Quick Start

TensorLayer Implementation of YOLOv4: Optimal Speed and Accuracy of Object Detection

TensorLayer Implementation of Optimizing Network Structure for 3D Human Pose Estimation(ICCV2019)

YOLOv4

Yolov4 was trained on COCO 2017 Dataset in this demo.

Data

Download yolov4.weights file yolov4_model.npz, Password: idsz, and put yolov4.weights under the folder ./examples/app_tutorials/model/. Your directory structure should look like this:

${root}/examples
    └── app_tutorials
            └── model
                ├── yolov4_model.npz
                ├── coco.names
                └── yolov4_weights_congfig.txt

You can put an image or a video under the folder ./examples/app_tutorials/data/,like:

${root}/examples
    └──app_tutorials
            └──data
                └── *.jpg/*.png/*.mp4/..  

demo

1. Image

   Modify image_path in ./examples/app_tutorials/tutorial_object_detection_yolov4_image.py according to your demand, then

python tutorial_object_detection_yolov4_image.py

2. Video

   Modify video_path in ./examples/app_tutorials/tutorial_object_detection_yolov4_video.py according to your demand, then

python tutorial_object_detection_yolov4_video.py

3. Output

   -Image

   

   -Video

   

3D Human Pose Estimation

Data

Download 3D Human Pose Estimation model weights lcn_model.npz, Password:ec07,and put it under the folder ./examples/app_tutorials/model/, Your directory structure should look like this:

${root}/examples
    └── app_tutorials
            └── model
                ├── lcn_model.npz
                └── pose_weights_config.txt

Download finetuned Stacked Hourglass detections and preprocessed H3.6M data(H36M.rar,Password:kw9i), then uncompress and put them under the folder ./examples/app_tutorials/data/, like:

${root}/examples
    └──app_tutorials
            └──data
                ├── h36m_sh_dt_ft.pkl
                ├── h36m_test.pkl
                └── h36m_train.pkl

Each sample is a list with the length of 34 in three .pkl files. The list represents [x,y] of 17 human pose points:

If you would like to know how to prepare the H3.6M data, please have a look at the pose_lcn.

Demo

For a quick demo, simply run

python tutorial_human_3dpose_estimation_LCN.py

This will produce a visualization similar to this:

This demo maps 2D poses to 3D space. Each 3D space result list represents [x,y,z] of 17 human pose points.

Acknowledgement

Yolov4 is bulit on https://github.com/AlexeyAB/darknet and https://github.com/hunglc007/tensorflow-yolov4-tflite. 3D Human Pose Estimation is bulit on https://github.com/rujiewu/pose_lcn and https://github.com/una-dinosauria/3d-pose-baseline. We would like to thank the authors for publishing their code.