Merge branch 4.x

doc/tutorials/dnn/dnn_face/dnn_face.markdown

@@ -0,0 +1,95 @@
+# DNN-based Face Detection And Recognition {#tutorial_dnn_face}
+
+@tableofcontents
+
+@prev_tutorial{tutorial_dnn_text_spotting}
+@next_tutorial{pytorch_cls_tutorial_dnn_conversion}
+
+| | |
+| -: | :- |
+| Original Author | Chengrui Wang, Yuantao Feng |
+| Compatibility | OpenCV >= 4.5.1 |
+
+## Introduction
+
+In this section, we introduce the DNN-based module for face detection and face recognition. Models can be obtained in [Models](#Models). The usage of `FaceDetectorYN` and `FaceRecognizer` are presented in [Usage](#Usage).
+
+## Models
+
+There are two models (ONNX format) pre-trained and required for this module:
+- [Face Detection](https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx):
+    - Size: 337KB
+    - Results on WIDER Face Val set: 0.830(easy), 0.824(medium), 0.708(hard)
+- [Face Recognition](https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view?usp=sharing)
+    - Size: 36.9MB
+    - Results:
+
+    | Database | Accuracy | Threshold (normL2) | Threshold (cosine) |
+    | -------- | -------- | ------------------ | ------------------ |
+    | LFW      | 99.60%   | 1.128              | 0.363              |
+    | CALFW    | 93.95%   | 1.149              | 0.340              |
+    | CPLFW    | 91.05%   | 1.204              | 0.275              |
+    | AgeDB-30 | 94.90%   | 1.202              | 0.277              |
+    | CFP-FP   | 94.80%   | 1.253              | 0.212              |
+
+## Usage
+
+### DNNFaceDetector
+
+```cpp
+// Initialize FaceDetectorYN
+Ptr<FaceDetectorYN> faceDetector = FaceDetectorYN::create(onnx_path, "", image.size(), score_thresh, nms_thresh, top_k);
+
+// Forward
+Mat faces;
+faceDetector->detect(image, faces);
+```
+
+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:
+
+```
+x1, y1, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt, x_rcm, y_rcm, x_lcm, y_lcm
+```
+, where `x1, y1, w, h` are the top-left coordinates, width and height of the face bounding box, `{x, y}_{re, le, nt, rcm, lcm}` stands for the coordinates of right eye, left eye, nose tip, the right corner and left corner of the mouth respectively.
+
+
+### Face Recognition
+
+Following Face Detection, run codes below to extract face feature from facial image.
+
+```cpp
+// Initialize FaceRecognizer with model path (cv::String)
+Ptr<FaceRecognizer> faceRecognizer = FaceRecognizer::create(model_path, "");
+
+// 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();
+```
+
+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);
+```
+
+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.
+
+## Reference:
+
+- https://github.com/ShiqiYu/libfacedetection
+- https://github.com/ShiqiYu/libfacedetection.train
+- https://github.com/zhongyy/SFace
+
+## Acknowledgement
+
+Thanks [Professor Shiqi Yu](https://github.com/ShiqiYu/) and [Yuantao Feng](https://github.com/fengyuentau) for training and providing the face detection model.
+
+Thanks [Professor Deng](http://www.whdeng.cn/), [PhD Candidate Zhong](https://github.com/zhongyy/) and [Master Candidate Wang](https://github.com/crywang/) for training and providing the face recognition model.

doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown

@@ -3,7 +3,7 @@
 @tableofcontents
 
 @prev_tutorial{tutorial_dnn_OCR}
-@next_tutorial{pytorch_cls_tutorial_dnn_conversion}
+@next_tutorial{tutorial_dnn_face}
 
 |    |    |
 | -: | :- |
@@ -26,6 +26,11 @@ Before recognition, you should `setVocabulary` and `setDecodeType`.
     - `T` is the sequence length
     - `B` is the batch size (only support `B=1` in inference)
     - and `Dim` is the length of vocabulary +1('Blank' of CTC is at the index=0 of Dim).
+- "CTC-prefix-beam-search", the output of the text recognition model should be a probability matrix same with "CTC-greedy".
+    - The algorithm is proposed at Hannun's [paper](https://arxiv.org/abs/1408.2873).
+    - `setDecodeOptsCTCPrefixBeamSearch` could be used to control the beam size in search step.
+    - To futher optimize for big vocabulary, a new option `vocPruneSize` is introduced to avoid iterate the whole vocbulary
+       but only the number of `vocPruneSize` tokens with top probabilty.
 
 @ref cv::dnn::TextRecognitionModel::recognize() is the main function for text recognition.
 - The input image should be a cropped text image or an image with `roiRects`

doc/tutorials/dnn/table_of_content_dnn.markdown

@@ -10,6 +10,7 @@ Deep Neural Networks (dnn module) {#tutorial_table_of_content_dnn}
 -   @subpage tutorial_dnn_custom_layers
 -   @subpage tutorial_dnn_OCR
 -   @subpage tutorial_dnn_text_spotting
+-   @subpage tutorial_dnn_face
 
 #### PyTorch models with OpenCV
 In this section you will find the guides, which describe how to run classification, segmentation and detection PyTorch DNN models with OpenCV.