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Debug-Stuff, Part turned 4x for partArray, Unbelieveable slow when layer is on...

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Unbelieveable slow when layer is on... -> may be a solver problem?
Solution found (see slack). Pömpel ignored. No Seg-Faults anymore
This commit is contained in:
JRauer 2018-01-27 12:35:14 +01:00
parent 9e2abfd64d
commit be49687f31
3 changed files with 25 additions and 17 deletions
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2
Source/functions/AbstractionLayers/DestructionPower/DestructionPower.h
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@ -1,6 +1,6 @@
#pragma once #pragma once
//TODO!! increase Destructioncount //TODO!! increase Destructioncount
#define DESTRUCTION_COUNT 1 #define DESTRUCTION_COUNT 2
#include "DestructionPower_Properties.h" #include "DestructionPower_Properties.h"
#include "../AbstraktionLayer_Base.h" #include "../AbstraktionLayer_Base.h"

38
Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures.cpp
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@ -23,6 +23,7 @@ bool AbstractionLayer_SURFFeatures::PreProcessing(coor mySize, const vector<Part
if(!PreProcessingFullImg(mySize)) return false; if(!PreProcessingFullImg(mySize)) return false;
if(!PreProcessingPieces(mySize, partArray)) return false; if(!PreProcessingPieces(mySize, partArray)) return false;
cout << "Done!" << endl;
return true; return true;
} }
@ -33,8 +34,9 @@ bool AbstractionLayer_SURFFeatures::EvaluateQuality (coor constraintCoordinate,
{ {
float diff = abs(m_constraintMatrix[constraintCoordinate.col][constraintCoordinate.row].m_numberOfFeaturesDetected - qVector[i].second->m_a4.m_numberOfFeaturesDetected); float diff = abs(m_constraintMatrix[constraintCoordinate.col][constraintCoordinate.row].m_numberOfFeaturesDetected - qVector[i].second->m_a4.m_numberOfFeaturesDetected);
qVector[i].first = 1 - diff; qVector[i].first = 1 - diff;
//cout << fixed << qVector[i].first << endl; // cout << i << " " << fixed << qVector[i].first << endl;
} }
// cout << " Matrix: " << m_constraintMatrix[constraintCoordinate.col][constraintCoordinate.row].m_numberOfFeaturesDetected << endl;
return true; return true;
} }
@ -69,7 +71,7 @@ bool AbstractionLayer_SURFFeatures::PreProcessingFullImg(coor mySize)
//cout << "POST: " << image.cols << " x " << image.rows << endl; //cout << "POST: " << image.cols << " x " << image.rows << endl;
// PARAMETERS (for description see top of file) // PARAMETERS (for description see top of file)
int maxCorners = 10000; int maxCorners = 12000;
double qualityLevel = 0.01; double qualityLevel = 0.01;
double minDistance = .5; double minDistance = .5;
Mat mask; Mat mask;
@ -81,11 +83,13 @@ bool AbstractionLayer_SURFFeatures::PreProcessingFullImg(coor mySize)
goodFeaturesToTrack( image, corners, maxCorners, qualityLevel, minDistance, mask, blockSize, useHarrisDetector, k ); goodFeaturesToTrack( image, corners, maxCorners, qualityLevel, minDistance, mask, blockSize, useHarrisDetector, k );
// Empty the matrix // Empty the matrix
for( int j = 0; j < mySize.col ; j++ ) for( int j = 0; j < mySize.row ; j++ )
{ for( int i = 0; i < mySize.row; i++ ) { for( int i = 0; i < mySize.col; i++ )
{ {
m_constraintMatrix[j][i].m_numberOfFeaturesDetected = 0; m_constraintMatrix[i][j].m_numberOfFeaturesDetected = 0;
//cout << m_constraintMatrix[i][j].m_numberOfFeaturesDetected << " ";
} }
//cout << endl;
} }
int pieceColSize = image.cols/mySize.col; int pieceColSize = image.cols/mySize.col;
@ -100,21 +104,23 @@ bool AbstractionLayer_SURFFeatures::PreProcessingFullImg(coor mySize)
// Get minimal and maximal number of features -> TODO: Do in first loop to safe time? // Get minimal and maximal number of features -> TODO: Do in first loop to safe time?
int minFeatures = int(m_constraintMatrix[0][0].m_numberOfFeaturesDetected); int minFeatures = int(m_constraintMatrix[0][0].m_numberOfFeaturesDetected);
int maxFeatures = int(m_constraintMatrix[0][0].m_numberOfFeaturesDetected); int maxFeatures = int(m_constraintMatrix[0][0].m_numberOfFeaturesDetected);
for( int j = 0; j < mySize.col ; j++ ) for( int j = 0; j < mySize.row ; j++ )
{ {
for( int i = 0; i < mySize.row; i++ ) for( int i = 0; i < mySize.col; i++ )
{ {
if(m_constraintMatrix[j][i].m_numberOfFeaturesDetected < minFeatures) minFeatures = int(m_constraintMatrix[j][i].m_numberOfFeaturesDetected); if(m_constraintMatrix[i][j].m_numberOfFeaturesDetected < minFeatures) minFeatures = int(m_constraintMatrix[i][j].m_numberOfFeaturesDetected);
if(m_constraintMatrix[j][i].m_numberOfFeaturesDetected > maxFeatures) maxFeatures = int(m_constraintMatrix[j][i].m_numberOfFeaturesDetected); if(m_constraintMatrix[i][j].m_numberOfFeaturesDetected > maxFeatures) maxFeatures = int(m_constraintMatrix[i][j].m_numberOfFeaturesDetected);
//cout << fixed << m_constraintMatrix[i][j].m_numberOfFeaturesDetected << " ";
} }
//cout << endl;
} }
// Calculate percentage from 0 to 100% (normalized 0-1) with numberOfFeatures and safe it // Calculate percentage from 0 to 100% (normalized 0-1) with numberOfFeatures and safe it
for( int j = 0; j < mySize.col ; j++ ) for( int j = 0; j < mySize.row ; j++ )
{ {
for( int i = 0; i < mySize.row; i++ ) for( int i = 0; i < mySize.col; i++ )
{ {
m_constraintMatrix[j][i].m_numberOfFeaturesDetected = (m_constraintMatrix[j][i].m_numberOfFeaturesDetected - minFeatures) / (maxFeatures - minFeatures); m_constraintMatrix[i][j].m_numberOfFeaturesDetected = (m_constraintMatrix[i][j].m_numberOfFeaturesDetected - minFeatures) / (maxFeatures - minFeatures);
//cout << fixed << m_constraintMatrix[i][j].m_numberOfFeaturesDetected << " "; //cout << fixed << m_constraintMatrix[i][j].m_numberOfFeaturesDetected << " ";
} }
//cout << endl; //cout << endl;
@ -161,19 +167,21 @@ bool AbstractionLayer_SURFFeatures::PreProcessingPieces(coor mySize, const vecto
useHarrisDetector, k); useHarrisDetector, k);
if (corners.size() < minFeatures) minFeatures = corners.size(); if (corners.size() < minFeatures) minFeatures = corners.size();
if (corners.size() > maxFeatures) maxFeatures = corners.size(); if (corners.size() > maxFeatures) maxFeatures = corners.size();
partArray->at(imgID)->m_a4.m_numberOfFeaturesDetected = corners.size(); for(int rotate = 0; rotate < 4; rotate++)
partArray->at(imgID*4 + rotate)->m_a4.m_numberOfFeaturesDetected = corners.size();
//cout << imgID << ":" << partArray->at(imgID*4)->m_a4.m_numberOfFeaturesDetected << endl;
//cout << imgID << " " << corners.size() << endl;
/*for( size_t i = 0; i < corners.size(); i++ ) { /*for( size_t i = 0; i < corners.size(); i++ ) {
cv::circle( src, corners[i], 2, cv::Scalar( 255. ), -1 ); cv::circle( src, corners[i], 2, cv::Scalar( 255. ), -1 );
} }
cv::namedWindow( "Output", CV_WINDOW_AUTOSIZE ); cv::namedWindow( "Output", CV_WINDOW_AUTOSIZE );
cv::imshow( "Output", src ); cv::imshow( "Output", src );
cout << count << " " << corners.size() << endl;
cv::waitKey(0);*/ cv::waitKey(0);*/
} }
} }
// Calculate percentage from 0 to 100% (normalized 0-1) with numberOfFeatures and safe it // Calculate percentage from 0 to 100% (normalized 0-1) with numberOfFeatures and safe it
for( unsigned i = 0; i < mySize.col*mySize.row; i++ ) for( unsigned i = 0; i < partArray->size(); i++ )
{ {
partArray->at(i)->m_a4.m_numberOfFeaturesDetected = (partArray->at(i)->m_a4.m_numberOfFeaturesDetected - minFeatures) / (maxFeatures - minFeatures); partArray->at(i)->m_a4.m_numberOfFeaturesDetected = (partArray->at(i)->m_a4.m_numberOfFeaturesDetected - minFeatures) / (maxFeatures - minFeatures);
//cout << fixed << partArray->at(i)->m_a4.m_numberOfFeaturesDetected << endl; //cout << fixed << partArray->at(i)->m_a4.m_numberOfFeaturesDetected << endl;

2
Source/functions/AbstractionLayers/Layer_SURFFeatures/AbstractionLayer_SURFFeatures.h
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@ -6,7 +6,7 @@
#include "opencv2/imgproc/imgproc.hpp" #include "opencv2/imgproc/imgproc.hpp"
#ifdef _WIN32 #ifdef _WIN32
#define PATH_FULL_PUZZLE "..\\..\\..\\puzzle_img\\puzzle1.jpg" #define PATH_FULL_PUZZLE "..\\..\\..\\puzzle_img\\puzzle2.jpg"
#elif defined __unix__ #elif defined __unix__
#define PATH_FULL_PUZZLE "..//..//..//puzzle_img//puzzle1.jpg" #define PATH_FULL_PUZZLE "..//..//..//puzzle_img//puzzle1.jpg"
#elif defined __APPLE__ #elif defined __APPLE__