opencv-ts

import { Mat } from '../core/Mat'; declare module FeatureDetection { enum HoughModes { HOUGH_STANDARD = 0, HOUGH_PROBABILISTIC = 1, HOUGH_MULTI_SCALE = 2, HOUGH_GRADIENT = 3, HOUGH_GRADIENT_ALT = 4, } interface _HoughModes { HOUGH_STANDARD: HoughModes.HOUGH_STANDARD; HOUGH_PROBABILISTIC: HoughModes.HOUGH_PROBABILISTIC; HOUGH_MULTI_SCALE: HoughModes.HOUGH_MULTI_SCALE; HOUGH_GRADIENT: HoughModes.HOUGH_GRADIENT; HOUGH_GRADIENT_ALT: HoughModes.HOUGH_GRADIENT_ALT; } enum LineSegmentDetectorModes { LSD_REFINE_NONE = 0, LSD_REFINE_STD = 1, LSD_REFINE_ADV = 2, } interface _LineSegmentDetectorModes { LSD_REFINE_NONE: LineSegmentDetectorModes.LSD_REFINE_NONE; LSD_REFINE_STD: LineSegmentDetectorModes.LSD_REFINE_STD; LSD_REFINE_ADV: LineSegmentDetectorModes.LSD_REFINE_ADV; } interface FeatureDetection { /** * Finds edges in an image using the Canny algorithm * @param image 8-bit input image. * @param edges output edge map; single channels 8-bit image, which has the same size as image * @param threshold1 first threshold for the hysteresis procedure * @param threshold2 second threshold for the hysteresis procedure * @param apertureSize aperture size for the Sobel operator */ Canny(image: Mat, edges: Mat, threshold1: number, threshold2: number): void; /** * Finds edges in an image using the Canny algorithm * @param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3). * @param dy 16-bit y derivative of input image (same type as dx). * @param edges output edge map; single channels 8-bit image, which has the same size as image * @param threshold1 first threshold for the hysteresis procedure * @param threshold2 second threshold for the hysteresis procedure */ Canny1(dx: Mat, dy: Mat, edges: Mat, threshold1: number, threshold2: number): void; /** * Harris corner detector * @param src Input single-channel 8-bit or floating-point image. * @param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same size as src * @param blockSize Neighborhood size * @param ksize Aperture parameter for the Sobel operator * @param k Harris detector free parameter. See the formula above */ cornerHarris(src: Mat, dst: Mat, blockSize: number, ksize: number, k: number): void; /** * Calculates the minimal eigenvalue of gradient matrices for corner detection * @param src Input single-channel 8-bit or floating-point image * @param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as src * @param blockSize Neighborhood size */ cornerMinEigenVal(src: Mat, dst: Mat, blockSize: number): void; /** * Determines strong corners on an image * @param image Input 8-bit or floating-point 32-bit, single-channel image * @param corners Output vector of detected corners * @param 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 * @param 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. * @param minDistance Minimum possible Euclidean distance between the returned corners */ goodFeaturesToTrack( image: Mat, corners: Mat, maxCorners: number, qualityLevel: number, minDistance: number ): void; /** *Determines strong corners on an image * @param image Input 8-bit or floating-point 32-bit, single-channel image * @param corners Output vector of detected corners * @param 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 * @param 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 * @param minDistance Minimum possible Euclidean distance between the returned corners * @param mask 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 * @param blockSize Size of an average block for computing a derivative covariation matrix over each pixel neighborhood * @param gradientSize Aperture parameter for the Sobel operator used for derivatives computation */ goodFeaturesToTrack1( image: Mat, corners: Mat, maxCorners: number, qualityLevel: number, minDistance: number, mask: Mat, blockSize: number, gradientSize?: number | 3 ): void; /** * Finds circles in a grayscale image using the Hough transform * @param image 8-bit, single-channel, grayscale input image. * @param circles Output vector of found circles. Each vector is encoded as 3 or 4 element floating-point vector (x,y,radius) or (x,y,radius,votes) * @param method Detection method, see HoughModes. The available methods are HOUGH_GRADIENT and HOUGH_GRADIENT_ALT. * @param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected * @param minDist Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed * this two parameter will be helpful to increase the accuracy of the circle detection * this two parameter are also available in opencv original documentation * @param param1 it is the higher threshold of the two passed to the Canny edge detector (the lower canny threshold is twice smaller) * @param param2 it is the accumulator threshold for the circle centers at the detection stage as discussed above. */ HoughCircles( image: Mat, circles: Mat, method: HoughModes, dp: number, minDist: number, param1?: number, param2?: number ): void; /** * Finds lines in a binary image using the standard Hough transform * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 2 or 3 element vector (ρ,θ) or (ρ,θ,votes) . ρ is the distance from the coordinate origin (0,0) (top-left corner of the image). θ is the line rotation angle in radians ( 0∼vertical line,π/2∼horizontal line ). votes is the value of accumulator * @param rho Distance resolution of the accumulator in pixels * @param theta Angle resolution of the accumulator in radians * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough votes ( >𝚝𝚑𝚛𝚎𝚜𝚑𝚘𝚕𝚍 ). */ HoughLines(image: Mat, lines: Mat, rho: number, theta: number, threshold: number): void; /** * Finds line segments in a binary image using the probabilistic Hough transform * @param image 8-bit, single-channel binary source image. The image may be modified by the function. * @param lines Output vector of lines. Each line is represented by a 2 or 3 element vector (ρ,θ) or (ρ,θ,votes) . ρ is the distance from the coordinate origin (0,0) (top-left corner of the image). θ is the line rotation angle in radians ( 0∼vertical line,π/2∼horizontal line ). votes is the value of accumulator * @param rho Distance resolution of the accumulator in pixels * @param theta Angle resolution of the accumulator in radians * @param threshold Accumulator threshold parameter. Only those lines are returned that get enough votes ( >𝚝𝚑𝚛𝚎𝚜𝚑𝚘𝚕𝚍 ). */ HoughLinesP(image: Mat, lines: Mat, rho: number, theta: number, threshold: number): void; } } export = FeatureDetection;