started to integrate DOT detector
This commit is contained in:
Maria Dimashova
parent
0a8c7d274b
commit
cd981f4d13
@ -426,6 +426,8 @@ protected:
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Ptr<CvHaarClassifierCascade> oldCascade;
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Ptr<CvHaarClassifierCascade> oldCascade;
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};
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};
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void CV_EXPORTS_W groupRectangles( vector<Rect>& rectList, int groupThreshold, double eps, vector<int>* weights, vector<double>* levelWeights );
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//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
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//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
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struct CV_EXPORTS_W HOGDescriptor
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struct CV_EXPORTS_W HOGDescriptor
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@ -574,6 +576,110 @@ protected:
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FernClassifier fernClassifier;
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FernClassifier fernClassifier;
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};
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};
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/****************************************************************************************\
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* Dominant Orientation Templates *
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\****************************************************************************************/
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class CV_EXPORTS DOTDetector
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{
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public:
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struct TrainParams
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{
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enum { BIN_COUNT = 7 };
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static double BIN_RANGE() { return 180.0 / BIN_COUNT; }
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TrainParams();
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TrainParams( const Size& winSize, int regionSize=7, int minMagnitude=60,
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int maxStrongestCount=7, int maxNonzeroBits=6,
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float minRatio=0.85f );
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void read( FileNode& fn );
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void write( FileStorage& fs ) const;
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void asserts() const;
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Size winSize;
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int regionSize;
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int minMagnitude;
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int maxStrongestCount;
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int maxNonzeroBits;
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float minRatio;
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};
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struct DetectParams
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{
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DetectParams();
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DetectParams( float minRatio, int minRegionSize, int maxRegionSize, int regionSizeStep,
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bool isGroup, int groupThreshold, double groupEps );
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void asserts( float minTrainRatio=1.f) const;
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float minRatio;
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int minRegionSize;
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int maxRegionSize;
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int regionSizeStep;
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bool isGroup;
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int groupThreshold;
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double groupEps;
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};
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struct DOTTemplate
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{
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DOTTemplate();
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DOTTemplate( const cv::Mat& quantizedImage, int classID,
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const cv::Mat& maskedImage=cv::Mat(), const cv::Mat& gradientMask=cv::Mat() );
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void addClassID( int classID, const cv::Mat& maskedImage=cv::Mat(), const cv::Mat& gradientMask=cv::Mat() );
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static float computeTexturelessRatio( const cv::Mat& quantizedImage );
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void read( FileNode& fn );
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void write( FileStorage& fs ) const;
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cv::Mat quantizedImage;
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std::vector<int> classIDs;
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float texturelessRatio;
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std::vector<cv::Mat> maskedImages;
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std::vector<cv::Mat> gradientMasks;
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};
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DOTDetector();
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DOTDetector( const std::string& filename ); // load from xml-file
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virtual ~DOTDetector();
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void clear();
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void read( FileNode& fn );
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void write( FileStorage& fs ) const;
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void load( const std::string& filename );
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void save( const std::string& filename ) const;
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void train( const string& baseDirName, const TrainParams& trainParams=TrainParams(), bool isAddImageAndGradientMask=false );
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void detectMultiScale( const Mat& image, vector<vector<Rect> >& rects,
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const DetectParams& detectParams=DetectParams(),
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vector<vector<float> >*ratios=0, vector<vector<int> >* trainTemplateIndices=0 ) const;
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const vector<DOTTemplate>& getDOTTemplates() const;
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const vector<string>& getClassNames() const;
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static void groupRectanglesList( std::vector<std::vector<cv::Rect> >& rectList, int groupThreshold, double eps );
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protected:
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void detectQuantized( const Mat& queryQuantizedImage, float minRatio,
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vector<vector<Rect> >& rects, vector<vector<float> >& ratios, vector<vector<int> >& trainTemlateIdxs ) const;
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TrainParams trainParams;
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bool isAddImageAndGradientMask;
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std::vector<std::string> classNames;
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std::vector<DOTTemplate> dotTemplates;
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};
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}
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}
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/****************************************************************************************\
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/****************************************************************************************\
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855
modules/objdetect/src/dotdetector.cpp
Normal file
855
modules/objdetect/src/dotdetector.cpp
Normal file
@ -0,0 +1,855 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include <iostream>
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#include <fstream>
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using namespace std;
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namespace cv
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{
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/*
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* Functions
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*/
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static void readDirContent( 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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static void computeGradients( const Mat& image, Mat& magnitudes, Mat& angles )
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{
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Mat dx, dy;
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cv::Sobel( image, dx, CV_32F, 1, 0, 3 );
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cv::Sobel( image, dy, CV_32F, 0, 1, 3 );
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cv::cartToPolar( dx, dy, magnitudes, angles, true );
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CV_Assert( magnitudes.type() == CV_32FC1 );
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CV_Assert( angles.type() == CV_32FC1 );
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}
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static void computeWinData( const Mat& image, const Mat& mask, const Size& winSize,
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Mat& winImage, Mat& winMask,
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Mat& winMagnitudes, Mat& winAngles, int border=0 )
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{
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CV_Assert( border >= 0 );
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Size extSize;
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extSize.width = winSize.width + 2*border;
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extSize.height = winSize.height + 2*border;
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if( mask.empty() )
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{
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image.copyTo( winImage );
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winMask.release();
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}
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else
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{
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vector<Point> points;
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points.reserve( image.rows * image.cols );
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for( int y = 0; y < mask.rows; y++ )
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{
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for( int x = 0; x < mask.cols; x++ )
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{
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if( mask.at<uchar>(y,x) )
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points.push_back( cv::Point(x,y) );
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}
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}
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cv::Rect_<float> brect = cv::boundingRect( cv::Mat(points) );
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float ratio = std::min( (float)winSize.width/ brect.width, (float)winSize.height / brect.height );
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float rectWidth = winSize.width / ratio;
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float rectHeight = winSize.height / ratio;
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float scaledBorder = border / ratio;
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brect.x -= (rectWidth - brect.width) / 2.f + scaledBorder ;
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brect.y -= (rectHeight - brect.height) / 2.f + scaledBorder;
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brect.width = rectWidth + 2*scaledBorder;
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brect.height = rectHeight + 2*scaledBorder;
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// TODO the following cases:
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assert( Rect(0, 0, image.cols, image.rows ).contains( brect.tl() ) );
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assert( Rect(0, 0, image.cols, image.rows ).contains( brect.br() ) );
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Mat subImage( image, brect );
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Mat subMask( mask, brect );
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cv::resize( subImage, winImage, extSize );
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cv::resize( subMask, winMask, extSize );
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CV_Assert( winImage.size() == extSize );
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CV_Assert( winMask.size() == extSize );
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}
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computeGradients( winImage, winMagnitudes, winAngles );
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// Mat bluredWinImage;
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// cv::GaussianBlur( winImage, bluredWinImage, Size(), 0.5, 0.5 );
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// computeGradients( bluredWinImage, magnitudes, angles );
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}
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inline int getBin( double angle )
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{
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double angle1 = angle >= 180 ? angle - 180 : angle;
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int orientationBin = (int)(angle1 / DOTDetector::TrainParams::BIN_RANGE() );
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assert( orientationBin < 7 );
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return orientationBin;
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}
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static void copyTrainData( const Mat& magnitudesSrc, const Mat& anglesSrc, const Mat& mask,
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Mat& magnitudesDst, Mat& anglesDst )
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{
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magnitudesDst = Mat( magnitudesSrc.size(), magnitudesSrc.type(), Scalar::all(-1) );
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anglesDst = Mat( anglesSrc.size(), anglesSrc.type(), Scalar::all(-1) );
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magnitudesSrc.copyTo( magnitudesDst, mask );
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anglesSrc.copyTo( anglesDst, mask );
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}
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inline int countNonZeroBits( uchar val )
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{
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uchar v = val;
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v = (v & 0x55) + ((v >> 1) & 0x55);
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v = (v & 0x33) + ((v >> 2) & 0x33);
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return (v & 0x0f) + ((v >> 4) & 0x0f);
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}
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inline void countNonZeroAndTexturelessBits( const Mat& mat, int& nonZeroBitsCount, int& texturelessBitsCount )
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{
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CV_Assert( mat.type() == CV_8UC1 );
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nonZeroBitsCount = 0;
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texturelessBitsCount = 0;
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const uchar texturelessValue = 1 << DOTDetector::TrainParams::BIN_COUNT;
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for( int y = 0; y < mat.rows; y++ )
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{
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for( int x = 0; x < mat.cols; x++ )
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{
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int curCount = countNonZeroBits( mat.at<uchar>(y,x) );
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if( curCount )
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{
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nonZeroBitsCount += curCount;
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if( mat.at<uchar>(y,x) == texturelessValue )
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texturelessBitsCount++;
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}
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}
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}
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}
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static void quantizeToTrain( const Mat& _magnitudesExt, const Mat& _anglesExt, const Mat& maskExt,
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Mat& quantizedImage, const DOTDetector::TrainParams& params )
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{
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CV_DbgAssert( params.winSize.height % params.regionSize == 0 );
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CV_DbgAssert( params.winSize.width % params.regionSize == 0 );
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CV_DbgAssert( params.regionSize % 2 == 1 );
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const int regionSize_2 = params.regionSize / 2;
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Mat magnitudesExt, anglesExt;
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copyTrainData( _magnitudesExt, _anglesExt, maskExt, magnitudesExt, anglesExt );
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const int verticalRegionCount = params.winSize.height / params.regionSize;
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const int horizontalRegionCount = params.winSize.width / params.regionSize;
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quantizedImage = Mat( verticalRegionCount, horizontalRegionCount, CV_8UC1, Scalar::all(0) );
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Rect curRect( regionSize_2, regionSize_2, params.regionSize, params.regionSize );
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for( int vRegIdx = 0; vRegIdx < verticalRegionCount; vRegIdx++ )
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{
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for( int hRegIdx = 0; hRegIdx < horizontalRegionCount; hRegIdx++ )
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{
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uchar curRectBits = 0;
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for( int yShift = -regionSize_2; yShift <= regionSize_2; yShift++ ) // TODO yShift += regionSize/2
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{
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Rect shiftedRect = curRect;
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shiftedRect.y = curRect.y + yShift;
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for( int xShift = -regionSize_2; xShift <= regionSize_2; xShift++ ) // TODO xShift += regionSize/2
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{
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shiftedRect.x = curRect.x + xShift;
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Mat subMagnitudes( magnitudesExt, shiftedRect ), subMagnitudesCopy;
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subMagnitudes.copyTo( subMagnitudesCopy );
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Mat subAngles( anglesExt, shiftedRect );
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double maxMagnitude;
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int strongestCount = 0;
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for( ; strongestCount < params.maxStrongestCount; strongestCount++ )
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{
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Point maxLoc;
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cv::minMaxLoc( subMagnitudesCopy, 0, &maxMagnitude, 0, &maxLoc );
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if( maxMagnitude < params.minMagnitude )
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break;
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subMagnitudesCopy.at<float>( maxLoc ) = -1;
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double angle = subAngles.at<float>( maxLoc );
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int orientationBin = getBin( angle );
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curRectBits |= 1 << orientationBin;
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}
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if( strongestCount == 0 && maxMagnitude > 0 )
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curRectBits |= 1 << DOTDetector::TrainParams::BIN_COUNT;
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}
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}
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if( !( curRectBits == (1 << DOTDetector::TrainParams::BIN_COUNT) && cv::countNonZero(magnitudesExt(curRect) == -1) ) )
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{
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if( countNonZeroBits( curRectBits ) <= params.maxNonzeroBits )
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quantizedImage.at<uchar>(vRegIdx, hRegIdx) = curRectBits;
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}
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curRect.x += params.regionSize;
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}
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||||||
|
curRect.x = regionSize_2;
|
||||||
|
curRect.y += params.regionSize;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
static void quantizeToDetect( const Mat& _magnitudes, const Mat& angles, Mat& quantizedImage, const DOTDetector::TrainParams& params )
|
||||||
|
{
|
||||||
|
Mat magnitudes; _magnitudes.copyTo( magnitudes );
|
||||||
|
|
||||||
|
const int verticalRegionCount = magnitudes.rows / params.regionSize;
|
||||||
|
const int horizontalRegionCount = magnitudes.cols / params.regionSize;
|
||||||
|
|
||||||
|
quantizedImage = Mat( verticalRegionCount, horizontalRegionCount, CV_8UC1, Scalar::all(0) );
|
||||||
|
|
||||||
|
Rect curRect(0, 0, params.regionSize, params.regionSize);
|
||||||
|
const int maxStrongestCount = 1;
|
||||||
|
for( int vRegIdx = 0; vRegIdx < verticalRegionCount; vRegIdx++ )
|
||||||
|
{
|
||||||
|
for( int hRegIdx = 0; hRegIdx < horizontalRegionCount; hRegIdx++ )
|
||||||
|
{
|
||||||
|
uchar curRectBits = 0;
|
||||||
|
|
||||||
|
Mat subMagnitudes( magnitudes, curRect ), subMagnitudesCopy;
|
||||||
|
subMagnitudes.copyTo( subMagnitudesCopy );
|
||||||
|
Mat subAngles( angles, curRect );
|
||||||
|
|
||||||
|
double maxMagnitude = -1;
|
||||||
|
int strongestCount = 0;
|
||||||
|
|
||||||
|
for( ; strongestCount < maxStrongestCount; strongestCount++ )
|
||||||
|
{
|
||||||
|
Point maxLoc;
|
||||||
|
cv::minMaxLoc( subMagnitudesCopy, 0, &maxMagnitude, 0, &maxLoc );
|
||||||
|
|
||||||
|
if( maxMagnitude < params.minMagnitude )
|
||||||
|
break;
|
||||||
|
|
||||||
|
subMagnitudesCopy.at<float>( maxLoc ) = -1;
|
||||||
|
|
||||||
|
double angle = subAngles.at<float>( maxLoc );
|
||||||
|
int orientationBin = getBin( angle );
|
||||||
|
|
||||||
|
curRectBits |= 1 << orientationBin;
|
||||||
|
}
|
||||||
|
if( strongestCount == 0 && maxMagnitude > 0 )
|
||||||
|
curRectBits |= 1 << DOTDetector::TrainParams::BIN_COUNT;
|
||||||
|
|
||||||
|
quantizedImage.at<uchar>(vRegIdx, hRegIdx) = curRectBits;
|
||||||
|
curRect.x += params.regionSize;
|
||||||
|
}
|
||||||
|
curRect.x = 0;
|
||||||
|
curRect.y += params.regionSize;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
inline void andQuantizedImages( const Mat& queryQuantizedImage, const Mat& trainQuantizedImage, float& ratio, float& texturelessRatio )
|
||||||
|
{
|
||||||
|
int nonZeroCount = 0, texturelessCount = 0;
|
||||||
|
countNonZeroAndTexturelessBits( trainQuantizedImage & queryQuantizedImage, nonZeroCount, texturelessCount );
|
||||||
|
|
||||||
|
CV_Assert( nonZeroCount > 0 );
|
||||||
|
int area = cv::countNonZero( trainQuantizedImage );
|
||||||
|
|
||||||
|
ratio = (float)nonZeroCount / area;
|
||||||
|
texturelessRatio = texturelessCount / nonZeroCount;
|
||||||
|
}
|
||||||
|
|
||||||
|
static void computeTrainUsedStrongestMask( const Mat& _magnitudesExt, const Mat& _anglesExt, const Mat& maskExt, const Mat& quantizedImage,
|
||||||
|
Mat& winUsedStrongestMask, int regionSize, int minMagnitude )
|
||||||
|
{
|
||||||
|
const int usedLabel = 255;
|
||||||
|
const int regionSize_2 = regionSize / 2;
|
||||||
|
|
||||||
|
Mat magnitudesExt, anglesExt;
|
||||||
|
copyTrainData( _magnitudesExt, _anglesExt, maskExt, magnitudesExt, anglesExt );
|
||||||
|
|
||||||
|
const int verticalRegionCount = quantizedImage.rows;
|
||||||
|
const int horizontalRegionCount = quantizedImage.cols;
|
||||||
|
|
||||||
|
Mat binsExt( anglesExt.size(), CV_32SC1, Scalar::all(-1) );
|
||||||
|
for( int y = 0; y < binsExt.rows; y++ )
|
||||||
|
{
|
||||||
|
for( int x = 0; x < binsExt.cols; x++ )
|
||||||
|
{
|
||||||
|
if( magnitudesExt.at<float>(y,x) >= minMagnitude )
|
||||||
|
{
|
||||||
|
binsExt.at<int>(y,x) = getBin( anglesExt.at<float>(y,x) );
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
Rect curRect( 0, 0, regionSize + 2*regionSize_2, regionSize + 2*regionSize_2 );
|
||||||
|
|
||||||
|
Mat colorsExt( anglesExt.size(), CV_8UC1, Scalar::all(0) );
|
||||||
|
for( int vRegIdx = 0; vRegIdx < verticalRegionCount; vRegIdx++ )
|
||||||
|
{
|
||||||
|
for( int hRegIdx = 0; hRegIdx < horizontalRegionCount; hRegIdx++ )
|
||||||
|
{
|
||||||
|
Mat subColors = colorsExt( curRect );
|
||||||
|
Mat subBins = binsExt( curRect );
|
||||||
|
|
||||||
|
uchar bits = quantizedImage.at<uchar>(vRegIdx, hRegIdx);
|
||||||
|
|
||||||
|
for( int binIdx = 0; binIdx < DOTDetector::TrainParams::BIN_COUNT; binIdx++ )
|
||||||
|
{
|
||||||
|
if( bits & (1 << binIdx) )
|
||||||
|
{
|
||||||
|
float gray = usedLabel/* * weights[vRegIdx*horizontalRegionCount+hRegIdx][binIdx]*/;
|
||||||
|
subColors.setTo( Scalar((uchar)cvRound(gray)), subBins == binIdx );
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
curRect.x += regionSize;
|
||||||
|
}
|
||||||
|
|
||||||
|
curRect.x = 0;
|
||||||
|
curRect.y += regionSize;
|
||||||
|
}
|
||||||
|
|
||||||
|
Mat colors = colorsExt( Rect(regionSize_2, regionSize_2, binsExt.cols - 2*regionSize_2, binsExt.rows - 2*regionSize_2) );
|
||||||
|
|
||||||
|
colors.convertTo( winUsedStrongestMask, CV_8UC1 );
|
||||||
|
}
|
||||||
|
|
||||||
|
/*
|
||||||
|
* DOTDetector::Params
|
||||||
|
*/
|
||||||
|
|
||||||
|
DOTDetector::TrainParams::TrainParams() : winSize(Size(84,84)), regionSize(7),
|
||||||
|
minMagnitude(60), maxStrongestCount(7), maxNonzeroBits(6),
|
||||||
|
minRatio(0.85f) {}
|
||||||
|
|
||||||
|
DOTDetector::TrainParams::TrainParams( const Size& _winSize, int _regionSize, int _minMagnitude,
|
||||||
|
int _maxStrongestCount, int _maxNonzeroBits,
|
||||||
|
float _minRatio ) :
|
||||||
|
winSize(_winSize), regionSize(_regionSize), minMagnitude(_minMagnitude),
|
||||||
|
maxStrongestCount(_maxStrongestCount), maxNonzeroBits(_maxNonzeroBits),
|
||||||
|
minRatio(_minRatio)
|
||||||
|
{
|
||||||
|
asserts();
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::TrainParams::asserts() const
|
||||||
|
{
|
||||||
|
CV_Assert( winSize.width > 0 && winSize.height > 0 );
|
||||||
|
CV_Assert( regionSize > 0 && regionSize % 2 == 1);
|
||||||
|
|
||||||
|
CV_Assert( winSize.width % regionSize == 0 );
|
||||||
|
CV_Assert( winSize.height % regionSize == 0 );
|
||||||
|
|
||||||
|
CV_Assert( minMagnitude > 0 );
|
||||||
|
|
||||||
|
CV_Assert( maxStrongestCount > 0 && maxStrongestCount <= BIN_COUNT );
|
||||||
|
CV_Assert( maxNonzeroBits > 0 && maxNonzeroBits <= BIN_COUNT );
|
||||||
|
|
||||||
|
CV_Assert( minRatio > 0.f && minRatio < 1.f );
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::TrainParams::read( FileNode& fn )
|
||||||
|
{
|
||||||
|
winSize.width = fn["winSize.width"];
|
||||||
|
winSize.height = fn["winSize.height"];
|
||||||
|
regionSize = fn["regionSize"];
|
||||||
|
|
||||||
|
minMagnitude = fn["minMagnitude"];
|
||||||
|
maxStrongestCount = fn["maxStrongestCount"];
|
||||||
|
maxNonzeroBits = fn["maxNonzeroBits"];
|
||||||
|
|
||||||
|
minRatio = fn["minRatio"];
|
||||||
|
|
||||||
|
asserts();
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::TrainParams::write( FileStorage& fs ) const
|
||||||
|
{
|
||||||
|
CV_Assert( fs.isOpened() );
|
||||||
|
|
||||||
|
fs << "winSize.width" << winSize.width;
|
||||||
|
fs << "winSize.height" << winSize.height;
|
||||||
|
fs << "regionSize" << regionSize;
|
||||||
|
|
||||||
|
fs << "minMagnitude" << minMagnitude;
|
||||||
|
fs << "maxStrongestCount" << maxStrongestCount;
|
||||||
|
fs << "maxNonzeroBits" << maxNonzeroBits;
|
||||||
|
|
||||||
|
fs << "minRatio" << minRatio;
|
||||||
|
}
|
||||||
|
|
||||||
|
DOTDetector::DetectParams::DetectParams() : minRatio(0.8f), minRegionSize(7), maxRegionSize(9), regionSizeStep(2),
|
||||||
|
isGroup(true), groupThreshold(3), groupEps(0.2) {}
|
||||||
|
|
||||||
|
DOTDetector::DetectParams::DetectParams( float _minRatio, int _minRegionSize, int _maxRegionSize, int _regionSizeStep,
|
||||||
|
bool _isGroup, int _groupThreshold, double _groupEps ) :
|
||||||
|
minRatio(_minRatio), minRegionSize(_minRegionSize), maxRegionSize(_maxRegionSize), regionSizeStep(_regionSizeStep),
|
||||||
|
isGroup(_isGroup), groupThreshold(_groupThreshold), groupEps(_groupEps)
|
||||||
|
{
|
||||||
|
asserts();
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::DetectParams::asserts( float minTrainRatio ) const
|
||||||
|
{
|
||||||
|
CV_Assert( minRatio > 0 && minRatio < 1 );
|
||||||
|
CV_Assert( minRatio <= minTrainRatio );
|
||||||
|
|
||||||
|
CV_Assert( minRegionSize > 0 && minRegionSize % 2 == 1 );
|
||||||
|
CV_Assert( maxRegionSize > 0 && maxRegionSize % 2 == 1 );
|
||||||
|
CV_Assert( minRegionSize <= maxRegionSize );
|
||||||
|
|
||||||
|
CV_Assert( regionSizeStep % 2 == 0 );
|
||||||
|
|
||||||
|
if( isGroup )
|
||||||
|
{
|
||||||
|
CV_Assert( groupThreshold > 0 );
|
||||||
|
CV_Assert( groupEps > 0 && groupEps < 1 );
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
/*
|
||||||
|
* DOTDetector::DOTTemplate
|
||||||
|
*/
|
||||||
|
|
||||||
|
DOTDetector::DOTTemplate::DOTTemplate() : texturelessRatio(-1.f) {}
|
||||||
|
|
||||||
|
DOTDetector::DOTTemplate::DOTTemplate( const cv::Mat& _quantizedImage, int _classID, const cv::Mat& _maskedImage, const cv::Mat& _gradientMask ) :
|
||||||
|
quantizedImage(_quantizedImage), texturelessRatio(computeTexturelessRatio(_quantizedImage))
|
||||||
|
{
|
||||||
|
addClassID( _classID, _maskedImage, _gradientMask );
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::DOTTemplate::addClassID( int _classID, const cv::Mat& _maskedImage, const cv::Mat& _gradientMask )
|
||||||
|
{
|
||||||
|
CV_Assert( _classID >= 0 );
|
||||||
|
bool isFound = false;
|
||||||
|
|
||||||
|
for( size_t i = 0; i < classIDs.size(); i++ )
|
||||||
|
{
|
||||||
|
if( classIDs[i] == _classID )
|
||||||
|
{
|
||||||
|
isFound = true;
|
||||||
|
break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if( !isFound )
|
||||||
|
{
|
||||||
|
classIDs.push_back( _classID );
|
||||||
|
if( !_maskedImage.empty() )
|
||||||
|
{
|
||||||
|
CV_Assert( !_gradientMask.empty() );
|
||||||
|
maskedImages.push_back( _maskedImage );
|
||||||
|
gradientMasks.push_back( _gradientMask );
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
float DOTDetector::DOTTemplate::computeTexturelessRatio( const cv::Mat& quantizedImage )
|
||||||
|
{
|
||||||
|
const uchar TEXTURELESS_VAL = 1 << DOTDetector::TrainParams::BIN_COUNT;
|
||||||
|
int texturelessCount = 0;
|
||||||
|
for( int y = 0; y < quantizedImage.rows; y++ )
|
||||||
|
{
|
||||||
|
for( int x = 0; x < quantizedImage.cols; x++ )
|
||||||
|
{
|
||||||
|
if( quantizedImage.at<uchar>(y,x) & TEXTURELESS_VAL )
|
||||||
|
texturelessCount++;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return (float)texturelessCount/ (float)(quantizedImage.cols * quantizedImage.rows);
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::DOTTemplate::read( FileNode& fn )
|
||||||
|
{
|
||||||
|
fn["template"] >> quantizedImage;
|
||||||
|
fn["classIDs"] >> classIDs;
|
||||||
|
texturelessRatio = fn["texturelessRatio"];
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::DOTTemplate::write( FileStorage& fs ) const
|
||||||
|
{
|
||||||
|
fs << "template" << quantizedImage;
|
||||||
|
fs << "classIDs" << classIDs;
|
||||||
|
fs << "texturelessRatio" << texturelessRatio;
|
||||||
|
}
|
||||||
|
|
||||||
|
/*
|
||||||
|
* DOTDetector
|
||||||
|
*/
|
||||||
|
|
||||||
|
DOTDetector::DOTDetector() : isAddImageAndGradientMask( false )
|
||||||
|
{
|
||||||
|
}
|
||||||
|
|
||||||
|
DOTDetector::DOTDetector( const std::string& filename ) : isAddImageAndGradientMask( false )
|
||||||
|
{
|
||||||
|
load( filename );
|
||||||
|
}
|
||||||
|
|
||||||
|
DOTDetector::~DOTDetector()
|
||||||
|
{
|
||||||
|
clear();
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::clear()
|
||||||
|
{
|
||||||
|
classNames.clear();
|
||||||
|
dotTemplates.clear();
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::read( FileNode& fn )
|
||||||
|
{
|
||||||
|
clear();
|
||||||
|
|
||||||
|
// read params
|
||||||
|
FileNode fn_params = fn["train_params"];
|
||||||
|
trainParams.read( fn_params );
|
||||||
|
|
||||||
|
// read class names
|
||||||
|
int classCount = fn["class_count"];
|
||||||
|
FileNodeIterator fni = fn["class_names"].begin();
|
||||||
|
for( int i = 0; i < classCount; i++ )
|
||||||
|
{
|
||||||
|
string name;
|
||||||
|
fni >> name;
|
||||||
|
classNames.push_back( name );
|
||||||
|
}
|
||||||
|
|
||||||
|
// read DOT templates
|
||||||
|
int templatesCount = fn["templates_count"];
|
||||||
|
fni = fn["templates"].begin();
|
||||||
|
for( int i = 0; i < templatesCount; i++ )
|
||||||
|
{
|
||||||
|
dotTemplates.push_back( DOTTemplate() );
|
||||||
|
FileNode cur_fn = *fni;
|
||||||
|
dotTemplates.rbegin()->read( cur_fn );
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::write( FileStorage& fs ) const
|
||||||
|
{
|
||||||
|
// write params
|
||||||
|
fs << "train_params" << "{";
|
||||||
|
trainParams.write( fs );
|
||||||
|
fs << "}"; //params
|
||||||
|
|
||||||
|
// write class names
|
||||||
|
fs << "class_count" << (int)classNames.size();
|
||||||
|
fs << "class_names" << "[";
|
||||||
|
for( size_t i = 0; i < classNames.size(); i++ )
|
||||||
|
{
|
||||||
|
fs << classNames[i];
|
||||||
|
}
|
||||||
|
fs << "]";
|
||||||
|
|
||||||
|
// write dot templates
|
||||||
|
fs << "templates_count" << (int)dotTemplates.size();
|
||||||
|
fs << "templates" << "[";
|
||||||
|
for( size_t i = 0; i < dotTemplates.size(); i++ )
|
||||||
|
{
|
||||||
|
dotTemplates[i].write( fs );
|
||||||
|
}
|
||||||
|
fs << "]";
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::load( const std::string& filename )
|
||||||
|
{
|
||||||
|
FileStorage fs( filename, FileStorage::READ );
|
||||||
|
if( fs.isOpened() )
|
||||||
|
{
|
||||||
|
FileNode fn = fs.getFirstTopLevelNode();
|
||||||
|
read( fn );
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::save( const std::string& filename ) const
|
||||||
|
{
|
||||||
|
FileStorage fs( filename, FileStorage::WRITE );
|
||||||
|
if( fs.isOpened() )
|
||||||
|
{
|
||||||
|
fs << "dot_detector" << "{";
|
||||||
|
write( fs );
|
||||||
|
fs << "}";
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::train( const string& _baseDirName, const TrainParams& _trainParams, bool _isAddImageAndGradientMask )
|
||||||
|
{
|
||||||
|
clear();
|
||||||
|
|
||||||
|
trainParams = _trainParams;
|
||||||
|
trainParams.asserts();
|
||||||
|
|
||||||
|
string baseDirName = _baseDirName + (*(_baseDirName.end()-1) == '/' ? "" : "/");
|
||||||
|
const int regionSize_2 = trainParams.regionSize / 2;
|
||||||
|
|
||||||
|
readDirContent( baseDirName+"objects.txt", classNames );
|
||||||
|
|
||||||
|
for( size_t objIdx = 0; objIdx < classNames.size(); objIdx++ )
|
||||||
|
{
|
||||||
|
string curObjDirName = baseDirName + classNames[objIdx] + "/";
|
||||||
|
|
||||||
|
cout << "===============" << classNames[objIdx] << "===============" << endl;
|
||||||
|
vector<string> imagesFilenames;
|
||||||
|
readDirContent( curObjDirName + "images.txt", imagesFilenames );
|
||||||
|
|
||||||
|
if( imagesFilenames.empty() )
|
||||||
|
continue;
|
||||||
|
|
||||||
|
int countSamples = 0;
|
||||||
|
for( size_t imgIdx = 0; imgIdx < imagesFilenames.size(); imgIdx++ )
|
||||||
|
{
|
||||||
|
cout << imagesFilenames[imgIdx] ;
|
||||||
|
Mat image = cv::imread( curObjDirName + imagesFilenames[imgIdx], 0 );
|
||||||
|
if( image.empty() )
|
||||||
|
continue;
|
||||||
|
|
||||||
|
Mat mask;
|
||||||
|
{
|
||||||
|
Mat _mask = cv::imread( curObjDirName + imagesFilenames[imgIdx] + ".mask.png", 0 );
|
||||||
|
if( _mask.empty() )
|
||||||
|
{
|
||||||
|
cout << " - FAIL" << endl;
|
||||||
|
continue;
|
||||||
|
}
|
||||||
|
mask = _mask;
|
||||||
|
}
|
||||||
|
cout << " - OK" << endl;
|
||||||
|
|
||||||
|
countSamples++;
|
||||||
|
|
||||||
|
Mat trainImageExt, trainMaskExt, trainQuantizedImage, detectQuantizedImage;
|
||||||
|
Mat trainMagnitudesExt, trainAnglesExt;
|
||||||
|
|
||||||
|
computeWinData( image, mask, trainParams.winSize,
|
||||||
|
trainImageExt, trainMaskExt,
|
||||||
|
trainMagnitudesExt, trainAnglesExt, regionSize_2 );
|
||||||
|
|
||||||
|
quantizeToTrain( trainMagnitudesExt, trainAnglesExt, trainMaskExt, trainQuantizedImage, trainParams );
|
||||||
|
|
||||||
|
quantizeToDetect( trainMagnitudesExt, trainAnglesExt, detectQuantizedImage, trainParams );
|
||||||
|
|
||||||
|
vector<vector<Rect> > rects;
|
||||||
|
vector<vector<float> > ratios;
|
||||||
|
vector<vector<int> > trainTemplatesIdxs;
|
||||||
|
detectQuantized( detectQuantizedImage, trainParams.minRatio, rects, ratios, trainTemplatesIdxs );
|
||||||
|
|
||||||
|
Mat maskedTrainImage, trainGradientMask;
|
||||||
|
if( isAddImageAndGradientMask )
|
||||||
|
{
|
||||||
|
trainImageExt.copyTo( maskedTrainImage, trainMaskExt);
|
||||||
|
computeTrainUsedStrongestMask( trainMagnitudesExt, trainAnglesExt, trainMaskExt, trainQuantizedImage,
|
||||||
|
trainGradientMask, trainParams.regionSize, trainParams.minMagnitude );
|
||||||
|
}
|
||||||
|
int classID = classNames.size()-1;
|
||||||
|
bool isFound = false;
|
||||||
|
for( size_t cIdx = 0; cIdx < trainTemplatesIdxs.size(); cIdx++ )
|
||||||
|
{
|
||||||
|
if( trainTemplatesIdxs[cIdx].size() )
|
||||||
|
{
|
||||||
|
for( size_t i = 0; i < trainTemplatesIdxs[cIdx].size(); i++ )
|
||||||
|
{
|
||||||
|
int tIdx = trainTemplatesIdxs[cIdx][i];
|
||||||
|
|
||||||
|
dotTemplates[tIdx].addClassID( classID, maskedTrainImage, trainGradientMask );
|
||||||
|
isFound = true;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
if( !isFound )
|
||||||
|
{
|
||||||
|
dotTemplates.push_back( DOTTemplate(trainQuantizedImage, classID, maskedTrainImage, trainGradientMask) );
|
||||||
|
}
|
||||||
|
|
||||||
|
cout << "dot templates size = " << dotTemplates.size() << endl;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::detectQuantized( const Mat& testQuantizedImage, float minRatio,
|
||||||
|
vector<vector<Rect> >& rects, vector<vector<float> >& ratios, vector<vector<int> >& trainTemlateIdxs ) const
|
||||||
|
{
|
||||||
|
if( dotTemplates.empty() )
|
||||||
|
return;
|
||||||
|
|
||||||
|
const int regionsPerRow = dotTemplates[0].quantizedImage.rows;
|
||||||
|
const int regionsPerCol = dotTemplates[0].quantizedImage.cols;
|
||||||
|
|
||||||
|
int classCount = classNames.size();
|
||||||
|
|
||||||
|
rects.resize( classCount );
|
||||||
|
ratios.resize( classCount );
|
||||||
|
trainTemlateIdxs.resize( classCount );
|
||||||
|
|
||||||
|
for( size_t tIdx = 0; tIdx < dotTemplates.size(); tIdx++ )
|
||||||
|
{
|
||||||
|
Rect r( 0, 0, regionsPerCol, regionsPerRow );
|
||||||
|
for( r.y = 0; r.y <= testQuantizedImage.rows-r.height; r.y++ )
|
||||||
|
{
|
||||||
|
for( r.x = 0; r.x <= testQuantizedImage.cols-r.width; r.x++ )
|
||||||
|
{
|
||||||
|
float ratio, texturelessRatio;
|
||||||
|
andQuantizedImages( testQuantizedImage(r), dotTemplates[tIdx].quantizedImage, ratio, texturelessRatio );
|
||||||
|
if( ratio > minRatio && texturelessRatio < dotTemplates[tIdx].texturelessRatio )
|
||||||
|
{
|
||||||
|
for( size_t cIdx = 0; cIdx < dotTemplates[tIdx].classIDs.size(); cIdx++ )
|
||||||
|
{
|
||||||
|
int classID = dotTemplates[tIdx].classIDs[cIdx];
|
||||||
|
rects[classID].push_back( r );
|
||||||
|
ratios[classID].push_back( ratio );
|
||||||
|
trainTemlateIdxs[classID].push_back( tIdx );
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::detectMultiScale( const Mat& image, vector<vector<Rect> >& rects,
|
||||||
|
const DetectParams& detectParams, vector<vector<float> >* ratios, vector<vector<int> >* trainTemplateIndices ) const
|
||||||
|
{
|
||||||
|
detectParams.asserts( trainParams.minRatio );
|
||||||
|
|
||||||
|
int classCount = classNames.size();
|
||||||
|
rects.resize( classCount );
|
||||||
|
if( ratios )
|
||||||
|
{
|
||||||
|
ratios->clear();
|
||||||
|
if( !detectParams.isGroup )
|
||||||
|
ratios->resize( classCount );
|
||||||
|
}
|
||||||
|
if( trainTemplateIndices )
|
||||||
|
{
|
||||||
|
trainTemplateIndices->clear();
|
||||||
|
if( !detectParams.isGroup )
|
||||||
|
trainTemplateIndices->resize( classCount );
|
||||||
|
}
|
||||||
|
|
||||||
|
Mat magnitudes, angles;
|
||||||
|
computeGradients( image, magnitudes, angles );
|
||||||
|
for( int regionSize = detectParams.minRegionSize; regionSize <= detectParams.maxRegionSize; regionSize += detectParams.regionSizeStep )
|
||||||
|
{
|
||||||
|
Mat quantizedImage;
|
||||||
|
vector<vector<Rect> > curRects;
|
||||||
|
vector<vector<float> > curRatios;
|
||||||
|
vector<vector<int> > curTrainTemlateIdxs;
|
||||||
|
quantizeToDetect( magnitudes, angles, quantizedImage, trainParams );
|
||||||
|
detectQuantized( quantizedImage, detectParams.minRatio, curRects, curRatios, curTrainTemlateIdxs );
|
||||||
|
|
||||||
|
for( int ci = 0; ci < classCount; ci++ )
|
||||||
|
{
|
||||||
|
for( size_t ri = 0; ri < curRects[ci].size(); ri++ )
|
||||||
|
{
|
||||||
|
Rect r = curRects[ci][ri];
|
||||||
|
r.x *= regionSize;
|
||||||
|
r.y *= regionSize;
|
||||||
|
r.width *= regionSize;
|
||||||
|
r.height *= regionSize;
|
||||||
|
|
||||||
|
rects[ci].push_back( r );
|
||||||
|
if( ratios && !detectParams.isGroup )
|
||||||
|
(*ratios)[ci].push_back( curRatios[ci][ri] );
|
||||||
|
if( trainTemplateIndices && !detectParams.isGroup )
|
||||||
|
(*trainTemplateIndices)[ci].push_back( curTrainTemlateIdxs[ci][ri] );
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if( detectParams.isGroup )
|
||||||
|
groupRectanglesList( rects, detectParams.groupThreshold, detectParams.groupEps );
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
const vector<DOTDetector::DOTTemplate>& DOTDetector::getDOTTemplates() const
|
||||||
|
{
|
||||||
|
return dotTemplates;
|
||||||
|
}
|
||||||
|
|
||||||
|
const vector<string>& DOTDetector::getClassNames() const
|
||||||
|
{
|
||||||
|
return classNames;
|
||||||
|
}
|
||||||
|
|
||||||
|
void DOTDetector::groupRectanglesList( std::vector<std::vector<cv::Rect> >& rectList, int groupThreshold, double eps )
|
||||||
|
{
|
||||||
|
for( size_t i = 0; i < rectList.size(); i++ )
|
||||||
|
groupRectangles( rectList[i], groupThreshold, eps );
|
||||||
|
}
|
||||||
|
|
||||||
|
} // namespace cv
|
||||||
|
|
||||||
|
/* End of file. */
|
||||||
134
samples/cpp/dot.cpp
Normal file
134
samples/cpp/dot.cpp
Normal file
@ -0,0 +1,134 @@
|
|||||||
|
#include <opencv2/imgproc/imgproc.hpp>
|
||||||
|
#include <opencv2/highgui/highgui.hpp>
|
||||||
|
#include <opencv2/objdetect/objdetect.hpp>
|
||||||
|
#include <iostream>
|
||||||
|
#include <fstream>
|
||||||
|
|
||||||
|
using namespace cv;
|
||||||
|
using namespace std;
|
||||||
|
|
||||||
|
#define SHOW_ALL_RECTS_BY_ONE 1
|
||||||
|
static void fillColors( vector<Scalar>& colors )
|
||||||
|
{
|
||||||
|
cv::RNG rng = theRNG();
|
||||||
|
|
||||||
|
for( size_t ci = 0; ci < colors.size(); ci++ )
|
||||||
|
colors[ci] = Scalar( rng(256), rng(256), rng(256) );
|
||||||
|
}
|
||||||
|
|
||||||
|
static void readTestImageNames( const string& descrFilename, vector<string>& names )
|
||||||
|
{
|
||||||
|
names.clear();
|
||||||
|
|
||||||
|
ifstream file( descrFilename.c_str() );
|
||||||
|
if ( !file.is_open() )
|
||||||
|
return;
|
||||||
|
|
||||||
|
while( !file.eof() )
|
||||||
|
{
|
||||||
|
string str; getline( file, str );
|
||||||
|
if( str.empty() ) break;
|
||||||
|
if( str[0] == '#' ) continue; // comment
|
||||||
|
names.push_back(str);
|
||||||
|
}
|
||||||
|
file.close();
|
||||||
|
}
|
||||||
|
|
||||||
|
int main( int argc, char **argv )
|
||||||
|
{
|
||||||
|
if( argc != 1 && argc != 3 )
|
||||||
|
{
|
||||||
|
cout << "Format: base; " << endl << "or without arguments to use default data" << endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
string baseDirName, testDirName;
|
||||||
|
if( argc == 1 )
|
||||||
|
{
|
||||||
|
baseDirName = "../MultiScaleDOT/Data/train/";
|
||||||
|
testDirName = "../MultiScaleDOT/Data/test/";
|
||||||
|
}
|
||||||
|
else
|
||||||
|
{
|
||||||
|
baseDirName = argv[1];
|
||||||
|
testDirName = argv[2];
|
||||||
|
baseDirName += (*(baseDirName.end()-1) == '/' ? "" : "/");
|
||||||
|
testDirName += (*(testDirName.end()-1) == '/' ? "" : "/");
|
||||||
|
}
|
||||||
|
|
||||||
|
DOTDetector::TrainParams trainParams;
|
||||||
|
trainParams.winSize = Size(84, 84);
|
||||||
|
trainParams.regionSize = 7;
|
||||||
|
trainParams.minMagnitude = 60; // we ignore pixels with magnitude less then minMagnitude
|
||||||
|
trainParams.maxStrongestCount = 7; // we find such count of strongest gradients for each region
|
||||||
|
trainParams.maxNonzeroBits = 6; // we filter very textured regions (that have more then maxUnzeroBits count of 1s (ones) in the template)
|
||||||
|
trainParams.minRatio = 0.85f;
|
||||||
|
|
||||||
|
// 1. Train detector
|
||||||
|
DOTDetector dotDetector;
|
||||||
|
dotDetector.train( baseDirName, trainParams, true );
|
||||||
|
|
||||||
|
const vector<string>& classNames = dotDetector.getClassNames();
|
||||||
|
const vector<DOTDetector::DOTTemplate>& dotTemplates = dotDetector.getDOTTemplates();
|
||||||
|
|
||||||
|
vector<Scalar> colors( classNames.size() );
|
||||||
|
fillColors( colors );
|
||||||
|
cout << "Templates count " << dotTemplates.size() << endl;
|
||||||
|
|
||||||
|
vector<string> testFilenames;
|
||||||
|
readTestImageNames( testDirName + "images.txt", testFilenames );
|
||||||
|
if( testFilenames.empty() )
|
||||||
|
{
|
||||||
|
cout << "Can not read no one test images" << endl;
|
||||||
|
return -1;
|
||||||
|
}
|
||||||
|
|
||||||
|
// 2. Detect objects
|
||||||
|
DOTDetector::DetectParams detectParams;
|
||||||
|
detectParams.minRatio = 0.8f;
|
||||||
|
detectParams.minRegionSize = 5;
|
||||||
|
detectParams.maxRegionSize = 11;
|
||||||
|
#if SHOW_ALL_RECTS_BY_ONE
|
||||||
|
detectParams.isGroup = false;
|
||||||
|
#endif
|
||||||
|
|
||||||
|
for( size_t imgIdx = 0; imgIdx < testFilenames.size(); imgIdx++ )
|
||||||
|
{
|
||||||
|
string curFilename = testDirName + testFilenames[imgIdx];
|
||||||
|
cout << curFilename << endl;
|
||||||
|
Mat queryImage = imread( curFilename, 0 );
|
||||||
|
|
||||||
|
if( queryImage.empty() )
|
||||||
|
continue;
|
||||||
|
|
||||||
|
cout << "Detection start ..." << endl;
|
||||||
|
vector<vector<Rect> > rects;
|
||||||
|
vector<vector<float> > ratios;
|
||||||
|
vector<vector<int> > trainTemlateIdxs;
|
||||||
|
dotDetector.detectMultiScale( queryImage, rects, detectParams, &ratios, &trainTemlateIdxs );
|
||||||
|
cout << "end" << endl;
|
||||||
|
|
||||||
|
Mat draw;
|
||||||
|
cvtColor( queryImage, draw, CV_GRAY2BGR );
|
||||||
|
|
||||||
|
#if SHOW_ALL_RECTS_BY_ONE
|
||||||
|
DOTDetector::groupRectanglesList( rects, 3, 0.2 );
|
||||||
|
#endif
|
||||||
|
|
||||||
|
const int textStep = 25;
|
||||||
|
for( size_t ci = 0; ci < classNames.size(); ci++ )
|
||||||
|
{
|
||||||
|
putText( draw, classNames[ci], Point(textStep, textStep*(1+ci)), 1, 2, colors[ci], 3 );
|
||||||
|
for( size_t ri = 0; ri < rects[ci].size(); ri++ )
|
||||||
|
{
|
||||||
|
rectangle( draw, rects[ci][ri], colors[ci], 3 );
|
||||||
|
}
|
||||||
|
}
|
||||||
|
Mat scaledDraw;
|
||||||
|
cv::resize( draw, scaledDraw, Size(640, 480) );
|
||||||
|
imshow( "detection result", scaledDraw );
|
||||||
|
|
||||||
|
cv::waitKey();
|
||||||
|
}
|
||||||
|
|
||||||
|
}
|
||||||
Loading…
x
Reference in New Issue
Block a user