HoughLines

Image processingfunctionOpenCV 5.0.0

import { HoughLines } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: Hough line detection →

Let edge pixels vote for lines in a parameter space.

VISUAL WALKTHROUGHGeometry

REFERENCE INPUTEdge observations

A supported shape is mixed with a few unrelated edge samples.

→

RESULTRecover a supported line

The recovered geometry follows the consistent observations; isolated samples do not define the final shape.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

example: samples/cpp/tutorial_code/ImgTrans/houghlines.cpp An example using the Hough line detector example: samples/python/snippets/houghlines.py An example using the Hough line detector in python Finds lines in a binary image using the standard Hough transform.

The function implements the standard or standard multi-scale Hough transform algorithm for line detection. See https://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm for a good explanation of Hough transform.

HoughLines(image: Mat, lines: Mat, rho: number, theta: number, threshold: number, srn: number, stn: number, min_theta: number, max_theta: number, use_edgeval: boolean): void;

6 available overloads

HoughLines(image: Mat, lines: Mat, rho: number, theta: number, threshold: number): void;

HoughLines(image: Mat, lines: Mat, rho: number, theta: number, threshold: number, srn: number): void;

HoughLines(image: Mat, lines: Mat, rho: number, theta: number, threshold: number, srn: number, stn: number): void;

HoughLines(image: Mat, lines: Mat, rho: number, theta: number, threshold: number, srn: number, stn: number, min_theta: number): void;

HoughLines(image: Mat, lines: Mat, rho: number, theta: number, threshold: number, srn: number, stn: number, min_theta: number, max_theta: number): void;

HoughLines(image: Mat, lines: Mat, rho: number, theta: number, threshold: number, srn: number, stn: number, min_theta: number, max_theta: number, use_edgeval: boolean): void;

image

8-bit, single-channel binary source image. The image may be modified by the function.

lines

Output destination, filled by the native operation. Output vector of lines. Each line is represented by a 2 or 3 element vector (\rho, \theta) or (\rho, \theta, \textrm{votes}), where \rho is the distance from the coordinate origin (0,0) (top-left corner of the image), \theta is the line rotation angle in radians ( 0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line} ), and \textrm{votes} is the value of accumulator.

rho

Distance resolution of the accumulator in pixels.

theta

Angle resolution of the accumulator in radians.

threshold

%Accumulator threshold parameter. Only those lines are returned that get enough votes ( >\texttt{threshold} ).

srn

For the multi-scale Hough transform, it is a divisor for the distance resolution rho. The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.

stn

For the multi-scale Hough transform, it is a divisor for the distance resolution theta.

min_theta

For standard and multi-scale Hough transform, minimum angle to check for lines. Must fall between 0 and max_theta.

max_theta

For standard and multi-scale Hough transform, an upper bound for the angle. Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly less than max_theta, depending on the parameters min_theta and theta.

use_edgeval

True if you want to use weighted Hough transform.

Upstream declaration ↗

These signatures describe this package. Upstream documentation can mention optional backends that are absent from this build. Check runtime compatibility before choosing a backend or file format.