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22d64ae08f
[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
145 lines
4.9 KiB
C++
145 lines
4.9 KiB
C++
#include <iostream>
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#include <fstream>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/dnn/dnn.hpp>
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using namespace cv;
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using namespace cv::dnn;
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String keys =
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"{ help h | | Print help message. }"
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"{ inputImage i | | Path to an input image. Skip this argument to capture frames from a camera. }"
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"{ modelPath mp | | Path to a binary .onnx file contains trained CRNN text recognition model. "
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"Download links are provided in doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown}"
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"{ RGBInput rgb |0| 0: imread with flags=IMREAD_GRAYSCALE; 1: imread with flags=IMREAD_COLOR. }"
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"{ evaluate e |false| false: predict with input images; true: evaluate on benchmarks. }"
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"{ evalDataPath edp | | Path to benchmarks for evaluation. "
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"Download links are provided in doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown}"
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"{ vocabularyPath vp | alphabet_36.txt | Path to recognition vocabulary. "
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"Download links are provided in doc/tutorials/dnn/dnn_text_spotting/dnn_text_spotting.markdown}";
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String convertForEval(String &input);
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int main(int argc, char** argv)
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{
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// Parse arguments
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CommandLineParser parser(argc, argv, keys);
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parser.about("Use this script to run the PyTorch implementation of "
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"An End-to-End Trainable Neural Network for Image-based SequenceRecognition and Its Application to Scene Text Recognition "
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"(https://arxiv.org/abs/1507.05717)");
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if (argc == 1 || parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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String modelPath = parser.get<String>("modelPath");
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String vocPath = parser.get<String>("vocabularyPath");
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int imreadRGB = parser.get<int>("RGBInput");
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if (!parser.check())
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{
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parser.printErrors();
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return 1;
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}
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// Load the network
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CV_Assert(!modelPath.empty());
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TextRecognitionModel recognizer(modelPath);
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// Load vocabulary
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CV_Assert(!vocPath.empty());
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std::ifstream vocFile;
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vocFile.open(samples::findFile(vocPath));
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CV_Assert(vocFile.is_open());
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String vocLine;
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std::vector<String> vocabulary;
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while (std::getline(vocFile, vocLine)) {
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vocabulary.push_back(vocLine);
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}
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recognizer.setVocabulary(vocabulary);
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recognizer.setDecodeType("CTC-greedy");
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// Set parameters
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double scale = 1.0 / 127.5;
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Scalar mean = Scalar(127.5, 127.5, 127.5);
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Size inputSize = Size(100, 32);
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recognizer.setInputParams(scale, inputSize, mean);
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if (parser.get<bool>("evaluate"))
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{
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// For evaluation
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String evalDataPath = parser.get<String>("evalDataPath");
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CV_Assert(!evalDataPath.empty());
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String gtPath = evalDataPath + "/test_gts.txt";
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std::ifstream evalGts;
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evalGts.open(gtPath);
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CV_Assert(evalGts.is_open());
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String gtLine;
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int cntRight=0, cntAll=0;
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TickMeter timer;
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timer.reset();
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while (std::getline(evalGts, gtLine)) {
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size_t splitLoc = gtLine.find_first_of(' ');
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String imgPath = evalDataPath + '/' + gtLine.substr(0, splitLoc);
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String gt = gtLine.substr(splitLoc+1);
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// Inference
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Mat frame = imread(samples::findFile(imgPath), imreadRGB);
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CV_Assert(!frame.empty());
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timer.start();
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std::string recognitionResult = recognizer.recognize(frame);
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timer.stop();
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if (gt == convertForEval(recognitionResult))
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cntRight++;
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cntAll++;
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}
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std::cout << "Accuracy(%): " << (double)(cntRight) / (double)(cntAll) << std::endl;
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std::cout << "Average Inference Time(ms): " << timer.getTimeMilli() / (double)(cntAll) << std::endl;
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}
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else
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{
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// Create a window
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static const std::string winName = "Input Cropped Image";
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// Open an image file
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CV_Assert(parser.has("inputImage"));
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Mat frame = imread(samples::findFile(parser.get<String>("inputImage")), imreadRGB);
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CV_Assert(!frame.empty());
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// Recognition
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std::string recognitionResult = recognizer.recognize(frame);
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imshow(winName, frame);
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std::cout << "Predition: '" << recognitionResult << "'" << std::endl;
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waitKey();
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}
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return 0;
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}
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// Convert the predictions to lower case, and remove other characters.
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// Only for Evaluation
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String convertForEval(String & input)
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{
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String output;
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for (uint i = 0; i < input.length(); i++){
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char ch = input[i];
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if ((int)ch >= 97 && (int)ch <= 122) {
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output.push_back(ch);
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} else if ((int)ch >= 65 && (int)ch <= 90) {
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output.push_back((char)(ch + 32));
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} else {
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continue;
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}
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}
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return output;
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}
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