Hi,
I am trying to implement a function which takes an image (type: `CV_64FC3`) and applies two operations on it:
1. Each RGB pixel is replaced by the
minimum channel value resulting in a
`CV_64C1` type image.
2. A minimum filter
of size `blockSize` is applied.
The function is performing as required but it takes approximately 1600ms to process a single image of resolution `720 x 576` with `blockSize` of 15. My question is whether I can do anything to make it computationally efficient, particularly `for` loop. Code:
Mat darkChannel(Mat im, int blockSize)
{
int padSize = (blockSize - 1) / 2;
double minVal;
Mat temp, minChannel, borderMinChannel, bgr[3];
split(im, bgr); //split into RGB channels
(cv::min)(bgr[0], bgr[1], temp); //find minimum between R and G channels
(cv::min)(temp, bgr[2], minChannel);
copyMakeBorder(minChannel, borderMinChannel, padSize, padSize, padSize, padSize, BORDER_REPLICATE); //padding
Mat dc = Mat::zeros(borderMinChannel.rows, borderMinChannel.cols, CV_64FC1); //create Mat to store final result
double* p;
for (int i = padSize; i < borderMinChannel.rows - padSize; i++)
{
p = dc.ptr(i);
for (int j = padSize; j < borderMinChannel.cols - padSize; j++)
{
minMaxLoc(borderMinChannel(Rect(j - padSize, i - padSize, 2 * padSize + 1, 2 * padSize + 1)), &minVal, NULL); //find the minimum value in a block
p[j] = minVal; //put the minimum value in Mat
}
}
dc = dc(Rect(padSize, padSize, minChannel.cols, minChannel.rows)); //remove padding to return to original size
return dc;
}
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