/sports_counting_by_pose_estimation

An android demo for real-time single person sports counting. It is based on MoveNet and deployed with ncnn.

Primary LanguageC++

sports_counting_by_pose_estimation

It is interesting to use pose estimation for sports counting. This project serves as a demo for this goal, which contains a whole pipeline of model conversion and ncnn Android deployment.

Sit up counting is supported now. It is easy to add other sports support.

situp

Here is a demo result for Sit up counting.

model conversion and preparation

MoveNet is used in this project. Google provides its pretrained model in tensorflow format. What I did to convert it to ncnn format is recorded as following.

  1. Download the tflite model from tensorflow model hub

    • the Lightning version of MoveNet is used in this project because it is intended for latency-critical applications.

  2. Convert tfllite model to ONNX model using tensorflow-onnx

    python -m tf2onnx.convert --tflite movenet.tflite --output movenet.onnx

  3. Optimize the ONNX model using onnx-simplifier

    python -m onnxsim movenet.onnx sim_movenet.onnx

  4. Edit the optimized ONNX model

    • This step is necessary because some of the post processing operators are not supported in ncnn.

    • What we need to do are:

      • Remove the pre-processing and post-processing operators.
        • They will be implemented in raw C++
        • tensorflow use NHWC format while ONNX use NCHW format. So the Transpose operators in pre-processing and post-processing operators should also be removed.
      • rename the model input and output.

    • Then we get modified_sim_movenet.onnx

      ⭐⭐ onnx-modifier is highly recommended for efficient and intuitive ONNX editing! ⭐⭐

  5. Convert the ONNX model to ncnn format

    # onnx2ncnn is provided in ncnn
onnx2ncnn modified_sim_movenet.onnx movenet.param movenet.bin

  6. Optimize the converted ncnn model

    # ncnnoptimize is provided in ncnn
ncnnoptimize movenet.param movenet.bin movenet_opt.param movenet_opt.bin 0

Then the ncnn model is ready! What I got is saved here

ncnn Android deployment

clone this repo and follow the instructions to build and run the Android demo.

step1

https://github.com/Tencent/ncnn/releases

  • Download ncnn-YYYYMMDD-android-vulkan.zip or build ncnn for android yourself
  • Extract ncnn-YYYYMMDD-android-vulkan.zip into app/src/main/jni and change the ncnn_DIR path to yours in app/src/main/jni/CMakeLists.txt

step2

https://github.com/nihui/opencv-mobile

  • Download opencv-mobile-XYZ-android.zip
  • Extract opencv-mobile-XYZ-android.zip into app/src/main/jni and change the OpenCV_DIR path to yours in app/src/main/jni/CMakeLists.txt

step3

  • Open this project with Android Studio, build it and enjoy!

The prebuilt android apk file can be downloaded here

how counting works

Take the supported Sit up for example, The angle between joints are calculated and serves as clues for counting.

/* The map from joint index to joint:
* 0 : neck; 1 & 2 : eyes; 3 & 4 : ears
* 5 & 6 : shoulders; 7 & 8 : elbows; 9 & 10 : hands
* 11 & 12 : hips; 13 & 14 : knees;
* 15 & 16 : feet
*/
float neck_hip_foot_angle = angle(points[0], points[11], points[15]);
if (!count_lock && neck_hip_foot_angle < 120) {
    count_lock = true;
    count_number += 1;
}
if (count_lock && neck_hip_foot_angle > 150) {
    count_lock = false;
}

The count_lock is a bool flag to avoid duplicated counting.

reference