OpenCV : Reversing the negative areas of an image - c++

Question

i'm using OpenCV 3.4.6 in a c++/Objective C project and given an image with negative rectangular areas, like this one:
I should detect those negative areas, reverse them and finally get the original image.
I tried to use findContours, enhancing the contrast of the original image or adding a threshold but the rectangles are not detected.
Here one of the test i've tried:
Mat contrasted = [self enhanceContrastTo: matOriginal];
Mat thresholded;
threshold(contrasted, thresholded, 125, 241, THRESH_BINARY);
std::vector<std::vector<cv::Point> > contours;
std::vector<Vec4i> hierarchy;
findContours( thresholded, contours, hierarchy, CV_RETR_EXTERNAL, CV_RETR_TREE );
/* contrast method */
+(Mat)enhanceContrastTo:(Mat)image {
cv::Mat lab_image;
cv::cvtColor(image, lab_image, CV_BGR2Lab);
// Extract the L channel
std::vector<cv::Mat> lab_planes(3);
cv::split(lab_image, lab_planes); // now we have the L image in lab_planes[0]
// apply the CLAHE algorithm to the L channel
cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE();
// clahe->setClipLimit(4);
clahe->setClipLimit(3);
cv::Mat dst;
clahe->apply(lab_planes[0], dst);
// Merge the the color planes back into an Lab image
dst.copyTo(lab_planes[0]);
cv::merge(lab_planes, lab_image);
// convert back to RGB
cv::Mat image_clahe;
cv::cvtColor(lab_image, image_clahe, CV_Lab2BGR);
return image_clahe;
}
The rectangles are clearly visible to the naked eye, I hope that opencv can also identify them but I don't know how.
Any idea?
Thanks

Answer 1

This particular question isn't too complicated but even minor variants can get complex. I can advise you on a couple of simple ideas that should suffice to solve the problem.
1) Instead of contour you can check whether neighboring points are close to reverse - this should filter out most irrelevant edges. But just checking for near-reverse is not sufficient as monotone grey area (127) fits the criteria too. Require also minimal threshold difference.
2) Since rectangles are parallel to axes - you can simply go along each row and column and count the number of pixels that are potentially edges of the reversed rectangles. It is better not to just count the number - but to check whether you have continuous large sequences of such pixels and record where exactly these segments are.
3) Use the found segments (or just indexes of rows and columns) of reversed edge-pixels to make candidates for reversed rectangles and then make final verifications.
This is but an algo draft - it will surely require refining. I am not sure why you wanted to use the contour function, tho.