End-to-end face detection, cropping, norm estimation, and landmark detection in a single onnx model, model.onnx.
You can try this model at the following link. Thanks for hysts.
See src.
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.
This is simply a merged model of the following underlying models with some pre- and post-processing.
| 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 |
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.
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.