A simple regression based implementation/VGG16 of pose estimation with tensorflow (python) based on JakeRenn's repository. Please read my post for more details about approaches, results, analyses and comprehension of papers: S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh. Convolutional pose machines. In CVPR, 2016.
The images in data/input/ is not complete. If you just want to run the code with demo images, the codes can run without modification. If you want to train and test with complete images:
human-pose-estimation-by-deep-learning/data/input/test_imgs: ~4000 test imageshuman-pose-estimation-by-deep-learning/data/input/train_imgs: ~60000 train images, use half of them. Each of them are labeled with 15 articulate points.
Please contact with JakeRenn for data (maybe not available currently). And modify the parametaer TAG = "_demo" to TAG = "" in corresponding files like train.py, test.py and draw_point.py.
cd human-pose-estimation-by-deep-learning
python train.py # or: nohup python train.py &A directory log will be generated to log every training. A directory params_demo will also be generated to store your models. With the log and models, you can use tensorboard for visualisation.
tensorboard --logdir ./log/train_log/ --port=8008
# then visit: http://hostname:8008/Also, you can modify parameters like batch size, number of max training iteration, number of checkpoint iteration in train.py.
python test.pyThen an annos file with predicted position of demo test images should be generated in human-pose-estimation-by-deep-learning/labels/txt/output/test_annos_demo.txt".
Files in human-pose-estimation-by-deep-learning\labels\python\ are some utils (in python). For example, you can use draw_point.py to draw the points indicated with annos file to images.
cd human-pose-estimation-by-deep-learning\labels\python\
python draw_point.pyThen the images with 15 points should be generated in human-pose-estimation-by-deep-learning\data\output\train_imgs_demo\. Modify the parameter PHASE to test will draw test images' points.