161 lines
4.5 KiB
C++
161 lines
4.5 KiB
C++
//
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// Created by Niko on 1/11/2018.
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//
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#include "../../../header.h"
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#include "AbstractionLayer_Histogram.h"
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using namespace cv;
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Mat HistogramComparer::readImages(int count)
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{
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char name[100];
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Mat corr;
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Mat ref_gray;
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sprintf(name, PATH, count);
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Mat src = imread(name, 1);
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if (!src.data)
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{
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cerr << "Problem loading image!!!" << endl;
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return src;
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}
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if(DISPLAY)imshow("src",src);
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Mat im_color;
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cvtColor(src, im_color, COLOR_BGR2HSV);
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return im_color;
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}
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bool AbstractionLayer_Histogram::PreProcessing(coor mySize, const vector<Part*>* partArray){
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HistogramComparer localImage;
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cout << "Abstraction 2 Preprocessing... " << flush;
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const vector<Part*>& ref_partArray = *partArray;
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analyseParts analyse(mySize.row*mySize.col);
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Part buf;
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int iterator=0;
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if(!analyse.getImages())
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{
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cerr << "Error occured in getImages!" << endl;
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return false;
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}
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else // hier werden alle vier verschiedenen Rotationsarten 'gleichzeitig' abgespeichert
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//TODO rows and cols
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for(int i = 0; i < mySize.row*mySize.col; i++)
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{
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Mat src_img1 = localImage.readImages(i);
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Mat hsv_img1;
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/// Convert to HSV
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cvtColor(src_img1, hsv_img1, COLOR_BGR2HSV);
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/// Using 50 bins for hue and 60 for saturation
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int h_bins = 50;
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int s_bins = 60;
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int histSize[] = {h_bins, s_bins};
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// hue varies from 0 to 179, saturation from 0 to 255
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float h_ranges[] = {0, 180};
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float s_ranges[] = {0, 256};
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const float *ranges[] = {h_ranges, s_ranges};
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// Use the o-th and 1-st channels
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int channels[] = {0, 1};
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/// Histograms
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MatND hist_img1;
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/// Calculate the histograms for the HSV images
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calcHist(&hsv_img1, 1, channels, Mat(), hist_img1, 2, histSize, ranges, true, false);
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// normalize(hist_img1, hist_img1, 0, 1, NORM_MINMAX, -1, Mat());
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ref_partArray[iterator]->m_aHistogram.image=hsv_img1;
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iterator++;
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}
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InitialiseConstraintMatrixSize(mySize.col, mySize.row); //col row switched in this function
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cout << "Done!" << endl;
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return true;
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}
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bool AbstractionLayer_Histogram::EvaluateQuality (const coor constraintCoordinate, qualityVector& qVector){
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//evaluateQuality = evaluateProbabilaty
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for(int i = 0;i < qVector.size();i++)
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{
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if(PlaceOfPartGood(constraintCoordinate, qVector[i].second->m_aHistogram.image))
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{
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qVector[i].first=1;
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continue;
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}
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qVector[i].first=0;
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}
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}
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bool AbstractionLayer_Histogram::PlaceOfPartGood(coor myCoor, Mat& myPart)
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{
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HistogramComparer localComparer;
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//sets coordinates to correct position for layer
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myCoor.row++;
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myCoor.col++;
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if( myCoor.row == 1 && myCoor.col == 1){return true;}
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else if(myCoor.col == 1 && myCoor.row >1){
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if(localComparer.CompareHistogram(m_constraintMatrix[myCoor.col][myCoor.row-1].image, myPart)){
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return true;
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}
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else return false;
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}
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else if( myCoor.row == 1 && myCoor.col >1){
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if(localComparer.CompareHistogram(m_constraintMatrix[myCoor.col-1][myCoor.row].image, myPart)){
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return true;
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}
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else return false;
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}
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else if (myCoor.col > 1 && myCoor.row >1){
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if( localComparer.CompareHistogram(m_constraintMatrix[myCoor.col][myCoor.row-1].image, myPart) &&
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localComparer.CompareHistogram(m_constraintMatrix[myCoor.col-1][myCoor.row].image, myPart)){
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return true;
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}
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else return false;
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}else return false;
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}
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bool HistogramComparer::CompareHistogram(Mat hist_img1,Mat hist_img2)
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{
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// Correlation
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double Correlation = compareHist(hist_img1, hist_img2, CV_COMP_CORREL);
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if(Correlation > 0.95 ){
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return true;
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}
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else
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return false;
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}
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bool AbstractionLayer_Histogram::SetConstraintOnPosition(const coor constraintCoordinate, const AbstractionLayer_Histogram_Properties constraint)
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{
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m_constraintMatrix[constraintCoordinate.col+1][constraintCoordinate.row+1].image=constraint.image;
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//m_constraintMatrix[constraintCoordinate.col+1][constraintCoordinate.row+1].m_connections=constraint.m_connections;
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}
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bool AbstractionLayer_Histogram::RemoveConstraintOnPosition(const coor constraintCoordinate)
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{
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Mat dummy(1,1,0);
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m_constraintMatrix[constraintCoordinate.col+1][constraintCoordinate.row+1].image = dummy;
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}
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