EdgeDetect Class Reference

#include <edgeDetect.h>

Collaboration diagram for EdgeDetect:

Public Member Functions
	EdgeDetect (QImage *image)
	~EdgeDetect ()
int	getNumClusters ()
PixelCluster *	getClusters ()
int *	getSmoothHist ()
int *	getPeaks ()
QImage *	getEdgeImage ()
int *	getClusterMap ()

Private Member Functions
void	allocateAndInitObjects ()
void	constructGSLClut ()
void	fillLumMapAndLumHistogram ()
void	smoothLumHistogram ()
void	computeEdgeMagAndGSLCmaps ()
int	pixelLum (int x, int y)
void	findPixelClusters ()
void	computeClusterStatistics ()
void	computeClusterThresholds ()
void	constructEdgeImage ()
void	deallocateObjects ()

Private Attributes
LUTentry	LUT [256]
QImage *	image
int	lumHist [256]
	luminosity and smooth luminosity histograms
int	smoothLumHist [256]
int	clusterPeaks [256]
int *	lumMap
float *	edgeMagMap
int *	GSLCmap
int	numClusters
PixelCluster *	clusters
int	minClusterSize
int	maxClusterSize

Detailed Description

Definition at line 45 of file edgeDetect.h.

Constructor & Destructor Documentation

EdgeDetect()

EdgeDetect::EdgeDetect

(

QImage *

image

)

Definition at line 195 of file edgeDetect.cpp.

{                     
  //load image
  this->image = image;
 
  //allocate and initialize objects used for edge detection
  allocateAndInitObjects();
 
  //fill lum map and lum histogram
  fillLumMapAndLumHistogram();
  
  //fill smoothed histogram
  smoothLumHistogram();
  
  //compute edge magnitude and GSLC maps
  computeEdgeMagAndGSLCmaps();
  
  //determine pixel clusters
  findPixelClusters();
  
  computeClusterStatistics();  
 
  computeClusterThresholds();
  
  constructEdgeImage();
}

References allocateAndInitObjects(), computeClusterStatistics(), computeClusterThresholds(), computeEdgeMagAndGSLCmaps(), constructEdgeImage(), fillLumMapAndLumHistogram(), findPixelClusters(), image, and smoothLumHistogram().

~EdgeDetect()

EdgeDetect::~EdgeDetect

(

)

Definition at line 222 of file edgeDetect.cpp.

{
  deallocateObjects();
}

References deallocateObjects().

Member Function Documentation

allocateAndInitObjects()

void EdgeDetect::allocateAndInitObjects ( )

private

Definition at line 267 of file edgeDetect.cpp.

{
  //initialize: 
  //-luminosity histogram
  //-smoothed luminosity histogram
  //-identified peak regions
  int i;
  for(i=0; i<256; i++)
  { 
    lumHist[i] = 0; 
    smoothLumHist[i] = 0;
    clusterPeaks[i] = 0;
  }
  
  //allocate luminance map
  lumMap = new int[image->width() * image->height()];
  
  //allocate edge magnitude map
  edgeMagMap = new float[image->width() * image->height()];
  
  //allocate GSLC map
  GSLCmap = new int[image->width() * image->height()];
  
  //construct LUT
  constructGSLClut();
}

References clusterPeaks, constructGSLClut(), edgeMagMap, GSLCmap, image, lumHist, lumMap, and smoothLumHist.

Referenced by EdgeDetect().

computeClusterStatistics()

void EdgeDetect::computeClusterStatistics ( )

private

Definition at line 549 of file edgeDetect.cpp.

{
  //initialize cluster stats
  int cluster;
  for(cluster=0; cluster<numClusters; cluster++)
  {
    int i;
    for(i=0; i<256; i++)
    {
      clusters[cluster].edgeMagHistogram[i] = 0;
    }
    clusters[cluster].totalEdgeMagnitude=0.0f;
    clusters[cluster].numPixels = 0;
  }
  
  //iterate over all pixels
  int i;
  for(i=0; i<image->width()*image->height(); i++)
  {
    //skip pixels that don't belong to peaks
    if( clusterPeaks[ lumMap[i] ] != 1)
      continue;
    
    //determine cluster pixel belongs to
    int cluster;
    for(cluster=0; cluster<numClusters; cluster++)
    {
      if( lumMap[i] >= clusters[cluster].minLuminance &&
          lumMap[i] <= clusters[cluster].maxLuminance )
      {      
        clusters[cluster].totalEdgeMagnitude+= edgeMagMap[i]; 
        clusters[cluster].numPixels++;
        clusters[cluster].edgeMagHistogram[ MIN( MAX( (int)edgeMagMap[i], 0), 255) ]++;
        break;
      }
    } //cluster
  } //pixel i
  
  //iterate over clusters to determine min and max peak cluster sizes
  minClusterSize = clusters[0].numPixels;
  maxClusterSize = clusters[0].numPixels;
  for(cluster=1; cluster<numClusters; cluster++)
  {
    if(clusters[cluster].numPixels < minClusterSize)
      minClusterSize = clusters[cluster].numPixels;
 
    if(clusters[cluster].numPixels > maxClusterSize)
      maxClusterSize = clusters[cluster].numPixels;
  }
 
  //iterate over clusters one final time to deduce normalized inputs to fuzzy logic process
  int JND = 255/50;
  for(cluster=0; cluster<numClusters; cluster++)
  {
    clusters[cluster].meanMode = MIN( clusters[cluster].totalEdgeMagnitude / clusters[cluster].numPixels,
                                       3*JND );
    
    int i;
    int mode = 0;
    for(i=1; i<256; i++)
    {
      if( clusters[cluster].edgeMagHistogram[i] > clusters[cluster].edgeMagHistogram[ mode ] )
        mode = i;
    }
    clusters[cluster].mode = MIN( mode, 2*JND );
        
    clusters[cluster].pixelCount = ((float)(clusters[cluster].numPixels - minClusterSize)) / 
                                   (maxClusterSize - minClusterSize);  
  }
}

References clusterPeaks, clusters, PixelCluster::edgeMagHistogram, edgeMagMap, image, lumMap, MAX, maxClusterSize, PixelCluster::meanMode, MIN, minClusterSize, PixelCluster::mode, numClusters, PixelCluster::numPixels, PixelCluster::pixelCount, and PixelCluster::totalEdgeMagnitude.

Referenced by EdgeDetect().

computeClusterThresholds()

void EdgeDetect::computeClusterThresholds ( )

private

Definition at line 621 of file edgeDetect.cpp.

{
  //iterate over each cluster
  int cluster;
  float S1,M1,L1;
  float S2,M2,L2;
  float S3,L3;
  float outS, outM, outL;
  
  int JND = 255/50;
  
  for(cluster=0; cluster<numClusters; cluster++)
  {
    //----
    //compute S,M, and L values for each input
    //----
    S1 = MAX( 1.0f - ((clusters[cluster].meanMode/JND) / 1.5f), 0 );
 
    if( (clusters[cluster].meanMode/JND) <= 1.5f )
      M1 = MAX( (clusters[cluster].meanMode/JND) - 0.5f, 0 );
    else
      M1 = MAX( 2.5f - (clusters[cluster].meanMode/JND), 0 );
    
    L1 = MAX( ((clusters[cluster].meanMode/JND) - 1.5f) / 1.5f, 0 );
    //----
    S2 = MAX( 1.0f - (clusters[cluster].mode/JND), 0 );
    
    if( (clusters[cluster].mode/JND) <= 1.0f )
      M2 = MAX( -1.0f + 2*(clusters[cluster].mode/JND), 0 );
    else
      M2 = MAX( 3.0f - 2*(clusters[cluster].mode/JND), 0 );
    
    L2 = MAX( (clusters[cluster].mode/JND) - 1.0, 0 );
    //----
    S3 = MAX( 1.0f - 2*clusters[cluster].pixelCount, 0 );
    L3 = MAX( -1.0f + 2*clusters[cluster].pixelCount, 0 );
    //----
    
    //Compute M,L for outputs using set of 18 rules.
    //outS is inherantly S given the ruleset provided
    outS = 0.0f;
    outM = 0.0f;
    outL = 0.0f;
    //Out 1
    if( numClusters > 2 )
    {
      outM += S1*S2*S3;   //rule 1
      
      //rule 2
      if( clusters[cluster].meanMode < clusters[cluster].mode )
        outS += S1*S2*L3;   
      else
        outM += S1*S2*L3;
 
      outM += S1*M2*S3;   //rule 3
      outM += S1*M2*L3;   //rule 4
      outM += S1*L2*S3;   //rule 5
      outM += S1*L2*L3;   //rule 6
      outM += M1*S2*S3;   //rule 7
      outM += M1*S2*L3;   //rule 8
      outM += M1*M2*S3;   //rule 9
      outL += M1*M2*L3;   //rule 10
      outM += M1*L2*S3;   //rule 11
      outL += M1*L2*L3;   //rule 12
      outM += L1*S2*S3;   //rule 13
      outL += L1*S2*L3;   //rule 14
      outM += L1*M2*S3;   //rule 15
      outL += L1*M2*L3;   //rule 16
      outL += L1*L2*S3;   //rule 17
      outL += L1*L2*L3;   //rule 18
    }
    //Out 2
    else
    {
      outL += S1*S2*S3;   //rule 1
      outL += S1*S2*L3;   //rule 2
      outM += S1*M2*S3;   //rule 3
      outL += S1*M2*L3;   //rule 4
      outM += S1*L2*S3;   //rule 5
      outM += S1*L2*L3;   //rule 6
      outL += M1*S2*S3;   //rule 7
      outL += M1*S2*L3;   //rule 8
      outL += M1*M2*S3;   //rule 9
      outL += M1*M2*L3;   //rule 10
      outL += M1*L2*S3;   //rule 11
      outL += M1*L2*L3;   //rule 12
      outL += L1*S2*S3;   //rule 13
      outL += L1*S2*L3;   //rule 14
      outL += L1*M2*S3;   //rule 15
      outL += L1*M2*L3;   //rule 16
      outL += L1*L2*S3;   //rule 17
      outL += L1*L2*L3;   //rule 18
    }
 
    //find centroid - Beta[k]
    float A = outM + 0.5f;
    float B = 2.5f - outM;
    float C = 1.5f * (outL + 1);
    float D = 1.5f * (outM + 1);
    float E = 2.5f - outL;
 
    //---------------------------------------------------------------
    //Case 1: Both outM and outL are above intersection point of diagonals
    if( outM > 0.5f && outL > 0.5f )
    {
      //find area of 7 subregions
      float area1 = ((A-0.5f)*outM)/2;
      float area2 = outM * (B-A);
      float area3 = ((2.1f-B) * (outM - 0.5)) / 2;
      float area4 = (2.1 - B) * 0.5f;
      float area5 = ((C - 2.1f) * (outL - 0.5)) / 2;
      float area6 = (C - 2.1f) * 0.5f;
      float area7 = (3.0f - C) * outL;
     
      //find half of total area
      float halfArea = (area1 + area2 + area3 + area4 + area5 + area6 + area7) / 2;
      
      //determine which region split will be within and resulting horizontal midpoint
      
      //Within area 1
      if( area1 > halfArea )
      {
        clusters[cluster].beta = 0.5f + (float)sqrt(2*halfArea);       
      }
      //Within area 2
      else if( area1 + area2 > halfArea )
      {
        clusters[cluster].beta = ((halfArea - area1) / outM) + A;        
      }
      //Within area 3-4
      else if( area1 + area2 + area3 + area4 > halfArea )
      {
        float a = -0.5f;
        float b = 2.8f;
        float c = area1 + area2 + area3 - halfArea - B/2 - 2.625f;
        clusters[cluster].beta = (-b + (float)sqrt( b*b - 4*a*c )) / (2*a);
      }
      //Within area 5-6
      else if( area1 + area2 + area3 + area4 + area5 + area6 > halfArea )
      {
        float a = 1.0f/3;
        float b = -0.7f;
        float c = area1 + area2 + area3 + area4 - halfArea;
        clusters[cluster].beta = (-b + (float)sqrt( b*b - 4*a*c )) / (2*a);
      }
      //Within area 7
      else
      {
        clusters[cluster].beta = ((halfArea - (area1 + area2 + area3 + area4 + area5 + area6) ) / outL) + C;       
      }
    } //end case 1
    //---------------------------------------------------------------
    //Case 2
    else if ( outM < 0.5f && outL > outM )
    {
      //find area of 5 subregions
      float area1 = (outM*(A-0.5f)) / 2;
      float area2 = (D-A) * outM;
      float area3 = ((C-D) * (outL - outM)) / 2;
      float area4 = (C-D) * outM;
      float area5 = (3.0f - C) * outL;
      
      //find half of total area
      float halfArea = (area1 + area2 + area3 + area4 + area5) / 2;
      
      //determine which region split will be within and resulting horizontal midpoint
 
      //Within area 1
      if( area1 > halfArea )
      {
        clusters[cluster].beta = 0.5f + (float)sqrt(2*halfArea);
      }
      //Within area 2
      else if( area1 + area2 > halfArea )
      {
        clusters[cluster].beta = ((halfArea - area1) / outM) + A;
      }
      //Within area 3-4
      else if( area1 + area2 + area3 + area4 > halfArea )
      {
        float a = 1.0f/3.0f;
        float b = outM - 0.5f - D/3;
        float c = area1 + area2 - D*outM + D/2 - halfArea;
        clusters[cluster].beta = (-b + (float)sqrt( b*b - 4*a*c )) / (2*a);
      }
      //Within area5
      else
      {
        clusters[cluster].beta = ((halfArea - (area1 + area2 + area3 + area4) ) / outL) + C;
      }
    } //end case 2
    //---------------------------------------------------------------
    //Case 3
    else
    {
      //find area of 5 subregions
      float area1 = (outM*(A-0.5f)) / 2;
      float area2 = (B-A) * outM;
      float area3 = ((E-B) * (outM - outL)) / 2;
      float area4 = (E-B) * outL;
      float area5 = (3.0f - E) * outL;
      
      //find half of total area
      float halfArea = (area1 + area2 + area3 + area4 + area5) / 2;
      
      //determine which region split will be within and resulting horizontal midpoint
      
      //Within area 1
      if( area1 > halfArea )
      {
        clusters[cluster].beta = 0.5f + (float)sqrt(2*halfArea);
      }
      //Within area 2
      else if( area1 + area2 > halfArea )
      {
        clusters[cluster].beta = ((halfArea - area1) / outM) + A;
      }
      //Within area 3-4
      else if( area1 + area2 + area3 + area4 > halfArea )
      {
        float a = -0.5f;
        float b = E/2 + 2.5f/2; 
        float c = area3 - 2.5f*E/2;
        clusters[cluster].beta = (-b + (float)sqrt( b*b - 4*a*c )) / (2*a);
      }
      //Within area5
      else
      {
        clusters[cluster].beta = ((halfArea - (area1 + area2 + area3 + area4) ) / outL) + E;
      }
    } //end case 3
    //---------------------------------------------------------------
    
    //Compute edge threshold
    int lumJND = 255/50;
    clusters[cluster].edgeThreshold = clusters[cluster].mode + clusters[cluster].beta*lumJND;
 
  } //end for cluster
  
}

References B, b, PixelCluster::beta, clusters, PixelCluster::edgeThreshold, MAX, PixelCluster::mode, and numClusters.

Referenced by EdgeDetect().

computeEdgeMagAndGSLCmaps()

void EdgeDetect::computeEdgeMagAndGSLCmaps ( )

private

Definition at line 344 of file edgeDetect.cpp.

{
  int x, y;
  int idealPattern[9];
  int pixelLums[9];
  
  //-------  
  //iterate over all pixels
  for( y=0; y<image->height(); y++)
  {   
    for( x=0; x<image->width(); x++)
    {
      //compute pixel luminances for entire grid
      pixelLums[0] = pixelLum(x-1,y-1);
      pixelLums[1] = pixelLum(x  ,y-1);
      pixelLums[2] = pixelLum(x+1,y-1);
      pixelLums[3] = pixelLum(x-1,y  );
      pixelLums[4] = pixelLum(x  ,y  );
      pixelLums[5] = pixelLum(x+1,y  );
      pixelLums[6] = pixelLum(x-1,y+1);
      pixelLums[7] = pixelLum(x  ,y+1);
      pixelLums[8] = pixelLum(x+1,y+1);
      
      //compute average
      float avg = 0;
      int i;
      for(i=0; i<=8; i++)
      {
        avg+= pixelLums[i];
      }
      avg = avg / 9;
      
      //determine ideal pattern and I0 and I1 averages
      int centerPixelLum = pixelLums[4];
      float centerDiff = centerPixelLum - avg;
      
      float I0avg = 0;
      int I0count = 0;
      
      float I1avg = 0;
      int I1count = 0;
      
      for(i=0; i<=8; i++)
      {
        if( centerDiff * (pixelLums[i]-avg) >=0 )
        { 
          I1avg+=pixelLums[i];
          I1count++;
          idealPattern[i] = 1; 
        }
        else 
        { 
          I0avg+=pixelLums[i];
          I0count++;
          idealPattern[i] = 0; 
        }
      }
 
      //compute and store edge magnitude
      if(I0count > 0) I0avg = I0avg/I0count;
      if(I1count > 0) I1avg = I1avg/I1count;     
      edgeMagMap[x + y*image->width()] = QABS( I1avg - I0avg );
      
      //compute and store GSLC
      int GSLC=0;
      int weight = 1;
      for(i=0; i<9; i++)
      {
        //skip center
        if(i == 4) continue;
        
        if(idealPattern[i] == 1)
        { GSLC+=weight; }
        
        weight = weight*2;
      }
      GSLCmap[x + y*image->width()] = GSLC;
    } //x
  } //y
}

References edgeMagMap, GSLCmap, image, and pixelLum().

Referenced by EdgeDetect().

constructEdgeImage()

void EdgeDetect::constructEdgeImage ( )

private

Definition at line 862 of file edgeDetect.cpp.

{
  int x, y;
  QRgb* rgb;
  
  uchar* scanLine;
  for( y=0; y<image->height(); y++)
  {   
    scanLine = image->scanLine(y);
    for( x=0; x<image->width(); x++)
    {
      //initialize pixel to black 
      rgb = ((QRgb*)scanLine+x);
      *rgb = qRgb( 0, 0, 0 );
      
      //lookup ESF for this pixel
      float ESF = LUT[ GSLCmap[x + y*image->width()] ].ESF;
 
      //If ESF value for this pixel is 0 skip
      if( ESF == 0.0f ) continue;
 
      //lookup edge magnitude threshold
      float lum = lumMap[x + y*image->width()];
      float edgeMagThresh = -1.0f;
      int cluster;
      for(cluster=0; cluster<numClusters; cluster++)
      {
        if(lum >= clusters[cluster].minLuminance &&
           lum <= clusters[cluster].maxLuminance)
        {
          edgeMagThresh = clusters[cluster].edgeThreshold;
          break;
        }
      }
 
      //if cluster not found bail
      if( cluster >= numClusters )
      {
//        cout << "Error! Could not find cluster pixel belonged to!\n";
        continue;
      }
      
      //if edge mag below thresh then skip
      if( edgeMagMap[x + y*image->width()] < edgeMagThresh ) continue;
        
      //ok, last checks implement NMS (non-maximum supression)
      int direction = LUT[ GSLCmap[x + y*image->width()] ].direction;
      int neighborIndex1 = -1;
      int neighborIndex2 = -1;
 
      if( direction == 0)
      {
        if( x > 0) 
          neighborIndex1 = x-1 + y*image->width();
        if( x < image->width() - 1 ) 
          neighborIndex2 = x+1 + y*image->width();
      }
      else if(direction == 1)
      {
        if( x > 0 && y < image->height() - 1 )
          neighborIndex1 = x-1 + (y+1)*image->width();
        if( x < image->width() - 1 && y > 0 )
          neighborIndex2 = x+1 + (y-1)*image->width();
      }
      else if(direction == 2)
      {
        if( y < image->height() - 1 ) 
          neighborIndex1 = x + (y+1)*image->width();
        if( y > 0) 
          neighborIndex2 = x + (y-1)*image->width();
      }
      else if(direction == 3)
      {
        if( x < image->width() - 1 && y < image->height() - 1 )
          neighborIndex1 = x+1 + (y+1)*image->width();
        if( x > 0 && y > 0 )
          neighborIndex2 = x-1 + (y-1)*image->width();
      }
 
      //neighbor 1 has higher confidence, skip!
      if( neighborIndex1 != -1 &&
          LUT[ GSLCmap[neighborIndex1] ].ESF * edgeMagMap[neighborIndex1] >
          ESF * edgeMagMap[x + y*image->width()] )
        continue;
      
      //neighbor 2 has higher confidence, skip!
      if( neighborIndex2 != -1 &&
          LUT[ GSLCmap[neighborIndex2] ].ESF * edgeMagMap[neighborIndex2] >
          ESF * edgeMagMap[x + y*image->width()] )
        continue;
      
      //All tests passed! Mark edge!
      *rgb = qRgb( 255, 255, 255 );
    } //x
  } //y
  
  //blur image - all of it
  blurImage( *image, 2.0f );
 
  //normalize image
  enhanceImageContrast( image );
  
}

References blurImage(), clusters, LUTentry::direction, edgeMagMap, PixelCluster::edgeThreshold, enhanceImageContrast(), LUTentry::ESF, GSLCmap, height, image, lumMap, LUT, numClusters, and width.

Referenced by EdgeDetect().

constructGSLClut()

void EdgeDetect::constructGSLClut ( )

private

Definition at line 974 of file edgeDetect.cpp.

{
  //----------------------
  //First fill entire table with 0 ESF's and invalid directions
  int i;
  for(i=0; i<256; i++)
  {
    LUT[i].ESF = 0.0f;
    LUT[i].direction = -1;
  }
  //----------------------
  //Next code in all pattern that are highly 
  //likely to be on edges as described in the paper
  //----------------------
  //Pattern (a)
 
  // ###
  // ##.
  // ...
  LUT[15].ESF = 0.179f;
  LUT[15].direction = 3;
 
  // ...
  // .##
  // ###
  LUT[240].ESF = 0.179f;
  LUT[240].direction = 3;
 
  // ###
  // .##
  // ...
  LUT[23].ESF = 0.179f;
  LUT[23].direction = 1;
  
  // ...
  // ##.
  // ###
  LUT[232].ESF = 0.179f;
  LUT[232].direction = 1;
   
  // ##.
  // ##.
  // #..
  LUT[43].ESF = 0.179f;
  LUT[43].direction = 3;
  
  // ..#
  // .##
  // .##
  LUT[212].ESF = 0.179f;
  LUT[212].direction = 3;
 
  // #..
  // ##.
  // ##.
  LUT[105].ESF = 0.179f;
  LUT[105].direction = 1;
 
  // .##
  // .##
  // ..#
  LUT[150].ESF = 0.179f;
  LUT[150].direction = 1;
  //----------------------
  //Pattern (b)
 
  // ###
  // ###
  // ...
  LUT[31].ESF = 0.137f;
  LUT[31].direction = 2;
 
  // ...
  // ###
  // ###
  LUT[248].ESF = 0.137f;
  LUT[248].direction = 2;
  
  // ##.
  // ##.
  // ##.
  LUT[107].ESF = 0.137f;
  LUT[107].direction = 0;
  
  // .##
  // .##
  // .##
  LUT[214].ESF = 0.137f;
  LUT[214].direction = 0;
  //----------------------
  //Pattern (c)
  
  // ###
  // .#.
  // ...
  LUT[7].ESF = 0.126f;
  LUT[7].direction = 2;
  
  // ...
  // .#.
  // ###
  LUT[224].ESF = 0.126f;
  LUT[224].direction = 2;
 
  // #..
  // ##.
  // #..
  LUT[41].ESF = 0.126f;
  LUT[41].direction = 0; 
  
  // ..#
  // .##
  // ..#
  LUT[148].ESF = 0.126f;
  LUT[148].direction = 0;
  //----------------------
  //Pattern (d)
  
  // ###
  // ##.
  // #..
  LUT[47].ESF = 0.10f;
  LUT[47].direction = 3;
   
  // ..#
  // .##
  // ###
  LUT[244].ESF = 0.10f;
  LUT[244].direction = 3;
 
  // ###
  // .##
  // ..#
  LUT[151].ESF = 0.10f;
  LUT[151].direction = 1;
 
  // #..
  // ##.
  // ###
  LUT[233].ESF = 0.10f;
  LUT[233].direction = 1;
  //----------------------
  //Pattern (e)
  
  // ##.
  // ##.
  // ...
  LUT[11].ESF = 0.10f;
  LUT[11].direction = 3;
  
  // ...
  // .##
  // .##
  LUT[208].ESF = 0.10f;
  LUT[208].direction = 3;
 
  // .##
  // .##
  // ...
  LUT[22].ESF = 0.10f;
  LUT[22].direction = 1;
  
  // ...
  // ##.
  // ##.
  LUT[104].ESF = 0.10f;
  LUT[104].direction = 1; 
  //----------------------    
}

References LUTentry::direction, LUTentry::ESF, and LUT.

Referenced by allocateAndInitObjects().

deallocateObjects()

void EdgeDetect::deallocateObjects ( )

private

Definition at line 966 of file edgeDetect.cpp.

{
  delete[] lumMap;      lumMap = NULL;
  delete[] edgeMagMap;  edgeMagMap = NULL;
  delete[] GSLCmap;     GSLCmap = NULL;
  delete[] clusters;    clusters = NULL;
}

References clusters, edgeMagMap, GSLCmap, and lumMap.

Referenced by ~EdgeDetect().

fillLumMapAndLumHistogram()

void EdgeDetect::fillLumMapAndLumHistogram ( )

private

Definition at line 294 of file edgeDetect.cpp.

{
  int x, y;
  QRgb* rgb;
  uchar* scanLine;
  int lumVal;
  for( y=0; y<image->height(); y++)
  {   
    scanLine = image->scanLine(y);
    for( x=0; x<image->width(); x++)
    {
      //get lum value for this pixel
      rgb = ((QRgb*)scanLine+x);
      lumVal = qGray(*rgb);
      
      //store in lum map
      lumMap[x + y*image->width()] = lumVal;
      
      //update lum histogram
      lumHist[ lumVal ]++;
    }
  }
}

References image, lumHist, and lumMap.

Referenced by EdgeDetect().

findPixelClusters()

void EdgeDetect::findPixelClusters ( )

private

Definition at line 432 of file edgeDetect.cpp.

{
  //find max count
  int maxCount = 0;
  int i;
  for(i=0; i<256; i++)
  {
    if(smoothLumHist[i] > maxCount)
      maxCount = smoothLumHist[i];
  }
 
  //compute JND for histogram (2% of total spread)
  int histJND = maxCount/50;
 
  //construct temporary array for valley locations
  //1's will indicate a valley midpoint
  int tmpValleyArray[256];
  for(i=0; i<256; i++) { tmpValleyArray[i] = 0; }
  
  //move across histogram finding valley midpoints
  int curTrackedMin = smoothLumHist[0];
  
  //first and last indices tracked min was observed
  int firstMinIndex = 0;
  int lastMinIndex = 0;
  
  //only add valley midpoint if finished tracking a descent
  bool slopeNeg = false;
  
  for(i = 1; i<256; i++ )
  {
    if( smoothLumHist[i] < curTrackedMin - histJND )
    {
      //found a descent!
      slopeNeg = true;
      curTrackedMin = smoothLumHist[i];
      firstMinIndex = i;
    }
    //starting to go up again, add last min to list
    else if( smoothLumHist[i] > curTrackedMin + histJND )
    {
      //if finished tracing a negative slope find midpoint and set location to true
      if(slopeNeg)
      {
        tmpValleyArray[ (firstMinIndex + lastMinIndex)/2 ] = 1;
      }
      
      curTrackedMin = smoothLumHist[i];
      slopeNeg = false;
    }
    else
    {
      //still tracking a min, update the right 
      //hand index. center of valley is found
      //by averaging first and last min index
      lastMinIndex = i;
    }
  }
  
  //count valleys
  int numValleys = 0;
  for(i=0; i<256; i++)
  {
    if(tmpValleyArray[i] == 1 ) numValleys++;
  }
 
  //determine number of clusters
  numClusters = numValleys-1;
  if(tmpValleyArray[0] != 1)
    numClusters++;
  if(tmpValleyArray[255] != 1)
    numClusters++;
  
  //allocate clusters
  clusters = new PixelCluster[numClusters];
  
  //automatically start first cluster
  int cluster=0;
  clusters[cluster].minLuminance = 0;
  
  //initialize left and right boundaries of all clusters
  for(i=1; i<256; i++)
  {
    //reached next valley, end cluster
    if( tmpValleyArray[i] == 1)
    {
      clusters[cluster].maxLuminance = i-1;
      cluster++;
      clusters[cluster].minLuminance = i;
    }
    //end last cluster automatically at end
    else if(i == 255)
    {
      clusters[cluster].maxLuminance = i;
    }
  }
  
  //determine cluster peaks
  for(cluster=0; cluster<numClusters; cluster++)
  {
    //find max for current cluster
    int maxIndex = clusters[cluster].minLuminance;
    for(i=clusters[cluster].minLuminance; i<=clusters[cluster].maxLuminance; i++)
    {
      if(smoothLumHist[i] > smoothLumHist[maxIndex])
         maxIndex = i;
    }
    
    //mark peaks  
    int lumJND = 255/50;
    for(i=MAX(0, maxIndex-lumJND); i<MIN(256, maxIndex+lumJND); i++)
    { 
      clusterPeaks[i] = 1; 
    }
  }
}

References clusterPeaks, clusters, MAX, PixelCluster::maxLuminance, MIN, PixelCluster::minLuminance, numClusters, and smoothLumHist.

Referenced by EdgeDetect().

getClusterMap()

int * EdgeDetect::getClusterMap

(

)

Definition at line 244 of file edgeDetect.cpp.

{
  //construct map
  int* clusterMap = new int[image->width() * image->height()];
  
  //iterate over all pixels, determine cluster each pixel belongs to
  int i, cluster;
  for(i=0; i<image->width()*image->height(); i++)
  {
    for(cluster=0; cluster<numClusters; cluster++)
    {
      if( lumMap[i] >= clusters[cluster].minLuminance &&
          lumMap[i] <= clusters[cluster].maxLuminance )
      {
        clusterMap[i] = cluster;
        break;
      }
    } //cluster
  } //pixel
 
  return clusterMap;
}

References clusters, image, lumMap, and numClusters.

Referenced by GrainEditor::GrainEditor().

getClusters()

PixelCluster * EdgeDetect::getClusters

(

)

Definition at line 230 of file edgeDetect.cpp.

{ return clusters; }

References clusters.

getEdgeImage()

QImage * EdgeDetect::getEdgeImage

(

)

Definition at line 239 of file edgeDetect.cpp.

{
  return image; 
}

References image.

getNumClusters()

int EdgeDetect::getNumClusters

(

)

Definition at line 227 of file edgeDetect.cpp.

{ return numClusters; }

References numClusters.

Referenced by GrainEditor::GrainEditor().

getPeaks()

int * EdgeDetect::getPeaks

(

)

Definition at line 233 of file edgeDetect.cpp.

{ return clusterPeaks; }

References clusterPeaks.

getSmoothHist()

int * EdgeDetect::getSmoothHist

(

)

Definition at line 236 of file edgeDetect.cpp.

{ return smoothLumHist; }

References smoothLumHist.

pixelLum()

int EdgeDetect::pixelLum ( int x, int y )

private

Definition at line 425 of file edgeDetect.cpp.

{
  int clampedX = MAX( MIN( x, image->width()-1), 0);
  int clampedY = MAX( MIN( y, image->height()-1), 0);
  return lumMap[ clampedX + clampedY * image->width() ];
}

References image, lumMap, MAX, and MIN.

Referenced by computeEdgeMagAndGSLCmaps().

smoothLumHistogram()

void EdgeDetect::smoothLumHistogram ( )

private

Definition at line 318 of file edgeDetect.cpp.

{
  #define FILTER_SIZE 5
  int filter[FILTER_SIZE] = {2, 5, 8, 5, 2};
  
  int i,j;
  int filterIndex, sum, total;
  for(i = 0; i<256; i++)
  {
    sum = 0;
    total = 0;
    
    for( j= -FILTER_SIZE/2; j <= FILTER_SIZE/2; j++)
    {
      if( i+j > 0 && i+j < 256 )
      {
        filterIndex = j+ FILTER_SIZE/2;
        total+= filter[filterIndex] * lumHist[i+j];
        sum  += filter[filterIndex];
      }
    }
    
    smoothLumHist[i] = total / sum;
  }
}

References FILTER_SIZE, lumHist, and smoothLumHist.

Referenced by EdgeDetect().

Member Data Documentation

clusterPeaks

int EdgeDetect::clusterPeaks[256]

private

Definition at line 100 of file edgeDetect.h.

Referenced by allocateAndInitObjects(), computeClusterStatistics(), findPixelClusters(), and getPeaks().

clusters

PixelCluster* EdgeDetect::clusters

private

Definition at line 113 of file edgeDetect.h.

Referenced by computeClusterStatistics(), computeClusterThresholds(), constructEdgeImage(), deallocateObjects(), findPixelClusters(), getClusterMap(), and getClusters().

edgeMagMap

float* EdgeDetect::edgeMagMap

private

Definition at line 106 of file edgeDetect.h.

Referenced by allocateAndInitObjects(), computeClusterStatistics(), computeEdgeMagAndGSLCmaps(), constructEdgeImage(), and deallocateObjects().

GSLCmap

int* EdgeDetect::GSLCmap

private

Definition at line 109 of file edgeDetect.h.

Referenced by allocateAndInitObjects(), computeEdgeMagAndGSLCmaps(), constructEdgeImage(), and deallocateObjects().

image

QImage* EdgeDetect::image

private

Definition at line 93 of file edgeDetect.h.

Referenced by allocateAndInitObjects(), computeClusterStatistics(), computeEdgeMagAndGSLCmaps(), constructEdgeImage(), EdgeDetect(), fillLumMapAndLumHistogram(), getClusterMap(), getEdgeImage(), and pixelLum().

lumHist

int EdgeDetect::lumHist[256]

private

luminosity and smooth luminosity histograms

Definition at line 96 of file edgeDetect.h.

Referenced by allocateAndInitObjects(), fillLumMapAndLumHistogram(), and smoothLumHistogram().

lumMap

int* EdgeDetect::lumMap

private

Definition at line 103 of file edgeDetect.h.

Referenced by allocateAndInitObjects(), computeClusterStatistics(), constructEdgeImage(), deallocateObjects(), fillLumMapAndLumHistogram(), getClusterMap(), and pixelLum().

LUT

LUTentry EdgeDetect::LUT[256]

private

Definition at line 90 of file edgeDetect.h.

Referenced by constructEdgeImage(), and constructGSLClut().

maxClusterSize

int EdgeDetect::maxClusterSize

private

Definition at line 116 of file edgeDetect.h.

Referenced by computeClusterStatistics().

minClusterSize

int EdgeDetect::minClusterSize

private

Definition at line 116 of file edgeDetect.h.

Referenced by computeClusterStatistics().

numClusters

int EdgeDetect::numClusters

private

Definition at line 112 of file edgeDetect.h.

Referenced by computeClusterStatistics(), computeClusterThresholds(), constructEdgeImage(), findPixelClusters(), getClusterMap(), and getNumClusters().

smoothLumHist

int EdgeDetect::smoothLumHist[256]

private

Definition at line 97 of file edgeDetect.h.

Referenced by allocateAndInitObjects(), findPixelClusters(), getSmoothHist(), and smoothLumHistogram().

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The documentation for this class was generated from the following files:

• edgeDetect.h
• edgeDetect.cpp