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Open Source Computer Vision Library
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157 lines
4.9 KiB
157 lines
4.9 KiB
#include <opencv2/imgproc/imgproc.hpp> |
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#include <opencv2/highgui/highgui.hpp> |
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#include <opencv2/objdetect/objdetect.hpp> |
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#include <iostream> |
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#include <fstream> |
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using namespace cv; |
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using namespace std; |
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#define SHOW_ALL_RECTS_BY_ONE 0 |
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static void fillColors( vector<Scalar>& colors ) |
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{ |
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cv::RNG rng = theRNG(); |
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for( size_t ci = 0; ci < colors.size(); ci++ ) |
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colors[ci] = Scalar( rng(256), rng(256), rng(256) ); |
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} |
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static void readTestImageNames( const string& descrFilename, vector<string>& names ) |
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{ |
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names.clear(); |
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ifstream file( descrFilename.c_str() ); |
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if ( !file.is_open() ) |
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return; |
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while( !file.eof() ) |
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{ |
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string str; getline( file, str ); |
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if( str.empty() ) break; |
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if( str[0] == '#' ) continue; // comment |
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names.push_back(str); |
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} |
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file.close(); |
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} |
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// find -name "image_*.png" | grep -v mask | sed 's/.\///' >> images.txt |
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int main( int argc, char **argv ) |
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{ |
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if( argc != 1 && argc != 3 ) |
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{ |
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cout << "Format: train_data test_data; " << endl << "or without arguments to use default data" << endl; |
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return -1; |
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} |
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string baseDirName, testDirName; |
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if( argc == 1 ) |
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{ |
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baseDirName = "../../opencv/samples/cpp/dot_data/train/"; |
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testDirName = "../../opencv/samples/cpp/dot_data/test/"; |
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} |
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else |
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{ |
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baseDirName = argv[1]; |
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testDirName = argv[2]; |
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baseDirName += (*(baseDirName.end()-1) == '/' ? "" : "/"); |
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testDirName += (*(testDirName.end()-1) == '/' ? "" : "/"); |
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} |
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DOTDetector::TrainParams trainParams; |
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trainParams.winSize = Size(84, 84); |
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trainParams.regionSize = 7; |
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trainParams.minMagnitude = 60; // we ignore pixels with magnitude less then minMagnitude |
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trainParams.maxStrongestCount = 7; // we find such count of strongest gradients for each region |
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trainParams.maxNonzeroBits = 6; // we filter very textured regions (that have more then maxUnzeroBits count of 1s (ones) in the template) |
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trainParams.minRatio = 0.85f; |
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// 1. Train detector |
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DOTDetector dotDetector; |
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dotDetector.train( baseDirName, trainParams, true ); |
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// dotDetector.save( "../../dot.xml.gz" ); |
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// dotDetector.load( "../../dot.xml.gz" ); |
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const vector<string>& objectClassNames = dotDetector.getObjectClassNames(); |
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const vector<DOTDetector::DOTTemplate>& dotTemplates = dotDetector.getDOTTemplates(); |
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vector<Scalar> colors( objectClassNames.size() ); |
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fillColors( colors ); |
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cout << "Templates count " << dotTemplates.size() << endl; |
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vector<string> testFilenames; |
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readTestImageNames( testDirName + "images.txt", testFilenames ); |
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if( testFilenames.empty() ) |
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{ |
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cout << "Can not read no one test images" << endl; |
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return -1; |
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} |
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// 2. Detect objects |
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DOTDetector::DetectParams detectParams; |
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detectParams.minRatio = 0.8f; |
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detectParams.minRegionSize = 5; |
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detectParams.maxRegionSize = 11; |
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#if SHOW_ALL_RECTS_BY_ONE |
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detectParams.isGroup = false; |
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#endif |
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for( size_t imgIdx = 0; imgIdx < testFilenames.size(); imgIdx++ ) |
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{ |
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string curFilename = testDirName + testFilenames[imgIdx]; |
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cout << curFilename << endl; |
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Mat queryImage = imread( curFilename, 0 ); |
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if( queryImage.empty() ) |
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continue; |
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cout << "Detection start ..." << endl; |
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vector<vector<Rect> > rects; |
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#if SHOW_ALL_RECTS_BY_ONE |
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vector<vector<float> > ratios; |
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vector<vector<int> > dotTemlateIndices; |
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dotDetector.detectMultiScale( queryImage, rects, detectParams, &ratios, &dotTemlateIndices ); |
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const vector<DOTDetector::DOTTemplate>& dotTemplates = dotDetector.getDOTTemplates(); |
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#else |
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dotDetector.detectMultiScale( queryImage, rects, detectParams ); |
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#endif |
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cout << "end" << endl; |
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Mat draw; |
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cvtColor( queryImage, draw, CV_GRAY2BGR ); |
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const int textStep = 25; |
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for( size_t ci = 0; ci < objectClassNames.size(); ci++ ) |
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{ |
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putText( draw, objectClassNames[ci], Point(textStep, textStep*(1+ci)), 1, 2, colors[ci], 3 ); |
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for( size_t ri = 0; ri < rects[ci].size(); ri++ ) |
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{ |
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rectangle( draw, rects[ci][ri], colors[ci], 3 ); |
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#if SHOW_ALL_RECTS_BY_ONE |
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int dotTemplateIndex = dotTemlateIndices[ci][ri]; |
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const DOTDetector::DOTTemplate::TrainData* trainData = dotTemplates[dotTemplateIndex].getTrainData(ci); |
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imshow( "maskedImage", trainData->maskedImage ); |
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imshow( "strongestGradientsMask", trainData->strongestGradientsMask ); |
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Mat scaledDraw; |
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cv::resize( draw, scaledDraw, Size(640, 480) ); |
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imshow( "detection result", scaledDraw ); |
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cv::waitKey(); |
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#endif |
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} |
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} |
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Mat scaledDraw; |
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cv::resize( draw, scaledDraw, Size(640, 480) ); |
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imshow( "detection result", scaledDraw ); |
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cv::waitKey(); |
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} |
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}
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