OpenCV deallocates the memory automatically, as well as automatically allocates the memory for output function parameters most of the time. So, if a function has one or more input arrays (cv::Mat instances) and some output arrays, the output arrays are automatically allocated or reallocated. The size and type of the output arrays are determined ...

To solve this problem, the algorithm uses the least square method, RANSAC, LMEDS and PROSAC (can be set by parameters). So the correct estimates provided by good matches are called inliers, and the rest are called outliers. cv2.findHomography() returns a mask that determines the inlier and outlier points.

To estimate the homography in OpenCV is a simple task, it's a one line of code: H, __ = cv2.findHomography(srcPoints, dstPoints, cv2.RANSAC, 5) Before starting coding stitching algorithm we need to swap image inputs. So "img_" now will take right image and "img" will take left image. So lets jump into stiching coding:

To solve this problem, algorithm uses Ransac or LMedS (which can be specificed in the Method option). So good matches which provide correct estimation are called inliers and remaining are called outliers. cv.findHomography returns a mask which specifies the inlier and outlier points. Options Q #1: Right, the findHomography tries to find the best transform between two sets of points. It uses something smarter than least squares, called RANSAC, which has the ability to reject outliers - if at least 50% + 1 of your data points are OK, RANSAC will do its best to find them, and build a reliable transform. The following are 30 code examples for showing how to use cv2.RANSAC().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.