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< 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. More...

Detailed Description

Function Documentation

Canny()

GMat cv::gapi::Canny	(	const GMat &	image,
		double	threshold1,
		double	threshold2,
		int	apertureSize = 3,
		bool	L2gradient = false
	)

#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<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:
	retval	=	cv.gapi.goodFeaturesToTrack(	image, maxCorners, qualityLevel, minDistance[, mask[, blockSize[, useHarrisDetector[, k]]]]	)

#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 [223]

• 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.