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409 lines
14 KiB
409 lines
14 KiB
/* |
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* webcam-demo.cpp |
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* |
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* A demo program of End-to-end Scene Text Detection and Recognition. |
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* |
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* Created on: Jul 31, 2014 |
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* Author: Lluis Gomez i Bigorda <lgomez AT cvc.uab.es> |
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*/ |
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#include "opencv2/text.hpp" |
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#include "opencv2/core/utility.hpp" |
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#include "opencv2/highgui.hpp" |
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#include "opencv2/imgproc.hpp" |
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#include "opencv2/features2d.hpp" |
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#include <iostream> |
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using namespace std; |
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using namespace cv; |
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using namespace cv::text; |
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//ERStat extraction is done in parallel for different channels |
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class Parallel_extractCSER: public cv::ParallelLoopBody |
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{ |
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private: |
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vector<Mat> &channels; |
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vector< vector<ERStat> > ®ions; |
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vector< Ptr<ERFilter> > er_filter1; |
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vector< Ptr<ERFilter> > er_filter2; |
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public: |
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Parallel_extractCSER(vector<Mat> &_channels, vector< vector<ERStat> > &_regions, |
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vector<Ptr<ERFilter> >_er_filter1, vector<Ptr<ERFilter> >_er_filter2) |
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: channels(_channels),regions(_regions),er_filter1(_er_filter1),er_filter2(_er_filter2){} |
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virtual void operator()( const cv::Range &r ) const |
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{ |
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for (int c=r.start; c < r.end; c++) |
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{ |
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er_filter1[c]->run(channels[c], regions[c]); |
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er_filter2[c]->run(channels[c], regions[c]); |
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} |
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} |
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Parallel_extractCSER & operator=(const Parallel_extractCSER &a); |
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}; |
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//OCR recognition is done in parallel for different detections |
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template <class T> |
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class Parallel_OCR: public cv::ParallelLoopBody |
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{ |
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private: |
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vector<Mat> &detections; |
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vector<string> &outputs; |
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vector< vector<Rect> > &boxes; |
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vector< vector<string> > &words; |
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vector< vector<float> > &confidences; |
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vector< Ptr<T> > &ocrs; |
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public: |
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Parallel_OCR(vector<Mat> &_detections, vector<string> &_outputs, vector< vector<Rect> > &_boxes, |
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vector< vector<string> > &_words, vector< vector<float> > &_confidences, |
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vector< Ptr<T> > &_ocrs) |
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: detections(_detections), outputs(_outputs), boxes(_boxes), words(_words), |
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confidences(_confidences), ocrs(_ocrs) |
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{} |
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virtual void operator()( const cv::Range &r ) const |
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{ |
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for (int c=r.start; c < r.end; c++) |
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{ |
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ocrs[c%ocrs.size()]->run(detections[c], outputs[c], &boxes[c], &words[c], &confidences[c], OCR_LEVEL_WORD); |
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} |
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} |
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Parallel_OCR & operator=(const Parallel_OCR &a); |
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}; |
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//Discard wrongly recognised strings |
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bool isRepetitive(const string& s); |
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//Draw ER's in an image via floodFill |
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void er_draw(vector<Mat> &channels, vector<vector<ERStat> > ®ions, vector<Vec2i> group, Mat& segmentation); |
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//Perform text detection and recognition from webcam |
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int main(int argc, char* argv[]) |
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{ |
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cout << endl << argv[0] << endl << endl; |
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cout << "A demo program of End-to-end Scene Text Detection and Recognition using webcam." << endl << endl; |
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cout << " Usage: " << argv[0] << " [camera_index]" << endl << endl; |
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cout << " Press 'r' to switch between MSER/CSER regions." << endl; |
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cout << " Press 'g' to switch between Horizontal and Arbitrary oriented grouping." << endl; |
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cout << " Press 'o' to switch between OCRTesseract/OCRHMMDecoder recognition." << endl; |
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cout << " Press 's' to scale down frame size to 320x240." << endl; |
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cout << " Press 'ESC' to exit." << endl << endl; |
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namedWindow("recognition",WINDOW_NORMAL); |
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bool downsize = false; |
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int REGION_TYPE = 1; |
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int GROUPING_ALGORITHM = 0; |
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int RECOGNITION = 0; |
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char *region_types_str[2] = {const_cast<char *>("ERStats"), const_cast<char *>("MSER")}; |
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char *grouping_algorithms_str[2] = {const_cast<char *>("exhaustive_search"), const_cast<char *>("multioriented")}; |
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char *recognitions_str[2] = {const_cast<char *>("Tesseract"), const_cast<char *>("NM_chain_features + KNN")}; |
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Mat frame,grey,orig_grey,out_img; |
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vector<Mat> channels; |
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vector<vector<ERStat> > regions(2); //two channels |
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// Create ERFilter objects with the 1st and 2nd stage default classifiers |
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// since er algorithm is not reentrant we need one filter for channel |
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vector< Ptr<ERFilter> > er_filters1; |
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vector< Ptr<ERFilter> > er_filters2; |
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for (int i=0; i<2; i++) |
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{ |
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Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),8,0.00015f,0.13f,0.2f,true,0.1f); |
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Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"),0.5); |
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er_filters1.push_back(er_filter1); |
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er_filters2.push_back(er_filter2); |
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} |
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//double t_r = getTickCount(); |
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//Initialize OCR engine (we initialize 10 instances in order to work several recognitions in parallel) |
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cout << "Initializing OCR engines ..." << endl; |
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int num_ocrs = 10; |
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vector< Ptr<OCRTesseract> > ocrs; |
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for (int o=0; o<num_ocrs; o++) |
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{ |
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ocrs.push_back(OCRTesseract::create()); |
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} |
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Mat transition_p; |
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string filename = "OCRHMM_transitions_table.xml"; |
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FileStorage fs(filename, FileStorage::READ); |
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fs["transition_probabilities"] >> transition_p; |
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fs.release(); |
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Mat emission_p = Mat::eye(62,62,CV_64FC1); |
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string voc = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"; |
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vector< Ptr<OCRHMMDecoder> > decoders; |
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for (int o=0; o<num_ocrs; o++) |
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{ |
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decoders.push_back(OCRHMMDecoder::create(loadOCRHMMClassifierNM("OCRHMM_knn_model_data.xml.gz"), |
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voc, transition_p, emission_p)); |
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} |
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cout << " Done!" << endl; |
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//cout << "TIME_OCR_INITIALIZATION_ALT = "<< ((double)getTickCount() - t_r)*1000/getTickFrequency() << endl; |
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int cam_idx = 0; |
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if (argc > 1) |
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cam_idx = atoi(argv[1]); |
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VideoCapture cap(cam_idx); |
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if(!cap.isOpened()) |
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{ |
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cout << "ERROR: Cannot open default camera (0)." << endl; |
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return -1; |
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} |
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while (cap.read(frame)) |
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{ |
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double t_all = (double)getTickCount(); |
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if (downsize) |
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resize(frame,frame,Size(320,240)); |
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/*Text Detection*/ |
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cvtColor(frame,grey,COLOR_RGB2GRAY); |
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grey.copyTo(orig_grey); |
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// Extract channels to be processed individually |
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channels.clear(); |
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channels.push_back(grey); |
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channels.push_back(255-grey); |
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regions[0].clear(); |
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regions[1].clear(); |
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//double t_d = (double)getTickCount(); |
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switch (REGION_TYPE) |
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{ |
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case 0: |
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{ |
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parallel_for_(cv::Range(0,(int)channels.size()), Parallel_extractCSER(channels,regions,er_filters1,er_filters2)); |
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break; |
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} |
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case 1: |
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{ |
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//Extract MSER |
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vector<vector<Point> > contours; |
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vector<Rect> bboxes; |
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Ptr<MSER> mser = MSER::create(21,(int)(0.00002*grey.cols*grey.rows),(int)(0.05*grey.cols*grey.rows),1,0.7); |
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mser->detectRegions(grey, contours, bboxes); |
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//Convert the output of MSER to suitable input for the grouping/recognition algorithms |
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if (contours.size() > 0) |
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MSERsToERStats(grey, contours, regions); |
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break; |
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} |
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case 2: |
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{ |
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break; |
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} |
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} |
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//cout << "TIME_REGION_DETECTION_ALT = " << ((double)getTickCount() - t_d)*1000/getTickFrequency() << endl; |
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// Detect character groups |
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//double t_g = getTickCount(); |
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vector< vector<Vec2i> > nm_region_groups; |
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vector<Rect> nm_boxes; |
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switch (GROUPING_ALGORITHM) |
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{ |
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case 0: |
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{ |
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erGrouping(frame, channels, regions, nm_region_groups, nm_boxes, ERGROUPING_ORIENTATION_HORIZ); |
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break; |
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} |
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case 1: |
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{ |
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erGrouping(frame, channels, regions, nm_region_groups, nm_boxes, ERGROUPING_ORIENTATION_ANY, "./trained_classifier_erGrouping.xml", 0.5); |
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break; |
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} |
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} |
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//cout << "TIME_GROUPING_ALT = " << ((double)getTickCount() - t_g)*1000/getTickFrequency() << endl; |
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/*Text Recognition (OCR)*/ |
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frame.copyTo(out_img); |
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float scale_img = (float)(600.f/frame.rows); |
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float scale_font = (float)(2-scale_img)/1.4f; |
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vector<string> words_detection; |
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float min_confidence1 = 0.f, min_confidence2 = 0.f; |
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if (RECOGNITION == 0) |
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{ |
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min_confidence1 = 51.f; min_confidence2 = 60.f; |
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} |
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vector<Mat> detections; |
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//t_r = getTickCount(); |
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for (int i=0; i<(int)nm_boxes.size(); i++) |
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{ |
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rectangle(out_img, nm_boxes[i].tl(), nm_boxes[i].br(), Scalar(255,255,0),3); |
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Mat group_img = Mat::zeros(frame.rows+2, frame.cols+2, CV_8UC1); |
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er_draw(channels, regions, nm_region_groups[i], group_img); |
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group_img(nm_boxes[i]).copyTo(group_img); |
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copyMakeBorder(group_img,group_img,15,15,15,15,BORDER_CONSTANT,Scalar(0)); |
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detections.push_back(group_img); |
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} |
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vector<string> outputs((int)detections.size()); |
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vector< vector<Rect> > boxes((int)detections.size()); |
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vector< vector<string> > words((int)detections.size()); |
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vector< vector<float> > confidences((int)detections.size()); |
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// parallel process detections in batches of ocrs.size() (== num_ocrs) |
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for (int i=0; i<(int)detections.size(); i=i+(int)num_ocrs) |
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{ |
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Range r; |
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if (i+(int)num_ocrs <= (int)detections.size()) |
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r = Range(i,i+(int)num_ocrs); |
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else |
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r = Range(i,(int)detections.size()); |
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switch(RECOGNITION) |
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{ |
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case 0: |
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parallel_for_(r, Parallel_OCR<OCRTesseract>(detections, outputs, boxes, words, confidences, ocrs)); |
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break; |
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case 1: |
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parallel_for_(r, Parallel_OCR<OCRHMMDecoder>(detections, outputs, boxes, words, confidences, decoders)); |
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break; |
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} |
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} |
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for (int i=0; i<(int)detections.size(); i++) |
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{ |
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outputs[i].erase(remove(outputs[i].begin(), outputs[i].end(), '\n'), outputs[i].end()); |
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//cout << "OCR output = \"" << outputs[i] << "\" lenght = " << outputs[i].size() << endl; |
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if (outputs[i].size() < 3) |
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continue; |
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for (int j=0; j<(int)boxes[i].size(); j++) |
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{ |
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boxes[i][j].x += nm_boxes[i].x-15; |
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boxes[i][j].y += nm_boxes[i].y-15; |
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//cout << " word = " << words[j] << "\t confidence = " << confidences[j] << endl; |
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if ((words[i][j].size() < 2) || (confidences[i][j] < min_confidence1) || |
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((words[i][j].size()==2) && (words[i][j][0] == words[i][j][1])) || |
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((words[i][j].size()< 4) && (confidences[i][j] < min_confidence2)) || |
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isRepetitive(words[i][j])) |
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continue; |
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words_detection.push_back(words[i][j]); |
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rectangle(out_img, boxes[i][j].tl(), boxes[i][j].br(), Scalar(255,0,255),3); |
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Size word_size = getTextSize(words[i][j], FONT_HERSHEY_SIMPLEX, (double)scale_font, (int)(3*scale_font), NULL); |
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rectangle(out_img, boxes[i][j].tl()-Point(3,word_size.height+3), boxes[i][j].tl()+Point(word_size.width,0), Scalar(255,0,255),-1); |
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putText(out_img, words[i][j], boxes[i][j].tl()-Point(1,1), FONT_HERSHEY_SIMPLEX, scale_font, Scalar(255,255,255),(int)(3*scale_font)); |
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} |
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} |
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//cout << "TIME_OCR_ALT = " << ((double)getTickCount() - t_r)*1000/getTickFrequency() << endl; |
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t_all = ((double)getTickCount() - t_all)*1000/getTickFrequency(); |
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char buff[100]; |
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sprintf(buff, "%2.1f Fps. @ 640x480", (float)(1000/t_all)); |
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string fps_info = buff; |
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rectangle(out_img, Point(out_img.rows-160,out_img.rows-70), Point(out_img.cols,out_img.rows), Scalar(255,255,255),-1); |
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putText(out_img, fps_info, Point(10,out_img.rows-10), FONT_HERSHEY_DUPLEX, scale_font, Scalar(255,0,0)); |
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putText(out_img, region_types_str[REGION_TYPE], Point(out_img.rows-150,out_img.rows-50), FONT_HERSHEY_DUPLEX, scale_font, Scalar(255,0,0)); |
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putText(out_img, grouping_algorithms_str[GROUPING_ALGORITHM], Point(out_img.rows-150,out_img.rows-30), FONT_HERSHEY_DUPLEX, scale_font, Scalar(255,0,0)); |
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putText(out_img, recognitions_str[RECOGNITION], Point(out_img.rows-150,out_img.rows-10), FONT_HERSHEY_DUPLEX, scale_font, Scalar(255,0,0)); |
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imshow("recognition", out_img); |
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//imwrite("recognition_alt.jpg", out_img); |
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int key = waitKey(30); |
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if (key == 27) //wait for key |
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{ |
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cout << "esc key pressed" << endl; |
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break; |
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} |
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else |
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{ |
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switch (key) |
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{ |
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case 103: //g |
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GROUPING_ALGORITHM = (GROUPING_ALGORITHM+1)%2; |
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cout << "Grouping switched to " << grouping_algorithms_str[GROUPING_ALGORITHM] << endl; |
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break; |
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case 111: //o |
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RECOGNITION = (RECOGNITION+1)%2; |
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cout << "OCR switched to " << recognitions_str[RECOGNITION] << endl; |
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break; |
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case 114: //r |
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REGION_TYPE = (REGION_TYPE+1)%2; |
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cout << "Regions switched to " << region_types_str[REGION_TYPE] << endl; |
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break; |
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case 115: //s |
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downsize = !downsize; |
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break; |
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default: |
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break; |
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} |
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} |
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} |
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return 0; |
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} |
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bool isRepetitive(const string& s) |
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{ |
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int count = 0; |
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int count2 = 0; |
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int count3 = 0; |
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int first=(int)s[0]; |
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int last=(int)s[(int)s.size()-1]; |
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for (int i=0; i<(int)s.size(); i++) |
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{ |
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if ((s[i] == 'i') || |
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(s[i] == 'l') || |
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(s[i] == 'I')) |
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count++; |
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if((int)s[i]==first) |
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count2++; |
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if((int)s[i]==last) |
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count3++; |
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} |
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if ((count > ((int)s.size()+1)/2) || (count2 == (int)s.size()) || (count3 > ((int)s.size()*2)/3)) |
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{ |
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return true; |
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} |
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return false; |
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} |
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void er_draw(vector<Mat> &channels, vector<vector<ERStat> > ®ions, vector<Vec2i> group, Mat& segmentation) |
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{ |
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for (int r=0; r<(int)group.size(); r++) |
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{ |
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ERStat er = regions[group[r][0]][group[r][1]]; |
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if (er.parent != NULL) // deprecate the root region |
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{ |
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int newMaskVal = 255; |
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int flags = 4 + (newMaskVal << 8) + FLOODFILL_FIXED_RANGE + FLOODFILL_MASK_ONLY; |
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floodFill(channels[group[r][0]],segmentation,Point(er.pixel%channels[group[r][0]].cols,er.pixel/channels[group[r][0]].cols), |
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Scalar(255),0,Scalar(er.level),Scalar(0),flags); |
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} |
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} |
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}
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