/// <summary>
/// show the histogram in an image object
/// </summary>
/// <param name="img"></param>
public void Show(classimage img)
{
int prev_x = 0, prev_y = 0;
img.clear();
for (int i = 0; i < no_of_levels; i++)
{
int x = i * img.width / no_of_levels;
int y = img.height - (level[i] * (img.height * 8 / 10) / maxval);
if (i > 0)
{
img.drawLine(prev_x, prev_y, x, y, 0, 255, 0, 0);
}
prev_x = x;
prev_y = y;
}
}
//---------------------------------------------------------------------------------------------
// Detect edges within the given binary image
//---------------------------------------------------------------------------------------------
public void detectEdgesBinary(classimage sourceimage, int tx, int ty, int bx, int by)
{
int x,y,xx,yy,c;
bool found;
if (tx<1) tx=1;
if (ty<1) ty=1;
if (bx>=width-1) bx=width-2;
if (by>=height-1) by=height-2;
for (y=ty;y<by;y++)
{
for (x=tx;x<bx;x++)
{
found=false;
if (sourceimage.image[x,y,0]>0)
{
xx=x-1;
while ((!found) && (xx<=x+1))
{
yy=y-1;
while ((!found) && (yy<=y+1))
{
if (!((xx==x) && (yy==y)))
{
if (sourceimage.image[xx,yy,0] != sourceimage.image[x,y,0]) found=true;
}
yy++;
}
xx++;
}
}
for (c=0;c<3;c++)
if (found) image[x,y,c]=255; else image[x,y,c]=0;
}
}
}
//------------------------------------------------------------------------------------------------------------------------
//update from another image
//------------------------------------------------------------------------------------------------------------------------
public void updateFromImage(classimage img)
{
int x, y, xx, yy;
for (x = 0; x < width; x++)
{
xx = (x * img.width) / width;
for (y = 0; y < height; y++)
{
yy = (y * img.height) / height;
image[x, y] = img.image[xx, yy, 0];
}
}
}
public void smoothImage(classimage sourceimg, int regionSize, int multiplier)
{
const int max_mask_size = 10;
int x,y,xx,yy,c;
int dx,dy,dist,pixels;
int[] tot = new int[3];
int halfRegion = regionSize/2;
int[] mask = new int[max_mask_size*max_mask_size];
int[,] distLookup= new int[max_mask_size,max_mask_size];
float angle,fraction;
int i1;
if (halfRegion<max_mask_size)
{
//create the mask
for (dist=0;dist<=halfRegion;dist++)
{
angle = (dist*3.1415927f) / halfRegion;
fraction = ((float)Math.Cos(angle) + 1)/2.0f;
fraction *= 1.3f;
fraction -= 0.3f;
mask[dist] = (int)(255 * fraction);
}
for (xx=0;xx<regionSize;xx++)
{
dx = xx - halfRegion;
for (yy=0;yy<regionSize;yy++)
{
dy = yy - halfRegion;
distLookup[xx,yy] = (int)Math.Sqrt((dx*dx)+(dy*dy));
}
}
//filter using the mask
for (x=0;x<width;x++)
for (y=0;y<height;y++)
{
pixels=0;
for (c=0;c<3;c++) tot[c]=0;
dx=0;
for (xx=x-halfRegion;xx<x+halfRegion;xx++)
{
if ((xx>=0) && (xx<width))
{
dy=0;
for (yy=y-halfRegion;yy<y+halfRegion;yy++)
{
if ((yy>=0) && (yy<height))
{
//dy = yy - (y-halfRegion);
dist = distLookup[dx,dy];
for (c=0;c<3;c++)
{
i1 = sourceimg.image[xx,yy,c];
tot[c] += (i1 * mask[dist]);
}
pixels++;
}
dy++;
}
}
dx++;
}
for (c=0;c<3;c++)
{
tot[c] /= (pixels*255);
if (tot[c]<0) tot[c]=0;
tot[c] = (tot[c] * multiplier)/10;
if (tot[c]>255) tot[c]=255;
image[x,y,c] = (Byte)tot[c];
}
}
}
}
//------------------------------------------------------------------------------------------------------------------------
//rotate the image
//------------------------------------------------------------------------------------------------------------------------
public void Rotate(classimage sourceImage, int angle)
{
float rot,hyp,ang;
int cx,cy,x,y,c,xx,yy;
if (rotationLookup==null)
{
imageRotation = angle;
//create the lookup table
rotationLookup = new int [width,height,2];
for (x=0;x<width;x++)
{
for (y=0;y<height;y++)
{
rotationLookup[x,y,0]=999;
rotationLookup[x,y,1]=999;
}
}
//rotate the image using floating point maths
rot = (float)((float)angle / 180.0 * 3.1415927);
cx = sourceImage.width/2;
cy = sourceImage.height/2;
for (x=0;x<sourceImage.width;x++)
{
for (y=0;y<sourceImage.height;y++)
{
hyp = (float)Math.Sqrt(((x-cx)*(x-cx))+((y-cy)*(y-cy)));
if (hyp>0)
{
ang = (float)Math.Acos((y-cy)/hyp);
if (x-cx>0) ang = (float)((2*3.1415927)-ang);
xx = cx + (int)(hyp * Math.Sin(rot-ang));
yy = cy + (int)(hyp * Math.Cos(rot-ang));
}
else
{
xx=x;
yy=y;
}
if ((xx>=0) && (xx<width) && (yy>=0) && (yy<height))
{
rotationLookup[x,y,0]=xx;
rotationLookup[x,y,1]=yy;
}
else
{
rotationLookup[x,y,0] = 0;
rotationLookup[x,y,1] = 0;
}
}
}
}
else
{
//just use the lookup table as a quick method to rotate the image
for (x=0;x<width;x++)
{
for (y=0;y<height;y++)
{
if (rotationLookup[x,y,0]!=999)
{
xx = rotationLookup[x,y,0];
yy = rotationLookup[x,y,1];
for (c=0;c<3;c++)
image[x,y,c] = sourceImage.image[xx,yy,c];
}
}
}
//recalculate the integral image
updateIntegralImageMono();
}
}
//---------------------------------------------------------------------------------------------
// simple stereo using a subtracted binary image
//---------------------------------------------------------------------------------------------
public void stereoSimple(classimage binaryimage, int max_disparity, classimage edges_left, classimage edges_right)
{
int x,y,start_x=0,cx,c,xx,max_width,x2,x3;
int state,disparity,p;
bool found_left,found_right;
const int STATE_SEARCHING=0;
const int STATE_FOUND=1;
max_width=width/2;
clear();
for (y=1;y<height-1;y++)
{
state=STATE_SEARCHING;
for (x=1;x<width-1;x++)
{
if ((binaryimage.image[x,y,0] > 0) && (state==STATE_SEARCHING))
{
state=STATE_FOUND;
start_x=x;
}
if ((binaryimage.image[x+1,y,0] == 0) && (binaryimage.image[x,y,0] == 0) && (state==STATE_FOUND))
{
state=STATE_SEARCHING;
cx = start_x + (((x-1)-start_x)/2);
disparity = cx - start_x;
p = (disparity*255)/max_disparity;
x2=x+((disparity*130)/100)+1;
found_right=false;
while ((!found_right) && (x2-x<max_width) && (x2<width))
{
if (edges_right.image[x2,y,0] > 0)
found_right=true;
else
x2++;
}
x3=x+((disparity*130)/100)+1;
found_left=false;
while ((!found_left) && (x3-x<max_width) && (x3<width))
{
if (edges_left.image[x3,y,0] > 0)
found_left=true;
else
x3++;
}
x3= (x3 - x)+start_x+disparity;
if ((found_left) || (found_right))
{
if (x3>x2) x2=x3;
if (x2>=width) x2=width-1;
for (xx=start_x;xx<x2;xx++) for (c=0;c<3;c++) image[xx,y,c]=(Byte)p;
}
}
}
}
}
//---------------------------------------------------------------------------------------------
// produces a smoothed depth map using shape from shading
//---------------------------------------------------------------------------------------------
public void smoothDepthMap(classimage image_raw, classimage disparity)
{
int x,y,c,start_x,min_width,i;
int localintensity,min_intensity,max_intensity;
int start_depth,end_depth,min_depth,max_depth,depth,grad_x;
int deviation,max_deviation,min_deviation,depth_grad_x;
int min_grad_x,max_grad_x,min_depth_grad_x,max_depth_grad_x;
clear();
min_width=width/30;
if (min_width<2) min_width=2;
for (y=1;y<height;y++)
{
start_x=-1;
for (x=0;x<width;x++)
{
if (disparity.image[x,y,0]>0)
{
if (start_x==-1) start_x=x;
}
else
{
if ((start_x>-1) && (x-start_x-1>min_width))
{
//get the depth range
start_depth = (disparity.image[start_x,y,0] + disparity.image[start_x+1,y,0])/2;
end_depth = (disparity.image[x-1,y,0] + disparity.image[x-2,y,0])/2;
min_intensity=255;
max_intensity=0;
min_depth=255;
max_depth=0;
max_deviation=-999;
min_deviation=999;
max_grad_x=-999;
min_grad_x=999;
max_depth_grad_x=-999;
min_depth_grad_x=999;
grad_x=0;
deviation=0;
for (i=start_x;i<x;i++)
{
//get the local intensity of the raw image at this point
localintensity=0;
for (c=0;c<3;c++)
{
localintensity += image_raw.image[i,y,c];
localintensity += image_raw.image[i,y-1,c];
}
localintensity /= 6;
//calculate gradient
if (i>start_x)
{
grad_x = localintensity - (int)(disparity.image[i-1,y,1]);
if (grad_x>max_grad_x) max_grad_x=grad_x;
if (grad_x<min_grad_x) min_grad_x=grad_x;
depth_grad_x = (int)(disparity.image[i,y,0]) - (int)(disparity.image[i-1,y,0]);
if (depth_grad_x>max_grad_x) max_depth_grad_x=depth_grad_x;
if (depth_grad_x<min_grad_x) min_depth_grad_x=depth_grad_x;
}
deviation += grad_x;
if (deviation>max_deviation) max_deviation = deviation;
if (deviation<min_deviation) min_deviation = deviation;
//temporarily store the value in the disparity map for later use
disparity.image[i,y,1]=(Byte)localintensity;
//get the depth range
depth = disparity.image[i,y,0];
if (depth>max_depth) max_depth=depth;
if (depth<min_depth) min_depth=depth;
//get the intensity range
if (localintensity>max_intensity) max_intensity=localintensity;
if (localintensity<min_intensity) min_intensity=localintensity;
}
deviation=0;
for (i=start_x;i<x;i++)
{
if (max_deviation>min_deviation)
{
//curved surface
if (i>start_x)
{
//calculate gradient
grad_x = (int)(disparity.image[i,y,1]) - (int)(disparity.image[i-1,y,1]);
deviation += grad_x;
grad_x = min_depth_grad_x + (((grad_x - min_grad_x)*(max_depth_grad_x-min_depth_grad_x))/(max_grad_x-min_grad_x));
grad_x = ((grad_x - min_grad_x)*150)/(max_grad_x-min_grad_x);
//this probably isn't right
depth_grad_x = (int)(disparity.image[i,y,0]) - (int)(disparity.image[i-1,y,0]);
depth = (int)(disparity.image[i-1,y,0]) + (((depth_grad_x*8) + (grad_x*2))/10);
}
else depth=start_depth;
}
else
{
//flat surface
depth = start_depth + (((i-start_x)*(end_depth-start_depth))/(x-start_x-1));
}
if (depth<0) depth=0;
if (depth>255) depth=255;
for (c=0;c<3;c++) image[i,y,c]=(Byte)depth;
}
}
start_x=-1;
}
}
}
}
//---------------------------------------------------------------------------------------------
//finds maximal points within the image - used for attention system
//---------------------------------------------------------------------------------------------
public void findMaximalPoints(classimage source_img, int search_radius)
{
int i,j,k,x,y,tx,ty,bx,by,r,c,xx,yy,max,mx=0,my=0,dx,dy,tot_x,tot_y,hits,mindist,dist;
int prev_NoOfAttentionPoints,flow_radius,winner;
//store the previous attention points, so that they can be compared against
//new ones to estimate direction of movement
prev_NoOfAttentionPoints = NoOfAttentionPoints;
for (i=0;i<prev_NoOfAttentionPoints;i++)
for (c=0;c<3;c++) prev_attentionPoint[i,c] = attentionPoint[i,c];
r = search_radius;
//clear the image
for (x=0;x<width;x++)
for (y=0;y<height;y++)
for (c=0;c<3;c++) image[x,y,c]=0;
NoOfAttentionPoints=0;
for (x=r;x<width-r;x+=r/2)
{
for (y=r;y<height-r;y+=r/2)
{
tx = x-r;
ty = y-r;
bx = x+r;
by = y+r;
max=0;
for (xx=tx;xx<bx;xx++)
{
for (yy=ty;yy<by;yy++)
{
if (source_img.image[xx,yy,0]>max)
{
mx = xx;
my = yy;
max = source_img.image[xx,yy,0];
}
}
}
if (max>0)
{
if (NoOfAttentionPoints<100)
{
attentionPoint[NoOfAttentionPoints,0] = mx;
attentionPoint[NoOfAttentionPoints,1] = my;
attentionPoint[NoOfAttentionPoints,2] = 1;
NoOfAttentionPoints++;
}
}
}
}
mindist=width/6;
for (i=0;i<NoOfAttentionPoints;i++)
{
if (attentionPoint[i,2]==1)
{
mx = attentionPoint[i,0];
my = attentionPoint[i,1];
hits=1;
tot_x = mx;
tot_y = my;
for (j=0;j<NoOfAttentionPoints;j++)
{
if ((i!=j) && (attentionPoint[j,2]==1))
{
x = attentionPoint[j,0];
y = attentionPoint[j,1];
dx = mx-x;
dy = my-y;
dist = (int)Math.Sqrt((dx*dx)+(dy*dy));
if (dist<mindist)
{
attentionPoint[j,2]=0;
tot_x += x;
tot_y += y;
hits++;
}
}
}
mx = tot_x / hits;
my = tot_y / hits;
attentionPoint[i,0] = mx;
attentionPoint[i,1] = my;
for (c=0;c<3;c++) image[mx,my,c]=255;
}
}
flow_radius=search_radius;
j=0;
for (i=0;i<NoOfAttentionPoints;i++)
{
if (attentionPoint[i,2]==1)
{
for (c=0;c<3;c++) attentionPoint[j,c] = attentionPoint[i,c];
//find the most similar previous point
mindist=9999;
winner=-1;
for (k=0;k<prev_NoOfAttentionPoints;k++)
{
dx = Math.Abs(attentionPoint[j,0] - prev_attentionPoint[k,0]);
dy = Math.Abs(attentionPoint[j,1] - prev_attentionPoint[k,1]);
if ((dx<flow_radius) && (dy<flow_radius))
{
dist = dx + dy;
if (dist<mindist)
{
mindist=dist;
winner=k;
}
}
}
//update the direction of movement of the attention point
if (winner>-1)
{
dx = attentionPoint[j,0] - prev_attentionPoint[winner,0];
dy = attentionPoint[j,1] - prev_attentionPoint[winner,1];
attentionPoint[j,3] = dx;
attentionPoint[j,4] = dy;
}
else
{
attentionPoint[j,3] = 0;
attentionPoint[j,4] = 0;
}
j++;
}
}
NoOfAttentionPoints=j;
}
//---------------------------------------------------------------------------------------------
//returns the attention point with the maximum response in the given source image
//---------------------------------------------------------------------------------------------
public int getMaxAttentionPoint(classimage source_img, int search_radius)
{
int i,x,y,c,winner,cx=0,cy=0,mx,my;
long max,pixels,p;
max=0;
winner=-1;
for (i=0;i<NoOfAttentionPoints;i++)
{
mx = attentionPoint[i,0];
my = attentionPoint[i,1];
source_img.CG(mx-search_radius,my-search_radius,mx+search_radius,my+search_radius,0,0,0,ref cx,ref cy,0);
attentionPoint[i,0] = cx;
attentionPoint[i,1] = cy;
p=0;
pixels=1;
for (x=cx-search_radius;x<cx+search_radius;x++)
{
if ((x>=0) && (x<width))
{
for (y=cy-search_radius;y<cy+search_radius;y++)
{
if ((y>=0) && (y<height))
{
for (c=0;c<3;c++) p += source_img.image[x,y,c];
}
pixels++;
}
}
}
if (p>max)
{
max = p;
winner=i;
}
}
attentionWinner=winner;
return(winner);
}
//---------------------------------------------------------------------------------------------
//enclosure image
//---------------------------------------------------------------------------------------------
public void enclosure(classimage source_img, int search_radius, int threshold)
{
int x,y,tx,ty,bx,by,r;
int[] p = new int[4];
int c,value,pixels,minval;
source_img.updateIntegralImageMono();
r = search_radius;
for (x=0;x<width;x++)
{
for (y=0;y<height;y++)
{
tx = x-r;
ty = y-r;
bx = x+r;
by = y+r;
if (tx<0) tx=0;
if (bx>=width) bx=width-1;
if (ty<0) ty=0;
if (by>=height) by=height-1;
pixels = (bx-tx)*(by-ty);
minval = pixels*threshold/100;
value=0;
p[0] = (int)source_img.getIntegral(tx,ty,x,y);
if (p[0]>minval)
{
p[1] = (int)source_img.getIntegral(x,ty,bx,y);
if (p[1]>minval)
{
p[2] = (int)source_img.getIntegral(tx,y,x,by);
if (p[2]>minval)
{
p[3] = (int)source_img.getIntegral(x,y,bx,by);
if (p[3]>minval)
{
value = (Math.Abs(p[0]-p[1]) + Math.Abs(p[2]-p[3]) + Math.Abs(p[0]-p[3]) + Math.Abs(p[0]-p[2]))/(pixels/2);
value = value*value;
if (value>255) value=255;
value=255-value;
}
}
}
}
for (c=0;c<3;c++) image[x,y,c]=(Byte)value;
}
}
}
//---------------------------------------------------------------------------------------------
//estimate the horizontal and vertical optical flow with a crude subtraction method
//---------------------------------------------------------------------------------------------
public void estimateMovement(classimage prev_img, int search_radius, ref int horizontal, ref int vertical)
{
int horizontal_search,vertical_search,half_horizontal,half_vertical;
int x,y,xx,yy,c,w,h,diff,min_diff;
const int increment=3;
horizontal_search = (width*search_radius)/100;
half_horizontal = horizontal_search/2;
vertical_search = (height*search_radius)/100;
half_vertical = vertical_search/2;
w = width-horizontal_search-1;
h = height-vertical_search-1;
horizontal=0;
vertical=0;
min_diff=-1;
for (x=0;x<horizontal_search;x+=increment)
{
for (y=0;y<vertical_search;y+=increment)
{
diff=0;
for (xx=0;xx<w;xx+=increment)
{
for (yy=0;yy<h;yy+=increment)
{
for (c=0;c<3;c++)
diff += Math.Abs(image[x+xx,y+yy,c] - prev_img.image[half_horizontal+xx,half_vertical+yy,c]);
}
}
if ((min_diff==-1) || (diff<min_diff))
{
min_diff=diff;
horizontal = x - half_horizontal;
vertical = y - half_vertical;
}
}
}
if (Math.Abs(horizontal)<2) horizontal=0;
if (Math.Abs(vertical)<2) vertical=0;
}
//---------------------------------------------------------------------------------------------
//find maximal gravity wells in the image
//---------------------------------------------------------------------------------------------
public void findGravityWells(classimage source_img, int search_radius)
{
int x,y,c,border,cx=0,cy=0,tx,ty,bx,by,inhibit_radius;
//bool found;
border = search_radius/2;
inhibit_radius = border/2;
for (x=0;x<width;x++)
for (y=0;y<height;y++)
for (c=0;c<3;c++)
image[x,y,c]=0;
for (x=0;x<width;x++)
for (y=0;y<height;y++)
{
tx = x-border;
ty = y-border;
bx = x+border;
by = y+border;
source_img.CG(tx,ty,bx,by,0,0,0,ref cx,ref cy,0);
if (!((cx==0) && (cy==0)))
{
//are there any pixels within the inhibition region?
//found=false;
if ((cx>tx+inhibit_radius) && (cx<bx-inhibit_radius))
{
if ((cy>ty+inhibit_radius) && (cy<by-inhibit_radius))
{
for (c=0;c<3;c++) image[cx,cy,c]=255;
}
}
}
}
}
//---------------------------------------------------------------------------------------------
// detects circle shapes and highlights the centre points
// requires a binary blob image and binary edges image
//---------------------------------------------------------------------------------------------
public void detectCircles(classimage binaryimage, classimage edgesimage, int radius)
{
int x,y,xx,yy,c,hits,maxhits;
int innerRadius=radius/3;
if (innerRadius<1) innerRadius=1;
clear();
for (x=radius;x<width-radius;x++)
{
for (y=radius;y<height-radius;y++)
{
if (binaryimage.image[x,y,0]>0)
{
//check that there is nothing in the middle
hits=0;
maxhits=2;
xx=x-innerRadius;
while ((hits<maxhits) && (xx<=x+innerRadius))
{
yy=y-innerRadius;
while ((hits<maxhits) && (yy<=y+innerRadius))
{
if (edgesimage.image[xx,yy,0]>0) hits++;
yy++;
}
xx++;
}
if (hits<maxhits)
{
for (c=0;c<3;c++) image[x,y,c]=255;
}
}
}
}
}
//---------------------------------------------------------------------------------------------
// skeletonises the given image
//---------------------------------------------------------------------------------------------
public void skeletonize(classimage binaryimage)
{
int x,y,start_x=0,start_y=0,cx,cy,c;
int state;
const int STATE_SEARCHING=0;
const int STATE_FOUND=1;
clear();
for (y=1;y<height-1;y++)
{
state=STATE_SEARCHING;
for (x=1;x<width-1;x++)
{
if ((binaryimage.image[x,y,0] > 0) && (state==STATE_SEARCHING))
{
state=STATE_FOUND;
start_x=x;
}
if ((binaryimage.image[x+1,y,0] == 0) && (binaryimage.image[x,y,0] == 0) && (state==STATE_FOUND))
{
state=STATE_SEARCHING;
cx = start_x + (((x-1)-start_x)/2);
if (binaryimage.image[cx,y,0]>0) for (c=0;c<3;c++) image[cx,y,c]=255;
}
}
state=STATE_SEARCHING;
for (x=width-2;x>0;x--)
{
if ((binaryimage.image[x,y,0] > 0) && (state==STATE_SEARCHING))
{
state=STATE_FOUND;
start_x=x;
}
if ((binaryimage.image[x+1,y,0] == 0) && (binaryimage.image[x,y,0] == 0) && (state==STATE_FOUND))
{
state=STATE_SEARCHING;
cx = start_x + (((x-1)-start_x)/2);
if (binaryimage.image[cx,y,0]>0) for (c=0;c<3;c++) image[cx,y,c]=255;
}
}
}
for (x=1;x<width-1;x++)
{
state=STATE_SEARCHING;
for (y=1;y<height-1;y++)
{
if ((binaryimage.image[x,y,0] > 0) && (state==STATE_SEARCHING))
{
state=STATE_FOUND;
start_y=y;
}
if ((binaryimage.image[x,y+1,0] == 0) && (binaryimage.image[x,y,0] == 0) && (state==STATE_FOUND))
{
state=STATE_SEARCHING;
cy = start_y + (((y-1)-start_y)/2);
if (binaryimage.image[x,cy,0]>0)
{
for (c=0;c<3;c++) image[x,cy,c]=255;
}
}
}
}
}
//---------------------------------------------------------------------------------------------
// colour constancy
//---------------------------------------------------------------------------------------------
public void colourConstancy(classimage sourceimg, int multiplier)
{
int x,y,c,p;
int[] col= new int[3];
for (x=0;x<width;x++)
for (y=0;y<height;y++)
{
for (c=0;c<3;c++) col[c] = sourceimg.image[x,y,c];
p = Math.Abs(col[0]-col[1]);
p += Math.Abs(col[0]-col[2]);
p += Math.Abs(col[1]-col[2]);
p = (p*multiplier)/3;
if (p>254) p=254;
p = 255-p;
for (c=0;c<3;c++) image[x,y,c]=(Byte)p;
}
}
//---------------------------------------------------------------------------------------------
//produces an image with the given attention point at its centre
//---------------------------------------------------------------------------------------------
public void attentionCentredImage(int attentionIndex, classimage source_img)
{
int x,y,c,xx,yy,dx,dy,w,h;
if (NoOfAttentionPoints>attentionIndex)
{
w = width/6;
h = height/6;
//clear the image
for (x=0;x<width;x++)
for (y=0;y<height;y++)
for (c=0;c<3;c++) image[x,y,c]=0;
if (attentionIndex>=0)
{
//select the given attention point
cx = attentionPoint[attentionIndex,0];
cy = attentionPoint[attentionIndex,1];
}
else
{
//randomly shift the attention point
cx += (int)(((Rnd()-0.5f)*width)/5);
cy += (int)(((Rnd()-0.5f)*height)/5);
if (cx<0) cx=0;
if (cx>=width) cx=width-1;
if (cy<0) cy=0;
if (cy>=height) cy=height-1;
}
clear();
for (x=0;x<width;x++)
{
dx = x - cx;
xx = (width/2) + dx;
//if (xx>-1)
{
for (y=0;y<height;y++)
{
dy = y - cy;
yy = (height/2) + dy;
//if (yy>-1)
{
if ((xx>w) && (xx<width-w) && (yy>h) && (yy<height-h))
for (c=0;c<3;c++) image[xx,yy,c] = source_img.image[x,y,c];
}
}
}
}
}
}
//---------------------------------------------------------------------------------------------
// magnetize the image
//---------------------------------------------------------------------------------------------
public void magnetize(classimage left_image, classimage right_image, classimage temp_image, int radius)
{
int x, y, xx, x1=0, x2=0, diff1, diff2, c;
bool found;
radius=width/4;
clear();
for (y=1;y<height-1;y++)
{
for (x=1;x<width-1;x++)
{
//min_distance=0;
xx=x+0;
found=false;
while ((!found) && (xx>0) && (x-xx<radius))
{
if (xx<width)
if (left_image.image[xx,y,0]>0) { found=true; x1=xx; }
xx--;
}
if (found)
{
xx=x-0;
found=false;
while ((!found) && (xx<width) && (xx-x<radius))
{
if (xx>0)
if (right_image.image[xx,y,0]>0) { found=true; x2=xx; }
xx++;
}
if (found)
{
//balance
diff1 = (x2-x)-(x-x1);
if (diff1<0) diff1=-diff1;
diff1 = ((radius-diff1)*255)/radius;
//distance
diff2 = x2-x1;
if (diff2<0) diff2=-diff1;
diff2 = (diff2*diff2)/50;
//diff2 = (radius-diff2);
if (diff2>255) diff2=255;
if (diff2<0) diff2=0;
diff2 = (diff2*255)/radius;
if (diff2>255) diff2=255;
diff1 = ((diff1*5) + (diff2*5))/10;
//if (diff<254) diff=0;
for (c=0;c<3;c++) image[x,y,c]=(Byte)diff1;
}
}
}
}
}
//---------------------------------------------------------------------------------------------
//returns the average difference for the two images
//---------------------------------------------------------------------------------------------
public int averageDifference(classimage otherimage, int multiplier)
{
int x,y,c,diff,p1,p2,hits;
int av=0;
const int minval=0;
hits=0;
for (x=0;x<width;x++)
{
for (y=0;y<height;y++)
{
p1=0;
p2=0;
for (c=0;c<3;c++)
{
p1 += image[x,y,c];
p2 += otherimage.image[x,y,c];
}
diff = Math.Abs((p1 * multiplier)-(p2 * multiplier));
if (diff>minval)
{
//if (diff>255) diff=255;
av+=diff;
hits++;
}
}
}
if (hits>0) av /= hits; //else av=255;
return(av);
}
//---------------------------------------------------------------------------------------------
// stereo matching
//---------------------------------------------------------------------------------------------
public void stereoMatch(classimage left_image_raw, classimage right_image_raw, classimage left_image, classimage left_image2, classimage left_image3, classimage left_image4, classimage right_image, classimage right_image2, classimage right_image3, classimage right_image4, int max_disparity, int weight_distance, int weight_colour, int weight_sequence, int match_threshold, int blobsize, int offset_x, int offset_y, int[,,] disparity_map)
{
int x,y,x2,xx,yy,c,dx,score,diff,i,xx2,yy2;
int max_score,val1=0,val2=0,disp=0,prev_disp,prev_disp2;
int[,] probableMatches = new int[20,2];
int prob_index,pp,prob_hits,pp2,pp3,max_rays;
long av_score,av_hits,quality_threshold,overall_max_score,max_diff;
classimage image1;
classimage image2;
//the maximum number of rays per correlation search
max_rays=3;
av_score=0;
av_hits=0;
overall_max_score=0;
image1 = left_image;
image2 = right_image;
for (y=0;y<height-1-offset_y;y++)
{
prev_disp=0;
prev_disp2=0;
for (x=blobsize;x<width-blobsize-1;x++)
{
if (image1.image[x+offset_x,y+offset_y,0]>0)
{
max_score=match_threshold*100;
max_diff=999999;
//clear probabilities
prob_hits=0;
prob_index=0;
for (pp=0;pp<20;pp++) probableMatches[pp,0]=-1;
for (x2=x-max_disparity+offset_x;x2<x+max_disparity+offset_x;x2+=3)
{
if ((x2>0) && (x2<width))
{
score=0;
if (image2.image[x2,y+offset_y,0]>0)
{
dx = x2-x;
if (dx<0) dx=-dx;
diff = (max_disparity-dx);
score = (diff*diff*weight_distance)/max_disparity;
//compare single points
diff=0;
for (c=0;c<3;c++)
{
//colour matching
for (xx2=-1;xx2<1;xx2++)
for (yy2=-1;yy2<1;yy2++)
{
if ((x+xx2>blobsize) && (x2+xx2>blobsize) && (y+yy2>0) && (y+yy2<height)) diff += Math.Abs(left_image_raw.image[x-blobsize+xx2,y+yy2,c] - right_image_raw.image[x2-blobsize+xx2,y+yy2,c]); else diff+=100;
if ((x+xx2<width-blobsize) && (x2+xx2<width-blobsize) && (y+yy2>0) && (y+yy2<height)) diff += Math.Abs(left_image_raw.image[x+blobsize+xx2,y+yy2,c] - right_image_raw.image[x2+blobsize+xx2,y+yy2,c]); else diff+=100;
if ((x+xx2>0) && (x2+xx2>0) && (y+yy2>0) && (x+xx2<width) && (y+yy2<height))
diff += Math.Abs(left_image_raw.image[x+xx2,y+yy2,c] - right_image_raw.image[x2+xx2,y+yy2,c]);
else diff+=50;
}
//orientation matching
diff += Math.Abs(image1.image[x+offset_x,y+offset_y,c] - image2.image[x2,y+offset_y,c]);
diff += Math.Abs(left_image2.image[x+offset_x,y+offset_y,c] -right_image2.image[x2,y+offset_y,c]);
if ((y>blobsize+offset_y) && (diff<max_diff))
{
diff += Math.Abs(image1.image[x+offset_x,y-blobsize+offset_y,c] - image2.image[x2,y-blobsize+offset_y,c]);
diff += Math.Abs(left_image2.image[x+offset_x,y-blobsize+offset_y,c] -right_image2.image[x2,y-blobsize+offset_y,c]);
} else val1+=50;
if ((y<height-blobsize-offset_y) && (diff<max_diff))
{
diff += Math.Abs(image1.image[x+offset_x,y+blobsize+offset_y,c] - image2.image[x2,y+blobsize+offset_y,c]);
diff += Math.Abs(left_image2.image[x+offset_x,y+blobsize+offset_y,c] -right_image2.image[x2,y+blobsize+offset_y,c]);
} else val1+=50;
if ((x2>=blobsize) && (y<height-offset_y) && (diff<max_diff))
{
diff += Math.Abs(image1.image[x+offset_x-blobsize,y+offset_y,c] - image2.image[x2-blobsize,y+offset_y,c]);
diff += Math.Abs(left_image2.image[x+offset_x-blobsize,y+offset_y,c] -right_image2.image[x2-blobsize,y+offset_y,c]);
} else val1+=50;
if ((x+offset_x>=blobsize+blobsize) && (x2>=blobsize+blobsize) && (y<height-offset_y) && (diff<max_diff))
{
diff += Math.Abs(image1.image[x+offset_x-blobsize-blobsize,y+offset_y,c] - image2.image[x2-blobsize-blobsize,y+offset_y,c]);
diff += Math.Abs(left_image2.image[x+offset_x-blobsize-blobsize,y+offset_y,c] -right_image2.image[x2-blobsize-blobsize,y+offset_y,c]);
} else val1+=50;
if ((x>=blobsize*3+offset_x) && (x2>=blobsize*3) && (y<height-offset_y) && (diff<max_diff))
{
diff += Math.Abs(image1.image[x+offset_x-blobsize-blobsize-blobsize,y+offset_y,c] - image2.image[x2-blobsize-blobsize-blobsize,y+offset_y,c]);
diff += Math.Abs(left_image2.image[x+offset_x-blobsize-blobsize-blobsize,y+offset_y,c] -right_image2.image[x2-blobsize-blobsize-blobsize,y+offset_y,c]);
} else val1+=50;
if ((x<width-offset_x-blobsize) && (x2<width-blobsize) && (y<height-offset_y) && (diff<max_diff))
{
diff += Math.Abs(image1.image[x+offset_x+blobsize,y+offset_y,c] - image2.image[x2+blobsize,y+offset_y,c]);
diff += Math.Abs(left_image2.image[x+offset_x+blobsize,y+offset_y,c] -right_image2.image[x2+blobsize,y+offset_y,c]);
} else val1+=50;
if ((x<width-blobsize-blobsize-offset_x) && (x2<width-blobsize-blobsize) && (y<height-offset_y) && (diff<max_diff))
{
diff += Math.Abs(image1.image[x+offset_x+blobsize+blobsize,y+offset_y,c] - image2.image[x2+blobsize+blobsize,y+offset_y,c]);
diff += Math.Abs(left_image2.image[x+offset_x+blobsize+blobsize,y+offset_y,c] -right_image2.image[x2+blobsize+blobsize,y+offset_y,c]);
} else val1+=50;
if ((x<width-blobsize-blobsize-blobsize-offset_x) && (x2<width-blobsize-blobsize-blobsize) && (y<height-offset_y) && (diff<max_diff))
{
diff += Math.Abs(image1.image[x+offset_x+blobsize+blobsize+blobsize,y+offset_y,c] - image2.image[x2+blobsize+blobsize+blobsize,y+offset_y,c]);
diff += Math.Abs(left_image2.image[x+offset_x+blobsize+blobsize+blobsize,y+offset_y,c] -right_image2.image[x2+blobsize+blobsize+blobsize,y+offset_y,c]);
} else val1+=50;
}
if (diff<max_diff)
{
disp = (Math.Abs(x2-x)*255)/max_disparity;
if (prev_disp<prev_disp2)
val1 = Math.Abs(disp - prev_disp);
else
val1 = Math.Abs(disp - prev_disp2);
prev_disp2=prev_disp;
prev_disp=disp;
val2 = diff + (val1*val1/4);
score += (5000-val2)*weight_colour;
}
if (score>=max_score)
{
max_score = score;
max_diff=diff;
if (score>overall_max_score) overall_max_score=score;
probableMatches[prob_index,0]=disp;
probableMatches[prob_index,1]=score;
prob_index++;
prob_hits++;
if (prob_index>=20) prob_index=0;
}
}
}
}
//select the top probabilities
pp=prob_index-1;
pp2=prob_hits;
pp3=0;
while ((pp>-1) && (pp2>0) && (pp2>prob_hits-max_rays))
{
score=probableMatches[pp,1];
av_score += score;
av_hits++;
for (xx=x;xx<x+blobsize;xx++)
for (yy=y;yy<y+blobsize;yy++)
{
//update the disparity map
disparity_map[xx,yy,1+(pp3*2)]=probableMatches[pp,0];
disparity_map[xx,yy,2+(pp3*2)]=score;
}
pp3++;
pp2--;
pp--;
if (pp<0) pp = 19;
}
for (xx=x;xx<x+blobsize;xx++)
for (yy=y;yy<y+blobsize;yy++)
disparity_map[xx,yy,0]=pp3;
}
}
}
//calculate average matching score
if (av_hits>0) av_score /= av_hits;
//eliminate poor quality disparities
clear();
quality_threshold = 50000;
//if (quality_threshold<240000) quality_threshold=240000;
for (y=0;y<height;y++)
{
for (x=0;x<width;x++)
{
max_score=-1;
for (i=0;i<disparity_map[x,y,0];i++)
{
score = disparity_map[x,y,2+(2*i)];
//if ((score<quality_threshold) || (disparity_map[x,y,1+(2*i)]>255))
if (score<quality_threshold)
{
disparity_map[x,y,1+(2*i)]=-1;
//if (i==0)
// for (c=0;c<3;c++) image[x,y,c] = 0;
}
else
{
disparity_map[x,y,2+(2*i)]=(int)((score*100)/overall_max_score);
if (score>max_score)
{
for (c=0;c<3;c++) image[x,y,c] = (Byte)(disparity_map[x,y,1+(2*i)]);
max_score=score;
}
}
}
}
}
//clean up
for (y=1;y<height-blobsize-1;y++)
{
for (x=1;x<width-blobsize-1;x++)
{
if (image[x,y,0]>180)
{
if (((image[x-1,y,0]<80) && (image[x+blobsize+1,y,0]<80)) ||
((image[x,y-1,0]<80) && (image[x,y+blobsize+1,0]<80)))
{
for (c=0;c<3;c++) image[x,y,c] = 0;
disparity_map[x,y,1]=0;
}
}
}
}
}
//---------------------------------------------------------------------------------------------
//adjust contrast
//---------------------------------------------------------------------------------------------
public void adjustContrast(classimage sourceimage, int contrastPercent, int centrepoint)
{
int x,y,c,diff,p;
int illumination;
int illumination_centre;
if (centrepoint!=0)
illumination_centre = centrepoint;
else
illumination_centre = sourceimage.getAverageIntensity(0,0,width,height);
for (x=0;x<width;x++)
for (y=0;y<height;y++)
{
illumination=0;
for (c=0;c<3;c++) illumination += sourceimage.image[x,y,c];
illumination /=3;
diff = ((illumination - illumination_centre)*contrastPercent)/100;
for (c=0;c<3;c++)
{
p = sourceimage.image[x,y,c];
p += diff;
if (p<0) p=0;
if (p>255) p=255;
image[x,y,c]=(Byte)p;
}
}
}
//---------------------------------------------------------------------------------------------
// colour normalisation
//---------------------------------------------------------------------------------------------
public void colourNormalise(classimage sourceimg, int multiplier)
{
int x,y,c,p,av_r=0,av_g=0,av_b=0;
int[] col = new int[3];
sourceimg.averageColour(0,0,width,height,ref av_r,ref av_g,ref av_b);
for (x=0;x<width;x++)
for (y=0;y<height;y++)
{
for (c=0;c<3;c++) col[c] = sourceimg.image[x,y,c];
col[0] -= av_r;
col[1] -= av_g;
col[2] -= av_b;
for (c=0;c<3;c++)
{
p = 127 + (col[c]*multiplier);
if (p<1) p=1;
if (p>255) p=255;
image[x,y,c]=(Byte)p;
}
}
}
//---------------------------------------------------------------------------------------------
//subtracts two images to produce a difference image
//---------------------------------------------------------------------------------------------
public void subtractImages(classimage source1, classimage source2, int multiplier)
{
int x,y,c,diff,p1,p2;
for (x=0;x<width;x++)
for (y=0;y<height;y++)
for (c=0;c<3;c++)
{
p1 = source1.image[x,y,c];
p2 = source2.image[x,y,c];
diff = Math.Abs(p1-p2);
diff *= multiplier;
if (diff>255) diff=255;
image[x,y,c] = (Byte)diff;
}
}
//------------------------------------------------------------------------------------------------------------------------
// polar transform
//------------------------------------------------------------------------------------------------------------------------
public void updatePolar(classimage source_img)
{
int x,y,xx,yy,x2,y2,c;
float angle;
if (!initialised)
{
createImage(source_img.width/reductionFactor_x,source_img.height/reductionFactor_y);
initialised=true;
}
if (polarLookup==null)
{
//generate the lookup array if necessary
polarLookup = new int [source_img.width,source_img.height,2];
for (x=0;x<source_img.width;x++)
{
for (y=0;y<source_img.height;y++)
{
polarLookup[x,y,0]=0;
polarLookup[x,y,1]=0;
}
}
}
if (currPolarRadius != source_img.width/2)
{
//re-create the lookup table
for (x=0;x<source_img.width;x++)
{
angle = (x * 3.1415927f * 2) / source_img.width;
for (y=0;y<source_img.height;y++)
{
xx = (source_img.width/2) + (int)(y * Math.Sin(angle));
yy = (source_img.height/2) + (int)(y * Math.Cos(angle));
if ((xx>=0) && (yy>=0) && (xx<source_img.width) && (yy<source_img.height))
{
polarLookup[xx,yy,0]=x;
polarLookup[xx,yy,1]=y;
}
}
}
currPolarRadius = source_img.width/2;
}
for (x=0;x<width;x++)
{
x2 = (x * source_img.width) / width;
for (y=0;y<height;y++)
{
y2 = (y * source_img.height) / height;
if ((x2>=0) && (y2>=0) && (x2<source_img.width) && (y2<source_img.height))
{
xx = (polarLookup[x2,y2,0] * width) / source_img.width;
yy = (polarLookup[x2,y2,1] * height) / source_img.height;
if ((xx>=0) && (yy>=0) && (xx<width) && (yy<height))
{
for (c=0;c<3;c++)
image[xx,yy,c] = source_img.image[x2,y2,c];
}
}
}
}
updateIntegralImageMono();
}
//---------------------------------------------------------------------------------------------
//subtracts two images to produce a difference image in mono only
//---------------------------------------------------------------------------------------------
public void subtractImagesMono(classimage source1, classimage source2, int multiplier,int minThreshold)
{
int x,y,c,diff,p1,p2,dx,dy;
for (x=1;x<source1.width-1;x++)
{
for (y=1;y<source1.height-1;y++)
{
p1=0;
p2=0;
for (dx=-1;dx<=1;dx++)
{
for (dy=-1;dy<=1;dy++)
{
for (c=0;c<3;c++)
{
p1 += source1.image[x+dx,y+dy,c];
p2 += source2.image[x+dx,y+dy,c];
}
}
}
p1 /= 9;
p2 /= 9;
diff=0;
if ((p1>minThreshold) || (p2>minThreshold))
{
diff = (Math.Abs(p1-p2)*multiplier)/3;
if (diff>255) diff=255;
}
for (c=0;c<3;c++) image[x,y,c] = (Byte)diff;
}
}
}
//------------------------------------------------------------------------------------------------------------------------
//flood fill from the given point using the given colour
//------------------------------------------------------------------------------------------------------------------------
public void floodFill(int x, int y, int r, int g, int b, int depth, ref int tx, ref int ty, ref int bx, ref int by, ref long pixels, ref long av_r, ref long av_g, ref long av_b, classimage sourceImage, ref long av_x, ref long av_y, bool EdgeDetection, int minEdgeLength, classimage tempimage, classimage colour_image)
{
int c,xx,yy;
if ((image[x,y,0]==0) && (image[x,y,1]==0) && (image[x,y,2]==0) && (sourceImage.image[x,y,0]>0) && (depth<7000))
{
if (x<tx) tx=x;
if (x>bx) bx=x;
if (y<ty) ty=y;
if (y>by) by=y;
if (colour_image.width!=width)
{
xx = (x*colour_image.width)/width;
yy = (y*colour_image.height)/height;
}
else
{
xx=x;
yy=y;
}
av_r += colour_image.image[xx,yy,0];
av_g += colour_image.image[xx,yy,1];
av_b += colour_image.image[xx,yy,2];
av_x += x;
av_y += y;
pixels++;
if ((r==0) && (g==0) && (b==0))
{
r=1;
g=1;
b=1;
}
for (c=0;c<3;c++) tempimage.image[x,y,c]=255;
image[x,y,0]=(Byte)r;
image[x,y,1]=(Byte)g;
image[x,y,2]=(Byte)b;
if (x>0)
{
floodFill(x-1,y,r,g,b,depth+1,ref tx,ref ty,ref bx,ref by,ref pixels,ref av_r,ref av_g,ref av_b,sourceImage,ref av_x,ref av_y,EdgeDetection,minEdgeLength,tempimage,colour_image);
if (y>0)
{
floodFill(x-1,y-1,r,g,b,depth+1,ref tx,ref ty,ref bx,ref by,ref pixels,ref av_r,ref av_g,ref av_b,sourceImage,ref av_x,ref av_y,EdgeDetection,minEdgeLength,tempimage,colour_image);
if (x<width-1)
{
floodFill(x+1,y-1,r,g,b,depth+1,ref tx,ref ty,ref bx,ref by,ref pixels,ref av_r,ref av_g,ref av_b,sourceImage,ref av_x,ref av_y,EdgeDetection,minEdgeLength,tempimage,colour_image);
}
}
if (x<width-1)
{
floodFill(x+1,y,r,g,b,depth+1,ref tx,ref ty,ref bx,ref by,ref pixels,ref av_r,ref av_g,ref av_b,sourceImage,ref av_x,ref av_y,EdgeDetection,minEdgeLength,tempimage,colour_image);
if (y<height-1)
{
floodFill(x+1,y+1,r,g,b,depth+1,ref tx,ref ty,ref bx,ref by,ref pixels,ref av_r,ref av_g,ref av_b,sourceImage,ref av_x,ref av_y,EdgeDetection,minEdgeLength,tempimage,colour_image);
}
}
}
if (y>0)
{
floodFill(x,y-1,r,g,b,depth+1,ref tx,ref ty,ref bx,ref by,ref pixels,ref av_r,ref av_g,ref av_b,sourceImage,ref av_x,ref av_y,EdgeDetection,minEdgeLength,tempimage,colour_image);
}
if (y<height-1)
{
floodFill(x,y+1,r,g,b,depth+1,ref tx,ref ty,ref bx,ref by,ref pixels,ref av_r,ref av_g,ref av_b,sourceImage,ref av_x,ref av_y,EdgeDetection,minEdgeLength,tempimage,colour_image);
}
}
}
//---------------------------------------------------------------------------------------------
//copy from an image
//---------------------------------------------------------------------------------------------
public void copyImage(classimage source_img)
{
int x,y,c;
Byte v;
for (x=0;x<width;x++)
{
for (y=0;y<height;y++)
{
for (c=0;c<3;c++)
{
v = source_img.image[x,y,c];
image[x,y,c] = v;
}
}
}
}
//------------------------------------------------------------------------------------------------------------------------
// sample the given image within the given bounding box
//------------------------------------------------------------------------------------------------------------------------
public void sampleFromImage(classimage example_img, int tx, int ty, int bx, int by)
{
int x, y, xx, yy, dx, dy;
dx = bx - tx;
dy = by - ty;
for (x = 0; x < width; x++)
{
xx = tx + ((x * dx) / width);
for (y = 0; y < height; y++)
{
yy = ty + ((y * dy) / height);
image[x, y] = example_img.image[xx, yy, 0];
}
}
//updateIntegralImage();
}
//---------------------------------------------------------------------------------------------
//create a texture image
//---------------------------------------------------------------------------------------------
public void textureImage(classimage source_img)
{
int x,y,w,h,xx,yy,texture,c;
w = source_img.width / width;
if (w<1) w=1;
h = source_img.height / height;
if (h<1) h=1;
for (x=0;x<width;x++)
{
xx = (x*source_img.width)/width;
for (y=0;y<height;y++)
{
yy = (y*source_img.height)/height;
texture = source_img.texture(xx-w,yy-h,xx+w,yy+h);
for (c=0;c<3;c++) image[x,y,c] = (Byte)texture;
}
}
}
//---------------------------------------------------------------------------------------------
//copy from an image
//---------------------------------------------------------------------------------------------
public void copyImage(classimage source_img)
{
int x, y;
Byte v;
for (x = 0; x < width; x++)
{
for (y = 0; y < height; y++)
{
v = (Byte)((source_img.image[x, y, 0] +
source_img.image[x, y, 1] +
source_img.image[x, y, 2]) / 3);
image[x, y] = v;
}
}
}
//---------------------------------------------------------------------------------------------
// Detect edges OFF
//---------------------------------------------------------------------------------------------
public void detectEdgesOff(classimage sourceimage, int minThresh, int patchSize)
{
int x,y,c;
int halfPatch = patchSize/2;
int quarterPatch = halfPatch/2;
int p1,p2,diff,pixels;
if (halfPatch<2) halfPatch=2;
if (quarterPatch<1) quarterPatch=1;
pixels = patchSize*patchSize;
sourceimage.updateIntegralImageMono();
for (y=halfPatch;y<height-halfPatch;y++)
{
for (x=halfPatch;x<width-halfPatch;x++)
{
p1 = (int)sourceimage.getIntegral(x-halfPatch,y-halfPatch,x+halfPatch,y+halfPatch);
p2 = (int)sourceimage.getIntegral(x-quarterPatch,y-quarterPatch,x+quarterPatch,y+quarterPatch);
diff = ((p2*2)-p1);
if (diff>0)
{
diff = Math.Abs(diff);
diff/=(pixels*10);
diff*=diff;
if (diff>255) diff=255;
}
else diff=0;
diff=255-diff;
if (diff<minThresh) diff=0;
for (c=0;c<3;c++) image[x,y,c] = (Byte)diff;
}
}
}
