facemesh_onnx_tensorrt

Verify that the post-processing merged into FaceMesh works correctly. The object detection model can be anything other than BlazeFace. YOLOv4 and FaceMesh committed to this repository have modified post-processing.

  • YOLOv4 + Modified FaceMesh

    Kazam_screencast_00067_.mp4

    Kazam_screencast_00073_.mp4 

    python demo_video.py

1. Pre-trained model

  1. https://github.com/PINTO0309/facemesh_onnx_tensorrt/releases
  2. https://github.com/PINTO0309/PINTO_model_zoo/tree/main/032_FaceMesh
  3. https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark.tflite

2. ONNX Modification Tools

  1. https://github.com/PINTO0309/simple-onnx-processing-tools
  2. https://github.com/PINTO0309/tflite2tensorflow
  3. https://github.com/PINTO0309/openvino2tensorflow

3. ModelCard

  1. https://drive.google.com/file/d/1QvwWNfFoweGVjsXF3DXzcrCnz-mx-Lha/preview
  2. https://github.com/tensorflow/tfjs-models/tree/master/face-landmarks-detection

  • Standard facial landmark 68 points

    image

  • Full facemesh 468 points

    image

  • INPUT:

    • input: float32 [N, 3, 192, 192]

      Object detection models for face detection have very narrow or wide detection areas, depending on the model type. Therefore, in order for FaceMesh to accurately detect feature points, the area to be cropped must not be too narrow, but must be expanded to fit the entire face as much as possible before cropping. In this case, it is necessary to crop a region that extends the actual x1, y1, x2, y2 area detected by object detection to some extent in the vertical and horizontal directions. This is the green area in the figure below. The cropped face area with a margin is resized to 192×192 without considering the aspect ratio, and then input into the FaceMesh model. If the area to be cropped is a large enough area, it may not be necessary to provide a margin. The 25% margin listed in the official model card is based on BlazeFace. BlazeFace has a very small face detection area.

    • crop_x1: int32 [N, 1]

      Coordinates reflecting about 25% margin on the X1 coordinates of the object detection result.

    • crop_y1: int32 [N, 1]

      Coordinates reflecting about 25% margin on the Y1 coordinates of the object detection result.

    • crop_width: int32 [N, 1]

      Width of face image reflecting about 25% margin to the left and right of object detection results.

    • crop_height: int32 [N, 1]

      Height of face image reflecting about 25% margin to the top and bottom object detection results.

    icon_design drawio (2)

  • OUTPUT:

    • final_landmarks: int32 [N, 468, 3]

      468 key points. X, Y, and Z coordinates.

    • score: float32 [N, 1]

      Probability value indicating whether a facial feature point has been successfully detected.

4. Benchmark

4-1. 1 batch + ONNX + TensorRT, 10 times loop

$ sit4onnx \
--input_onnx_file_path face_mesh_192x192_post.onnx

INFO: file: face_mesh_192x192_post.onnx
INFO: providers: ['TensorrtExecutionProvider', 'CPUExecutionProvider']
INFO: input_name.1: input shape: [1, 3, 192, 192] dtype: float32
INFO: input_name.2: crop_x1 shape: [1, 1] dtype: int32
INFO: input_name.3: crop_y1 shape: [1, 1] dtype: int32
INFO: input_name.4: crop_width shape: [1, 1] dtype: int32
INFO: input_name.5: crop_height shape: [1, 1] dtype: int32
INFO: test_loop_count: 10
INFO: total elapsed time:  5.5561065673828125 ms
INFO: avg elapsed time per pred:  0.5556106567382812 ms
INFO: output_name.1: score shape: [1, 1] dtype: float32
INFO: output_name.2: final_landmarks shape: [1, 468, 3] dtype: int32

4-2. 100 batch + ONNX + TensorRT, 10 times loop

$ sit4onnx \
--input_onnx_file_path face_mesh_Nx3x192x192_post.onnx \
--batch_size 100

INFO: file: face_mesh_Nx3x192x192_post.onnx
INFO: providers: ['TensorrtExecutionProvider', 'CPUExecutionProvider']
INFO: input_name.1: input shape: [100, 3, 192, 192] dtype: float32
INFO: input_name.2: crop_x1 shape: [100, 1] dtype: int32
INFO: input_name.3: crop_y1 shape: [100, 1] dtype: int32
INFO: input_name.4: crop_width shape: [100, 1] dtype: int32
INFO: input_name.5: crop_height shape: [100, 1] dtype: int32
INFO: test_loop_count: 10
INFO: total elapsed time:  103.13057899475098 ms
INFO: avg elapsed time per pred:  10.313057899475098 ms
INFO: output_name.1: score shape: [100, 1] dtype: float32
INFO: output_name.2: final_landmarks shape: [100, 468, 3] dtype: int32

5. Structure of PINTO Special FaceMesh model

face_mesh_Nx3x192x192_post onnx (1)

GitHub

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