How to run findContours() on meanShiftSegmentation() output? - c++

Question

I'm trying to rewrite my very slow naive segmentation using floodFill to something faster. I ruled out meanShiftFiltering a year ago because of the difficulty in labelling the colours and then finding their contours.
The current version of opencv seems to have a fast new function that labels segments using mean shift: gpu::meanShiftSegmentation(). It produces images like the following:
(source: ekran.org)
So this looks to me pretty close to being able to generating contours. How can I run findContours to generate segments?
Seems to me, this would be done by extracting the labelled colours from the image, and then testing which pixel values in the image match each label colour to make a boolean image suitable for findContours. This is what I have done in the following (but its a bit slow and strikes me there should be a better way):
Mat image = imread("test.png");
...
// gpu operations on image resulting in gpuOpen
...
// Mean shift
TermCriteria iterations = TermCriteria(CV_TERMCRIT_ITER, 2, 0);
gpu::meanShiftSegmentation(gpuOpen, segments, 10, 20, 300, iterations);
// convert to greyscale (HSV image)
vector<Mat> channels;
split(segments, channels);
// get labels from histogram of image.
int size = 256;
labels = Mat(256, 1, CV_32SC1);
calcHist(&channels.at(2), 1, 0, Mat(), labels, 1, &size, 0);
// Loop through hist bins
for (int i=0; i<256; i++) {
float count = labels.at<float>(i);
// Does this bin represent a label in the image?
if (count > 0) {
// find areas of the image that match this label and findContours on the result.
Mat label = Mat(channels.at(2).rows, channels.at(2).cols, CV_8UC1, Scalar::all(i)); // image filled with label colour.
Mat boolImage = (channels.at(2) == label); // which pixels in labeled image are identical to this label?
vector<vector<Point>> labelContours;
findContours(boolImage, labelContours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Loop through contours.
for (int idx = 0; idx < labelContours.size(); idx++) {
// get bounds for this contour.
bounds = boundingRect(labelContours[idx]);
// create ROI for bounds to extract this region
Mat patchROI = image(bounds);
Mat maskROI = boolImage(bounds);
}
}
}
Is this the best approach or is there a better way to get the label colours? Seems it would be logical for meanShiftSegmentation to provide this information? (vector of colour values, or vector of masks for each label, etc.)
Thank you.

Answer 1

Following is another way of doing this without thowing away the colour information in the meanShiftSegmentation results. I did not compare the two for performance.
// Loop through whole image, pixel and pixel and then use the colour to index an array of bools indicating presence.
vector<Scalar> colours;
vector<Scalar>::iterator colourIter;
vector< vector< vector<bool> > > colourSpace;
vector< vector< vector<bool> > >::iterator colourSpaceBIter;
vector< vector<bool> >::iterator colourSpaceGIter;
vector<bool>::iterator colourSpaceRIter;
// Initialize 3D Vector
colourSpace.resize(256);
for (int i = 0; i < 256; i++) {
colourSpace[i].resize(256);
for (int j = 0; j < 256; j++) {
colourSpace[i][j].resize(256);
}
}
// Loop through pixels in the image (should be fastish, look into LUT for faster)
uchar r, g, b;
for (int i = 0; i < segments.rows; i++)
{
Vec3b* pixel = segments.ptr<Vec3b>(i); // point to first pixel in row
for (int j = 0; j < segments.cols; j++)
{
b = pixel[j][0];
g = pixel[j][1];
r = pixel[j][2];
colourSpace[b][g][r] = true; // this colour is in the image.
//cout << "BGR: " << int(b) << " " << int(g) << " " << int(r) << endl;
}
}
// Get all the unique colours from colourSpace
// loop through colourSpace
int bi=0;
for (colourSpaceBIter = colourSpace.begin(); colourSpaceBIter != colourSpace.end(); colourSpaceBIter++) {
int gi=0;
for (colourSpaceGIter = colourSpaceBIter->begin(); colourSpaceGIter != colourSpaceBIter->end(); colourSpaceGIter++) {
int ri=0;
for (colourSpaceRIter = colourSpaceGIter->begin(); colourSpaceRIter != colourSpaceGIter->end(); colourSpaceRIter++) {
if (*colourSpaceRIter)
colours.push_back( Scalar(bi,gi,ri) );
ri++;
}
gi++;
}
bi++;
}
// For each colour
int segmentCount = 0;
for (colourIter = colours.begin(); colourIter != colours.end(); colourIter++) {
Mat label = Mat(segments.rows, segments.cols, CV_8UC3, *colourIter); // image filled with label colour.
Mat boolImage = Mat(segments.rows, segments.cols, CV_8UC3);
inRange(segments, *colourIter, *colourIter, boolImage); // which pixels in labeled image are identical to this label?
vector<vector<Point> > labelContours;
findContours(boolImage, labelContours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Loop through contours.
for (int idx = 0; idx < labelContours.size(); idx++) {
// get bounds for this contour.
Rect bounds = boundingRect(labelContours[idx]);
float area = contourArea(labelContours[idx]);
// Draw this contour on a new blank image
Mat maskImage = Mat::zeros(boolImage.rows, boolImage.cols, boolImage.type());
drawContours(maskImage, labelContours, idx, Scalar(255,255,255), CV_FILLED);
Mat patchROI = frame(bounds);
Mat maskROI = maskImage(bounds);
}
segmentCount++;
}