步态识别-基于图卷积网络+人体骨架实现的步态识别算法-附项目源码-优质项目分享.zip

# GaitGraph  ## Quick Start ### Prerequisites - Python >= 3.6 - CUDA >= 10 First, create a virtual environment or install dependencies directly with: ```shell pip3 install -r requirements.txt ``` ### Data preparation The extraction of the pose data from CASIA-B can either run the commands bellow or download the preprocessed data using: ```shell cd data sh ./download_data.sh ``` Optional: If you choose to run the preprocessing, [download](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) the dataset and run the following commands. ```shell # Download required weights cd models sh ./download_weights.sh # Copy extraction script # <PATH_TO_CASIA-B> should be something like: /home/ ... /datasets/CASIA_Gait_Dataset/DatasetB cd ../data cp extract_frames.sh <PATH_TO_CASIA-B> cd <PATH_TO_CASIA-B> mkdir frames sh extract_frames.sh cd frames find . -type f -regex ".*\.jpg" -print | sort | grep -v bkgrd > ../casia-b_all_frames.csv cp ../casia-b_all_frames.csv <PATH_TO_REPO>/data cd <PATH_TO_REPO>/src export PYTHONPATH=${PWD}:$PYTHONPATH cd preparation python3 prepare_detection.py <PATH_TO_CASIA-B> ../../data/casia-b_all_frames.csv ../../data/casia-b_detections.csv python3 prepare_pose_estimation.py <PATH_TO_CASIA-B> ../../data/casia-b_detections.csv ../../data/casia-b_pose_coco.csv python3 split_casia-b.py ../../data/casia-b_pose_coco.csv --output_dir ../../data ``` ### Train To train the model you can run the `train.py` script. To see all options run: ```shell cd src export PYTHONPATH=${PWD}:$PYTHONPATH python3 train.py --help ``` Check `experiments/1_train_*.sh` to see the configurations used in the paper. Optionally start the tensorboard with: ```shell tensorboard --logdir=save/casia-b_tensorboard ``` ### Evaluation Evaluate the models using `evaluate.py` script. To see all options run: ```shell python3 evaluate.py --help ``` ## Main Results Top-1 Accuracy per probe angle excluding identical-view cases for the provided models on [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) dataset. | | 0 | 18 | 36 | 54 | 72 | 90 | 108 | 126 | 144 | 162 | 180 | mean | |:-------|-----:|-----:|-----:|-----:|-----:|-----:|------:|------:|------:|------:|------:|-------:| | NM#5-6 | 85.3 | 88.5 | 91 | 92.5 | 87.2 | 86.5 | 88.4 | 89.2 | 87.9 | 85.9 | 81.9 | 87.7 | | BG#1-2 | 75.8 | 76.7 | 75.9 | 76.1 | 71.4 | 73.9 | 78 | 74.7 | 75.4 | 75.4 | 69.2 | 74.8 | | CL#1-2 | 69.6 | 66.1 | 68.8 | 67.2 | 64.5 | 62 | 69.5 | 65.6 | 65.7 | 66.1 | 64.3 | 66.3 | The pre-trained model is available in `wgts`.