Skip to content

O
opencv
Commit 15808067 authored by Maria Dimashova's avatar Maria Dimashova
Browse files
Options

removed DOT implementation

parent 6616606e
Show whitespace changes
Inline Side-by-side
Showing with 0 additions and 1317 deletions
modules/objdetect/include/opencv2/objdetect/objdetect.hpp
View file @ 15808067
......@@ -576,120 +576,6 @@ protected:
FernClassifier fernClassifier;
};
/****************************************************************************************\
* Dominant Orientation Templates *
\****************************************************************************************/
class CV_EXPORTS DOTDetector
{
public:
struct CV_EXPORTS TrainParams
{
enum { BIN_COUNT = 7 };
static double BIN_RANGE() { return 180.0 / BIN_COUNT; }
TrainParams();
TrainParams( const Size& winSize, int regionSize=7, int minMagnitude=60,
int maxStrongestCount=7, int maxNonzeroBits=6,
float minRatio=0.85f );
void read( FileNode& fn );
void write( FileStorage& fs ) const;
void isConsistent() const;
Size winSize;
int regionSize;
int minMagnitude;
int maxStrongestCount;
int maxNonzeroBits;
float minRatio;
};
struct CV_EXPORTS DetectParams
{
DetectParams();
DetectParams( float minRatio, int minRegionSize, int maxRegionSize, int regionSizeStep,
bool isGroup, int groupThreshold=3, double groupEps=0.2f );
void isConsistent( float minTrainRatio=1.f ) const;
float minRatio;
int minRegionSize;
int maxRegionSize;
int regionSizeStep;
bool isGroup;
int groupThreshold;
double groupEps;
};
struct CV_EXPORTS DOTTemplate
{
struct CV_EXPORTS TrainData
{
TrainData();
TrainData( const Mat& maskedImage, const cv::Mat& strongestGradientsMask );
cv::Mat maskedImage;
cv::Mat strongestGradientsMask;
};
DOTTemplate();
DOTTemplate( const cv::Mat& quantizedImage, int objectClassID,
const cv::Mat& maskedImage=cv::Mat(), const cv::Mat& strongestGradientsMask=cv::Mat() );
void addObjectClassID( int objectClassID, const cv::Mat& maskedImage=cv::Mat(), const cv::Mat& strongestGradientsMask=cv::Mat() );
const TrainData* getTrainData( int objectClassID ) const;
static float computeTexturelessRatio( const cv::Mat& quantizedImage );
void read( FileNode& fn );
void write( FileStorage& fs ) const;
cv::Mat quantizedImage;
float texturelessRatio;
int area;
std::vector<int> objectClassIDs;
std::vector<TrainData> trainData;
};
DOTDetector();
DOTDetector( const std::string& filename ); // load from xml-file
virtual ~DOTDetector();
void clear();
void read( FileNode& fn );
void write( FileStorage& fs ) const;
void load( const std::string& filename );
void save( const std::string& filename ) const;
void train( const string& baseDirName, const TrainParams& trainParams=TrainParams(), bool isAddImageAndGradientMask=false );
void detectMultiScale( const Mat& image, vector<vector<Rect> >& rects, const DetectParams& detectParams=DetectParams(),
vector<vector<float> >* ratios=0, vector<vector<int> >* dotTemplateIndices=0 ) const;
const vector<DOTTemplate>& getDOTTemplates() const;
const vector<string>& getObjectClassNames() const;
static void groupRectanglesList( std::vector<std::vector<cv::Rect> >& rectList, int groupThreshold, double eps );
protected:
void detectQuantized( const Mat& queryQuantizedImage, float minRatio,
vector<vector<Rect> >& rects,
vector<vector<float> >* ratios,
vector<vector<int> >* dotTemplateIndices ) const;
TrainParams trainParams;
std::vector<std::string> objectClassNames;
std::vector<DOTTemplate> dotTemplates;
};
struct CV_EXPORTS DataMatrixCode {
char msg[4]; //TODO std::string
Mat original;
......
modules/objdetect/src/dotdetector.cpp deleted 100644 → 0
View file @ 6616606e
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <iostream>
#include <fstream>
using namespace std;
namespace cv
{
/*
* Functions
*/
static void readDirContent( const string& descrFilename, vector<string>& names )
{
names.clear();
ifstream file( descrFilename.c_str(), ifstream::in );
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();
}
inline void computeGradients( const Mat& image, Mat& magnitudes, Mat& angles )
{
Mat dx, dy;
cv::Sobel( image, dx, CV_32F, 1, 0, 3 );
cv::Sobel( image, dy, CV_32F, 0, 1, 3 );
cv::cartToPolar( dx, dy, magnitudes, angles, true );
CV_Assert( magnitudes.type() == CV_32FC1 );
CV_Assert( angles.type() == CV_32FC1 );
}
static void computeWinData( const Mat& image, const Mat& mask, const Size& winSize,
Mat& winImage, Mat& winMask,
Mat& winMagnitudes, Mat& winAngles, int border=0 )
{
CV_Assert( border >= 0 );
Size extSize;
extSize.width = winSize.width + 2*border;
extSize.height = winSize.height + 2*border;
if( mask.empty() )
{
image.copyTo( winImage );
winMask.release();
}
else
{
vector<Point> points;
points.reserve( image.rows * image.cols );
for( int y = 0; y < mask.rows; y++ )
{
for( int x = 0; x < mask.cols; x++ )
{
if( mask.at<uchar>(y,x) )
points.push_back( cv::Point(x,y) );
}
}
cv::Rect_<float> brect = cv::boundingRect( cv::Mat(points) );
float ratio = std::min( (float)winSize.width/ brect.width, (float)winSize.height / brect.height );
float rectWidth = winSize.width / ratio;
float rectHeight = winSize.height / ratio;
float scaledBorder = border / ratio;
brect.x -= (rectWidth - brect.width) / 2.f + scaledBorder ;
brect.y -= (rectHeight - brect.height) / 2.f + scaledBorder;
brect.width = rectWidth + 2*scaledBorder;
brect.height = rectHeight + 2*scaledBorder;
// TODO the following cases:
assert( Rect(0, 0, image.cols, image.rows ).contains( brect.tl() ) );
assert( Rect(0, 0, image.cols, image.rows ).contains( brect.br() ) );
Mat subImage( image, brect );
Mat subMask( mask, brect );
cv::resize( subImage, winImage, extSize );
cv::resize( subMask, winMask, extSize );
CV_Assert( winImage.size() == extSize );
CV_Assert( winMask.size() == extSize );
}
computeGradients( winImage, winMagnitudes, winAngles );
// Mat bluredWinImage;
// cv::GaussianBlur( winImage, bluredWinImage, Size(), 0.5, 0.5 );
// computeGradients( bluredWinImage, magnitudes, angles );
}
inline int getBin( double angle )
{
double angle1 = angle >= 180 ? angle - 180 : angle;
int orientationBin = (int)(angle1 / DOTDetector::TrainParams::BIN_RANGE() );
assert( orientationBin < 7 );
return orientationBin;
}
static void copyTrainData( const Mat& magnitudesSrc, const Mat& anglesSrc, const Mat& mask,
Mat& magnitudesDst, Mat& anglesDst )
{
magnitudesDst = Mat( magnitudesSrc.size(), magnitudesSrc.type(), Scalar::all(-1) );
anglesDst = Mat( anglesSrc.size(), anglesSrc.type(), Scalar::all(-1) );
magnitudesSrc.copyTo( magnitudesDst, mask );
anglesSrc.copyTo( anglesDst, mask );
}
inline int countNonZeroBits( uchar val )
{
uchar v = val;
v = (v & 0x55) + ((v >> 1) & 0x55);
v = (v & 0x33) + ((v >> 2) & 0x33);
return (v & 0x0f) + ((v >> 4) & 0x0f);
}
const uchar texturelessValue = 1 << DOTDetector::TrainParams::BIN_COUNT;
inline void countNonZeroAndTexturelessBits( const Mat& mat, int& nonZeroBitsCount, int& texturelessBitsCount )
{
CV_DbgAssert( mat.type() == CV_8UC1 );
nonZeroBitsCount = 0;
texturelessBitsCount = 0;
int step = mat.step1();
for( int y = 0; y < mat.rows; y++ )
{
const uchar* rowPtr = mat.data + y*step;
for( int x = 0; x < mat.cols; x++ )
{
int curCount = countNonZeroBits( rowPtr[x] );
if( curCount )
{
nonZeroBitsCount += curCount;
if( rowPtr[x] == texturelessValue )
texturelessBitsCount++;
}
}
}
}
struct TrainImageQuantizer
{
TrainImageQuantizer( const DOTDetector::TrainParams& _params, const Mat& _magnitudesExt, const Mat& _anglesExt, Mat& _quantizedImage ) :
params(_params), verticalRegionCount(_params.winSize.height/_params.regionSize), horizontalRegionCount(_params.winSize.width/_params.regionSize),
regionSize_2(params.regionSize/2), magnitudesExt(_magnitudesExt), anglesExt(_anglesExt), quantizedImage(&_quantizedImage)
{
quantizedImage->create(verticalRegionCount, horizontalRegionCount, CV_8UC1 );
quantizedImage->setTo( Scalar::all(0) );
}
void operator()( const cv::BlockedRange& range) const
{
Rect curRect( regionSize_2, regionSize_2 + params.regionSize*range.begin(), params.regionSize, params.regionSize );
for( int vRegIdx = range.begin(); vRegIdx < range.end(); vRegIdx++)
{
for( int hRegIdx = 0; hRegIdx < horizontalRegionCount; hRegIdx++ )
{
uchar curRectBits = 0;
for( int yShift = -regionSize_2; yShift <= regionSize_2; yShift++ ) // TODO yShift += regionSize/2
{
Rect shiftedRect = curRect;
shiftedRect.y = curRect.y + yShift;
for( int xShift = -regionSize_2; xShift <= regionSize_2; xShift++ ) // TODO xShift += regionSize/2
{
shiftedRect.x = curRect.x + xShift;
Mat subMagnitudes( magnitudesExt, shiftedRect ), subMagnitudesCopy;
subMagnitudes.copyTo( subMagnitudesCopy );
Mat subAngles( anglesExt, shiftedRect );
double maxMagnitude = 0;
int strongestCount = 0;
for( ; strongestCount < params.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;
}
}
if( !( curRectBits == (1 << DOTDetector::TrainParams::BIN_COUNT) && cv::countNonZero(magnitudesExt(curRect) == -1) ) )
{
if( countNonZeroBits( curRectBits ) <= params.maxNonzeroBits )
quantizedImage->at<uchar>(vRegIdx, hRegIdx) = curRectBits;
}
curRect.x += params.regionSize;
}
curRect.x = regionSize_2;
curRect.y += params.regionSize;
}
}
const DOTDetector::TrainParams& params;
const int verticalRegionCount;
const int horizontalRegionCount;
const int regionSize_2;
const Mat& magnitudesExt;
const Mat& anglesExt;
// Result matrix
Mat* quantizedImage;
TrainImageQuantizer& operator=(const TrainImageQuantizer&);
};
static void quantizeToTrain( const Mat& _magnitudesExt, const Mat& _anglesExt, const Mat& maskExt,
Mat& quantizedImage, const DOTDetector::TrainParams& params )
{
CV_DbgAssert( params.winSize.height % params.regionSize == 0 );
CV_DbgAssert( params.winSize.width % params.regionSize == 0 );
CV_DbgAssert( params.regionSize % 2 == 1 );
Mat magnitudesExt, anglesExt;
copyTrainData( _magnitudesExt, _anglesExt, maskExt, magnitudesExt, anglesExt );
const int verticalRegionCount = params.winSize.height / params.regionSize;
TrainImageQuantizer quantizer( params, magnitudesExt, anglesExt, quantizedImage );
parallel_for( cv::BlockedRange(0, verticalRegionCount), quantizer );
}
struct DetectImageQuantizer
{
DetectImageQuantizer( const DOTDetector::TrainParams& _params, int _regionSize, const Mat& _magnitudes, const Mat& _angles, Mat& _quantizedImage ) :
params(_params), verticalRegionCount(_magnitudes.rows/_regionSize), horizontalRegionCount(_magnitudes.cols/_regionSize),
regionSize(_regionSize), regionSize_2(_regionSize/2), magnitudes(_magnitudes), angles(_angles), quantizedImage(&_quantizedImage)
{
quantizedImage->create(verticalRegionCount, horizontalRegionCount, CV_8UC1 );
quantizedImage->setTo( Scalar::all(0) );
}
void operator()( const cv::BlockedRange& range) const
{
Rect curRect( 0, regionSize*range.begin(), regionSize, regionSize );
const int maxStrongestCount = 1;
for( int vRegIdx = range.begin(); vRegIdx < range.end(); 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 += regionSize;
}
curRect.x = 0;
curRect.y += regionSize;
}
}
const DOTDetector::TrainParams& params;
const int verticalRegionCount;
const int horizontalRegionCount;
const int regionSize;
const int regionSize_2;
const Mat& magnitudes;
const Mat& angles;
// Result matrix
Mat* quantizedImage;
DetectImageQuantizer& operator=(const DetectImageQuantizer&);
};
static void quantizeToDetect( const Mat& _magnitudes, const Mat& angles,
Mat& quantizedImage, int regionSize, const DOTDetector::TrainParams& params )
{
Mat magnitudes; _magnitudes.copyTo( magnitudes );
const int verticalRegionCount = magnitudes.rows / regionSize;
DetectImageQuantizer quantizer( params, regionSize, magnitudes, angles, quantizedImage );
parallel_for( cv::BlockedRange(0, verticalRegionCount), quantizer );
}
inline void andQuantizedImages( const Mat& queryQuantizedImage, const DOTDetector::DOTTemplate& trainTemplate, float& ratio, float& texturelessRatio )
{
int nonZeroCount = 0, texturelessCount = 0;
countNonZeroAndTexturelessBits( trainTemplate.quantizedImage & queryQuantizedImage, nonZeroCount, texturelessCount );
CV_Assert( nonZeroCount > 0 );
ratio = (float)nonZeroCount / trainTemplate.area;
texturelessRatio = (float)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)
{
isConsistent();
}
void DOTDetector::TrainParams::isConsistent() 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"];
isConsistent();
}
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)
{
isConsistent();
}
void DOTDetector::DetectParams::isConsistent( 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::TrainData::TrainData()
{
}
DOTDetector::DOTTemplate::TrainData::TrainData( const Mat& _maskedImage, const cv::Mat& _strongestGradientsMask )
: maskedImage( _maskedImage ), strongestGradientsMask( _strongestGradientsMask )
{
}
DOTDetector::DOTTemplate::DOTTemplate() : texturelessRatio(-1.f), area(0) {}
DOTDetector::DOTTemplate::DOTTemplate( const cv::Mat& _quantizedImage, int _objectClassID, const cv::Mat& _maskedImage, const cv::Mat& _strongestGradientsMask ) :
quantizedImage(_quantizedImage), texturelessRatio(computeTexturelessRatio(_quantizedImage)), area(cv::countNonZero(_quantizedImage))
{
addObjectClassID( _objectClassID, _maskedImage, _strongestGradientsMask );
}
void DOTDetector::DOTTemplate::addObjectClassID( int _objectClassID, const cv::Mat& _maskedImage, const cv::Mat& _strongestGradientsMask )
{
CV_Assert( _objectClassID >= 0 );
bool isFound = false;
for( size_t i = 0; i < objectClassIDs.size(); i++ )
{
if( objectClassIDs[i] == _objectClassID )
{
isFound = true;
break;
}
}
if( !isFound )
{
objectClassIDs.push_back( _objectClassID );
if( !_maskedImage.empty() )
{
CV_Assert( !_strongestGradientsMask.empty() );
trainData.push_back( TrainData(_maskedImage, _strongestGradientsMask) );
}
}
}
const DOTDetector::DOTTemplate::TrainData* DOTDetector::DOTTemplate::getTrainData( int objectClassID ) const
{
if( objectClassID >= 0 )
{
for( size_t i = 0; i < objectClassIDs.size(); i++ )
{
if( objectClassID == objectClassIDs[i] )
return &trainData[i];
}
}
return 0;
}
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;
for( FileNodeIterator fni = fn["objectClassIDs"].begin(); fni != fn["objectClassIDs"].end(); fni++ )
{
objectClassIDs.push_back( *fni );
}
texturelessRatio = fn["texturelessRatio"];
}
void DOTDetector::DOTTemplate::write( FileStorage& fs ) const
{
fs << "template" << quantizedImage;
fs << "objectClassIDs" << "[";
for( size_t i = 0; i < objectClassIDs.size(); i++ )
{
fs << objectClassIDs[i];
}
fs << "]";
fs << "texturelessRatio" << texturelessRatio;
}
/*
* DOTDetector
*/
DOTDetector::DOTDetector()
{
}
DOTDetector::DOTDetector( const std::string& filename )
{
load( filename );
}
DOTDetector::~DOTDetector()
{
clear();
}
void DOTDetector::clear()
{
objectClassNames.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["object_class_count"];
FileNodeIterator fni = fn["object_class_names"].begin();
for( int i = 0; i < classCount; i++ )
{
string name;
fni >> name;
objectClassNames.push_back( name );
}
// read DOT templates
int templatesCount = fn["templates_count"];
for( int i = 0; i < templatesCount; i++ )
{
stringstream ss;
ss << "template_" << i;
dotTemplates.push_back( DOTTemplate() );
FileNode cur_fn = fn["templates"][ss.str()];
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 << "object_class_count" << (int)objectClassNames.size();
fs << "object_class_names" << "[";
for( size_t i = 0; i < objectClassNames.size(); i++ )
{
fs << objectClassNames[i];
}
fs << "]";
// write dot templates
fs << "templates_count" << (int)dotTemplates.size();
fs << "templates" << "{";
for( size_t i = 0; i < dotTemplates.size(); i++ )
{
stringstream ss;
ss << "template_" << i;
fs << ss.str() << "{";
dotTemplates[i].write( fs );
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.isConsistent();
string baseDirName = _baseDirName + (*(_baseDirName.end()-1) == '/' ? "" : "/");
const int regionSize_2 = trainParams.regionSize / 2;
vector<string> allObjectClassNames;
readDirContent( baseDirName + "objects.txt", allObjectClassNames );
for( size_t objIdx = 0; objIdx < allObjectClassNames.size(); objIdx++ )
{
string curObjDirName = baseDirName + allObjectClassNames[objIdx] + "/";
cout << "===============" << allObjectClassNames[objIdx] << "===============" << endl;
vector<string> imagesFilenames;
readDirContent( curObjDirName + "images.txt", imagesFilenames );
if( imagesFilenames.empty() )
continue;
objectClassNames.push_back( allObjectClassNames[objIdx] );
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, queryQuantizedImage;
Mat trainMagnitudesExt, trainAnglesExt;
computeWinData( image, mask, trainParams.winSize,
trainImageExt, trainMaskExt,
trainMagnitudesExt, trainAnglesExt, regionSize_2 );
static int index_ = 0;
{
stringstream ss;
ss << "/files/Datasets/test_temp/" << index_ << ".png";
index_++;
imwrite( ss.str(), trainImageExt );
}
quantizeToTrain( trainMagnitudesExt, trainAnglesExt, trainMaskExt, trainQuantizedImage, trainParams );
quantizeToDetect( trainMagnitudesExt, trainAnglesExt, queryQuantizedImage,
trainParams.regionSize, trainParams );
vector<vector<Rect> > rects;
vector<vector<float> > ratios;
vector<vector<int> > dotTemplateIndices;
detectQuantized( queryQuantizedImage, trainParams.minRatio, rects, &ratios, &dotTemplateIndices );
Mat trainMaskedImage, trainStrongestGradientMask;
if( isAddImageAndGradientMask )
{
trainImageExt.copyTo( trainMaskedImage, trainMaskExt );
computeTrainUsedStrongestMask( trainMagnitudesExt, trainAnglesExt, trainMaskExt, trainQuantizedImage,
trainStrongestGradientMask, trainParams.regionSize, trainParams.minMagnitude );
}
int objectClassID = objectClassNames.size()-1;
bool isFound = false;
for( size_t cIdx = 0; cIdx < dotTemplateIndices.size(); cIdx++ )
{
if( dotTemplateIndices[cIdx].size() )
{
for( size_t i = 0; i < dotTemplateIndices[cIdx].size(); i++ )
{
int tIdx = dotTemplateIndices[cIdx][i];
if( isAddImageAndGradientMask )
dotTemplates[tIdx].addObjectClassID( objectClassID, trainMaskedImage, trainStrongestGradientMask );
else
dotTemplates[tIdx].addObjectClassID( objectClassID );
isFound = true;
}
}
}
if( !isFound )
{
if( isAddImageAndGradientMask )
dotTemplates.push_back( DOTTemplate(trainQuantizedImage, objectClassID, trainMaskedImage, trainStrongestGradientMask) );
else
dotTemplates.push_back( DOTTemplate(trainQuantizedImage, objectClassID ) );
}
cout << "dot templates size = " << dotTemplates.size() << endl;
}
}
}
#ifdef HAVE_TBB
typedef tbb::concurrent_vector<float> ConcurrentFloatVector;
typedef tbb::concurrent_vector<int> ConcurrentIntVector;
#else
typedef std::vector<float> ConcurrentFloatVector;
typedef std::vector<int> ConcurrentIntVector;
#endif
struct TemplateComparator
{
TemplateComparator( const Mat& _queryQuantizedImage, const vector<DOTDetector::DOTTemplate>& _dotTemplates,
float _minRatio,
vector<ConcurrentRectVector>& _concurrRects,
vector<ConcurrentFloatVector>* _concurrRatiosPtr, vector<ConcurrentIntVector>* _concurrTemplateIndicesPtr )
: regionsPerRow(_dotTemplates[0].quantizedImage.rows), regionsPerCol(_dotTemplates[0].quantizedImage.cols), minRatio(_minRatio),
queryQuantizedImage(_queryQuantizedImage), dotTemplates(_dotTemplates), concurrRectsPtr(&_concurrRects),
concurrRatiosPtr(_concurrRatiosPtr), concurrTemplateIndicesPtr(_concurrTemplateIndicesPtr)
{};
void operator()( const cv::BlockedRange& range ) const
{
for( int tIdx = range.begin(); tIdx < range.end(); tIdx++ )
{
Rect r( 0, 0, regionsPerCol, regionsPerRow );
for( r.y = 0; r.y <= queryQuantizedImage.rows-r.height; r.y++ )
{
for( r.x = 0; r.x <= queryQuantizedImage.cols-r.width; r.x++ )
{
float ratio, texturelessRatio;
andQuantizedImages( queryQuantizedImage(r), dotTemplates[tIdx], ratio, texturelessRatio );
if( ratio > minRatio && texturelessRatio < dotTemplates[tIdx].texturelessRatio )
{
for( size_t cIdx = 0; cIdx < dotTemplates[tIdx].objectClassIDs.size(); cIdx++ )
{
int objectClassID = dotTemplates[tIdx].objectClassIDs[cIdx];
(*concurrRectsPtr)[objectClassID].push_back( r );
if( concurrRatiosPtr )
(*concurrRatiosPtr)[objectClassID].push_back( ratio );
if( concurrTemplateIndicesPtr )
(*concurrTemplateIndicesPtr)[objectClassID].push_back( tIdx );
}
}
}
}
}
}
const int regionsPerRow;
const int regionsPerCol;
const float minRatio;
const Mat& queryQuantizedImage;
const vector<DOTDetector::DOTTemplate>& dotTemplates;
vector<ConcurrentRectVector>* concurrRectsPtr;
vector<ConcurrentFloatVector>* concurrRatiosPtr;
vector<ConcurrentIntVector>* concurrTemplateIndicesPtr;
TemplateComparator& operator=(const TemplateComparator&);
};
void DOTDetector::detectQuantized( const Mat& queryQuantizedImage, float minRatio,
vector<vector<Rect> >& rects,
vector<vector<float> >* ratios,
vector<vector<int> >* dotTemplateIndices ) const
{
if( dotTemplates.empty() )
return;
int objectClassCount = objectClassNames.size();
vector<ConcurrentRectVector> concurrRects( objectClassCount );
vector<ConcurrentFloatVector> concurrRatios;
vector<ConcurrentIntVector> concurrTemplateIndices;
vector<ConcurrentFloatVector>* concurrRatiosPtr = 0;
vector<ConcurrentIntVector>* concurrTemplateIndicesPtr = 0;
if( ratios )
{
concurrRatios.resize( objectClassCount );
concurrRatiosPtr = &concurrRatios;
}
if( dotTemplateIndices )
{
concurrTemplateIndices.resize( objectClassCount );
concurrTemplateIndicesPtr = &concurrTemplateIndices;
}
TemplateComparator templatesComparator( queryQuantizedImage, dotTemplates, minRatio, concurrRects, concurrRatiosPtr, concurrTemplateIndicesPtr );
parallel_for( cv::BlockedRange(0, dotTemplates.size()), templatesComparator );
// copy to the output vectors
rects.resize( objectClassCount );
if( ratios )
ratios->resize( objectClassCount );
if( dotTemplateIndices )
dotTemplateIndices->resize( objectClassCount );
for( int i = 0; i < objectClassCount; i++ )
{
rects[i].clear();
rects[i].insert( rects[i].end(), concurrRects[i].begin(), concurrRects[i].end() );
if( ratios )
{
(*ratios)[i].clear();
(*ratios)[i].insert( (*ratios)[i].end(), (*concurrRatiosPtr)[i].begin(), (*concurrRatiosPtr)[i].end() );
}
if( dotTemplateIndices )
{
(*dotTemplateIndices)[i].clear();
(*dotTemplateIndices)[i].insert( (*dotTemplateIndices)[i].end(), (*concurrTemplateIndicesPtr)[i].begin(), (*concurrTemplateIndicesPtr)[i].end() );
}
}
}
void DOTDetector::detectMultiScale( const Mat& image, vector<vector<Rect> >& rects,
const DetectParams& detectParams, vector<vector<float> >* ratios, vector<vector<int> >* dotTemplateIndices ) const
{
detectParams.isConsistent( trainParams.minRatio );
int objectClassCount = objectClassNames.size();
rects.resize( objectClassCount );
if( ratios )
{
ratios->clear();
if( !detectParams.isGroup )
ratios->resize( objectClassCount );
}
if( dotTemplateIndices )
{
dotTemplateIndices->clear();
if( !detectParams.isGroup )
dotTemplateIndices->resize( objectClassCount );
}
vector<vector<Rect> > curRects;
vector<vector<float> > curRatios;
vector<vector<int> > curDotTemlateIndices;
Mat magnitudes, angles;
computeGradients( image, magnitudes, angles );
for( int regionSize = detectParams.minRegionSize; regionSize <= detectParams.maxRegionSize; regionSize += detectParams.regionSizeStep )
{
Mat quantizedImage;
quantizeToDetect( magnitudes, angles, quantizedImage, regionSize, trainParams );
detectQuantized( quantizedImage, detectParams.minRatio, curRects,
ratios ? &curRatios : 0, dotTemplateIndices ? &curDotTemlateIndices : 0 );
for( int ci = 0; ci < objectClassCount; 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( dotTemplateIndices && !detectParams.isGroup )
(*dotTemplateIndices)[ci].push_back( curDotTemlateIndices[ci][ri] );
}
}
}
if( detectParams.isGroup )
groupRectanglesList( rects, detectParams.groupThreshold, detectParams.groupEps );
}
const vector<DOTDetector::DOTTemplate>& DOTDetector::getDOTTemplates() const
{
return dotTemplates;
}
const vector<string>& DOTDetector::getObjectClassNames() const
{
return objectClassNames;
}
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. */
samples/cpp/dot.cpp deleted 100644 → 0
View file @ 6616606e
#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 0
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();
}
// find -name "image_*.png" | grep -v mask | sed 's/.\///' >> images.txt
int main( int argc, char **argv )
{
if( argc != 1 && argc != 3 )
{
cout << "Format: train_data test_data; " << endl << "or without arguments to use default data" << endl;
return -1;
}
string baseDirName, testDirName;
if( argc == 1 )
{
baseDirName = "../../opencv/samples/cpp/dot_data/train/";
testDirName = "../../opencv/samples/cpp/dot_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 );
// dotDetector.save( "../../dot.xml.gz" );
// dotDetector.load( "../../dot.xml.gz" );
const vector<string>& objectClassNames = dotDetector.getObjectClassNames();
const vector<DOTDetector::DOTTemplate>& dotTemplates = dotDetector.getDOTTemplates();
vector<Scalar> colors( objectClassNames.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;
#if SHOW_ALL_RECTS_BY_ONE
vector<vector<float> > ratios;
vector<vector<int> > dotTemlateIndices;
dotDetector.detectMultiScale( queryImage, rects, detectParams, &ratios, &dotTemlateIndices );
const vector<DOTDetector::DOTTemplate>& dotTemplates = dotDetector.getDOTTemplates();
#else
dotDetector.detectMultiScale( queryImage, rects, detectParams );
#endif
cout << "end" << endl;
Mat draw;
cvtColor( queryImage, draw, CV_GRAY2BGR );
const int textStep = 25;
for( size_t ci = 0; ci < objectClassNames.size(); ci++ )
{
putText( draw, objectClassNames[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 );
#if SHOW_ALL_RECTS_BY_ONE
int dotTemplateIndex = dotTemlateIndices[ci][ri];
const DOTDetector::DOTTemplate::TrainData* trainData = dotTemplates[dotTemplateIndex].getTrainData(ci);
imshow( "maskedImage", trainData->maskedImage );
imshow( "strongestGradientsMask", trainData->strongestGradientsMask );
Mat scaledDraw;
cv::resize( draw, scaledDraw, Size(640, 480) );
imshow( "detection result", scaledDraw );
cv::waitKey();
#endif
}
}
Mat scaledDraw;
cv::resize( draw, scaledDraw, Size(640, 480) );
imshow( "detection result", scaledDraw );
cv::waitKey();
}
}
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment