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Merge pull request #20957 from sturkmen72:update-documentation

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Update documentation

* Update DNN-based Face Detection And Recognition tutorial

* samples(dnn/face): update face_detect.cpp

* final changes

Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
This commit is contained in:
Suleyman TURKMEN
2021-11-28 15:56:28 +03:00
committed by GitHub
parent b594ed99b8
commit a97f21ba4e
5 changed files with 333 additions and 275 deletions
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doc/tutorials/dnn/dnn_face/dnn_face.markdown
+42 -25
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@@ -36,14 +36,34 @@ There are two models (ONNX format) pre-trained and required for this module:
### DNNFaceDetector
```cpp
// Initialize FaceDetectorYN
Ptr<FaceDetectorYN> faceDetector = FaceDetectorYN::create(onnx_path, "", image.size(), score_thresh, nms_thresh, top_k);
@add_toggle_cpp
- **Downloadable code**: Click
[here](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detect.cpp)
// Forward
Mat faces;
faceDetector->detect(image, faces);
```
- **Code at glance:**
@include samples/dnn/face_detect.cpp
@end_toggle
@add_toggle_python
- **Downloadable code**: Click
[here](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detect.py)
- **Code at glance:**
@include samples/dnn/face_detect.py
@end_toggle
Explanation
-----------
@add_toggle_cpp
@snippet dnn/face_detect.cpp initialize_FaceDetectorYN
@snippet dnn/face_detect.cpp inference
@end_toggle
@add_toggle_python
@snippet dnn/face_detect.py initialize_FaceDetectorYN
@snippet dnn/face_detect.py inference
@end_toggle
The detection output `faces` is a two-dimension array of type CV_32F, whose rows are the detected face instances, columns are the location of a face and 5 facial landmarks. The format of each row is as follows:
@@ -57,28 +77,25 @@ x1, y1, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt, x_rcm, y_rcm, x_lcm, y_lcm
Following Face Detection, run codes below to extract face feature from facial image.
```cpp
// Initialize FaceRecognizerSF with model path (cv::String)
Ptr<FaceRecognizerSF> faceRecognizer = FaceRecognizerSF::create(model_path, "");
@add_toggle_cpp
@snippet dnn/face_detect.cpp initialize_FaceRecognizerSF
@snippet dnn/face_detect.cpp facerecognizer
@end_toggle
// Aligning and cropping facial image through the first face of faces detected by dnn_face::DNNFaceDetector
Mat aligned_face;
faceRecognizer->alignCrop(image, faces.row(0), aligned_face);
// Run feature extraction with given aligned_face (cv::Mat)
Mat feature;
faceRecognizer->feature(aligned_face, feature);
feature = feature.clone();
```
@add_toggle_python
@snippet dnn/face_detect.py initialize_FaceRecognizerSF
@snippet dnn/face_detect.py facerecognizer
@end_toggle
After obtaining face features *feature1* and *feature2* of two facial images, run codes below to calculate the identity discrepancy between the two faces.
```cpp
// Calculating the discrepancy between two face features by using cosine distance.
double cos_score = faceRecognizer->match(feature1, feature2, FaceRecognizer::DisType::COSINE);
// Calculating the discrepancy between two face features by using normL2 distance.
double L2_score = faceRecognizer->match(feature1, feature2, FaceRecognizer::DisType::NORM_L2);
```
@add_toggle_cpp
@snippet dnn/face_detect.cpp match
@end_toggle
@add_toggle_python
@snippet dnn/face_detect.py match
@end_toggle
For example, two faces have same identity if the cosine distance is greater than or equal to 0.363, or the normL2 distance is less than or equal to 1.128.