onnx-facial-lmk-detector

End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model, model.onnx.

Demo

You can try this model at the following link. Thanks for hysts.

• https://huggingface.co/spaces/hysts/atksh-onnx-facial-lmk-detector

Code

See src.

Example

import onnxruntime as ort
import cv2

sess = ort.InferenceSession("model.onnx")
img = cv2.imread("input.jpg")

scores, bboxes, keypoints, aligned_imgs, landmarks, affine_matrices = sess.run(None, {"input": img})
# float32 int64 int64 uint8 int64 float32
# (N,) (N, 4) (N, 5, 2) (N, 224, 224, 3) (N, 106, 2) (N, 2, 3)

This model requires onnxruntime>=1.11.

How does it work?

This is simply a merged model of the following underlying models with some pre- and post-processing.

Underlying models

	model	reference
face detection	SCRFD_10G_KPS	https://github.com/deepinsight/insightface/tree/master/detection/scrfd#pretrained-models
landmark detection	2d106det	https://github.com/deepinsight/insightface/blob/master/alignment/coordinate_reg/README.md#pretrained-models

Pre- and Post-Processing

Implemented the following processing by PyTorch and exported to ONNX.

• Input transform:

  • Resize and pad to (1920, 1920)
  • BGR to RGB conversion
  • Transpose (H, W, C) to (C, H, W)

• (Face Detection)

• Post-processing of face detection

  • Predicted bounding boxes and Confidence Score Processing
  • NMS (ONNX Operator)

• Norm estimation and face cropping

  • Estimate the norm and apply an affine transformation to each face.
  • Crop the faces and resize them to (192, 192).

• (Landmark Detection)

• Perform post-processing for landmark detection.

  • Process the predicted landmarks and apply the inverse affine transform to each face.

Note

Please check with the model provider regarding the license for your use.

This model includes the work that is distributed in the Apache License 2.0.