/*
This has a funny way of dealing with edges. In case of a Gaussian blur, divides by the sum of all of the numbers in the mask by definition. Might prove harsh on edges. In all other cases, divides by 8 no matter what, so that can equalize at edges. Also, when one of the pixels needed in the mask calculation is off the bounds of the image, the center pixel value is substituted, so that there is less difference. This doesnt pick up as many stray lines on edges, but also proves to often not pick up enough lines.
*/
DoubleArray Mask::applyMask(DoubleArray array){
ICoord size = array.size();
DoubleArray newarray = DoubleArray(size);
width = (int)maskArray.size()(0)/2; /* in case the maskArray was edited */
for (DoubleArray::iterator i = array.begin();i !=array.end(); ++i){
ICoord curr = i.coord();
double num = 0;
double c = 0;
int d = 1;
for (int i = -width; i <= width; ++i){ // scans through the mask
for (int j = -width; j <= width; ++j){
ICoord a = ICoord(i,j);
if (pixelInBounds(curr + a, size)){
num = num + array[curr+a]*maskArray[ICoord(i + width, j + width)];
c = c + maskArray[ICoord(i + width, j + width)];
}
else{
num = num + array[curr]*maskArray[ICoord(i + width, j + width)]; /* if pixel needed for mask is out of the image, then substitute the center pixel */
d = d + 1; /* count number of pixels out of bounds */
}
}
}
if (type == GAUSSIAN_MASK)
newarray[curr] = num/159;
else if (type == SMALL_GAUSSIAN_MASK)
newarray[curr] = num/99;
else
newarray[curr] = num/8;
}
return newarray;
}
