tf-pose-estimation-master-韦访源码分析带注释.zip

# tf-pose-estimation 'Openpose', human pose estimation algorithm, have been implemented using Tensorflow. It also provides several variants that have 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 a descent FPS!** Original Repo(Caffe) : https://github.com/CMU-Perceptual-Computing-Lab/openpose | CMU's Original Model</br> on Macbook Pro 15" | Mobilenet-thin </br>on Macbook Pro 15" | Mobilenet-thin</br>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 - 2019.3.12 Add new models using mobilenet-v2 architecture. See : [experiments.md](./etcs/experiments.md) - 2018.5.21 Post-processing part is implemented in c++. It is required compiling the part. See: https://github.com/ildoonet/tf-pose-estimation/tree/master/src/pafprocess - 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 - slidingwindow - https://github.com/adamrehn/slidingwindow - I copied from the above git repo to modify few things. ### Install Clone the repo and install 3rd-party libraries. ```bash $ git clone https://www.github.com/ildoonet/tf-pose-estimation $ cd tf-pose-estimation $ pip3 install -r requirements.txt ``` Build c++ library for post processing. See : https://github.com/ildoonet/tf-pose-estimation/tree/master/tf_pose/pafprocess ``` $ cd tf_pose/pafprocess $ swig -python -c++ pafprocess.i && python3 setup.py build_ext --inplace ``` ### Package Install Alternatively, you can install this repo as a shared package using pip. ```bash $ git clone https://www.github.com/ildoonet/tf-pose-estimation $ cd tf-openpose $ python setup.py install # Or, `pip install -e .` ``` ## Models & Performances See [experiments.md](./etc/experiments.md) ### 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) - mobilenet_v2_large (trained in 432x368) - mobilenet_v2_small (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 ``` ## Demo ### Test Inference You can test the inference feature with a single image. ``` $ python run.py --model=mobilenet_thin --resize=432x368 --image=./images/p1.jpg ``` 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 ``` $ python run_webcam.py --model=mobilenet_thin --resize=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) ``` If you installed it as a package, ```python import tf_pose coco_style = tf_pose.infer(image_path) ``` ## ROS Support See : [etcs/ros.md](./etcs/ros.md) ## Training See : [etcs/training.md](./etcs/training.md) ## References See : [etcs/reference.md](./etcs/reference.md)