128 lines
4.4 KiB
C++
128 lines
4.4 KiB
C++
/*
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* textdetection.cpp
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*
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* A demo program of the Extremal Region Filter algorithm described in
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* Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012
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*
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* Created on: Sep 23, 2013
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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/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include <vector>
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#include <iostream>
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#include <iomanip>
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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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void show_help_and_exit(const char *cmd);
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void groups_draw(Mat &src, vector<Rect> &groups);
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void er_show(vector<Mat> &channels, vector<vector<ERStat> > ®ions);
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int main(int argc, const char * argv[])
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{
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cout << endl << argv[0] << endl << endl;
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cout << "Demo program of the Extremal Region Filter algorithm described in " << endl;
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cout << "Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012" << endl << endl;
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if (argc < 2) show_help_and_exit(argv[0]);
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Mat src = imread(argv[1]);
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// Extract channels to be processed individually
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vector<Mat> channels;
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computeNMChannels(src, channels);
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int cn = (int)channels.size();
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// Append negative channels to detect ER- (bright regions over dark background)
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for (int c = 0; c < cn-1; c++)
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channels.push_back(255-channels[c]);
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// Create ERFilter objects with the 1st and 2nd stage default classifiers
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Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"),16,0.00015f,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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vector<vector<ERStat> > regions(channels.size());
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// Apply the default cascade classifier to each independent channel (could be done in parallel)
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cout << "Extracting Class Specific Extremal Regions from " << (int)channels.size() << " channels ..." << endl;
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cout << " (...) this may take a while (...)" << endl << endl;
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for (int c=0; c<(int)channels.size(); c++)
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{
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er_filter1->run(channels[c], regions[c]);
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er_filter2->run(channels[c], regions[c]);
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}
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// Detect character groups
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cout << "Grouping extracted ERs ... ";
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vector< vector<Vec2i> > region_groups;
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vector<Rect> groups_boxes;
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erGrouping(src, channels, regions, region_groups, groups_boxes, ERGROUPING_ORIENTATION_HORIZ);
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//erGrouping(src, channels, regions, region_groups, groups_boxes, ERGROUPING_ORIENTATION_ANY, "./trained_classifier_erGrouping.xml", 0.5);
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// draw groups
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groups_draw(src, groups_boxes);
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imshow("grouping",src);
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cout << "Done!" << endl << endl;
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cout << "Press 'space' to show the extracted Extremal Regions, any other key to exit." << endl << endl;
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if ((waitKey()&0xff) == ' ')
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er_show(channels,regions);
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// memory clean-up
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er_filter1.release();
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er_filter2.release();
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regions.clear();
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if (!groups_boxes.empty())
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{
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groups_boxes.clear();
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}
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}
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// helper functions
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void show_help_and_exit(const char *cmd)
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{
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cout << " Usage: " << cmd << " <input_image> " << endl;
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cout << " Default classifier files (trained_classifierNM*.xml) must be in current directory" << endl << endl;
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exit(-1);
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}
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void groups_draw(Mat &src, vector<Rect> &groups)
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{
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for (int i=(int)groups.size()-1; i>=0; i--)
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{
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if (src.type() == CV_8UC3)
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rectangle(src,groups.at(i).tl(),groups.at(i).br(),Scalar( 0, 255, 255 ), 3, 8 );
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else
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rectangle(src,groups.at(i).tl(),groups.at(i).br(),Scalar( 255 ), 3, 8 );
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}
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}
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void er_show(vector<Mat> &channels, vector<vector<ERStat> > ®ions)
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{
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for (int c=0; c<(int)channels.size(); c++)
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{
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Mat dst = Mat::zeros(channels[0].rows+2,channels[0].cols+2,CV_8UC1);
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for (int r=0; r<(int)regions[c].size(); r++)
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{
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ERStat er = regions[c][r];
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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[c],dst,Point(er.pixel%channels[c].cols,er.pixel/channels[c].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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char buff[20]; char *buff_ptr = buff;
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sprintf(buff, "channel %d", c);
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imshow(buff_ptr, dst);
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}
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waitKey(-1);
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}
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