I am trying to find the dominant colors in dresses.
1) First step is to remove the background. I did this using the solution mentioned here. It works perfectly and makes the background black.
2) Now with the result of the first step I am trying to find dominant colors using the solution mentioned here. But I am getting black (the background) as one of the dominant colours.
How can I ignore the background pixels in step 2?
Depending on the case, you could find the bounding rectangle of the region that you're interested in. If the number of color pixels is much higher than the number of black pixels inside that bounding rectangle, black shouldn't be detected as the dominant color.
Call findContours(binaryMask) on the binary image of your mask. Make sure you found just the contour you were looking for. If not, filter them to get the best one for the application. Then call boundingRect(cnt) on the contour. Then crop the image using that rectangle and run your function. If that's insufficient, try minAreaRect(cnt), but the cropping is a bit trickier: see this answer.
If that doesn't work, I'd probably go for the "dumb" solution, by changing the color of the mask to a color that will for 99% not appear on a dress and then - knowing it exact RGB values - filter it out from the results.
Next time please remember to provide an image of your case, so the answers may be more accurate.
One easy way to do it would be to simply discard black as a dominant colour. Grab one more cluster than you really want, ignore black. If black may genuinely be the dominant colour, repeat the operation with a different background colour and discard that; compare results. This would be slow, but simple to do.
Alternatively, you could only sample from pixels in your foreground. From your foreground extraction method, you should have a binary black and white foreground/background mask. If you only sample from white areas of the mask, then only these colours should be taken into consideration.
I have a rough C++ implementation of this, but it's almost certainly not the most efficient possible. Maybe it's a start you could work from?
Mat src; //Your source image
Mat mask; //Your black & white foreground/background image
Mat samples(src.rows * src.cols, 3, CV_32F);
//Set up samples with only foreground pixels
for (int y = 0; y < src.rows; y++) {
for (int x = 0; x < src.cols; x++) {
if (mask.at<uchar>(y, x) == 255) {
for (int z = 0; z < 3; z++) {
samples.at<float>(y + x*src.rows, z) = src.at<Vec3b>(y, x)[z];
}
}
}
}
int clusterNo = 3;
int attempts = 5;
Mat labels;
Mat centers;
kmeans(samples, clusterNo, labels, TermCriteria(), attempts, KMEANS_RANDOM_CENTERS, centers);
Your dominant colours will be stored in the rows of centres, where you can do what you want with them.
Remove the background. That gives you a binary image - foreground and background pixels. Now do a morphological closing to close up little holes in foreground images and generally clean up the contours. Finally substitute pixels back in again to get a colour foreground image.
