OpenCV  4.6.0-dev Open Source Computer Vision
Graph API: Image Feature Detection

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. More...

GArray< Point2fcv::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. More...

◆ Canny()

 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

Note
Function textual ID is "org.opencv.imgproc.feature.canny"
Parameters
 image 8-bit 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 ).

◆ goodFeaturesToTrack()

 GArray 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 [226]

• Function calculates the corner quality measure at every source image pixel using the cornerMinEigenVal or cornerHarris .
• Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
• The corners with the minimal eigenvalue less than $$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)$$ are rejected.
• The remaining corners are sorted by the quality measure in the descending order.
• Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

Note
• If the function is called with different values A and B of the parameter qualityLevel , and A > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .
• Function textual ID is "org.opencv.imgproc.feature.goodFeaturesToTrack"
Parameters
 image Input 8-bit or floating-point 32-bit, single-channel 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.
Returns
vector of detected corners.