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HISTORY.md

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+# History
+
+* [master](https://github.com/vita-epfl/openpifpaf/compare/v0.6.0...master)
+* [0.6.0](https://github.com/vita-epfl/openpifpaf/compare/v0.5.1...v0.6.0) (2019-05-07)
+    * Torch 1.1.0 compatibility (older versions work but have modified learning rate schedule due to https://github.com/pytorch/pytorch/pull/7889)
+    * more aggressive NMS
+    * multi-scale support for `eval_coco`: `--two-scale`, `--three-scale`, `--multi-scale`
+    * guaranteed complete poses with `--force-complete-pose`
+* [0.5.1](https://github.com/vita-epfl/openpifpaf/compare/v0.5.0...v0.5.1) (2019-05-01)

LICENSE

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+Copyright 2019 by EPFL/VITA. All rights reserved.
+
+This project and all its files are licensed under
+GNU AGPLv3 or later version.
+
+If this license is not suitable for your business or project
+please contact us for a full commercial license.

MANIFEST.in

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+include openpifpaf/functional.pyx
+include openpifpaf/functional.c

all.sh

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+
+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.video --batch-size 1 --jaad_batch_size 1 --checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190525-002719.pkl.epoch006

backprop.sh

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+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.backprop --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190526-203030.pkl.epoch006 \
+--jaad_train "singletxt_pre_train_1s" --jaad_val "singletxt_val_1s" --jaad_pre_train "singletxt_pre_train_1s" \

docs/datasets.md

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+# Datasets
+
+Download MSCOCO data:
+
+```sh
+mkdir data-mscoco
+cd data-mscoco
+gsutil ls gs://images.cocodataset.org  # to list available directories
+
+mkdir -p images/val2017
+gsutil -m rsync gs://images.cocodataset.org/val2017 images/val2017
+
+mkdir -p images/train2017
+gsutil -m rsync gs://images.cocodataset.org/train2017 images/train2017
+
+gsutil cp gs://images.cocodataset.org/annotations/annotations_trainval2017.zip .
+# or
+wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
+unzip annotations_trainval2017.zip
+wget http://images.cocodataset.org/annotations/image_info_test2017.zip
+unzip image_info_test2017.zip
+
+# test images: run inside of images directory
+wget http://images.cocodataset.org/zips/test2017.zip
+unzip test2017.zip
+```
+
+Download MPII data:
+
+```sh
+mkdir data-mpii
+cd data-mpii
+wget https://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1.tar.gz
+wget https://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1_u12_2.zip
+```

eval.sh

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+
+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.eval --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190525-002719.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "trained0s_predict0s_truncated.txt" \
+--jaad_train "singletxt_pre_train_0s_truncated" --jaad_val "singletxt_val_0s_truncated" --jaad_pre_train "singletxt_pre_train_0s_truncated"
+
+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.eval --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190525-002719.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "trained0s_predict1s_truncated.txt" \
+--jaad_train "singletxt_pre_train_1s_truncated" --jaad_val "singletxt_val_1s_truncated" --jaad_pre_train "singletxt_pre_train_1s_truncated"
+
+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.eval --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190525-002719.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "trained0s_predict2s_truncated.txt" \
+--jaad_train "singletxt_pre_train_2s_truncated" --jaad_val "singletxt_val_2s_truncated" --jaad_pre_train "singletxt_pre_train_2s_truncated"
+
+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.eval --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190526-203030.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "trained1s_predict0s_truncated.txt" \
+--jaad_train "singletxt_pre_train_0s_truncated" --jaad_val "singletxt_val_0s_truncated" --jaad_pre_train "singletxt_pre_train_0s_truncated"
+
+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.eval --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190526-203030.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "trained1s_predict1s_truncated.txt" \
+--jaad_train "singletxt_pre_train_1s_truncated" --jaad_val "singletxt_val_1s_truncated" --jaad_pre_train "singletxt_pre_train_1s_truncated"
+
+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.eval --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190526-203030.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "trained1s_predict2s_truncated.txt" \
+--jaad_train "singletxt_pre_train_2s_truncated" --jaad_val "singletxt_val_2s_truncated" --jaad_pre_train "singletxt_pre_train_2s_truncated"

eval_coco.sh

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+CUDA_VISIBLE_DEVICES=None python3 -m openpifpaf.eval_coco --checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190526-203030.pkl.epoch006 --long-edge=321 --loader-workers=1

eval_crm_recall.sh

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+CUDA_VISIBLE_DEVICES="1" python3 -m openpifpaf.eval_crm_recall --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190526-203030.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "bbox-recall-crm.txt" \
+--jaad_train "singletxt_pre_train_1s" --jaad_val "singletxt_val_1s" --jaad_pre_train "singletxt_pre_train_1s"

eval_optimistic.sh

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+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.eval_optimistic --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190526-203030.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "trained1s.txt" \
+--jaad_train "singletxt_pre_train_0s" --jaad_val "singletxt_val_0s" --jaad_pre_train "singletxt_pre_train_0s"
+
+CUDA_VISIBLE_DEVICES="0" python3 -m openpifpaf.eval_optimistic --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190527-174900.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "trained2s.txt" \
+--jaad_train "singletxt_pre_train_0s" --jaad_val "singletxt_val_0s" --jaad_pre_train "singletxt_pre_train_0s"

eval_pifpaf_recall.sh

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+CUDA_VISIBLE_DEVICES="1" python3 -m openpifpaf.eval_pifpaf_recall --batch-size 1 --jaad_batch_size 1 \
+--checkpoint outputs/resnet50block5-pif-paf-crm-edge401-190526-203030.pkl.epoch006 \
+--truncate 0 \
+--final_frame_offset 0 \
+--result "bbox-recall-pifpaf.txt" \
+--jaad_train "singletxt_pre_train_1s" --jaad_val "singletxt_val_1s" --jaad_pre_train "singletxt_pre_train_1s"

openpifpaf.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: openpifpaf
+Version: 0.6.0
+Summary: PifPaf: Composite Fields for Human Pose Estimation
+Home-page: https://github.com/vita-epfl/openpifpaf
+Author: Sven Kreiss
+Author-email: [email protected]
+License: GNU AGPLv3
+Description: # openpifpaf
+
+        [![Build Status](https://travis-ci.org/vita-epfl/openpifpaf.svg?branch=master)](https://travis-ci.org/vita-epfl/openpifpaf)
+
+        > We propose a new bottom-up method for multi-person 2D human pose
+        > estimation that is particularly well suited for urban mobility such as self-driving cars
+        > and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF) to
+        > localize body parts and a Part Association Field (PAF) to associate body parts with each other to form
+        > full human poses.
+        > Our method outperforms previous methods at low resolution and in crowded,
+        > cluttered and occluded scenes
+        > thanks to (i) our new composite field PAF encoding fine-grained information and (ii) the choice of Laplace loss for regressions which incorporates a notion of uncertainty.
+        > Our architecture is based on a fully
+        > convolutional, single-shot, box-free design.
+        > We perform on par with the existing
+        > state-of-the-art bottom-up method on the standard COCO keypoint task
+        > and produce state-of-the-art results on a modified COCO keypoint task for
+        > the transportation domain.
+
+
+        ```
+        @article{kreiss2019pifpaf,
+          title={PifPaf: Composite Fields for Human Pose Estimation},
+          author={Kreiss, Sven and Bertoni, Lorenzo and Alahi, Alexandre},
+          journal={CVPR, arXiv preprint arXiv:1903.06593},
+          year={2019}
+        }
+        ```
+
+        [arxiv.org/abs/1903.06593](https://arxiv.org/abs/1903.06593)
+
+
+        # Demo
+
+        ![example image with overlaid pose skeleton](docs/coco/000000081988.jpg.skeleton.png)
+
+        Image credit: "[Learning to surf](https://www.flickr.com/photos/fotologic/6038911779/in/photostream/)" by fotologic which is licensed under [CC-BY-2.0].<br />
+        Created with:
+        `python3 -m openpifpaf.predict --show docs/coco/000000081988.jpg`
+
+        For more demos, see the
+        [openpifpafwebdemo](https://github.com/vita-epfl/openpifpafwebdemo) project and
+        the `openpifpaf.webcam` command.
+        There is also a [Google Colab demo](https://colab.research.google.com/drive/1H8T4ZE6wc0A9xJE4oGnhgHpUpAH5HL7W).
+
+        <img src="docs/wave3.gif" height=250 alt="example image" />
+
+
+        # Install
+
+        Python 3 is required. Python 2 is not supported.
+        Do not clone this repository
+        and make sure there is no folder named `openpifpaf` in your current directory.
+
+        ```sh
+        pip3 install openpifpaf
+        ```
+
+        For a live demo, we recommend to try the
+        [openpifpafwebdemo](https://github.com/vita-epfl/openpifpafwebdemo) project.
+        Alternatively, `openpifpaf.webcam` provides a live demo as well.
+        It requires OpenCV. To use a globally installed
+        OpenCV from inside a virtual environment, create the virtualenv with the
+        `--system-site-packages` option and verify that you can do `import cv2`.
+
+        For development of the openpifpaf source code itself, you need to clone this repository and then:
+
+        ```sh
+        pip3 install numpy cython
+        pip3 install --editable '.[train,test]'
+        ```
+
+        The last command installs the Python package in the current directory
+        (signified by the dot) with the optional dependencies needed for training and
+        testing. The current changelog and the changelogs for prior releases are
+        in [HISTORY.md](HISTORY.md).
+
+
+        # Interfaces
+
+        * `python3 -m openpifpaf.predict --help`
+        * `python3 -m openpifpaf.webcam --help`
+        * `python3 -m openpifpaf.train --help`
+        * `python3 -m openpifpaf.eval_coco --help`
+        * `python3 -m openpifpaf.logs --help`
+
+        Example commands to try:
+
+        ```sh
+        # live demo
+        MPLBACKEND=macosx python3 -m openpifpaf.webcam --scale 0.1 --source=0
+
+        # single image
+        python3 -m openpifpaf.predict my_image.jpg --show
+        ```
+
+
+        # Pre-trained Models
+
+        Put the files from this
+        [Google Drive](https://drive.google.com/drive/folders/1v8UNDjZbqeMZY64T33tSDOq1jtcBJBy7?usp=sharing>)
+        into your `outputs` folder.
+        Alternative downloads:
+
+        | models            | [Cloudflare IPFS gateway] to https | [IPFS]      | [DAT] (broken?) |
+        |-------------------|------------------------------------|-------------|-----------------|
+        | ResNet50 (97MB)   | [CF R50]                           | [IPFS R50]  | [DAT repo]      |
+        | ResNet101 (169MB) | [CF R101]                          | [IPFS R101] | [DAT repo]      |
+        | ResNet152 (229MB) | [CF R152]                          | [IPFS R152] | [DAT repo]      |
+
+        [Cloudflare IPFS gateway]: https://blog.cloudflare.com/distributed-web-gateway/
+        [IPFS]: https://ipfs.io/
+        [DAT]: https://datproject.org/
+        [CF R50]: https://cloudflare-ipfs.com/ipfs/QmVySuAqA2MMMPDHM1p4wLpNo93FPsZC21nApUdsqX7uDB
+        [IPFS R50]: ipfs://QmVySuAqA2MMMPDHM1p4wLpNo93FPsZC21nApUdsqX7uDB
+        [CF R101]: https://cloudflare-ipfs.com/ipfs/QmdmrxTRnDucpkEQhPY9bKFcP5sb9cqRZKPC7tGKWkfqJw
+        [IPFS R101]: ipfs://QmdmrxTRnDucpkEQhPY9bKFcP5sb9cqRZKPC7tGKWkfqJw
+        [CF R152]: https://cloudflare-ipfs.com/ipfs/QmPhJVjt3jw9q6bpKQgvuN2aszcwSBFYszyU9Lkeg9pPZu
+        [IPFS R152]: ipfs://QmPhJVjt3jw9q6bpKQgvuN2aszcwSBFYszyU9Lkeg9pPZu
+        [DAT repo]: dat://339c3ba0e0cc7e1dd3d3d6d2241004fda7ceabb36d17216027da87d48fd3ece8
+
+
+        Visualize logs:
+
+        ```sh
+        python3 -m pifpaf.logs \
+          outputs/resnet50block5-pif-paf-edge401-190424-122009.pkl.log \
+          outputs/resnet101block5-pif-paf-edge401-190412-151013.pkl.log \
+          outputs/resnet152block5-pif-paf-edge401-190412-121848.pkl.log
+        ```
+
+
+        # Train
+
+        See [datasets](docs/datasets.md) for setup instructions.
+        See [studies.ipynb](docs/studies.ipynb) for previous studies.
+
+        Train a model:
+
+        ```sh
+        python3 -m openpifpaf.train \
+          --lr=1e-3 \
+          --momentum=0.95 \
+          --epochs=75 \
+          --lr-decay 60 70 \
+          --batch-size=8 \
+          --basenet=resnet50block5 \
+          --head-quad=1 \
+          --headnets pif paf \
+          --square-edge=401 \
+          --regression-loss=laplace \
+          --lambdas 30 2 2 50 3 3 \
+          --freeze-base=1
+        ```
+
+        You can refine an existing model with the `--checkpoint` option.
+
+        To produce evaluations at every epoch, check the directory for new
+        snapshots every 5 minutes:
+
+        ```
+        while true; do \
+          CUDA_VISIBLE_DEVICES=0 find outputs/ -name "resnet101block5-pif-paf-l1-190109-113346.pkl.epoch???" -exec \
+            python3 -m openpifpaf.eval_coco --checkpoint {} -n 500 --long-edge=641 --skip-existing \; \
+          ; \
+          sleep 300; \
+        done
+        ```
+
+
+        # Person Skeletons
+
+        COCO / kinematic tree / dense:
+
+        <img src="docs/skeleton_coco.png" height="250" /><img src="docs/skeleton_kinematic_tree.png" height="250" /><img src="docs/skeleton_dense.png" height="250" />
+
+        Created with `python3 -m openpifpaf.data`.
+
+
+        # Video
+
+        Processing a video frame by frame from `video.avi` to `video.pose.mp4` using ffmpeg:
+
+        ```sh
+        export VIDEO=video.avi  # change to your video file
+
+        mkdir ${VIDEO}.images
+        ffmpeg -i ${VIDEO} -qscale:v 2 -vf scale=641:-1 -f image2 ${VIDEO}.images/%05d.jpg
+        python3 -m openpifpaf.predict --checkpoint resnet152 ${VIDEO}.images/*.jpg
+        ffmpeg -framerate 24 -pattern_type glob -i ${VIDEO}.images/'*.jpg.skeleton.png' -vf scale=640:-1 -c:v libx264 -pix_fmt yuv420p ${VIDEO}.pose.mp4
+        ```
+
+        In this process, ffmpeg scales the video to `641px` which can be adjusted.
+
+
+        # Evaluations
+
+        See [evaluation logs](docs/eval_logs.md) for a long list.
+        This result was produced with `python -m openpifpaf.eval_coco --checkpoint outputs/resnet101block5-pif-paf-edge401-190313-100107.pkl --long-edge=641 --loader-workers=8`:
+
+        ```
+         Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.657
+         Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.866
+         Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.719
+         Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.619
+         Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.718
+         Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.712
+         Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.895
+         Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.768
+         Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.660
+         Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.785
+        Decoder 0: decoder time = 875.4406125545502s
+        total processing time = 1198.353811264038s
+        ```
+
+
+        # Profiling Decoder
+
+        Run predict with the `--profile` option:
+
+        ```sh
+        python3 -m openpifpaf.predict \
+          --checkpoint outputs/resnet152block5-pif-paf-edge401-190412-121848.pkl \
+          docs/coco/000000081988.jpg --show --profile --debug
+        ```
+
+        This will write a stats table to the terminal and also produce a `decoder.prof` file.
+        You can use flameprof (`pip install flameprof`) to get a flame graph with
+        `flameprof decoder.prof > docs/decoder_flame.svg`:
+
+        ![flame graph for decoder](docs/decoder_flame.svg)
+
+
+        [CC-BY-2.0]: https://creativecommons.org/licenses/by/2.0/
+
+Platform: UNKNOWN
+Description-Content-Type: text/markdown
+Provides-Extra: train
+Provides-Extra: test