I am using the function findHomography
of OpenCV with the RANSAC method in order to find the homography that relates two images linked with a set of keypoints.
I used the findHomography method after applying keypoint matching.
Then you can use the mask output to extract the subset of correct matches from all matches.
There is an example in Python 3.6 & OpenCV 3.4.1:
good_kp = [gray_kp[m.queryIdx].pt for m in good_matches]
correct_matched_kp = [good_kp[i] for i in range(len(good_kp)) if mask[i]]
The mask returned by findHomography
is an 8-bit, single-channel cv::Mat
(or std::vector<uchar>
, if you prefer) containing either 0
or 1
indicating the outlier status.
EDIT: You access each element of the mask by calling .at<double>
, which is leading to the confusing output. You should be using .at<uchar>
, which will interpret the matrix value correctly.