +| 文档版本号 | 修改说明 | 修改者 | 日期 | +|------------|----------|------------|----------| +| V1.0 | 初版 | SDK 部 | 2023-9-4 | + +## 1. 版本信息 + +| 所属产品 | 版本号 | 发布日期 | +|----------|--------|----------| +| K230 CanMV | V1.0.0 | 2023-9-4| + +## 2.常见问题解答 diff --git "a/zh/CanMV-K230\345\277\253\351\200\237\345\205\245\351\227\250\346\214\207\345\215\227.md" "b/zh/CanMV-K230\345\277\253\351\200\237\345\205\245\351\227\250\346\214\207\345\215\227.md" index 1c97e9c..8177a28 100755 --- "a/zh/CanMV-K230\345\277\253\351\200\237\345\205\245\351\227\250\346\214\207\345\215\227.md" +++ "b/zh/CanMV-K230\345\277\253\351\200\237\345\205\245\351\227\250\346\214\207\345\215\227.md" @@ -1,4 +1,4 @@ -# K230 SDK使用说明 +# CanMV-K230 快速入门指南 ![cover](images/canaan-cover.png) diff --git "a/zh/CanMV-K230\347\211\210\346\234\254\350\257\264\346\230\216.md" "b/zh/CanMV-K230\347\211\210\346\234\254\350\257\264\346\230\216.md" new file mode 100755 index 0000000..0f03907 --- /dev/null +++ "b/zh/CanMV-K230\347\211\210\346\234\254\350\257\264\346\230\216.md" @@ -0,0 +1,113 @@ +# CanMV-K230 版本说明 + +![cover](images/canaan-cover.png) + +版权所有©2023北京嘉楠捷思信息技术有限公司 + +
+ +## 免责声明 + +您购买的产品、服务或特性等应受北京嘉楠捷思信息技术有限公司（“本公司”，下同）及其关联公司的商业合同和条款的约束，本文档中描述的全部或部分产品、服务或特性可能不在您的购买或使用范围之内。除非合同另有约定，本公司不对本文档的任何陈述、信息、内容的正确性、可靠性、完整性、适销性、符合特定目的和不侵权提供任何明示或默示的声明或保证。除非另有约定，本文档仅作为使用指导参考。 + +由于产品版本升级或其他原因，本文档内容将可能在未经任何通知的情况下，不定期进行更新或修改。 + +## 商标声明 + +![logo](images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 + +**版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** +非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 + +
+ +## 目录 + +[TOC] + +## 前言 + +### 概述 + +本文档主要介绍了K230 CanMV V0.2.0 版本发布相关的内容，包括当前版本支持的硬件、功能、使用限制等。 + +### 读者对象 + +本文档（本指南）主要适用于以下人员： + +- 技术支持工程师 +- 软件开发工程师 + +### 缩略词定义 + +| 简称 | 说明 | +| --- | --- | + +### 修订记录 + +| 文档版本号 | 修改说明 | 修改者 | 日期 | +| --- | --- | --- | --- | +| V0.2.0 | 初版 | SDK 部 | 2023-11-23 | + +## 1. 版本信息 + +| 所属产品 | 版本号 | 发布日期 | +|----------|--------|----------| +| K230 CanMV | V0.2.0 | 2023-11-23 | + +## 2. 支持的硬件 + +K230平台支持CanMV-K230等主板 + +## 3. 版本使用限制说明 + +无 + +## 4. 版本功能统计 + +### 4.1 基本功能 + +| ID | 支持版本 | 功能概要 | 功能描述 | 备注 | +| --- | --- | --- | --- | --- | +| 1 | V0.2.0 | Camera | 支持获取sensor图像 | | +| 2 | V0.2.0 | Display | 支持HDMI显示 | | +| 3 | V0.2.0 | 加密模块 | 支持硬件SHA256、AES | | +| 4 | V0.2.0 | VPU | 支持编解码 | | +| 5 | V0.2.0 | audio | 支持音频、内置编解码器 | | +| 6 | V0.2.0 | gpio | 支持GPIO | | +| 7 | V0.2.0 | adc | 支持模数转换 | | +| 8 | V0.2.0 | fft | 支持傅里叶变换 | | +| 9 | V0.2.0 | i2c | 支持i2c通信 | | +| 10 | V0.2.0 | pwm | 支持输出PWM | | +| 11 | V0.2.0 | spi | 支持SPI通信 | | +| 12 | V0.2.0 | timer | 支持定时器 | | +| 13 | V0.2.0 | wdt | 支持看门狗 | | + +### 4.2 AI Demo + +| ID | 支持版本 | 功能概要 | 功能描述 | 备注 | +| --- | --- | --- | --- | --- | +| 1 | V0.2.0 | 人脸检测 | | | +| 2 | V0.2.0 | COCO目标检测 | | | +| 3 | V0.2.0 | yolov8 | | |-seg +| 4 | V0.2.0 | 车牌检测 | | | +| 5 | V0.2.0 | OCR识别 | | | +| 6 | V0.2.0 | 手掌检测 | | | +| 7 | V0.2.0 | 人体检测 | | | +| 8 | V0.2.0 | 人体姿态估计 | | | +| 9 | V0.2.0 | KWS | | | +| 10 | V0.2.0 | 人脸关键点检测 | | | +| 11 | V0.2.0 | 人脸解析 | | | +| 12 | V0.2.0 | 人脸识别 | | | +| 13 | V0.2.0 | OCR检测 | | | +| 14 | V0.2.0 | 车牌识别 | | | +| 15 | V0.2.0 | 人脸姿态角 | | | +| 16 | V0.2.0 | 石头剪刀布 | | | +| 17 | V0.2.0 | 手掌关键点检测 | | | +| 18 | V0.2.0 | 静态手势识别 | | | +| 19 | V0.2.0 | 人脸mesh | | | + +## 5. 版本的遗留问题及限制说明 + +| ID | 功能模块 | 问题/限制描述 | 备注 | +| --- | --- | --- | --- | diff --git a/zh/api/api.rst b/zh/api/api.rst new file mode 100755 index 0000000..5ba1ee7 --- /dev/null +++ b/zh/api/api.rst @@ -0,0 +1,25 @@ +CanMV API 手册 +=========== +.. toctree:: + :maxdepth: 1 + + cipher/K230_CanMV_Hashlib模块API手册.md + cipher/K230_CanMV_Ucryptolib模块API手册.md + extmod/K230_CanMV_uctypes模块API手册.md + machine/K230_CanMV_ADC模块API手册.md + machine/K230_CanMV_FFT模块API手册.md + machine/K230_CanMV_GPIO模块API手册.md + machine/K230_CanMV_I2C模块API手册.md + machine/K230_CanMV_IOMUX_API手册.md + machine/K230_CanMV_PWM模块API手册.md + machine/K230_CanMV_SPI模块API手册.md + machine/K230_CanMV_Timer模块API手册.md + machine/K230_CanMV_WDT模块API手册.md + mpp/K230_CanMV_Audio模块API手册.md + mpp/K230_CanMV_Camera模块API手册.md + mpp/K230_CanMV_Display模块API手册.md + mpp/K230_CanMV_Media模块API手册.md + mpp/K230_CanMV_MP4模块API手册.md + mpp/K230_CanMV_VDEC模块API手册.md + mpp/K230_CanMV_VENC模块API手册.md + mpp/K230_CanMV_播放器模块API手册.md \ No newline at end of file diff --git "a/zh/01_software/cipher/K230_CanMV_Hashlib\346\250\241\345\235\227API\346\211\213\345\206\214.md" "b/zh/api/cipher/K230_CanMV_Hashlib\346\250\241\345\235\227API\346\211\213\345\206\214.md" old mode 100644 new mode 100755 similarity index 100% rename from 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z;z}dT-rCwP$;?Ggw49RpF%-7V>=ILnYoCs@_S2?f!*c0-M=@44UkQ!dl7B{YY}BxJ zW~5sWEfQK};}f|n(u?`Nl$NNJbmCD2i^wur)*fZg4KNW zTWi7@ZU^+es)RZGVVll&OHCQl>3d%1I<>n;0oTi&pqtDx*L7J2dC_r}L--0k_uemq7li9dlZPAfaOI6v1-_aWr25hmPYVhSBn-#t$=P!%kn6D?BsTHq zU=HXnFloB+Jn-^G$gdR9Y{CZiO`-W0JVy+MoeA&e*U52}V6fXJ&q?$Iu;-h=t<13%fjaXLMS* zAj_j(%69w z?RW5zO;%ZFq@lacCno#Mydbg#ldoX4Da#agv60;)Pc)JBvczu%-n>t zEn|U@gyS*{@vG4B>xoXjsl1f2vXndD@#~FF*><0bSKC5f886)(LonqVdG8-Mt~G&5 zuB1mTHCE6=`U}(-vq_+)g*RY)_*K=U ztphos*S7otcnbtC^ZI}oa!JNNVR~RJa-(!(fcJEf-5q26EM?=G{qfo~bHljX?=@r1 z)tjATN2bBr+}}bI2J%-{Tb@ck!Ej2l!lSE!??{WI>8G38!5Kp|?!xykJTD~Wvo6+p zVy{+ChVVRr1AOFt%LOd=zvZ1qr#rG+Ue~AQOg41?SM(IqwG}%g{BMfIgX(9LZo7r? z|J0Ly + +## 免责声明 + +您购买的产品、服务或特性等应受北京嘉楠捷思信息技术有限公司（“本公司”，下同）及其关联公司的商业合同和条款的约束，本文档中描述的全部或部分产品、服务或特性可能不在您的购买或使用范围之内。除非合同另有约定，本公司不对本文档的任何陈述、信息、内容的正确性、可靠性、完整性、适销性、符合特定目的和不侵权提供任何明示或默示的声明或保证。除非另有约定，本文档仅作为使用指导参考。 + +由于产品版本升级或其他原因，本文档内容将可能在未经任何通知的情况下，不定期进行更新或修改。 + +## 商标声明 + +![logo](../images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 + +**版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** +非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 + +

+ +## 目录 + +[TOC] + +## 前言 + +### 概述 + +本文档主要介绍machine模块下的SPI类API。 + +### 读者对象 + +本文档（本指南）主要适用于以下人员： + +- 技术支持工程师 +- 软件开发工程师 + +### 缩略词定义 + +| 简称 | 说明 | +| ---- | ---- | +| SPI | Serial Peripheral Interface | + +### 修订记录 + +| 文档版本号 | 修改说明 | 修改者 | 日期 | +| ---------- | -------- | ---------- | ---------- | +| V1.0 | 初版 | 软件部 | 2023-10-23 | + +## 1. 概述 + +K230内部包含三个SPI硬件模块，片选的极性可配置；支持时钟速率可配。 +通道输出IO配置参考IOMUX模块。 + +## 2. API描述 + +SPI类位于machine模块下 + +### 示例 + +```python +from machine import SPI +# spi init clock 5MHz, polarity 0, phase 0, data bitwide 8bits +spi = SPI(id, baudrate=5000000, polarity=0, phase=0, bits=8) +# Sending data to the slave +spi.write(buf) +# Sending data while reading data to variables +spi.write_readinto(write_buf, read_buf) +# close spi +spi.deinit() +``` + +### 构造函数 + +```python +spi = machine.SPI(id, baudrate=20, polarity=0, phase=0, bits=8) +``` + +【参数】 + +- id： SPI ID, [0~2] (spi.SPI0~spi.SPI2) +- baudrate: SPI时钟baudrate，Fsclk_out=Fssi_clk/BAUDR +- polarity: 极性 +- phase: 相位 +- bits: 数据位宽 + +### write + +```python +spi.write(buf) +``` + +发送数据 + +【参数】 + +- buf： bytearray 类型， 定义了数据及长度 + +【返回值】 + +无 + +### write_readinto + +```python +spi.write_readinto(write_buf, read_buf) +``` + +发送数据，同时读取数据到变量 + +【参数】 + +- write_buf： bytearray 类型， 定义了需要发送的数据及长度 +- read_buf： bytearray 类型， 定义了接收数据存放的位置 + +【返回值】 + +无 + +### deinit + +```python +spi.deinit() +``` + +注销spi + +【参数】 + +无 + +【返回值】 + +无 diff --git "a/zh/01_software/machine/K230 CanMV Timer API\346\211\213\345\206\214.md" "b/zh/api/machine/K230_CanMV_Timer\346\250\241\345\235\227API\346\211\213\345\206\214.md" old mode 100644 new mode 100755 similarity index 95% rename from "zh/01_software/machine/K230 CanMV Timer API\346\211\213\345\206\214.md" rename to "zh/api/machine/K230_CanMV_Timer\346\250\241\345\235\227API\346\211\213\345\206\214.md" index f7f3ef5..37a5f01 --- "a/zh/01_software/machine/K230 CanMV Timer API\346\211\213\345\206\214.md" +++ "b/zh/api/machine/K230_CanMV_Timer\346\250\241\345\235\227API\346\211\213\345\206\214.md" @@ -1,6 +1,6 @@ # K230 CanMV Timer API手册 -![cover](../../template/images/canaan-cover.png) +![cover](../images/canaan-cover.png) 版权所有©2023北京嘉楠捷思信息技术有限公司 @@ -14,7 +14,7 @@ ## 商标声明 -![logo](../../template/images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 +![logo](../images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 **版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** 非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 diff --git "a/zh/01_software/machine/K230 CanMV WDT API\346\211\213\345\206\214.md" "b/zh/api/machine/K230_CanMV_WDT\346\250\241\345\235\227API\346\211\213\345\206\214.md" old mode 100644 new mode 100755 similarity index 93% rename from "zh/01_software/machine/K230 CanMV WDT API\346\211\213\345\206\214.md" rename to "zh/api/machine/K230_CanMV_WDT\346\250\241\345\235\227API\346\211\213\345\206\214.md" index 9c51411..945c758 --- "a/zh/01_software/machine/K230 CanMV WDT API\346\211\213\345\206\214.md" +++ "b/zh/api/machine/K230_CanMV_WDT\346\250\241\345\235\227API\346\211\213\345\206\214.md" @@ -1,6 +1,6 @@ # K230 CanMV WDT API手册 -![cover](../../template/images/canaan-cover.png) +![cover](../images/canaan-cover.png) 版权所有©2023北京嘉楠捷思信息技术有限公司 @@ -14,7 +14,7 @@ ## 商标声明 -![logo](../../template/images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 +![logo](../images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 **版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** 非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 diff --git a/zh/01_software/machine/images/canaan-cover.png b/zh/api/machine/images/canaan-cover.png old mode 100644 new mode 100755 similarity index 100% rename from zh/01_software/machine/images/canaan-cover.png rename to zh/api/machine/images/canaan-cover.png diff --git a/zh/01_software/machine/images/canaan-lable.png b/zh/api/machine/images/canaan-lable.png old mode 100644 new mode 100755 similarity index 100% rename from zh/01_software/machine/images/canaan-lable.png rename to zh/api/machine/images/canaan-lable.png diff --git a/zh/01_software/machine/images/logo.png b/zh/api/machine/images/logo.png similarity index 100% rename from zh/01_software/machine/images/logo.png rename to zh/api/machine/images/logo.png diff --git "a/zh/01_software/mpp/K230_CanMV_Audio\346\250\241\345\235\227API\346\211\213\345\206\214.md" "b/zh/api/mpp/K230_CanMV_Audio\346\250\241\345\235\227API\346\211\213\345\206\214.md" old mode 100644 new mode 100755 similarity index 100% rename from "zh/01_software/mpp/K230_CanMV_Audio\346\250\241\345\235\227API\346\211\213\345\206\214.md" rename to "zh/api/mpp/K230_CanMV_Audio\346\250\241\345\235\227API\346\211\213\345\206\214.md" diff --git "a/zh/01_software/mpp/K230_CanMV_Camera\346\250\241\345\235\227API\346\211\213\345\206\214.md" "b/zh/api/mpp/K230_CanMV_Camera\346\250\241\345\235\227API\346\211\213\345\206\214.md" similarity index 100% rename from "zh/01_software/mpp/K230_CanMV_Camera\346\250\241\345\235\227API\346\211\213\345\206\214.md" rename to "zh/api/mpp/K230_CanMV_Camera\346\250\241\345\235\227API\346\211\213\345\206\214.md" diff --git "a/zh/01_software/mpp/K230_CanMV_Display\346\250\241\345\235\227API\346\211\213\345\206\214.md" "b/zh/api/mpp/K230_CanMV_Display\346\250\241\345\235\227API\346\211\213\345\206\214.md" old mode 100644 new mode 100755 similarity index 100% rename from "zh/01_software/mpp/K230_CanMV_Display\346\250\241\345\235\227API\346\211\213\345\206\214.md" rename to "zh/api/mpp/K230_CanMV_Display\346\250\241\345\235\227API\346\211\213\345\206\214.md" diff --git "a/zh/01_software/mpp/K230_CanMV_MP4\346\250\241\345\235\227API\346\211\213\345\206\214.md" "b/zh/api/mpp/K230_CanMV_MP4\346\250\241\345\235\227API\346\211\213\345\206\214.md" old mode 100644 new mode 100755 similarity index 100% rename from "zh/01_software/mpp/K230_CanMV_MP4\346\250\241\345\235\227API\346\211\213\345\206\214.md" rename to "zh/api/mpp/K230_CanMV_MP4\346\250\241\345\235\227API\346\211\213\345\206\214.md" diff --git "a/zh/01_software/mpp/K230_CanMV_Media\346\250\241\345\235\227API\346\211\213\345\206\214.md" "b/zh/api/mpp/K230_CanMV_Media\346\250\241\345\235\227API\346\211\213\345\206\214.md" similarity index 100% rename from "zh/01_software/mpp/K230_CanMV_Media\346\250\241\345\235\227API\346\211\213\345\206\214.md" rename to "zh/api/mpp/K230_CanMV_Media\346\250\241\345\235\227API\346\211\213\345\206\214.md" diff --git "a/zh/api/mpp/K230_CanMV_VDEC\346\250\241\345\235\227API\346\211\213\345\206\214.md" "b/zh/api/mpp/K230_CanMV_VDEC\346\250\241\345\235\227API\346\211\213\345\206\214.md" new file mode 100755 index 0000000..951d565 --- /dev/null +++ "b/zh/api/mpp/K230_CanMV_VDEC\346\250\241\345\235\227API\346\211\213\345\206\214.md" @@ -0,0 +1,330 @@ +# K230 CanMV VDEC 模块API手册 + +![cover](../images/canaan-cover.png) + +版权所有©2023北京嘉楠捷思信息技术有限公司 + +
+ +## 免责声明 + +您购买的产品、服务或特性等应受北京嘉楠捷思信息技术有限公司（“本公司”，下同）及其关联公司的商业合同和条款的约束，本文档中描述的全部或部分产品、服务或特性可能不在您的购买或使用范围之内。除非合同另有约定，本公司不对本文档的任何陈述、信息、内容的正确性、可靠性、完整性、适销性、符合特定目的和不侵权提供任何明示或默示的声明或保证。除非另有约定，本文档仅作为使用指导参考。 + +由于产品版本升级或其他原因，本文档内容将可能在未经任何通知的情况下，不定期进行更新或修改。 + +## 商标声明 + +![logo](../images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 + +**版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** +非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 + +
+ +## 目录 + +[TOC] + +## 前言 + +### 概述 + +本文档主要介绍K230_CanMV VDEC模块API的使用。 + +### 读者对象 + +本文档（本指南）主要适用于以下人员： + +- 技术支持工程师 +- 软件开发工程师 + +### 缩略词定义 + +| 简称 | 说明 | +| ---- | ---- | +| VDEC | Video Decoder | + +### 修订记录 + +| 文档版本号 | 修改说明 | 修改者 | 日期 | +| ---------- | -------- | ---------- | ---------- | +| V1.0 | 初版 | 孙小朋 | 2023-10-27 | + +## 1. 概述 + +此文档介绍K230_CanMV VDEC模块API，可支持264,265解码，并于vo模块绑定，将解码数据输出到vo显示。 + +## 2. API描述 + +提供Decoder类，该类提供如下方法： + +### Decoder.\_\_init__ + +【描述】 + +构造函数 + +【语法】 + +```python +decoder = Decoder(K_PT_H264) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +|-----------------|-------------------------------|-----------| +| type | 编码类型 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +|---------|---------------------------------| +| 0 | 成功 | +| 非 0 | 失败 | + +【注意】 + +VDEC最多支持4路解码 + +【举例】 + +无 + +【相关主题】 + +无 + +### Decoder.Create + +【描述】 + +创建解码器 + +【语法】 + +```python +decoder = Decoder(K_PT_H264) +decoder.create() +``` + +【参数】 + +无 + +【返回值】 + +| 返回值 | 描述 | +|---------|---------------------------------| +| 0 | 成功 | +| 非 0 | 失败 | + +【举例】 + +无 + +【相关主题】 + +无 + +### Decoder.destroy + +【描述】 + +销毁解码器 + +【语法】 + +```python +decoder = Decoder(K_PT_H264) +decoder.destroy() +``` + +【参数】 + +无 + +【返回值】 + +| 返回值 | 描述 | +|--------|------| +| 0 | 成功 | +| 非0 | 失败 | + +【注意】 + +无 + +【举例】 + +无 + +### Decoder.Start + +【描述】 + +开始编码 + +【语法】 + +```python +decoder = Decoder(K_PT_H264) +decoder.Start() +``` + +【参数】 + +【返回值】 + +| 返回值 | 描述 | +|--------|-----| +| 0 | 成功 | +| 非0 | 失败 | + +【注意】 + +无 + +【举例】 + +无 + +### Decoder.decode + +【描述】 + +解码一帧数据 + +【语法】 + +```python +decoder = Decoder(K_PT_H264) +decoder.decode(stream_data) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +|----------|-----|----------| +| stream_data | 编码数据 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +|--------|------| +| 0 | 成功 | +| 非0 | 失败 | + +【注意】 + +无 + +【举例】 + +无 + +### Decoder.stop + +【描述】 + +释放一帧码流buffer + +【语法】 + +```python +decoder = Decoder(K_PT_H264) +decoder.stop() +``` + +【参数】 + +无 + +【返回值】 + +| 返回值 | 描述 | +|--------|------| +| 0 | 成功 | +| 非0 | 失败 | + +【注意】 + +无 + +【举例】 + +无 + +## 3. 数据结构描述 + +### StreamData + +【说明】 + +码流结构体 + +【定义】 + +```python +class StreamData: + def __init__(self): + self.data + self.pts +``` + +【成员】 + +| 成员名称 | 描述 | +|---------|------| +| data | 码流数据 | +| pts | 时间戳 | + +【注意事项】 + +无 + +## 4. 示例程序 + +### 例程1 + +```python +from media.media import * +from mpp.payload_struct import * +import media.vdecoder as vdecoder +from media.display import * + +def vdec_vo_test(): + #init display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(400, 200, 1280, 720, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + + #init vdec + vdec = vdecoder.Decoder(K_PT_H265) + ret = media.buffer_init() + if ret: + print("buffer_init failed") + + vdec.create() + vdec.start() + + #bind vdec -> vo + meida_source = media_device(VIDEO_DECODE_MOD_ID, VDEC_DEV_ID, VDEC_CHN_ID_0) + meida_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(meida_source, meida_sink) + + #show vdec data + stream_data = StreamData() + while(1): + stream_data.data = get_one_frame_data()#get one frame + vdec.decode(stream_data) + + #deinit + vdec.stop() + media.destroy_link(meida_source, meida_sink) + vdec.destroy() + time.sleep(1) + display.deinit() + +vdec_vo_test() + +``` diff --git "a/zh/01_software/mpp/K230_CanMV_VENC\346\250\241\345\235\227API\346\211\213\345\206\214.md" "b/zh/api/mpp/K230_CanMV_VENC\346\250\241\345\235\227API\346\211\213\345\206\214.md" old mode 100644 new mode 100755 similarity index 98% rename from "zh/01_software/mpp/K230_CanMV_VENC\346\250\241\345\235\227API\346\211\213\345\206\214.md" rename to "zh/api/mpp/K230_CanMV_VENC\346\250\241\345\235\227API\346\211\213\345\206\214.md" index 86bc54b..420223f --- "a/zh/01_software/mpp/K230_CanMV_VENC\346\250\241\345\235\227API\346\211\213\345\206\214.md" +++ "b/zh/api/mpp/K230_CanMV_VENC\346\250\241\345\235\227API\346\211\213\345\206\214.md" @@ -85,7 +85,7 @@ Encoder.Create(chn, chnAttr) | 非 0 | 失败 | 【注意】 -VENC最多支持4路编码，编码通道号取值范围[0, 3] +VENC最多支持4路编码，编码通道号取值范围[0, 3]，其中第4路固定用于 IDE 图像传输，除非不调用 compress_for_ide ，不然建议只使用 [0, 2] 【举例】 diff --git "a/zh/api/mpp/K230_CanMV_\346\222\255\346\224\276\345\231\250\346\250\241\345\235\227API\346\211\213\345\206\214.md" "b/zh/api/mpp/K230_CanMV_\346\222\255\346\224\276\345\231\250\346\250\241\345\235\227API\346\211\213\345\206\214.md" new file mode 100755 index 0000000..7e44e73 --- /dev/null +++ "b/zh/api/mpp/K230_CanMV_\346\222\255\346\224\276\345\231\250\346\250\241\345\235\227API\346\211\213\345\206\214.md" @@ -0,0 +1,306 @@ +# K230 CanMV 播放器 模块API手册 + +![cover](../images/canaan-cover.png) + +版权所有©2023北京嘉楠捷思信息技术有限公司 + +
+ +## 免责声明 + +您购买的产品、服务或特性等应受北京嘉楠捷思信息技术有限公司（“本公司”，下同）及其关联公司的商业合同和条款的约束，本文档中描述的全部或部分产品、服务或特性可能不在您的购买或使用范围之内。除非合同另有约定，本公司不对本文档的任何陈述、信息、内容的正确性、可靠性、完整性、适销性、符合特定目的和不侵权提供任何明示或默示的声明或保证。除非另有约定，本文档仅作为使用指导参考。 + +由于产品版本升级或其他原因，本文档内容将可能在未经任何通知的情况下，不定期进行更新或修改。 + +## 商标声明 + +![logo](../images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 + +**版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** +非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 + +
+ +## 目录 + +[TOC] + +## 前言 + +### 概述 + +本文档主要介绍K230_CanMV 播放器模块API的使用。 + +### 读者对象 + +本文档（本指南）主要适用于以下人员： + +- 技术支持工程师 +- 软件开发工程师 + +### 修订记录 + +| 文档版本号 | 修改说明 | 修改者 | 日期 | +| ---------- | -------- | ---------- | ---------- | +| V1.0 | 初版 | 孙小朋 | 2023-10-27 | + +## 1. 概述 + +此文档介绍K230_CanMV 播放器模块API，可播放mp4格式文件。支持音视频同时播放，音频支持g711a/u，视频支持H264/H265。 + +## 2. API描述 + +提供Player类，该类提供如下方法： + +### Player.load + +【描述】 + +加载文件，当前版本只支持mp4格式文件 + +【语法】 + +```python +player=Player() +player.load("test.mp4") +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +|-----------------|-------------------------------|-----------| +| filename | 文件名称 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +|---------|---------------------------------| +| 0 | 成功 | +| 非 0 | 失败 | + +【注意】 + +当前版本只支持播放mp4格式文件 + +【举例】 + +无 + +【相关主题】 + +无 + +### Player.start + +【描述】 + +开始播放 + +【语法】 + +```python +player=Player() +player.start() +``` + +【参数】 + +无 + +【返回值】 + +| 返回值 | 描述 | +|--------|------| +| 0 | 成功 | +| 非0 | 失败 | + +【注意】 + +无 + +【举例】 + +无 + +### Player.pause + +【描述】 + +暂停播放 + +【语法】 + +```python +player=Player() +player.pause() +``` + +【参数】 + +无 + +【返回值】 + +| 返回值 | 描述 | +|--------|-----| +| 0 | 成功 | +| 非0 | 失败 | + +【注意】 + +无 + +【举例】 + +无 + +### Player.resume + +【描述】 + +继续播放 + +【语法】 + +```python +player=Player() +player.resume() +``` + +【参数】 + +无 + +【返回值】 + +| 返回值 | 描述 | +|--------|------| +| 0 | 成功 | +| 非0 | 失败 | + +【注意】 + +无 + +【举例】 + +无 + +### Player.stop + +【描述】 + +停止播放 + +【语法】 + +```python +player=Player() +player.stop() +``` + +【参数】 + +无 + +【返回值】 + +| 返回值 | 描述 | +|--------|------| +| 0 | 成功 | +| 非0 | 失败 | + +【注意】 + +无 + +【举例】 + +无 + +### Player.set_event_callback + +【描述】 + +设置播放事件回调函数 + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +|-----------------|-------------------------------|-----------| +| callback | 回调函数名 | 输入 | + +【语法】 + +```python + +def player_event(event,data): + pass + +player=Player() +player.set_event_callback(callback=player_event) +``` + +【参数】 + +无 + +【返回值】 + +| 返回值 | 描述 | +|--------|------| +| 0 | 成功 | +| 非0 | 失败 | + +【注意】 + +无 + +【举例】 + +无 + +## 3. 数据结构描述 + +### playe_event_type + +【描述】 + +编码格式类型 + +【成员】 + +| 成员名称 | 描述 | +|---------|------| +| K_PLAYER_EVENT_EOF | 播放结束 | +| K_PLAYER_EVENT_PROGRESS| 播放进度 | + +## 4. 示例程序 + +### 例程1 + +```python +from media.player import * + +start_play = False +def player_event(event,data): + global start_play + if(event == K_PLAYER_EVENT_EOF): + start_play = False + +def play_mp4_test(filename): + global start_play + player=Player() + player.load(filename) + player.set_event_callback(player_event) + player.start() + start_play = True + + while(start_play): + time.sleep(0.1) + + player.stop() + print("play over") + +play_mp4_test("/sdcard/app/tests/test.mp4") +``` diff --git a/zh/01_software/mpp/images/canaan-cover.png b/zh/api/mpp/images/canaan-cover.png old mode 100644 new mode 100755 similarity index 100% rename from zh/01_software/mpp/images/canaan-cover.png rename to zh/api/mpp/images/canaan-cover.png diff --git a/zh/01_software/mpp/images/canaan-lable.png b/zh/api/mpp/images/canaan-lable.png old mode 100644 new mode 100755 similarity index 100% rename from zh/01_software/mpp/images/canaan-lable.png rename to zh/api/mpp/images/canaan-lable.png diff --git a/zh/01_software/mpp/images/k230-canmv-camera-top.png b/zh/api/mpp/images/k230-canmv-camera-top.png similarity index 100% rename from zh/01_software/mpp/images/k230-canmv-camera-top.png rename to zh/api/mpp/images/k230-canmv-camera-top.png diff --git a/zh/01_software/mpp/images/logo.png b/zh/api/mpp/images/logo.png similarity index 100% rename from zh/01_software/mpp/images/logo.png rename to zh/api/mpp/images/logo.png diff --git "a/zh/api/nncase_runtime/K230_CanMV_nncase_runtime_API\346\211\213\345\206\214.md" "b/zh/api/nncase_runtime/K230_CanMV_nncase_runtime_API\346\211\213\345\206\214.md" new file mode 100755 index 0000000..0e4a6b6 --- /dev/null +++ "b/zh/api/nncase_runtime/K230_CanMV_nncase_runtime_API\346\211\213\345\206\214.md" @@ -0,0 +1,524 @@ +# K230 CanMV nncase_runtime 模块API手册 + +![cover](../images/canaan-cover.png) + +版权所有©2023北京嘉楠捷思信息技术有限公司 + +
+ +## 免责声明 + +您购买的产品、服务或特性等应受北京嘉楠捷思信息技术有限公司（“本公司”，下同）及其关联公司的商业合同和条款的约束，本文档中描述的全部或部分产品、服务或特性可能不在您的购买或使用范围之内。除非合同另有约定，本公司不对本文档的任何陈述、信息、内容的正确性、可靠性、完整性、适销性、符合特定目的和不侵权提供任何明示或默示的声明或保证。除非另有约定，本文档仅作为使用指导参考。 + +由于产品版本升级或其他原因，本文档内容将可能在未经任何通知的情况下，不定期进行更新或修改。 + +## 商标声明 + +![logo](../images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 + +**版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** +非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 + +
+ +## 目录 + +[toc] + +## 前言 + +### 概述 + +此文档介绍CanMV nncase_runtime模块，用于指导开发人员使用MicroPython调用KPU和AI2D模块。 + +### 读者对象 + +本文档（本指南）主要适用于以下人员： + +- 技术支持工程师 +- 软件开发工程师 + +### 缩略词定义 + +| 简称 | 说明 | +| ------------------- | ------------------------------------------- | +| nncase_runtime | k230 nncase runtime包,包含KPU模块和AI2D模块 | +| nncase_runtime.kpu | kpu模块 | +| nncase_runtime.ai2d | ai2d模块 | + +### 修订记录 + +| 文档版本号 | 修改说明 | 修改者 | 日期 | +| ---------- | -------- | ------ | ---------- | +| V1.0 | 初版 | 杨浩琪 | 2023-09-18 | + +## 1. 概述 + +此文档介绍CanMV nncase_runtime模块，用于指导开发人员使用MicroPython调用KPU和AI2D模块。 + +## 2. API描述 + +### 2.1 from_numpy + +【描述】 +从MicroPython中ulab.numpy创建runtime_tensor。 + +【语法】 + +```Python +runtime_tensor = nncase_runtime.from_numpy(ulab.numpy) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +| ---------- | --------- | --------- | +| ulab.numpy | numpy对象 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +| -------------- | ---------------------------- | +| runtime_tensor | 返回创建好的runtime_tensor。 | +| 其他 | 失败，抛出C++异常。 | + +### 2.2 to_numpy + +【描述】 + +将runtime_tensor转为ulab.numpy。 + +【语法】 + +```Python +runtime_tensor = kpu.get_output_tensor(0) +result = runtime_tensor.to_numpy() +``` + +【参数】 + +无。 + +【返回值】 + +| 返回值 | 描述 | +| ---------- | ---------------------------------------- | +| ulab.numpy | 返回从runtime_tensor转换后的ulab.numpy。 | +| 其他 | 失败 | + +### 2.3 nncase_runtime.kpu + +kpu模块提供调用KPU硬件来完成开发板上推理神经网络模型的基础函数，主要包括加载模型，设置输入数据，执行推理，获取输出结果等功能。 + +#### 2.3.1 load_kmodel + +【描述】 + +加载编译生成的kmodel格式的神经网络。 + +【语法】 + +```Python +load_kmodel(read_bin) +load_kmodel(path) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +| -------- | ------------------ | --------- | +| read_bin | kmodel的二进制内容 | 输入 | +| path | kmodel的路径 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +| ------ | ------------------- | +| 无 | 成功。 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.3.2 set_input_tensor + +【描述】 + +设置kmodel推理时的runtime_tensor。 + +【语法】 + +```Python +set_input_tensor(index, runtime_tensor) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +| -------------- | ------------------ | --------- | +| index | kmodel的输入索引。 | 输入 | +| runtime_tensor | 包含输入数据信息。 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +| ------ | ------------------- | +| 无 | 成功。 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.3.3 get_input_tensor + +【描述】 + +获取kmodel推理时的runtime_tensor。 + +【语法】 + +```Python +get_input_tensor(index) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +| -------- | ------------------ | --------- | +| index | kmodel的输入索引。 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +| -------------- | ------------------- | +| runtime_tensor | 包含输入数据信息。 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.3.4 set_output_tensor + +【描述】 + +设置kmodel推理后的输出结果。 + +【语法】 + +```Python +set_output_tensor(index, runtime_tensor) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +| -------------- | ------------------ | --------- | +| index | kmodel的输出索引。 | 输入 | +| runtime_tensor | 输出结果。 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +| ------ | ------------------- | +| 无 | 成功。 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.3.5 get_output_tensor + +【描述】 + +获取kmodel推理后的输出结果。 + +【语法】 + +```Python +get_output_tensor(index) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +| -------- | ------------------ | --------- | +| index | kmodel的输出索引。 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +| :------------- | ----------------------------- | +| runtime_tensor | 获取第index个runtime_tensor。 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.3.6 run + +【描述】 + +启动kmodel推理 + +【语法】 + +```Python +run() +``` + +【返回值】 + +| 返回值 | 描述 | +| :----- | ------------------- | +| 无 | 推理成功 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.3.7 inputs_size + +【描述】 + +获取kmodel的输入个数。 + +【语法】 + +```Python +inputs_size() +``` + +【返回值】 + +| 返回值 | 描述 | +| :----- | ------------------- | +| size_t | kmodel的输入个数。 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.3.8 outputs_size + +【描述】 + +获取kmodel的输出个数。 + +【语法】 + +```Python +outputs_size() +``` + +【返回值】 + +| 返回值 | 描述 | +| :----- | ------------------- | +| size_t | kmodel的输出个数。 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.3.9 get_input_desc + +【描述】 + +获取指定索引的输入的描述信息。 + +【语法】 + +```Python +get_input_desc(index) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +| -------- | ------------------ | --------- | +| index | kmodel的输入索引。 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +| :---------- | -------------------------------------------------- | +| MemoryRange | 第index个输入信息:`dtype`, `start`, `size`。 | + +#### 2.3.10 get_output_desc + +【描述】 + +获取指定索引的输出的描述信息。 + +【语法】 + +```Python +get_output_desc(index) +``` + +【参数】 + +| 参数名称 | 描述 | 输入/输出 | +| -------- | ------------------ | --------- | +| index | kmodel的输入索引。 | 输入 | + +【返回值】 + +| 返回值 | 描述 | +| :---------- | -------------------------------------------------- | +| MemoryRange | 第index个输出信息:`dtype`, `start`, `size`。 | + +### 2.4 nncase_runtime.ai2d + +#### 2.4.1 build + +【描述】 + +ai2d_builder的构造函数. + +【语法】 + +```Python +build(input_shape, output_shape) +``` + +【参数】 + +| 名称 | 描述 | +| ------------ | -------- | +| input_shape | 输入形状 | +| output_shape | 输出形状 | + +【返回值 】 + +| 返回值 | 描述 | +| :----------- | ---------------------------- | +| ai2d_builder | 返回ai2d_builder，用于执行。 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.4.2 run + +配置寄存器并启动AI2D的计算. + +【定义】 + +```Python +ai2d_builder.run(input_tensor, output_tensor) +``` + +【参数】 + +| 名称 | 描述 | +| ------------- | ---------- | +| input_tensor | 输入tensor | +| output_tensor | 输出tensor | + +【返回值】 + +| 返回值 | 描述 | +| ------ | ------------------- | +| 无 | 成功。 | +| 其他 | 失败，抛出C++异常。 | + +#### 2.4.3 set_dtype + +【描述】 + +用于设置AI2D计算过程中的数据类型. + +【定义】 + +```Python +set_dtype(src_format, dst_format, src_type, dst_type) +``` + +【参数】 + +| 名称 | 类型 | 描述 | +| ---------- | ----------- | ------------ | +| src_format | ai2d_format | 输入数据格式 | +| dst_format | ai2d_format | 输出数据格式 | +| src_type | datatype_t | 输入数据类型 | +| dst_type | datatype_t | 输出数据类型 | + +#### 2.4.4 set_crop_param + +【描述】 + +用于配置crop相关的参数. + +【定义】 + +```Python +set_crop_param(crop_flag, start_x, start_y, width, height) +``` + +【参数】 + +| 名称 | 类型 | 描述 | +| --------- | ---- | ------------------ | +| crop_flag | bool | 是否开启crop功能 | +| start_x | int | 宽度方向的起始像素 | +| start_y | int | 高度方向的起始像素 | +| width | int | 宽度方向的crop长度 | +| height | int | 高度方向的crop长度 | + +#### 2.4.5 set_shift_param + +【描述】 + +用于配置shift相关的参数. + +【定义】 + +```Python +set_shift_param(shift_flag, shift_val) +``` + +【参数】 + +| 名称 | 类型 | 描述 | +| ---------- | ---- | ----------------- | +| shift_flag | bool | 是否开启shift功能 | +| shift_val | int | 右移的比特数 | + +#### 2.4.6 set_pad_param + +【描述】 + +用于配置pad相关的参数. + +【定义】 + +```Python +set_pad_param(pad_flag, paddings, pad_mode, pad_val) +``` + +【参数】 + +| 名称 | 类型 | 描述 | +| -------- | ---- | --------------------------------------------------------------------------------------------- | +| pad_flag | bool | 是否开启pad功能 | +| paddings | list | 各个维度的padding, size=8，分别表示dim0到dim4的前后padding的个数，其中dim0/dim1固定配置{0, 0} | +| pad_mode | int | 只支持pad constant，配置0即可 | +| pad_val | list | 每个channel的padding value | + +#### 2.4.7 set_resize_param + +【描述】 + +用于配置resize相关的参数. + +【定义】 + +```Python +set_resize_param(resize_flag, interp_method, interp_mode) +``` + +【参数】 + +| 名称 | 类型 | 描述 | +| ------------- | ------------------ | ------------------ | +| resize_flag | bool | 是否开启resize功能 | +| interp_method | ai2d_interp_method | resize插值方法 | +| interp_mode | ai2d_interp_mode | resize模式 | + +#### 2.4.8 set_affine_param + +【描述】 + +用于配置affine相关的参数. + +【定义】 + +```Python +set_affine_param(affine_flag, interp_method, cord_round, bound_ind, bound_val, bound_smooth, M) +``` + +【参数】 + +| 名称 | 类型 | 描述 | +| ------------- | ------------------ | ------------------------------------------------------------------------------------------------------------------------ | +| affine_flag | bool | 是否开启affine功能 | +| interp_method | ai2d_interp_method | Affine采用的插值方法 | +| cord_round | uint32_t | 整数边界0或者1 | +| bound_ind | uint32_t | 边界像素模式0或者1 | +| bound_val | uint32_t | 边界填充值 | +| bound_smooth | uint32_t | 边界平滑0或者1 | +| M | list | 仿射变换矩阵对应的vector，仿射变换为Y=\[a_0, a_1; a_2, a_3\] \cdot X + \[b_0, b_1\] $, 则 M=[a_0,a_1,b_0,a_2,a_3,b_1 ] | diff --git "a/zh/example/K230_CanMV_AI_Demo\347\244\272\344\276\213\350\257\264\346\230\216.md" "b/zh/example/K230_CanMV_AI_Demo\347\244\272\344\276\213\350\257\264\346\230\216.md" new file mode 100755 index 0000000..98ef584 --- /dev/null +++ "b/zh/example/K230_CanMV_AI_Demo\347\244\272\344\276\213\350\257\264\346\230\216.md" @@ -0,0 +1,9859 @@ +# K230 CanMV AI Demo示例说明 + +![cover](images/canaan-cover.png) + +版权所有©2023北京嘉楠捷思信息技术有限公司 + +
+ +## 免责声明 + +您购买的产品、服务或特性等应受北京嘉楠捷思信息技术有限公司（“本公司”，下同）及其关联公司的商业合同和条款的约束，本文档中描述的全部或部分产品、服务或特性可能不在您的购买或使用范围之内。除非合同另有约定，本公司不对本文档的任何陈述、信息、内容的正确性、可靠性、完整性、适销性、符合特定目的和不侵权提供任何明示或默示的声明或保证。除非另有约定，本文档仅作为使用指导参考。 + +由于产品版本升级或其他原因，本文档内容将可能在未经任何通知的情况下，不定期进行更新或修改。 + +## 商标声明 + +![logo](images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 + +**版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** +非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 + +
+ +## 目录 + +[TOC] + +## 前言 + +### 概述 + +本文档主要介绍如何写AI Demo。 + +### 读者对象 + +本文档（本指南）主要适用于以下人员： + +- 技术支持工程师 +- 软件开发工程师 + +## 一、概述 + +本文档包括19个AI Demo，这些示例程序都实现从摄像头采集数据、kpu推理到显示器展示的流程，应用到了K230 CanMV 平台的多个硬件模块：AI2D，KPU，Camera，Display等。 + +这些AI Demo分为两种类型：单模型、多模型，涵盖物体、人脸、人手、人体、车牌、OCR等方向；参考该文档，k230用户可以更快上手K230 AI应用的开发，实现预期效果。 + +更多AI Demo后续即将解锁。 + +| 单模型示例 | 多模型示例 | +| ------------ | -------------- | +| 人脸检测 | 人脸关键点检测 | +| COCO目标检测 | 人脸识别 | +| yolov8-seg | 人脸姿态角 | +| 车牌检测 | 人脸解析 | +| OCR检测 | 车牌识别 | +| 手掌检测 | 石头剪刀布 | +| 人体检测 | OCR识别 | +| 人体姿态估计 | 手掌关键点检测 | +| KWS | 静态手势识别 | +| | 人脸mesh | + +## 二、AI Demo单模型示例解析 + +### 1.分模块解析 + +#### 1.1 模块引入 + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行时模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import aidemo #aidemo模块，封装ai demo相关后处理、画图操作 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 +``` + +| 模块 | 说明 | +| ---------------------- | ------------------------------------------------------------ | +| image（必选） | 图像模块，主要用于读取、图像绘制元素（框、点等）等操作 | +| media.camera（必选） | [摄像头模块](https://github.com/kendryte/k230_canmv_docs/blob/main/zh/01_software/mpp/K230_CanMV_Camera%E6%A8%A1%E5%9D%97API%E6%89%8B%E5%86%8C.md) | +| media.display （必选） | [显示模块](https://github.com/kendryte/k230_canmv_docs/blob/main/zh/01_software/mpp/K230_CanMV_Display%E6%A8%A1%E5%9D%97API%E6%89%8B%E5%86%8C.md) | +| media.media（必选） | [媒体软件抽象模块，主要封装媒体数据链路以及媒体缓冲区](https://github.com/kendryte/k230_canmv_docs/blob/main/zh/01_software/mpp/K230_CanMV_Media%E6%A8%A1%E5%9D%97API%E6%89%8B%E5%86%8C.md) | +| nncase_runtime（必选） | nncase运行时模块， 封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 | +| aidemo（可选） | 封装部分ai demo相关后处理、复杂画图操作 | +| aicube（可选） | 封装基于ai cube训练的检测分割等任务的后处理 | +| ulab.numpy （可选） | [类似python numpy操作，但也会有一些接口不同](https://micropython-ulab.readthedocs.io/en/latest/ ) | +| time（可选） | 时间统计 | +| gc（可选） | [垃圾回收模块](https://docs.micropython.org/en/latest/library/gc.html)，自动回收 | + +#### 1.2 参数配置 + +不同模型（kmodel）根据自己需要设置参数信息，包括显示、AI原图、kmodel参数、文件配置、调试模式等。 + +```python +#********************for config.py******************** +# display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) # 显示宽度要求16位对齐 +DISPLAY_HEIGHT = 1080 + +# ai原图分辨率，sensor默认出图为16:9，若需不形变原图，最好按照16:9比例设置宽高 +OUT_RGB888P_WIDTH = ALIGN_UP(1024, 16) # ai原图宽度要求16位对齐 +OUT_RGB888P_HEIGH = 576 + +# kmodel相关参数设置 +# kmodel输入shape,NCHW,RGB +kmodel_input_shape = (1,3,320,320) +# ai原图padding +rgb_mean = [104,117,123] +# 其它kmodel参数 +...... + +# 文件配置 +root_dir = '/sdcard/app/tests/' +kmodel_file = root_dir + 'kmodel/face_detection_320.kmodel' # kmodel路径 +anchors_path = root_dir + 'utils/prior_data_320.bin' # kmodel anchor +# 调试模型，0：不调试，>0：打印对应级别调试信息 +debug_mode = 0 +``` + +#### 1.3 时间统计工具 + +ScopedTiming 类是一个用来测量代码块执行时间的上下文管理器。上下文管理器通过定义包含 `__enter__` 和 `__exit__` 方法的类来创建。当在 with 语句中使用该类的实例时，`__enter__` 在进入 with 块时被调用，`__exit__` 在离开时被调用。 + +```python +#********************for scoped_timing.py******************** +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") +``` + +**使用示例：** + +```python +import ulab.numpy as np +debug_mode = 1 +a = np.ones(10**6, dtype=np.float) +with ScopedTiming("kpu_pre_process",debug_mode > 0): + a = a * 1.000001 + 0.000001 +``` + +#### 1.4 nncase使用：ai2d + +ai2d主要是用于对输入原图预处理进行硬件加速，然后把预处理结果喂给kmodel。 + +##### （1）ai2d基础用法示例 + +```python +import nncase_runtime as nn +# 注：此示例仅为基础用法，其它demo可根据实际情况调整 +def get_pad_one_side_param(): + # 右padding或下padding，获取padding参数 + dst_w = kmodel_input_shape[3] # kmodel输入宽（w） + dst_h = kmodel_input_shape[2] # kmodel输入高（h） + + # OUT_RGB888P_WIDTH：原图宽（w） + # OUT_RGB888P_HEIGH：原图高（h） + # 计算最小的缩放比例，等比例缩放 + ratio_w = dst_w / OUT_RGB888P_WIDTH + ratio_h = dst_h / OUT_RGB888P_HEIGH + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + # 计算经过缩放后的新宽和新高 + new_w = (int)(ratio * OUT_RGB888P_WIDTH) + new_h = (int)(ratio * OUT_RGB888P_HEIGH) + + # 计算需要添加的padding，以使得kmodel输入的宽高和原图一致 + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + # 四舍五入，确保padding是整数 + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +def ai2d_demo(rgb888p_img): + # 初始化AI2D模块 + # rgb888p_img为Image对象 + with ScopedTiming("ai2d_init",debug_mode > 0): + # (1)创建ai2d实例 + ai2d = nn.ai2d() + # (2)设置ai2d参数 + # 设置ai2d输入、输出格式和数据类型 + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + # 设置padding参数 + ai2d.set_pad_param(True, get_pad_one_side_param(), 0, rgb_mean) + # 设置resize参数 + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + # （3）根据ai2d参数构建ai2d_builder + global ai2d_builder + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], kmodel_input_shape) + + # （4）创建ai2d输出，用于保存ai2d输出结果 + global ai2d_output_tensor + data = np.ones(kmodel_input_shape, dtype=np.uint8) + ai2d_output_tensor = nn.from_numpy(data) + + # （5）创建ai2d输入对象，并将对象从numpy转换为tensor + ai2d_input = rgb888p_img.to_numpy_ref() + ai2d_input_tensor = nn.from_numpy(ai2d_input) + + # （6）根据输入，ai2d参数，运行得到ai2d输出，将结果保存到ai2d_output_tensor中 + ai2d_builder.run(ai2d_input_tensor, ai2d_output_tensor) + # dump ai2d结果查看，查看结果是否正确（此时保存格式为nn.ai2d_format.NCHW_FMT，NCHW、RGB格式，需要转换为图片格式后再查看） + #ai2d_out_data = fp_ai2d_output_tensor.to_numpy() # + #with open("/sdcard/app/ai2d_out.bin", "wb") as file: + #file.write(ai2d_out_data.tobytes()) + + # （7）删除 ai2d、ai2d_input_tensor、ai2d_output_tensor、ai2d_builder 变量，释放对它所引用对象的内存引用 + del ai2d + del ai2d_input_tensor + del ai2d_output_tensor + del ai2d_builder +``` + +##### （2）ai2d示例用法一：ai2d参数固定 + +**ai2d参数固定**：针对视频流的不同帧，ai2d参数固定使用示例 + +eg：人脸检测模型，对于模型输入，需要将原图进行预处理（padding、resize）之后，然后再喂给kmodel；若是原图从sensor中取出，分辨率固定，则padding的上下左右位置是固定的，此时可以使用以下模板对原图进行预处理 + +```python +import nncase_runtime as nn +#ai2d：ai2d实例 +#ai2d_input_tensor：ai2d输入 +#ai2d_output_tensor：ai2d输出 +#ai2d_builder：根据ai2d参数，构建的ai2d_builder对象 +global ai2d,ai2d_input_tensor,ai2d_output_tensor,ai2d_builder + +def get_pad_one_side_param(): + # 右padding或下padding，获取padding参数 + ..... + return [0, 0, 0, 0, top, bottom, left, right] + +def ai2d_init(): + # 初始化AI2D模块 + with ScopedTiming("ai2d_init",debug_mode > 0): + global ai2d + # （1）创建ai2d实例 + ai2d = nn.ai2d() + # （2）设置ai2d参数 + # 设置ai2d输入、输出格式和数据类型 + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + # 设置padding参数 + ai2d.set_pad_param(True, get_pad_one_side_param(), 0, rgb_mean) + # 设置resize参数 + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + # （3）创建ai2d输出，用于保存ai2d输出结果 + global ai2d_output_tensor + data = np.ones(kmodel_input_shape, dtype=np.uint8) + ai2d_output_tensor = nn.from_numpy(data) + + # （4）根据ai2d参数构建ai2d_builder，因为ai2d的参数不变，因此只需创建一次ai2d_builder + global ai2d_builder + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], kmodel_input_shape) + + +def ai2d_run(rgb888p_img): + with ScopedTiming("ai2d_run",debug_mode > 0): + global ai2d_input_tensor,ai2d_output_tensor + # （1）创建ai2d输入对象，并将对象从numpy转换为tensor + ai2d_input = rgb888p_img.to_numpy_ref() + ai2d_input_tensor = nn.from_numpy(ai2d_input) + + # （2）根据输入，ai2d参数，运行得到ai2d输出，将结果保存到ai2d_output_tensor中 + ai2d_builder.run(ai2d_input_tensor, ai2d_output_tensor) + +def ai2d_release(): + with ScopedTiming("ai2d_release",debug_mode > 0): + global ai2d_input_tensor + # 删除 ai2d_input_tensor 变量，释放对它所引用对象的内存引用 + del ai2d_input_tensor +``` + +**使用示例：** + +```python +ai2d_init() # ai2d初始化 +while True: + rgb888p_img = camera_read(CAM_DEV_ID_0) # 从sensor拿到一帧图像 + ai2d_run(rgb888p_img) # 对sensor原图像预处理 + ai2d_release() # 释放ai2d_input_tensor，因为每帧原图不同，ai2d_input_tensor指向的对象都会改变，所以每次都需释放内存 +...... +global ai2d,ai2d_output_tensor,ai2d_builder #只需释放一次 +del ai2d # 释放ai2d，因为ai2d指向的对象是固定的 +del ai2d_output_tensor # 释放ai2d_output_tensor，因为ai2d_output_tensor指向的对象是固定的 +del ai2d_builder # 释放ai2d_builder，因为ai2d的参数未变，ai2d_builder指向的对象是固定的 +``` + +##### （3）ai2d示例用法二：ai2d参数不固定 + +**ai2d参数不固定**：针对视频流的不同帧，ai2d参数实时变化 + +eg：人脸关键点检测模型，对于模型输入，需要将原图进行预处理（affine）之后，然后再喂给kmodel；即使原图从sensor中取出，分辨率固定，但是affine的参数是实时改变的，此时可以使用以下模板对原图进行预处理。 + +```python +import nncase_runtime as nn +# fld_ai2d：人脸关键点ai2d实例 +# fld_ai2d_input_tensor：人脸关键点ai2d输入 +# fld_ai2d_output_tensor：人脸关键点ai2d输出 +# fld_ai2d_builder：根据人脸关键点ai2d参数，构建的人脸关键点ai2d_builder对象 +global fld_ai2d,fld_ai2d_input_tensor,fld_ai2d_output_tensor,fld_ai2d_builder +# affine参数 +global matrix_dst + +def get_affine_matrix(bbox): + # 根据人脸检测框获取仿射矩阵，用于将边界框映射到模型输入空间 + with ScopedTiming("get_affine_matrix", debug_mode > 1): + ...... + return matrix_dst + +def fld_ai2d_init(): + with ScopedTiming("fld_ai2d_init",debug_mode > 0): + #for face landmark + global fld_ai2d + # （1）创建人脸关键点ai2d对象 + fld_ai2d = nn.ai2d() + + global fld_ai2d_output_tensor + # （2）创建人脸关键点ai2d_output_tensor对象 + data = np.ones(fld_kmodel_input_shape, dtype=np.uint8) + fld_ai2d_output_tensor = nn.from_numpy(data) + +def fld_ai2d_run(rgb888p_img,det): + # 人脸关键点ai2d运行，rgb888p_img是Image对象，det为人脸检测框 + with ScopedTiming("fld_ai2d_run",debug_mode > 0): + global fld_ai2d,fld_ai2d_input_tensor,fld_ai2d_output_tensor + # （1）创建ai2d_input_tensor + # Image对象转换为numpy对象 + ai2d_input = rgb888p_img.to_numpy_ref() + # 将numpy对象转换为ai2d_tensor + fld_ai2d_input_tensor = nn.from_numpy(ai2d_input) + + # （2）设置ai2d参数 + # 设置ai2d输入、输出格式、数据类型 + fld_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + global matrix_dst + # 根据检测框获取affine参数 + matrix_dst = get_affine_matrix(det) + affine_matrix = [matrix_dst[0][0],matrix_dst[0][1],matrix_dst[0][2], + matrix_dst[1][0],matrix_dst[1][1],matrix_dst[1][2]] + # 设置affine参数 + fld_ai2d.set_affine_param(True,nn.interp_method.cv2_bilinear,0, 0, 127, 1,affine_matrix) + + global fld_ai2d_builder + # （3）根据新的ai2d affine参数，创建新的ai2d_builder对象 + fld_ai2d_builder = fld_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fld_kmodel_input_shape) + # （4）ai2d_builder运行，将结果保存到fld_ai2d_output_tensor + fld_ai2d_builder.run(fld_ai2d_input_tensor, fld_ai2d_output_tensor) + +def fld_ai2d_release(): + with ScopedTiming("fld_ai2d_release",debug_mode > 0): + global fld_ai2d_input_tensor,fld_ai2d_builder + del fld_ai2d_input_tensor #删除fld_ai2d_input_tensor变量，释放对它所引用对象的内存引用 + del fld_ai2d_builder #删除fld_ai2d_builder变量，释放对它所引用对象的内存引用 +``` + +**使用示例：** + +```python +# ************main************ +fld_ai2d_init() # ai2d初始化 +while True: + rgb888p_img = camera_read(CAM_DEV_ID_0) # 从sensor拿到一帧图像 + fld_ai2d_run(rgb888p_img,det) # 根据det，对sensor原图预处理 + fld_ai2d_release() # 释放ai2d_input_tensor、ai2d_builder，因为每帧原图不同，ai2d_input_tensor指向的对象都会改变，所以每次都需释放内存;因为ai2d的参数实时改变，ai2d_builder对象需要每次创建，也需要每次都释放内存 +...... +global fld_ai2d,fld_ai2d_output_tensor #只需释放一次 +del fld_ai2d # 释放fld_ai2d，因为ai2d指向的对象是固定的 +del fld_ai2d_output_tensor # 释放fld_ai2d_output_tensor，因为fld_ai2d_output_tensor指向的对象是固定的 +``` + +#### 1.5 nncase使用：kpu + +##### （1）kpu基础用法 + +```python +import nncase_runtime as nn + +# （1）初始化kpu对象 +kpu_obj = nn.kpu() +# （2）加载kmodel +kpu_obj.load_kmodel(kmodel_file) + +# （3）设置kpu输入 +kmodel_input_shape = (1,3,320,320) +data = np.ones(kmodel_input_shape, dtype=np.uint8) +kmodel_input_tensor = nn.from_numpy(data) +kpu_obj.set_input_tensor(0, kmodel_input_tensor) + +# （4）kpu运行 +kpu_obj.run() + +# （5）获取kpu模型输出 +results = [] +for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data #tensor对象用完之后释放内存 + results.append(result) + +#（6）kpu后处理 +...... + +# （7）释放kpu对象 +del kpu_obj +``` + +##### （2）kpu示例用法：kpu配合ai2d使用 + +```python +import nncase_runtime as nn +global ai2d,ai2d_input_tensor,ai2d_output_tensor,ai2d_builder #ai2d相关对象 +global current_kmodel_obj #当前kpu对象 + +def kpu_init(kmodel_file): + # 初始化kpu对象，并加载kmodel + with ScopedTiming("kpu_init",debug_mode > 0): + # （1）初始化kpu对象 + kpu_obj = nn.kpu() + # （2）加载kmodel + kpu_obj.load_kmodel(kmodel_file) + # （3）ai2d初始化，模型输入需要预处理的情况下，kpu需要配合ai2d使用 + ai2d_init() + return kpu_obj + +def kpu_pre_process(rgb888p_img): + # kpu预处理，rgb888p_img是Image对象，原图 + # （1）ai2d运行，对原图进行预处理 + ai2d_run(rgb888p_img) + with ScopedTiming("kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,ai2d_output_tensor + # （2）将ai2d输出tensor设置为kpu模型输入 + current_kmodel_obj.set_input_tensor(0, ai2d_output_tensor) + +def kpu_get_output(): + # 获取kpu输出 + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取kpu输出，将输出转换为numpy格式，以便进行后处理 + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data #tensor对象用完之后释放内存 + results.append(result) + return results + +def kpu_run(kpu_obj,rgb888p_img): + # kpu推理运行 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）kpu预处理 + kpu_pre_process(rgb888p_img) + with ScopedTiming("kpu_run",debug_mode > 0): + # （2）kpu运行 + kpu_obj.run() + # （3）ai2d释放 + ai2d_release() + # （4）获取模型输出 + results = kpu_get_output() + # （5）kpu后处理，获取检测结果 + with ScopedTiming("kpu_post",debug_mode > 0): + post_ret = aidemo.face_det_post_process(confidence_threshold,nms_threshold,kmodel_input_shape[2],prior_data,[OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH],results) + + # （6）返回人脸检测框 + if len(post_ret)==0: + return post_ret + else: + return post_ret[0] + +# kpu释放 +def kpu_deinit(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d,ai2d_output_tensor + del kpu_obj #删除kpu_obj变量，释放对它所引用对象的内存引用 + del ai2d #删除ai2d变量，释放对它所引用对象的内存引用 + del ai2d_output_tensor #删除ai2d_output_tensor变量，释放对它所引用对象的内存引用 +``` + +使用示例： + +```python +kmodel_file = "/sdcard/app/kmodel/face_detection_320.kmodel" +fd_kmodel = kpu_init(kmodel_file) +while True: + rgb888p_img = camera_read(CAM_DEV_ID_0) # 从sensor拿到一帧图像 + dets = kpu_run(fd_kmodel,rgb888p_img) # kmodel推理 + ...... +...... +kpu_deinit(fd_kmodel) # 释放kmodel +``` + +#### 1.6 媒体使用 + +##### 1.6.1 camera + +```python +from media.camera import * +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +OUT_RGB888P_WIDTH = ALIGN_UP(1024, 16) +OUT_RGB888P_HEIGH = 576 + +def camera_init(dev_id): + # 根据设备id初始化camera + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # 设置camera的两路输出，一路输出用于显示，一路输出用于ai + + # （1）设置显示输出 + # 设置指定设备id的chn0的输出宽高 + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + # 设置指定设备id的chn0的输出格式为yuv420sp + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # （2）设置AI输出 + # 设置指定设备id的chn2的输出宽高 + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + # 设置指定设备id的chn2的输出格式为rgb88planar + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +def camera_start(dev_id): + # 启动sensor + camera.start_stream(dev_id) + +def camera_read(dev_id): + # 读取指定设备chn2的一帧图像，即获取一帧AI原图 + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +def camera_release_image(dev_id,rgb888p_img): + # 释放指定设备chn2一帧图像 + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +def camera_stop(dev_id): + # 释放sensor + camera.stop_stream(dev_id) +``` + +**使用示例：** + +```python +from media.camera import * + +# 初始化camera 0 +camera_init(CAM_DEV_ID_0) +......（camera_start还要配合其它操作，稍后在media模块介绍） +# 启动sensor +camera_start(CAM_DEV_ID_0) +time.sleep(5) +rgb888p_img = None +while True: + # 读取一帧图像 + rgb888p_img = camera_read(CAM_DEV_ID_0) + if rgb888p_img == -1: + # 若是未成功获取图像 + print("face_detect_test, capture_image failed") + # 释放当前图像 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + ....... + # 释放当前图像 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None +``` + +##### 1.6.2 display + +```python +from media.display import * +# draw_img：用于画图 +# osd_img：用于显示 +global draw_img,osd_img + +#for display +def display_init(): + # 使用hdmi用于显示 + display.init(LT9611_1920X1080_30FPS) + # 设置显示宽高、格式等 + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +def display_deinit(): + # 释放与显示相关的资源 + display.deinit() + +def display_draw(dets): + # 将检测框画到显示上 + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + if dets: + # 清空draw_img + draw_img.clear() + + # 画检测框 + for det in dets: + x, y, w, h = map(lambda x: int(round(x, 0)), det[:4]) + x = x * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + y = y * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + w = w * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + h = h * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + # 先将框画到draw_img，argb + draw_img.draw_rectangle(x,y, w, h, color=(255, 255, 0, 255)) + # 将draw_img拷贝到osd_img + draw_img.copy_to(osd_img) + # 将osd_img显示到hdmi上 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + # 清空draw_img + draw_img.clear() + # 将draw_img拷贝到osd_img + draw_img.copy_to(osd_img) + # 将透明图显示到hdmi上 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) +``` + +**使用示例：** + +```python +from media.display import * +# 显示初始化 +display_init() +while True: + ...... + dets = kpu_run(kpu_face_detect,rgb888p_img) + # 将检测框画到显示屏幕 + display_draw(dets) + ...... +# 显示资源释放 +display_deinit() +``` + +##### 1.6.3 media + +```python +from media.media import * + +global buffer,media_source,media_sink #for media +def media_init(): + # （1）配置媒体缓冲区 + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + ret = media.buffer_config(config) + + # （2）创建由参数media_source和media_sink指定的媒体链路，链路创建成功后，数据流会自动从media_source流入media_sink，无需用户干预 + global media_source, media_sink + # 创建指向指定sensor id，chn0的source + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + # 创建指向指定显示的sink + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + # 创建子sensor的chn0的输出，到指定显示的链路 + media.create_link(media_source, media_sink) + + # （3）初始化K230 CanMV平台媒体缓冲区 + ret = media.buffer_init() + if ret: + return ret + + global buffer, draw_img, osd_img + # （4）构建用于画图的对象 + # 使用media模块构建osd_image内存，用于显示 + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 创建用于画框、画点的Image + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 用于画框、画点结果，防止画的过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +def media_deinit(): + # 释放media资源 + global buffer,media_source, media_sink + # （1）释放buffer + media.release_buffer(buffer) + # （2）销毁已经创建的媒体链路 + media.destroy_link(media_source, media_sink) + + # （3）去初始化K230 CanMV平台媒体缓冲区 + ret = media.buffer_deinit() + return ret +``` + +**使用示例：** + +```python +# camera初始化 +camera_init(CAM_DEV_ID_0) +# 显示初始化 +display_init() + +rgb888p_img = None +try: + ret = media_init() #媒体初始化（注：媒体初始化必须在camera_start之前，确保media缓冲区已配置完全） + if ret: + print("face_detect_test, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) # 启动camera + time.sleep(5) # sleep 5s保证camera启动完全，可以拿到正确的图像 + while True: + with ScopedTiming("total",1): + # （1）读取一帧图像 + rgb888p_img = camera_read(CAM_DEV_ID_0) + if rgb888p_img == -1: + # 若读取失败，则释放当前帧 + print("face_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + # （2）kpu推理，获取推理结果 + dets = kpu_run(kpu_face_detect,rgb888p_img) + # （3）将推理结果画到原图 + display_draw(dets) + + # （4）释放当前帧 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None +except Exception as e: + print(f"An error occurred during buffer used: {e}") +finally: + # 注：无论程序是否正常停止，确保释放以下资源。保证下次的正确运行 + if rgb888p_img is not None: + #先release掉申请的内存，再stop camera + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + # 释放camera资源 + camera_stop(CAM_DEV_ID_0) + # 释放显示资源 + display_deinit() + # 释放kpu资源 + kpu_deinit(kpu_face_detect) + # 垃圾回收 + gc.collect() + time.sleep(1) + # 释放媒体资源 + ret = media_deinit() + if ret: + print("face_detect_test, buffer_deinit failed") + return ret +``` + +### 2.人脸检测 + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import aidemo #aidemo模块，封装ai demo相关后处理、画图操作 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 + +#********************for config.py******************** +# display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) # 显示宽度要求16位对齐 +DISPLAY_HEIGHT = 1080 + +# ai原图分辨率，sensor默认出图为16:9，若需不形变原图，最好按照16:9比例设置宽高 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) # ai原图宽度要求16位对齐 +OUT_RGB888P_HEIGH = 1080 + +# kmodel参数设置 +# kmodel输入shape +kmodel_input_shape = (1,3,320,320) +# ai原图padding +rgb_mean = [104,117,123] +# kmodel其它参数设置 +confidence_threshold = 0.5 +top_k = 5000 +nms_threshold = 0.2 +keep_top_k = 750 +vis_thres = 0.5 +variance = [0.1, 0.2] +anchor_len = 4200 +score_dim = 2 +det_dim = 4 +keypoint_dim = 10 + +# 文件配置 +# kmodel文件配置 +root_dir = '/sdcard/app/tests/' +kmodel_file = root_dir + 'kmodel/face_detection_320.kmodel' +# anchor文件配置 +anchors_path = root_dir + 'utils/prior_data_320.bin' +# 调试模型，0：不调试，>0：打印对应级别调试信息 +debug_mode = 0 + +#********************for scoped_timing.py******************** +# 时间统计类 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#********************for ai_utils.py******************** +# 当前kmodel +global current_kmodel_obj +# ai2d： ai2d实例 +# ai2d_input_tensor： ai2d输入 +# ai2d_output_tensor：ai2d输出 +# ai2d_builder： 根据ai2d参数，构建的ai2d_builder对象 +global ai2d,ai2d_input_tensor,ai2d_output_tensor,ai2d_builder #for ai2d +print('anchors_path:',anchors_path) +# 读取anchor文件，为后处理做准备 +prior_data = np.fromfile(anchors_path, dtype=np.float) +prior_data = prior_data.reshape((anchor_len,det_dim)) + +def get_pad_one_side_param(): + # 右padding或下padding，获取padding参数 + dst_w = kmodel_input_shape[3] # kmodel输入宽（w） + dst_h = kmodel_input_shape[2] # kmodel输入高（h） + + # OUT_RGB888P_WIDTH：原图宽（w） + # OUT_RGB888P_HEIGH：原图高（h） + # 计算最小的缩放比例，等比例缩放 + ratio_w = dst_w / OUT_RGB888P_WIDTH + ratio_h = dst_h / OUT_RGB888P_HEIGH + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + # 计算经过缩放后的新宽和新高 + new_w = (int)(ratio * OUT_RGB888P_WIDTH) + new_h = (int)(ratio * OUT_RGB888P_HEIGH) + + # 计算需要添加的padding，以使得kmodel输入的宽高和原图一致 + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + # 四舍五入，确保padding是整数 + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +def ai2d_init(): + # 人脸检测模型ai2d初始化 + with ScopedTiming("ai2d_init",debug_mode > 0): + # （1）创建ai2d对象 + global ai2d + ai2d = nn.ai2d() + # （2）设置ai2d参数 + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d.set_pad_param(True, get_pad_one_side_param(), 0, rgb_mean) + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + # （3）创建ai2d_output_tensor，用于保存ai2d输出 + global ai2d_output_tensor + data = np.ones(kmodel_input_shape, dtype=np.uint8) + ai2d_output_tensor = nn.from_numpy(data) + + # （4）ai2d_builder，根据ai2d参数、输入输出大小创建ai2d_builder对象 + global ai2d_builder + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], kmodel_input_shape) + + +def ai2d_run(rgb888p_img): + # 对原图rgb888p_img进行预处理 + with ScopedTiming("ai2d_run",debug_mode > 0): + global ai2d_input_tensor,ai2d_output_tensor + # （1）根据原图构建ai2d_input_tensor对象 + ai2d_input = rgb888p_img.to_numpy_ref() + ai2d_input_tensor = nn.from_numpy(ai2d_input) + # （2）运行ai2d_builder，将结果保存到ai2d_output_tensor中 + ai2d_builder.run(ai2d_input_tensor, ai2d_output_tensor) + +def ai2d_release(): + # 释放ai2d_input_tensor + with ScopedTiming("ai2d_release",debug_mode > 0): + global ai2d_input_tensor + del ai2d_input_tensor + +def kpu_init(kmodel_file): + # 初始化kpu对象，并加载kmodel + with ScopedTiming("kpu_init",debug_mode > 0): + # 初始化kpu对象 + kpu_obj = nn.kpu() + # 加载kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化ai2d + ai2d_init() + return kpu_obj + +def kpu_pre_process(rgb888p_img): + # 使用ai2d对原图进行预处理（padding，resize） + ai2d_run(rgb888p_img) + with ScopedTiming("kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,ai2d_output_tensor + # 将ai2d输出设置为kpu输入 + current_kmodel_obj.set_input_tensor(0, ai2d_output_tensor) + +def kpu_get_output(): + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取模型输出，并将结果转换为numpy，以便进行人脸检测后处理 + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data + results.append(result) + return results + +def kpu_run(kpu_obj,rgb888p_img): + # kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）原图预处理，并设置模型输入 + kpu_pre_process(rgb888p_img) + # （2）kpu推理 + with ScopedTiming("kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放ai2d资源 + ai2d_release() + # （4）获取kpu输出 + results = kpu_get_output() + # （5）kpu结果后处理 + with ScopedTiming("kpu_post",debug_mode > 0): + post_ret = aidemo.face_det_post_process(confidence_threshold,nms_threshold,kmodel_input_shape[2],prior_data,[OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH],results) + + # （6）返回人脸检测框 + if len(post_ret)==0: + return post_ret + else: + return post_ret[0] + + +def kpu_deinit(kpu_obj): + # kpu释放 + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d,ai2d_output_tensor + del kpu_obj #删除kpu_obj变量，释放对它所引用对象的内存引用 + del ai2d #删除ai2d变量，释放对它所引用对象的内存引用 + del ai2d_output_tensor #删除ai2d_output_tensor变量，释放对它所引用对象的内存引用 + +#********************for media_utils.py******************** +global draw_img,osd_img #for display +global buffer,media_source,media_sink #for media + +# for display，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.2 +def display_init(): + # hdmi显示初始化 + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +def display_deinit(): + # 释放显示资源 + display.deinit() + +def display_draw(dets): + # hdmi画检测框 + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + if dets: + draw_img.clear() + for det in dets: + x, y, w, h = map(lambda x: int(round(x, 0)), det[:4]) + x = x * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + y = y * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + w = w * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + h = h * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + draw_img.draw_rectangle(x,y, w, h, color=(255, 255, 0, 255), thickness = 2) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.1 +def camera_init(dev_id): + # camera初始化 + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +def camera_start(dev_id): + # camera启动 + camera.start_stream(dev_id) + +def camera_read(dev_id): + # 读取一帧图像 + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +def camera_release_image(dev_id,rgb888p_img): + # 释放一帧图像 + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +def camera_stop(dev_id): + # 停止camera + camera.stop_stream(dev_id) + +#for media，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.3 +def media_init(): + # meida初始化 + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +def media_deinit(): + # meida资源释放 + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + + +#********************for face_detect.py******************** +def face_detect_inference(): + print("face_detect_test start") + # kpu初始化 + kpu_face_detect = kpu_init(kmodel_file) + # camera初始化 + camera_init(CAM_DEV_ID_0) + # 显示初始化 + display_init() + + rgb888p_img = None + # 注意：将一定要将一下过程包在try中，用于保证程序停止后，资源释放完毕；确保下次程序仍能正常运行 + try: + # 注意：媒体初始化（注：媒体初始化必须在camera_start之前，确保media缓冲区已配置完全） + ret = media_init() + if ret: + print("face_detect_test, buffer init failed") + return ret + # 启动camera + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + # （1）读取一帧图像 + rgb888p_img = camera_read(CAM_DEV_ID_0) + # （2）若读取失败，释放当前帧 + if rgb888p_img == -1: + print("face_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # （3）若读取成功，推理当前帧 + if rgb888p_img.format() == image.RGBP888: + # （3.1）推理当前图像，并获取检测结果 + dets = kpu_run(kpu_face_detect,rgb888p_img) + # （3.2）将结果画到显示器 + display_draw(dets) + + # （4）释放当前帧 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + + except Exception as e: + # 捕捉运行运行中异常，并打印错误 + print(f"An error occurred during buffer used: {e}") + finally: + # 释放当前帧 + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + # 停止camera + camera_stop(CAM_DEV_ID_0) + # 释放显示资源 + display_deinit() + # 释放kpu资源 + kpu_deinit(kpu_face_detect) + # 垃圾回收 + gc.collect() + time.sleep(1) + # 释放媒体资源 + ret = media_deinit() + if ret: + print("face_detect_test, buffer_deinit failed") + return ret + + print("face_detect_test end") + return 0 + +if __name__ == '__main__': + face_detect_inference() +``` + +### 3.COCO目标检测 + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +#ai 原图输入分辨率 +OUT_RGB888P_WIDTH = ALIGN_UP(320, 16) +OUT_RGB888P_HEIGHT = 320 + +#多目标检测 kmodel 输入 shape +kmodel_input_shape = (1,3,320,320) + +#多目标检测 相关参数设置 +confidence_threshold = 0.2 # 多目标检测分数阈值 +nms_threshold = 0.2 # 非最大值抑制阈值 +x_factor = float(OUT_RGB888P_WIDTH)/kmodel_input_shape[3] # 原始图像分辨率宽与kmodel宽输入大小比值 +y_factor = float(OUT_RGB888P_HEIGHT)/kmodel_input_shape[2] # 原始图像分辨率高与kmodel高输入大小比值 +keep_top_k = 50 # 最大输出检测框的数量 + +#文件配置 +root_dir = '/sdcard/app/tests/' +kmodel_file = root_dir + 'kmodel/yolov8n_320.kmodel' # kmodel文件的路径 +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） + +#颜色板 用于作图 +color_four = [(255, 220, 20, 60), (255, 119, 11, 32), (255, 0, 0, 142), (255, 0, 0, 230), + (255, 106, 0, 228), (255, 0, 60, 100), (255, 0, 80, 100), (255, 0, 0, 70), + (255, 0, 0, 192), (255, 250, 170, 30), (255, 100, 170, 30), (255, 220, 220, 0), + (255, 175, 116, 175), (255, 250, 0, 30), (255, 165, 42, 42), (255, 255, 77, 255), + (255, 0, 226, 252), (255, 182, 182, 255), (255, 0, 82, 0), (255, 120, 166, 157), + (255, 110, 76, 0), (255, 174, 57, 255), (255, 199, 100, 0), (255, 72, 0, 118), + (255, 255, 179, 240), (255, 0, 125, 92), (255, 209, 0, 151), (255, 188, 208, 182), + (255, 0, 220, 176), (255, 255, 99, 164), (255, 92, 0, 73), (255, 133, 129, 255), + (255, 78, 180, 255), (255, 0, 228, 0), (255, 174, 255, 243), (255, 45, 89, 255), + (255, 134, 134, 103), (255, 145, 148, 174), (255, 255, 208, 186), + (255, 197, 226, 255), (255, 171, 134, 1), (255, 109, 63, 54), (255, 207, 138, 255), + (255, 151, 0, 95), (255, 9, 80, 61), (255, 84, 105, 51), (255, 74, 65, 105), + (255, 166, 196, 102), (255, 208, 195, 210), (255, 255, 109, 65), (255, 0, 143, 149), + (255, 179, 0, 194), (255, 209, 99, 106), (255, 5, 121, 0), (255, 227, 255, 205), + (255, 147, 186, 208), (255, 153, 69, 1), (255, 3, 95, 161), (255, 163, 255, 0), + (255, 119, 0, 170), (255, 0, 182, 199), (255, 0, 165, 120), (255, 183, 130, 88), + (255, 95, 32, 0), (255, 130, 114, 135), (255, 110, 129, 133), (255, 166, 74, 118), + (255, 219, 142, 185), (255, 79, 210, 114), (255, 178, 90, 62), (255, 65, 70, 15), + (255, 127, 167, 115), (255, 59, 105, 106), (255, 142, 108, 45), (255, 196, 172, 0), + (255, 95, 54, 80), (255, 128, 76, 255), (255, 201, 57, 1), (255, 246, 0, 122), + (255, 191, 162, 208)] + +#标签 多目标检测的所有可识别类别 +labels = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"] + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + + +#ai_utils.py +global current_kmodel_obj # 定义全局的 kpu 对象 +global ai2d,ai2d_input_tensor,ai2d_output_tensor,ai2d_builder # 定义全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder + + +# 多目标检测 非最大值抑制方法实现 +def py_cpu_nms(boxes,scores,thresh): + """Pure Python NMS baseline.""" + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = np.argsort(scores,axis = 0)[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + new_x1 = [] + new_x2 = [] + new_y1 = [] + new_y2 = [] + new_areas = [] + for order_i in order: + new_x1.append(x1[order_i]) + new_x2.append(x2[order_i]) + new_y1.append(y1[order_i]) + new_y2.append(y2[order_i]) + new_areas.append(areas[order_i]) + new_x1 = np.array(new_x1) + new_x2 = np.array(new_x2) + new_y1 = np.array(new_y1) + new_y2 = np.array(new_y2) + xx1 = np.maximum(x1[i], new_x1) + yy1 = np.maximum(y1[i], new_y1) + xx2 = np.minimum(x2[i], new_x2) + yy2 = np.minimum(y2[i], new_y2) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + + new_areas = np.array(new_areas) + ovr = inter / (areas[i] + new_areas - inter) + new_order = [] + for ovr_i,ind in enumerate(ovr): + if ind < thresh: + new_order.append(order[ovr_i]) + order = np.array(new_order,dtype=np.uint8) + return keep + +# 多目标检测 接收kmodel输出的后处理方法 +def kpu_post_process(output_data): + with ScopedTiming("kpu_post_process", debug_mode > 0): + boxes_ori = output_data[:,0:4] + scores_ori = output_data[:,4:] + confs_ori = np.max(scores_ori,axis=-1) + inds_ori = np.argmax(scores_ori,axis=-1) + + boxes = [] + scores = [] + inds = [] + + for i in range(len(boxes_ori)): + if confs_ori[i] > confidence_threshold: + scores.append(confs_ori[i]) + inds.append(inds_ori[i]) + x = boxes_ori[i,0] + y = boxes_ori[i,1] + w = boxes_ori[i,2] + h = boxes_ori[i,3] + left = int((x - 0.5 * w) * x_factor) + top = int((y - 0.5 * h) * y_factor) + right = int((x + 0.5 * w) * x_factor) + bottom = int((y + 0.5 * h) * y_factor) + boxes.append([left,top,right,bottom]) + + if len(boxes)==0: + return [] + + boxes = np.array(boxes) + scores = np.array(scores) + inds = np.array(inds) + + # do NMS + keep = py_cpu_nms(boxes,scores,nms_threshold) + dets = np.concatenate((boxes, scores.reshape((len(boxes),1)), inds.reshape((len(boxes),1))), axis=1) + + dets_out = [] + for keep_i in keep: + dets_out.append(dets[keep_i]) + dets_out = np.array(dets_out) + + # keep top-K faster NMS + dets_out = dets_out[:keep_top_k, :] + return dets_out + +# ai2d 初始化 +def ai2d_init(): + with ScopedTiming("ai2d_init",debug_mode > 0): + global ai2d + ai2d = nn.ai2d() + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + global ai2d_out_tensor + data = np.ones(kmodel_input_shape, dtype=np.uint8) + ai2d_out_tensor = nn.from_numpy(data) + + global ai2d_builder + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH], kmodel_input_shape) + +# ai2d 运行 +def ai2d_run(rgb888p_img): + with ScopedTiming("ai2d_run",debug_mode > 0): + global ai2d_input_tensor,ai2d_out_tensor + ai2d_input = rgb888p_img.to_numpy_ref() + ai2d_input_tensor = nn.from_numpy(ai2d_input) + + ai2d_builder.run(ai2d_input_tensor, ai2d_out_tensor) + +# ai2d 释放内存 +def ai2d_release(): + with ScopedTiming("ai2d_release",debug_mode > 0): + global ai2d_input_tensor + del ai2d_input_tensor + +# kpu 初始化 +def kpu_init(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + + ai2d_init() + return kpu_obj + +# kpu 输入预处理 +def kpu_pre_process(rgb888p_img): + ai2d_run(rgb888p_img) + with ScopedTiming("kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,ai2d_out_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, ai2d_out_tensor) + +# kpu 获得 kmodel 输出 +def kpu_get_output(): + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + data = current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + + result = result.reshape((result.shape[0] * result.shape[1], result.shape[2])) + result = result.transpose() + tmp2 = result.copy() + del data + results.append(tmp2) + return results + +# kpu 运行 +def kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1) 原图预处理，并设置模型输入 + kpu_pre_process(rgb888p_img) + # (2) kpu运行 + with ScopedTiming("kpu_run",debug_mode > 0): + kpu_obj.run() + # (3) 释放ai2d资源 + ai2d_release() + # (4) 获取kpu输出 + results = kpu_get_output() + # (5) kpu结果后处理 + dets = kpu_post_process(results[0]) + # (6) 返回多目标检测结果 + return dets + +# kpu 释放内存 +def kpu_deinit(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d,ai2d_out_tensor + del kpu_obj + del ai2d + del ai2d_out_tensor + +#media_utils.py +global draw_img,osd_img #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +# display 作图过程 将所有目标检测框以及类别、分数值的作图 +def display_draw(dets): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + if dets: + draw_img.clear() + for det in dets: + x1, y1, x2, y2 = map(lambda x: int(round(x, 0)), det[:4]) + w = (x2 - x1) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + h = (y2 - y1) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGHT + draw_img.draw_rectangle(x1 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH, + y1 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGHT, w, h, color=color_four[int(det[5])]) + draw_img.draw_string( int(x1 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) , int(y1 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGHT)-50, + " " + labels[int(det[5])] + " " + str(round(det[4],2)) , color=color_four[int(det[5])] , scale=4) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + + +#**********for ob_detect.py********** +def ob_detect_inference(): + print("ob_detect start") + kpu_ob_detect = kpu_init(kmodel_file) # 创建多目标检测的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("ob_detect, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图片 + if rgb888p_img == -1: + print("ob_detect, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + dets = kpu_run(kpu_ob_detect,rgb888p_img) # 执行多目标检测 kpu运行 以及 后处理过程 + display_draw(dets) # 将得到的检测结果 绘制到 display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图像 + rgb888p_img = None + # gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止camera + display_deinit() # 释放 display + kpu_deinit(kpu_ob_detect) # 释放 kpu + gc.collect() + time.sleep(1) + ret = media_deinit() # 释放 整个media + if ret: + print("ob_detect, buffer_deinit failed") + return ret + + print("ob_detect_test end") + return 0 + +if __name__ == '__main__': + ob_detect_inference() +``` + +### 4.yolov8-seg + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 +import aidemo #aidemo模块，封装ai demo相关后处理、画图操作 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +#ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(320, 16) +OUT_RGB888P_HEIGHT = 320 + +#多目标分割 kmodel 输入参数配置 +kmodel_input_shape = (1,3,320,320) # kmodel输入分辨率 +rgb_mean = [114,114,114] # ai2d padding 值 + +#多目标分割 相关参数设置 +confidence_threshold = 0.2 # 多目标分割分数阈值 +nms_threshold = 0.5 # 非最大值抑制阈值 +mask_thres = 0.5 # 多目标分割掩码阈值 + +#文件配置 +root_dir = '/sdcard/app/tests/' +kmodel_file = root_dir + 'kmodel/yolov8n_seg_320.kmodel' # kmodel文件的路径 +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） + +#标签 多目标分割的所有可识别类别 +labels = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"] + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + + +#ai_utils.py +global current_kmodel_obj # 定义全局的 kpu 对象 +global ai2d,ai2d_input_tensor,ai2d_output_tensor,ai2d_builder # 定义全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder + +# 多目标分割 接收kmodel输出的后处理方法 +def kpu_post_process(output_datas): + with ScopedTiming("kpu_post_process", debug_mode > 0): + global masks + mask_dets = aidemo.segment_postprocess(output_datas,[OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH],[kmodel_input_shape[2],kmodel_input_shape[3]],[DISPLAY_HEIGHT,DISPLAY_WIDTH],confidence_threshold,nms_threshold,mask_thres,masks) + return mask_dets + +# 获取kmodel输入图像resize比例 以及 padding的上下左右像素数量 +def get_pad_param(): + #右padding或下padding + dst_w = kmodel_input_shape[3] + dst_h = kmodel_input_shape[2] + + ratio_w = float(dst_w) / OUT_RGB888P_WIDTH + ratio_h = float(dst_h) / OUT_RGB888P_HEIGHT + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + + new_w = (int)(ratio * OUT_RGB888P_WIDTH) + new_h = (int)(ratio * OUT_RGB888P_HEIGHT) + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + + top = (int)(round(dh - 0.1)) + bottom = (int)(round(dh + 0.1)) + left = (int)(round(dw - 0.1)) + right = (int)(round(dw + 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +# ai2d 初始化 +def ai2d_init(): + with ScopedTiming("ai2d_init",debug_mode > 0): + global ai2d + ai2d = nn.ai2d() + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d.set_pad_param(True, get_pad_param(), 0, rgb_mean) + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + global ai2d_out_tensor + data = np.ones(kmodel_input_shape, dtype=np.uint8) + ai2d_out_tensor = nn.from_numpy(data) + + global ai2d_builder + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH], kmodel_input_shape) + +# ai2d 运行 +def ai2d_run(rgb888p_img): + with ScopedTiming("ai2d_run",debug_mode > 0): + global ai2d_input_tensor,ai2d_out_tensor + ai2d_input = rgb888p_img.to_numpy_ref() + ai2d_input_tensor = nn.from_numpy(ai2d_input) + + ai2d_builder.run(ai2d_input_tensor, ai2d_out_tensor) + +# ai2d 释放内存 +def ai2d_release(): + with ScopedTiming("ai2d_release",debug_mode > 0): + global ai2d_input_tensor + del ai2d_input_tensor + +# kpu 初始化 +def kpu_init(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + + ai2d_init() + return kpu_obj + +# kpu 输入预处理 +def kpu_pre_process(rgb888p_img): + ai2d_run(rgb888p_img) + with ScopedTiming("kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,ai2d_out_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, ai2d_out_tensor) + +# kpu 获得 kmodel 输出 +def kpu_get_output(): + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + data_0 = current_kmodel_obj.get_output_tensor(0) + result_0 = data_0.to_numpy() + del data_0 + results.append(result_0) + + data_1 = current_kmodel_obj.get_output_tensor(1) + result_1 = data_1.to_numpy() + del data_1 + results.append(result_1) + + return results + +# kpu 运行 +def kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1) 原图预处理，并设置模型输入 + kpu_pre_process(rgb888p_img) + # (2) kpu 运行 + with ScopedTiming("kpu_run",debug_mode > 0): + kpu_obj.run() + # (3) 释放ai2d资源 + ai2d_release() + # (4) 获取kpu输出 + results = kpu_get_output() + # (5) kpu结果后处理 + seg_res = kpu_post_process(results) + # (6) 返回 分割 mask 结果 + return seg_res + +# kpu 释放内存 +def kpu_deinit(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d,ai2d_out_tensor + del kpu_obj + del ai2d + del ai2d_out_tensor + +#media_utils.py +global draw_img,osd_img,masks #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +# display 作图过程 将所有目标分割对象以及类别、分数值的作图 +def display_draw(seg_res): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img,masks + if seg_res[0]: + dets = seg_res[0] + ids = seg_res[1] + scores = seg_res[2] + + for i, det in enumerate(dets): + x1, y1, w, h = map(lambda x: int(round(x, 0)), det) + draw_img.draw_string( int(x1) , int(y1)-50, " " + labels[int(ids[i])] + " " + str(round(scores[i],2)) , color=(255,0,0,0), scale=4) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img, masks + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + masks = np.zeros((1,DISPLAY_HEIGHT,DISPLAY_WIDTH,4)) + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888,alloc=image.ALLOC_REF,data=masks) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + + +#**********for seg.py********** +def seg_inference(): + print("seg start") + kpu_seg = kpu_init(kmodel_file) # 创建多目标分割的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("seg, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图片 + if rgb888p_img == -1: + print("seg, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + seg_res = kpu_run(kpu_seg,rgb888p_img) # 执行多目标分割 kpu 运行 以及 后处理过程 + display_draw(seg_res) # 将得到的分割结果 绘制到 display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图像 + rgb888p_img = None + #gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止 camera + display_deinit() # 释放 display + kpu_deinit(kpu_seg) # 释放 kpu + gc.collect() + time.sleep(1) + ret = media_deinit() # 释放 整个media + if ret: + print("seg, buffer_deinit failed") + return ret + + print("seg end") + return 0 + +if __name__ == '__main__': + seg_inference() +``` + +### 5.车牌检测 + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 +import aidemo #aidemo模块，封装ai demo相关后处理、画图操作 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +#ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) +OUT_RGB888P_HEIGHT = 1080 + +#车牌检测 kmodel 输入shape +kmodel_input_shape = (1,3,640,640) + +#车牌检测 相关参数设置 +obj_thresh = 0.2 #车牌检测分数阈值 +nms_thresh = 0.2 #检测框 非极大值抑制 阈值 + +#文件配置 +root_dir = '/sdcard/app/tests/' +kmodel_file = root_dir + 'kmodel/LPD_640.kmodel' # kmodel 文件的路径 +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + + +#ai_utils.py +global current_kmodel_obj # 定义全局的 kpu 对象 +global ai2d,ai2d_input_tensor,ai2d_output_tensor,ai2d_builder # 定义全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder + +# 车牌检测 接收kmodel输出的后处理方法 +def kpu_post_process(output_data): + with ScopedTiming("kpu_post_process", debug_mode > 0): + results = aidemo.licence_det_postprocess(output_data,[OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH],[kmodel_input_shape[2],kmodel_input_shape[3]],obj_thresh,nms_thresh) + return results + +# ai2d 初始化 +def ai2d_init(): + with ScopedTiming("ai2d_init",debug_mode > 0): + global ai2d + ai2d = nn.ai2d() + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + global ai2d_out_tensor + data = np.ones(kmodel_input_shape, dtype=np.uint8) + ai2d_out_tensor = nn.from_numpy(data) + + global ai2d_builder + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH], kmodel_input_shape) + +# ai2d 运行 +def ai2d_run(rgb888p_img): + with ScopedTiming("ai2d_run",debug_mode > 0): + global ai2d_input_tensor,ai2d_out_tensor + ai2d_input = rgb888p_img.to_numpy_ref() + ai2d_input_tensor = nn.from_numpy(ai2d_input) + + ai2d_builder.run(ai2d_input_tensor, ai2d_out_tensor) + +# ai2d 释放内存 +def ai2d_release(): + with ScopedTiming("ai2d_release",debug_mode > 0): + global ai2d_input_tensor + del ai2d_input_tensor + +# kpu 初始化 +def kpu_init(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + + ai2d_init() + return kpu_obj + +# kpu 输入预处理 +def kpu_pre_process(rgb888p_img): + ai2d_run(rgb888p_img) + with ScopedTiming("kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,ai2d_out_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, ai2d_out_tensor) + +# kpu 获得 kmodel 输出 +def kpu_get_output(): + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + tmp2 = result.copy() + del data + results.append(tmp2) + return results + +# kpu 运行 +def kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1) 原图预处理，并设置模型输入 + kpu_pre_process(rgb888p_img) + # (2) kpu 运行 + with ScopedTiming("kpu_run",debug_mode > 0): + kpu_obj.run() + # (3) 释放ai2d资源 + ai2d_release() + # (4) 获取kpu输出 + results = kpu_get_output() + # (5) kpu结果后处理 + dets = kpu_post_process(results) + # (6) 返回 车牌检测框 结果 + return dets + +# kpu 释放内存 +def kpu_deinit(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d,ai2d_out_tensor + del kpu_obj + del ai2d + del ai2d_out_tensor + +#media_utils.py +global draw_img,osd_img #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +# display 作图过程 将所有车牌检测框绘制到屏幕上 +def display_draw(dets): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + if dets: + draw_img.clear() + point_8 = np.zeros((8),dtype=np.int16) + for det in dets: + for i in range(4): + x = det[i * 2 + 0]/OUT_RGB888P_WIDTH*DISPLAY_WIDTH + y = det[i * 2 + 1]/OUT_RGB888P_HEIGHT*DISPLAY_HEIGHT + point_8[i * 2 + 0] = int(x) + point_8[i * 2 + 1] = int(y) + for i in range(4): + draw_img.draw_line(point_8[i * 2 + 0],point_8[i * 2 + 1],point_8[(i+1) % 4 * 2 + 0],point_8[(i+1) % 4 * 2 + 1],color=(255, 0, 255, 0),thickness=2) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_BGR_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + + +#**********for licence_det.py********** +def licence_det_inference(): + print("licence_det start") + kpu_licence_det = kpu_init(kmodel_file) # 创建车牌检测的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("licence_det, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图片 + if rgb888p_img == -1: + print("licence_det, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + dets = kpu_run(kpu_licence_det,rgb888p_img) # 执行车牌检测 kpu 运行 以及后处理过程 + display_draw(dets) # 将得到的 检测结果 绘制到 display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图像 + rgb888p_img = None + # gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止 camera + display_deinit() # 释放 display + kpu_deinit(kpu_licence_det) # 释放 kpu + gc.collect() + time.sleep(1) + ret = media_deinit() # 释放整个media + if ret: + print("licence_det, buffer_deinit failed") + return ret + + print("licence_det end") + return 0 + +if __name__ == '__main__': + licence_det_inference() +``` + +### 6.OCR检测 + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 +import os #操作系统接口模块 +import aicube #aicube模块，封装检测分割等任务相关后处理 + +# display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +# ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(640, 16) +OUT_RGB888P_HEIGH = 360 + +# kmodel输入参数配置 +kmodel_input_shape_det = (1,3,640,640) # kmodel输入分辨率 +rgb_mean = [0,0,0] # ai2d padding的值 + +# kmodel相关参数设置 +mask_threshold = 0.25 # 二值化mask阈值 +box_threshold = 0.3 # 检测框分数阈值 + +# 文件配置 +root_dir = '/sdcard/app/tests/' +kmodel_file_det = root_dir + 'kmodel/ocr_det_int16.kmodel' # kmodel加载路径 +debug_mode = 0 # 调试模式 大于0（调试）、 反之 （不调试） + +# scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +# ai utils +global current_kmodel_obj # 定义全局kpu对象 +global ai2d_det,ai2d_input_tensor_det,ai2d_output_tensor_det,ai2d_builder_det,ai2d_input_det # 定义全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder + +# padding方法，一边padding，右padding或者下padding +def get_pad_one_side_param(out_img_size,input_img_size): + # 右padding或下padding + dst_w = out_img_size[0] + dst_h = out_img_size[1] + + input_width = input_img_size[0] + input_high = input_img_size[1] + + ratio_w = dst_w / input_width + ratio_h = dst_h / input_high + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + + new_w = (int)(ratio * input_width) + new_h = (int)(ratio * input_high) + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + + +# ai2d 初始化，用于实现输入的预处理 +def ai2d_init_det(): + with ScopedTiming("ai2d_init",debug_mode > 0): + global ai2d_det + ai2d_det = nn.ai2d() + ai2d_det.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d_det.set_pad_param(True, get_pad_one_side_param([kmodel_input_shape_det[3],kmodel_input_shape_det[2]], [OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH]), 0, [0, 0, 0]) + ai2d_det.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + global ai2d_output_tensor_det + data = np.ones(kmodel_input_shape_det, dtype=np.uint8) + ai2d_output_tensor_det = nn.from_numpy(data) + global ai2d_builder_det + ai2d_builder_det = ai2d_det.build([1, 3, OUT_RGB888P_HEIGH, OUT_RGB888P_WIDTH], [1, 3, kmodel_input_shape_det[2], kmodel_input_shape_det[3]]) + + +# ai2d 运行，完成ai2d_init_det设定的预处理 +def ai2d_run_det(rgb888p_img): + with ScopedTiming("ai2d_run",debug_mode > 0): + global ai2d_input_tensor_det,ai2d_builder_det,ai2d_input_det + ai2d_input_det = rgb888p_img.to_numpy_ref() + ai2d_input_tensor_det = nn.from_numpy(ai2d_input_det) + global ai2d_output_tensor_det + ai2d_builder_det.run(ai2d_input_tensor_det, ai2d_output_tensor_det) + + +# ai2d 释放输入tensor +def ai2d_release_det(): + with ScopedTiming("ai2d_release",debug_mode > 0): + global ai2d_input_tensor_det + del ai2d_input_tensor_det + +# kpu 初始化 +def kpu_init_det(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + ai2d_init_det() + return kpu_obj + +# 预处理方法 +def kpu_pre_process_det(rgb888p_img): + # 运行ai2d，将ai2d预处理的输出设置为kmodel的输入tensor + ai2d_run_det(rgb888p_img) + with ScopedTiming("kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,ai2d_output_tensor_det + # set kpu input + current_kmodel_obj.set_input_tensor(0, ai2d_output_tensor_det) + +# 获取kmodel的推理输出 +def kpu_get_output(): + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data + results.append(result) + return results + +# kpu 运行 +def kpu_run_det(kpu_obj,rgb888p_img): + # kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + #（1）原图像预处理并设置模型输入 + kpu_pre_process_det(rgb888p_img) + #（2）kpu推理 + with ScopedTiming("kpu_run",debug_mode > 0): + kpu_obj.run() + #（3）释放ai2d资源 + ai2d_release_det() + #（4）获取kpu输出 + results = kpu_get_output() + #（5）CHW转HWC + global ai2d_input_det + tmp = (ai2d_input_det.shape[0], ai2d_input_det.shape[1], ai2d_input_det.shape[2]) + ai2d_input_det = ai2d_input_det.reshape((ai2d_input_det.shape[0], ai2d_input_det.shape[1] * ai2d_input_det.shape[2])) + ai2d_input_det = ai2d_input_det.transpose() + tmp2 = ai2d_input_det.copy() + tmp2 = tmp2.reshape((tmp[1], tmp[2], tmp[0])) + #（6）后处理，aicube.ocr_post_process接口说明： + # 接口：aicube.ocr_post_process(threshold_map,ai_isp,kmodel_input_shape,isp_shape,mask_threshold,box_threshold); + # 参数说明： + # threshold_map: DBNet模型的输出为（N,kmodel_input_shape_det[2],kmodel_input_shape_det[3],2），两个通道分别为threshold map和segmentation map + # 后处理过程只使用threshold map，因此将results[0][:,:,:,0] reshape成一维传给接口使用。 + # ai_isp：后处理还会返回基于原图的检测框裁剪数据，因此要将原图数据reshape为一维传给接口处理。 + # kmodel_input_shape：kmodel输入分辨率。 + # isp_shape：AI原图分辨率。要将kmodel输出分辨率的检测框坐标映射到原图分辨率上，需要使用这两个分辨率的值。 + # mask_threshold：用于二值化图像获得文本区域。 + # box_threshold：检测框分数阈值，低于该阈值的检测框不计入结果。 + with ScopedTiming("kpu_post",debug_mode > 0): + # 调用aicube模块的ocr_post_process完成ocr检测的后处理 + # det_results结构为[[crop_array_nhwc,[p1_x,p1_y,p2_x,p2_y,p3_x,p3_y,p4_x,p4_y]],...] + det_results = aicube.ocr_post_process(results[0][:, :, :, 0].reshape(-1), tmp2.reshape(-1), + [kmodel_input_shape_det[3], kmodel_input_shape_det[2]], + [OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH], mask_threshold, box_threshold) + return det_results + + +# kpu 释放内存 +def kpu_deinit_det(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d_det,ai2d_output_tensor_det,ai2d_input_tensor_det + del kpu_obj + del ai2d_det + del ai2d_output_tensor_det + #del ai2d_input_tensor_det + +#********************for media_utils.py******************** + +global draw_img,osd_img #for display +global buffer,media_source,media_sink #for media + +#display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +# display 作图过程，将OCR检测后处理得到的框绘制到OSD上并显示 +def display_draw(det_results): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + if det_results: + draw_img.clear() + # 循环绘制所有检测到的框 + for j in det_results: + # 将原图的坐标点转换成显示的坐标点，循环绘制四条直线，得到一个矩形框 + for i in range(4): + x1 = j[1][(i * 2)] / OUT_RGB888P_WIDTH * DISPLAY_WIDTH + y1 = j[1][(i * 2 + 1)] / OUT_RGB888P_HEIGH * DISPLAY_HEIGHT + x2 = j[1][((i + 1) * 2) % 8] / OUT_RGB888P_WIDTH * DISPLAY_WIDTH + y2 = j[1][((i + 1) * 2 + 1) % 8] / OUT_RGB888P_HEIGH * DISPLAY_HEIGHT + draw_img.draw_line((int(x1), int(y1), int(x2), int(y2)), color=(255, 0, 0, 255), + thickness=5) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 启动视频流 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 捕获一帧图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 释放内存 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# 停止视频流 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放buffer，销毁link +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +# +def ocr_det_inference(): + print("ocr_det_test start") + kpu_ocr_det = kpu_init_det(kmodel_file_det) # 创建ocr检测任务的kpu对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + rgb888p_img = None + try: + ret = media_init() + if ret: + print("ocr_det_test, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图像 + if rgb888p_img == -1: + print("ocr_det_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + det_results = kpu_run_det(kpu_ocr_det,rgb888p_img) # kpu运行获取kmodel的推理输出 + display_draw(det_results) # 绘制检测结果，并显示 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # 释放内存 + rgb888p_img = None + # gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止camera + display_deinit() # 释放display + kpu_deinit_det(kpu_ocr_det) # 释放kpu + gc.collect() + time.sleep(1) + ret = media_deinit() # 释放整个media + if ret: + print("ocr_det_test, buffer_deinit failed") + return ret + + print("ocr_det_test end") + return 0 + +if __name__ == '__main__': + ocr_det_inference() +``` + +### 7.手掌检测 + +```python +import aicube #aicube模块，封装检测分割等任务相关后处理 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 + +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 + +import time #时间统计 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 + +import gc #垃圾回收模块 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +##ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) +OUT_RGB888P_HEIGH = 1080 + +#kmodel输入shape +kmodel_input_shape = (1,3,512,512) # kmodel输入分辨率 + +#kmodel相关参数设置 +confidence_threshold = 0.2 # 手掌检测阈值，用于过滤roi +nms_threshold = 0.5 # 手掌检测框阈值，用于过滤重复roi +kmodel_frame_size = [512,512] # 手掌检测输入图片尺寸 +frame_size = [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH] # 直接输入图片尺寸 +strides = [8,16,32] # 输出特征图的尺寸与输入图片尺寸的比 +num_classes = 1 # 模型输出类别数 +nms_option = False # 是否所有检测框一起做NMS，False则按照不同的类分别应用NMS +labels = ["hand"] # 模型输出类别名称 + +root_dir = '/sdcard/app/tests/' +kmodel_file = root_dir + 'kmodel/hand_det.kmodel' # kmodel文件的路径 +anchors = [26,27, 53,52, 75,71, 80,99, 106,82, 99,134, 140,113, 161,172, 245,276] #anchor设置 + +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#ai_utils.py +global current_kmodel_obj # 定义全局的 kpu 对象 +global ai2d,ai2d_input_tensor,ai2d_output_tensor,ai2d_builder # 定义全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder + + +# ai2d 初始化 +def ai2d_init(): + with ScopedTiming("ai2d_init",debug_mode > 0): + global ai2d + global ai2d_builder + global ai2d_output_tensor + # 计算padding值 + ori_w = OUT_RGB888P_WIDTH + ori_h = OUT_RGB888P_HEIGH + width = kmodel_frame_size[0] + height = kmodel_frame_size[1] + ratiow = float(width) / ori_w + ratioh = float(height) / ori_h + if ratiow < ratioh: + ratio = ratiow + else: + ratio = ratioh + new_w = int(ratio * ori_w) + new_h = int(ratio * ori_h) + dw = float(width - new_w) / 2 + dh = float(height - new_h) / 2 + top = int(round(dh - 0.1)) + bottom = int(round(dh + 0.1)) + left = int(round(dw - 0.1)) + right = int(round(dw - 0.1)) + + ai2d = nn.ai2d() + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d.set_pad_param(True, [0,0,0,0,top,bottom,left,right], 0, [114,114,114]) + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel ) + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,height,width]) + data = np.ones(kmodel_input_shape, dtype=np.uint8) + ai2d_output_tensor = nn.from_numpy(data) + +# ai2d 运行 +def ai2d_run(rgb888p_img): + with ScopedTiming("ai2d_run",debug_mode > 0): + global ai2d_input_tensor,ai2d_output_tensor + ai2d_input = rgb888p_img.to_numpy_ref() + ai2d_input_tensor = nn.from_numpy(ai2d_input) + ai2d_builder.run(ai2d_input_tensor, ai2d_output_tensor) + +# ai2d 释放内存 +def ai2d_release(): + with ScopedTiming("ai2d_release",debug_mode > 0): + global ai2d_input_tensor + del ai2d_input_tensor + +# kpu 初始化 +def kpu_init(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + + ai2d_init() + return kpu_obj + +# kpu 输入预处理 +def kpu_pre_process(rgb888p_img): + ai2d_run(rgb888p_img) + with ScopedTiming("kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,ai2d_output_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, ai2d_output_tensor) + +# kpu 获得 kmodel 输出 +def kpu_get_output(): + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + + result = result.reshape((result.shape[0]*result.shape[1]*result.shape[2]*result.shape[3])) + tmp2 = result.copy() + del result + results.append(tmp2) + return results + +# kpu 运行 +def kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1)原图预处理，并设置模型输入 + kpu_pre_process(rgb888p_img) + # (2)手掌检测 kpu 运行 + with ScopedTiming("kpu_run",debug_mode > 0): + current_kmodel_obj.run() + # (3)释放手掌检测 ai2d 资源 + ai2d_release() + # (4)获取手掌检测 kpu 输出 + results = kpu_get_output() + # (5)手掌检测 kpu 结果后处理 + dets = aicube.anchorbasedet_post_process( results[0], results[1], results[2], kmodel_frame_size, frame_size, strides, num_classes, confidence_threshold, nms_threshold, anchors, nms_option) + # (6)返回手掌检测结果 + return dets + +# kpu 释放内存 +def kpu_deinit(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d, ai2d_output_tensor + del kpu_obj + del ai2d + del ai2d_output_tensor + +#media_utils.py +global draw_img,osd_img,masks #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +# display 作图过程 框出所有检测到的手以及标出得分 +def display_draw(dets): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + + if dets: + draw_img.clear() + for det_box in dets: + x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5] + w = float(x2 - x1) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + h = float(y2 - y1) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + + x1 = int(x1 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y1 = int(y1 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + x2 = int(x2 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y2 = int(y2 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + + if (h<(0.1*DISPLAY_HEIGHT)): + continue + if (w<(0.25*DISPLAY_WIDTH) and ((x1<(0.03*DISPLAY_WIDTH)) or (x2>(0.97*DISPLAY_WIDTH)))): + continue + if (w<(0.15*DISPLAY_WIDTH) and ((x1<(0.01*DISPLAY_WIDTH)) or (x2>(0.99*DISPLAY_WIDTH)))): + continue + draw_img.draw_rectangle(x1 , y1 , int(w) , int(h) , color=(255, 0, 255, 0)) + draw_img.draw_string( x1 , y1-50, " " + labels[det_box[0]] + " " + str(round(det_box[1],2)) , color=(255,0, 255, 0), scale=4) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +#**********for hand_detect.py********** +def hand_detect_inference(): + print("hand_detect_test start") + kpu_hand_detect = kpu_init(kmodel_file) # 创建手掌检测的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("hand_detect_test, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图片 + if rgb888p_img == -1: + print("hand_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + dets = kpu_run(kpu_hand_detect,rgb888p_img) # 执行手掌检测 kpu 运行 以及 后处理过程 + display_draw(dets) # 将得到的检测结果 绘制到 display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图像 + rgb888p_img = None + #gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止 camera + display_deinit() # 释放 display + kpu_deinit(kpu_hand_detect) # 释放 kpu + gc.collect() + ret = media_deinit() # 释放 整个media + if ret: + print("hand_detect_test, buffer_deinit failed") + return ret + + print("hand_detect_test end") + return 0 + +if __name__ == '__main__': + hand_detect_inference() +``` + +### 8.人体检测 + +```python +import aicube #aicube模块，封装检测分割等任务相关后处理 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 + +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 + +import time #时间统计 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 + +import gc #垃圾回收模块 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +##ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) +OUT_RGB888P_HEIGH = 1080 + +#kmodel输入shape +kmodel_input_shape = (1,3,640,640) # kmodel输入分辨率 + +#kmodel相关参数设置 +confidence_threshold = 0.2 # 行人检测阈值，用于过滤roi +nms_threshold = 0.6 # 行人检测框阈值，用于过滤重复roi +kmodel_frame_size = [640,640] # 行人检测输入图片尺寸 +frame_size = [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH] # 直接输入图片尺寸 +strides = [8,16,32] # 输出特征图的尺寸与输入图片尺寸的比 +num_classes = 1 # 模型输出类别数 +nms_option = False # 是否所有检测框一起做NMS，False则按照不同的类分别应用NMS +labels = ["person"] # 模型输出类别名称 + +root_dir = '/sdcard/app/tests/' +kmodel_file = root_dir + 'kmodel/person_detect_yolov5n.kmodel' # kmodel文件的路径 +anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326] #anchor设置 + +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） +total_debug_mode = 1 + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#ai_utils.py +global current_kmodel_obj # 定义全局的 kpu 对象 +global ai2d,ai2d_input_tensor,ai2d_output_tensor,ai2d_builder # 定义全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder + + +# ai2d 初始化 +def ai2d_init(): + with ScopedTiming("ai2d_init",debug_mode > 0): + global ai2d + global ai2d_builder + global ai2d_output_tensor + # 计算padding值 + ori_w = OUT_RGB888P_WIDTH + ori_h = OUT_RGB888P_HEIGH + width = kmodel_frame_size[0] + height = kmodel_frame_size[1] + ratiow = float(width) / ori_w + ratioh = float(height) / ori_h + if ratiow < ratioh: + ratio = ratiow + else: + ratio = ratioh + new_w = int(ratio * ori_w) + new_h = int(ratio * ori_h) + dw = float(width - new_w) / 2 + dh = float(height - new_h) / 2 + top = int(round(dh - 0.1)) + bottom = int(round(dh + 0.1)) + left = int(round(dw - 0.1)) + right = int(round(dw - 0.1)) + + ai2d = nn.ai2d() + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d.set_pad_param(True, [0,0,0,0,top,bottom,left,right], 0, [114,114,114]) + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel ) + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,height,width]) + data = np.ones(kmodel_input_shape, dtype=np.uint8) + ai2d_output_tensor = nn.from_numpy(data) + +# ai2d 运行 +def ai2d_run(rgb888p_img): + with ScopedTiming("ai2d_run",debug_mode > 0): + global ai2d_input_tensor,ai2d_output_tensor + ai2d_input = rgb888p_img.to_numpy_ref() + ai2d_input_tensor = nn.from_numpy(ai2d_input) + ai2d_builder.run(ai2d_input_tensor, ai2d_output_tensor) + +# ai2d 释放内存 +def ai2d_release(): + with ScopedTiming("ai2d_release",debug_mode > 0): + global ai2d_input_tensor + del ai2d_input_tensor + +# kpu 初始化 +def kpu_init(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + + ai2d_init() + return kpu_obj + +# kpu 输入预处理 +def kpu_pre_process(rgb888p_img): + ai2d_run(rgb888p_img) + with ScopedTiming("kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,ai2d_output_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, ai2d_output_tensor) + +# kpu 获得 kmodel 输出 +def kpu_get_output(): + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + + result = result.reshape((result.shape[0]*result.shape[1]*result.shape[2]*result.shape[3])) + tmp2 = result.copy() + del result + results.append(tmp2) + return results + +# kpu 运行 +def kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1)原图预处理，并设置模型输入 + kpu_pre_process(rgb888p_img) + # (2)行人检测 kpu 运行 + with ScopedTiming("kpu_run",debug_mode > 0): + current_kmodel_obj.run() + # (3)释放行人检测 ai2d 资源 + ai2d_release() + # (4)获取行人检测 kpu 输出 + results = kpu_get_output() + # (5)行人检测 kpu 结果后处理 + with ScopedTiming("kpu_post_process",debug_mode > 0): + dets = aicube.anchorbasedet_post_process( results[0], results[1], results[2], kmodel_frame_size, frame_size, strides, num_classes, confidence_threshold, nms_threshold, anchors, nms_option) + # (6)返回行人检测结果 + return dets + +# kpu 释放内存 +def kpu_deinit(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d, ai2d_output_tensor + del kpu_obj + del ai2d + del ai2d_output_tensor + +#media_utils.py +global draw_img,osd_img,masks #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +# display 作图过程 框出所有检测到的人以及标出得分 +def display_draw(dets): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + + if dets: + draw_img.clear() + for det_box in dets: + x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5] + w = float(x2 - x1) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + h = float(y2 - y1) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + + x1 = int(x1 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y1 = int(y1 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + x2 = int(x2 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y2 = int(y2 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + + if (h<(0.1*DISPLAY_HEIGHT)): + continue + if (w<(0.25*DISPLAY_WIDTH) and ((x1<(0.03*DISPLAY_WIDTH)) or (x2>(0.97*DISPLAY_WIDTH)))): + continue + if (w<(0.15*DISPLAY_WIDTH) and ((x1<(0.01*DISPLAY_WIDTH)) or (x2>(0.99*DISPLAY_WIDTH)))): + continue + draw_img.draw_rectangle(x1 , y1 , int(w) , int(h) , color=(255, 0, 255, 0),thickness = 2) + draw_img.draw_string( x1 , y1-50, " " + labels[det_box[0]] + " " + str(round(det_box[1],2)) , color=(255,0, 255, 0), scale=4) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +#**********for person_detect.py********** +def person_detect_inference(): + print("person_detect_test start") + kpu_person_detect = kpu_init(kmodel_file) # 创建行人检测的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("person_detect_test, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + while True: + with ScopedTiming("total",total_debug_mode): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图片 + if rgb888p_img == -1: + print("person_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + dets = kpu_run(kpu_person_detect,rgb888p_img) # 执行行人检测 kpu 运行 以及 后处理过程 + display_draw(dets) # 将得到的检测结果 绘制到 display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图像 + rgb888p_img = None + # gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止 camera + display_deinit() # 释放 display + kpu_deinit(kpu_person_detect) # 释放 kpu + gc.collect() + ret = media_deinit() # 释放 整个media + if ret: + print("person_detect_test, buffer_deinit failed") + return ret + + print("person_detect_test end") + return 0 + +if __name__ == '__main__': + person_detect_inference() +``` + +### 9.人体姿态估计 + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 +import aidemo #aidemo模块，封装ai demo相关后处理、画图操作 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +#ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) +OUT_RGB888P_HEIGHT = 1080 + +#人体关键点检测 kmodel 输入参数配置 +kmodel_input_shape = (1,3,640,640) # kmodel输入分辨率 +rgb_mean = [114,114,114] # ai2d padding 值 + +#人体关键点 相关参数设置 +confidence_threshold = 0.2 # 人体关键点检测分数阈值 +nms_threshold = 0.5 # 非最大值抑制阈值 + +#文件配置 +root_dir = '/sdcard/app/tests/' +kmodel_file = root_dir + 'kmodel/yolov8n-pose.kmodel' # kmodel文件的路径 +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） + +#骨骼信息 +SKELETON = [(16, 14),(14, 12),(17, 15),(15, 13),(12, 13),(6, 12),(7, 13),(6, 7),(6, 8),(7, 9),(8, 10),(9, 11),(2, 3),(1, 2),(1, 3),(2, 4),(3, 5),(4, 6),(5, 7)] +#肢体颜色 +LIMB_COLORS = [(255, 51, 153, 255),(255, 51, 153, 255),(255, 51, 153, 255),(255, 51, 153, 255),(255, 255, 51, 255),(255, 255, 51, 255),(255, 255, 51, 255),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 0, 255, 0),(255, 0, 255, 0),(255, 0, 255, 0),(255, 0, 255, 0),(255, 0, 255, 0),(255, 0, 255, 0),(255, 0, 255, 0)] +#关键点颜色 +KPS_COLORS = [(255, 0, 255, 0),(255, 0, 255, 0),(255, 0, 255, 0),(255, 0, 255, 0),(255, 0, 255, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 255, 128, 0),(255, 51, 153, 255),(255, 51, 153, 255),(255, 51, 153, 255),(255, 51, 153, 255),(255, 51, 153, 255),(255, 51, 153, 255)] + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + + +#ai_utils.py +global current_kmodel_obj # 定义全局的 kpu 对象 +global ai2d,ai2d_input_tensor,ai2d_output_tensor,ai2d_builder # 定义全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder + +# 人体关键点检测 接收kmodel输出的后处理方法 +def kpu_post_process(output_datas): + with ScopedTiming("kpu_post_process", debug_mode > 0): + results = aidemo.person_kp_postprocess(output_datas,[OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH],[kmodel_input_shape[2],kmodel_input_shape[3]],confidence_threshold,nms_threshold) + return results + +# 获取kmodel输入图像resize比例 以及 padding的上下左右像素数量 +def get_pad_param(): + #右padding或下padding + dst_w = kmodel_input_shape[3] + dst_h = kmodel_input_shape[2] + + ratio_w = float(dst_w) / OUT_RGB888P_WIDTH + ratio_h = float(dst_h) / OUT_RGB888P_HEIGHT + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + + new_w = (int)(ratio * OUT_RGB888P_WIDTH) + new_h = (int)(ratio * OUT_RGB888P_HEIGHT) + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + + top = (int)(round(dh)) + bottom = (int)(round(dh)) + left = (int)(round(dw)) + right = (int)(round(dw)) + return [0, 0, 0, 0, top, bottom, left, right] + +# ai2d 初始化 +def ai2d_init(): + with ScopedTiming("ai2d_init",debug_mode > 0): + global ai2d + ai2d = nn.ai2d() + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d.set_pad_param(True, get_pad_param(), 0, rgb_mean) + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + global ai2d_out_tensor + data = np.ones(kmodel_input_shape, dtype=np.uint8) + ai2d_out_tensor = nn.from_numpy(data) + + global ai2d_builder + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH], kmodel_input_shape) + +# ai2d 运行 +def ai2d_run(rgb888p_img): + with ScopedTiming("ai2d_run",debug_mode > 0): + global ai2d_input_tensor,ai2d_out_tensor + ai2d_input = rgb888p_img.to_numpy_ref() + ai2d_input_tensor = nn.from_numpy(ai2d_input) + + ai2d_builder.run(ai2d_input_tensor, ai2d_out_tensor) + +# ai2d 释放内存 +def ai2d_release(): + with ScopedTiming("ai2d_release",debug_mode > 0): + global ai2d_input_tensor + del ai2d_input_tensor + +# kpu 初始化 +def kpu_init(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + + ai2d_init() + return kpu_obj + +# kpu 输入预处理 +def kpu_pre_process(rgb888p_img): + ai2d_run(rgb888p_img) + with ScopedTiming("kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,ai2d_out_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, ai2d_out_tensor) + +# kpu 获得 kmodel 输出 +def kpu_get_output(): + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + data = current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + del data + + return result + +# kpu 运行 +def kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1) 原图预处理，并设置模型输入 + kpu_pre_process(rgb888p_img) + # (2) kpu 运行 + with ScopedTiming("kpu_run",debug_mode > 0): + kpu_obj.run() + # (3) 释放ai2d资源 + ai2d_release() + # (4) 获取kpu输出 + results = kpu_get_output() + # (5) kpu结果后处理 + kp_res = kpu_post_process(results) + # (6) 返回 人体关键点检测 结果 + return kp_res + +# kpu 释放内存 +def kpu_deinit(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d,ai2d_out_tensor + del kpu_obj + del ai2d + del ai2d_out_tensor + +#media_utils.py +global draw_img,osd_img #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +# display 作图过程 将所有目标关键点作图 +def display_draw(res): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + if res[0]: + draw_img.clear() + kpses = res[1] + for i in range(len(res[0])): + for k in range(17+2): + if (k < 17): + kps_x = round(kpses[i][k][0]) + kps_y = round(kpses[i][k][1]) + kps_s = kpses[i][k][2] + + kps_x1 = int(float(kps_x) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + kps_y1 = int(float(kps_y) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGHT) + + if (kps_s > 0): + draw_img.draw_circle(kps_x1,kps_y1,5,KPS_COLORS[k],4) + ske = SKELETON[k] + pos1_x = round(kpses[i][ske[0]-1][0]) + pos1_y = round(kpses[i][ske[0]-1][1]) + + pos1_x_ = int(float(pos1_x) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + pos1_y_ = int(float(pos1_y) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGHT) + + pos2_x = round(kpses[i][(ske[1] -1)][0]) + pos2_y = round(kpses[i][(ske[1] -1)][1]) + + pos2_x_ = int(float(pos2_x) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + pos2_y_ = int(float(pos2_y) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGHT) + + pos1_s = kpses[i][(ske[0] -1)][2] + pos2_s = kpses[i][(ske[1] -1)][2] + + if (pos1_s > 0.0 and pos2_s >0.0): + draw_img.draw_line(pos1_x,pos1_y,pos2_x,pos2_y,LIMB_COLORS[k],4) + + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + + +#**********for person_kp_detect.py********** +def person_kp_detect_inference(): + print("person_kp_detect start") + kpu_person_kp_detect = kpu_init(kmodel_file) # 创建人体关键点检测的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("person_kp_detect, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图片 + if rgb888p_img == -1: + print("person_kp_detect, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + person_kp_detect_res = kpu_run(kpu_person_kp_detect,rgb888p_img) # 执行人体关键点检测 kpu 运行 以及 后处理过程 + display_draw(person_kp_detect_res) # 将得到的人体关键点结果 绘制到 display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图像 + rgb888p_img = None + #gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止 camera + display_deinit() # 释放 display + kpu_deinit(kpu_person_kp_detect) # 释放 kpu + gc.collect() + time.sleep(1) + ret = media_deinit() # 释放 整个media + if ret: + print("person_kp_detect, buffer_deinit failed") + return ret + + print("person_kp_detect end") + return 0 + +if __name__ == '__main__': + person_kp_detect_inference() +``` + +### 10.KWS + +```python +from media.pyaudio import * # 音频模块 +from media.media import * # 软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import media.wave as wave # wav音频处理模块 +import nncase_runtime as nn # nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +import ulab.numpy as np # 类似python numpy操作，但也会有一些接口不同 +import aidemo # aidemo模块，封装ai demo相关前处理、后处理等操作 +import time # 时间统计 +import struct # 字节字符转换模块 +import gc # 垃圾回收模块 +import os # 操作系统接口模块 + +# key word spotting任务 +# 检测阈值 +THRESH = 0.5 +# 有关音频流的宏变量 +SAMPLE_RATE = 16000 # 采样率16000Hz,即每秒采样16000次 +CHANNELS = 1 # 通道数 1为单声道，2为立体声 +FORMAT = paInt16 # 音频输入输出格式 paInt16 +CHUNK = int(0.3 * 16000) # 每次读取音频数据的帧数，设置为0.3s的帧数16000*0.3=4800 + +root_dir='/sdcard/app/tests/' +kmodel_file_kws = root_dir+"kmodel/kws.kmodel" # kmodel加载路径 +reply_wav_file = root_dir+"utils/wozai.wav" # kws唤醒词回复音频路径 +debug_mode = 0 # 调试模式，大于0（调试）、 反之 （不调试） + + +# scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +global current_kmodel_obj # 定义全局kpu对象 +global p,cache_np,fp,input_stream,output_stream,audio_input_tensor,cache_input_tensor # 定义全局音频流对象，输入输出流对象，并且定义kws处理接口FeaturePipeline对象fp,输入输出tensor和缓冲cache_np + +# 初始化kws音频流相关变量 +def init_kws(): + with ScopedTiming("init_kws",debug_mode > 0): + global p,cache_np,fp,input_stream,output_stream,cache_input_tensor + # 初始化模型的cache输入 + cache_np = np.zeros((1, 256, 105), dtype=np.float) + cache_input_tensor = nn.from_numpy(cache_np) + # 初始化音频预处理接口 + fp = aidemo.kws_fp_create() + # 初始化音频流 + p = PyAudio() + p.initialize(CHUNK) + ret = media.buffer_init() + if ret: + print("record_audio, buffer_init failed") + # 用于采集实时音频数据 + input_stream = p.open( + format=FORMAT, + channels=CHANNELS, + rate=SAMPLE_RATE, + input=True, + frames_per_buffer=CHUNK + ) + + # 用于播放回复音频 + output_stream = p.open( + format=FORMAT, + channels=CHANNELS, + rate=SAMPLE_RATE, + output=True, + frames_per_buffer=CHUNK + ) + +# kws 初始化kpu +def kpu_init_kws(): + with ScopedTiming("init_kpu",debug_mode > 0): + # 初始化kpu并加载kmodel + kpu = nn.kpu() + kpu.load_kmodel(kmodel_file_kws) + return kpu + +# kws音频预处理 +def kpu_pre_process_kws(pcm_data_list): + global current_kmodel_obj + global fp,input_stream,audio_input_tensor,cache_input_tensor + with ScopedTiming("pre_process",debug_mode > 0): + # 将pcm数据处理为模型输入的特征向量 + mp_feats = aidemo.kws_preprocess(fp, pcm_data_list)[0] + mp_feats_np = np.array(mp_feats) + mp_feats_np = mp_feats_np.reshape((1, 30, 40)) + audio_input_tensor = nn.from_numpy(mp_feats_np) + cache_input_tensor = nn.from_numpy(cache_np) + current_kmodel_obj.set_input_tensor(0, audio_input_tensor) + current_kmodel_obj.set_input_tensor(1, cache_input_tensor) + +# kws任务kpu运行并完成后处理 +def kpu_run_kws(kpu_obj,pcm_data_list): + global current_kmodel_obj,cache_np,output_stream + current_kmodel_obj = kpu_obj + # （1）kws音频数据预处理 + kpu_pre_process_kws(pcm_data_list) + # （2）kpu推理 + with ScopedTiming("kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）获取模型输出 + logits = kpu_obj.get_output_tensor(0) + cache_tensor = kpu_obj.get_output_tensor(1) # 更新缓存输入 + logits_np = logits.to_numpy() + cache_np=cache_tensor.to_numpy() + del logits + del cache_tensor + # （4）后处理argmax + max_logits = np.max(logits_np, axis=1)[0] + max_p = np.max(max_logits) + idx = np.argmax(max_logits) + # 如果分数大于阈值，且idx==1(即包含唤醒词)，播放回复音频 + if max_p > THRESH and idx == 1: + print("====Detected XiaonanXiaonan!====") + wf = wave.open(reply_wav_file, "rb") + wav_data = wf.read_frames(CHUNK) + while wav_data: + output_stream.write(wav_data) + wav_data = wf.read_frames(CHUNK) + time.sleep(1) # 时间缓冲，用于播放声音 + wf.close() + else: + print("Deactivated!") + + +# kws推理过程 +def kws_inference(): + # 记录音频帧帧数 + global p,fp,input_stream,output_stream + # 初始化 + init_kws() + kpu_kws=kpu_init_kws() + pcm_data_list = [] + try: + while True: + with ScopedTiming("total", 1): + pcm_data_list.clear() + # 对实时音频流进行推理 + pcm_data = input_stream.read() # 获取的音频流数据字节数，len(pcm_data)=0.3*16000*2=9600，即以16000Hz的采样率采样0.3s，每次采样数据为paInt16格式占2个字节 + # 获取音频流数据 + for i in range(0, len(pcm_data), 2): + # 每两个字节组织成一个有符号整数，然后将其转换为浮点数，即为一次采样的数据，加入到当前一帧（0.3s）的数据列表中 + int_pcm_data = struct.unpack("0：打印对应级别调试信息 +debug_mode = 0 + +# 人脸关键点不同部位关键点列表 +dict_kp_seq = [ + [43, 44, 45, 47, 46, 50, 51, 49, 48], # left_eyebrow + [97, 98, 99, 100, 101, 105, 104, 103, 102], # right_eyebrow + [35, 36, 33, 37, 39, 42, 40, 41], # left_eye + [89, 90, 87, 91, 93, 96, 94, 95], # right_eye + [34, 88], # pupil + [72, 73, 74, 86], # bridge_nose + [77, 78, 79, 80, 85, 84, 83], # wing_nose + [52, 55, 56, 53, 59, 58, 61, 68, 67, 71, 63, 64], # out_lip + [65, 54, 60, 57, 69, 70, 62, 66], # in_lip + [1, 9, 10, 11, 12, 13, 14, 15, 16, 2, 3, 4, 5, 6, 7, 8, 0, 24, 23, 22, 21, 20, 19, 18, 32, 31, 30, 29, 28, 27, 26, 25, 17] # basin +] + +# 人脸关键点不同部位（顺序同dict_kp_seq）颜色配置，argb +color_list_for_osd_kp = [ + (255, 0, 255, 0), + (255, 0, 255, 0), + (255, 255, 0, 255), + (255, 255, 0, 255), + (255, 255, 0, 0), + (255, 255, 170, 0), + (255, 255, 255, 0), + (255, 0, 255, 255), + (255, 255, 220, 50), + (255, 30, 30, 255) +] + +#********************for scoped_timing.py******************** +# 时间统计类 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#********************for ai_utils.py******************** +global current_kmodel_obj #当前kpu对象 +# fd_ai2d： 人脸检测ai2d实例 +# fd_ai2d_input_tensor： 人脸检测ai2d输入 +# fd_ai2d_output_tensor： 人脸检测ai2d输入 +# fd_ai2d_builder： 根据人脸检测ai2d参数，构建的人脸检测ai2d_builder对象 +global fd_ai2d,fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder +# fld_ai2d： 人脸关键点ai2d实例 +# fld_ai2d_input_tensor： 人脸关键点ai2d输入 +# fld_ai2d_output_tensor：人脸关键点ai2d输入 +# fld_ai2d_builder： 根据人脸关键点ai2d参数，构建的人脸关键点ai2d_builder对象 +global fld_ai2d,fld_ai2d_input_tensor,fld_ai2d_output_tensor,fld_ai2d_builder +global matrix_dst #人脸仿射变换矩阵 + +#读取anchor文件，为人脸检测后处理做准备 +print('anchors_path:',anchors_path) +prior_data = np.fromfile(anchors_path, dtype=np.float) +prior_data = prior_data.reshape((anchor_len,det_dim)) + +def get_pad_one_side_param(): + # 右padding或下padding，获取padding参数 + dst_w = fd_kmodel_input_shape[3] # kmodel输入宽（w） + dst_h = fd_kmodel_input_shape[2] # kmodel输入高（h） + + # OUT_RGB888P_WIDTH：原图宽（w） + # OUT_RGB888P_HEIGH：原图高（h） + # 计算最小的缩放比例，等比例缩放 + ratio_w = dst_w / OUT_RGB888P_WIDTH + ratio_h = dst_h / OUT_RGB888P_HEIGH + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + # 计算经过缩放后的新宽和新高 + new_w = (int)(ratio * OUT_RGB888P_WIDTH) + new_h = (int)(ratio * OUT_RGB888P_HEIGH) + + # 计算需要添加的padding，以使得kmodel输入的宽高和原图一致 + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + # 四舍五入，确保padding是整数 + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +def fd_ai2d_init(): + # 人脸检测模型ai2d初始化 + with ScopedTiming("fd_ai2d_init",debug_mode > 0): + # （1）创建人脸检测ai2d对象 + global fd_ai2d + fd_ai2d = nn.ai2d() + # （2）设置人脸检测ai2d参数 + fd_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + fd_ai2d.set_pad_param(True, get_pad_one_side_param(), 0, rgb_mean) + fd_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + #（3）人脸检测ai2d_builder，根据人脸检测ai2d参数、输入输出大小创建ai2d_builder对象 + global fd_ai2d_builder + fd_ai2d_builder = fd_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fd_kmodel_input_shape) + + #（4）创建人脸检测ai2d_output_tensor，用于保存人脸检测ai2d输出 + global fd_ai2d_output_tensor + data = np.ones(fd_kmodel_input_shape, dtype=np.uint8) + fd_ai2d_output_tensor = nn.from_numpy(data) + +def fd_ai2d_run(rgb888p_img): + # 根据人脸检测ai2d参数，对原图rgb888p_img进行预处理 + with ScopedTiming("fd_ai2d_run",debug_mode > 0): + global fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder + # （1）根据原图构建ai2d_input_tensor对象 + ai2d_input = rgb888p_img.to_numpy_ref() + fd_ai2d_input_tensor = nn.from_numpy(ai2d_input) + # （2）运行人脸检测ai2d_builder，将结果保存到人脸检测ai2d_output_tensor中 + fd_ai2d_builder.run(fd_ai2d_input_tensor, fd_ai2d_output_tensor) + +def fd_ai2d_release(): + # 释放人脸检测ai2d_input_tensor + with ScopedTiming("fd_ai2d_release",debug_mode > 0): + global fd_ai2d_input_tensor + del fd_ai2d_input_tensor + + +def fd_kpu_init(kmodel_file): + # 初始化人脸检测kpu对象，并加载kmodel + with ScopedTiming("fd_kpu_init",debug_mode > 0): + # 初始化人脸检测kpu对象 + kpu_obj = nn.kpu() + # 加载人脸检测kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸检测ai2d + fd_ai2d_init() + return kpu_obj + +def fd_kpu_pre_process(rgb888p_img): + # 设置人脸检测kpu输入 + # 使用人脸检测ai2d对原图进行预处理（padding，resize） + fd_ai2d_run(rgb888p_img) + with ScopedTiming("fd_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fd_ai2d_output_tensor + # 设置人脸检测kpu输入 + current_kmodel_obj.set_input_tensor(0, fd_ai2d_output_tensor) + +def fd_kpu_get_output(): + # 获取人脸检测kpu输出 + with ScopedTiming("fd_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取模型输出，并将结果转换为numpy，以便进行人脸检测后处理 + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data + results.append(result) + return results + +def fd_kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）原图预处理，并设置模型输入 + fd_kpu_pre_process(rgb888p_img) + # （2）人脸检测kpu推理 + with ScopedTiming("fd kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸检测ai2d资源 + fd_ai2d_release() + # （4）获取人俩检测kpu输出 + results = fd_kpu_get_output() + # （5）人脸检测kpu结果后处理 + with ScopedTiming("fd kpu_post",debug_mode > 0): + post_ret = aidemo.face_det_post_process(confidence_threshold,nms_threshold,fd_kmodel_input_shape[2],prior_data, + [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH],results) + # （6）返回人脸检测框 + if len(post_ret)==0: + return post_ret + else: + return post_ret[0] #0:det,1:landm,2:score + +def fd_kpu_deinit(kpu_obj): + # kpu释放 + with ScopedTiming("fd_kpu_deinit",debug_mode > 0): + global fd_ai2d, fd_ai2d_output_tensor + del kpu_obj #删除人脸检测kpu_obj变量，释放对它所引用对象的内存引用 + del fd_ai2d #删除人脸检测ai2d变量，释放对它所引用对象的内存引用 + del fd_ai2d_output_tensor #删除人脸检测ai2d_output_tensor变量，释放对它所引用对象的内存引用 + +###############for face recognition############### +def get_affine_matrix(bbox): + # 获取仿射矩阵，用于将边界框映射到模型输入空间 + with ScopedTiming("get_affine_matrix", debug_mode > 1): + # 从边界框提取坐标和尺寸 + x1, y1, w, h = map(lambda x: int(round(x, 0)), bbox[:4]) + # 计算缩放比例，使得边界框映射到模型输入空间的一部分 + scale_ratio = (fld_kmodel_input_shape[2]) / (max(w, h) * 1.5) + # 计算边界框中心点在模型输入空间的坐标 + cx = (x1 + w / 2) * scale_ratio + cy = (y1 + h / 2) * scale_ratio + # 计算模型输入空间的一半长度 + half_input_len = fld_kmodel_input_shape[2] / 2 + + # 创建仿射矩阵并进行设置 + matrix_dst = np.zeros((2, 3), dtype=np.float) + matrix_dst[0, 0] = scale_ratio + matrix_dst[0, 1] = 0 + matrix_dst[0, 2] = half_input_len - cx + matrix_dst[1, 0] = 0 + matrix_dst[1, 1] = scale_ratio + matrix_dst[1, 2] = half_input_len - cy + return matrix_dst + +def fld_ai2d_init(): + # 人脸关键点ai2d初始化 + with ScopedTiming("fld_ai2d_init",debug_mode > 0): + # （1）创建人脸关键点ai2d对象 + global fld_ai2d + fld_ai2d = nn.ai2d() + + # （2）创建人脸关键点ai2d_output_tensor对象，用于存放ai2d输出 + global fld_ai2d_output_tensor + data = np.ones(fld_kmodel_input_shape, dtype=np.uint8) + fld_ai2d_output_tensor = nn.from_numpy(data) + +def fld_ai2d_run(rgb888p_img,det): + # 人脸关键点ai2d推理 + with ScopedTiming("fld_ai2d_run",debug_mode > 0): + global fld_ai2d,fld_ai2d_input_tensor,fld_ai2d_output_tensor + #（1）根据原图ai2d_input_tensor对象 + ai2d_input = rgb888p_img.to_numpy_ref() + fld_ai2d_input_tensor = nn.from_numpy(ai2d_input) + + # （2）根据新的det设置新的人脸关键点ai2d参数 + fld_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + global matrix_dst + matrix_dst = get_affine_matrix(det) + affine_matrix = [matrix_dst[0][0],matrix_dst[0][1],matrix_dst[0][2], + matrix_dst[1][0],matrix_dst[1][1],matrix_dst[1][2]] + fld_ai2d.set_affine_param(True,nn.interp_method.cv2_bilinear,0, 0, 127, 1,affine_matrix) + + # （3）根据新的人脸关键点ai2d参数，构建人脸关键点ai2d_builder + global fld_ai2d_builder + fld_ai2d_builder = fld_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fld_kmodel_input_shape) + # （4）推理人脸关键点ai2d，将预处理的结果保存到fld_ai2d_output_tensor + fld_ai2d_builder.run(fld_ai2d_input_tensor, fld_ai2d_output_tensor) + +def fld_ai2d_release(): + # 释放人脸关键点ai2d_input_tensor、ai2d_builder + with ScopedTiming("fld_ai2d_release",debug_mode > 0): + global fld_ai2d_input_tensor,fld_ai2d_builder + del fld_ai2d_input_tensor + del fld_ai2d_builder + +def fld_kpu_init(kmodel_file): + # 人脸关键点kpu初始化 + with ScopedTiming("fld_kpu_init",debug_mode > 0): + # 初始化人脸关键点kpu对象 + kpu_obj = nn.kpu() + # 加载人脸关键点kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸关键点ai2d + fld_ai2d_init() + return kpu_obj + +def fld_kpu_pre_process(rgb888p_img,det): + # 人脸关键点kpu预处理 + # 人脸关键点ai2d推理，根据det对原图进行预处理 + fld_ai2d_run(rgb888p_img,det) + with ScopedTiming("fld_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fld_ai2d_output_tensor + # 将人脸关键点ai2d输出设置为人脸关键点kpu输入 + current_kmodel_obj.set_input_tensor(0, fld_ai2d_output_tensor) + #ai2d_out_data = fld_ai2d_output_tensor.to_numpy() + #with open("/sdcard/app/ai2d_out.bin", "wb") as file: + #file.write(ai2d_out_data.tobytes()) + +def fld_kpu_get_output(): + with ScopedTiming("fld_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取人脸关键点kpu输出 + data = current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + del data + return result + +def fld_kpu_post_process(pred): + # 人脸关键点kpu推理结果后处理 + with ScopedTiming("fld_kpu_post_process",debug_mode > 0): + # （1）将人脸关键点输出变换模型输入 + half_input_len = fld_kmodel_input_shape[2] // 2 + pred = pred.flatten() + for i in range(len(pred)): + pred[i] += (pred[i] + 1) * half_input_len + + # （2）获取仿射矩阵的逆矩阵 + global matrix_dst + matrix_dst_inv = aidemo.invert_affine_transform(matrix_dst) + matrix_dst_inv = matrix_dst_inv.flatten() + + # （3）对每个关键点进行逆变换 + half_out_len = len(pred) // 2 + for kp_id in range(half_out_len): + old_x = pred[kp_id * 2] + old_y = pred[kp_id * 2 + 1] + + # 逆变换公式 + new_x = old_x * matrix_dst_inv[0] + old_y * matrix_dst_inv[1] + matrix_dst_inv[2] + new_y = old_x * matrix_dst_inv[3] + old_y * matrix_dst_inv[4] + matrix_dst_inv[5] + + pred[kp_id * 2] = new_x + pred[kp_id * 2 + 1] = new_y + + return pred + +def fld_kpu_run(kpu_obj,rgb888p_img,det): + # 人脸关键点kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）人脸关键点kpu预处理，设置kpu输入 + fld_kpu_pre_process(rgb888p_img,det) + # （2）人脸关键点kpu推理 + with ScopedTiming("fld_kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸关键点ai2d + fld_ai2d_release() + # （4）获取人脸关键点kpu输出 + result = fld_kpu_get_output() + # （5）人脸关键点后处理 + result = fld_kpu_post_process(result) + return result + +def fld_kpu_deinit(kpu_obj): + # 人脸关键点kpu释放 + with ScopedTiming("fld_kpu_deinit",debug_mode > 0): + global fld_ai2d,fld_ai2d_output_tensor + del kpu_obj # 删除kpu_obj变量，释放对它所引用对象的内存引用 + del fld_ai2d # 删除fld_ai2d变量，释放对它所引用对象的内存引用 + del fld_ai2d_output_tensor # 删除fld_ai2d_output_tensor变量，释放对它所引用对象的内存引用 + +#********************for media_utils.py******************** +global draw_img_ulab,draw_img,osd_img #for display +global buffer,media_source,media_sink #for media + +# for display，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.2 +def display_init(): + # 设置使用hdmi进行显示 + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +def display_deinit(): + # 释放显示资源 + display.deinit() + +def display_draw(dets,landmark_preds): + # 在显示器画人脸轮廓 + with ScopedTiming("display_draw",debug_mode >0): + global draw_img_ulab,draw_img,osd_img + if dets: + draw_img.clear() + for pred in landmark_preds: + # （1）获取单个人脸框对应的人脸关键点 + for sub_part_index in range(len(dict_kp_seq)): + # （2）构建人脸某个区域关键点集 + sub_part = dict_kp_seq[sub_part_index] + face_sub_part_point_set = [] + for kp_index in range(len(sub_part)): + real_kp_index = sub_part[kp_index] + x, y = pred[real_kp_index * 2], pred[real_kp_index * 2 + 1] + + x = int(x * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y = int(y * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + face_sub_part_point_set.append((x, y)) + + # （3）画人脸不同区域的轮廓 + if sub_part_index in (9, 6): + color = np.array(color_list_for_osd_kp[sub_part_index],dtype = np.uint8) + face_sub_part_point_set = np.array(face_sub_part_point_set) + + aidemo.polylines(draw_img_ulab, face_sub_part_point_set,False,color,5,8,0) + + elif sub_part_index == 4: + color = color_list_for_osd_kp[sub_part_index] + for kp in face_sub_part_point_set: + x,y = kp[0],kp[1] + draw_img.draw_circle(x,y ,2, color, 1) + else: + color = np.array(color_list_for_osd_kp[sub_part_index],dtype = np.uint8) + face_sub_part_point_set = np.array(face_sub_part_point_set) + aidemo.contours(draw_img_ulab, face_sub_part_point_set,-1,color,2,8) + + # （4）将轮廓结果拷贝到osd + draw_img.copy_to(osd_img) + # （5）将osd显示到屏幕 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + # （1）清空用来画框的图像 + draw_img.clear() + # （2）清空osd + draw_img.copy_to(osd_img) + # （3）显示透明图层 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.1 +def camera_init(dev_id): + # camera初始化 + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +def camera_start(dev_id): + # camera启动 + camera.start_stream(dev_id) + +def camera_read(dev_id): + # 读取一帧图像 + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +def camera_release_image(dev_id,rgb888p_img): + # 释放一帧图像 + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +def camera_stop(dev_id): + # 停止camera + camera.stop_stream(dev_id) + +#for media，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.3 +def media_init(): + # meida初始化 + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img_ulab,draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 用于画框 + draw_img_ulab = np.zeros((DISPLAY_HEIGHT,DISPLAY_WIDTH,4),dtype=np.uint8) + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_REF,data = draw_img_ulab) + # 用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +def media_deinit(): + # meida资源释放 + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +#********************for face_detect.py******************** +def face_landmark_inference(): + print("face_landmark_test start") + # 人脸检测kpu初始化 + kpu_face_detect = fd_kpu_init(fd_kmodel_file) + # 人脸关键点kpu初始化 + kpu_face_landmark = fld_kpu_init(fr_kmodel_file) + # camera初始化 + camera_init(CAM_DEV_ID_0) + # 显示初始化 + display_init() + + rgb888p_img = None + # 注意：将一定要将一下过程包在try中，用于保证程序停止后，资源释放完毕；确保下次程序仍能正常运行 + try: + # 注意：媒体初始化（注：媒体初始化必须在camera_start之前，确保media缓冲区已配置完全） + ret = media_init() + if ret: + print("face_detect_test, buffer init failed") + return ret + + # 启动camera + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + # （1）读取一帧图像 + rgb888p_img = camera_read(CAM_DEV_ID_0) + # （2）若读取失败，释放当前帧 + if rgb888p_img == -1: + print("face_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + # （3）若读取成功，推理当前帧 + if rgb888p_img.format() == image.RGBP888: + # （3.1）推理当前图像，并获取人脸检测结果 + dets = fd_kpu_run(kpu_face_detect,rgb888p_img) + # （3.2）针对每个人脸框，推理得到对应人脸关键点 + landmark_result = [] + for det in dets: + ret = fld_kpu_run(kpu_face_landmark,rgb888p_img,det) + landmark_result.append(ret) + # （3.3）将人脸关键点画到屏幕上 + display_draw(dets,landmark_result) + + # （4）释放当前帧 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + #with ScopedTiming("gc collect", debug_mode > 0): + #gc.collect() + except Exception as e: + # 捕捉运行运行中异常，并打印错误 + print(f"An error occurred during buffer used: {e}") + finally: + # 释放当前帧 + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + # 停止camera + camera_stop(CAM_DEV_ID_0) + # 释放显示资源 + display_deinit() + # 释放kpu资源 + fd_kpu_deinit(kpu_face_detect) + fld_kpu_deinit(kpu_face_landmark) + # 垃圾回收 + gc.collect() + time.sleep(1) + # 释放媒体资源 + ret = media_deinit() + if ret: + print("face_landmark_test, buffer_deinit failed") + return ret + + print("face_landmark_test end") + return 0 + +if __name__ == '__main__': + face_landmark_inference() +``` + +### 2. 人脸识别 + +**人脸注册：** + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +import aidemo #aidemo模块，封装ai demo相关后处理、画图操作 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 +import os #操作系统接口模块 +import math #数学模块 + +#********************for config.py******************** +# kmodel输入shape +# 人脸检测kmodel输入shape +fd_kmodel_input_shape = (1,3,320,320) +# 人脸识别kmodel输入shape +fr_kmodel_input_shape = (1,3,112,112) +# ai原图padding +rgb_mean = [104,117,123] + +#kmodel相关参数设置 +#人脸检测 +confidence_threshold = 0.5 #人脸检测阈值 +top_k = 5000 +nms_threshold = 0.2 +keep_top_k = 750 +vis_thres = 0.5 +variance = [0.1, 0.2] +anchor_len = 4200 +score_dim = 2 +det_dim = 4 +keypoint_dim = 10 +#人脸识别 +max_register_face = 100 #数据库最多人脸个数 +feature_num = 128 #人脸识别特征维度 + +# 文件配置 +# 人脸检测kmodel +root_dir = '/sdcard/app/tests/' +fd_kmodel_file = root_dir + 'kmodel/face_detection_320.kmodel' +# 人脸识别kmodel +fr_kmodel_file = root_dir + 'kmodel/face_recognition.kmodel' +# 人脸检测anchor +anchors_path = root_dir + 'utils/prior_data_320.bin' +# 人脸注册数据库 +database_dir = root_dir + 'utils/db/' +# 人脸注册数据库原图 +database_img_dir = root_dir + 'utils/db_img/' +# 调试模型，0：不调试，>0：打印对应级别调试信息 +debug_mode = 0 + +#********************for scoped_timing.py******************** +# 时间统计类 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#********************for ai_utils.py******************** +global current_kmodel_obj #当前kpu实例 +# fd_ai2d： 人脸检测ai2d实例 +# fd_ai2d_input_tensor： 人脸检测ai2d输入 +# fd_ai2d_output_tensor： 人脸检测ai2d输入 +# fd_ai2d_builder： 根据人脸检测ai2d参数，构建的人脸检测ai2d_builder对象 +global fd_ai2d,fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder +# fr_ai2d： 人脸识别ai2d实例 +# fr_ai2d_input_tensor： 人脸识别ai2d输入 +# fr_ai2d_output_tensor： 人脸识别ai2d输入 +# fr_ai2d_builder： 根据人脸识别ai2d参数，构建的人脸识别ai2d_builder对象 +global fr_ai2d,fr_ai2d_input_tensor,fr_ai2d_output_tensor,fr_ai2d_builder +global valid_register_face #数据库中有效人脸个数 + +#读取anchor文件，为人脸检测后处理做准备 +print('anchors_path:',anchors_path) +prior_data = np.fromfile(anchors_path, dtype=np.float) +prior_data = prior_data.reshape((anchor_len,det_dim)) + +def get_pad_one_side_param(rgb888p_img): + # 右padding或下padding，获取padding参数 + with ScopedTiming("get_pad_one_side_param", debug_mode > 1): + dst_w = fd_kmodel_input_shape[3] # kmodel输入宽（w） + dst_h = fd_kmodel_input_shape[2] # kmodel输入高（h） + + # 计算最小的缩放比例，等比例缩放 + ratio_w = dst_w / rgb888p_img.shape[3] + ratio_h = dst_h / rgb888p_img.shape[2] + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + + # 计算经过缩放后的新宽和新高 + new_w = (int)(ratio * rgb888p_img.shape[3]) + new_h = (int)(ratio * rgb888p_img.shape[2]) + + # 计算需要添加的padding，以使得kmodel输入的宽高和原图一致 + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + # 四舍五入，确保padding是整数 + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +def fd_ai2d_init(): + # 人脸检测模型ai2d初始化 + with ScopedTiming("fd_ai2d_init",debug_mode > 0): + # （1）创建人脸检测ai2d对象 + global fd_ai2d + fd_ai2d = nn.ai2d() + + #（2）创建人脸检测ai2d_output_tensor，用于保存人脸检测ai2d输出 + global fd_ai2d_output_tensor + data = np.ones(fd_kmodel_input_shape, dtype=np.uint8) + fd_ai2d_output_tensor = nn.from_numpy(data) + +def fd_ai2d_run(rgb888p_img): + # 根据人脸检测ai2d参数，对原图rgb888p_img进行预处理 + with ScopedTiming("fd_ai2d_run",debug_mode > 0): + global fd_ai2d,fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder + # （1）根据原图构建ai2d_input_tensor对象 + fd_ai2d_input_tensor = nn.from_numpy(rgb888p_img) + # （2）根据新的图像设置新的人脸检测ai2d参数 + fd_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + fd_ai2d.set_pad_param(True, get_pad_one_side_param(rgb888p_img), 0, rgb_mean) + fd_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + # （3）根据新的人脸检测ai2d参数，构建人脸检测ai2d_builder + fd_ai2d_builder = fd_ai2d.build(rgb888p_img.shape, fd_kmodel_input_shape) + # （4）运行人脸检测ai2d_builder，将结果保存到人脸检测ai2d_output_tensor中 + fd_ai2d_builder.run(fd_ai2d_input_tensor, fd_ai2d_output_tensor) + +def fd_ai2d_release(): + # 释放人脸检测ai2d部分资源 + with ScopedTiming("fd_ai2d_release",debug_mode > 0): + global fd_ai2d_input_tensor,fd_ai2d_builder + del fd_ai2d_input_tensor + del fd_ai2d_builder + + +def fd_kpu_init(kmodel_file): + # 初始化人脸检测kpu对象，并加载kmodel + with ScopedTiming("fd_kpu_init",debug_mode > 0): + # 初始化人脸检测kpu对象 + kpu_obj = nn.kpu() + # 加载人脸检测kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸检测ai2d + fd_ai2d_init() + return kpu_obj + +def fd_kpu_pre_process(rgb888p_img): + # 设置人脸检测kpu输入 + # 使用人脸检测ai2d对原图进行预处理（padding，resize） + fd_ai2d_run(rgb888p_img) + with ScopedTiming("fd_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fd_ai2d_output_tensor + # 设置人脸检测kpu输入 + current_kmodel_obj.set_input_tensor(0, fd_ai2d_output_tensor) + +def fd_kpu_get_output(): + # 获取人脸检测kpu输出 + with ScopedTiming("fd_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取模型输出，并将结果转换为numpy，以便进行人脸检测后处理 + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data + results.append(result) + return results + +def fd_kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）原图预处理，并设置模型输入 + fd_kpu_pre_process(rgb888p_img) + # （2）人脸检测kpu推理 + with ScopedTiming("fd kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸检测ai2d资源 + fd_ai2d_release() + # （4）获取人俩检测kpu输出 + results = fd_kpu_get_output() + # （5）人脸检测kpu结果后处理 + with ScopedTiming("fd kpu_post",debug_mode > 0): + post_ret = aidemo.face_det_post_process(confidence_threshold,nms_threshold,fd_kmodel_input_shape[2],prior_data, + [rgb888p_img.shape[3],rgb888p_img.shape[2]],results) + # （6）返回人脸关键点 + if len(post_ret)==0: + return post_ret + else: + return post_ret[0],post_ret[1] #0:det,1:landm,2:score + +def fd_kpu_deinit(kpu_obj): + # kpu释放 + with ScopedTiming("fd_kpu_deinit",debug_mode > 0): + global fd_ai2d, fd_ai2d_output_tensor + del kpu_obj #删除人脸检测kpu_obj变量，释放对它所引用对象的内存引用 + del fd_ai2d #删除人脸检测ai2d变量，释放对它所引用对象的内存引用 + del fd_ai2d_output_tensor #删除人脸检测ai2d_output_tensor变量，释放对它所引用对象的内存引用 + +###############for face recognition############### +# 标准5官 +umeyama_args_112 = [ + 38.2946 , 51.6963 , + 73.5318 , 51.5014 , + 56.0252 , 71.7366 , + 41.5493 , 92.3655 , + 70.7299 , 92.2041 +] + +def svd22(a): + # svd + s = [0.0, 0.0] + u = [0.0, 0.0, 0.0, 0.0] + v = [0.0, 0.0, 0.0, 0.0] + + s[0] = (math.sqrt((a[0] - a[3]) ** 2 + (a[1] + a[2]) ** 2) + math.sqrt((a[0] + a[3]) ** 2 + (a[1] - a[2]) ** 2)) / 2 + s[1] = abs(s[0] - math.sqrt((a[0] - a[3]) ** 2 + (a[1] + a[2]) ** 2)) + v[2] = math.sin((math.atan2(2 * (a[0] * a[1] + a[2] * a[3]), a[0] ** 2 - a[1] ** 2 + a[2] ** 2 - a[3] ** 2)) / 2) if \ + s[0] > s[1] else 0 + v[0] = math.sqrt(1 - v[2] ** 2) + v[1] = -v[2] + v[3] = v[0] + u[0] = -(a[0] * v[0] + a[1] * v[2]) / s[0] if s[0] != 0 else 1 + u[2] = -(a[2] * v[0] + a[3] * v[2]) / s[0] if s[0] != 0 else 0 + u[1] = (a[0] * v[1] + a[1] * v[3]) / s[1] if s[1] != 0 else -u[2] + u[3] = (a[2] * v[1] + a[3] * v[3]) / s[1] if s[1] != 0 else u[0] + v[0] = -v[0] + v[2] = -v[2] + + return u, s, v + + +def image_umeyama_112(src): + # 使用Umeyama算法计算仿射变换矩阵 + SRC_NUM = 5 + SRC_DIM = 2 + src_mean = [0.0, 0.0] + dst_mean = [0.0, 0.0] + + for i in range(0,SRC_NUM * 2,2): + src_mean[0] += src[i] + src_mean[1] += src[i + 1] + dst_mean[0] += umeyama_args_112[i] + dst_mean[1] += umeyama_args_112[i + 1] + + src_mean[0] /= SRC_NUM + src_mean[1] /= SRC_NUM + dst_mean[0] /= SRC_NUM + dst_mean[1] /= SRC_NUM + + src_demean = [[0.0, 0.0] for _ in range(SRC_NUM)] + dst_demean = [[0.0, 0.0] for _ in range(SRC_NUM)] + + for i in range(SRC_NUM): + src_demean[i][0] = src[2 * i] - src_mean[0] + src_demean[i][1] = src[2 * i + 1] - src_mean[1] + dst_demean[i][0] = umeyama_args_112[2 * i] - dst_mean[0] + dst_demean[i][1] = umeyama_args_112[2 * i + 1] - dst_mean[1] + + A = [[0.0, 0.0], [0.0, 0.0]] + for i in range(SRC_DIM): + for k in range(SRC_DIM): + for j in range(SRC_NUM): + A[i][k] += dst_demean[j][i] * src_demean[j][k] + A[i][k] /= SRC_NUM + + T = [[1, 0, 0], [0, 1, 0], [0, 0, 1]] + U, S, V = svd22([A[0][0], A[0][1], A[1][0], A[1][1]]) + + T[0][0] = U[0] * V[0] + U[1] * V[2] + T[0][1] = U[0] * V[1] + U[1] * V[3] + T[1][0] = U[2] * V[0] + U[3] * V[2] + T[1][1] = U[2] * V[1] + U[3] * V[3] + + scale = 1.0 + src_demean_mean = [0.0, 0.0] + src_demean_var = [0.0, 0.0] + for i in range(SRC_NUM): + src_demean_mean[0] += src_demean[i][0] + src_demean_mean[1] += src_demean[i][1] + + src_demean_mean[0] /= SRC_NUM + src_demean_mean[1] /= SRC_NUM + + for i in range(SRC_NUM): + src_demean_var[0] += (src_demean_mean[0] - src_demean[i][0]) * (src_demean_mean[0] - src_demean[i][0]) + src_demean_var[1] += (src_demean_mean[1] - src_demean[i][1]) * (src_demean_mean[1] - src_demean[i][1]) + + src_demean_var[0] /= SRC_NUM + src_demean_var[1] /= SRC_NUM + + scale = 1.0 / (src_demean_var[0] + src_demean_var[1]) * (S[0] + S[1]) + T[0][2] = dst_mean[0] - scale * (T[0][0] * src_mean[0] + T[0][1] * src_mean[1]) + T[1][2] = dst_mean[1] - scale * (T[1][0] * src_mean[0] + T[1][1] * src_mean[1]) + T[0][0] *= scale + T[0][1] *= scale + T[1][0] *= scale + T[1][1] *= scale + return T + +def get_affine_matrix(sparse_points): + # 获取affine变换矩阵 + with ScopedTiming("get_affine_matrix", debug_mode > 1): + # 使用Umeyama算法计算仿射变换矩阵 + matrix_dst = image_umeyama_112(sparse_points) + matrix_dst = [matrix_dst[0][0],matrix_dst[0][1],matrix_dst[0][2], + matrix_dst[1][0],matrix_dst[1][1],matrix_dst[1][2]] + return matrix_dst + +def fr_ai2d_init(): + with ScopedTiming("fr_ai2d_init",debug_mode > 0): + # （1）人脸识别ai2d初始化 + global fr_ai2d + fr_ai2d = nn.ai2d() + + # （2）人脸识别ai2d_output_tensor初始化，用于存放ai2d输出 + global fr_ai2d_output_tensor + data = np.ones(fr_kmodel_input_shape, dtype=np.uint8) + fr_ai2d_output_tensor = nn.from_numpy(data) + +def fr_ai2d_run(rgb888p_img,sparse_points): + # 人脸识别ai2d推理 + with ScopedTiming("fr_ai2d_run",debug_mode > 0): + global fr_ai2d,fr_ai2d_input_tensor,fr_ai2d_output_tensor + #（1）根据原图创建人脸识别ai2d_input_tensor对象 + fr_ai2d_input_tensor = nn.from_numpy(rgb888p_img) + #（2）根据新的人脸关键点设置新的人脸识别ai2d参数 + fr_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + affine_matrix = get_affine_matrix(sparse_points) + fr_ai2d.set_affine_param(True,nn.interp_method.cv2_bilinear,0, 0, 127, 1,affine_matrix) + global fr_ai2d_builder + # （3）根据新的人脸识别ai2d参数，构建识别ai2d_builder + fr_ai2d_builder = fr_ai2d.build(rgb888p_img.shape, fr_kmodel_input_shape) + # （4）推理人脸识别ai2d，将预处理的结果保存到fr_ai2d_output_tensor + fr_ai2d_builder.run(fr_ai2d_input_tensor, fr_ai2d_output_tensor) + +def fr_ai2d_release(): + # 释放人脸识别ai2d_input_tensor、ai2d_builder + with ScopedTiming("fr_ai2d_release",debug_mode > 0): + global fr_ai2d_input_tensor,fr_ai2d_builder + del fr_ai2d_input_tensor + del fr_ai2d_builder + +def fr_kpu_init(kmodel_file): + # 人脸识别kpu初始化 + with ScopedTiming("fr_kpu_init",debug_mode > 0): + # 初始化人脸识别kpu对象 + kpu_obj = nn.kpu() + # 加载人脸识别kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸识别ai2d + fr_ai2d_init() + return kpu_obj + +def fr_kpu_pre_process(rgb888p_img,sparse_points): + # 人脸识别kpu预处理 + # 人脸识别ai2d推理，根据关键点对原图进行预处理 + fr_ai2d_run(rgb888p_img,sparse_points) + with ScopedTiming("fr_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fr_ai2d_output_tensor + # 将人脸识别ai2d输出设置为人脸识别kpu输入 + current_kmodel_obj.set_input_tensor(0, fr_ai2d_output_tensor) + + #ai2d_out_data = fr_ai2d_output_tensor.to_numpy() + #print('ai2d_out_data.shape:',ai2d_out_data.shape) + #with open("/sdcard/app/ai2d_out.bin", "wb") as file: + #file.write(ai2d_out_data.tobytes()) + +def fr_kpu_get_output(): + # 获取人脸识别kpu输出 + with ScopedTiming("fr_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + data = current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + del data + return result[0] + +def fr_kpu_run(kpu_obj,rgb888p_img,sparse_points): + # 人脸识别kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）人脸识别kpu预处理,设置kpu输入 + fr_kpu_pre_process(rgb888p_img,sparse_points) + # （2）人脸识别kpu推理 + with ScopedTiming("fr kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸识别ai2d + fr_ai2d_release() + # （4）获取人脸识别kpu输出 + results = fr_kpu_get_output() + return results + +def fr_kpu_deinit(kpu_obj): + # 人脸识别kpu相关资源释放 + with ScopedTiming("fr_kpu_deinit",debug_mode > 0): + global fr_ai2d + del kpu_obj + del fr_ai2d + +#********************for face_detect.py******************** +def image2rgb888array(img): #4维 + # 将Image转换为rgb888格式 + with ScopedTiming("fr_kpu_deinit",debug_mode > 0): + img_data_rgb888=img.to_rgb888() + # hwc,rgb888 + img_hwc=img_data_rgb888.to_numpy_ref() + shape=img_hwc.shape + img_tmp = img_hwc.reshape((shape[0] * shape[1], shape[2])) + img_tmp_trans = img_tmp.transpose() + img_res=img_tmp_trans.copy() + # chw,rgb888 + img_return=img_res.reshape((1,shape[2],shape[0],shape[1])) + return img_return + +def face_registration_inference(): + print("face_registration_test start") + # 人脸检测kpu初始化 + kpu_face_detect = fd_kpu_init(fd_kmodel_file) + # 人脸识别kpu初始化 + kpu_face_reg = fr_kpu_init(fr_kmodel_file) + try: + # 获取图像列表 + img_list = os.listdir(database_img_dir) + for img_file in img_list: + with ScopedTiming("total",1): + # （1）读取一张图像 + full_img_file = database_img_dir + img_file + print(full_img_file) + img = image.Image(full_img_file) + rgb888p_img_ndarry = image2rgb888array(img) + + #（2）推理得到人脸检测kpu，得到人脸检测框、人脸五点 + dets,landms = fd_kpu_run(kpu_face_detect,rgb888p_img_ndarry) + if dets: + if dets.shape[0] == 1: + #（3）若是只检测到一张人脸，则将该人脸注册到数据库 + db_i_name = img_file.split('.')[0] + for landm in landms: + reg_result = fr_kpu_run(kpu_face_reg,rgb888p_img_ndarry,landm) + #print('\nwrite bin:',database_dir+'{}.bin'.format(db_i_name)) + with open(database_dir+'{}.bin'.format(db_i_name), "wb") as file: + file.write(reg_result.tobytes()) + else: + print('Only one person in a picture when you sign up') + else: + print('No person detected') + + gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + # 释放kpu资源 + fd_kpu_deinit(kpu_face_detect) + fr_kpu_deinit(kpu_face_reg) + # 垃圾回收 + gc.collect() + time.sleep(1) + + print("face_registration_test end") + return 0 + +if __name__ == '__main__': + face_registration_inference() +``` + +**人脸识别：** + +```python +import ulab.numpy as np # 类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn # nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * # 摄像头模块 +from media.display import * # 显示模块 +from media.media import * # 软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import aidemo # aidemo模块，封装ai demo相关后处理、画图操作 +import image # 图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time # 时间统计 +import gc # 垃圾回收模块 +import os # 操作系统接口模块 +import math # 数学模块 + +#********************for config.py******************** +# display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) # 显示宽度要求16位对齐 +DISPLAY_HEIGHT = 1080 + +# ai原图分辨率，sensor默认出图为16:9，若需不形变原图，最好按照16:9比例设置宽高 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) # ai原图宽度要求16位对齐 +OUT_RGB888P_HEIGH = 1080 + +# kmodel输入shape +# 人脸检测kmodel输入shape +fd_kmodel_input_shape = (1,3,320,320) +# 人脸识别kmodel输入shape +fr_kmodel_input_shape = (1,3,112,112) +# ai原图padding +rgb_mean = [104,117,123] + +#kmodel相关参数设置 +#人脸检测 +confidence_threshold = 0.5 #人脸检测阈值 +top_k = 5000 +nms_threshold = 0.2 +keep_top_k = 750 +vis_thres = 0.5 +variance = [0.1, 0.2] +anchor_len = 4200 +score_dim = 2 +det_dim = 4 +keypoint_dim = 10 +#人脸识别 +max_register_face = 100 # 数据库最多人脸个数 +feature_num = 128 # 人脸识别特征维度 +face_recognition_threshold = 0.75 # 人脸识别阈值 + +#文件配置 +# 人脸检测kmodel +root_dir = '/sdcard/app/tests/' +fd_kmodel_file = root_dir + 'kmodel/face_detection_320.kmodel' +# 人脸识别kmodel +fr_kmodel_file = root_dir + 'kmodel/face_recognition.kmodel' +# 人脸检测anchor +anchors_path = root_dir + 'utils/prior_data_320.bin' +# 人脸数据库 +database_dir = root_dir + 'utils/db/' +# 调试模型，0：不调试，>0：打印对应级别调试信息 +debug_mode = 0 + +#********************for scoped_timing.py******************** +# 时间统计类 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#********************for ai_utils.py******************** +global current_kmodel_obj #当前kpu实例 +# fd_ai2d： 人脸检测ai2d实例 +# fd_ai2d_input_tensor： 人脸检测ai2d输入 +# fd_ai2d_output_tensor： 人脸检测ai2d输入 +# fd_ai2d_builder： 根据人脸检测ai2d参数，构建的人脸检测ai2d_builder对象 +global fd_ai2d,fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder +# fr_ai2d： 人脸识别ai2d实例 +# fr_ai2d_input_tensor： 人脸识别ai2d输入 +# fr_ai2d_output_tensor： 人脸识别ai2d输入 +# fr_ai2d_builder： 根据人脸识别ai2d参数，构建的人脸识别ai2d_builder对象 +global fr_ai2d,fr_ai2d_input_tensor,fr_ai2d_output_tensor,fr_ai2d_builder +# valid_register_face： 数据库中有效人脸个数 +# db_name： 数据库人名列表 +# db_data： 数据库特征列表 +global valid_register_face,db_name，db_data + +#读取anchor文件，为人脸检测后处理做准备 +print('anchors_path:',anchors_path) +prior_data = np.fromfile(anchors_path, dtype=np.float) +prior_data = prior_data.reshape((anchor_len,det_dim)) + +def get_pad_one_side_param(): + # 右padding或下padding，获取padding参数 + dst_w = fd_kmodel_input_shape[3] # kmodel输入宽（w） + dst_h = fd_kmodel_input_shape[2] # kmodel输入高（h） + + # OUT_RGB888P_WIDTH：原图宽（w） + # OUT_RGB888P_HEIGH：原图高（h） + # 计算最小的缩放比例，等比例缩放 + ratio_w = dst_w / OUT_RGB888P_WIDTH + ratio_h = dst_h / OUT_RGB888P_HEIGH + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + # 计算经过缩放后的新宽和新高 + new_w = (int)(ratio * OUT_RGB888P_WIDTH) + new_h = (int)(ratio * OUT_RGB888P_HEIGH) + + # 计算需要添加的padding，以使得kmodel输入的宽高和原图一致 + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + # 四舍五入，确保padding是整数 + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +def fd_ai2d_init(): + # 人脸检测模型ai2d初始化 + with ScopedTiming("fd_ai2d_init",debug_mode > 0): + # （1）创建人脸检测ai2d对象 + global fd_ai2d + fd_ai2d = nn.ai2d() + # （2）设置人脸检测ai2d参数 + fd_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + fd_ai2d.set_pad_param(True, get_pad_one_side_param(), 0, rgb_mean) + fd_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + #（3）人脸检测ai2d_builder，根据人脸检测ai2d参数、输入输出大小创建ai2d_builder对象 + global fd_ai2d_builder + fd_ai2d_builder = fd_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fd_kmodel_input_shape) + + #（4）创建人脸检测ai2d_output_tensor，用于保存人脸检测ai2d输出 + global fd_ai2d_output_tensor + data = np.ones(fd_kmodel_input_shape, dtype=np.uint8) + fd_ai2d_output_tensor = nn.from_numpy(data) + +def fd_ai2d_run(rgb888p_img): + # 根据人脸检测ai2d参数，对原图rgb888p_img进行预处理 + with ScopedTiming("fd_ai2d_run",debug_mode > 0): + global fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder + # （1）根据原图构建ai2d_input_tensor对象 + ai2d_input = rgb888p_img.to_numpy_ref() + fd_ai2d_input_tensor = nn.from_numpy(ai2d_input) + # （2）运行人脸检测ai2d_builder，将结果保存到人脸检测ai2d_output_tensor中 + fd_ai2d_builder.run(fd_ai2d_input_tensor, fd_ai2d_output_tensor) + +def fd_ai2d_release(): + # 释放人脸检测ai2d_input_tensor + with ScopedTiming("fd_ai2d_release",debug_mode > 0): + global fd_ai2d_input_tensor + del fd_ai2d_input_tensor + + +def fd_kpu_init(kmodel_file): + # 初始化人脸检测kpu对象，并加载kmodel + with ScopedTiming("fd_kpu_init",debug_mode > 0): + # 初始化人脸检测kpu对象 + kpu_obj = nn.kpu() + # 加载人脸检测kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸检测ai2d + fd_ai2d_init() + return kpu_obj + +def fd_kpu_pre_process(rgb888p_img): + # 设置人脸检测kpu输入 + # 使用人脸检测ai2d对原图进行预处理（padding，resize） + fd_ai2d_run(rgb888p_img) + with ScopedTiming("fd_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fd_ai2d_output_tensor + # 设置人脸检测kpu输入 + current_kmodel_obj.set_input_tensor(0, fd_ai2d_output_tensor) + +def fd_kpu_get_output(): + # 获取人脸检测kpu输出 + with ScopedTiming("fd_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取模型输出，并将结果转换为numpy，以便进行人脸检测后处理 + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data + results.append(result) + return results + +def fd_kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）原图预处理，并设置模型输入 + fd_kpu_pre_process(rgb888p_img) + # （2）人脸检测kpu推理 + with ScopedTiming("fd kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸检测ai2d资源 + fd_ai2d_release() + # （4）获取人俩检测kpu输出 + results = fd_kpu_get_output() + # （5）人脸检测kpu结果后处理 + with ScopedTiming("fd kpu_post",debug_mode > 0): + post_ret = aidemo.face_det_post_process(confidence_threshold,nms_threshold,fd_kmodel_input_shape[2],prior_data, + [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH],results) + # （6）返回人脸检测框 + if len(post_ret)==0: + return post_ret,post_ret + else: + return post_ret[0],post_ret[1] #0:det,1:landm,2:score + +def fd_kpu_deinit(kpu_obj): + # kpu释放 + with ScopedTiming("fd_kpu_deinit",debug_mode > 0): + global fd_ai2d, fd_ai2d_output_tensor + del kpu_obj #删除人脸检测kpu_obj变量，释放对它所引用对象的内存引用 + del fd_ai2d #删除人脸检测ai2d变量，释放对它所引用对象的内存引用 + del fd_ai2d_output_tensor #删除人脸检测ai2d_output_tensor变量，释放对它所引用对象的内存引用 + +###############for face recognition############### +##for database +def database_init(): + # 数据初始化，构建数据库人名列表和数据库特征列表 + with ScopedTiming("database_init", debug_mode > 1): + global valid_register_face,db_name,db_data + valid_register_face = 0 + db_name = [] + db_data = [] + + db_file_list = os.listdir(database_dir) + for db_file in db_file_list: + if not db_file.endswith('.bin'): + continue + if valid_register_face >= max_register_face: + break + valid_index = valid_register_face + full_db_file = database_dir + db_file + with open(full_db_file, 'rb') as f: + data = f.read() + feature = np.frombuffer(data, dtype=np.float) + db_data.append(feature) + name = db_file.split('.')[0] + db_name.append(name) + valid_register_face += 1 + +def database_reset(): + # 数据库清空 + with ScopedTiming("database_reset", debug_mode > 1): + global valid_register_face,db_name,db_data + print("database clearing...") + db_name = [] + db_data = [] + valid_register_face = 0 + print("database clear Done!") + +def database_search(feature): + # 数据库查询 + with ScopedTiming("database_search", debug_mode > 1): + global valid_register_face,db_name,db_data + v_id = -1 + v_score_max = 0.0 + + # 将当前人脸特征归一化 + feature /= np.linalg.norm(feature) + # 遍历当前人脸数据库，统计最高得分 + for i in range(valid_register_face): + db_feature = db_data[i] + db_feature /= np.linalg.norm(db_feature) + # 计算数据库特征与当前人脸特征相似度 + v_score = np.dot(feature, db_feature)/2 + 0.5 + if v_score > v_score_max: + v_score_max = v_score + v_id = i + + if v_id == -1: + # 数据库中无人脸 + return 'unknown' + elif v_score_max < face_recognition_threshold: + # 小于人脸识别阈值，未识别 + print('v_score_max:',v_score_max) + return 'unknown' + else: + # 识别成功 + result = 'name: {}, score:{}'.format(db_name[v_id],v_score_max) + return result + +# 标准5官 +umeyama_args_112 = [ + 38.2946 , 51.6963 , + 73.5318 , 51.5014 , + 56.0252 , 71.7366 , + 41.5493 , 92.3655 , + 70.7299 , 92.2041 +] + +def svd22(a): + # svd + s = [0.0, 0.0] + u = [0.0, 0.0, 0.0, 0.0] + v = [0.0, 0.0, 0.0, 0.0] + + s[0] = (math.sqrt((a[0] - a[3]) ** 2 + (a[1] + a[2]) ** 2) + math.sqrt((a[0] + a[3]) ** 2 + (a[1] - a[2]) ** 2)) / 2 + s[1] = abs(s[0] - math.sqrt((a[0] - a[3]) ** 2 + (a[1] + a[2]) ** 2)) + v[2] = math.sin((math.atan2(2 * (a[0] * a[1] + a[2] * a[3]), a[0] ** 2 - a[1] ** 2 + a[2] ** 2 - a[3] ** 2)) / 2) if \ + s[0] > s[1] else 0 + v[0] = math.sqrt(1 - v[2] ** 2) + v[1] = -v[2] + v[3] = v[0] + u[0] = -(a[0] * v[0] + a[1] * v[2]) / s[0] if s[0] != 0 else 1 + u[2] = -(a[2] * v[0] + a[3] * v[2]) / s[0] if s[0] != 0 else 0 + u[1] = (a[0] * v[1] + a[1] * v[3]) / s[1] if s[1] != 0 else -u[2] + u[3] = (a[2] * v[1] + a[3] * v[3]) / s[1] if s[1] != 0 else u[0] + v[0] = -v[0] + v[2] = -v[2] + + return u, s, v + + +def image_umeyama_112(src): + # 使用Umeyama算法计算仿射变换矩阵 + SRC_NUM = 5 + SRC_DIM = 2 + src_mean = [0.0, 0.0] + dst_mean = [0.0, 0.0] + + for i in range(0,SRC_NUM * 2,2): + src_mean[0] += src[i] + src_mean[1] += src[i + 1] + dst_mean[0] += umeyama_args_112[i] + dst_mean[1] += umeyama_args_112[i + 1] + + src_mean[0] /= SRC_NUM + src_mean[1] /= SRC_NUM + dst_mean[0] /= SRC_NUM + dst_mean[1] /= SRC_NUM + + src_demean = [[0.0, 0.0] for _ in range(SRC_NUM)] + dst_demean = [[0.0, 0.0] for _ in range(SRC_NUM)] + + for i in range(SRC_NUM): + src_demean[i][0] = src[2 * i] - src_mean[0] + src_demean[i][1] = src[2 * i + 1] - src_mean[1] + dst_demean[i][0] = umeyama_args_112[2 * i] - dst_mean[0] + dst_demean[i][1] = umeyama_args_112[2 * i + 1] - dst_mean[1] + + A = [[0.0, 0.0], [0.0, 0.0]] + for i in range(SRC_DIM): + for k in range(SRC_DIM): + for j in range(SRC_NUM): + A[i][k] += dst_demean[j][i] * src_demean[j][k] + A[i][k] /= SRC_NUM + + T = [[1, 0, 0], [0, 1, 0], [0, 0, 1]] + U, S, V = svd22([A[0][0], A[0][1], A[1][0], A[1][1]]) + + T[0][0] = U[0] * V[0] + U[1] * V[2] + T[0][1] = U[0] * V[1] + U[1] * V[3] + T[1][0] = U[2] * V[0] + U[3] * V[2] + T[1][1] = U[2] * V[1] + U[3] * V[3] + + scale = 1.0 + src_demean_mean = [0.0, 0.0] + src_demean_var = [0.0, 0.0] + for i in range(SRC_NUM): + src_demean_mean[0] += src_demean[i][0] + src_demean_mean[1] += src_demean[i][1] + + src_demean_mean[0] /= SRC_NUM + src_demean_mean[1] /= SRC_NUM + + for i in range(SRC_NUM): + src_demean_var[0] += (src_demean_mean[0] - src_demean[i][0]) * (src_demean_mean[0] - src_demean[i][0]) + src_demean_var[1] += (src_demean_mean[1] - src_demean[i][1]) * (src_demean_mean[1] - src_demean[i][1]) + + src_demean_var[0] /= SRC_NUM + src_demean_var[1] /= SRC_NUM + + scale = 1.0 / (src_demean_var[0] + src_demean_var[1]) * (S[0] + S[1]) + T[0][2] = dst_mean[0] - scale * (T[0][0] * src_mean[0] + T[0][1] * src_mean[1]) + T[1][2] = dst_mean[1] - scale * (T[1][0] * src_mean[0] + T[1][1] * src_mean[1]) + T[0][0] *= scale + T[0][1] *= scale + T[1][0] *= scale + T[1][1] *= scale + return T + +def get_affine_matrix(sparse_points): + # 获取放射变换矩阵 + with ScopedTiming("get_affine_matrix", debug_mode > 1): + # 使用Umeyama算法计算仿射变换矩阵 + matrix_dst = image_umeyama_112(sparse_points) + matrix_dst = [matrix_dst[0][0],matrix_dst[0][1],matrix_dst[0][2], + matrix_dst[1][0],matrix_dst[1][1],matrix_dst[1][2]] + return matrix_dst + +def fr_ai2d_init(): + with ScopedTiming("fr_ai2d_init",debug_mode > 0): + # （1）人脸识别ai2d初始化 + global fr_ai2d + fr_ai2d = nn.ai2d() + + # （2）人脸识别ai2d_output_tensor初始化，用于存放ai2d输出 + global fr_ai2d_output_tensor + data = np.ones(fr_kmodel_input_shape, dtype=np.uint8) + fr_ai2d_output_tensor = nn.from_numpy(data) + +def fr_ai2d_run(rgb888p_img,sparse_points): + # 人脸识别ai2d推理 + with ScopedTiming("fr_ai2d_run",debug_mode > 0): + global fr_ai2d,fr_ai2d_input_tensor,fr_ai2d_output_tensor + #（1）根据原图创建人脸识别ai2d_input_tensor对象 + ai2d_input = rgb888p_img.to_numpy_ref() + fr_ai2d_input_tensor = nn.from_numpy(ai2d_input) + #（2）根据新的人脸关键点设置新的人脸识别ai2d参数 + fr_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + affine_matrix = get_affine_matrix(sparse_points) + fr_ai2d.set_affine_param(True,nn.interp_method.cv2_bilinear,0, 0, 127, 1,affine_matrix) + global fr_ai2d_builder + # （3）根据新的人脸识别ai2d参数，构建识别ai2d_builder + fr_ai2d_builder = fr_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fr_kmodel_input_shape) + # （4）推理人脸识别ai2d，将预处理的结果保存到fr_ai2d_output_tensor + fr_ai2d_builder.run(fr_ai2d_input_tensor, fr_ai2d_output_tensor) + +def fr_ai2d_release(): + # 释放人脸识别ai2d_input_tensor、ai2d_builder + with ScopedTiming("fr_ai2d_release",debug_mode > 0): + global fr_ai2d_input_tensor,fr_ai2d_builder + del fr_ai2d_input_tensor + del fr_ai2d_builder + +def fr_kpu_init(kmodel_file): + # 人脸识别kpu初始化 + with ScopedTiming("fr_kpu_init",debug_mode > 0): + # 初始化人脸识别kpu对象 + kpu_obj = nn.kpu() + # 加载人脸识别kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸识别ai2d + fr_ai2d_init() + # 数据库初始化 + database_init() + return kpu_obj + +def fr_kpu_pre_process(rgb888p_img,sparse_points): + # 人脸识别kpu预处理 + # 人脸识别ai2d推理，根据关键点对原图进行预处理 + fr_ai2d_run(rgb888p_img,sparse_points) + with ScopedTiming("fr_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fr_ai2d_output_tensor + # 将人脸识别ai2d输出设置为人脸识别kpu输入 + current_kmodel_obj.set_input_tensor(0, fr_ai2d_output_tensor) + + #ai2d_out_data = fr_ai2d_output_tensor.to_numpy() + #print('ai2d_out_data.shape:',ai2d_out_data.shape) + #with open("/sdcard/app/ai2d_out.bin", "wb") as file: + #file.write(ai2d_out_data.tobytes()) + +def fr_kpu_get_output(): + # 获取人脸识别kpu输出 + with ScopedTiming("fr_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + data = current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + del data + return result[0] + +def fr_kpu_run(kpu_obj,rgb888p_img,sparse_points): + # 人脸识别kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）人脸识别kpu预处理,设置kpu输入 + fr_kpu_pre_process(rgb888p_img,sparse_points) + # （2）人脸识别kpu推理 + with ScopedTiming("fr kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸识别ai2d + fr_ai2d_release() + # （4）获取人脸识别kpu输出 + results = fr_kpu_get_output() + # （5）在数据库中查找当前人脸特征 + recg_result = database_search(results) + return recg_result + +def fr_kpu_deinit(kpu_obj): + # 人脸识别kpu相关资源释放 + with ScopedTiming("fr_kpu_deinit",debug_mode > 0): + global fr_ai2d + del kpu_obj + del fr_ai2d + +#********************for media_utils.py******************** +global draw_img,osd_img #for display +global buffer,media_source,media_sink #for media + +# for display，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.2 +def display_init(): + # hdmi显示初始化 + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +def display_deinit(): + # 释放显示资源 + display.deinit() + +def display_draw(dets,recg_results): + # 在显示器上写人脸识别结果 + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + if dets: + draw_img.clear() + for i,det in enumerate(dets): + # （1）画人脸框 + x1, y1, w, h = map(lambda x: int(round(x, 0)), det[:4]) + x1 = x1 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + y1 = y1 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + w = w * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + h = h * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + draw_img.draw_rectangle(x1,y1, w, h, color=(255,0, 255, 0)) + + # （2）写人脸识别结果 + recg_text = recg_results[i] + draw_img.draw_string(x1,y1,recg_text,color=(255,0, 0, 255),scale=4) + + # （3）将画图结果拷贝到osd + draw_img.copy_to(osd_img) + # （4）将osd显示到屏幕 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + # （1）清空用来画框的图像 + draw_img.clear() + # （2）清空osd + draw_img.copy_to(osd_img) + # （3）显示透明图层 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.1 +def camera_init(dev_id): + # camera初始化 + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_BGR_888_PLANAR) + +def camera_start(dev_id): + # camera启动 + camera.start_stream(dev_id) + +def camera_read(dev_id): + # 读取一帧图像 + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +def camera_release_image(dev_id,rgb888p_img): + # 释放一帧图像 + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +def camera_stop(dev_id): + # 停止camera + camera.stop_stream(dev_id) + +#for media，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.3 +def media_init(): + # meida初始化 + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +def media_deinit(): + # meida资源释放 + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +#********************for face_detect.py******************** +def face_recognition_inference(): + print("face_recognition_test start") + # 人脸检测kpu初始化 + kpu_face_detect = fd_kpu_init(fd_kmodel_file) + # 人脸关键点kpu初始化 + kpu_face_recg = fr_kpu_init(fr_kmodel_file) + # camera初始化 + camera_init(CAM_DEV_ID_0) + # 显示初始化 + display_init() + + rgb888p_img = None + # 注意：将一定要将一下过程包在try中，用于保证程序停止后，资源释放完毕；确保下次程序仍能正常运行 + try: + # 注意：媒体初始化（注：媒体初始化必须在camera_start之前，确保media缓冲区已配置完全） + ret = media_init() + if ret: + print("face_detect_test, buffer init failed") + return ret + + # 启动camera + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + # （1）读取一帧图像 + rgb888p_img = camera_read(CAM_DEV_ID_0) + # （2）若读取失败，释放当前帧 + if rgb888p_img == -1: + print("face_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # （3）若读取成功，推理当前帧 + if rgb888p_img.format() == image.RGBP888: + # （3.1）推理当前图像，并获取人脸检测结果 + dets,landms = fd_kpu_run(kpu_face_detect,rgb888p_img) + recg_result = [] + for landm in landms: + # （3.2）针对每个人脸五官点，推理得到人脸特征，并计算特征在数据库中相似度 + ret = fr_kpu_run(kpu_face_recg,rgb888p_img,landm) + recg_result.append(ret) + # （3.3）将识别结果画到显示器上 + display_draw(dets,recg_result) + + # （4）释放当前帧 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + gc.collect() + except Exception as e: + # 捕捉运行运行中异常，并打印错误 + print(f"An error occurred during buffer used: {e}") + finally: + # 释放当前帧 + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + # 停止camera + camera_stop(CAM_DEV_ID_0) + # 释放显示资源 + display_deinit() + # 释放kpu资源 + fd_kpu_deinit(kpu_face_detect) + fr_kpu_deinit(kpu_face_recg) + # 垃圾回收 + gc.collect() + time.sleep(1) + # 释放媒体资源 + ret = media_deinit() + if ret: + print("face_recognition_test, buffer_deinit failed") + return ret + + print("face_recognition_test end") + return 0 + +if __name__ == '__main__': + face_recognition_inference() +``` + +### 3.人脸姿态角 + +```python +import ulab.numpy as np # 类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn # nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * # 摄像头模块 +from media.display import * # 显示模块 +from media.media import * # 软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import aidemo # aidemo模块，封装ai demo相关后处理、画图操作 +import image # 图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time # 时间统计 +import gc # 垃圾回收模块 +import os # 操作系统接口模块 +import math # 数学模块 + +#********************for config.py******************** +# display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) # 显示宽度要求16位对齐 +DISPLAY_HEIGHT = 1080 + +# ai原图分辨率，sensor默认出图为16:9，若需不形变原图，最好按照16:9比例设置宽高 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) # ai原图宽度要求16位对齐 +OUT_RGB888P_HEIGH = 1080 + +# kmodel参数设置 +# 人脸检测kmodel输入shape +fd_kmodel_input_shape = (1,3,320,320) +# 人脸姿态估计kmodel输入shape +fp_kmodel_input_shape = (1,3,120,120) +# ai原图padding +rgb_mean = [104,117,123] + +#人脸检测kmodel其它参数设置 +confidence_threshold = 0.5 # 人脸检测阈值 +top_k = 5000 +nms_threshold = 0.2 +keep_top_k = 750 +vis_thres = 0.5 +variance = [0.1, 0.2] +anchor_len = 4200 +score_dim = 2 +det_dim = 4 +keypoint_dim = 10 + +# 文件配置 +# 人脸检测kmodel文件配置 +root_dir = '/sdcard/app/tests/' +fd_kmodel_file = root_dir + 'kmodel/face_detection_320.kmodel' +# 人脸姿态估计kmodel文件配置 +fp_kmodel_file = root_dir + 'kmodel/face_pose.kmodel' +# anchor文件配置 +anchors_path = root_dir + 'utils/prior_data_320.bin' +# 调试模型，0：不调试，>0：打印对应级别调试信息 +debug_mode = 0 + +#********************for scoped_timing.py******************** +# 时间统计类 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#********************for ai_utils.py******************** +global current_kmodel_obj #当前kpu对象 +# fd_ai2d： 人脸检测ai2d实例 +# fd_ai2d_input_tensor： 人脸检测ai2d输入 +# fd_ai2d_output_tensor： 人脸检测ai2d输入 +# fd_ai2d_builder： 根据人脸检测ai2d参数，构建的人脸检测ai2d_builder对象 +global fd_ai2d,fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder +# fld_ai2d： 人脸姿态估计ai2d实例 +# fld_ai2d_input_tensor： 人脸姿态估计ai2d输入 +# fld_ai2d_output_tensor：人脸姿态估计ai2d输入 +# fld_ai2d_builder： 根据人脸姿态估计ai2d参数，构建的人脸姿态估计ai2d_builder对象 +global fp_ai2d,fp_ai2d_input_tensor,fp_ai2d_output_tensor,fp_ai2d_builder +global matrix_dst #人脸仿射变换矩阵 +#读取anchor文件，为人脸检测后处理做准备 +print('anchors_path:',anchors_path) +prior_data = np.fromfile(anchors_path, dtype=np.float) +prior_data = prior_data.reshape((anchor_len,det_dim)) + +def get_pad_one_side_param(): + # 右padding或下padding，获取padding参数 + dst_w = fd_kmodel_input_shape[3] # kmodel输入宽（w） + dst_h = fd_kmodel_input_shape[2] # kmodel输入高（h） + + # OUT_RGB888P_WIDTH：原图宽（w） + # OUT_RGB888P_HEIGH：原图高（h） + # 计算最小的缩放比例，等比例缩放 + ratio_w = dst_w / OUT_RGB888P_WIDTH + ratio_h = dst_h / OUT_RGB888P_HEIGH + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + # 计算经过缩放后的新宽和新高 + new_w = (int)(ratio * OUT_RGB888P_WIDTH) + new_h = (int)(ratio * OUT_RGB888P_HEIGH) + + # 计算需要添加的padding，以使得kmodel输入的宽高和原图一致 + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + # 四舍五入，确保padding是整数 + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +def fd_ai2d_init(): + # 人脸检测模型ai2d初始化 + with ScopedTiming("fd_ai2d_init",debug_mode > 0): + # （1）创建人脸检测ai2d对象 + global fd_ai2d + fd_ai2d = nn.ai2d() + # （2）设置人脸检测ai2d参数 + fd_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + fd_ai2d.set_pad_param(True, get_pad_one_side_param(), 0, rgb_mean) + fd_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + #（3）人脸检测ai2d_builder，根据人脸检测ai2d参数、输入输出大小创建ai2d_builder对象 + global fd_ai2d_builder + fd_ai2d_builder = fd_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fd_kmodel_input_shape) + + #（4）创建人脸检测ai2d_output_tensor，用于保存人脸检测ai2d输出 + global fd_ai2d_output_tensor + data = np.ones(fd_kmodel_input_shape, dtype=np.uint8) + fd_ai2d_output_tensor = nn.from_numpy(data) + +def fd_ai2d_run(rgb888p_img): + # 根据人脸检测ai2d参数，对原图rgb888p_img进行预处理 + with ScopedTiming("fd_ai2d_run",debug_mode > 0): + global fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder + # （1）根据原图构建ai2d_input_tensor对象 + ai2d_input = rgb888p_img.to_numpy_ref() + fd_ai2d_input_tensor = nn.from_numpy(ai2d_input) + # （2）运行人脸检测ai2d_builder，将结果保存到人脸检测ai2d_output_tensor中 + fd_ai2d_builder.run(fd_ai2d_input_tensor, fd_ai2d_output_tensor) + +def fd_ai2d_release(): + # 释放人脸检测ai2d_input_tensor + with ScopedTiming("fd_ai2d_release",debug_mode > 0): + global fd_ai2d_input_tensor + del fd_ai2d_input_tensor + + +def fd_kpu_init(kmodel_file): + # 初始化人脸检测kpu对象，并加载kmodel + with ScopedTiming("fd_kpu_init",debug_mode > 0): + # 初始化人脸检测kpu对象 + kpu_obj = nn.kpu() + # 加载人脸检测kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸检测ai2d + fd_ai2d_init() + return kpu_obj + +def fd_kpu_pre_process(rgb888p_img): + # 设置人脸检测kpu输入 + # 使用人脸检测ai2d对原图进行预处理（padding，resize） + fd_ai2d_run(rgb888p_img) + with ScopedTiming("fd_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fd_ai2d_output_tensor + # 设置人脸检测kpu输入 + current_kmodel_obj.set_input_tensor(0, fd_ai2d_output_tensor) + +def fd_kpu_get_output(): + # 获取人脸检测kpu输出 + with ScopedTiming("fd_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取模型输出，并将结果转换为numpy，以便进行人脸检测后处理 + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data + results.append(result) + return results + +def fd_kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）原图预处理，并设置模型输入 + fd_kpu_pre_process(rgb888p_img) + # （2）人脸检测kpu推理 + with ScopedTiming("fd kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸检测ai2d资源 + fd_ai2d_release() + # （4）获取人俩检测kpu输出 + results = fd_kpu_get_output() + # （5）人脸检测kpu结果后处理 + with ScopedTiming("fd kpu_post",debug_mode > 0): + post_ret = aidemo.face_det_post_process(confidence_threshold,nms_threshold,fd_kmodel_input_shape[2],prior_data, + [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH],results) + # （6）返回人脸检测框 + if len(post_ret)==0: + return post_ret + else: + return post_ret[0] #0:det,1:landm,2:score + +def fd_kpu_deinit(kpu_obj): + # kpu释放 + with ScopedTiming("fd_kpu_deinit",debug_mode > 0): + global fd_ai2d, fd_ai2d_output_tensor + del kpu_obj #删除人脸检测kpu_obj变量，释放对它所引用对象的内存引用 + del fd_ai2d #删除人脸检测ai2d变量，释放对它所引用对象的内存引用 + del fd_ai2d_output_tensor #删除人脸检测ai2d_output_tensor变量，释放对它所引用对象的内存引用 + +###############for face recognition############### +def get_affine_matrix(bbox): + # 获取仿射矩阵，用于将边界框映射到模型输入空间 + with ScopedTiming("get_affine_matrix", debug_mode > 1): + # 设置缩放因子 + factor = 2.7 + # 从边界框提取坐标和尺寸 + x1, y1, w, h = map(lambda x: int(round(x, 0)), bbox[:4]) + # 模型输入大小 + edge_size = fp_kmodel_input_shape[2] + # 平移距离，使得模型输入空间的中心对准原点 + trans_distance = edge_size / 2.0 + # 计算边界框中心点的坐标 + center_x = x1 + w / 2.0 + center_y = y1 + h / 2.0 + # 计算最大边长 + maximum_edge = factor * (h if h > w else w) + # 计算缩放比例 + scale = edge_size * 2.0 / maximum_edge + # 计算平移参数 + cx = trans_distance - scale * center_x + cy = trans_distance - scale * center_y + # 创建仿射矩阵 + affine_matrix = [scale, 0, cx, 0, scale, cy] + return affine_matrix + +def build_projection_matrix(det): + x1, y1, w, h = map(lambda x: int(round(x, 0)), det[:4]) + + # 计算边界框中心坐标 + center_x = x1 + w / 2.0 + center_y = y1 + h / 2.0 + + # 定义后部（rear）和前部（front）的尺寸和深度 + rear_width = 0.5 * w + rear_height = 0.5 * h + rear_depth = 0 + factor = np.sqrt(2.0) + front_width = factor * rear_width + front_height = factor * rear_height + front_depth = factor * rear_width # 使用宽度来计算深度，也可以使用高度，取决于需求 + + # 定义立方体的顶点坐标 + temp = [ + [-rear_width, -rear_height, rear_depth], + [-rear_width, rear_height, rear_depth], + [rear_width, rear_height, rear_depth], + [rear_width, -rear_height, rear_depth], + [-front_width, -front_height, front_depth], + [-front_width, front_height, front_depth], + [front_width, front_height, front_depth], + [front_width, -front_height, front_depth] + ] + + projections = np.array(temp) + # 返回投影矩阵和中心坐标 + return projections, (center_x, center_y) + +def rotation_matrix_to_euler_angles(R): + # 将旋转矩阵（3x3 矩阵）转换为欧拉角（pitch、yaw、roll） + # 计算 sin(yaw) + sy = np.sqrt(R[0, 0] ** 2 + R[1, 0] ** 2) + + if sy < 1e-6: + # 若 sin(yaw) 过小，说明 pitch 接近 ±90 度 + pitch = np.arctan2(-R[1, 2], R[1, 1]) * 180 / np.pi + yaw = np.arctan2(-R[2, 0], sy) * 180 / np.pi + roll = 0 + else: + # 计算 pitch、yaw、roll 的角度 + pitch = np.arctan2(R[2, 1], R[2, 2]) * 180 / np.pi + yaw = np.arctan2(-R[2, 0], sy) * 180 / np.pi + roll = np.arctan2(R[1, 0], R[0, 0]) * 180 / np.pi + return [pitch,yaw,roll] + +def get_euler(data): + # 获取旋转矩阵和欧拉角 + R = data[:3, :3].copy() + eular = rotation_matrix_to_euler_angles(R) + return R,eular + +def fp_ai2d_init(): + # 人脸姿态估计ai2d初始化 + with ScopedTiming("fp_ai2d_init",debug_mode > 0): + # （1）创建人脸姿态估计ai2d对象 + global fp_ai2d + fp_ai2d = nn.ai2d() + + # （2）创建人脸姿态估计ai2d_output_tensor对象 + global fp_ai2d_output_tensor + data = np.ones(fp_kmodel_input_shape, dtype=np.uint8) + fp_ai2d_output_tensor = nn.from_numpy(data) + +def fp_ai2d_run(rgb888p_img,det): + # 人脸姿态估计ai2d推理 + with ScopedTiming("fp_ai2d_run",debug_mode > 0): + global fp_ai2d,fp_ai2d_input_tensor,fp_ai2d_output_tensor + #（1）根据原图构建人脸姿态估计ai2d_input_tensor + ai2d_input = rgb888p_img.to_numpy_ref() + fp_ai2d_input_tensor = nn.from_numpy(ai2d_input) + #（2）设置人脸姿态估计ai2d参数 + fp_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + global matrix_dst + matrix_dst = get_affine_matrix(det) + fp_ai2d.set_affine_param(True,nn.interp_method.cv2_bilinear,0, 0, 127, 1,matrix_dst) + # （3）构建人脸姿态估计ai2d_builder + global fp_ai2d_builder + fp_ai2d_builder = fp_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fp_kmodel_input_shape) + # （4）推理人脸姿态估计ai2d，将结果保存到ai2d_output_tensor + fp_ai2d_builder.run(fp_ai2d_input_tensor, fp_ai2d_output_tensor) + +def fp_ai2d_release(): + # 释放部分人脸姿态估计ai2d资源 + with ScopedTiming("fp_ai2d_release",debug_mode > 0): + global fp_ai2d_input_tensor,fp_ai2d_builder + del fp_ai2d_input_tensor + del fp_ai2d_builder + +def fp_kpu_init(kmodel_file): + # 初始化人脸姿态估计kpu及ai2d + with ScopedTiming("fp_kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + fp_ai2d_init() + return kpu_obj + +def fp_kpu_pre_process(rgb888p_img,det): + # 人脸姿态估计kpu预处理 + fp_ai2d_run(rgb888p_img,det) + with ScopedTiming("fp_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fp_ai2d_output_tensor + current_kmodel_obj.set_input_tensor(0, fp_ai2d_output_tensor) + #ai2d_out_data = _ai2d_output_tensor.to_numpy() + #with open("/sdcard/app/ai2d_out.bin", "wb") as file: + #file.write(ai2d_out_data.tobytes()) + +def fp_kpu_get_output(): + # 获取人脸姿态估计kpu输出 + with ScopedTiming("fp_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + data = current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + result = result[0] + del data + return result + +def fp_kpu_post_process(pred): + # 人脸姿态估计kpu推理结果后处理 + R,eular = get_euler(pred) + return R,eular + +def fp_kpu_run(kpu_obj,rgb888p_img,det): + # 人脸姿态估计kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）根据人脸检测框进行人脸姿态估计kpu预处理 + fp_kpu_pre_process(rgb888p_img,det) + # （2）人脸姿态估计kpu推理 + with ScopedTiming("fp_kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸姿态估计ai2d资源 + fp_ai2d_release() + # （4）释放人脸姿态估计kpu推理输出 + result = fp_kpu_get_output() + # （5）释放人脸姿态估计后处理 + R,eular = fp_kpu_post_process(result) + return R,eular + +def fp_kpu_deinit(kpu_obj): + # 释放人脸姿态估计kpu及ai2d资源 + with ScopedTiming("fp_kpu_deinit",debug_mode > 0): + global fp_ai2d,fp_ai2d_output_tensor + del kpu_obj + del fp_ai2d + del fp_ai2d_output_tensor + +#********************for media_utils.py******************** +global draw_img_ulab,draw_img,osd_img #for display +global buffer,media_source,media_sink #for media + +# for display，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.2 +def display_init(): + # 设置使用hdmi进行显示 + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +def display_deinit(): + # 释放显示资源 + display.deinit() + +def display_draw(dets,pose_results): + # 在显示器画人脸轮廓 + with ScopedTiming("display_draw",debug_mode >0): + global draw_img_ulab,draw_img,osd_img + if dets: + draw_img.clear() + line_color = np.array([255, 0, 0 ,255],dtype = np.uint8) #bgra + for i,det in enumerate(dets): + # （1）获取人脸姿态矩阵和欧拉角 + projections,center_point = build_projection_matrix(det) + R,euler = pose_results[i] + + # （2）遍历人脸投影矩阵的关键点，进行投影，并将结果画在图像上 + first_points = [] + second_points = [] + for pp in range(8): + sum_x, sum_y = 0.0, 0.0 + for cc in range(3): + sum_x += projections[pp][cc] * R[cc][0] + sum_y += projections[pp][cc] * (-R[cc][1]) + + center_x,center_y = center_point[0],center_point[1] + x = (sum_x + center_x) / OUT_RGB888P_WIDTH * DISPLAY_WIDTH + y = (sum_y + center_y) / OUT_RGB888P_HEIGH * DISPLAY_HEIGHT + x = max(0, min(x, DISPLAY_WIDTH)) + y = max(0, min(y, DISPLAY_HEIGHT)) + + if pp < 4: + first_points.append((x, y)) + else: + second_points.append((x, y)) + first_points = np.array(first_points,dtype=np.float) + aidemo.polylines(draw_img_ulab,first_points,True,line_color,2,8,0) + second_points = np.array(second_points,dtype=np.float) + aidemo.polylines(draw_img_ulab,second_points,True,line_color,2,8,0) + + for ll in range(4): + x0, y0 = int(first_points[ll][0]),int(first_points[ll][1]) + x1, y1 = int(second_points[ll][0]),int(second_points[ll][1]) + draw_img.draw_line(x0, y0, x1, y1, color = (255, 0, 0 ,255), thickness = 2) + + # （3）将绘制好的图像拷贝到显示缓冲区，并在显示器上展示 + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + # （1）清空用来画框的图像 + draw_img.clear() + # （2）清空osd + draw_img.copy_to(osd_img) + # （3）显示透明图层 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.1 +def camera_init(dev_id): + # camera初始化 + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +def camera_start(dev_id): + # camera启动 + camera.start_stream(dev_id) + +def camera_read(dev_id): + # 读取一帧图像 + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +def camera_release_image(dev_id,rgb888p_img): + # 释放一帧图像 + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +def camera_stop(dev_id): + # 停止camera + camera.stop_stream(dev_id) + +#for media，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.3 +def media_init(): + # meida初始化 + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img_ulab,draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 用于画框，draw_img->draw_img_ulab(两者指向同一块内存) + draw_img_ulab = np.zeros((DISPLAY_HEIGHT,DISPLAY_WIDTH,4),dtype=np.uint8) + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_REF,data = draw_img_ulab) + # 用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +def media_deinit(): + # meida资源释放 + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +#********************for face_detect.py******************** +def face_pose_inference(): + print("face_pose_test start") + # 人脸检测kpu初始化 + kpu_face_detect = fd_kpu_init(fd_kmodel_file) + # 人脸姿态估计kpu初始化 + kpu_face_pose = fp_kpu_init(fp_kmodel_file) + # camera初始化 + camera_init(CAM_DEV_ID_0) + # 显示初始化 + display_init() + + rgb888p_img = None + # 注意：将一定要将一下过程包在try中，用于保证程序停止后，资源释放完毕；确保下次程序仍能正常运行 + try: + # 注意：媒体初始化（注：媒体初始化必须在camera_start之前，确保media缓冲区已配置完全） + ret = media_init() + if ret: + print("face_detect_test, buffer init failed") + return ret + + # 启动camera + camera_start(CAM_DEV_ID_0) + time.sleep(5) + + gc_count = 0 + while True: + with ScopedTiming("total",1): + # （1）读取一帧图像 + rgb888p_img = camera_read(CAM_DEV_ID_0) + # （2）若读取失败，释放当前帧 + if rgb888p_img == -1: + print("face_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # （3）若读取成功，推理当前帧 + if rgb888p_img.format() == image.RGBP888: + # （3.1）推理当前图像，并获取人脸检测结果 + dets = fd_kpu_run(kpu_face_detect,rgb888p_img) + # （3.2）针对每个人脸框，推理得到对应人脸旋转矩阵、欧拉角 + pose_results = [] + for det in dets: + R,eular = fp_kpu_run(kpu_face_pose,rgb888p_img,det) + pose_results.append((R,eular)) + # （3.3）将人脸姿态估计结果画到显示器上 + display_draw(dets,pose_results) + + # （4）释放当前帧 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + + except Exception as e: + # 捕捉运行运行中异常，并打印错误 + print(f"An error occurred during buffer used: {e}") + finally: + # 释放当前帧 + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + # 停止camera + camera_stop(CAM_DEV_ID_0) + # 释放显示资源 + display_deinit() + # 释放kpu资源 + fd_kpu_deinit(kpu_face_detect) + fp_kpu_deinit(kpu_face_pose) + # 垃圾回收 + gc.collect() + time.sleep(1) + # 释放媒体资源 + ret = media_deinit() + if ret: + print("face_pose_test, buffer_deinit failed") + return ret + + print("face_pose_test end") + return 0 + +if __name__ == '__main__': + face_pose_inference() +``` + +### 4. 人脸解析 + +```python +import ulab.numpy as np # 类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn # nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * # 摄像头模块 +from media.display import * # 显示模块 +from media.media import * # 软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import aidemo # aidemo模块，封装ai demo相关后处理、画图操作 +import image # 图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time # 时间统计 +import gc # 垃圾回收模块 +import os # 操作系统接口模块 +import math # 数学模块 + +#********************for config.py******************** +# display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) # 显示宽度要求16位对齐 +DISPLAY_HEIGHT = 1080 + +# ai原图分辨率，sensor默认出图为16:9，若需不形变原图，最好按照16:9比例设置宽高 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) # ai原图宽度要求16位对齐 +OUT_RGB888P_HEIGH = 1080 + +# kmodel参数设置 +# 人脸检测kmodel输入shape +fd_kmodel_input_shape = (1,3,320,320) +# 人脸解析kmodel输入shape +fp_kmodel_input_shape = (1,3,320,320) +# ai原图padding +rgb_mean = [104,117,123] + +#人脸检测kmodel其它参数设置 +confidence_threshold = 0.5 # 人脸检测阈值 +top_k = 5000 +nms_threshold = 0.2 +keep_top_k = 750 +vis_thres = 0.5 +variance = [0.1, 0.2] +anchor_len = 4200 +score_dim = 2 +det_dim = 4 +keypoint_dim = 10 + +# 文件配置 +# 人脸检测kmodel +root_dir = '/sdcard/app/tests/' +fd_kmodel_file = root_dir + 'kmodel/face_detection_320.kmodel' +# 人脸解析kmodel +fp_kmodel_file = root_dir + 'kmodel/face_parse.kmodel' +# anchor文件 +anchors_path = root_dir + 'utils/prior_data_320.bin' +# 调试模型，0：不调试，>0：打印对应级别调试信息 +debug_mode = 0 + +#********************for scoped_timing.py******************** +# 时间统计类 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#********************for ai_utils.py******************** +global current_kmodel_obj #当前kpu对象 +# fd_ai2d： 人脸检测ai2d实例 +# fd_ai2d_input_tensor： 人脸检测ai2d输入 +# fd_ai2d_output_tensor： 人脸检测ai2d输入 +# fd_ai2d_builder： 根据人脸检测ai2d参数，构建的人脸检测ai2d_builder对象 +global fd_ai2d,fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder +# fld_ai2d： 人脸解析ai2d实例 +# fld_ai2d_input_tensor： 人脸解析ai2d输入 +# fld_ai2d_output_tensor：人脸解析ai2d输入 +# fld_ai2d_builder： 根据人脸解析ai2d参数，构建的人脸解析ai2d_builder对象 +global fp_ai2d,fp_ai2d_input_tensor,fp_ai2d_output_tensor,fp_ai2d_builder +global matrix_dst #人脸仿射变换矩阵 + +#读取anchor文件，为人脸检测后处理做准备 +print('anchors_path:',anchors_path) +prior_data = np.fromfile(anchors_path, dtype=np.float) +prior_data = prior_data.reshape((anchor_len,det_dim)) + +def get_pad_one_side_param(): + # 右padding或下padding，获取padding参数 + dst_w = fd_kmodel_input_shape[3] # kmodel输入宽（w） + dst_h = fd_kmodel_input_shape[2] # kmodel输入高（h） + + # OUT_RGB888P_WIDTH：原图宽（w） + # OUT_RGB888P_HEIGH：原图高（h） + # 计算最小的缩放比例，等比例缩放 + ratio_w = dst_w / OUT_RGB888P_WIDTH + ratio_h = dst_h / OUT_RGB888P_HEIGH + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + # 计算经过缩放后的新宽和新高 + new_w = (int)(ratio * OUT_RGB888P_WIDTH) + new_h = (int)(ratio * OUT_RGB888P_HEIGH) + + # 计算需要添加的padding，以使得kmodel输入的宽高和原图一致 + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + # 四舍五入，确保padding是整数 + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +def fd_ai2d_init(): + # 人脸检测模型ai2d初始化 + with ScopedTiming("fd_ai2d_init",debug_mode > 0): + # （1）创建人脸检测ai2d对象 + global fd_ai2d + fd_ai2d = nn.ai2d() + # （2）设置人脸检测ai2d参数 + fd_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + fd_ai2d.set_pad_param(True, get_pad_one_side_param(), 0, rgb_mean) + fd_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + #（3）人脸检测ai2d_builder，根据人脸检测ai2d参数、输入输出大小创建ai2d_builder对象 + global fd_ai2d_builder + fd_ai2d_builder = fd_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fd_kmodel_input_shape) + + #（4）创建人脸检测ai2d_output_tensor，用于保存人脸检测ai2d输出 + global fd_ai2d_output_tensor + data = np.ones(fd_kmodel_input_shape, dtype=np.uint8) + fd_ai2d_output_tensor = nn.from_numpy(data) + +def fd_ai2d_run(rgb888p_img): + # 根据人脸检测ai2d参数，对原图rgb888p_img进行预处理 + with ScopedTiming("fd_ai2d_run",debug_mode > 0): + global fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder + # （1）根据原图构建ai2d_input_tensor对象 + ai2d_input = rgb888p_img.to_numpy_ref() + fd_ai2d_input_tensor = nn.from_numpy(ai2d_input) + # （2）运行人脸检测ai2d_builder，将结果保存到人脸检测ai2d_output_tensor中 + fd_ai2d_builder.run(fd_ai2d_input_tensor, fd_ai2d_output_tensor) + +def fd_ai2d_release(): + # 释放人脸检测ai2d_input_tensor + with ScopedTiming("fd_ai2d_release",debug_mode > 0): + global fd_ai2d_input_tensor + del fd_ai2d_input_tensor + + +def fd_kpu_init(kmodel_file): + # 初始化人脸检测kpu对象，并加载kmodel + with ScopedTiming("fd_kpu_init",debug_mode > 0): + # 初始化人脸检测kpu对象 + kpu_obj = nn.kpu() + # 加载人脸检测kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸检测ai2d + fd_ai2d_init() + return kpu_obj + +def fd_kpu_pre_process(rgb888p_img): + # 设置人脸检测kpu输入 + # 使用人脸检测ai2d对原图进行预处理（padding，resize） + fd_ai2d_run(rgb888p_img) + with ScopedTiming("fd_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fd_ai2d_output_tensor + # 设置人脸检测kpu输入 + current_kmodel_obj.set_input_tensor(0, fd_ai2d_output_tensor) + +def fd_kpu_get_output(): + # 获取人脸检测kpu输出 + with ScopedTiming("fd_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取模型输出，并将结果转换为numpy，以便进行人脸检测后处理 + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data + results.append(result) + return results + +def fd_kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）原图预处理，并设置模型输入 + fd_kpu_pre_process(rgb888p_img) + # （2）人脸检测kpu推理 + with ScopedTiming("fd kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸检测ai2d资源 + fd_ai2d_release() + # （4）获取人俩检测kpu输出 + results = fd_kpu_get_output() + # （5）人脸检测kpu结果后处理 + with ScopedTiming("fd kpu_post",debug_mode > 0): + post_ret = aidemo.face_det_post_process(confidence_threshold,nms_threshold,fd_kmodel_input_shape[2],prior_data, + [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH],results) + # （6）返回人脸检测框 + if len(post_ret)==0: + return post_ret + else: + return post_ret[0] #0:det,1:landm,2:score + +def fd_kpu_deinit(kpu_obj): + # kpu释放 + with ScopedTiming("fd_kpu_deinit",debug_mode > 0): + global fd_ai2d, fd_ai2d_output_tensor + del kpu_obj #删除人脸检测kpu_obj变量，释放对它所引用对象的内存引用 + del fd_ai2d #删除人脸检测ai2d变量，释放对它所引用对象的内存引用 + del fd_ai2d_output_tensor #删除人脸检测ai2d_output_tensor变量，释放对它所引用对象的内存引用 + +###############for face recognition############### +def get_affine_matrix(bbox): + # 获取仿射矩阵，用于将边界框映射到模型输入空间 + with ScopedTiming("get_affine_matrix", debug_mode > 1): + # 设置缩放因子 + factor = 2.7 + # 从边界框提取坐标和尺寸 + x1, y1, w, h = map(lambda x: int(round(x, 0)), bbox[:4]) + # 模型输入大小 + edge_size = fp_kmodel_input_shape[2] + # 平移距离，使得模型输入空间的中心对准原点 + trans_distance = edge_size / 2.0 + # 计算边界框中心点的坐标 + center_x = x1 + w / 2.0 + center_y = y1 + h / 2.0 + # 计算最大边长 + maximum_edge = factor * (h if h > w else w) + # 计算缩放比例 + scale = edge_size * 2.0 / maximum_edge + # 计算平移参数 + cx = trans_distance - scale * center_x + cy = trans_distance - scale * center_y + # 创建仿射矩阵 + affine_matrix = [scale, 0, cx, 0, scale, cy] + return affine_matrix + +def fp_ai2d_init(): + # 人脸解析ai2d初始化 + with ScopedTiming("fp_ai2d_init",debug_mode > 0): + # （1）创建人脸解析ai2d对象 + global fp_ai2d + fp_ai2d = nn.ai2d() + + # （2）创建人脸解析ai2d_output_tensor对象 + global fp_ai2d_output_tensor + data = np.ones(fp_kmodel_input_shape, dtype=np.uint8) + fp_ai2d_output_tensor = nn.from_numpy(data) + +def fp_ai2d_run(rgb888p_img,det): + # 人脸解析ai2d推理 + with ScopedTiming("fp_ai2d_run",debug_mode > 0): + global fp_ai2d,fp_ai2d_input_tensor,fp_ai2d_output_tensor + #（1）根据原图构建人脸解析ai2d_input_tensor + ai2d_input = rgb888p_img.to_numpy_ref() + fp_ai2d_input_tensor = nn.from_numpy(ai2d_input) + #（2）设置人脸解析ai2d参数 + fp_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + global matrix_dst + matrix_dst = get_affine_matrix(det) + fp_ai2d.set_affine_param(True,nn.interp_method.cv2_bilinear,0, 0, 127, 1,matrix_dst) + + # （3）构建人脸解析ai2d_builder + global fp_ai2d_builder + fp_ai2d_builder = fp_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fp_kmodel_input_shape) + # （4）推理人脸解析ai2d，将结果保存到ai2d_output_tensor + fp_ai2d_builder.run(fp_ai2d_input_tensor, fp_ai2d_output_tensor) + +def fp_ai2d_release(): + # 释放部分人脸解析ai2d资源 + with ScopedTiming("fp_ai2d_release",debug_mode > 0): + global fp_ai2d_input_tensor,fp_ai2d_builder + del fp_ai2d_input_tensor + del fp_ai2d_builder + +def fp_kpu_init(kmodel_file): + # 初始化人脸解析kpu及ai2d + with ScopedTiming("fp_kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + fp_ai2d_init() + return kpu_obj + +def fp_kpu_pre_process(rgb888p_img,det): + # 人脸解析kpu预处理 + fp_ai2d_run(rgb888p_img,det) + with ScopedTiming("fp_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fp_ai2d_output_tensor + current_kmodel_obj.set_input_tensor(0, fp_ai2d_output_tensor) + #ai2d_out_data = fp_ai2d_output_tensor.to_numpy() + #with open("/sdcard/app/ai2d_out.bin", "wb") as file: + #file.write(ai2d_out_data.tobytes()) + +def fp_kpu_get_output(): + # 获取人脸解析kpu输出 + with ScopedTiming("fp_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + data = current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + del data + return result + +def fp_kpu_run(kpu_obj,rgb888p_img,det): + # 人脸解析kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）根据人脸检测框进行人脸解析kpu预处理 + fp_kpu_pre_process(rgb888p_img,det) + # （2）人脸解析kpu推理 + with ScopedTiming("fp_kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸解析ai2d资源 + fp_ai2d_release() + # （4）释放人脸解析kpu输出 + result = fp_kpu_get_output() + return result + +def fp_kpu_deinit(kpu_obj): + # 释放人脸解析kpu和ai2d资源 + with ScopedTiming("fp_kpu_deinit",debug_mode > 0): + global fp_ai2d,fp_ai2d_output_tensor + del kpu_obj + del fp_ai2d + del fp_ai2d_output_tensor + +#********************for media_utils.py******************** +global draw_img_ulab,draw_img,osd_img #for display +global buffer,media_source,media_sink #for media + +#for display +def display_init(): + # 设置使用hdmi进行显示 + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +def display_deinit(): + # 释放显示资源 + display.deinit() + +def display_draw(dets,parse_results): + # 在显示器画出人脸解析结果 + with ScopedTiming("display_draw",debug_mode >0): + global draw_img_ulab,draw_img,osd_img + if dets: + draw_img.clear() + for i,det in enumerate(dets): + # （1）将人脸检测框画到draw_img + x, y, w, h = map(lambda x: int(round(x, 0)), det[:4]) + x = x * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + y = y * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + w = w * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + h = h * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + draw_img.draw_rectangle(x,y, w, h, color=(255, 255, 0, 255)) + # （2）将人脸解析结果画到draw_img（draw_img_ulab和draw_img指同一内存） + aidemo.face_parse_post_process(draw_img_ulab,[OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH], + [DISPLAY_WIDTH,DISPLAY_HEIGHT],fp_kmodel_input_shape[2],det.tolist(),parse_results[i]) + # （3）将绘制好的图像拷贝到显示缓冲区，并在显示器上展示 + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + # （1）清空用来画框的图像 + draw_img.clear() + # （2）清空osd + draw_img.copy_to(osd_img) + # （3）显示透明图层 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.1 +def camera_init(dev_id): + # camera初始化 + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +def camera_start(dev_id): + # camera启动 + camera.start_stream(dev_id) + +def camera_read(dev_id): + # 读取一帧图像 + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +def camera_release_image(dev_id,rgb888p_img): + # 释放一帧图像 + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +def camera_stop(dev_id): + # 停止camera + camera.stop_stream(dev_id) + +#for media，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.3 +def media_init(): + # meida初始化 + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + + global buffer, draw_img_ulab,draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 用于画框，draw_img->draw_img_ulab(两者指向同一块内存) + draw_img_ulab = np.zeros((DISPLAY_HEIGHT,DISPLAY_WIDTH,4),dtype=np.uint8) + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_REF,data = draw_img_ulab) + # 用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +def media_deinit(): + # meida资源释放 + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +#********************for face_detect.py******************** +def face_parse_inference(): + print("face_parse_test start") + # 人脸检测kpu初始化 + kpu_face_detect = fd_kpu_init(fd_kmodel_file) + # 人脸解析kpu初始化 + kpu_face_parse = fp_kpu_init(fp_kmodel_file) + # camera初始化 + camera_init(CAM_DEV_ID_0) + # 显示初始化 + display_init() + + rgb888p_img = None + # 注意：将一定要将一下过程包在try中，用于保证程序停止后，资源释放完毕；确保下次程序仍能正常运行 + try: + # 注意：媒体初始化（注：媒体初始化必须在camera_start之前，确保media缓冲区已配置完全） + ret = media_init() + if ret: + print("face_detect_test, buffer init failed") + return ret + + # 启动camera + camera_start(CAM_DEV_ID_0) + time.sleep(5) + + gc_count = 0 + while True: + with ScopedTiming("total",1): + # （1）读取一帧图像 + rgb888p_img = camera_read(CAM_DEV_ID_0) + # （2）若读取失败，释放当前帧 + if rgb888p_img == -1: + print("face_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # （3）若读取成功，推理当前帧 + if rgb888p_img.format() == image.RGBP888: + # （3.1）推理当前图像，并获取人脸检测结果 + dets = fd_kpu_run(kpu_face_detect,rgb888p_img) + # （3.2）针对每个人脸框，推理得到对应人脸解析结果 + parse_results = [] + for det in dets: + parse_ret = fp_kpu_run(kpu_face_parse,rgb888p_img,det) + parse_results.append(parse_ret) + # （3.3）将人脸解析结果画到显示器上 + display_draw(dets,parse_results) + + # （4）释放当前帧 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + except Exception as e: + # 捕捉运行运行中异常，并打印错误 + print(f"An error occurred during buffer used: {e}") + finally: + # 释放当前帧 + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + # 停止camera + camera_stop(CAM_DEV_ID_0) + # 释放显示资源 + display_deinit() + # 释放kpu资源 + fd_kpu_deinit(kpu_face_detect) + fp_kpu_deinit(kpu_face_parse) + # 垃圾回收 + gc.collect() + time.sleep(1) + # 释放媒体资源 + ret = media_deinit() + if ret: + print("face_parse_test, buffer_deinit failed") + return ret + + print("face_parse_test end") + return 0 + +if __name__ == '__main__': + face_parse_inference() +``` + +### 5. 车牌识别 + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 +import aidemo #aidemo模块，封装ai demo相关后处理、画图操作 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +#ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(640, 16) +OUT_RGB888P_HEIGHT = 480 + +#车牌检测 和 车牌识别 kmodel输入shape +det_kmodel_input_shape = (1,3,640,640) +rec_kmodel_input_shape = (1,1,32,220) + +#车牌检测 相关参数设置 +obj_thresh = 0.2 #车牌检测分数阈值 +nms_thresh = 0.2 #检测框 非极大值抑制 阈值 + +#文件配置 +root_dir = '/sdcard/app/tests/' +det_kmodel_file = root_dir + 'kmodel/LPD_640.kmodel' # 车牌检测 kmodel 文件路径 +rec_kmodel_file = root_dir + 'kmodel/licence_reco.kmodel' # 车牌识别 kmodel 文件路径 +#dict_rec = ["挂", "使", "领", "澳", "港", "皖", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "京", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "警", "学", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "_", "-"] +dict_rec = ["gua","shi","ling","ao","gang","wan","hu","jin","yu","ji","jin","meng","liao","ji","hei","su","zhe","jing","min","gan","lu","yu","e","xiang","yue","gui","qiong","chuan","gui","yun","zang","shan","gan","qing","ning","xin","jing","xue","0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "_", "-"] +dict_size = len(dict_rec) +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + + +#ai_utils.py +global det_current_kmodel_obj,rec_current_kmodel_obj # 定义全局的 kpu 对象 +global det_ai2d,det_ai2d_input_tensor,det_ai2d_output_tensor,det_ai2d_builder # 定义车牌检测 ai2d 对象 ，并且定义 ai2d 的输入、输出 以及 builder +global rec_ai2d,rec_ai2d_input_tensor,rec_ai2d_output_tensor,rec_ai2d_builder # 定义车牌识别 ai2d 对象 ，并且定义 ai2d 的输入、输出 以及 builder + +# 车牌检测 接收kmodel输出的后处理方法 +def det_kpu_post_process(output_data): + with ScopedTiming("det_kpu_post_process", debug_mode > 0): + results = aidemo.licence_det_postprocess(output_data,[OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH],[det_kmodel_input_shape[2],det_kmodel_input_shape[3]],obj_thresh,nms_thresh) + return results + +# 车牌识别 接收kmodel输出的后处理方法 +def rec_kpu_post_process(output_data): + with ScopedTiming("rec_kpu_post_process", debug_mode > 0): + size = rec_kmodel_input_shape[3] / 4 + result = [] + for i in range(size): + maxs = float("-inf") + index = -1 + for j in range(dict_size): + if (maxs < float(output_data[i * dict_size +j])): + index = j + maxs = output_data[i * dict_size +j] + result.append(index) + + result_str = "" + for i in range(size): + if (result[i] >= 0 and result[i] != 0 and not(i > 0 and result[i-1] == result[i])): + result_str += dict_rec[result[i]-1] + return result_str + +# 车牌检测 ai2d 初始化 +def det_ai2d_init(): + with ScopedTiming("det_ai2d_init",debug_mode > 0): + global det_ai2d + det_ai2d = nn.ai2d() + det_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + det_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + global det_ai2d_out_tensor + data = np.ones(det_kmodel_input_shape, dtype=np.uint8) + det_ai2d_out_tensor = nn.from_numpy(data) + + global det_ai2d_builder + det_ai2d_builder = det_ai2d.build([1,3,OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH], det_kmodel_input_shape) + +# 车牌识别 ai2d 初始化 +def rec_ai2d_init(): + with ScopedTiming("rec_ai2d_init",debug_mode > 0): + global rec_ai2d + rec_ai2d = nn.ai2d() + rec_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + + global rec_ai2d_out_tensor + data = np.ones(rec_kmodel_input_shape, dtype=np.uint8) + rec_ai2d_out_tensor = nn.from_numpy(data) + + rec_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + +# 车牌检测 ai2d 运行 +def det_ai2d_run(rgb888p_img): + with ScopedTiming("det_ai2d_run",debug_mode > 0): + global det_ai2d_input_tensor,det_ai2d_out_tensor,det_ai2d_builder + det_ai2d_input = rgb888p_img.to_numpy_ref() + det_ai2d_input_tensor = nn.from_numpy(det_ai2d_input) + + det_ai2d_builder.run(det_ai2d_input_tensor, det_ai2d_out_tensor) + +# 车牌识别 ai2d 运行 +def rec_ai2d_run(img_array): + with ScopedTiming("rec_ai2d_run",debug_mode > 0): + global rec_ai2d_input_tensor,rec_ai2d_out_tensor,rec_ai2d_builder + rec_ai2d_builder = rec_ai2d.build([1,1,img_array.shape[2],img_array.shape[3]], rec_kmodel_input_shape) + rec_ai2d_input_tensor = nn.from_numpy(img_array) + + rec_ai2d_builder.run(rec_ai2d_input_tensor, rec_ai2d_out_tensor) + +# 车牌检测 ai2d 释放内存 +def det_ai2d_release(): + with ScopedTiming("det_ai2d_release",debug_mode > 0): + global det_ai2d_input_tensor + del det_ai2d_input_tensor + +# 车牌识别 ai2d 释放内存 +def rec_ai2d_release(): + with ScopedTiming("rec_ai2d_release",debug_mode > 0): + global rec_ai2d_input_tensor, rec_ai2d_builder + del rec_ai2d_input_tensor + del rec_ai2d_builder + +# 车牌检测 kpu 初始化 +def det_kpu_init(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("det_kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + + det_ai2d_init() + return kpu_obj + +# 车牌识别 kpu 初始化 +def rec_kpu_init(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("rec_kpu_init",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + + rec_ai2d_init() + return kpu_obj + +# 车牌检测 kpu 输入预处理 +def det_kpu_pre_process(rgb888p_img): + det_ai2d_run(rgb888p_img) + with ScopedTiming("det_kpu_pre_process",debug_mode > 0): + global det_current_kmodel_obj,det_ai2d_out_tensor + # set kpu input + det_current_kmodel_obj.set_input_tensor(0, det_ai2d_out_tensor) + +# 车牌识别 kpu 输入预处理 +def rec_kpu_pre_process(img_array): + rec_ai2d_run(img_array) + with ScopedTiming("rec_kpu_pre_process",debug_mode > 0): + global rec_current_kmodel_obj,rec_ai2d_out_tensor + # set kpu input + rec_current_kmodel_obj.set_input_tensor(0, rec_ai2d_out_tensor) + +# 车牌识别 抠图 +def rec_array_pre_process(rgb888p_img,dets): + with ScopedTiming("rec_array_pre_process",debug_mode > 0): + isp_image = rgb888p_img.to_numpy_ref() + imgs_array_boxes = aidemo.ocr_rec_preprocess(isp_image,[OUT_RGB888P_HEIGHT,OUT_RGB888P_WIDTH],dets) + return imgs_array_boxes + +# 车牌检测 获取 kmodel 输出 +def det_kpu_get_output(): + with ScopedTiming("det_kpu_get_output",debug_mode > 0): + global det_current_kmodel_obj + results = [] + for i in range(det_current_kmodel_obj.outputs_size()): + data = det_current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + tmp2 = result.copy() + del data + results.append(tmp2) + return results + +# 车牌识别 获取 kmodel 输出 +def rec_kpu_get_output(): + with ScopedTiming("rec_kpu_get_output",debug_mode > 0): + global rec_current_kmodel_obj + data = rec_current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + result = result.reshape((result.shape[0] * result.shape[1] * result.shape[2])) + tmp = result.copy() + del data + return tmp + +# 车牌检测 kpu 运行 +def det_kpu_run(kpu_obj,rgb888p_img): + global det_current_kmodel_obj + det_current_kmodel_obj = kpu_obj + # (1) 原图预处理，并设置模型输入 + det_kpu_pre_process(rgb888p_img) + # (2) kpu 运行 + with ScopedTiming("det_kpu_run",debug_mode > 0): + kpu_obj.run() + # (3) 释放ai2d资源 + det_ai2d_release() + # (4) 获取kpu输出 + results = det_kpu_get_output() + # (5) kpu结果后处理 + dets = det_kpu_post_process(results) + # 返回 车牌检测结果 + return dets + +# 车牌识别 kpu 运行 +def rec_kpu_run(kpu_obj,rgb888p_img,dets): + global rec_current_kmodel_obj + if (len(dets) == 0): + return [] + rec_current_kmodel_obj = kpu_obj + # (1) 原始图像抠图，车牌检测结果 points 排序 + imgs_array_boxes = rec_array_pre_process(rgb888p_img,dets) + imgs_array = imgs_array_boxes[0] + boxes = imgs_array_boxes[1] + recs = [] + for img_array in imgs_array: + # (2) 抠出后的图像 进行预处理，设置模型输入 + rec_kpu_pre_process(img_array) + # (3) kpu 运行 + with ScopedTiming("rec_kpu_run",debug_mode > 0): + kpu_obj.run() + # (4) 释放ai2d资源 + rec_ai2d_release() + # (5) 获取 kpu 输出 + result = rec_kpu_get_output() + # (6) kpu 结果后处理 + rec = rec_kpu_post_process(result) + recs.append(rec) + # (7) 返回 车牌检测 和 识别结果 + return [boxes,recs] + + +# 车牌检测 kpu 释放内存 +def det_kpu_deinit(kpu_obj): + with ScopedTiming("det_kpu_deinit",debug_mode > 0): + global det_ai2d, det_ai2d_builder, det_ai2d_out_tensor + del kpu_obj + del det_ai2d + del det_ai2d_builder + del det_ai2d_out_tensor + +# 车牌识别 kpu 释放内存 +def rec_kpu_deinit(kpu_obj): + with ScopedTiming("rec_kpu_deinit",debug_mode > 0): + global rec_ai2d, rec_ai2d_out_tensor + del kpu_obj + del rec_ai2d + del rec_ai2d_out_tensor + +#media_utils.py +global draw_img,osd_img #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +# display 作图过程 将所有车牌检测框 和 识别结果绘制到屏幕 +def display_draw(dets_recs): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + if dets_recs: + dets = dets_recs[0] + recs = dets_recs[1] + draw_img.clear() + point_8 = np.zeros((8),dtype=np.int16) + for det_index in range(len(dets)): + for i in range(4): + x = dets[det_index][i * 2 + 0]/OUT_RGB888P_WIDTH*DISPLAY_WIDTH + y = dets[det_index][i * 2 + 1]/OUT_RGB888P_HEIGHT*DISPLAY_HEIGHT + point_8[i * 2 + 0] = int(x) + point_8[i * 2 + 1] = int(y) + for i in range(4): + draw_img.draw_line(point_8[i * 2 + 0],point_8[i * 2 + 1],point_8[(i+1) % 4 * 2 + 0],point_8[(i+1) % 4 * 2 + 1],color=(255, 0, 255, 0),thickness=2) + draw_img.draw_string( point_8[6], point_8[7] + 20, recs[det_index] , color=(255,255,153,18) , scale=4) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_BGR_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + + +#**********for licence_det_rec.py********** +def licence_det_inference(): + print("licence_det start") + kpu_licence_det = det_kpu_init(det_kmodel_file) # 创建车牌检测的 kpu 对象 + kpu_licence_rec = rec_kpu_init(rec_kmodel_file) # 创建车牌识别的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("licence_det, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图片 + if rgb888p_img == -1: + print("licence_det, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + dets = det_kpu_run(kpu_licence_det,rgb888p_img) # 执行车牌检测 kpu 运行 以及 后处理过程 + dets_recs = rec_kpu_run(kpu_licence_rec,rgb888p_img,dets) # 执行车牌识别 kpu 运行 以及 后处理过程 + display_draw(dets_recs) # 将得到的检测结果和识别结果 绘制到display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图像 + rgb888p_img = None + # gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止 camera + display_deinit() # 释放 display + det_kpu_deinit(kpu_licence_det) # 释放 车牌检测 kpu + rec_kpu_deinit(kpu_licence_rec) # 释放 车牌识别 kpu + gc.collect() + time.sleep(1) + ret = media_deinit() # 释放 整个media + if ret: + print("licence_det, buffer_deinit failed") + return ret + + print("licence_det end") + return 0 + +if __name__ == '__main__': + licence_det_inference() +``` + +### 6. 石头剪刀布 + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +from random import randint #随机整数生成 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 +import aicube #aicube模块，封装ai cube 相关后处理 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +#ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) +OUT_RGB888P_HEIGH = 1080 + +#手掌检测 和 手掌关键点检测 kmodel输入shape +hd_kmodel_input_shape = (1,3,512,512) +hk_kmodel_input_shape = (1,3,256,256) + +#手掌检测 相关参数设置 +confidence_threshold = 0.2 #手掌检测 分数阈值 +nms_threshold = 0.5 #非极大值抑制 阈值 +hd_kmodel_frame_size = [512,512] #手掌检测kmodel输入 w h +hd_frame_size = [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH] #手掌检测原始输入图像 w h +strides = [8,16,32] #手掌检测模型 下采样输出倍数 +num_classes = 1 #检测类别数， 及手掌一种 +nms_option = False #控制最大值抑制的方式 False 类内 True 类间 +labels = ["hand"] #标签名称 +anchors = [26,27,53,52,75,71,80,99,106,82,99,134,140,113,161,172,245,276] #手掌检测模型 锚框 +#手掌关键点检测 相关参数 +hk_kmodel_frame_size = [256,256] #手掌关键点检测 kmodel 输入 w h + +# kmodel 路径 +root_dir = '/sdcard/app/tests/' +hd_kmodel_file = root_dir + 'kmodel/hand_det.kmodel' #手掌检测kmodel路径 +hk_kmodel_file = root_dir + 'kmodel/handkp_det.kmodel' #手掌关键点kmodel路径 +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） + +# 猜拳模式 0 玩家稳赢 ， 1 玩家必输 ， n > 2 多局多胜 +guess_mode = 3 + +# 读取石头剪刀布的bin文件方法 +def read_file(file_name): + image_arr = np.fromfile(file_name,dtype=np.uint8) + image_arr = image_arr.reshape((400,400,4)) + return image_arr +# 石头剪刀布的 array +five_image = read_file(root_dir + "utils/five.bin") +fist_image = read_file(root_dir + "utils/fist.bin") +shear_image = read_file(root_dir + "utils/shear.bin") + + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#ai_utils.py +global current_kmodel_obj # 定义全局的 kpu 对象 +global hd_ai2d,hd_ai2d_input_tensor,hd_ai2d_output_tensor,hd_ai2d_builder # 定义手掌检测 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder +global hk_ai2d,hk_ai2d_input_tensor,hk_ai2d_output_tensor,hk_ai2d_builder # 定义手掌关键点检测 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder +global counts_guess, player_win, k230_win, sleep_end, set_stop_id # 定义猜拳游戏的参数：猜拳次数、玩家赢次、k230赢次、是否停顿、是狗暂停 + +# 手掌检测 ai2d 初始化 +def hd_ai2d_init(): + with ScopedTiming("hd_ai2d_init",debug_mode > 0): + global hd_ai2d + global hd_ai2d_builder + # 计算padding值 + ori_w = OUT_RGB888P_WIDTH + ori_h = OUT_RGB888P_HEIGH + width = hd_kmodel_frame_size[0] + height = hd_kmodel_frame_size[1] + ratiow = float(width) / ori_w + ratioh = float(height) / ori_h + if ratiow < ratioh: + ratio = ratiow + else: + ratio = ratioh + new_w = int(ratio * ori_w) + new_h = int(ratio * ori_h) + dw = float(width - new_w) / 2 + dh = float(height - new_h) / 2 + top = int(round(dh - 0.1)) + bottom = int(round(dh + 0.1)) + left = int(round(dw - 0.1)) + right = int(round(dw - 0.1)) + + # init kpu and load kmodel + hd_ai2d = nn.ai2d() + hd_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + hd_ai2d.set_pad_param(True, [0,0,0,0,top,bottom,left,right], 0, [114,114,114]) + hd_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel ) + hd_ai2d_builder = hd_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,height,width]) + + global hd_ai2d_output_tensor + data = np.ones(hd_kmodel_input_shape, dtype=np.uint8) + hd_ai2d_output_tensor = nn.from_numpy(data) + +# 手掌检测 ai2d 运行 +def hd_ai2d_run(rgb888p_img): + with ScopedTiming("hd_ai2d_run",debug_mode > 0): + global hd_ai2d_input_tensor,hd_ai2d_output_tensor + hd_ai2d_input = rgb888p_img.to_numpy_ref() + hd_ai2d_input_tensor = nn.from_numpy(hd_ai2d_input) + + hd_ai2d_builder.run(hd_ai2d_input_tensor, hd_ai2d_output_tensor) + +# 手掌检测 ai2d 释放内存 +def hd_ai2d_release(): + with ScopedTiming("hd_ai2d_release",debug_mode > 0): + global hd_ai2d_input_tensor + del hd_ai2d_input_tensor + +# 手掌检测 kpu 初始化 +def hd_kpu_init(hd_kmodel_file): + # init kpu and load kmodel + with ScopedTiming("hd_kpu_init",debug_mode > 0): + hd_kpu_obj = nn.kpu() + hd_kpu_obj.load_kmodel(hd_kmodel_file) + + hd_ai2d_init() + return hd_kpu_obj + +# 手掌检测 kpu 输入预处理 +def hd_kpu_pre_process(rgb888p_img): + hd_ai2d_run(rgb888p_img) + with ScopedTiming("hd_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,hd_ai2d_output_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, hd_ai2d_output_tensor) + +# 手掌检测 kpu 获取 kmodel 输出 +def hd_kpu_get_output(): + with ScopedTiming("hd_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + result = result.reshape((result.shape[0]*result.shape[1]*result.shape[2]*result.shape[3])) + tmp2 = result.copy() + del result + results.append(tmp2) + return results + +# 手掌检测 kpu 运行 +def hd_kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1) 原始图像预处理，并设置模型输入 + hd_kpu_pre_process(rgb888p_img) + # (2) kpu 运行 + with ScopedTiming("hd_kpu_run",debug_mode > 0): + current_kmodel_obj.run() + # (3) 释放ai2d资源 + hd_ai2d_release() + # (4) 获取kpu输出 + results = hd_kpu_get_output() + # (5) kpu结果后处理 + dets = aicube.anchorbasedet_post_process( results[0], results[1], results[2], hd_kmodel_frame_size, hd_frame_size, strides, num_classes, confidence_threshold, nms_threshold, anchors, nms_option) + # (6) 返回 手掌检测 结果 + return dets + +# 手掌检测 kpu 释放内存 +def hd_kpu_deinit(kpu_obj): + with ScopedTiming("hd_kpu_deinit",debug_mode > 0): + global hd_ai2d,hd_ai2d_output_tensor + del kpu_obj + del hd_ai2d + del hd_ai2d_output_tensor + +# 手掌关键点检测 ai2d 初始化 +def hk_ai2d_init(): + with ScopedTiming("hk_ai2d_init",debug_mode > 0): + global hk_ai2d + hk_ai2d = nn.ai2d() + hk_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + + global hk_ai2d_output_tensor + data = np.ones(hk_kmodel_input_shape, dtype=np.uint8) + hk_ai2d_output_tensor = nn.from_numpy(data) + +# 手掌关键点检测 ai2d 运行 +def hk_ai2d_run(rgb888p_img, x, y, w, h): + with ScopedTiming("hk_ai2d_run",debug_mode > 0): + global hk_ai2d,hk_ai2d_input_tensor,hk_ai2d_output_tensor + hk_ai2d_input = rgb888p_img.to_numpy_ref() + hk_ai2d_input_tensor = nn.from_numpy(hk_ai2d_input) + + hk_ai2d.set_crop_param(True, x, y, w, h) + hk_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel ) + + global hk_ai2d_builder + hk_ai2d_builder = hk_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,hk_kmodel_frame_size[1],hk_kmodel_frame_size[0]]) + hk_ai2d_builder.run(hk_ai2d_input_tensor, hk_ai2d_output_tensor) + +# 手掌关键点检测 ai2d 释放内存 +def hk_ai2d_release(): + with ScopedTiming("hk_ai2d_release",debug_mode > 0): + global hk_ai2d_input_tensor + del hk_ai2d_input_tensor + +# 手掌关键点检测 kpu 初始化 +def hk_kpu_init(hk_kmodel_file): + # init kpu and load kmodel + with ScopedTiming("hk_kpu_init",debug_mode > 0): + hk_kpu_obj = nn.kpu() + hk_kpu_obj.load_kmodel(hk_kmodel_file) + + hk_ai2d_init() + return hk_kpu_obj + +# 手掌关键点检测 kpu 输入预处理 +def hk_kpu_pre_process(rgb888p_img, x, y, w, h): + hk_ai2d_run(rgb888p_img, x, y, w, h) + with ScopedTiming("hk_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,hk_ai2d_output_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, hk_ai2d_output_tensor) + +# 手掌关键点检测 kpu 获得 kmodel 输出 +def hk_kpu_get_output(): + with ScopedTiming("hk_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + + result = result.reshape((result.shape[0]*result.shape[1])) + tmp2 = result.copy() + del result + results.append(tmp2) + return results + +# 手掌关键点检测 接收kmodel结果的后处理 +def hk_kpu_post_process(results, x, y, w, h): + results_show = np.zeros(results.shape,dtype=np.int16) + # results_show = np.zeros(len(results),dtype=np.int16) + results_show[0::2] = results[0::2] * w + x + results_show[1::2] = results[1::2] * h + y + return results_show + +# 手掌关键点检测 kpu 运行 +def hk_kpu_run(kpu_obj,rgb888p_img, x, y, w, h): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1) 原图预处理，并设置模型输入 + hk_kpu_pre_process(rgb888p_img, x, y, w, h) + # (2) kpu 运行 + with ScopedTiming("hk_kpu_run",debug_mode > 0): + current_kmodel_obj.run() + # (3) 释放ai2d资源 + hk_ai2d_release() + # (4) 获取kpu输出 + results = hk_kpu_get_output() + # (5) kpu结果后处理 + result = hk_kpu_post_process(results[0],x,y,w,h) + # (6) 返回 关键点检测 结果 + return result + +# 手掌关键点检测 kpu 释放内存 +def hk_kpu_deinit(kpu_obj): + with ScopedTiming("hk_kpu_deinit",debug_mode > 0): + global hk_ai2d,hk_ai2d_output_tensor + del kpu_obj + del hk_ai2d + del hk_ai2d_output_tensor + +# 手掌关键点检测 计算角度 +def hk_vector_2d_angle(v1,v2): + v1_x = v1[0] + v1_y = v1[1] + v2_x = v2[0] + v2_y = v2[1] + v1_norm = np.sqrt(v1_x * v1_x+ v1_y * v1_y) + v2_norm = np.sqrt(v2_x * v2_x + v2_y * v2_y) + dot_product = v1_x * v2_x + v1_y * v2_y + cos_angle = dot_product/(v1_norm*v2_norm) + angle = np.acos(cos_angle)*180/np.pi + # if (angle>180): + # return 65536 + return angle + +# 利用手掌关键点检测的结果 判断手掌手势 +def hk_gesture(kpu_hand_keypoint_detect,rgb888p_img,det_box): + x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5] + w = int(float(x2 - x1) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + h = int(float(y2 - y1) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + + x1 = int(x1 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y1 = int(y1 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + x2 = int(x2 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y2 = int(y2 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + + if (h<(0.1*DISPLAY_HEIGHT)): + return + if (w<(0.25*DISPLAY_WIDTH) and ((x1<(0.03*DISPLAY_WIDTH)) or (x2>(0.97*DISPLAY_WIDTH)))): + return + if (w<(0.15*DISPLAY_WIDTH) and ((x1<(0.01*DISPLAY_WIDTH)) or (x2>(0.99*DISPLAY_WIDTH)))): + return + + length = max(w,h)/2 + cx = (x1+x2)/2 + cy = (y1+y2)/2 + ratio_num = 1.26*length + + x1_kp = int(max(0,cx-ratio_num)) + y1_kp = int(max(0,cy-ratio_num)) + x2_kp = int(min(DISPLAY_WIDTH-1, cx+ratio_num)) + y2_kp = int(min(DISPLAY_HEIGHT-1, cy+ratio_num)) + w_kp = int(x2_kp - x1_kp + 1) + h_kp = int(y2_kp - y1_kp + 1) + + results = hk_kpu_run(kpu_hand_keypoint_detect,rgb888p_img, x1_kp, y1_kp, w_kp, h_kp) + + angle_list = [] + for i in range(5): + angle = hk_vector_2d_angle([(results[0]-results[i*8+4]), (results[1]-results[i*8+5])],[(results[i*8+6]-results[i*8+8]),(results[i*8+7]-results[i*8+9])]) + angle_list.append(angle) + + thr_angle = 65. + thr_angle_thumb = 53. + thr_angle_s = 49. + gesture_str = None + if 65535. not in angle_list: + if (angle_list[0]>thr_angle_thumb) and (angle_list[1]>thr_angle) and (angle_list[2]>thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle): + gesture_str = "fist" + elif (angle_list[0]thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle): + gesture_str = "gun" + elif (angle_list[0]thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]5) and (angle_list[1]thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle): + gesture_str = "one" + elif (angle_list[0]thr_angle) and (angle_list[2]>thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]thr_angle_thumb) and (angle_list[1]thr_angle): + gesture_str = "three" + elif (angle_list[0]thr_angle) and (angle_list[2]>thr_angle) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle): + gesture_str = "thumbUp" + elif (angle_list[0]>thr_angle_thumb) and (angle_list[1]thr_angle) and (angle_list[4]>thr_angle): + gesture_str = "yeah" + + return gesture_str + + +#media_utils.py +global draw_img,osd_img,masks #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img, masks + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + masks = np.zeros((OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH,4),dtype=np.uint8) + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888,alloc=image.ALLOC_REF,data=masks) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + + +#**********for finger_guessing.py********** +def finger_guessing_inference(): + print("finger_guessing_test start") + kpu_hand_detect = hd_kpu_init(hd_kmodel_file) # 创建手掌检测的 kpu 对象 + kpu_hand_keypoint_detect = hk_kpu_init(hk_kmodel_file) # 创建手掌关键点检测的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("finger_guessing, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) # 开启 camera + counts_guess = -1 # 猜拳次数 计数 + player_win = 0 # 玩家 赢次计数 + k230_win = 0 # k230 赢次计数 + sleep_end = False # 是否 停顿 + set_stop_id = True # 是否 暂停猜拳 + LIBRARY = ["fist","yeah","five"] # 猜拳 石头剪刀布 三种方案的dict + + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图像 + if rgb888p_img == -1: + print("finger_guessing, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + with ScopedTiming("trigger time", debug_mode > 0): + dets = hd_kpu_run(kpu_hand_detect,rgb888p_img) # 执行手掌检测 kpu 运行 以及 后处理过程 + gesture = "" + draw_img.clear() + for det_box in dets: + gesture = hk_gesture(kpu_hand_keypoint_detect,rgb888p_img,det_box) # 执行手掌关键点检测 kpu 运行 以及 后处理过程 得到手势类型 + if (len(dets) >= 2): + draw_img.draw_string( 300 , 500, "Must have one hand !", color=(255,255,0,0), scale=7) + draw_img.copy_to(osd_img) + elif (guess_mode == 0): + if (gesture == "fist"): + masks[:400,:400,:] = shear_image + elif (gesture == "five"): + masks[:400,:400,:] = fist_image + elif (gesture == "yeah"): + masks[:400,:400,:] = five_image + draw_img.copy_to(osd_img) + elif (guess_mode == 1): + if (gesture == "fist"): + masks[:400,:400,:] = five_image + elif (gesture == "five"): + masks[:400,:400,:] = shear_image + elif (gesture == "yeah"): + masks[:400,:400,:] = fist_image + draw_img.copy_to(osd_img) + else: + if (sleep_end): + time.sleep_ms(2000) + sleep_end = False + if (len(dets) == 0): + set_stop_id = True + if (counts_guess == -1 and gesture != "fist" and gesture != "yeah" and gesture != "five"): + draw_img.draw_string( 400 , 450, "G A M E S T A R T", color=(255,255,0,0), scale=7) + draw_img.draw_string( 400 , 550, " 1 S E T ", color=(255,255,0,0), scale=7) + draw_img.copy_to(osd_img) + elif (counts_guess == guess_mode): + draw_img.clear() + if (k230_win > player_win): + draw_img.draw_string( 400 , 450, "Y O U L O S E", color=(255,255,0,0), scale=7) + elif (k230_win < player_win): + draw_img.draw_string( 400 , 450, "Y O U W I N", color=(255,255,0,0), scale=7) + else: + draw_img.draw_string( 400 , 450, "T I E G A M E", color=(255,255,0,0), scale=7) + draw_img.copy_to(osd_img) + counts_guess = -1 + player_win = 0 + k230_win = 0 + + sleep_end = True + else: + if (set_stop_id): + if (counts_guess == -1 and (gesture == "fist" or gesture == "yeah" or gesture == "five")): + counts_guess = 0 + if (counts_guess != -1 and (gesture == "fist" or gesture == "yeah" or gesture == "five")): + k230_guess = randint(1,10000) % 3 + if (gesture == "fist" and LIBRARY[k230_guess] == "yeah"): + player_win += 1 + elif (gesture == "fist" and LIBRARY[k230_guess] == "five"): + k230_win += 1 + if (gesture == "yeah" and LIBRARY[k230_guess] == "fist"): + k230_win += 1 + elif (gesture == "yeah" and LIBRARY[k230_guess] == "five"): + player_win += 1 + if (gesture == "five" and LIBRARY[k230_guess] == "fist"): + player_win += 1 + elif (gesture == "five" and LIBRARY[k230_guess] == "yeah"): + k230_win += 1 + + if (LIBRARY[k230_guess] == "fist"): + masks[:400,:400,:] = fist_image + elif (LIBRARY[k230_guess] == "five"): + masks[:400,:400,:] = five_image + elif (LIBRARY[k230_guess] == "yeah"): + masks[:400,:400,:] = shear_image + + counts_guess += 1; + draw_img.draw_string( 400 , 450, " " + str(counts_guess) + " S E T ", color=(255,255,0,0), scale=7) + draw_img.copy_to(osd_img) + set_stop_id = False + sleep_end = True + + else: + draw_img.draw_string( 400 , 450, " " + str(counts_guess+1) + " S E T ", color=(255,255,0,0), scale=7) + draw_img.copy_to(osd_img) + else: + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) # 将得到的图像 绘制到 display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图形 + rgb888p_img = None + gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止 camera + display_deinit() # 停止 display + hd_kpu_deinit(kpu_hand_detect) # 释放手掌检测 kpu + hk_kpu_deinit(kpu_hand_keypoint_detect) # 释放手掌关键点检测 kpu + + gc.collect() + ret = media_deinit() # 释放 整个 media + if ret: + print("finger_guessing, buffer_deinit failed") + return ret + + print("finger_guessing_test end") + return 0 + +if __name__ == '__main__': + finger_guessing_inference() +``` + +### 7. OCR识别 + +```python +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time #时间统计 +import gc #垃圾回收模块 +import aicube #aicube模块，封装检测分割等任务相关后处理 + +# display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +# ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(640, 16) +OUT_RGB888P_HEIGH = 360 + +#kmodel输入参数设置 +kmodel_input_shape_det = (1,3,640,640) # OCR检测模型的kmodel输入分辨率 +kmodel_input_shape_rec = (1,3,32,512) # OCR识别模型的kmodel输入分辨率 +rgb_mean = [0,0,0] # ai2d padding的值 + +#检测步骤kmodel相关参数设置 +mask_threshold = 0.25 # 二值化mask阈值 +box_threshold = 0.3 # 检测框分数阈值 + +#文件配置 +root_dir = '/sdcard/app/tests/' +kmodel_file_det = root_dir + 'kmodel/ocr_det_int16.kmodel' # 检测模型路径 +kmodel_file_rec = root_dir + "kmodel/ocr_rec_int16.kmodel" # 识别模型路径 +dict_path = root_dir + 'utils/dict.txt' # 调试模式 大于0（调试）、 反之 （不调试） +debug_mode = 0 + +# OCR字典读取 +with open(dict_path, 'r') as file: + line_one = file.read(100000) + line_list = line_one.split("\r\n") +DICT = {num: char.replace("\r", "").replace("\n", "") for num, char in enumerate(line_list)} + +# scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +# utils 设定全局变量 +global current_kmodel_obj # 设置全局kpu对象 +global ai2d_det,ai2d_input_tensor_det,ai2d_output_tensor_det,ai2d_builder_det,ai2d_input_det # 设置检测模型的ai2d对象，并定义ai2d的输入、输出和builder +global ai2d_rec,ai2d_input_tensor_rec,ai2d_output_tensor_rec,ai2d_builder_rec # 设置识别模型的ai2d对象，并定义ai2d的输入、输出和builder + +# padding方法，一边padding，右padding或者下padding +def get_pad_one_side_param(out_img_size,input_img_size): + dst_w = out_img_size[0] + dst_h = out_img_size[1] + + input_width = input_img_size[0] + input_high = input_img_size[1] + + ratio_w = dst_w / input_width + ratio_h = dst_h / input_high + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + + new_w = (int)(ratio * input_width) + new_h = (int)(ratio * input_high) + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +# 检测步骤ai2d初始化 +def ai2d_init_det(): + with ScopedTiming("ai2d_init_det",debug_mode > 0): + global ai2d_det + ai2d_det = nn.ai2d() + ai2d_det.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d_det.set_pad_param(True, get_pad_one_side_param([kmodel_input_shape_det[3],kmodel_input_shape_det[2]], [OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH]), 0, [0, 0, 0]) + ai2d_det.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + global ai2d_output_tensor_det + data = np.ones(kmodel_input_shape_det, dtype=np.uint8) + ai2d_output_tensor_det = nn.from_numpy(data) + global ai2d_builder_det + ai2d_builder_det = ai2d_det.build([1, 3, OUT_RGB888P_HEIGH, OUT_RGB888P_WIDTH], [1, 3, kmodel_input_shape_det[2], kmodel_input_shape_det[3]]) + + +# 检测步骤的ai2d 运行，完成ai2d_init_det预设的预处理 +def ai2d_run_det(rgb888p_img): + with ScopedTiming("ai2d_run_det",debug_mode > 0): + global ai2d_input_tensor_det,ai2d_builder_det,ai2d_input_det + ai2d_input_det = rgb888p_img.to_numpy_ref() + ai2d_input_tensor_det = nn.from_numpy(ai2d_input_det) + global ai2d_output_tensor_det + ai2d_builder_det.run(ai2d_input_tensor_det, ai2d_output_tensor_det) + +# 识别步骤ai2d初始化 +def ai2d_init_rec(): + with ScopedTiming("ai2d_init_res",debug_mode > 0): + global ai2d_rec,ai2d_output_tensor_rec + ai2d_rec = nn.ai2d() + ai2d_rec.set_dtype(nn.ai2d_format.RGB_packed, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d_out_data = np.ones((1, 3, kmodel_input_shape_rec[2], kmodel_input_shape_rec[3]), dtype=np.uint8) + ai2d_output_tensor_rec = nn.from_numpy(ai2d_out_data) + + +# 识别步骤ai2d运行 +def ai2d_run_rec(rgb888p_img): + with ScopedTiming("ai2d_run_rec",debug_mode > 0): + global ai2d_rec,ai2d_builder_rec,ai2d_input_tensor_rec,ai2d_output_tensor_rec + ai2d_rec.set_pad_param(True, get_pad_one_side_param([kmodel_input_shape_rec[3],kmodel_input_shape_rec[2]],[rgb888p_img.shape[2],rgb888p_img.shape[1]]), 0, [0, 0, 0]) + ai2d_rec.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + ai2d_builder_rec = ai2d_rec.build([rgb888p_img.shape[0], rgb888p_img.shape[1], rgb888p_img.shape[2],rgb888p_img.shape[3]], + [1, 3, kmodel_input_shape_rec[2], kmodel_input_shape_rec[3]]) + ai2d_input_tensor_rec = nn.from_numpy(rgb888p_img) + ai2d_builder_rec.run(ai2d_input_tensor_rec, ai2d_output_tensor_rec) + +# 检测步骤ai2d释放内存 +def ai2d_release_det(): + with ScopedTiming("ai2d_release_det",debug_mode > 0): + global ai2d_input_tensor_det + del ai2d_input_tensor_det + +# 识别步骤ai2d释放内存 +def ai2d_release_rec(): + with ScopedTiming("ai2d_release_rec",debug_mode > 0): + global ai2d_input_tensor_rec + del ai2d_input_tensor_rec + +# 检测步骤kpu初始化 +def kpu_init_det(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("kpu_init_det",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + ai2d_init_det() + return kpu_obj + +# 识别步骤kpu初始化 +def kpu_init_rec(kmodel_file): + # init kpu and load kmodel + with ScopedTiming("kpu_init_rec",debug_mode > 0): + kpu_obj = nn.kpu() + kpu_obj.load_kmodel(kmodel_file) + ai2d_init_rec() + return kpu_obj + +# 检测步骤预处理，调用ai2d_run_det实现，并将ai2d的输出设置为kmodel的输入 +def kpu_pre_process_det(rgb888p_img): + ai2d_run_det(rgb888p_img) + with ScopedTiming("kpu_pre_process_det",debug_mode > 0): + global current_kmodel_obj,ai2d_output_tensor_det + # set kpu input + current_kmodel_obj.set_input_tensor(0, ai2d_output_tensor_det) + +# 识别步骤预处理，调用ai2d_init_run_rec实现，并将ai2d的输出设置为kmodel的输入 +def kpu_pre_process_rec(rgb888p_img): + ai2d_run_rec(rgb888p_img) + with ScopedTiming("kpu_pre_process_rec",debug_mode > 0): + global current_kmodel_obj,ai2d_output_tensor_rec + # set kpu input + current_kmodel_obj.set_input_tensor(0, ai2d_output_tensor_rec) + + +# 获取kmodel的输出 +def kpu_get_output(): + with ScopedTiming("kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data + results.append(result) + return results + +# 检测步骤kpu运行 +def kpu_run_det(kpu_obj,rgb888p_img): + # kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + #（1）原图像预处理并设置模型输入 + kpu_pre_process_det(rgb888p_img) + #（2）kpu推理 + with ScopedTiming("kpu_run_det",debug_mode > 0): + # 检测运行 + kpu_obj.run() + #（3）检测释放ai2d资源 + ai2d_release_det() + #（4）获取检测kpu输出 + results = kpu_get_output() + #（5）CHW转HWC + global ai2d_input_det + tmp = (ai2d_input_det.shape[0], ai2d_input_det.shape[1], ai2d_input_det.shape[2]) + ai2d_input_det = ai2d_input_det.reshape((ai2d_input_det.shape[0], ai2d_input_det.shape[1] * ai2d_input_det.shape[2])) + ai2d_input_det = ai2d_input_det.transpose() + tmp2 = ai2d_input_det.copy() + tmp2 = tmp2.reshape((tmp[1], tmp[2], tmp[0])) + #（6）后处理，aicube.ocr_post_process接口说明： + # 接口：aicube.ocr_post_process(threshold_map,ai_isp,kmodel_input_shape,isp_shape,mask_threshold,box_threshold); + # 参数说明： + # threshold_map: DBNet模型的输出为（N,kmodel_input_shape_det[2],kmodel_input_shape_det[3],2），两个通道分别为threshold map和segmentation map + # 后处理过程只使用threshold map，因此将results[0][:,:,:,0] reshape成一维传给接口使用。 + # ai_isp：后处理还会返回基于原图的检测框裁剪数据，因此要将原图数据reshape为一维传给接口处理。 + # kmodel_input_shape：kmodel输入分辨率。 + # isp_shape：AI原图分辨率。要将kmodel输出分辨率的检测框坐标映射到原图分辨率上，需要使用这两个分辨率的值。 + # mask_threshold：用于二值化图像获得文本区域。 + # box_threshold：检测框分数阈值，低于该阈值的检测框不计入结果。 + with ScopedTiming("kpu_post",debug_mode > 0): + # 调用aicube模块的ocr_post_process完成ocr检测的后处理 + # det_results结构为[[crop_array_nhwc,[p1_x,p1_y,p2_x,p2_y,p3_x,p3_y,p4_x,p4_y]],...] + det_results = aicube.ocr_post_process(results[0][:, :, :, 0].reshape(-1), tmp2.reshape(-1), + [kmodel_input_shape_det[3], kmodel_input_shape_det[2]], + [OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH], mask_threshold, box_threshold) + return det_results + +# 识别步骤后处理 +def kpu_run_rec(kpu_obj,rgb888p_img): + # kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + #（1）识别预处理并设置模型输入 + kpu_pre_process_rec(rgb888p_img) + #（2）kpu推理 + with ScopedTiming("kpu_run_rec",debug_mode > 0): + # 识别运行 + kpu_obj.run() + #（3）识别释放ai2d资源 + ai2d_release_rec() + #（4）获取识别kpu输出 + results = kpu_get_output() + #（5）识别后处理，results结构为[(N,MAX_LENGTH,DICT_LENGTH),...],在axis=2维度上取argmax获取当前识别字符在字典中的索引 + preds = np.argmax(results[0], axis=2).reshape((-1)) + output_txt = "" + for i in range(len(preds)): + # 当前识别字符不是字典的最后一个字符并且和前一个字符不重复（去重），加入识别结果字符串 + if preds[i] != (len(DICT) - 1) and (not (i > 0 and preds[i - 1] == preds[i])): + output_txt = output_txt + DICT[preds[i]] + return output_txt + +# 释放检测步骤kpu、ai2d以及ai2d相关的tensor +def kpu_deinit_det(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d_det,ai2d_output_tensor_det + del kpu_obj + del ai2d_det + del ai2d_output_tensor_det + +# 释放识别步骤kpu +def kpu_deinit_rec(kpu_obj): + with ScopedTiming("kpu_deinit",debug_mode > 0): + global ai2d_rec,ai2d_output_tensor_rec + del kpu_obj + del ai2d_rec + del ai2d_output_tensor_rec + + +#********************for media_utils.py******************** + +global draw_img,osd_img #for display +global buffer,media_source,media_sink #for media + +# display初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# 释放display +def display_deinit(): + display.deinit() + +# display显示检测识别框 +def display_draw(det_results): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + if det_results: + draw_img.clear() + # 循环绘制所有检测到的框 + for j in det_results: + # 将原图的坐标点转换成显示的坐标点，循环绘制四条直线，得到一个矩形框 + for i in range(4): + x1 = j[1][(i * 2)] / OUT_RGB888P_WIDTH * DISPLAY_WIDTH + y1 = j[1][(i * 2 + 1)] / OUT_RGB888P_HEIGH * DISPLAY_HEIGHT + x2 = j[1][((i + 1) * 2) % 8] / OUT_RGB888P_WIDTH * DISPLAY_WIDTH + y2 = j[1][((i + 1) * 2 + 1) % 8] / OUT_RGB888P_HEIGH * DISPLAY_HEIGHT + draw_img.draw_line((int(x1), int(y1), int(x2), int(y2)), color=(255, 0, 0, 255), + thickness=5) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +# camera初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + # camera获取的通道0图像送display显示 + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + # camera获取的通道2图像送ai处理 + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 启动视频流 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 捕获一帧图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 释放内存 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 停止视频流 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放buffer，销毁link +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +def ocr_rec_inference(): + print("ocr_rec_test start") + kpu_ocr_det = kpu_init_det(kmodel_file_det) # 创建OCR检测kpu对象 + kpu_ocr_rec = kpu_init_rec(kmodel_file_rec) # 创建OCR识别kpu对象 + camera_init(CAM_DEV_ID_0) # camera初始化 + display_init() # display初始化 + rgb888p_img = None + try: + ret = media_init() + if ret: + print("ocr_rec_test, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图像 + if rgb888p_img == -1: + print("ocr_rec_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + det_results = kpu_run_det(kpu_ocr_det,rgb888p_img) # kpu运行获取OCR检测kmodel的推理输出 + ocr_results="" + if det_results: + for j in det_results: + ocr_result = kpu_run_rec(kpu_ocr_rec,j[0]) # j[0]为检测框的裁剪部分，kpu运行获取OCR识别kmodel的推理输出 + ocr_results = ocr_results+" ["+ocr_result+"] " + print("\n"+ocr_results) + display_draw(det_results) + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + # gc.collect() + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止camera + display_deinit() # 释放display + kpu_deinit_det(kpu_ocr_det) # 释放OCR检测步骤kpu + kpu_deinit_rec(kpu_ocr_rec) # 释放OCR识别步骤kpu + gc.collect() + time.sleep(1) + ret = media_deinit() # 释放整个media + if ret: + print("ocr_rec_test, buffer_deinit failed") + return ret + + print("ocr_rec_test end") + return 0 + +if __name__ == '__main__': + ocr_rec_inference() +``` + +### 8. 手掌关键点检测 + +```python +import aicube #aicube模块，封装检测分割等任务相关后处理 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 + +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 + +import time #时间统计 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 + +import gc #垃圾回收模块 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +##ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) +OUT_RGB888P_HEIGH = 1080 + +#--------for hand detection---------- +#kmodel输入shape +hd_kmodel_input_shape = (1,3,512,512) # 手掌检测kmodel输入分辨率 + +#kmodel相关参数设置 +confidence_threshold = 0.2 # 手掌检测阈值，用于过滤roi +nms_threshold = 0.5 # 手掌检测框阈值，用于过滤重复roi +hd_kmodel_frame_size = [512,512] # 手掌检测输入图片尺寸 +hd_frame_size = [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH] # 手掌检测直接输入图片尺寸 +strides = [8,16,32] # 输出特征图的尺寸与输入图片尺寸的比 +num_classes = 1 # 手掌检测模型输出类别数 +nms_option = False # 是否所有检测框一起做NMS，False则按照不同的类分别应用NMS + +root_dir = '/sdcard/app/tests/' +hd_kmodel_file = root_dir + "kmodel/hand_det.kmodel" # 手掌检测kmodel文件的路径 +anchors = [26,27, 53,52, 75,71, 80,99, 106,82, 99,134, 140,113, 161,172, 245,276] #anchor设置 + +#--------for hand keypoint detection---------- +#kmodel输入shape +hk_kmodel_input_shape = (1,3,256,256) # 手掌关键点检测kmodel输入分辨率 + +#kmodel相关参数设置 +hk_kmodel_frame_size = [256,256] # 手掌关键点检测输入图片尺寸 +hk_kmodel_file = root_dir + 'kmodel/handkp_det.kmodel' # 手掌关键点检测kmodel文件的路径 + +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#ai_utils.py +global current_kmodel_obj # 定义全局的 kpu 对象 +global hd_ai2d,hd_ai2d_input_tensor,hd_ai2d_output_tensor,hd_ai2d_builder # 定义手掌检测全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder +global hk_ai2d,hk_ai2d_input_tensor,hk_ai2d_output_tensor,hk_ai2d_builder # 定义手掌关键点检测全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder + +#-------hand detect--------: +# 手掌检测ai2d 初始化 +def hd_ai2d_init(): + with ScopedTiming("hd_ai2d_init",debug_mode > 0): + global hd_ai2d + global hd_ai2d_builder + global hd_ai2d_output_tensor + # 计算padding值 + ori_w = OUT_RGB888P_WIDTH + ori_h = OUT_RGB888P_HEIGH + width = hd_kmodel_frame_size[0] + height = hd_kmodel_frame_size[1] + ratiow = float(width) / ori_w + ratioh = float(height) / ori_h + if ratiow < ratioh: + ratio = ratiow + else: + ratio = ratioh + new_w = int(ratio * ori_w) + new_h = int(ratio * ori_h) + dw = float(width - new_w) / 2 + dh = float(height - new_h) / 2 + top = int(round(dh - 0.1)) + bottom = int(round(dh + 0.1)) + left = int(round(dw - 0.1)) + right = int(round(dw - 0.1)) + + # init kpu and load kmodel + hd_ai2d = nn.ai2d() + hd_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + hd_ai2d.set_pad_param(True, [0,0,0,0,top,bottom,left,right], 0, [114,114,114]) + hd_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel ) + hd_ai2d_builder = hd_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,height,width]) + data = np.ones(hd_kmodel_input_shape, dtype=np.uint8) + hd_ai2d_output_tensor = nn.from_numpy(data) + +# 手掌检测 ai2d 运行 +def hd_ai2d_run(rgb888p_img): + with ScopedTiming("hd_ai2d_run",debug_mode > 0): + global hd_ai2d_input_tensor,hd_ai2d_output_tensor + hd_ai2d_input = rgb888p_img.to_numpy_ref() + hd_ai2d_input_tensor = nn.from_numpy(hd_ai2d_input) + + hd_ai2d_builder.run(hd_ai2d_input_tensor, hd_ai2d_output_tensor) + +# 手掌检测 ai2d 释放内存 +def hd_ai2d_release(): + with ScopedTiming("hd_ai2d_release",debug_mode > 0): + global hd_ai2d_input_tensor + del hd_ai2d_input_tensor + +# 手掌检测 kpu 初始化 +def hd_kpu_init(hd_kmodel_file): + # init kpu and load kmodel + with ScopedTiming("hd_kpu_init",debug_mode > 0): + hd_kpu_obj = nn.kpu() + hd_kpu_obj.load_kmodel(hd_kmodel_file) + + hd_ai2d_init() + return hd_kpu_obj + +# 手掌检测 kpu 输入预处理 +def hd_kpu_pre_process(rgb888p_img): + hd_ai2d_run(rgb888p_img) + with ScopedTiming("hd_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,hd_ai2d_output_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, hd_ai2d_output_tensor) + +# 手掌检测 kpu 获得 kmodel 输出 +def hd_kpu_get_output(): + with ScopedTiming("hd_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + result = result.reshape((result.shape[0]*result.shape[1]*result.shape[2]*result.shape[3])) + tmp2 = result.copy() + del result + results.append(tmp2) + return results + +# 手掌检测 kpu 运行 +def hd_kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1)原图预处理，并设置模型输入 + hd_kpu_pre_process(rgb888p_img) + # (2)手掌检测 kpu 运行 + with ScopedTiming("hd_kpu_run",debug_mode > 0): + current_kmodel_obj.run() + # (3)释放手掌检测 ai2d 资源 + hd_ai2d_release() + # (4)获取手掌检测 kpu 输出 + results = hd_kpu_get_output() + # (5)手掌检测 kpu 结果后处理 + dets = aicube.anchorbasedet_post_process( results[0], results[1], results[2], hd_kmodel_frame_size, hd_frame_size, strides, num_classes, confidence_threshold, nms_threshold, anchors, nms_option) + # (6)返回手掌检测结果 + return dets + +# 手掌检测 kpu 释放内存 +def hd_kpu_deinit(kpu_obj): + with ScopedTiming("hd_kpu_deinit",debug_mode > 0): + global hd_ai2d, hd_ai2d_output_tensor + del kpu_obj + del hd_ai2d + del hd_ai2d_output_tensor + +#-------hand keypoint detection------: +# 手掌关键点检测 ai2d 初始化 +def hk_ai2d_init(): + with ScopedTiming("hk_ai2d_init",debug_mode > 0): + global hk_ai2d, hk_ai2d_output_tensor + hk_ai2d = nn.ai2d() + hk_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + data = np.ones(hk_kmodel_input_shape, dtype=np.uint8) + hk_ai2d_output_tensor = nn.from_numpy(data) + +# 手掌关键点检测 ai2d 运行 +def hk_ai2d_run(rgb888p_img, x, y, w, h): + with ScopedTiming("hk_ai2d_run",debug_mode > 0): + global hk_ai2d,hk_ai2d_input_tensor,hk_ai2d_output_tensor + hk_ai2d_input = rgb888p_img.to_numpy_ref() + hk_ai2d_input_tensor = nn.from_numpy(hk_ai2d_input) + + hk_ai2d.set_crop_param(True, x, y, w, h) + hk_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel ) + + global hk_ai2d_builder + hk_ai2d_builder = hk_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,hk_kmodel_frame_size[1],hk_kmodel_frame_size[0]]) + hk_ai2d_builder.run(hk_ai2d_input_tensor, hk_ai2d_output_tensor) + +# 手掌关键点检测 ai2d 释放内存 +def hk_ai2d_release(): + with ScopedTiming("hk_ai2d_release",debug_mode > 0): + global hk_ai2d_input_tensor + del hk_ai2d_input_tensor + +# 手掌关键点检测 kpu 初始化 +def hk_kpu_init(hk_kmodel_file): + # init kpu and load kmodel + with ScopedTiming("hk_kpu_init",debug_mode > 0): + hk_kpu_obj = nn.kpu() + hk_kpu_obj.load_kmodel(hk_kmodel_file) + + hk_ai2d_init() + return hk_kpu_obj + +# 手掌关键点检测 kpu 输入预处理 +def hk_kpu_pre_process(rgb888p_img, x, y, w, h): + hk_ai2d_run(rgb888p_img, x, y, w, h) + with ScopedTiming("hk_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,hk_ai2d_output_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, hk_ai2d_output_tensor) + +# 手掌关键点检测 kpu 获得 kmodel 输出 +def hk_kpu_get_output(): + with ScopedTiming("hk_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + + result = result.reshape((result.shape[0]*result.shape[1])) + tmp2 = result.copy() + del result + results.append(tmp2) + return results + +# 手掌关键点检测 kpu 运行 +def hk_kpu_run(kpu_obj,rgb888p_img, x, y, w, h): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1)原图预处理，并设置模型输入 + hk_kpu_pre_process(rgb888p_img, x, y, w, h) + # (2)手掌关键点检测 kpu 运行 + with ScopedTiming("hk_kpu_run",debug_mode > 0): + current_kmodel_obj.run() + # (3)释放手掌关键点检测 ai2d 资源 + hk_ai2d_release() + # (4)获取手掌关键点检测 kpu 输出 + results = hk_kpu_get_output() + # (5)返回手掌关键点检测结果 + return results + +# 手掌关键点检测 kpu 释放内存 +def hk_kpu_deinit(kpu_obj): + with ScopedTiming("hk_kpu_deinit",debug_mode > 0): + global hk_ai2d, hk_ai2d_output_tensor + del kpu_obj + del hk_ai2d + del hk_ai2d_output_tensor + +#media_utils.py +global draw_img,osd_img,masks #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +# display 作图过程 标出检测到的21个关键点并用不同颜色的线段连接 +def display_draw(results, x, y, w, h): + with ScopedTiming("display_draw",debug_mode >0): + global draw_img,osd_img + + if results: + results_show = np.zeros(results.shape,dtype=np.int16) + results_show[0::2] = results[0::2] * w + x + results_show[1::2] = results[1::2] * h + y + for i in range(len(results_show)/2): + draw_img.draw_circle(results_show[i*2], results_show[i*2+1], 1, color=(255, 0, 255, 0),fill=False) + for i in range(5): + j = i*8 + if i==0: + R = 255; G = 0; B = 0 + if i==1: + R = 255; G = 0; B = 255 + if i==2: + R = 255; G = 255; B = 0 + if i==3: + R = 0; G = 255; B = 0 + if i==4: + R = 0; G = 0; B = 255 + draw_img.draw_line(results_show[0], results_show[1],results_show[j+2], results_show[j+3],color=(255,R,G,B)) + draw_img.draw_line(results_show[j+2], results_show[j+3],results_show[j+4], results_show[j+5],color=(255,R,G,B)) + draw_img.draw_line(results_show[j+4], results_show[j+5],results_show[j+6], results_show[j+7],color=(255,R,G,B)) + draw_img.draw_line(results_show[j+6], results_show[j+7],results_show[j+8], results_show[j+9],color=(255,R,G,B)) + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +#**********for hand_keypoint_detect.py********** +def hand_keypoint_detect_inference(): + print("hand_keypoint_detect_test start") + kpu_hand_detect = hd_kpu_init(hd_kmodel_file) # 创建手掌检测的 kpu 对象 + kpu_hand_keypoint_detect = hk_kpu_init(hk_kmodel_file) # 创建手掌关键点检测的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("hand_detect_test, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图片 + if rgb888p_img == -1: + print("hand_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + dets = hd_kpu_run(kpu_hand_detect,rgb888p_img) # 执行手掌检测 kpu 运行 以及 后处理过程 + draw_img.clear() + + for det_box in dets: + x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5] + w = int(float(x2 - x1) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + h = int(float(y2 - y1) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + + x1 = int(x1 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y1 = int(y1 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + x2 = int(x2 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y2 = int(y2 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + + if (h<(0.1*DISPLAY_HEIGHT)): + continue + if (w<(0.25*DISPLAY_WIDTH) and ((x1<(0.03*DISPLAY_WIDTH)) or (x2>(0.97*DISPLAY_WIDTH)))): + continue + if (w<(0.15*DISPLAY_WIDTH) and ((x1<(0.01*DISPLAY_WIDTH)) or (x2>(0.99*DISPLAY_WIDTH)))): + continue + + length = max(w,h)/2 + cx = (x1+x2)/2 + cy = (y1+y2)/2 + ratio_num = 1.26*length + + x1_kp = int(max(0,cx-ratio_num)) + y1_kp = int(max(0,cy-ratio_num)) + x2_kp = int(min(DISPLAY_WIDTH-1, cx+ratio_num)) + y2_kp = int(min(DISPLAY_HEIGHT-1, cy+ratio_num)) + w_kp = int(x2_kp - x1_kp + 1) + h_kp = int(y2_kp - y1_kp + 1) + + hk_results = hk_kpu_run(kpu_hand_keypoint_detect,rgb888p_img, x1_kp, y1_kp, w_kp, h_kp) # 执行手掌关键点检测 kpu 运行 以及 后处理过程 + + draw_img.draw_rectangle(x1, y1, w, h, color=(255, 0, 255, 0)) # 将得到的手掌检测结果 绘制到 display + display_draw(hk_results[0], x1_kp, y1_kp, w_kp, h_kp) # 将得到的手掌关键点检测结果 绘制到 display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图像 + rgb888p_img = None + #gc.collect() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止 camera + display_deinit() # 释放 display + hd_kpu_deinit(kpu_hand_detect) # 释放手掌检测 kpu + hk_kpu_deinit(kpu_hand_keypoint_detect) # 释放手掌关键点检测 kpu + gc.collect() + ret = media_deinit() # 释放 整个media + if ret: + print("hand_detect_test, buffer_deinit failed") + return ret + + print("hand_detect_test end") + return 0 + +if __name__ == '__main__': + hand_keypoint_detect_inference() +``` + +### 9. 静态手势识别 + +```python +import aicube #aicube模块，封装检测分割等任务相关后处理 +from media.camera import * #摄像头模块 +from media.display import * #显示模块 +from media.media import * #软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 + +import nncase_runtime as nn #nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +import ulab.numpy as np #类似python numpy操作，但也会有一些接口不同 + +import time #时间统计 +import image #图像模块，主要用于读取、图像绘制元素（框、点等）等操作 + +import gc #垃圾回收模块 + +##config.py +#display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +##ai原图分辨率输入 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) +OUT_RGB888P_HEIGH = 1080 + +#--------for hand detection---------- +#kmodel输入shape +hd_kmodel_input_shape = (1,3,512,512) # 手掌检测kmodel输入分辨率 + +#kmodel相关参数设置 +confidence_threshold = 0.2 # 手掌检测阈值，用于过滤roi +nms_threshold = 0.5 # 手掌检测框阈值，用于过滤重复roi +hd_kmodel_frame_size = [512,512] # 手掌检测输入图片尺寸 +hd_frame_size = [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH] # 手掌检测直接输入图片尺寸 +strides = [8,16,32] # 输出特征图的尺寸与输入图片尺寸的比 +num_classes = 1 # 手掌检测模型输出类别数 +nms_option = False # 是否所有检测框一起做NMS，False则按照不同的类分别应用NMS + +root_dir = '/sdcard/app/tests/' +hd_kmodel_file = root_dir + 'kmodel/hand_det.kmodel' # 手掌检测kmodel文件的路径 +anchors = [26,27, 53,52, 75,71, 80,99, 106,82, 99,134, 140,113, 161,172, 245,276] #anchor设置 + +#--------for hand recognition---------- +#kmodel输入shape +hr_kmodel_input_shape = (1,3,224,224) # 手势识别kmodel输入分辨率 + +#kmodel相关参数设置 +hr_kmodel_frame_size = [224,224] # 手势识别输入图片尺寸 +labels = ["gun","other","yeah","five"] # 模型输出类别名称 + +hr_kmodel_file = root_dir + "kmodel/hand_reco.kmodel" # 手势识别kmodel文件的路径 + +debug_mode = 0 # debug模式 大于0（调试）、 反之 （不调试） + +#scoped_timing.py 用于debug模式输出程序块运行时间 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#ai_utils.py +global current_kmodel_obj # 定义全局的 kpu 对象 +global hd_ai2d,hd_ai2d_input_tensor,hd_ai2d_output_tensor,hd_ai2d_builder # 定义手掌检测全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder +global hr_ai2d,hr_ai2d_input_tensor,hr_ai2d_output_tensor,hr_ai2d_builder # 定义手势识别全局 ai2d 对象，并且定义 ai2d 的输入、输出 以及 builder + +#-------hand detect--------: +# 手掌检测 ai2d 初始化 +def hd_ai2d_init(): + with ScopedTiming("hd_ai2d_init",debug_mode > 0): + global hd_ai2d + global hd_ai2d_builder + global hd_ai2d_output_tensor + # 计算padding值 + ori_w = OUT_RGB888P_WIDTH + ori_h = OUT_RGB888P_HEIGH + width = hd_kmodel_frame_size[0] + height = hd_kmodel_frame_size[1] + ratiow = float(width) / ori_w + ratioh = float(height) / ori_h + if ratiow < ratioh: + ratio = ratiow + else: + ratio = ratioh + new_w = int(ratio * ori_w) + new_h = int(ratio * ori_h) + dw = float(width - new_w) / 2 + dh = float(height - new_h) / 2 + top = int(round(dh - 0.1)) + bottom = int(round(dh + 0.1)) + left = int(round(dw - 0.1)) + right = int(round(dw - 0.1)) + + # init kpu and load kmodel + hd_ai2d = nn.ai2d() + hd_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + hd_ai2d.set_pad_param(True, [0,0,0,0,top,bottom,left,right], 0, [114,114,114]) + hd_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel ) + hd_ai2d_builder = hd_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,height,width]) + data = np.ones(hd_kmodel_input_shape, dtype=np.uint8) + hd_ai2d_output_tensor = nn.from_numpy(data) + +# 手掌检测 ai2d 运行 +def hd_ai2d_run(rgb888p_img): + with ScopedTiming("hd_ai2d_run",debug_mode > 0): + global hd_ai2d_input_tensor,hd_ai2d_output_tensor + hd_ai2d_input = rgb888p_img.to_numpy_ref() + hd_ai2d_input_tensor = nn.from_numpy(hd_ai2d_input) + + hd_ai2d_builder.run(hd_ai2d_input_tensor, hd_ai2d_output_tensor) + +# 手掌检测 ai2d 释放内存 +def hd_ai2d_release(): + with ScopedTiming("hd_ai2d_release",debug_mode > 0): + global hd_ai2d_input_tensor + del hd_ai2d_input_tensor + +# 手掌检测 kpu 初始化 +def hd_kpu_init(hd_kmodel_file): + # init kpu and load kmodel + with ScopedTiming("hd_kpu_init",debug_mode > 0): + hd_kpu_obj = nn.kpu() + hd_kpu_obj.load_kmodel(hd_kmodel_file) + + hd_ai2d_init() + return hd_kpu_obj + +# 手掌检测 kpu 输入预处理 +def hd_kpu_pre_process(rgb888p_img): + hd_ai2d_run(rgb888p_img) + with ScopedTiming("hd_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,hd_ai2d_output_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, hd_ai2d_output_tensor) + +# 手掌检测 kpu 获得 kmodel 输出 +def hd_kpu_get_output(): + with ScopedTiming("hd_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + result = result.reshape((result.shape[0]*result.shape[1]*result.shape[2]*result.shape[3])) + tmp2 = result.copy() + del result + results.append(tmp2) + return results + +# 手掌检测 kpu 运行 +def hd_kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1)原图预处理，并设置模型输入 + hd_kpu_pre_process(rgb888p_img) + # (2)手掌检测 kpu 运行 + with ScopedTiming("hd_kpu_run",debug_mode > 0): + current_kmodel_obj.run() + # (3)释放手掌检测 ai2d 资源 + hd_ai2d_release() + # (4)获取手掌检测 kpu 输出 + results = hd_kpu_get_output() + # (5)手掌检测 kpu 结果后处理 + dets = aicube.anchorbasedet_post_process( results[0], results[1], results[2], hd_kmodel_frame_size, hd_frame_size, strides, num_classes, confidence_threshold, nms_threshold, anchors, nms_option) + # (6)返回手掌检测结果 + return dets + +# 手掌检测 kpu 释放内存 +def hd_kpu_deinit(kpu_obj): + with ScopedTiming("hd_kpu_deinit",debug_mode > 0): + global hd_ai2d, hd_ai2d_output_tensor + del kpu_obj + del hd_ai2d + del hd_ai2d_output_tensor + +#-------hand recognition--------: +# 手势识别 ai2d 初始化 +def hr_ai2d_init(): + with ScopedTiming("hr_ai2d_init",debug_mode > 0): + global hr_ai2d, hr_ai2d_output_tensor + hr_ai2d = nn.ai2d() + hr_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + data = np.ones(hr_kmodel_input_shape, dtype=np.uint8) + hr_ai2d_output_tensor = nn.from_numpy(data) + +# 手势识别 ai2d 运行 +def hr_ai2d_run(rgb888p_img, x, y, w, h): + with ScopedTiming("hr_ai2d_run",debug_mode > 0): + global hr_ai2d,hr_ai2d_input_tensor,hr_ai2d_output_tensor + hr_ai2d_input = rgb888p_img.to_numpy_ref() + hr_ai2d_input_tensor = nn.from_numpy(hr_ai2d_input) + + hr_ai2d.set_crop_param(True, x, y, w, h) + hr_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel ) + + global hr_ai2d_builder + hr_ai2d_builder = hr_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,hr_kmodel_frame_size[1],hr_kmodel_frame_size[0]]) + hr_ai2d_builder.run(hr_ai2d_input_tensor, hr_ai2d_output_tensor) + +# 手势识别 ai2d 释放内存 +def hr_ai2d_release(): + with ScopedTiming("hr_ai2d_release",debug_mode > 0): + global hr_ai2d_input_tensor + del hr_ai2d_input_tensor + +# 手势识别 kpu 初始化 +def hr_kpu_init(hr_kmodel_file): + # init kpu and load kmodel + with ScopedTiming("hr_kpu_init",debug_mode > 0): + hr_kpu_obj = nn.kpu() + hr_kpu_obj.load_kmodel(hr_kmodel_file) + + hr_ai2d_init() + return hr_kpu_obj + +# 手势识别 kpu 输入预处理 +def hr_kpu_pre_process(rgb888p_img, x, y, w, h): + hr_ai2d_run(rgb888p_img, x, y, w, h) + with ScopedTiming("hr_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,hr_ai2d_output_tensor + # set kpu input + current_kmodel_obj.set_input_tensor(0, hr_ai2d_output_tensor) + +# 手势识别 kpu 获得 kmodel 输出 +def hr_kpu_get_output(): + with ScopedTiming("hr_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + + result = result.reshape((result.shape[0]*result.shape[1])) + tmp2 = result.copy() + del result + results.append(tmp2) + return results + +# softmax实现 +def softmax(x): + x -= np.max(x) + x = np.exp(x) / np.sum(np.exp(x)) + return x + +# 手势识别 kpu 输出后处理 +def hr_kpu_post_process(results): + x_softmax = softmax(results[0]) + result = np.argmax(x_softmax) + text = " " + labels[result] + ": " + str(round(x_softmax[result],2)) + return text + +# 手势识别 kpu 运行 +def hr_kpu_run(kpu_obj,rgb888p_img, x, y, w, h): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # (1)原图预处理，并设置模型输入 + hr_kpu_pre_process(rgb888p_img, x, y, w, h) + # (2)手势识别 kpu 运行 + with ScopedTiming("hr_kpu_run",debug_mode > 0): + current_kmodel_obj.run() + # (3)释放手势识别 ai2d 资源 + hr_ai2d_release() + # (4)获取手势识别 kpu 输出 + results = hr_kpu_get_output() + # (5)手势识别 kpu 结果后处理 + result = hr_kpu_post_process(results) + # (6)返回手势识别结果 + return result + +# 手势识别 kpu 释放内存 +def hr_kpu_deinit(kpu_obj): + with ScopedTiming("hr_kpu_deinit",debug_mode > 0): + global hr_ai2d, hr_ai2d_output_tensor + del kpu_obj + del hr_ai2d + del hr_ai2d_output_tensor + +#media_utils.py +global draw_img,osd_img,masks #for display 定义全局 作图image对象 +global buffer,media_source,media_sink #for media 定义 media 程序中的中间存储对象 + +#for display 初始化 +def display_init(): + # use hdmi for display + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +# display 释放内存 +def display_deinit(): + display.deinit() + +#for camera 初始化 +def camera_init(dev_id): + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +# camera 开启 +def camera_start(dev_id): + camera.start_stream(dev_id) + +# camera 读取图像 +def camera_read(dev_id): + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +# camera 图像释放 +def camera_release_image(dev_id,rgb888p_img): + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +# camera 结束 +def camera_stop(dev_id): + camera.stop_stream(dev_id) + +#for media 初始化 +def media_init(): + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 图层1，用于画框 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + # 图层2，用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +# media 释放内存 +def media_deinit(): + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +#**********for hand_recognition.py********** +def hand_recognition_inference(): + print("hand_recognition start") + kpu_hand_detect = hd_kpu_init(hd_kmodel_file) # 创建手掌检测的 kpu 对象 + kpu_hand_recognition = hr_kpu_init(hr_kmodel_file) # 创建手势识别的 kpu 对象 + camera_init(CAM_DEV_ID_0) # 初始化 camera + display_init() # 初始化 display + + rgb888p_img = None + try: + ret = media_init() + if ret: + print("hand_recognition_test, buffer init failed") + return ret + + camera_start(CAM_DEV_ID_0) + while True: + with ScopedTiming("total",1): + rgb888p_img = camera_read(CAM_DEV_ID_0) # 读取一帧图片 + if rgb888p_img == -1: + print("hand_recognition_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + dets = hd_kpu_run(kpu_hand_detect,rgb888p_img) # 执行手掌检测 kpu 运行 以及 后处理过程 + draw_img.clear() + + for det_box in dets: + x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5] + w = int(float(x2 - x1) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + h = int(float(y2 - y1) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + + x1 = int(x1 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y1 = int(y1 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + x2 = int(x2 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH) + y2 = int(y2 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH) + + if (h<(0.1*DISPLAY_HEIGHT)): + continue + if (w<(0.25*DISPLAY_WIDTH) and ((x1<(0.03*DISPLAY_WIDTH)) or (x2>(0.97*DISPLAY_WIDTH)))): + continue + if (w<(0.15*DISPLAY_WIDTH) and ((x1<(0.01*DISPLAY_WIDTH)) or (x2>(0.99*DISPLAY_WIDTH)))): + continue + + length = max(w,h)/2 + cx = (x1+x2)/2 + cy = (y1+y2)/2 + ratio_num = 1.1*length + + x1_kp = int(max(0,cx-ratio_num)) + y1_kp = int(max(0,cy-ratio_num)) + x2_kp = int(min(DISPLAY_WIDTH-1, cx+ratio_num)) + y2_kp = int(min(DISPLAY_HEIGHT-1, cy+ratio_num)) + w_kp = int(x2_kp - x1_kp + 1) + h_kp = int(y2_kp - y1_kp + 1) + + hr_results = hr_kpu_run(kpu_hand_recognition,rgb888p_img, x1_kp, y1_kp, w_kp, h_kp) # 执行手势识别 kpu 运行 以及 后处理过程 + + draw_img.draw_rectangle(x1, y1, w, h, color=(255, 0, 255, 0)) # 将得到的检测结果 绘制到 display + draw_img.draw_string( x1 , y1-50, hr_results, color=(255,0, 255, 0), scale=4) # 将得到的识别结果 绘制到 display + + camera_release_image(CAM_DEV_ID_0,rgb888p_img) # camera 释放图像 + rgb888p_img = None + #gc.collect() + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + camera_stop(CAM_DEV_ID_0) # 停止 camera + display_deinit() # 释放 display + hd_kpu_deinit(kpu_hand_detect) # 释放手掌检测 kpu + hr_kpu_deinit(kpu_hand_recognition) # 释放手势识别 kpu + gc.collect() + ret = media_deinit() # 释放 整个media + if ret: + print("hand_recognition_test, buffer_deinit failed") + return ret + + print("hand_recognition_test end") + return 0 + +if __name__ == '__main__': + hand_recognition_inference() +``` + +### 10.人脸mesh + +```python +import ulab.numpy as np # 类似python numpy操作，但也会有一些接口不同 +import nncase_runtime as nn # nncase运行模块，封装了kpu（kmodel推理）和ai2d（图片预处理加速）操作 +from media.camera import * # 摄像头模块 +from media.display import * # 显示模块 +from media.media import * # 软件抽象模块，主要封装媒体数据链路以及媒体缓冲区 +import aidemo # aidemo模块，封装ai demo相关后处理、画图操作 +import image # 图像模块，主要用于读取、图像绘制元素（框、点等）等操作 +import time # 时间统计 +import gc # 垃圾回收模块 +import os # 操作系统接口模块 +import math # 数学模块 + + +#********************for config.py******************** +# display分辨率 +DISPLAY_WIDTH = ALIGN_UP(1920, 16) # 显示宽度要求16位对齐 +DISPLAY_HEIGHT = 1080 + +# ai原图分辨率，sensor默认出图为16:9，若需不形变原图，最好按照16:9比例设置宽高 +OUT_RGB888P_WIDTH = ALIGN_UP(1920, 16) # ai原图宽度要求16位对齐 +OUT_RGB888P_HEIGH = 1080 + +# kmodel参数设置 +# 人脸检测kmodel输入shape +fd_kmodel_input_shape = (1,3,320,320) +# 人脸mesh kmodel输入shape +fm_kmodel_input_shape = (1,3,120,120) +fmpost_kmodel_input_shapes = [(3,3),(3,1),(40,1),(10,1)] +# ai原图padding +rgb_mean = [104,117,123] + +#人脸检测kmodel其它参数设置 +confidence_threshold = 0.5 # 人脸检测阈值 +top_k = 5000 +nms_threshold = 0.2 +keep_top_k = 750 +vis_thres = 0.5 +variance = [0.1, 0.2] +anchor_len = 4200 +score_dim = 2 +det_dim = 4 +keypoint_dim = 10 + +# 文件配置 +# 人脸检测kmodel +root_dir = '/sdcard/app/tests/' +fd_kmodel_file = root_dir + 'kmodel/face_detection_320.kmodel' +# 人脸mesh kmodel +fm_kmodel_file = root_dir + 'kmodel/face_alignment.kmodel' +# 人脸mesh后处理kmodel +fmpost_kmodel_file = root_dir + 'kmodel/face_alignment_post.kmodel' +# anchor文件 +anchors_path = root_dir + 'utils/prior_data_320.bin' +# 人脸mesh参数均值 +param_mean = np.array([0.0003492636315058917,2.52790130161884e-07,-6.875197868794203e-07,60.1679573059082,-6.295513230725192e-07,0.0005757200415246189,-5.085391239845194e-05,74.2781982421875,5.400917189035681e-07,6.574138387804851e-05,0.0003442012530285865,-66.67157745361328,-346603.6875,-67468.234375,46822.265625,-15262.046875,4350.5888671875,-54261.453125,-18328.033203125,-1584.328857421875,-84566.34375,3835.960693359375,-20811.361328125,38094.9296875,-19967.85546875,-9241.3701171875,-19600.71484375,13168.08984375,-5259.14404296875,1848.6478271484375,-13030.662109375,-2435.55615234375,-2254.20654296875,-14396.5615234375,-6176.3291015625,-25621.919921875,226.39447021484375,-6326.12353515625,-10867.2509765625,868.465087890625,-5831.14794921875,2705.123779296875,-3629.417724609375,2043.9901123046875,-2446.6162109375,3658.697021484375,-7645.98974609375,-6674.45263671875,116.38838958740234,7185.59716796875,-1429.48681640625,2617.366455078125,-1.2070955038070679,0.6690792441368103,-0.17760828137397766,0.056725528091192245,0.03967815637588501,-0.13586315512657166,-0.09223993122577667,-0.1726071834564209,-0.015804484486579895,-0.1416848599910736],dtype=np.float) +# 人脸mesh参数方差 +param_std = np.array([0.00017632152594160289,6.737943476764485e-05,0.00044708489440381527,26.55023193359375,0.0001231376954820007,4.493021697271615e-05,7.923670636955649e-05,6.982563018798828,0.0004350444069132209,0.00012314890045672655,0.00017400001524947584,20.80303955078125,575421.125,277649.0625,258336.84375,255163.125,150994.375,160086.109375,111277.3046875,97311.78125,117198.453125,89317.3671875,88493.5546875,72229.9296875,71080.2109375,50013.953125,55968.58203125,47525.50390625,49515.06640625,38161.48046875,44872.05859375,46273.23828125,38116.76953125,28191.162109375,32191.4375,36006.171875,32559.892578125,25551.1171875,24267.509765625,27521.3984375,23166.53125,21101.576171875,19412.32421875,19452.203125,17454.984375,22537.623046875,16174.28125,14671.640625,15115.6884765625,13870.0732421875,13746.3125,12663.1337890625,1.5870834589004517,1.5077009201049805,0.5881357789039612,0.5889744758605957,0.21327851712703705,0.2630201280117035,0.2796429395675659,0.38030216097831726,0.16162841022014618,0.2559692859649658],dtype=np.float) +# 调试模型，0：不调试，>0：打印对应级别调试信息 +debug_mode = 0 + +#********************for scoped_timing.py******************** +# 时间统计类 +class ScopedTiming: + def __init__(self, info="", enable_profile=True): + self.info = info + self.enable_profile = enable_profile + + def __enter__(self): + if self.enable_profile: + self.start_time = time.time_ns() + return self + + def __exit__(self, exc_type, exc_value, traceback): + if self.enable_profile: + elapsed_time = time.time_ns() - self.start_time + print(f"{self.info} took {elapsed_time / 1000000:.2f} ms") + +#********************for ai_utils.py******************** +global current_kmodel_obj #当前kpu对象 +# fd_ai2d： 人脸检测ai2d实例 +# fd_ai2d_input_tensor： 人脸检测ai2d输入 +# fd_ai2d_output_tensor： 人脸检测ai2d输入 +# fd_ai2d_builder： 根据人脸检测ai2d参数，构建的人脸检测ai2d_builder对象 +global fd_ai2d,fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder +# fm_ai2d： 人脸mesh ai2d实例 +# fm_ai2d_input_tensor： 人脸mesh ai2d输入 +# fm_ai2d_output_tensor：人脸mesh ai2d输入 +# fm_ai2d_builder： 根据人脸mesh ai2d参数，构建的人脸mesh ai2d_builder对象 +global fm_ai2d,fm_ai2d_input_tensor,fm_ai2d_output_tensor,fm_ai2d_builder +global roi #人脸区域 +global vertices #3D关键点 + +#读取anchor文件，为人脸检测后处理做准备 +print('anchors_path:',anchors_path) +prior_data = np.fromfile(anchors_path, dtype=np.float) +prior_data = prior_data.reshape((anchor_len,det_dim)) + +def get_pad_one_side_param(): + # 右padding或下padding，获取padding参数 + dst_w = fd_kmodel_input_shape[3] # kmodel输入宽（w） + dst_h = fd_kmodel_input_shape[2] # kmodel输入高（h） + + # OUT_RGB888P_WIDTH：原图宽（w） + # OUT_RGB888P_HEIGH：原图高（h） + # 计算最小的缩放比例，等比例缩放 + ratio_w = dst_w / OUT_RGB888P_WIDTH + ratio_h = dst_h / OUT_RGB888P_HEIGH + if ratio_w < ratio_h: + ratio = ratio_w + else: + ratio = ratio_h + # 计算经过缩放后的新宽和新高 + new_w = (int)(ratio * OUT_RGB888P_WIDTH) + new_h = (int)(ratio * OUT_RGB888P_HEIGH) + + # 计算需要添加的padding，以使得kmodel输入的宽高和原图一致 + dw = (dst_w - new_w) / 2 + dh = (dst_h - new_h) / 2 + # 四舍五入，确保padding是整数 + top = (int)(round(0)) + bottom = (int)(round(dh * 2 + 0.1)) + left = (int)(round(0)) + right = (int)(round(dw * 2 - 0.1)) + return [0, 0, 0, 0, top, bottom, left, right] + +def fd_ai2d_init(): + # 人脸检测模型ai2d初始化 + with ScopedTiming("fd_ai2d_init",debug_mode > 0): + # （1）创建人脸检测ai2d对象 + global fd_ai2d + fd_ai2d = nn.ai2d() + # （2）设置人脸检测ai2d参数 + fd_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + fd_ai2d.set_pad_param(True, get_pad_one_side_param(), 0, rgb_mean) + fd_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + + #（3）人脸检测ai2d_builder，根据人脸检测ai2d参数、输入输出大小创建ai2d_builder对象 + global fd_ai2d_builder + fd_ai2d_builder = fd_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fd_kmodel_input_shape) + + #（4）创建人脸检测ai2d_output_tensor，用于保存人脸检测ai2d输出 + global fd_ai2d_output_tensor + data = np.ones(fd_kmodel_input_shape, dtype=np.uint8) + fd_ai2d_output_tensor = nn.from_numpy(data) + +def fd_ai2d_run(rgb888p_img): + # 根据人脸检测ai2d参数，对原图rgb888p_img进行预处理 + with ScopedTiming("fd_ai2d_run",debug_mode > 0): + global fd_ai2d_input_tensor,fd_ai2d_output_tensor,fd_ai2d_builder + # （1）根据原图构建ai2d_input_tensor对象 + ai2d_input = rgb888p_img.to_numpy_ref() + fd_ai2d_input_tensor = nn.from_numpy(ai2d_input) + # （2）运行人脸检测ai2d_builder，将结果保存到人脸检测ai2d_output_tensor中 + fd_ai2d_builder.run(fd_ai2d_input_tensor, fd_ai2d_output_tensor) + +def fd_ai2d_release(): + # 释放人脸检测ai2d_input_tensor + with ScopedTiming("fd_ai2d_release",debug_mode > 0): + global fd_ai2d_input_tensor + del fd_ai2d_input_tensor + +def fd_kpu_init(kmodel_file): + # 初始化人脸检测kpu对象，并加载kmodel + with ScopedTiming("fd_kpu_init",debug_mode > 0): + # 初始化人脸检测kpu对象 + kpu_obj = nn.kpu() + # 加载人脸检测kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸检测ai2d + fd_ai2d_init() + return kpu_obj + +def fd_kpu_pre_process(rgb888p_img): + # 设置人脸检测kpu输入 + # 使用人脸检测ai2d对原图进行预处理（padding，resize） + fd_ai2d_run(rgb888p_img) + with ScopedTiming("fd_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fd_ai2d_output_tensor + # 设置人脸检测kpu输入 + current_kmodel_obj.set_input_tensor(0, fd_ai2d_output_tensor) + +def fd_kpu_get_output(): + # 获取人脸检测kpu输出 + with ScopedTiming("fd_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取模型输出，并将结果转换为numpy，以便进行人脸检测后处理 + results = [] + for i in range(current_kmodel_obj.outputs_size()): + data = current_kmodel_obj.get_output_tensor(i) + result = data.to_numpy() + del data + results.append(result) + return results + +def fd_kpu_run(kpu_obj,rgb888p_img): + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）原图预处理，并设置模型输入 + fd_kpu_pre_process(rgb888p_img) + # （2）人脸检测kpu推理 + with ScopedTiming("fd kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸检测ai2d资源 + fd_ai2d_release() + # （4）获取人俩检测kpu输出 + results = fd_kpu_get_output() + # （5）人脸检测kpu结果后处理 + with ScopedTiming("fd kpu_post",debug_mode > 0): + post_ret = aidemo.face_det_post_process(confidence_threshold,nms_threshold,fd_kmodel_input_shape[2],prior_data, + [OUT_RGB888P_WIDTH,OUT_RGB888P_HEIGH],results) + # （6）返回人脸检测框 + if len(post_ret)==0: + return post_ret + else: + return post_ret[0] #0:det,1:landm,2:score + +def fd_kpu_deinit(kpu_obj): + # kpu释放 + with ScopedTiming("fd_kpu_deinit",debug_mode > 0): + global fd_ai2d, fd_ai2d_output_tensor + del kpu_obj #删除人脸检测kpu_obj变量，释放对它所引用对象的内存引用 + del fd_ai2d #删除人脸检测ai2d变量，释放对它所引用对象的内存引用 + del fd_ai2d_output_tensor #删除人脸检测ai2d_output_tensor变量，释放对它所引用对象的内存引用 + +###############for face recognition############### +def parse_roi_box_from_bbox(bbox): + # 获取人脸roi + x1, y1, w, h = map(lambda x: int(round(x, 0)), bbox[:4]) + old_size = (w + h) / 2 + center_x = x1 + w / 2 + center_y = y1 + h / 2 + old_size * 0.14 + size = int(old_size * 1.58) + + x0 = center_x - float(size) / 2 + y0 = center_y - float(size) / 2 + x1 = x0 + size + y1 = y0 + size + + x0 = max(0, min(x0, OUT_RGB888P_WIDTH)) + y0 = max(0, min(y0, OUT_RGB888P_HEIGH)) + x1 = max(0, min(x1, OUT_RGB888P_WIDTH)) + y1 = max(0, min(y1, OUT_RGB888P_HEIGH)) + + roi = (x0, y0, x1 - x0, y1 - y0) + return roi + +def fm_ai2d_init(): + # 人脸mesh ai2d初始化 + with ScopedTiming("fm_ai2d_init",debug_mode > 0): + # （1）创建人脸mesh ai2d对象 + global fm_ai2d + fm_ai2d = nn.ai2d() + + # （2）创建人脸mesh ai2d_output_tensor对象，用于存放ai2d输出 + global fm_ai2d_output_tensor + data = np.ones(fm_kmodel_input_shape, dtype=np.uint8) + fm_ai2d_output_tensor = nn.from_numpy(data) + +def fm_ai2d_run(rgb888p_img,det): + # 人脸mesh ai2d推理 + with ScopedTiming("fm_ai2d_run",debug_mode > 0): + global fm_ai2d,fm_ai2d_input_tensor,fm_ai2d_output_tensor + #（1）根据原图ai2d_input_tensor对象 + ai2d_input = rgb888p_img.to_numpy_ref() + fm_ai2d_input_tensor = nn.from_numpy(ai2d_input) + + # （2）根据新的det设置新的人脸mesh ai2d参数 + fm_ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + global roi + roi = parse_roi_box_from_bbox(det) + fm_ai2d.set_crop_param(True,int(roi[0]),int(roi[1]),int(roi[2]),int(roi[3])) + fm_ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + # （3）根据新的人脸mesh ai2d参数，构建人脸mesh ai2d_builder + global fm_ai2d_builder + fm_ai2d_builder = fm_ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], fm_kmodel_input_shape) + # （4）推理人脸mesh ai2d，将预处理的结果保存到fm_ai2d_output_tensor + fm_ai2d_builder.run(fm_ai2d_input_tensor, fm_ai2d_output_tensor) + +def fm_ai2d_release(): + # 释放人脸mesh ai2d_input_tensor、ai2d_builder + with ScopedTiming("fm_ai2d_release",debug_mode > 0): + global fm_ai2d_input_tensor,fm_ai2d_builder + del fm_ai2d_input_tensor + del fm_ai2d_builder + +def fm_kpu_init(kmodel_file): + # 人脸mesh kpu初始化 + with ScopedTiming("fm_kpu_init",debug_mode > 0): + # 初始化人脸mesh kpu对象 + kpu_obj = nn.kpu() + # 加载人脸mesh kmodel + kpu_obj.load_kmodel(kmodel_file) + # 初始化人脸mesh ai2d + fm_ai2d_init() + return kpu_obj + +def fm_kpu_pre_process(rgb888p_img,det): + # 人脸mesh kpu预处理 + # 人脸mesh ai2d推理，根据det对原图进行预处理 + fm_ai2d_run(rgb888p_img,det) + with ScopedTiming("fm_kpu_pre_process",debug_mode > 0): + global current_kmodel_obj,fm_ai2d_output_tensor + # 将人脸mesh ai2d输出设置为人脸mesh kpu输入 + current_kmodel_obj.set_input_tensor(0, fm_ai2d_output_tensor) + +def fm_kpu_get_output(): + with ScopedTiming("fm_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取人脸mesh kpu输出 + data = current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + del data + return result + +def fm_kpu_post_process(param): + # 人脸mesh kpu结果后处理，反标准化 + with ScopedTiming("fm_kpu_post_process",debug_mode > 0): + param = param * param_std + param_mean + return param + +def fm_kpu_run(kpu_obj,rgb888p_img,det): + # 人脸mesh kpu推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + # （1）人脸mesh kpu预处理，设置kpu输入 + fm_kpu_pre_process(rgb888p_img,det) + # （2）人脸mesh kpu推理 + with ScopedTiming("fm_kpu_run",debug_mode > 0): + kpu_obj.run() + # （3）释放人脸mesh ai2d + fm_ai2d_release() + # （4）获取人脸mesh kpu输出 + param = fm_kpu_get_output() + # （5）人脸mesh 后处理 + param = fm_kpu_post_process(param) + return param + +def fm_kpu_deinit(kpu_obj): + # 人脸mesh kpu释放 + with ScopedTiming("fm_kpu_deinit",debug_mode > 0): + global fm_ai2d,fm_ai2d_output_tensor + del kpu_obj # 删除kpu_obj变量，释放对它所引用对象的内存引用 + del fm_ai2d # 删除fm_ai2d变量，释放对它所引用对象的内存引用 + del fm_ai2d_output_tensor # 删除fm_ai2d_output_tensor变量，释放对它所引用对象的内存引用 + +def fmpost_kpu_init(kmodel_file): + # face mesh post模型初始化 + with ScopedTiming("fmpost_kpu_init",debug_mode > 0): + # 初始化人脸mesh kpu post对象 + kpu_obj = nn.kpu() + # 加载人脸mesh后处理kmodel + kpu_obj.load_kmodel(kmodel_file) + return kpu_obj + +def fmpost_kpu_pre_process(param): + # face mesh post模型预处理，param解析 + with ScopedTiming("fmpost_kpu_pre_process",debug_mode > 0): + param = param[0] + trans_dim, shape_dim, exp_dim = 12, 40, 10 + + # reshape前务必进行copy，否则会导致模型输入错误 + R_ = param[:trans_dim].copy().reshape((3, -1)) + R = R_[:, :3].copy() + offset = R_[:, 3].copy() + offset = offset.reshape((3, 1)) + alpha_shp = param[trans_dim:trans_dim + shape_dim].copy().reshape((-1, 1)) + alpha_exp = param[trans_dim + shape_dim:].copy().reshape((-1, 1)) + + R_tensor = nn.from_numpy(R) + current_kmodel_obj.set_input_tensor(0, R_tensor) + del R_tensor + + offset_tensor = nn.from_numpy(offset) + current_kmodel_obj.set_input_tensor(1, offset_tensor) + del offset_tensor + + alpha_shp_tensor = nn.from_numpy(alpha_shp) + current_kmodel_obj.set_input_tensor(2, alpha_shp_tensor) + del alpha_shp_tensor + + alpha_exp_tensor = nn.from_numpy(alpha_exp) + current_kmodel_obj.set_input_tensor(3, alpha_exp_tensor) + del alpha_exp_tensor + + return + +def fmpost_kpu_get_output(): + # 获取face mesh post模型输出 + with ScopedTiming("fmpost_kpu_get_output",debug_mode > 0): + global current_kmodel_obj + # 获取人脸mesh kpu输出 + data = current_kmodel_obj.get_output_tensor(0) + result = data.to_numpy() + del data + return result + +def fmpost_kpu_post_process(roi): + # face mesh post模型推理结果后处理 + with ScopedTiming("fmpost_kpu_post_process",debug_mode > 0): + x, y, w, h = map(lambda x: int(round(x, 0)), roi[:4]) + x = x * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + y = y * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + w = w * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + h = h * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + roi_array = np.array([x,y,w,h],dtype=np.float) + global vertices + aidemo.face_mesh_post_process(roi_array,vertices) + return + +def fmpost_kpu_run(kpu_obj,param): + # face mesh post模型推理 + global current_kmodel_obj + current_kmodel_obj = kpu_obj + fmpost_kpu_pre_process(param) + with ScopedTiming("fmpost_kpu_run",debug_mode > 0): + kpu_obj.run() + global vertices + vertices = fmpost_kpu_get_output() + global roi + fmpost_kpu_post_process(roi) + return + +def fmpost_kpu_deinit(kpu_obj): + # face mesh post模型释放 + with ScopedTiming("fm_kpu_deinit",debug_mode > 0): + del kpu_obj + +#********************for media_utils.py******************** +global draw_img_ulab,draw_img,osd_img #for display +global buffer,media_source,media_sink #for media + +# for display，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.2 +def display_init(): + # 设置使用hdmi进行显示 + display.init(LT9611_1920X1080_30FPS) + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + +def display_deinit(): + # 释放显示资源 + display.deinit() + +def display_draw(dets,vertices_list): + # 在显示器画人脸轮廓 + with ScopedTiming("display_draw",debug_mode >0): + global draw_img_ulab,draw_img,osd_img + if dets: + draw_img.clear() + for vertices in vertices_list: + aidemo.face_draw_mesh(draw_img_ulab, vertices) + # （4）将轮廓结果拷贝到osd + draw_img.copy_to(osd_img) + # （5）将osd显示到屏幕 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + # （1）清空用来画框的图像 + draw_img.clear() + # （2）清空osd + draw_img.copy_to(osd_img) + # （3）显示透明图层 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + +#for camera，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.1 +def camera_init(dev_id): + # camera初始化 + camera.sensor_init(dev_id, CAM_DEFAULT_SENSOR) + + # set chn0 output yuv420sp + camera.set_outsize(dev_id, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + camera.set_outfmt(dev_id, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # set chn2 output rgb88planar + camera.set_outsize(dev_id, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + camera.set_outfmt(dev_id, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + +def camera_start(dev_id): + # camera启动 + camera.start_stream(dev_id) + +def camera_read(dev_id): + # 读取一帧图像 + with ScopedTiming("camera_read",debug_mode >0): + rgb888p_img = camera.capture_image(dev_id, CAM_CHN_ID_2) + return rgb888p_img + +def camera_release_image(dev_id,rgb888p_img): + # 释放一帧图像 + with ScopedTiming("camera_release_image",debug_mode >0): + camera.release_image(dev_id, CAM_CHN_ID_2, rgb888p_img) + +def camera_stop(dev_id): + # 停止camera + camera.stop_stream(dev_id) + +#for media，已经封装好，无需自己再实现，直接调用即可，详细解析请查看1.6.3 +def media_init(): + # meida初始化 + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4 * DISPLAY_WIDTH * DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + + ret = media.buffer_config(config) + + global media_source, media_sink + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + media.create_link(media_source, media_sink) + + # 初始化多媒体buffer + ret = media.buffer_init() + if ret: + return ret + global buffer, draw_img_ulab,draw_img, osd_img + buffer = media.request_buffer(4 * DISPLAY_WIDTH * DISPLAY_HEIGHT) + # 用于画框 + draw_img_ulab = np.zeros((DISPLAY_HEIGHT,DISPLAY_WIDTH,4),dtype=np.uint8) + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_REF,data = draw_img_ulab) + # 用于拷贝画框结果，防止画框过程中发生buffer搬运 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, + phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + return ret + +def media_deinit(): + # meida资源释放 + global buffer,media_source, media_sink + media.release_buffer(buffer) + media.destroy_link(media_source, media_sink) + + ret = media.buffer_deinit() + return ret + +#********************for face_detect.py******************** +def face_mesh_inference(): + # 人脸检测kpu初始化 + kpu_face_detect = fd_kpu_init(fd_kmodel_file) + # 人脸mesh kpu初始化 + kpu_face_mesh = fm_kpu_init(fm_kmodel_file) + # face_mesh_post kpu初始化 + kpu_face_mesh_post = fmpost_kpu_init(fmpost_kmodel_file) + # camera初始化 + camera_init(CAM_DEV_ID_0) + # 显示初始化 + display_init() + + rgb888p_img = None + # 注意：将一定要将一下过程包在try中，用于保证程序停止后，资源释放完毕；确保下次程序仍能正常运行 + try: + # 注意：媒体初始化（注：媒体初始化必须在camera_start之前，确保media缓冲区已配置完全） + ret = media_init() + if ret: + print("face_detect_test, buffer init failed") + return ret + + # 启动camera + camera_start(CAM_DEV_ID_0) + time.sleep(5) + while True: + with ScopedTiming("total",1): + # （1）读取一帧图像 + rgb888p_img = camera_read(CAM_DEV_ID_0) + # （2）若读取失败，释放当前帧 + if rgb888p_img == -1: + print("face_detect_test, capture_image failed") + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + continue + # （3）若读取成功，推理当前帧 + if rgb888p_img.format() == image.RGBP888: + # （3.1）推理当前图像，并获取人脸检测结果 + dets = fd_kpu_run(kpu_face_detect,rgb888p_img) + ## （3.2）针对每个人脸框，推理得到对应人脸mesh + mesh_result = [] + for det in dets: + param = fm_kpu_run(kpu_face_mesh,rgb888p_img,det) + fmpost_kpu_run(kpu_face_mesh_post,param) + global vertices + mesh_result.append(vertices) + ## （3.3）将人脸mesh 画到屏幕上 + display_draw(dets,mesh_result) + + # （4）释放当前帧 + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + rgb888p_img = None + #with ScopedTiming("gc collect", debug_mode > 0): + #gc.collect() + except Exception as e: + # 捕捉运行运行中异常，并打印错误 + print(f"An error occurred during buffer used: {e}") + finally: + # 释放当前帧 + if rgb888p_img is not None: + #先release掉申请的内存再stop + camera_release_image(CAM_DEV_ID_0,rgb888p_img) + + # 停止camera + camera_stop(CAM_DEV_ID_0) + # 释放显示资源 + display_deinit() + # 释放kpu资源 + fd_kpu_deinit(kpu_face_detect) + fm_kpu_deinit(kpu_face_mesh) + fmpost_kpu_deinit(kpu_face_mesh_post) + # 垃圾回收 + gc.collect() + time.sleep(1) + # 释放媒体资源 + ret = media_deinit() + if ret: + print("face_mesh_test, buffer_deinit failed") + return ret + + #print("face_mesh_test end") + return 0 + +if __name__ == '__main__': + face_mesh_inference() +``` diff --git "a/zh/01_software/example/K230_Canmv\347\244\272\344\276\213\350\256\262\350\247\243-\344\272\272\350\204\270\346\243\200\346\265\213.md" "b/zh/example/K230_Canmv\347\244\272\344\276\213\350\256\262\350\247\243-\344\272\272\350\204\270\346\243\200\346\265\213.md" similarity index 100% rename from "zh/01_software/example/K230_Canmv\347\244\272\344\276\213\350\256\262\350\247\243-\344\272\272\350\204\270\346\243\200\346\265\213.md" rename to "zh/example/K230_Canmv\347\244\272\344\276\213\350\256\262\350\247\243-\344\272\272\350\204\270\346\243\200\346\265\213.md" diff --git a/zh/example/cipher/cipher.md b/zh/example/cipher/cipher.md new file mode 100755 index 0000000..4c76aa5 --- /dev/null +++ b/zh/example/cipher/cipher.md @@ -0,0 +1,316 @@ +# Cipher - Cipher 例程 + +本示例程序用于对 CanMV 开发板进行一个 Cipher 输出的功能展示。 + +```python +import uhashlib + +print('###################### SHA256 Test ##############################') +print('********************** Test-1: Only Call update() Once ******************') +# 初始化sha256对象 +obj = uhashlib.sha256() +# 输入消息message +msg = b'\x45\x11\x01\x25\x0e\xc6\xf2\x66\x52\x24\x9d\x59\xdc\x97\x4b\x73\x61\xd5\x71\xa8\x10\x1c\xdf\xd3\x6a\xba\x3b\x58\x54\xd3\xae\x08\x6b\x5f\xdd\x45\x97\x72\x1b\x66\xe3\xc0\xdc\x5d\x8c\x60\x6d\x96\x57\xd0\xe3\x23\x28\x3a\x52\x17\xd1\xf5\x3f\x2f\x28\x4f\x57\xb8' +# 标准哈希值 +dgst0 = b'\x1a\xaa\xf9\x28\x5a\xf9\x45\xb8\xa9\x7c\xf1\x4f\x86\x9b\x18\x90\x14\xc3\x84\xf3\xc7\xc2\xb7\xd2\xdf\x8a\x97\x13\xbf\xfe\x0b\xf1' +# 将消息更新到硬件IP中 +obj.update(msg) +# 计算哈希值 +dgst = obj.digest() +print(dgst0 == dgst) +# print(binascii.hexlify(dgst)) +print('********************** Test-2: Call update() Twice ******************') +dgst0 = b'\x93\x6a\x18\x5c\xaa\xa2\x66\xbb\x9c\xbe\x98\x1e\x9e\x05\xcb\x78\xcd\x73\x2b\x0b\x32\x80\xeb\x94\x44\x12\xbb\x6f\x8f\x8f\x07\xaf' +obj = uhashlib.sha256() +# 向硬件多次更新消息 +obj.update(b'hello') +obj.update(b'world') +dgst = obj.digest() +print(dgst0 == dgst) +# print('********************** Test-3: Call digest() Twice ******************') +# dgst0 = b'\x93\x6a\x18\x5c\xaa\xa2\x66\xbb\x9c\xbe\x98\x1e\x9e\x05\xcb\x78\xcd\x73\x2b\x0b\x32\x80\xeb\x94\x44\x12\xbb\x6f\x8f\x8f\x07\xaf' +# obj = uhashlib.sha256() +# obj.update(b'hello') +# obj.update(b'world') +# dgst = obj.digest() +# dgst1 = obj.digest() +# print(dgst0 == dgst) +# print(dgst == dgst1) + + +print('\n###################### AES-GCM Test ##############################') +import ucryptolib +import collections +# 创建一个具名元组并返回具名元组子类 +Aes = collections.namedtuple('Aes', ['key', 'iv', 'aad', 'pt', 'ct', 'tag']) +aes = [ + Aes(b'\xb5\x2c\x50\x5a\x37\xd7\x8e\xda\x5d\xd3\x4f\x20\xc2\x25\x40\xea\x1b\x58\x96\x3c\xf8\xe5\xbf\x8f\xfa\x85\xf9\xf2\x49\x25\x05\xb4', + b'\x51\x6c\x33\x92\x9d\xf5\xa3\x28\x4f\xf4\x63\xd7', + b'', + b'', + b'', + b'\xbd\xc1\xac\x88\x4d\x33\x24\x57\xa1\xd2\x66\x4f\x16\x8c\x76\xf0' + ), + + Aes(b'\x24\x50\x1a\xd3\x84\xe4\x73\x96\x3d\x47\x6e\xdc\xfe\x08\x20\x52\x37\xac\xfd\x49\xb5\xb8\xf3\x38\x57\xf8\x11\x4e\x86\x3f\xec\x7f', + b'\x9f\xf1\x85\x63\xb9\x78\xec\x28\x1b\x3f\x27\x94', + b'\xad\xb5\xec\x72\x0c\xcf\x98\x98\x50\x00\x28\xbf\x34\xaf\xcc\xbc\xac\xa1\x26\xef', + b'\x27', + b'\xeb', + b'\x63\x35\xe1\xd4\x9e\x89\x88\xea\xc4\x8e\x42\x19\x4e\x5f\x56\xdb' + ), + + Aes(b'\x1f\xde\xd3\x2d\x59\x99\xde\x4a\x76\xe0\xf8\x08\x21\x08\x82\x3a\xef\x60\x41\x7e\x18\x96\xcf\x42\x18\xa2\xfa\x90\xf6\x32\xec\x8a', + b'\x1f\x3a\xfa\x47\x11\xe9\x47\x4f\x32\xe7\x04\x62', + b'', + b'\x06\xb2\xc7\x58\x53\xdf\x9a\xeb\x17\xbe\xfd\x33\xce\xa8\x1c\x63\x0b\x0f\xc5\x36\x67\xff\x45\x19\x9c\x62\x9c\x8e\x15\xdc\xe4\x1e\x53\x0a\xa7\x92\xf7\x96\xb8\x13\x8e\xea\xb2\xe8\x6c\x7b\x7b\xee\x1d\x40\xb0', + b'\x91\xfb\xd0\x61\xdd\xc5\xa7\xfc\xc9\x51\x3f\xcd\xfd\xc9\xc3\xa7\xc5\xd4\xd6\x4c\xed\xf6\xa9\xc2\x4a\xb8\xa7\x7c\x36\xee\xfb\xf1\xc5\xdc\x00\xbc\x50\x12\x1b\x96\x45\x6c\x8c\xd8\xb6\xff\x1f\x8b\x3e\x48\x0f', + b'\x30\x09\x6d\x34\x0f\x3d\x5c\x42\xd8\x2a\x6f\x47\x5d\xef\x23\xeb' + ), + + Aes(b'\x24\x50\x1a\xd3\x84\xe4\x73\x96\x3d\x47\x6e\xdc\xfe\x08\x20\x52\x37\xac\xfd\x49\xb5\xb8\xf3\x38\x57\xf8\x11\x4e\x86\x3f\xec\x7f', + b'\x9f\xf1\x85\x63\xb9\x78\xec\x28\x1b\x3f\x27\x94', + b'\xad\xb5\xec\x72\x0c\xcf\x98\x98\x50\x00\x28\xbf\x34\xaf\xcc\xbc\xac\xa1\x26\xef', + b'\x27\xf3\x48\xf9\xcd\xc0\xc5\xbd\x5e\x66\xb1\xcc\xb6\x3a\xd9\x20\xff\x22\x19\xd1\x4e\x8d\x63\x1b\x38\x72\x26\x5c\xf1\x17\xee\x86\x75\x7a\xcc\xb1\x58\xbd\x9a\xbb\x38\x68\xfd\xc0\xd0\xb0\x74\xb5\xf0\x1b\x2c', + b'\xeb\x7c\xb7\x54\xc8\x24\xe8\xd9\x6f\x7c\x6d\x9b\x76\xc7\xd2\x6f\xb8\x74\xff\xbf\x1d\x65\xc6\xf6\x4a\x69\x8d\x83\x9b\x0b\x06\x14\x5d\xae\x82\x05\x7a\xd5\x59\x94\xcf\x59\xad\x7f\x67\xc0\xfa\x5e\x85\xfa\xb8', + b'\xbc\x95\xc5\x32\xfe\xcc\x59\x4c\x36\xd1\x55\x02\x86\xa7\xa3\xf0' + ) +] + +print('********************** Test-1: ivlen=12, ptlen=0, aadlen=0 ******************') +print('GCM Encrypt......') +# 初始化aes-gcm密码对象，参数包含密钥key、初始化向量iv以及附加数据aad +crypto = ucryptolib.aes(aes[0].key, 0, aes[0].iv, aes[0].aad) +# 输入明文数据 +inbuf = aes[0].pt +outbuf = bytearray(16) +# 加密，输出格式为：密文 + tag +val = crypto.encrypt(inbuf, outbuf) +# 标准数据 +val0 = aes[0].ct + aes[0].tag +print(val0 == val) +print('GCM Decrypt......') +crypto = ucryptolib.aes(aes[0].key, 0, aes[0].iv, aes[0].aad) +# 输入数据格式：密文 + tag +inbuf = aes[0].ct + aes[0].tag +# 解密，输出数据格式：明文 +val = crypto.decrypt(inbuf) +print(val[:1] == b'\x00') +# val0 = aes[0].pt +# print(val0 == val) + +print('********************** Test-2: ivlen=12, ptlen=1, aadlen=20 ******************') +print('GCM Encrypt......') +# 初始化 +crypto = ucryptolib.aes(aes[1].key, 0, aes[1].iv, aes[1].aad) +# 输入明文 +inbuf = aes[1].pt +outbuf = bytearray(17) +# 输出密文+tag +val = crypto.encrypt(inbuf, outbuf) +val0 = aes[1].ct + aes[1].tag +print(val0 == val) +print('GCM Decrypt......') +crypto = ucryptolib.aes(aes[1].key, 0, aes[1].iv, aes[1].aad) +# 输入密文+tag +inbuf = aes[1].ct + aes[1].tag +outbuf = bytearray(1) +# 输出明文 +val = crypto.decrypt(inbuf, outbuf) +val0 = aes[1].pt +print(val0 == val) + +print('********************** Test-3: ivlen=12, ptlen=51, aadlen=0 ******************') +print('GCM Encrypt......') +# 初始化 +crypto = ucryptolib.aes(aes[2].key, 0, aes[2].iv, aes[2].aad) +# 输入明文 +inbuf = aes[2].pt +outbuf = bytearray(67) +# 输出密文+tag +val = crypto.encrypt(inbuf, outbuf) +val0 = aes[2].ct + aes[2].tag +print(val0 == val) +print('GCM Decrypt......') +crypto = ucryptolib.aes(aes[2].key, 0, aes[2].iv, aes[2].aad) +# 输入密文+tag +inbuf = aes[2].ct + aes[2].tag +outbuf = bytearray(51) +# 输入明文 +val = crypto.decrypt(inbuf, outbuf) +val0 = aes[2].pt +print(val0 == val) + +print('********************** Test-4: ivlen=12, ptlen=51, aadlen=20 ******************') +print('GCM Encrypt......') +# 初始化 +crypto = ucryptolib.aes(aes[3].key, 0, aes[3].iv, aes[3].aad) +# 输入明文 +inbuf = aes[3].pt +outbuf = bytearray(67) +# 输出密文+tag +val = crypto.encrypt(inbuf, outbuf) +val0 = aes[3].ct + aes[3].tag +print(val0 == val) +print('GCM Decrypt......') +crypto = ucryptolib.aes(aes[3].key, 0, aes[3].iv, aes[3].aad) +# 输入密文+tag +inbuf = aes[3].ct + aes[3].tag +val = crypto.decrypt(inbuf) +val0 = aes[3].pt +print(val[:51] == val0) +# outbuf = bytearray(51) +# val = crypto.decrypt(inbuf, outbuf) +# val0 = aes[3].pt +# print(val0 == val) + + +print('\n###################### SM4-ECB/CFB/OFB/CBC/CTR Test ##############################') +Sm4 = collections.namedtuple('Sm4', ['key', 'iv', 'pt', 'ct']) +sm4 = [ + # ecb + Sm4(b'\x01\x23\x45\x67\x89\xab\xcd\xef\xfe\xdc\xba\x98\x76\x54\x32\x10', + None, + b'\xaa\xaa\xaa\xaa\xbb\xbb\xbb\xbb\xcc\xcc\xcc\xcc\xdd\xdd\xdd\xdd\xee\xee\xee\xee\xff\xff\xff\xff\xaa\xaa\xaa\xaa\xbb\xbb\xbb\xbb', + b'\x5e\xc8\x14\x3d\xe5\x09\xcf\xf7\xb5\x17\x9f\x8f\x47\x4b\x86\x19\x2f\x1d\x30\x5a\x7f\xb1\x7d\xf9\x85\xf8\x1c\x84\x82\x19\x23\x04' + ), + + # cbc + Sm4( + b'\x01\x23\x45\x67\x89\xab\xcd\xef\xfe\xdc\xba\x98\x76\x54\x32\x10', + b'\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f', + b'\xaa\xaa\xaa\xaa\xbb\xbb\xbb\xbb\xcc\xcc\xcc\xcc\xdd\xdd\xdd\xdd\xee\xee\xee\xee\xff\xff\xff\xff\xaa\xaa\xaa\xaa\xbb\xbb\xbb\xbb', + b'\x78\xeb\xb1\x1c\xc4\x0b\x0a\x48\x31\x2a\xae\xb2\x04\x02\x44\xcb\x4c\xb7\x01\x69\x51\x90\x92\x26\x97\x9b\x0d\x15\xdc\x6a\x8f\x6d' + ), + + # cfb + Sm4( + b'\x01\x23\x45\x67\x89\xab\xcd\xef\xfe\xdc\xba\x98\x76\x54\x32\x10', + b'\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f', + b'\xaa\xaa\xaa\xaa\xbb\xbb\xbb\xbb\xcc\xcc\xcc\xcc\xdd\xdd\xdd\xdd\xee\xee\xee\xee\xff\xff\xff\xff\xaa\xaa\xaa\xaa\xbb\xbb\xbb\xbb', + b'\xac\x32\x36\xcb\x86\x1d\xd3\x16\xe6\x41\x3b\x4e\x3c\x75\x24\xb7\x69\xd4\xc5\x4e\xd4\x33\xb9\xa0\x34\x60\x09\xbe\xb3\x7b\x2b\x3f' + ), + + # ofb + Sm4( + b'\x01\x23\x45\x67\x89\xab\xcd\xef\xfe\xdc\xba\x98\x76\x54\x32\x10', + b'\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f', + b'\xaa\xaa\xaa\xaa\xbb\xbb\xbb\xbb\xcc\xcc\xcc\xcc\xdd\xdd\xdd\xdd\xee\xee\xee\xee\xff\xff\xff\xff\xaa\xaa\xaa\xaa\xbb\xbb\xbb\xbb', + b'\xac\x32\x36\xcb\x86\x1d\xd3\x16\xe6\x41\x3b\x4e\x3c\x75\x24\xb7\x1d\x01\xac\xa2\x48\x7c\xa5\x82\xcb\xf5\x46\x3e\x66\x98\x53\x9b' + ), + + # ctr + Sm4( + b'\x01\x23\x45\x67\x89\xab\xcd\xef\xfe\xdc\xba\x98\x76\x54\x32\x10', + b'\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d\x0e\x0f', + b'\xaa\xaa\xaa\xaa\xaa\xaa\xaa\xaa\xbb\xbb\xbb\xbb\xbb\xbb\xbb\xbb\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xdd\xdd\xdd\xdd\xdd\xdd\xdd\xdd\xee\xee\xee\xee\xee\xee\xee\xee\xff\xff\xff\xff\xff\xff\xff\xff\xee\xee\xee\xee\xee\xee\xee\xee\xaa\xaa\xaa\xaa\xaa\xaa\xaa\xaa', + b'\xac\x32\x36\xcb\x97\x0c\xc2\x07\x91\x36\x4c\x39\x5a\x13\x42\xd1\xa3\xcb\xc1\x87\x8c\x6f\x30\xcd\x07\x4c\xce\x38\x5c\xdd\x70\xc7\xf2\x34\xbc\x0e\x24\xc1\x19\x80\xfd\x12\x86\x31\x0c\xe3\x7b\x92\x2a\x46\xb8\x94\xbe\xe4\xfe\xb7\x9a\x38\x22\x94\x0c\x93\x54\x05' + ) +] + +print('********************** Test-1: keybits=128, ptlen=32 ******************') +print('SM4-ECB Encrypt......') +# 初始化sm4密码对象，参数包含密钥key、加解密模式 +crypto = ucryptolib.sm4(sm4[0].key, 1) +# 输入明文数据 +inbuf = sm4[0].pt +outbuf = bytearray(32) +# 加密 +val = crypto.encrypt(inbuf, outbuf) +# golden数据 +val0 = sm4[0].ct +print(val0 == val) +print('SM4-ECB Decrypt......') +# 解密 +crypto = ucryptolib.sm4(sm4[0].key, 1) +inbuf = sm4[0].ct +outbuf = bytearray(32) +val = crypto.decrypt(inbuf, outbuf) +val0 = sm4[0].pt +print(val0 == val) + +print('********************** Test-2: keybits=128, ivlen=16, ptlen=32 ******************') +print('SM4-CBC Encrypt......') +# 初始化sm4密码对象 +crypto = ucryptolib.sm4(sm4[1].key, 2, sm4[1].iv) +# 输入明文数据 +inbuf = sm4[1].pt +outbuf = bytearray(32) +# 加密 +val = crypto.encrypt(inbuf, outbuf) +val0 = sm4[1].ct +print(val0 == val) +print('SM4-CBC Decrypt......') +# 解密 +crypto = ucryptolib.sm4(sm4[1].key, 2, sm4[1].iv) +inbuf = sm4[1].ct +val = crypto.decrypt(inbuf) +val0 = sm4[1].pt +print(val0 == val) +# outbuf = bytearray(32) +# val = crypto.decrypt(inbuf, outbuf) +# val0 = sm4[1].pt +# print(val0 == val) + +print('********************** Test-3: keybits=128, ivlen=16, ptlen=32 ******************') +print('SM4-CFB Encrypt......') +# 初始化sm4密码对象 +crypto = ucryptolib.sm4(sm4[2].key, 3, sm4[2].iv) +# 输入明文数据 +inbuf = sm4[2].pt +outbuf = bytearray(32) +# 加密 +val = crypto.encrypt(inbuf, outbuf) +val0 = sm4[2].ct +print(val0 == val) +print('SM4-CFB Decrypt......') +# 解密 +crypto = ucryptolib.sm4(sm4[2].key, 3, sm4[2].iv) +inbuf = sm4[2].ct +outbuf = bytearray(32) +val = crypto.decrypt(inbuf, outbuf) +val0 = sm4[2].pt +print(val0 == val) + +print('********************** Test-4: keybits=128, ivlen=16, ptlen=32 ******************') +print('SM4-OFB Encrypt......') +# 初始化sm4密码对象 +crypto = ucryptolib.sm4(sm4[3].key, 5, sm4[3].iv) +# 输入明文数据 +inbuf = sm4[3].pt +outbuf = bytearray(32) +# 加密 +val = crypto.encrypt(inbuf, outbuf) +val0 = sm4[3].ct +print(val0 == val) +print('SM4-OFB Decrypt......') +# 解密 +crypto = ucryptolib.sm4(sm4[3].key, 5, sm4[3].iv) +inbuf = sm4[3].ct +outbuf = bytearray(32) +val = crypto.decrypt(inbuf, outbuf) +val0 = sm4[3].pt +print(val0 == val) + +print('********************** Test-5: keybits=128, ivlen=16, ptlen=64 ******************') +print('SM4-CTR Encrypt......') +# 初始化sm4密码对象 +crypto = ucryptolib.sm4(sm4[4].key, 6, sm4[4].iv) +# 输入明文数据 +inbuf = sm4[4].pt +outbuf = bytearray(64) +# 加密 +val = crypto.encrypt(inbuf, outbuf) +val0 = sm4[4].ct +print(val0 == val) +print('SM4-CTR Decrypt......') +# 解密 +crypto = ucryptolib.sm4(sm4[4].key, 6, sm4[4].iv) +inbuf = sm4[4].ct +outbuf = bytearray(64) +val = crypto.decrypt(inbuf, outbuf) +val0 = sm4[4].pt +print(val0 == val) +``` + +具体接口定义请参考 [Cipher](../../api/cipher/K230_CanMV_Ucryptolib模块API手册.md)、[Hash](../../api/cipher/K230_CanMV_Hashlib模块API手册.md) diff --git a/zh/example/demo/face_detection.md b/zh/example/demo/face_detection.md new file mode 100755 index 0000000..0a092c5 --- /dev/null +++ b/zh/example/demo/face_detection.md @@ -0,0 +1,384 @@ +# face detection - 人脸检测画框示例 + +```python +import os + +from media.camera import * #导入camera模块，使用camera相关接口 +from media.display import * #导入display模块，使用display相关接口 +from media.media import * #导入media模块，使用meida相关接口 +from time import * + +import nncase_runtime as nn #导入nn模块，使用nn相关接口 +import ulab.numpy as np #导入np模块，使用np相关接口 + +import time +import image + +import random +import gc + +DISPLAY_WIDTH = ALIGN_UP(1920, 16) +DISPLAY_HEIGHT = 1080 + +OUT_RGB888P_WIDTH = ALIGN_UP(1024, 16) +OUT_RGB888P_HEIGH = 624 + +confidence_threshold = 0.5 +top_k = 5000 +nms_threshold = 0.2 +keep_top_k = 750 +vis_thres = 0.5 +variance = [0.1, 0.2] + +anchors_path = '/sdcard/app/tests/nncase_runtime/face_detection/prior_data_320.bin' +prior_data = np.fromfile(anchors_path, dtype=np.float) +prior_data = prior_data.reshape((4200,4)) + +scale = np.ones(4, dtype=np.uint8)*1024 +scale1 = np.ones(10, dtype=np.uint8)*1024 + + +def decode(loc, priors, variances): + boxes = np.concatenate( + (priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * np.exp(loc[:, 2:] * variances[1])), axis=1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + + +def decode_landm(pre, priors, variances): + landms = np.concatenate((priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:]) + , axis=1) + return landms + + +def py_cpu_nms(dets, thresh): + """Pure Python NMS baseline.""" + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + + order = np.argsort(scores,axis = 0)[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + new_x1 = [] + new_x2 = [] + new_y1 = [] + new_y2 = [] + new_areas = [] + for order_i in order: + new_x1.append(x1[order_i]) + new_x2.append(x2[order_i]) + new_y1.append(y1[order_i]) + new_y2.append(y2[order_i]) + new_areas.append(areas[order_i]) + new_x1 = np.array(new_x1) + new_x2 = np.array(new_x2) + new_y1 = np.array(new_y1) + new_y2 = np.array(new_y2) + xx1 = np.maximum(x1[i], new_x1) + yy1 = np.maximum(y1[i], new_y1) + xx2 = np.minimum(x2[i], new_x2) + yy2 = np.minimum(y2[i], new_y2) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + + new_areas = np.array(new_areas) + ovr = inter / (areas[i] + new_areas - inter) + new_order = [] + for ovr_i,ind in enumerate(ovr): + if ind < thresh: + new_order.append(order[ovr_i]) + order = np.array(new_order,dtype=np.uint8) + return keep + + +def pad_img_to_square(image, rgb_mean): + height, width, _ = image.shape + long_side = max(width, height) + image_t = np.empty((long_side, long_side, 3), dtype=image.dtype) + image_t[:, :] = rgb_mean + image_t[0:0 + height, 0:0 + width] = image + return image_t + + +def softmax(x): + x = x[0] + x_row_max = np.max(x,axis=-1) + x_row_max = x_row_max.reshape(tuple(list(x.shape)[:-1]+[1])) + x = x - x_row_max + x_exp = np.exp(x) + x_exp_row_sum = np.sum(x_exp,axis=-1).reshape(tuple(list(x.shape)[:-1]+[1])) + softmax = x_exp / x_exp_row_sum + + return softmax + + +def draw_image(img_raw,dets): + pass + + +def get_result(output_data): + loc = [] + loc = np.zeros((1, 4200, 4), dtype=np.float) + start_i = 0 + for _i in range(0, 3): + sum_shape = 1 + for sh_i in output_data[_i].shape: + sum_shape *= sh_i + output_data[_i] = output_data[_i].reshape((1, -1, loc.shape[2])) + loc[:,start_i:start_i + int(sum_shape/loc.shape[2]),:] = output_data[_i] + start_i = start_i + int(sum_shape/loc.shape[2]) + + #conf = [] + start_i = 0 + conf = np.zeros((1, 4200, 2), dtype=np.float) + for _i in range(3, 6): + sum_shape = 1 + for sh_i in output_data[_i].shape: + sum_shape *= sh_i + output_data[_i] = output_data[_i].reshape((1, -1, conf.shape[2])) + conf[:,start_i:start_i + int(sum_shape/conf.shape[2]),:] = output_data[_i] + start_i = start_i + int(sum_shape/conf.shape[2]) + conf = softmax(conf) + #landms = [] + #for _i in range(6, 9): + ##landms.append(np.reshape(np.transpose(output_data[_i], [0, 2, 3, 1]), [1, -1, 10])) + #landms.append(np.reshape(output_data[_i],(1, -1, 10))) + #landms = np.hstack(landms) + + boxes = decode(loc[0], prior_data, variance) + boxes = boxes * scale + scores = conf[:, 1] + #landms = decode_landm(np.squeeze(landms, axis=0), prior_data, variance) + #landms = landms * scale1 + + # ignore low scores + inds = [] + boxes_ind = [] + scores_ind = [] + for i in range(len(scores)): + if scores[i] > confidence_threshold: + inds.append(i) + boxes_ind.append(boxes[i]) + scores_ind.append(scores[i]) + + boxes_ind = np.array(boxes_ind) + scores_ind = np.array(scores_ind) + #landms = landms[inds] + + # keep top-K before NMS + order = np.argsort(scores_ind, axis=0)[::-1][:top_k] + boxes_order = [] + scores_order = [] + for order_i in order: + boxes_order.append(boxes_ind[order_i]) + scores_order.append(scores_ind[order_i]) + if len(boxes_order)==0: + return [] + #print('***',boxes_order,len(boxes_order)) + #print('***',scores_order,len(scores_order)) + boxes_order = np.array(boxes_order) + scores_order = np.array(scores_order).reshape((-1,1)) + #landms = landms[order] + + # do NMS + dets = np.concatenate((boxes_order, scores_order), axis=1) + keep = py_cpu_nms(dets, nms_threshold) + + dets_out = [] + for keep_i in keep: + dets_out.append(dets[keep_i]) + dets_out = np.array(dets_out) + #landms = landms[keep] + + # keep top-K faster NMS + dets_out = dets_out[:keep_top_k, :] + #landms = landms[:args.keep_top_k, :] + # dets = np.concatenate((dets, landms), axis=1) + return dets_out + + +def face_detect_test(): + print("face_detect_test start") + + #初始化KPU + kpu = nn.kpu() + #初始化AI2D + ai2d = nn.ai2d() + #加载模型 + kpu.load_kmodel("/sdcard/app/tests/nncase_runtime/face_detection/face_detection_320.kmodel") + ai2d.set_dtype(nn.ai2d_format.NCHW_FMT, + nn.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + ai2d.set_pad_param(True, [0,0,0,0,0,125,0,0], 0, [104,117,123]) + ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel ) + ai2d_builder = ai2d.build([1,3,OUT_RGB888P_HEIGH,OUT_RGB888P_WIDTH], [1,3,320,320]) + + + #初始化HDMI显示 + display.init(LT9611_1920X1080_30FPS) + + #创建osd图层使用的vb + config = k_vb_config() + config.max_pool_cnt = 1 + config.comm_pool[0].blk_size = 4*DISPLAY_WIDTH*DISPLAY_HEIGHT + config.comm_pool[0].blk_cnt = 1 + config.comm_pool[0].mode = VB_REMAP_MODE_NOCACHE + #配置媒体缓冲区 + ret = media.buffer_config(config) + #初始化默认sensor配置（OV5647） + camera.sensor_init(CAM_DEV_ID_0, CAM_DEFAULT_SENSOR) + + #设置通道0输出尺寸 + camera.set_outsize(CAM_DEV_ID_0, CAM_CHN_ID_0, DISPLAY_WIDTH, DISPLAY_HEIGHT) + #设置通道0输出格式 + camera.set_outfmt(CAM_DEV_ID_0, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + #创建媒体数据源设备 + meida_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + #创建媒体数据接收设备 + meida_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + #创建媒体链路，数据从源设备流到接收设备 + media.create_link(meida_source, meida_sink) + #设置视频层 + display.set_plane(0, 0, DISPLAY_WIDTH, DISPLAY_HEIGHT, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + + # set chn1 output rgb888 + #camera.set_outsize(CAM_DEV_ID_0, CAM_CHN_ID_1, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + #camera.set_outfmt(CAM_DEV_ID_0, CAM_CHN_ID_1, PIXEL_FORMAT_RGB_888) + + #设置通道2输出尺寸 + camera.set_outsize(CAM_DEV_ID_0, CAM_CHN_ID_2, OUT_RGB888P_WIDTH, OUT_RGB888P_HEIGH) + #设置通道2输出格式 + camera.set_outfmt(CAM_DEV_ID_0, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + + try: + #初始化媒体缓冲区 + ret = media.buffer_init() + if ret: + print("face_detect_test, buffer init failed") + return ret + #请求媒体缓冲区用于OSD + buffer = media.request_buffer(4*DISPLAY_WIDTH*DISPLAY_HEIGHT) + #创建用于绘制人脸框信息的image对象 + draw_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, alloc=image.ALLOC_MPGC) + #创建用于OSD显示的image对象 + osd_img = image.Image(DISPLAY_WIDTH, DISPLAY_HEIGHT, image.ARGB8888, poolid=buffer.pool_id, alloc=image.ALLOC_VB, phyaddr=buffer.phys_addr, virtaddr=buffer.virt_addr) + #启动摄像头 + camera.start_stream(CAM_DEV_ID_0) + time.sleep(5) + + rgb888p_img = None + while True: + #捕获摄像头数据 + rgb888p_img = camera.capture_image(CAM_DEV_ID_0, CAM_CHN_ID_2) + if rgb888p_img == -1: + print("face_detect_test, capture_image failed") + #释放捕获的数据 + camera.release_image(CAM_DEV_ID_0, CAM_CHN_ID_2, rgb888p_img) + continue + + # for rgb888planar + if rgb888p_img.format() == image.RGBP888: + ai2d_input = rgb888p_img.to_numpy_ref() + + ai2d_input_tensor = nn.from_numpy(ai2d_input) + + data = np.ones((1,3,320,320),dtype=np.uint8) + ai2d_out = nn.from_numpy(data) + + ai2d_builder.run(ai2d_input_tensor, ai2d_out) + + #设置kpu输入 + kpu.set_input_tensor(0, ai2d_out) + #运行kmodel + kpu.run() + + del ai2d_input_tensor + del ai2d_out + # get output + results = [] + for i in range(kpu.outputs_size()): + data = kpu.get_output_tensor(i) + result = data.to_numpy() + + tmp = (result.shape[0],result.shape[1],result.shape[2],result.shape[3]) + result = result.reshape((result.shape[0]*result.shape[1],result.shape[2]*result.shape[3])) + result = result.transpose() + tmp2 = result.copy() + tmp2 = tmp2.reshape((tmp[0],tmp[2],tmp[3],tmp[1])) + del result + results.append(tmp2) + gc.collect() + + #获取人脸检测结果 + dets = get_result(results) + if dets: + draw_img.clear() + for det in dets: + x1, y1, x2, y2 = map(lambda x: int(round(x, 0)), det[:4]) + w = (x2 - x1) * DISPLAY_WIDTH // OUT_RGB888P_WIDTH + h = (y2 - y1) * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH + #绘制人脸框 + draw_img.draw_rectangle(x1 * DISPLAY_WIDTH // OUT_RGB888P_WIDTH, y1 * DISPLAY_HEIGHT // OUT_RGB888P_HEIGH, w, h, color=(255,255,0,255)) + #拷贝人脸框信息到OSD对象 + draw_img.copy_to(osd_img) + #显示人脸框 + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + else: + draw_img.clear() + draw_img.draw_rectangle(0, 0, 128, 128, color=(0,0,0,0)) + draw_img.copy_to(osd_img) + display.show_image(osd_img, 0, 0, DISPLAY_CHN_OSD3) + #释放采集的图像 + camera.release_image(CAM_DEV_ID_0, CAM_CHN_ID_2, rgb888p_img) + rgb888p_img = None + + except Exception as e: + print(f"An error occurred during buffer used: {e}") + finally: + if rgb888p_img is not None: + camera.release_image(CAM_DEV_ID_0, CAM_CHN_ID_2, rgb888p_img) + #停止摄像头输出 + camera.stop_stream(CAM_DEV_ID_0) + #去初始化显示设备 + display.deinit() + #释放媒体缓冲区 + media.release_buffer(buffer) + #销毁媒体链路 + media.destroy_link(meida_source, meida_sink) + + del kpu #释放kpu资源 + del ai2d #释放ai2d资源 + gc.collect() + + time.sleep(1) + #去初始化媒体缓冲区资源 + ret = media.buffer_deinit() + if ret: + print("face_detect_test, buffer_deinit failed") + return ret + + print("face_detect_test end") + return 0 + + +face_detect_test() + +``` + +具体接口使用请参考相关文档说明 diff --git a/zh/example/example.rst b/zh/example/example.rst new file mode 100755 index 0000000..2d323c1 --- /dev/null +++ b/zh/example/example.rst @@ -0,0 +1,24 @@ +CanMV 例程讲解 +=========== +.. toctree:: + :maxdepth: 1 + + K230_CanMV_AI_Demo示例说明.md + K230_Canmv示例讲解-人脸检测.md + cipher/cipher.md + machine/adc/adc.md + machine/fft/fft.md + machine/fpioa/fpioa.md + machine/gpio/gpio.md + machine/pwm/pwm.md + machine/spi/spi.md + machine/timer/timer.md + machine/wdt/wdt.md + media/acodec.md + media/audio.md + media/camera.md + media/display.md + media/media.md + media/mp4muxer.md + media/player.md + media/venc.md \ No newline at end of file diff --git a/zh/template/images/canaan-cover.png b/zh/example/images/canaan-cover.png old mode 100644 new mode 100755 similarity index 100% rename from zh/template/images/canaan-cover.png rename to zh/example/images/canaan-cover.png diff --git a/zh/01_software/example/images/canmv-face-detect-result.jpg b/zh/example/images/canmv-face-detect-result.jpg similarity index 100% rename from zh/01_software/example/images/canmv-face-detect-result.jpg rename to zh/example/images/canmv-face-detect-result.jpg diff --git a/zh/01_software/example/images/canmv-face-detect-run.png b/zh/example/images/canmv-face-detect-run.png similarity index 100% rename from zh/01_software/example/images/canmv-face-detect-run.png rename to zh/example/images/canmv-face-detect-run.png diff --git a/zh/template/images/logo.png b/zh/example/images/logo.png similarity index 100% rename from zh/template/images/logo.png rename to zh/example/images/logo.png diff --git a/zh/example/machine/adc/adc.md b/zh/example/machine/adc/adc.md new file mode 100755 index 0000000..d1e4227 --- /dev/null +++ b/zh/example/machine/adc/adc.md @@ -0,0 +1,14 @@ +# adc - adc例程 + +本示例程序用于对 CanMV 开发板进行一个adc的功能展示。 + +```python +from machine import ADC + +adc = ADC(0,enable=True) #构造adc对象，通道0默认开启 +value = adc.value() #获取通道0的数值 +print("value = %d" % value) +adc.deinit() #注销adc对象 +``` + +具体接口定义请参考 [ADC](../../../api/machine/K230_CanMV_ADC模块API手册.md) diff --git a/zh/example/machine/fft/fft.md b/zh/example/machine/fft/fft.md new file mode 100755 index 0000000..003ac9f --- /dev/null +++ b/zh/example/machine/fft/fft.md @@ -0,0 +1,34 @@ +# fft - fft例程 + +本示例程序用于对 CanMV 开发板进行一个FFT的功能展示。 + +```python +from machine import FFT +import array +import math +from ulab import numpy as np +PI = 3.14159265358979323846264338327950288419716939937510 + +rx = [] +def input_data(): + for i in range(64): + data0 = 10 *math.cos(2* PI *i / 64) + data1 = 20 * math.cos(2 * 2* PI *i / 64) + data2 = 30* math.cos(3 *2* PI *i / 64) + data3 = 0.2* math.cos(4 *2 * PI * i / 64) + data4 = 1000* math.cos(5 *2* PI * i / 64) + rx.append((int(data0 + data1 + data2 + data3 + data4))) +input_data() #初始化需要进行FFT的数据，列表类型 +print(rx) +data = np.array(rx,dtype=np.uint16) #把列表数据转换成数组 +print(data) +fft1 = FFT(data, 64, 0x555) #创建一个FFT对象,运算点数为64，偏移是0x555 +res = fft1.run() #获取FFT转换后的数据 +print(res) +res = fft1.amplitude(res) #获取各个频率点的幅值 +print(res) +res = fft1.freq(64,38400) #获取所有频率点的频率值 +print(res) +``` + +具体接口定义请参考 [FFT](../../../api/machine/K230_CanMV_FFT模块API手册.md) diff --git a/zh/example/machine/fpioa/fpioa.md b/zh/example/machine/fpioa/fpioa.md new file mode 100755 index 0000000..d836e41 --- /dev/null +++ b/zh/example/machine/fpioa/fpioa.md @@ -0,0 +1,25 @@ +# fpioa - iomux fpioa例程 + +本示例程序用于对 CanMV 开发板进行iomux功能展示。 + +```python +from machine import FPIOA +a = FPIOA() + +help(a) #查看本模块所有帮助； + +for i in range(63): + a.help(i) #查看第i个管脚的帮助 + +for i in range(221): + a.get_Pin_num(i) #获取某个功能对应的管脚号 + +a.help(60) #查看第60个管脚的当前配置和可配置功能 +a.set_function(60,a.GPIO60) #把第60个管脚设置为gpio60功能。 +a.help(60) #查看第60个管脚的帮助 + +a.set_function(60,set_pd=1) #使能第60个管脚的下拉 其他可配选项为(set_sl=1,set_ie=0,set_oe=0,set_pd=1,set_pu=0,set_ds=6,set_st=0,set_di=1) +a.help(60) +``` + +具体接口定义请参考 [IOMUX_FPIOA](../../../api/machine/K230_CanMV_IOMUX_API手册.md) diff --git a/zh/example/machine/gpio/gpio.md b/zh/example/machine/gpio/gpio.md new file mode 100755 index 0000000..455af02 --- /dev/null +++ b/zh/example/machine/gpio/gpio.md @@ -0,0 +1,15 @@ +# gpio - gpio例程 + +本示例程序用于对 CanMV 开发板进行一个GPIO的功能展示。 + +```python +from machine import GPIO +gpio = GPIO(8, GPIO.OUT, GPIO.PULL_UP, value=0) #构造GPIO对象，gpio编号为8，设置为上拉输出低电平 +value = gpio.value() #获取gpio的值 +print("value = %d" % value) +gpio.value(1) #设置gpio输出为高电平 +value = gpio.value() +print("value = %d" % value) +``` + +具体接口定义请参考 [GPIO](../../../api/machine/K230_CanMV_GPIO模块API手册.md) diff --git a/zh/example/machine/i2c/i2c.md b/zh/example/machine/i2c/i2c.md new file mode 100755 index 0000000..ca7a0ff --- /dev/null +++ b/zh/example/machine/i2c/i2c.md @@ -0,0 +1,54 @@ +# i2c - i2c例程 + +本示例程序用于对 CanMV 开发板进行i2c读取hdmi id的功能展示。 + +```python +from machine import I2C +# 实例化I2C4,使用默认100KHz时钟,7 bit地址模式 +i2c4=I2C(4) + +# 扫描I2C4连接的hdmi地址 +a=i2c4.scan() +# 打印I2C4连接的hdmi地址，59 +print(a) + +# hdmi地址0x3b(即59),写hdmi页地址寄存器（0xff）为0x80,位宽为8bits +i2c4.writeto_mem(0x3b,0xff,bytes([0x80]),mem_size=8) +# hdmi地址0x3b(即59),读id地址0,位宽为8bits,值为0x17 +i2c4.readfrom_mem(0x3b,0x00,1,mem_size=8) +# hdmi地址0x3b(即59),读id地址1,位宽为8bits,值为0x2 +i2c4.readfrom_mem(0x3b,0x01,1,mem_size=8) + +# hdmi地址0x3b(即59),写hdmi页地址寄存器（0xff）为0x80 +i2c4.writeto(0x3b,bytes([0xff,0x80]),True) +# hdmi地址0x3b(即59)，发送要被读取的id地址0 +i2c4.writeto(0x3b,bytes([0x00]),True) +# hdmi地址0x3b(即59)，读返回的id,值为0x17 +i2c4.readfrom(0x3b,1) +# hdmi地址0x3b(即59)，发送要被读取的id地址1 +i2c4.writeto(0x3b,bytes([0x01]),True) +# hdmi地址0x3b(即59)，读返回的id,值为0x2 +i2c4.readfrom(0x3b,1) + +# hdmi地址0x3b(即59),写hdmi页地址寄存器（0xff）为0x80,位宽为8bits +i2c4.writeto_mem(0x3b,0xff,bytes([0x80]),mem_size=8) +# 创建长度为1的接收buff +a=bytearray(1) +# hdmi地址0x3b(即59),读id地址0,位宽为8bits +i2c4.readfrom_mem_into(0x3b,0x0,a,mem_size=8) +# 打印buff,值为0x17 +print(a) + +# hdmi地址0x3b(即59),写hdmi页地址寄存器（0xff）为0x80 +i2c4.writeto(0x3b,bytes([0xff,0x80]),True) +# hdmi地址0x3b(即59),发送要被读取的id地址0 +i2c4.writeto(0x3b,bytes([0x00]),True) +# 创建长度为1的接收buff +b=bytearray(1) +# hdmi地址0x3b(即59),读返回的id, +i2c4.readfrom_into(0x3b,b) +# 打印buff,值为0x17 +print(b) +``` + +具体接口定义请参考 [I2C](../../../api/machine/K230_CanMV_I2C模块API手册.md) diff --git a/zh/example/machine/pwm/pwm.md b/zh/example/machine/pwm/pwm.md new file mode 100755 index 0000000..43072fa --- /dev/null +++ b/zh/example/machine/pwm/pwm.md @@ -0,0 +1,19 @@ +# pwm - pwm例程 + +本示例程序用于对 CanMV 开发板进行一个PWM输出的功能展示。 + +```python +from machine import PWM +# 实例化PWM通道0，频率为1000Hz，占空比为50%，默认使能输出 +pwm0 = PWM(0, 1000, 50, enable = True) +# 关闭通道0输出 +pwm0.enable(0) +# 调整通道0频率为2000Hz +pwm0.freq(2000) +# 调整通道0占空比为40% +pwm0.duty(40) +# 打开通道0输出 +pwm0.enable(1) +``` + +具体接口定义请参考 [PWM](../../../api/machine/K230_CanMV_PWM模块API手册.md) diff --git a/zh/example/machine/spi/spi.md b/zh/example/machine/spi/spi.md new file mode 100755 index 0000000..8b7dad3 --- /dev/null +++ b/zh/example/machine/spi/spi.md @@ -0,0 +1,63 @@ +# spi - spi例程 + +本示例程序用于对 CanMV 开发板进行spi读取flash id的功能展示。 + +```python +from machine import SPI +from machine import FPIOA + +# 实例化SPI的gpio +a = FPIOA() + +# 打印gpio14的属性 +a.help(14) +# 设置gpio14为QSPI0_CS0功能 +a.set_function(14,a.QSPI0_CS0) +a.help(14) + +# 打印gpio15的属性 +a.help(15) +# 设置gpio15为QSPI0_CLK功能 +a.set_function(15,a.QSPI0_CLK) +a.help(15) + +# 打印gpio16的属性 +a.help(16) +# 设置gpio16为QSPI0_D0功能 +a.set_function(16,a.QSPI0_D0) +a.help(16) + +# 打印gpio17的属性 +a.help(17) +# 设置gpio17为QSPI0_D1功能 +a.set_function(17,a.QSPI0_D1) +a.help(17) + +# 实例化SPI，使用5MHz时钟，极性为0，数据位宽为8bit +spi=SPI(1,baudrate=5000000, polarity=0, phase=0, bits=8) + +# 使能 gd25lq128 复位 +spi.write(bytes([0x66])) +# gd25lq128 复位 +spi.write(bytes([0x99])) + +# 读id命令（0x9f） +a=bytes([0x9f]) +# 创建长度为3的接收buff +b=bytearray(3) +# 读id +spi.write_readinto(a,b) +# 打印为：bytearray(b'\xc8`\x18') +print(b) + +# 读id命令（0x90,0,0,0） +a=bytes([0x90,0,0,0]) +# 创建长度为2的接收buff +b=bytearray(2) +# 读id +spi.write_readinto(a,b) +# 打印为：bytearray(b'\xc8\x17') +print(b) +``` + +具体接口定义请参考 [I2C](../../../api/machine/K230_CanMV_SPI模块API手册.md) diff --git a/zh/example/machine/timer/timer.md b/zh/example/machine/timer/timer.md new file mode 100755 index 0000000..3d73101 --- /dev/null +++ b/zh/example/machine/timer/timer.md @@ -0,0 +1,14 @@ +# Timer - Timer例程 + +本示例程序用于对 CanMV 开发板进行一个定时器的功能展示。 + +```python +from machine import Timer +#定义定时器回调函数 +def on_timer(arg): + print("time up: %d" % arg) +#构造定时器对象，时间为3s，默认开启定时器 +tim = Timer(mode=Timer.MODE_ONE_SHOT,period=3, unit=Timer.UNIT_S, callback=on_timer, arg=1, start=True) +``` + +具体接口定义请参考 [Timer](../../../api/machine/K230_CanMV_Timer模块API手册.md) diff --git a/zh/example/machine/wdt/wdt.md b/zh/example/machine/wdt/wdt.md new file mode 100755 index 0000000..9ccc8c9 --- /dev/null +++ b/zh/example/machine/wdt/wdt.md @@ -0,0 +1,21 @@ +# wdt - wdt例程 + +本示例程序用于对 CanMV 开发板进行一个wdt的功能展示。 + +```python +import time +from machine import WDT + +wdt1 = WDT(1,3) #构造wdt对象，/dev/watchdog1,timeout为3s +print('into', wdt1) +time.sleep(2) #延时2s +print(time.ticks_ms()) +## 1.test wdt feed +wdt1.feed() #喂狗操作 +time.sleep(2) #延时2s +print(time.ticks_ms()) +## 2.test wdt stop +wdt1.stop() #停止喂狗 +``` + +具体接口定义请参考 [WDT](../../../api/machine/K230_CanMV_WDT模块API手册.md) diff --git a/zh/example/media/acodec.md b/zh/example/media/acodec.md new file mode 100755 index 0000000..25fc63f --- /dev/null +++ b/zh/example/media/acodec.md @@ -0,0 +1,145 @@ +# acodec - g711编解码例程 + +本示例程序用于对 CanMV 开发板进行一个g711编解码的功能展示。 + +```python +from media.pyaudio import * #导入pyaudio模块，用于采集和播放音频 +from media.media import * #导入media模块，用于初始化vb buffer +import media.g711 as g711 #导入g711模块，用于g711编解码 +from mpp.payload_struct import * #导入payload模块，用于获取音视频编解码类型 + +def encode_audio(filename, duration): + CHUNK = int(44100/25) #设置音频chunk值 + FORMAT = paInt16 #设置采样精度 + CHANNELS = 2 #设置声道数 + RATE = 44100 #设置采样率 + + p = PyAudio() + p.initialize(CHUNK) #初始化PyAudio对象 + enc = g711.Encoder(K_PT_G711A,CHUNK) #创建g711编码器对象 + ret = media.buffer_init() #vb buffer初始化 + if ret: + print("record_audio, buffer_init failed") + + enc.create() #创建编码器 + #创建音频输入流 + stream = p.open(format=FORMAT, + channels=CHANNELS, + rate=RATE, + input=True, + frames_per_buffer=CHUNK) + + frames = [] + #采集音频数据编码并存入列表 + for i in range(0, int(RATE / CHUNK * duration)): + frame_data = stream.read() #从音频输入流中读取音频数据 + data = enc.encode(frame_data) #编码音频数据为g711 + frames.append(data) #将g711编码数据保存到列表中 + + stream.stop_stream() #停止音频输入流 + stream.close() #关闭音频输入流 + p.terminate() #释放音频对象 + enc.destroy() #销毁g711音频编码器 + + #将g711编码数据存入文件中 + with open(filename,mode='w') as wf: + wf.write(b''.join(frames)) + + media.buffer_deinit() #释放vb buffer + +def decode_audio(filename): + wf = open(filename,mode='rb') #打开g711文件 + FORMAT = paInt16 #设置音频chunk值 + CHANNELS = 2 #设置声道数 + RATE = 44100 #设置采样率 + CHUNK = int(RATE/25) #设置音频chunk值 + + p = PyAudio() + p.initialize(CHUNK) #初始化PyAudio对象 + dec = g711.Decoder(K_PT_G711A,CHUNK) #创建g711解码器对象 + ret = media.buffer_init() #vb buffer初始化 + if ret: + print("play_audio, buffer_init failed") + + dec.create() #创建解码器 + + #创建音频输出流 + stream = p.open(format=FORMAT, + channels=CHANNELS, + rate=RATE, + output=True, + frames_per_buffer=CHUNK) + + stream_len = CHUNK*CHANNELS*2//2 #设置每次读取的g711数据流长度 + stream_data = wf.read(stream_len) #从g711文件中读取数据 + + #解码g711文件并播放 + while stream_data: + frame_data = dec.decode(stream_data) #解码g711文件 + stream.write(frame_data) #播放raw数据 + stream_data = wf.read(stream_len) #从g711文件中读取数据 + + stream.stop_stream() #停止音频输入流 + stream.close() #关闭音频输入流 + p.terminate() #释放音频对象 + dec.destroy() #销毁解码器 + wf.close() #关闭g711文件 + + media.buffer_deinit() #释放vb buffer + +def loop_codec(duration): + CHUNK = int(44100/25) #设置音频chunk值 + FORMAT = paInt16 #设置采样精度 + CHANNELS = 2 #设置声道数 + RATE = 44100 #设置采样率 + + p = PyAudio() + p.initialize(CHUNK) #初始化PyAudio对象 + dec = g711.Decoder(K_PT_G711A,CHUNK) #创建g711解码器对象 + enc = g711.Encoder(K_PT_G711A,CHUNK) #创建g711编码器对象 + ret = media.buffer_init() #vb buffer初始化 + if ret: + print("loop_audio, buffer_init failed") + + dec.create() #创建g711解码器 + enc.create() #创建g711编码器 + + #创建音频输入流 + input_stream = p.open(format=FORMAT, + channels=CHANNELS, + rate=RATE, + input=True, + frames_per_buffer=CHUNK) + + #创建音频输出流 + output_stream = p.open(format=FORMAT, + channels=CHANNELS, + rate=RATE, + output=True, + frames_per_buffer=CHUNK) + + #从音频输入流中获取数据->编码->解码->写入到音频输出流中 + for i in range(0, int(RATE / CHUNK * duration)): + frame_data = input_stream.read() #从音频输入流中获取raw音频数据 + stream_data = enc.encode(frame_data) #编码音频数据为g711 + frame_data = dec.decode(stream_data) #解码g711数据为raw数据 + output_stream.write(frame_data) #播放raw数据 + + + input_stream.stop_stream() #停止音频输入流 + output_stream.stop_stream() #停止音频输出流 + input_stream.close() #关闭音频输入流 + output_stream.close() #关闭音频输出流 + p.terminate() #释放音频对象 + dec.destroy() #销毁g711解码器 + enc.destroy() #销毁g711编码器 + + media.buffer_deinit() #释放vb buffer + +#encode_audio('/sdcard/app/test.g711a', 15) #采集并编码g711文件 +#decode_audio('/sdcard/app/test.g711a') #解码g711文件并输出 +loop_codec(15) #采集音频数据->编码g711->解码g711->播放音频 +print("audio codec sample done") +``` + +具体接口定义请参考 [acodec](../../api/mpp/K230_CanMV_播放器模块API手册.md) diff --git a/zh/example/media/audio.md b/zh/example/media/audio.md new file mode 100755 index 0000000..0039365 --- /dev/null +++ b/zh/example/media/audio.md @@ -0,0 +1,125 @@ +# audio - 音频采集输出例程 + +本示例程序用于对 CanMV 开发板进行一个音频采集和输出的功能展示。 + +```python +from media.pyaudio import * #导入pyaudio模块，用于采集和播放音频 +import media.wave as wave #导入wav模块，用于保存和加载wav音频文件 +from media.media import * #导入media模块，用于初始化vb buffer + +def record_audio(filename, duration): + CHUNK = int(44100/25) #设置音频chunk值 + FORMAT = paInt16 #设置采样精度 + CHANNELS = 2 #设置声道数 + RATE = 44100 #设置采样率 + + p = PyAudio() + p.initialize(CHUNK) #初始化PyAudio对象 + ret = media.buffer_init() #vb buffer初始化 + if ret: + print("record_audio, buffer_init failed") + + #创建音频输入流 + stream = p.open(format=FORMAT, + channels=CHANNELS, + rate=RATE, + input=True, + frames_per_buffer=CHUNK) + + frames = [] + #采集音频数据并存入列表 + for i in range(0, int(RATE / CHUNK * duration)): + data = stream.read() + frames.append(data) + + + stream.stop_stream() #停止采集音频数据 + stream.close()#关闭音频输入流 + p.terminate()#释放音频对象 + + #将列表中的数据保存到wav文件中 + wf = wave.open(filename, 'wb') #创建wav 文件 + wf.set_channels(CHANNELS) #设置wav 声道数 + wf.set_sampwidth(p.get_sample_size(FORMAT)) #设置wav 采样精度 + wf.set_framerate(RATE) #设置wav 采样率 + wf.write_frames(b''.join(frames)) #存储wav音频数据 + wf.close() #关闭wav文件 + + media.buffer_deinit() #释放vb buffer + +def play_audio(filename): + + wf = wave.open(filename, 'rb')#打开wav文件 + CHUNK = int(wf.get_framerate()/25)#设置音频chunk值 + + p = PyAudio() + p.initialize(CHUNK) #初始化PyAudio对象 + ret = media.buffer_init()#vb buffer初始化 + if ret: + print("play_audio, buffer_init failed") + + #创建音频输出流，设置的音频参数均为wave中获取到的参数 + stream = p.open(format=p.get_format_from_width(wf.get_sampwidth()), + channels=wf.get_channels(), + rate=wf.get_framerate(), + output=True,frames_per_buffer=CHUNK) + + data = wf.read_frames(CHUNK)#从wav文件中读取数一帧数据 + + while data: + stream.write(data) #将帧数据写入到音频输出流中 + data = wf.read_frames(CHUNK) #从wav文件中读取数一帧数据 + + stream.stop_stream() #停止音频输出流 + stream.close()#关闭音频输出流 + p.terminate()#释放音频对象 + wf.close()#关闭wav文件 + + media.buffer_deinit()#释放vb buffer + + +def loop_audio(duration): + CHUNK = int(44100/25)#设置音频chunck + FORMAT = paInt16 #设置音频采样精度 + CHANNELS = 2 #设置音频声道数 + RATE = 44100 #设置音频采样率 + + p = PyAudio() + p.initialize(CHUNK)#初始化PyAudio对象 + ret = media.buffer_init() #初始化vb buffer + if ret: + print("loop_audio, buffer_init failed") + + #创建音频输入流 + input_stream = p.open(format=FORMAT, + channels=CHANNELS, + rate=RATE, + input=True, + frames_per_buffer=CHUNK) + + #创建音频输出流 + output_stream = p.open(format=FORMAT, + channels=CHANNELS, + rate=RATE, + output=True,frames_per_buffer=CHUNK) + + #从音频输入流中获取数据写入到音频输出流中 + for i in range(0, int(RATE / CHUNK * duration)): + output_stream.write(input_stream.read()) + + + input_stream.stop_stream()#停止音频输入流 + output_stream.stop_stream()#停止音频输出流 + input_stream.close() #关闭音频输入流 + output_stream.close() #关闭音频输出流 + p.terminate() #释放音频对象 + + media.buffer_deinit() #释放vb buffer + +#play_audio('/sdcard/app/input.wav') #播放wav文件 +#record_audio('/sdcard/app/output.wav', 15) #录制wav文件 +loop_audio(15) #采集音频并输出 +print("audio sample done") +``` + +具体接口定义请参考 [audio](../../api/mpp/K230_CanMV_Audio模块API手册.md) diff --git a/zh/example/media/camera.md b/zh/example/media/camera.md new file mode 100755 index 0000000..7a601f6 --- /dev/null +++ b/zh/example/media/camera.md @@ -0,0 +1,132 @@ +# Camera - 摄像头预览及图像采集示例 + +```python +from media.camera import * #导入camera模块，使用camera相关接口 +from media.display import * #导入display模块，使用display相关接口 +from media.media import * #导入media模块，使用meida相关接口 +from time import * #导入time模块，使用time相关接口 +import time +import image #导入image模块，使用image相关接口 + + +def canmv_camera_test(): + print("canmv_camera_test") + + #初始化HDMI显示 + display.init(LT9611_1920X1080_30FPS) + + #初始化默认sensor配置（OV5647） + camera.sensor_init(CAM_DEV_ID_0, CAM_DEFAULT_SENSOR) + #camera.sensor_init(CAM_DEV_ID_0, CAM_IMX335_2LANE_1920X1080_30FPS_12BIT_LINEAR) + + out_width = 1920 + out_height = 1080 + # 设置输出宽度16字节对齐 + out_width = ALIGN_UP(out_width, 16) + + #设置通道0输出尺寸 + camera.set_outsize(CAM_DEV_ID_0, CAM_CHN_ID_0, out_width, out_height) + #设置通道0输出格式 + camera.set_outfmt(CAM_DEV_ID_0, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + #创建媒体数据源设备 + meida_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + #创建媒体数据接收设备 + meida_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + #创建媒体链路，数据从源设备流到接收设备 + media.create_link(meida_source, meida_sink) + #设置显示输出平面的属性 + display.set_plane(0, 0, out_width, out_height, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + + out_width = 640 + out_height = 480 + out_width = ALIGN_UP(out_width, 16) + + #设置通道1输出尺寸 + camera.set_outsize(CAM_DEV_ID_0, CAM_CHN_ID_1, out_width, out_height) + #设置通道1输出格式 + camera.set_outfmt(CAM_DEV_ID_0, CAM_CHN_ID_1, PIXEL_FORMAT_RGB_888) + + #设置通道2输出尺寸 + camera.set_outsize(CAM_DEV_ID_0, CAM_CHN_ID_2, out_width, out_height) + #设置通道2输出格式 + camera.set_outfmt(CAM_DEV_ID_0, CAM_CHN_ID_2, PIXEL_FORMAT_RGB_888_PLANAR) + + #初始化媒体缓冲区 + ret = media.buffer_init() + if ret: + print("canmv_camera_test, buffer init failed") + return ret + + #启动摄像头数据流 + camera.start_stream(CAM_DEV_ID_0) + time.sleep(15) + + capture_count = 0 + while capture_count < 100: + time.sleep(1) + for dev_num in range(CAM_DEV_ID_MAX): + if not camera.cam_dev[dev_num].dev_attr.dev_enable: + continue + + for chn_num in range(CAM_CHN_ID_MAX): + if not camera.cam_dev[dev_num].chn_attr[chn_num].chn_enable: + continue + + print(f"canmv_camera_test, dev({dev_num}) chn({chn_num}) capture frame.") + #从指定设备和通道捕获图像 + img = camera.capture_image(dev_num, chn_num) + if img == -1: + print("camera.capture_image failed") + continue + + if img.format() == image.YUV420: + suffix = "yuv420sp" + elif img.format() == image.RGB888: + suffix = "rgb888" + elif img.format() == image.RGBP888: + suffix = "rgb888p" + else: + suffix = "unkown" + + filename = f"/sdcard/dev_{dev_num:02d}_chn_{chn_num:02d}_{img.width()}x{img.height()}_{capture_count:04d}.{suffix}" + print("save capture image to file:", filename) + + with open(filename, "wb") as f: + if f: + img_data = uctypes.bytearray_at(img.virtaddr(), img.size()) + # save yuv data to sdcard. + #f.write(img_data) + else: + print(f"capture_image, open dump file failed({filename})") + + time.sleep(1) + #释放捕获的图像数据 + camera.release_image(dev_num, chn_num, img) + + capture_count += 1 + + #停止摄像头输出 + camera.stop_stream(CAM_DEV_ID_0) + + #去初始化显示设备 + display.deinit() + + #销毁媒体链路 + media.destroy_link(meida_source, meida_sink) + + time.sleep(1) + #去初始化媒体缓冲区资源 + ret = media.buffer_deinit() + if ret: + print("camera test, media_buffer_deinit failed") + return ret + + print("camera test exit") + return 0 + + +canmv_camera_test() +``` + +具体接口定义请参考 [camera](../../api/mpp/K230_CanMV_Camera模块API手册.md) diff --git a/zh/example/media/display.md b/zh/example/media/display.md new file mode 100755 index 0000000..3946619 --- /dev/null +++ b/zh/example/media/display.md @@ -0,0 +1,76 @@ +# Display - 图像采集显示实例 + +```python +from media.camera import * #导入camera模块，使用camera相关接口 +from media.display import * #导入display模块，使用display相关接口 +from media.media import * #导入media模块，使用meida相关接口 +from time import * #导入time模块，使用time相关接口 +import time #导入time模块，使用time相关接口 + + +def camera_display_test(): + CAM_OUTPUT_BUF_NUM = 6 + CAM_INPUT_BUF_NUM = 4 + + #定义输出窗口的宽度和高度，并进行对齐 + out_width = 1080 + out_height = 720 + out_width = ALIGN_UP(out_width, 16) + + #初始化HDMI显示 + display.init(LT9611_1920X1080_30FPS) + #初始化默认sensor配置（OV5647） + camera.sensor_init(CAM_DEV_ID_0, CAM_DEFAULT_SENSOR) + + #设置通道buffer数量 + camera.set_outbufs(CAM_DEV_ID_0, CAM_CHN_ID_0, CAM_OUTPUT_BUF_NUM) + #设置通道0输出尺寸 + camera.set_outsize(CAM_DEV_ID_0, CAM_CHN_ID_0, out_width, out_height) + #设置通道0输出格式 + camera.set_outfmt(CAM_DEV_ID_0, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + #创建媒体数据源设备 + meida_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + #创建媒体数据接收设备 + meida_sink = media_device(DISPLAY_MOD_ID, DISPLAY_DEV_ID, DISPLAY_CHN_VIDEO1) + #创建媒体链路，数据从源设备流到接收设备 + media.create_link(meida_source, meida_sink) + #设置显示输出平面的属性 + display.set_plane(400, 200, out_width, out_height, PIXEL_FORMAT_YVU_PLANAR_420, DISPLAY_MIRROR_NONE, DISPLAY_CHN_VIDEO1) + + #初始化媒体缓冲区 + ret = media.buffer_init() + if ret: + print("camera_display_test, buffer init failed") + return ret + + #启动摄像头数据流 + camera.start_stream(CAM_DEV_ID_0) + + #采集显示600s后，停止采集输出 + count = 0 + while count < 600: + time.sleep(1) + count += 1 + + #停止摄像头输出 + camera.stop_stream(CAM_DEV_ID_0) + #销毁媒体链路 + media.destroy_link(meida_source, meida_sink) + time.sleep(1) + #去初始化显示设备 + display.deinit() + #去初始化媒体缓冲区资源 + ret = media.buffer_deinit() + if ret: + print("camera_display_test, media_buffer_deinit failed") + return ret + + print("camera_display_test exit") + return 0 + +camera_display_test() + +``` + +具体接口定义请参考 [display](../../api/mpp/K230_CanMV_Display模块API手册.md) diff --git a/zh/example/media/media.md b/zh/example/media/media.md new file mode 100755 index 0000000..6cbe01e --- /dev/null +++ b/zh/example/media/media.md @@ -0,0 +1,131 @@ +# Meida - meida模块接口应用示例 + +```python +from media.media import * #导入media模块，用于初始化vb buffer + +def media_buf_test(): + print("media_buf_test start") + config = k_vb_config() #初始化vb config对象 + config.max_pool_cnt = 10 + + #配置vb pool + config.comm_pool[0].blk_size = 1024*1024 + config.comm_pool[0].blk_cnt = 10 + config.comm_pool[0].mode = VB_REMAP_MODE_NONE + + config.comm_pool[1].blk_size = 2*1024*1024 + config.comm_pool[1].blk_cnt = 10 + config.comm_pool[1].mode = VB_REMAP_MODE_NOCACHE + + config.comm_pool[2].blk_size = 3*1024*1024 + config.comm_pool[2].blk_cnt = 10 + config.comm_pool[3].mode = VB_REMAP_MODE_CACHED + + print("media_buf_test buffer_config 111") + #配置媒体缓冲区参数 + ret = media.buffer_config(config) + if ret: + print("media_buf_test, buffer_config failed") + return ret + + config.max_pool_cnt = 10 + + config.comm_pool[0].blk_size = 1024*1024 + config.comm_pool[0].blk_cnt = 10 + config.comm_pool[0].mode = VB_REMAP_MODE_NONE + + config.comm_pool[1].blk_size = 2*1024*1024 + config.comm_pool[1].blk_cnt = 10 + config.comm_pool[1].mode = VB_REMAP_MODE_NOCACHE + + config.comm_pool[2].blk_size = 3*1024*1024 + config.comm_pool[2].blk_cnt = 10 + config.comm_pool[3].mode = VB_REMAP_MODE_CACHED + + print("media_buf_test buffer_config 222") + #配置媒体缓冲区参数 + ret = media.buffer_config(config) + if ret: + print("media_buf_test, buffer_config failed") + return ret + + config.max_pool_cnt = 20 + + config.comm_pool[0].blk_size = 4*1024*1024 + config.comm_pool[0].blk_cnt = 3 + config.comm_pool[0].mode = VB_REMAP_MODE_NONE + + config.comm_pool[1].blk_size = 5*1024*1024 + config.comm_pool[1].blk_cnt = 3 + config.comm_pool[1].mode = VB_REMAP_MODE_NOCACHE + + config.comm_pool[2].blk_size = 6*1024*1024 + config.comm_pool[2].blk_cnt = 3 + config.comm_pool[3].mode = VB_REMAP_MODE_CACHED + + print("media_buf_test buffer_config 333") + #配置媒体缓冲区参数 + ret = media.buffer_config(config) + if ret: + print("media_buf_test, buffer_config failed") + return ret + + config.max_pool_cnt = 30 + + config.comm_pool[0].blk_size = 4*1024*1024 + config.comm_pool[0].blk_cnt = 5 + config.comm_pool[0].mode = VB_REMAP_MODE_NONE + + config.comm_pool[1].blk_size = 4*1024*1024 + config.comm_pool[1].blk_cnt = 5 + config.comm_pool[1].mode = VB_REMAP_MODE_NOCACHE + + config.comm_pool[2].blk_size = 4*1024*1024 + config.comm_pool[2].blk_cnt = 5 + config.comm_pool[3].mode = VB_REMAP_MODE_CACHED + + print("media_buf_test buffer_config 444") + #配置媒体缓冲区参数 + ret = media.buffer_config(config) + if ret: + print("media_buf_test, buffer_config failed") + return ret + + print("media_buf_test buffer_init") + #初始化媒体缓冲区 + ret = media.buffer_init() + if ret: + print("media_buf_test, buffer_init failed") + + + print("media_buf_test request_buffer") + #请求指定大小的媒体缓冲区 + buffer = media.request_buffer(4*1024*1024) + if buffer == -1: + print("media_buf_test, request_buffer failed") + else: + print(f"buffer handle({buffer.handle})") + print(f"buffer pool_id({buffer.pool_id})") + print(f"buffer phys_addr({buffer.phys_addr})") + print(f"buffer virt_addr({buffer.virt_addr})") + print(f"buffer size({buffer.size})") + #释放媒体缓冲区 + ret = media.release_buffer(buffer) + if ret: + print("media_buf_test, release_buffer failed") + + print("media_buf_test buffer_deinit") + #去初始化媒体缓冲区 + ret = media.buffer_deinit() + if ret: + print("media_buf_test, buffer_deinit failed") + return ret + + print("media_buf_test end") + return 0 + + +media_buf_test() +``` + +具体接口定义请参考 [meida](../../api/mpp/K230_CanMV_Media模块API手册.md) diff --git a/zh/example/media/mp4muxer.md b/zh/example/media/mp4muxer.md new file mode 100755 index 0000000..fe3e86c --- /dev/null +++ b/zh/example/media/mp4muxer.md @@ -0,0 +1,63 @@ +# venc - venc例程 + +本示例程序用于在 CanMV 开发板进行mp4 muxer的功能展示。 + +```python +from media.mp4format import * + +def canmv_mp4_muxer_test(): + width = 1280 + height = 720 + + # 实例化mp4 container + mp4_muxer = Mp4Container() + + mp4_cfg = Mp4CfgStr(mp4_muxer.MP4_CONFIG_TYPE_MUXER) + if mp4_cfg.type == mp4_muxer.MP4_CONFIG_TYPE_MUXER: + file_name = "/sdcard/app/tests/test.mp4" + mp4_cfg.SetMuxerCfg(file_name, mp4_muxer.MP4_CODEC_ID_H265, width, height, mp4_muxer.MP4_CODEC_ID_G711U) + + # 创建mp4 muxer + ret = mp4_muxer.Create(mp4_cfg) + if ret: + print("canmv_mp4_muxer_test, mp4 muxer Create failed.") + return -1 + + # 启动mp4 muxer + ret = mp4_muxer.Start() + if ret: + print("canmv_mp4_muxer_test, mp4 muxer Start failed.") + return -1 + + frame_count = 0 + + while True: + # 处理音视频数据，按MP4格式写入文件 + ret = mp4_muxer.Process() + if ret: + print("canmv_mp4_muxer_test, mp4 muxer Process failed.") + return -1 + + frame_count += 1 + print("frame_coutn = ", frame_count) + if frame_count >= 100: + break + + # 停止mp4 muxer + ret = mp4_muxer.Stop() + if ret: + print("canmv_mp4_muxer_test, mp4 muxer Stop failed.") + return -1 + + # 销毁mp4 muxer + ret = mp4_muxer.Destroy() + if ret: + print("canmv_mp4_muxer_test, mp4 muxer Destroy failed.") + return -1 + + return 0 + +canmv_mp4_muxer_test() +``` + +具体接口定义请参考 [VENC](../../api/mpp/K230_CanMV_MP4模块API手册.md) diff --git a/zh/example/media/player.md b/zh/example/media/player.md new file mode 100755 index 0000000..7df08d5 --- /dev/null +++ b/zh/example/media/player.md @@ -0,0 +1,32 @@ +# player - mp4文件播放器例程 + +本示例程序用于对 CanMV 开发板进行一个mp4文件播放器的功能展示。 + +```python +from media.player import * #导入播放器模块，用于播放mp4文件 + +start_play = False #播放结束flag +def player_event(event,data): + global start_play + if(event == K_PLAYER_EVENT_EOF): #播放结束标识 + start_play = False #设置播放结束标识 + +def play_mp4_test(filename): + global start_play + player=Player() #创建播放器对象 + player.load(filename) #加载mp4文件 + player.set_event_callback(player_event) #设置播放器事件回调 + player.start() #开始播放 + start_play = True + + #等待播放结束 + while(start_play): + time.sleep(0.1) + + player.stop() #停止播放 + print("play over") + +play_mp4_test("/sdcard/app/tests/test.mp4")#播放mp4文件 +``` + +具体接口定义请参考 [player](../../api/mpp/K230_CanMV_播放器模块API手册.md) diff --git a/zh/example/media/venc.md b/zh/example/media/venc.md new file mode 100755 index 0000000..4fc1b06 --- /dev/null +++ b/zh/example/media/venc.md @@ -0,0 +1,136 @@ +# venc - venc例程 + +本示例程序用于在 CanMV 开发板进行venc视频编码的功能展示。 + +```python +from media.vencoder import * +from media.camera import * +from media.media import * +from time import * +import time + +def canmv_venc_test(): + width = 1280 + height = 720 + venc_chn = VENC_CHN_ID_0 + + width = ALIGN_UP(width, 16) + # 初始化sensor + camera.sensor_init(CAM_DEV_ID_0, CAM_DEFAULT_SENSOR) + + # 设置camera 输出buffer + camera.set_outbufs(CAM_DEV_ID_0, CAM_CHN_ID_0, 6) + # 设置camera 输出buffer size + camera.set_outsize(CAM_DEV_ID_0, CAM_CHN_ID_0, width, height) + # 设置camera 输出格式 + camera.set_outfmt(CAM_DEV_ID_0, CAM_CHN_ID_0, PIXEL_FORMAT_YUV_SEMIPLANAR_420) + + # 实例化video encoder + encoder = Encoder() + # 设置video encoder 输出buffer + ret = encoder.SetOutBufs(venc_chn, 15, width, height) + if ret: + print("canmv_venc_test, encoder SetOutBufs failed.") + return -1 + + # 初始化设置的buffer + ret = media.buffer_init() + if ret: + print("canmv_venc_test, buffer_init failed.") + return ret + + chnAttr = ChnAttrStr(encoder.PAYLOAD_TYPE_H265, encoder.H265_PROFILE_MAIN, width, height) + streamData = StreamData() + + # 创建编码器 + ret = encoder.Create(venc_chn, chnAttr) + if ret < 0: + print("canmv_venc_test, vencoder create filed.") + return ret + + # 绑定camera和venc + media_source = media_device(CAMERA_MOD_ID, CAM_DEV_ID_0, CAM_CHN_ID_0) + media_sink = media_device(VIDEO_ENCODE_MOD_ID, VENC_DEV_ID, venc_chn) + ret = media.create_link(media_source, media_sink) + if ret: + print("cam_venc_test, create link with camera failed.") + return ret + + # 开始编码 + ret = encoder.Start(venc_chn) + if ret: + print("cam_venc_test, encoder start failed") + return ret + + # 启动camera + ret = camera.start_stream(CAM_DEV_ID_0) + if ret: + print("cam_venc_test, camera start failed") + return ret + + frame_count = 0 + if chnAttr.payload_type == encoder.PAYLOAD_TYPE_H265: + suffix = "265" + elif chnAttr.payload_type == encoder.PAYLOAD_TYPE_H264: + suffix = "264" + else: + suffix = "unkown" + print("cam_venc_test, venc payload_type unsupport") + return -1 + + out_file = f"/sdcard/app/tests/venc_chn_{venc_chn:02d}.{suffix}" + print("save stream to file: ", out_file) + + with open(out_file, "wb") as fo: + while True: + ret = encoder.GetStream(venc_chn, streamData) # 获取一帧码流 + if ret < 0: + print("cam_venc_test, venc get stream failed") + return ret + + for pack_idx in range(0, streamData.pack_cnt): + stream_data = uctypes.bytearray_at(streamData.data[pack_idx], streamData.data_size[pack_idx]) + fo.write(stream_data) # 码流写文件 + print("stream size: ", streamData.data_size[pack_idx], "stream type: ", streamData.stream_type[pack_idx]) + + ret = encoder.ReleaseStream(venc_chn, streamData) # 释放一帧码流 + if ret < 0: + print("cam_venc_test, venc release stream failed") + return ret + + frame_count += 1 + if frame_count >= 100: + break + + # 停止camera + camera.stop_stream(CAM_DEV_ID_0) + + # 销毁camera和venc的绑定 + ret = media.destroy_link(media_source, media_sink) + if ret: + print("cam_venc_test, venc destroy link with camera failed.") + return ret + + # 停止编码 + ret = encoder.Stop(venc_chn) + if ret < 0: + print("cam_venc_test, venc stop failed.") + return ret + + # 销毁编码器 + ret = encoder.Destroy(venc_chn) + if ret < 0: + print("cam_venc_test, venc destroy failed.") + return ret + + # 清理buffer + ret = media.buffer_deinit() + if ret: + print("cam_venc_test, media buffer deinit failed.") + return ret + + +canmv_venc_test() +``` + +具体接口定义请参考 [VENC](../../api/mpp/K230_CanMV_VENC模块API手册.md) diff --git "a/zh/template/K230_CanMV_XXX\346\250\241\345\235\227API\346\211\213\345\206\214_\346\250\241\346\235\277.md" "b/zh/template/K230_CanMV_XXX\346\250\241\345\235\227API\346\211\213\345\206\214_\346\250\241\346\235\277.md" deleted file mode 100644 index aded074..0000000 --- "a/zh/template/K230_CanMV_XXX\346\250\241\345\235\227API\346\211\213\345\206\214_\346\250\241\346\235\277.md" +++ /dev/null @@ -1,118 +0,0 @@ -# K230 CanMV xx 模块API手册 - -![cover](images/canaan-cover.png) - -版权所有©2023北京嘉楠捷思信息技术有限公司 - -
- -## 免责声明 - -您购买的产品、服务或特性等应受北京嘉楠捷思信息技术有限公司（“本公司”，下同）及其关联公司的商业合同和条款的约束，本文档中描述的全部或部分产品、服务或特性可能不在您的购买或使用范围之内。除非合同另有约定，本公司不对本文档的任何陈述、信息、内容的正确性、可靠性、完整性、适销性、符合特定目的和不侵权提供任何明示或默示的声明或保证。除非另有约定，本文档仅作为使用指导参考。 - -由于产品版本升级或其他原因，本文档内容将可能在未经任何通知的情况下，不定期进行更新或修改。 - -## 商标声明 - -![logo](images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 - -**版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** -非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 - -
- -## 目录 - -[TOC] - -## 前言 - -### 概述 - -本文档主要介绍xxx。 - -### 读者对象 - -本文档（本指南）主要适用于以下人员： - -- 技术支持工程师 -- 软件开发工程师 - -### 缩略词定义 - -| 简称 | 说明 | -| ---- | ---- | -| XXX | xx | -| XXX | xx | - -### 修订记录 - -| 文档版本号 | 修改说明 | 修改者 | 日期 | -| ---------- | -------- | ---------- | ---------- | -| V1.0 | 初版 | 软件部 | 2023-03-10 | -| V1.1 | | | | -| V1.2 | | | | - -## 1. 概述 - -此文档介绍xxx等。 - -## 2. API描述 - -### kd_xxx_func - -【描述】 - -示例API - -【语法】 - -```python -kd_xxx_func(a) -``` - -【参数】 - -| 参数名称 | 描述 | 输入/输出 | -|-----------------|-------------------------------|-----------| -| a | 示例参数 | 输入 | - -【返回值】 - -| 返回值 | 描述 | -|---------|---------------------------------| -| 0 | 成功。 | -| 非 0 | 失败，其值为\[错误码\] | - -【注意】 -无 - -【举例】 - -无 - -【相关主题】 - -无 - -## 3. 数据结构描述 - -### k_xxx_type - -【说明】 - -示例数据结结构 - -【定义】 - -【注意事项】 - -无 - -【相关数据类型及接口】 - -## 4. 示例程序 - -### 例程1 - -### 例程2 diff --git "a/zh/01_software/K230_CanMV ramdisk\344\275\277\347\224\250\350\257\264\346\230\216.md" "b/zh/userguide/K230_CanMV ramdisk\344\275\277\347\224\250\350\257\264\346\230\216.md" similarity index 100% rename from "zh/01_software/K230_CanMV ramdisk\344\275\277\347\224\250\350\257\264\346\230\216.md" rename to "zh/userguide/K230_CanMV ramdisk\344\275\277\347\224\250\350\257\264\346\230\216.md" diff --git "a/zh/01_software/K230_CanMV_IDE\344\275\277\347\224\250\350\257\264\346\230\216.md" "b/zh/userguide/K230_CanMV_IDE\344\275\277\347\224\250\350\257\264\346\230\216.md" old mode 100644 new mode 100755 similarity index 87% rename from "zh/01_software/K230_CanMV_IDE\344\275\277\347\224\250\350\257\264\346\230\216.md" rename to "zh/userguide/K230_CanMV_IDE\344\275\277\347\224\250\350\257\264\346\230\216.md" index f26d997..9a7d749 --- "a/zh/01_software/K230_CanMV_IDE\344\275\277\347\224\250\350\257\264\346\230\216.md" +++ "b/zh/userguide/K230_CanMV_IDE\344\275\277\347\224\250\350\257\264\346\230\216.md" @@ -91,3 +91,11 @@ K230 使用的 CanMV IDE 与 K210 的一致，可以在[这里](https://github.c 第三个选项`保存文件到CanMV Cam`可以保存一个指定的文件到开发板。 ![Save](images/ide-5.png) + +### 2.4 在 IDE 中显示图像 + +openmv 提供了 image 类，对 image 对象调用 `compress_for_ide()` 方法可以将图像发送到 IDE 进行显示，如果图像来源是 camera 并且像素格式为 YUV420SP，会自动使用硬件编码器进行图像编码，此时会占用 VENC 模块的第4通道。 + +![IDE 显示图像](images/ide_image.png) + +由于 CanMV IDE 是基于2.9.2版本的OpenMV IDE，使用Qt5，性能较低，图像帧率只能达到20FPS左右，使用4.0以上版本的 OpenMV IDE 可以获得较高的帧率。 diff --git "a/zh/userguide/K230_CanMV_nncase_runtime_\344\275\277\347\224\250\350\257\264\346\230\216.md" "b/zh/userguide/K230_CanMV_nncase_runtime_\344\275\277\347\224\250\350\257\264\346\230\216.md" new file mode 100755 index 0000000..10d3c05 --- /dev/null +++ "b/zh/userguide/K230_CanMV_nncase_runtime_\344\275\277\347\224\250\350\257\264\346\230\216.md" @@ -0,0 +1,177 @@ +# K230 CanMV nncase_runtime 使用说明 + +![cover](images/canaan-cover.png) + +版权所有©2023北京嘉楠捷思信息技术有限公司 + +
+ +## 免责声明 + +您购买的产品、服务或特性等应受北京嘉楠捷思信息技术有限公司（“本公司”，下同）及其关联公司的商业合同和条款的约束，本文档中描述的全部或部分产品、服务或特性可能不在您的购买或使用范围之内。除非合同另有约定，本公司不对本文档的任何陈述、信息、内容的正确性、可靠性、完整性、适销性、符合特定目的和不侵权提供任何明示或默示的声明或保证。除非另有约定，本文档仅作为使用指导参考。 + +由于产品版本升级或其他原因，本文档内容将可能在未经任何通知的情况下，不定期进行更新或修改。 + +## 商标声明 + +![logo](images/logo.png)、“嘉楠”和其他嘉楠商标均为北京嘉楠捷思信息技术有限公司及其关联公司的商标。本文档可能提及的其他所有商标或注册商标，由各自的所有人拥有。 + +**版权所有 © 2023北京嘉楠捷思信息技术有限公司。保留一切权利。** +非经本公司书面许可，任何单位和个人不得擅自摘抄、复制本文档内容的部分或全部，并不得以任何形式传播。 + +
+ +## 目录 + +[toc] + +## 前言 + +### 概述 + +此文档介绍CanMV nncase_runtime模块，用于指导开发人员使用MicroPython调用KPU和AI2D模块。 + +### 读者对象 + +本文档（本指南）主要适用于以下人员： + +- 技术支持工程师 +- 软件开发工程师 + +### 缩略词定义 + +| 简称 | 说明 | +| ---- | ---- | +| | | + +### 修订记录 + +| 文档版本号 | 修改说明 | 修改者 | 日期 | +| ---------- | -------- | ------ | ---------- | +| V1.0 | 初版 | 杨浩琪 | 2023-11-21 | + +## 1. 概述 + +此文档介绍CanMV nncase_runtime模块如何使用。 + +## 2. 功能介绍 + +### 2.1. 导入库 + +```Python +import nncase_runtime as nn +import ulab.numpy as np +``` + +### 2.2. KPU初始化 + +初始化模型推理模块 + +```Python +kpu = nn.kpu() +``` + +### 2.3. AI2D初始化 + +初始化图像处理模块 + +```Python +ai2d = nn.ai2d() +``` + +### 2.4. 读取模型 + +读取模型有两种方式，一种是通过文件路径，一种是通过二进制数据。 + +```Python +# 文件路径 +model = nn.load_model('test.kmodel') + +# 二进制数据 +with open("test.kmodel", "rb") as f: + data = f.read() + kpu.load_kmodel(data) +``` + +### 2.5. 单独使用KPU进行推理 + +#### 2.5.1. 设置模型输入 + +开始模型推理前，需要设置对应的模型输入数据 + +```Python +data = np.zeros((1,3,320,320),dtype=np.uint8) +kpu_input = nn.from_numpy(data) +kpu.set_input_tensor(0, kpu_input) + +# 模型存在多个输入 +kpu.set_input_tensor(1, kpu_input_1) +kpu.set_input_tensor(2, kpu_input_2) +``` + +#### 2.5.2. 执行推理并获取推理结果 + +```Python +kpu.run() + +result = kpu.get_output_tensor(i) # 返回第i个输出tensor +data = result.to_numpy() # 将输出tensor转换为numpy对象 +``` + +### 2.6. 使用AI2D+KPU进行推理 + +这里使用AI2D对图像进行预处理，然后使用KPU进行推理。如果使用摄像头等输入设备，请参考[AI Demo示例说明](../example/K230_CanMV_AI_Demo示例说明.md#14-nncase使用ai2d) + +#### 2.6.1. 配置AI2D参数 + +AI2D功能有：`crop`，`shift`，`pad`，`resize`，`affine`。可以根据实际需求配置对应的参数，不使用的功能不需要配置。 +各个场景的不同用法请参考[AI demo](../example/K230_CanMV_AI_Demo示例说明.md)中第三章<<三、AI Demo多模型示例解析>>。 + +```Python +# 基础配置： 输入、输出layout，输入、输出dtype +ai2d.set_dtype(nncase_runtime.ai2d_format.NCHW_FMT, + nncase_runtime.ai2d_format.NCHW_FMT, + np.uint8, np.uint8) + +# 功能配置，以pad和resize为例 +ai2d.set_pad_param(True, [0,0,0,0,1,1,2,2], 0, [127,127,127]) +ai2d.set_resize_param(True, nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel) + +# 执行配置,需要配置输入、输出shape +ai2d_builder = ai2d.build([1,3,224,224], [1,3,256,256]) +``` + +#### 2.6.2. 串联使用AI2D和KPU + +```Python +data = np.zeros((1,3,224,224),dtype=np.uint8) +ai2d_input = nn.from_numpy(data) + +# 获取KPU的输入tensor +kpu_input = kpu.get_input_tensor(0) + +# 将KPU的输入tensor设置为ai2d的输出 +ai2d_builder.run(ai2d_input, kpu_input) +kpu.run() + +# 获取KPU的输出tensor +result = kpu.get_output_tensor(i) # 返回第i个输出tensor +data = result.to_numpy() # 将输出tensor转换为numpy对象 +``` + +### 2.7. 释放内存 + +```Python +del kpu +del ai2d +del ai2d_builder + +# tensor = nn.from_numpy() +del tensor + +# input_tensor = kpu.get_input_tensor(i) +del input_tensor + +# output_tensor = kpu.get_output_tensor(i) +del output_tensor +``` diff --git "a/zh/01_software/K230_CanMV\344\275\277\347\224\250\350\257\264\346\230\216.md" "b/zh/userguide/K230_CanMV\344\275\277\347\224\250\350\257\264\346\230\216.md" old mode 100644 new mode 100755 similarity index 100% rename from "zh/01_software/K230_CanMV\344\275\277\347\224\250\350\257\264\346\230\216.md" rename to "zh/userguide/K230_CanMV\344\275\277\347\224\250\350\257\264\346\230\216.md" diff --git a/zh/userguide/images/balena-Etcher-finish.jpg b/zh/userguide/images/balena-Etcher-finish.jpg new file mode 100755 index 0000000000000000000000000000000000000000..8fbb39e329c7ca6ca20fc26e526a15a798a5a809 GIT binary patch literal 73170 zcmeFZbyOYMwl2P7K!OE#2<{Nvp>fz)aEHLg-CdGEaJP*UTsH1baCdiicZWy%^yyCb z@11-5{O%p&y*I`!{$SOpxz?QXTi=|kYObpNIQ6&$cncH}7Xdtb1^_&J`U5=90tBDV z{%gmNLH=taJoSG34nTr=cKd?p#WNznbEIc4ke)rZ0SJB=3H9QKz5hI3zJh{=dHw7S 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