onnx-facial-lmk-detector

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #rust #deep-learning #linux #nodejs #macos #awesome #cli #kubernetes #open-source

onnx-facial-lmk-detector

(★ 84)

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

파일 탐색기

최종 버전 다운로드 (.zip)

- 0.jpg
- 1.jpg
- 2.jpg
- 3.jpg
- 4.jpg
- 5.jpg
- 6.jpg
- 7.jpg
- 8.jpg
- - .gitkeep
- - __init__.py
  - affine.py
  - estimate_norm.py
  - merge.py
  - svd.py
  - utils.py
- .gitignore
- Dockerfile
- download.sh
- entrypoint.sh
- inference.py
- main.py
- prepare.py
- README.md
- requirements.txt
- sample.jpg
.gitignore
inference_sample.py
input.jpg
LICENSE
model.onnx
model.svg
output.png
README.md
requirements.txt
sample.jpg
sample_out.png

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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](/src).

## Example

![example](https://raw.githubusercontent.com/atksh/onnx-facial-lmk-detector/6ea090532acce1c228d1f860d27708d450416475/output.png?token=GHSAT0AAAAAABHJHGPX4XIAJZ4ALEVWPJTIYSJ6HKQ)

```python
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.

onnx-facial-lmk-detector

파일 탐색기

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

onnx-facial-lmk-detector

파일 탐색기

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README