I'm trying to convert the following Matlab code into openCV.
My code works really great for most of the examples I have(~700), except this one.
Original Image
 Here is the matlab output:  Here is the openCV output:  Here is what happens when I remove CV_Fill:  % Convert to BW image bwImg = im2bw(imageData, grayThresh); % If we end up with totally black or white image return false status. if all(bwImg(:)) || all(~bwImg(:)) status = false; cornerXY = []; centroidXY = []; centroidMedianDistance = []; return; end % Calculate each separated object area cDist=regionprops(bwImg, 'Area'); cDist=[cDist.Area]; % Label each object [bwImgLabeled, ~]=bwlabel(bwImg); % Calculate min and max object size based on assumptions on the color % checker size maxLabelSize = prod(size(imageData)./[4 6]); minLabelSize = prod(size(imageData)./[4 6]./10); % Find label indices for objects that are too large or too small remInd = find(cDist > maxLabelSize); remInd = [remInd find(cDist < minLabelSize)]; % Remove over/undersized objects for n=1:length(remInd) ri = bwImgLabeled == remInd(n); bwImgLabeled(ri) = 0; end % Fill any holes in the objects bwImgLabeled = imfill(bwImgLabeled,'holes'); % Re-label the result image objects bwImgLabeled(bwImgLabeled>0) = 1; [bwImgLabeled, nObjs] = bwlabel(bwImgLabeled); Here is my C++/OpenCV code
cv::Mat bwImg = Utilities::im2bw(imageData, grayThresh); double sum = cv::sum(bwImg)[0]; if(sum == bwImg.rows * bwImg.cols||sum == 0 ) { return AutomaticMacbethDetectionResults(false); } cv::vector> contours_1;
cv::vector hierarchy_1;
cv::findContours(bwImg,contours_1,hierarchy_1,CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE,cv::Point(0,0));*/
bwImg.convertTo(bwImg,CV_8U);
cv::vector> contours_1;
cv::vector hierarchy;
cv::findContours(bwImg,contours_1,hierarchy,CV_RETR_TREE,CV_CHAIN_APPROX_NONE,cv::Point(0,0));
double maxLabelSize = (bwImg.rows/4.0) * (bwImg.cols/6.0);
double minLabelSize = ((bwImg.rows/40.0) * (bwImg.cols/60.0));
//OPENCV hierarchy *[Next, Previous, First_Child, Parent]**
// http://docs.opencv.org/master/d9/d8b/tutorial_py_contours_hierarchy.html#gsc.tab=0
cv::vector> goodContours;
for (int i = 0; i < contours_1.size(); i++)
{
double size = cv::contourArea(contours_1[i]);
if (size < maxLabelSize && size > minLabelSize) //I added my check for hierarachy[i][2] ==-1 here!
{
goodContours.push_back(contours_1[i]);
}
}
cv::Mat filterContours = cv::Mat::zeros(bwImg.size(),CV_8UC3);
bwImg.release();
for (int i = 0; i < goodContours.size(); i++)
{
cv::RNG rng(12345);
cv::Scalar color = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
cv::drawContours(filterContours,goodContours,i,color,CV_FILLED);
}
imageData = filterContours;
filterContours.release();
/*% Re-label the result image objects
bwImgLabeled(bwImgLabeled > 0) = 1;*/
cv::threshold(imageData, imageData, 0 ,254,CV_THRESH_BINARY);
so as far as I understand I can use the Hierarchy feature in order to understand if there is a contour which holds other contours.
[Hierarchy ](http://docs.opencv.org/master/d9/d8b/tutorial_py_contours_hierarchy.html#gsc.tab=0)
and I added a switch to check for hierarchy[i][2] == -1 (meaning it has no children)
however this ruins my results for other images. Can you please explain the difference between regionprops and findcontours? and maybe suggests a way to solve my issue?
 Here is the matlab output:  Here is the openCV output:  Here is what happens when I remove CV_Fill:  % Convert to BW image bwImg = im2bw(imageData, grayThresh); % If we end up with totally black or white image return false status. if all(bwImg(:)) || all(~bwImg(:)) status = false; cornerXY = []; centroidXY = []; centroidMedianDistance = []; return; end % Calculate each separated object area cDist=regionprops(bwImg, 'Area'); cDist=[cDist.Area]; % Label each object [bwImgLabeled, ~]=bwlabel(bwImg); % Calculate min and max object size based on assumptions on the color % checker size maxLabelSize = prod(size(imageData)./[4 6]); minLabelSize = prod(size(imageData)./[4 6]./10); % Find label indices for objects that are too large or too small remInd = find(cDist > maxLabelSize); remInd = [remInd find(cDist < minLabelSize)]; % Remove over/undersized objects for n=1:length(remInd) ri = bwImgLabeled == remInd(n); bwImgLabeled(ri) = 0; end % Fill any holes in the objects bwImgLabeled = imfill(bwImgLabeled,'holes'); % Re-label the result image objects bwImgLabeled(bwImgLabeled>0) = 1; [bwImgLabeled, nObjs] = bwlabel(bwImgLabeled); Here is my C++/OpenCV code
cv::Mat bwImg = Utilities::im2bw(imageData, grayThresh); double sum = cv::sum(bwImg)[0]; if(sum == bwImg.rows * bwImg.cols||sum == 0 ) { return AutomaticMacbethDetectionResults(false); } cv::vector