14_topHat&balckHat礼帽和黑帽.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 礼帽与黑帽"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 礼帽（原图像和原图像开运算结果的差值）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① 礼帽 = 原始输入-开运算（开运算可以理解为先腐蚀后扩张）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 #opencv的缩写为cv2\n",
    "import matplotlib.pyplot as plt # matplotlib库用于绘图展示\n",
    "import numpy as np   # numpy数值计算工具包\n",
    "\n",
    "# 魔法指令，直接展示图，Jupyter notebook特有\n",
    "%matplotlib inline   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 礼帽 \n",
    "# 原始带刺，开运算不带刺，原始输入-开运算 = 刺（开运算可以理解为先腐蚀后扩张）\n",
    "img = cv2.imread('01_Picture/05_Dige.png')\n",
    "kernel = np.ones((5,5),np.uint8) # 创建尺寸大小为5×5的核，uint8代表无符号整数（0 to 255）\n",
    "tophat = cv2.morphologyEx(img,cv2.MORPH_TOPHAT,kernel)\n",
    "cv2.imshow('tophat',tophat)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ps:numpy中**ones函数**用于创建数组，详解https://www.runoob.com/numpy/numpy-array-creation.html"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行结果\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 黑帽（我的理解：主要为了得到汉字边缘）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "② 黑帽 = 闭运算-原始输入（闭运算可以理解为先扩张后腐蚀）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 黑帽  \n",
    "# 原始带刺，闭运算带刺并且比原始边界胖一点，闭运算-原始输入 = 原始整体\n",
    "img = img = cv2.imread('01_Picture/05_Dige.png')\n",
    "kernel = np.ones((5,5),np.uint8)\n",
    "blackhat = cv2.morphologyEx(img,cv2.MORPH_BLACKHAT,kernel)\n",
    "cv2.imshow('blackhat',blackhat)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行结果\n",
    "![image.png](attachment:image.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "study",
   "language": "python",
   "name": "study"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}