Merge pull request opencv#17570 from HannibalAPE:text_det_recog_demo

[GSoC] High Level API and Samples for Scene Text Detection and Recognition

* APIs and samples for scene text detection and recognition

* update APIs and tutorial for Text Detection and Recognition

* API updates:
(1) put decodeType into struct Voc
(2) optimize the post-processing of DB

* sample update:
(1) add transformation into scene_text_spotting.cpp
(2) modify text_detection.cpp with API update

* update tutorial

* simplify text recognition API
update tutorial

* update impl usage in recognize() and detect()

* dnn: refactoring public API of TextRecognitionModel/TextDetectionModel

* update provided models
update opencv.bib

* dnn: adjust text rectangle angle

* remove points ordering operation in model.cpp

* update gts of DB test in test_model.cpp

* dnn: ensure to keep text rectangle angle

- avoid 90/180 degree turns

* dnn(text): use quadrangle result in TextDetectionModel API

* dnn: update Text Detection API
(1) keep points' order consistent with (bl, tl, tr, br) in unclip
(2) update contourScore with boundingRect

Author: HannibalAPE

doc/opencv.bib

@@ -1261,3 +1261,26 @@ @inproceedings{forstner1987fast
   pages={281--305},
   year={1987}
 }
+@inproceedings{liao2020real,
+  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
+  title={Real-time Scene Text Detection with Differentiable Binarization},
+  booktitle={Proc. AAAI},
+  year={2020}
+}
+@article{shi2016end,
+  title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
+  author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
+  journal={IEEE transactions on pattern analysis and machine intelligence},
+  volume={39},
+  number={11},
+  pages={2298--2304},
+  year={2016},
+  publisher={IEEE}
+}
+@inproceedings{zhou2017east,
+  title={East: an efficient and accurate scene text detector},
+  author={Zhou, Xinyu and Yao, Cong and Wen, He and Wang, Yuzhi and Zhou, Shuchang and He, Weiran and Liang, Jiajun},
+  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
+  pages={5551--5560},
+  year={2017}
+}

doc/tutorials/dnn/dnn_OCR/dnn_OCR.markdown

@@ -1,6 +1,7 @@
 # How to run custom OCR model {#tutorial_dnn_OCR}
 
 @prev_tutorial{tutorial_dnn_custom_layers}
+@next_tutorial{tutorial_dnn_text_spotting}
 
 ## Introduction
 
@@ -43,4 +44,4 @@ The input of text recognition model is the output of the text detection model, w
 
 DenseNet_CTC has the smallest parameters and best FPS, and it is suitable for edge devices, which are very sensitive to the cost of calculation. If you have limited computing resources and want to achieve better accuracy, VGG_CTC is a good choice.
 
-CRNN_VGG_BiLSTM_CTC is suitable for scenarios that require high recognition accuracy.
+CRNN_VGG_BiLSTM_CTC is suitable for scenarios that require high recognition accuracy.

doc/tutorials/dnn/dnn_text_spotting/detect_test1.jpg

@@ -0,0 +1,316 @@
+# High Level API: TextDetectionModel and TextRecognitionModel {#tutorial_dnn_text_spotting}
+
+@prev_tutorial{tutorial_dnn_OCR}
+
+## Introduction
+In this tutorial, we will introduce the APIs for TextRecognitionModel and TextDetectionModel in detail.
+
+---
+#### TextRecognitionModel:
+
+In the current version, @ref cv::dnn::TextRecognitionModel only supports CNN+RNN+CTC based algorithms,
+and the greedy decoding method for CTC is provided.
+For more information, please refer to the [original paper](https://arxiv.org/abs/1507.05717)
+
+Before recognition, you should `setVocabulary` and `setDecodeType`.
+- "CTC-greedy", the output of the text recognition model should be a probability matrix.
+    The shape should be `(T, B, Dim)`, where
+    - `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).
+
+@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`
+- Other decoding methods may supported in the future
+
+---
+
+#### TextDetectionModel:
+
+@ref cv::dnn::TextDetectionModel API provides these methods for text detection:
+- cv::dnn::TextDetectionModel::detect() returns the results in std::vector<std::vector<Point>> (4-points quadrangles)
+- cv::dnn::TextDetectionModel::detectTextRectangles() returns the results in std::vector<cv::RotatedRect> (RBOX-like)
+
+In the current version, @ref cv::dnn::TextDetectionModel supports these algorithms:
+- use @ref cv::dnn::TextDetectionModel_DB with "DB" models
+- and use @ref cv::dnn::TextDetectionModel_EAST with "EAST" models
+
+The following provided pretrained models are variants of DB (w/o deformable convolution),
+and the performance can be referred to the Table.1 in the [paper]((https://arxiv.org/abs/1911.08947)).
+For more information, please refer to the [official code](https://github.com/MhLiao/DB)
+
+---
+
+You can train your own model with more data, and convert it into ONNX format.
+We encourage you to add new algorithms to these APIs.
+
+
+## Pretrained Models
+
+#### TextRecognitionModel:
+
+```
+crnn.onnx:
+url: https://drive.google.com/uc?export=dowload&id=1ooaLR-rkTl8jdpGy1DoQs0-X0lQsB6Fj
+sha: 270d92c9ccb670ada2459a25977e8deeaf8380d3,
+alphabet_36.txt: https://drive.google.com/uc?export=dowload&id=1oPOYx5rQRp8L6XQciUwmwhMCfX0KyO4b
+parameter setting: -rgb=0;
+description: The classification number of this model is 36 (0~9 + a~z).
+             The training dataset is MJSynth.
+
+crnn_cs.onnx:
+url: https://drive.google.com/uc?export=dowload&id=12diBsVJrS9ZEl6BNUiRp9s0xPALBS7kt
+sha: a641e9c57a5147546f7a2dbea4fd322b47197cd5
+alphabet_94.txt: https://drive.google.com/uc?export=dowload&id=1oKXxXKusquimp7XY1mFvj9nwLzldVgBR
+parameter setting: -rgb=1;
+description: The classification number of this model is 94 (0~9 + a~z + A~Z + punctuations).
+             The training datasets are MJsynth and SynthText.
+
+crnn_cs_CN.onnx:
+url: https://drive.google.com/uc?export=dowload&id=1is4eYEUKH7HR7Gl37Sw4WPXx6Ir8oQEG
+sha: 3940942b85761c7f240494cf662dcbf05dc00d14
+alphabet_3944.txt: https://drive.google.com/uc?export=dowload&id=18IZUUdNzJ44heWTndDO6NNfIpJMmN-ul
+parameter setting: -rgb=1;
+description: The classification number of this model is 3944 (0~9 + a~z + A~Z + Chinese characters + special characters).
+             The training dataset is ReCTS (https://rrc.cvc.uab.es/?ch=12).
+```
+
+More models can be found in [here](https://drive.google.com/drive/folders/1cTbQ3nuZG-EKWak6emD_s8_hHXWz7lAr?usp=sharing),
+which are taken from [clovaai](https://github.com/clovaai/deep-text-recognition-benchmark).
+You can train more models by [CRNN](https://github.com/meijieru/crnn.pytorch), and convert models by `torch.onnx.export`.
+
+#### TextDetectionModel:
+
+```
+- DB_IC15_resnet50.onnx:
+url: https://drive.google.com/uc?export=dowload&id=17_ABp79PlFt9yPCxSaarVc_DKTmrSGGf
+sha: bef233c28947ef6ec8c663d20a2b326302421fa3
+recommended parameter setting: -inputHeight=736, -inputWidth=1280;
+description: This model is trained on ICDAR2015, so it can only detect English text instances.
+
+- DB_IC15_resnet18.onnx:
+url: https://drive.google.com/uc?export=dowload&id=1sZszH3pEt8hliyBlTmB-iulxHP1dCQWV
+sha: 19543ce09b2efd35f49705c235cc46d0e22df30b
+recommended parameter setting: -inputHeight=736, -inputWidth=1280;
+description: This model is trained on ICDAR2015, so it can only detect English text instances.
+
+- DB_TD500_resnet50.onnx:
+url: https://drive.google.com/uc?export=dowload&id=19YWhArrNccaoSza0CfkXlA8im4-lAGsR
+sha: 1b4dd21a6baa5e3523156776970895bd3db6960a
+recommended parameter setting: -inputHeight=736, -inputWidth=736;
+description: This model is trained on MSRA-TD500, so it can detect both English and Chinese text instances.
+
+- DB_TD500_resnet18.onnx:
+url: https://drive.google.com/uc?export=dowload&id=1vY_KsDZZZb_svd5RT6pjyI8BS1nPbBSX
+sha: 8a3700bdc13e00336a815fc7afff5dcc1ce08546
+recommended parameter setting: -inputHeight=736, -inputWidth=736;
+description: This model is trained on MSRA-TD500, so it can detect both English and Chinese text instances.
+
+```
+
+We will release more models of DB [here](https://drive.google.com/drive/folders/1qzNCHfUJOS0NEUOIKn69eCtxdlNPpWbq?usp=sharing) in the future.
+
+```
+- EAST:
+Download link: https://www.dropbox.com/s/r2ingd0l3zt8hxs/frozen_east_text_detection.tar.gz?dl=1
+This model is based on https://github.com/argman/EAST
+```
+
+## Images for Testing
+
+```
+Text Recognition:
+url: https://drive.google.com/uc?export=dowload&id=1nMcEy68zDNpIlqAn6xCk_kYcUTIeSOtN
+sha: 89205612ce8dd2251effa16609342b69bff67ca3
+
+Text Detection:
+url: https://drive.google.com/uc?export=dowload&id=149tAhIcvfCYeyufRoZ9tmc2mZDKE_XrF
+sha: ced3c03fb7f8d9608169a913acf7e7b93e07109b
+```
+
+## Example for Text Recognition
+
+Step1. Loading images and models with a vocabulary
+
+```cpp
+    // Load a cropped text line image
+    // you can find cropped images for testing in "Images for Testing"
+    int rgb = IMREAD_COLOR; // This should be changed according to the model input requirement.
+    Mat image = imread("path/to/text_rec_test.png", rgb);
+
+    // Load models weights
+    TextRecognitionModel model("path/to/crnn_cs.onnx");
+
+    // The decoding method
+    // more methods will be supported in future
+    model.setDecodeType("CTC-greedy");
+
+    // Load vocabulary
+    // vocabulary should be changed according to the text recognition model
+    std::ifstream vocFile;
+    vocFile.open("path/to/alphabet_94.txt");
+    CV_Assert(vocFile.is_open());
+    String vocLine;
+    std::vector<String> vocabulary;
+    while (std::getline(vocFile, vocLine)) {
+        vocabulary.push_back(vocLine);
+    }
+    model.setVocabulary(vocabulary);
+```
+
+Step2. Setting Parameters
+
+```cpp
+    // Normalization parameters
+    double scale = 1.0 / 127.5;
+    Scalar mean = Scalar(127.5, 127.5, 127.5);
+
+    // The input shape
+    Size inputSize = Size(100, 32);
+
+    model.setInputParams(scale, inputSize, mean);
+```
+Step3. Inference
+```cpp
+    std::string recognitionResult = recognizer.recognize(image);
+    std::cout << "'" << recognitionResult << "'" << std::endl;
+```
+
+Input image:
+
+![Picture example](text_rec_test.png)
+
+Output:
+```
+'welcome'
+```
+
+
+## Example for Text Detection
+
+Step1. Loading images and models
+```cpp
+    // Load an image
+    // you can find some images for testing in "Images for Testing"
+    Mat frame = imread("/path/to/text_det_test.png");
+```
+
+Step2.a Setting Parameters (DB)
+```cpp
+    // Load model weights
+    TextDetectionModel_DB model("/path/to/DB_TD500_resnet50.onnx");
+
+    // Post-processing parameters
+    float binThresh = 0.3;
+    float polyThresh = 0.5;
+    uint maxCandidates = 200;
+    double unclipRatio = 2.0;
+    model.setBinaryThreshold(binThresh)
+         .setPolygonThreshold(polyThresh)
+         .setMaxCandidates(maxCandidates)
+         .setUnclipRatio(unclipRatio)
+    ;
+
+    // Normalization parameters
+    double scale = 1.0 / 255.0;
+    Scalar mean = Scalar(122.67891434, 116.66876762, 104.00698793);
+
+    // The input shape
+    Size inputSize = Size(736, 736);
+
+    model.setInputParams(scale, inputSize, mean);
+```
+
+Step2.b Setting Parameters (EAST)
+```cpp
+    TextDetectionModel_EAST model("EAST.pb");
+
+    float confThreshold = 0.5;
+    float nmsThreshold = 0.4;
+    model.setConfidenceThreshold(confThresh)
+         .setNMSThreshold(nmsThresh)
+    ;
+
+    double detScale = 1.0;
+    Size detInputSize = Size(320, 320);
+    Scalar detMean = Scalar(123.68, 116.78, 103.94);
+    bool swapRB = true;
+    model.setInputParams(detScale, detInputSize, detMean, swapRB);
+```
+
+
+Step3. Inference
+```cpp
+    std::vector<std::vector<Point>> detResults;
+    model.detect(detResults);
+
+    // Visualization
+    polylines(frame, results, true, Scalar(0, 255, 0), 2);
+    imshow("Text Detection", image);
+    waitKey();
+```
+
+Output:
+
+![Picture example](text_det_test_results.jpg)
+
+## Example for Text Spotting
+
+After following the steps above, it is easy to get the detection results of an input image.
+Then, you can do transformation and crop text images for recognition.
+For more information, please refer to **Detailed Sample**
+```cpp
+    // Transform and Crop
+    Mat cropped;
+    fourPointsTransform(recInput, vertices, cropped);
+
+    String recResult = recognizer.recognize(cropped);
+```
+
+Output Examples:
+
+![Picture example](detect_test1.jpg)
+
+![Picture example](detect_test2.jpg)
+
+## Source Code
+The [source code](https://github.com/opencv/opencv/blob/master/modules/dnn/src/model.cpp)
+of these APIs can be found in the DNN module.
+
+## Detailed Sample
+For more information, please refer to:
+- [samples/dnn/scene_text_recognition.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/scene_text_recognition.cpp)
+- [samples/dnn/scene_text_detection.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/scene_text_detection.cpp)
+- [samples/dnn/text_detection.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/text_detection.cpp)
+- [samples/dnn/scene_text_spotting.cpp](https://github.com/opencv/opencv/blob/master/samples/dnn/scene_text_spotting.cpp)
+
+#### Test with an image
+Examples:
+```bash
+example_dnn_scene_text_recognition -mp=path/to/crnn_cs.onnx -i=path/to/an/image -rgb=1 -vp=/path/to/alphabet_94.txt
+example_dnn_scene_text_detection -mp=path/to/DB_TD500_resnet50.onnx -i=path/to/an/image -ih=736 -iw=736
+example_dnn_scene_text_spotting -dmp=path/to/DB_IC15_resnet50.onnx -rmp=path/to/crnn_cs.onnx -i=path/to/an/image -iw=1280 -ih=736 -rgb=1 -vp=/path/to/alphabet_94.txt
+example_dnn_text_detection -dmp=path/to/EAST.pb -rmp=path/to/crnn_cs.onnx -i=path/to/an/image -rgb=1 -vp=path/to/alphabet_94.txt
+```
+
+#### Test on public datasets
+Text Recognition:
+
+The download link for testing images can be found in the **Images for Testing**
+
+
+Examples:
+```bash
+example_dnn_scene_text_recognition -mp=path/to/crnn.onnx -e=true -edp=path/to/evaluation_data_rec -vp=/path/to/alphabet_36.txt -rgb=0
+example_dnn_scene_text_recognition -mp=path/to/crnn_cs.onnx -e=true -edp=path/to/evaluation_data_rec -vp=/path/to/alphabet_94.txt -rgb=1
+```
+
+Text Detection:
+
+The download links for testing images can be found in the **Images for Testing**
+
+Examples:
+```bash
+example_dnn_scene_text_detection -mp=path/to/DB_TD500_resnet50.onnx -e=true -edp=path/to/evaluation_data_det/TD500 -ih=736 -iw=736
+example_dnn_scene_text_detection -mp=path/to/DB_IC15_resnet50.onnx -e=true -edp=path/to/evaluation_data_det/IC15 -ih=736 -iw=1280
+```

doc/tutorials/dnn/dnn_text_spotting/detect_test2.jpg

@@ -79,4 +79,14 @@ Deep Neural Networks (dnn module) {#tutorial_table_of_content_dnn}
 
     *Author:* Zihao Mu
 
-    In this tutorial you will learn how to use opencv_dnn module using custom OCR models.
+    In this tutorial you will learn how to use opencv_dnn module using custom OCR models.
+
+-   @subpage tutorial_dnn_text_spotting
+
+    *Languages:* C++
+
+    *Compatibility:* \> OpenCV 4.5
+
+    *Author:* Wenqing Zhang
+
+    In these tutorial, we'll introduce how to use the high-level APIs for text recognition and text detection