OpenCV
4.10.0dev
Open Source Computer Vision

Functions  
GMat  cv::gapi::Canny (const GMat &image, double threshold1, double threshold2, int apertureSize=3, bool L2gradient=false) 
Finds edges in an image using the Canny algorithm.  
GArray< Point2f >  cv::gapi::goodFeaturesToTrack (const GMat &image, int maxCorners, double qualityLevel, double minDistance, const Mat &mask=Mat(), int blockSize=3, bool useHarrisDetector=false, double k=0.04) 
Determines strong corners on an image.  
GMat cv::gapi::Canny  (  const GMat &  image, 
double  threshold1,  
double  threshold2,  
int  apertureSize = 3 , 

bool  L2gradient = false 

) 
Python:  

cv.gapi.Canny(  image, threshold1, threshold2[, apertureSize[, L2gradient]]  ) >  retval 
#include <opencv2/gapi/imgproc.hpp>
Finds edges in an image using the Canny algorithm.
The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See http://en.wikipedia.org/wiki/Canny_edge_detector
image  8bit input image. 
threshold1  first threshold for the hysteresis procedure. 
threshold2  second threshold for the hysteresis procedure. 
apertureSize  aperture size for the Sobel operator. 
L2gradient  a flag, indicating whether a more accurate \(L_2\) norm \(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=dI/dx+dI/dy\) is enough ( L2gradient=false ). 
GArray< Point2f > cv::gapi::goodFeaturesToTrack  (  const GMat &  image, 
int  maxCorners,  
double  qualityLevel,  
double  minDistance,  
const Mat &  mask = Mat() , 

int  blockSize = 3 , 

bool  useHarrisDetector = false , 

double  k = 0.04 

) 
Python:  

cv.gapi.goodFeaturesToTrack(  image, maxCorners, qualityLevel, minDistance[, mask[, blockSize[, useHarrisDetector[, k]]]]  ) >  retval 
#include <opencv2/gapi/imgproc.hpp>
Determines strong corners on an image.
The function finds the most prominent corners in the image or in the specified image region, as described in [244]
The function can be used to initialize a pointbased tracker of an object.
image  Input 8bit or floatingpoint 32bit, singlechannel image. 
maxCorners  Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned. 
qualityLevel  Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected. 
minDistance  Minimum possible Euclidean distance between the returned corners. 
mask  Optional region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected. 
blockSize  Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs . 
useHarrisDetector  Parameter indicating whether to use a Harris detector (see cornerHarris) or cornerMinEigenVal. 
k  Free parameter of the Harris detector. 