文件列表:

ok\.dockerignore (212, 2018-02-15)
ok\CMakeLists.txt (489, 2018-02-15)
ok\docker\Dockerfile (1846, 2018-02-15)
ok\Dockerfile (238, 2018-02-15)
ok\etcs\dance.mp4 (6226477, 2018-02-15)
ok\etcs\feature.md (618, 2018-02-15)
ok\etcs\inference_result2.png (699224, 2018-02-15)
ok\etcs\loss_ll_heat.png (54246, 2018-02-15)
ok\etcs\loss_ll_paf.png (55867, 2018-02-15)
ok\etcs\openpose_macbook13_mobilenet2.gif (2562970, 2018-02-15)
ok\etcs\openpose_macbook_cmu.gif (155586, 2018-02-15)
ok\etcs\openpose_macbook_mobilenet3.gif (2524305, 2018-02-15)
ok\etcs\openpose_p40_cmu.gif (5350103, 2018-02-15)
ok\etcs\openpose_p40_mobilenet.gif (5064093, 2018-02-15)
ok\etcs\openpose_tx2_mobilenet3.gif (4171863, 2018-02-15)
ok\etcs\ros.md (1151, 2018-02-15)
ok\etcs\training.md (4523, 2018-02-15)
ok\images\apink1.jpg (316990, 2018-02-15)
ok\images\apink1_crop.jpg (279404, 2018-02-15)
ok\images\apink1_crop_s1.jpg (209289, 2018-02-15)
ok\images\apink2.jpg (381428, 2018-02-15)
ok\images\apink3.jpg (128816, 2018-02-15)
ok\images\cat.jpg (140391, 2018-02-15)
ok\images\golf.jpg (223738, 2018-02-15)
ok\images\hand1.jpg (71638, 2018-02-15)
ok\images\hand1_small.jpg (130378, 2018-02-15)
ok\images\hand2.jpg (20092, 2018-02-15)
ok\images\handsup1.jpg (201499, 2018-02-15)
ok\images\p1.jpg (10832, 2018-02-15)
ok\images\p2.jpg (86864, 2018-02-15)
ok\images\p3.jpg (75588, 2018-02-15)
ok\images\p3_dance.png (575359, 2018-02-15)
ok\images\ski.jpg (305923, 2018-02-15)
ok\images\valid_person1.jpg (67644, 2018-02-15)
ok\launch\demo_video.launch (2377, 2018-02-15)
ok\LICENSE (11357, 2018-02-15)
ok\models\graph\cmu\download.sh (494, 2018-02-15)
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# tf-pose-estimation 'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for **real-time processing on the CPU or low-power embedded devices.** **You can even run this on your macbook with descent FPS!** Original Repo(Caffe) : https://github.com/CMU-Perceptual-Computing-Lab/openpose | CMU's Original Model
on Macbook Pro 15" | Mobilenet Variant
on Macbook Pro 15" | Mobilenet Variant
on Jetson TX2 | |:---------|:--------------------|:----------------| |  |  |  | | **~0.6 FPS** | **~4.2 FPS** @ 368x368 | **~10 FPS** @ 368x368 | | 2.8GHz Quad-core i7 | 2.8GHz Quad-core i7 | Jetson TX2 Embedded Board | Implemented features are listed here : [features](./etcs/feature.md) ## Important Updates 2018.2.7 Arguments in run.py script changed. Support dynamic input size. ## Install ### Dependencies You need dependencies below. - python3 - tensorflow 1.4.1+ - opencv3, protobuf, python3-tk ### Install ```bash $ git clone https://www.github.com/ildoonet/tf-openpose $ cd tf-openpose $ pip3 install -r requirements.txt ``` ## Models I have tried multiple variations of models to find optmized network architecture. Some of them are below and checkpoint files are provided for research purpose. - cmu - the model based VGG pretrained network which described in the original paper. - I converted Weights in Caffe format to use in tensorflow. - [pretrained weight download](https://www.dropbox.com/s/xh5s7sb7remu8tx/openpose_coco.npy?dl=0) - dsconv - Same architecture as the cmu version except for the **depthwise separable convolution** of mobilenet. - I trained it using 'transfer learning', but it provides not-enough speed and accuracy. - mobilenet - Based on the mobilenet paper, 12 convolutional layers are used as feature-extraction layers. - To improve on small person, **minor modification** on the architecture have been made. - Three models were learned according to network size parameters. - mobilenet - 368x368 : [checkpoint weight download](https://www.dropbox.com/s/09xivpuboecge56/mobilenet_0.75_0.50_model-388003.zip?dl=0) - mobilenet_fast - mobilenet_accurate - I published models which is not the best ones, but you can test them before you trained a model from the scratch. ### Download Tensorflow Graph File(pb file) Before running demo, you should download graph files. You can deploy this graph on your mobile or other platforms. - cmu (trained in 656x368) - mobilenet_thin (trained in 432x368) CMU's model graphs are too large for git, so I uploaded them on an external cloud. You should download them if you want to use cmu's original model. Download scripts are provided in the model folder. ``` $ cd models/graph/cmu $ bash download.sh ``` ### Inference Time | Dataset | Model | Inference Time
Macbook Pro i5 3.1G | Inference Time
Jetson TX2 | |---------|--------------------|----------------:|----------------:| | Coco | cmu | 10.0s @ 368x368 | OOM @ 368x368
5.5s @ 320x240| | Coco | dsconv | 1.10s @ 368x368 | | Coco | mobilenet_accurate | 0.40s @ 368x368 | 0.18s @ 368x368 | | Coco | mobilenet | 0.24s @ 368x368 | 0.10s @ 368x368 | | Coco | mobilenet_fast | 0.16s @ 368x368 | 0.07s @ 368x368 | ## Demo ### Test Inference You can test the inference feature with a single image. ``` $ python3 run.py --model=mobilenet_thin --resolution=432x368 --image=... ``` The image flag MUST be relative to the src folder with no "~", i.e: ``` --image ../../Desktop ``` Then you will see the screen as below with pafmap, heatmap, result and etc.  ### Realtime Webcam ``` $ python3 run_webcam.py --model=mobilenet_thin --resolution=432x368 --camera=0 ``` Then you will see the realtime webcam screen with estimated poses as below. This [Realtime Result](./etcs/openpose_macbook13_mobilenet2.gif) was recored on macbook pro 13" with 3.1Ghz Dual-Core CPU. ## Python Usage This pose estimator provides simple python classes that you can use in your applications. See [run.py](run.py) or [run_webcam.py](run_webcam.py) as references. ```python e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) humans = e.inference(image) image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) ``` ## ROS Support See : [etcs/ros.md](./etcs/ros.md) ## Training See : [etcs/training.md](./etcs/training.md) ## References ### OpenPose [1] https://github.com/CMU-Perceptual-Computing-Lab/openpose [2] Training Codes : https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation [3] Custom Caffe by Openpose : https://github.com/CMU-Perceptual-Computing-Lab/caffe_train [4] Keras Openpose : https://github.com/michalfaber/keras_Realtime_Multi-Person_Pose_Estimation ### Lifting from the deep [1] Arxiv Paper : https://arxiv.org/abs/1701.00295 [2] https://github.com/DenisTome/Lifting-from-the-Deep-release ### Mobilenet [1] Original Paper : https://arxiv.org/abs/1704.04861 [2] Pretrained model : https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.md ### Libraries [1] Tensorpack : https://github.com/ppwwyyxx/tensorpack ### Tensorflow Tips [1] Freeze graph : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py [2] Optimize graph : https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2

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