Gftt Opencv

This code first calculates the minimum eigenvalue of the gradient each block i.e., 3x3 neighborhood of image, as required for finding a Shi-Tomasi Harris corner.If this minimum eigenvalue is above some threshold, then the pixel where the block was centered is considered as a feature.

opencv--GFTTFASTSURFSIFTSTAR opencvlibdllopencvdllC92Windows92System32lib

GFTT is already similar with Harris method. it looks for points in an image where there's a change in intensity. it calculates 1-Image Gradients 2-Creates Autocorrelation Matrix 3-Selection

OpenCV has a function, cv.goodFeaturesToTrack. It finds N strongest corners in the image by Shi-Tomasi method or Harris Corner Detection, if you specify it. As usual, image should be a grayscale image. Then you specify number of corners you want to find. Then you specify the quality level, which is a value between 0-1, which denotes the

GFTT Good Features to Track GFTT is a feature detector only. GFTTDetector can be used to detect features using Harris named after the creator and GFTT corner detection algorithms. So, - Selection from Computer Vision with OpenCV 3 and Qt5 Book

This is the complete list of members for cvGFTTDetector, including all inherited members.

image Image. keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example SIFT duplicates keypoint with several dominant orientations for each orientation.

Constructor Detail. GFTTDetector protected GFTTDetector long addr Method Detail. __fromPtr__ public static GFTTDetector __fromPtr__ long addr create public static GFTTDetector create int maxCorners, double qualityLevel, double minDistance, int blockSize, boolean useHarrisDetector, double k

Python cv.GFTTDetector.create, maxCorners, qualityLevel, minDistance, blockSize, useHarrisDetector, k -gt retval cv.GFTTDetector.create

Tx for your follow-up. I know using goodFeaturesToTrack I will be able to adjust blockSize parameter value for GFTT feature. But as I said in the question I am trying to do that using only the pointer returned by FeatureDetectorcreate function.