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How to use houghline method in openCV

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by vsasikalabe dot icon Updated: Sep 19, 2023

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In image processing, researchers use the Hough Transform to detect any shape. You can identify the shape in mathematical form. It can still identify the shape even if it becomes slightly broken or distorted. 


HoughLines() returns an array of ( (\rho, \theta) values. We calculate \rho in pixels and \theta in radians. The Input image should be binary. So, apply threshold or use canny edge detection before applying hough transform. We use the OpenCV functions HoughLines() and HoughLinesP() to detect lines in an image. 


OpenCV uses the following Hough Line Transforms.   

  • (Standard Hough Transform, SHT)   
  • (Multi-Scale Hough Transform, MSHT). This point-to-curve transformation is for straight lines.   


Shape Detection is implementing shape detection algorithms. This is to identify and categorize shapes in images. First, an edge detection of the specific image is desirable to apply the Houghline method. The OpenCV line detection method uses the Houghline method two. This is to identify the edges of the specified image and find the particular shape. Sometimes, the distance between the line segments is less. So, the value of the function joins the line segments into a single line segment. HoughCircles function has inbuilt canny detection. So, it is not required to detect edges explicitly in it.   


The Hough Transform is a used method for processing the image to enable the detection of any shapes. It provides the details of the mathematical representation of that particular shape. The Hough Transform algorithm is also used in grayscale images. So, we need to convert the image to grayscale using the function. After Converting it, we reduced the noise from the image using any blur function.


Then, we apply the Canny Edge Detection algorithm to find the border in the image. We can draw the original image once we have detected the lines in the image. Many applications use it. Some examples include lane detection in self-driving cars, OCR, and edge detection. Two parameters (slope and intercept) represent a line. A circle has three parameters (the coordinates of the center and the radius (x, y, r)). 

Preview of the output that you will get on running this code from your IDE.

Code

In this solution, we used the openCV and Numpy libraries.

How can I improve the results of HoughLinesP() in my OpenCV Python script

PythondotLines of Code : 35dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
import cv2
import numpy as np

# read image as color not grayscale
img = cv2.imread("lines.png", cv2.IMREAD_COLOR)

# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# do threshold
thresh = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)[1]

# get hough line segments
threshold = 30
minLineLength =10
maxLineGap = 10
lines = cv2.HoughLinesP(thresh, 1, np.pi/360, threshold, minLineLength, maxLineGap)

# draw lines
results = img.copy()
for [line] in lines:
    print(line)
    x1 = line[0]
    y1 = line[1]
    x2 = line[2]
    y2 = line[3]
    cv2.line(results, (x1,y1), (x2,y2), (0,0,255), 1) 

# show lines
cv2.imshow("lines", results)
cv2.waitKey(0)

# write results
cv2.imwrite("lines_hough.png",results)

Instructions

Follow the steps carefully to get the output easily.

  1. Download and Install the PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install cv2 - pip install opencv-python.
  4. Install Numpy - pip install Numpy.
  5. Create a new Python file on your IDE.
  6. Copy the snippet using the 'copy' button and paste it into your python file.
  7. Write the image path in line no.5
  8. Run the current file to generate the output.


I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.


I found this code snippet by searching for ' How can I improve the results of HoughLinesP() in my OpenCV Python script' in Kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in PyCharm 2022.3.
  2. The solution is tested on Python 3.11.1
  3. openCV Python version- 4.8.0.76
  4. Numpy Version - 1.25.2


Using this solution, we are able to use the houghline method in openCV with simple steps. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help us to use the houghline method in openCV.

Dependent Libraries

opencvby opencv

C++ doticonstar image 69456 doticonVersion:4.7.0doticon
License: Permissive (Apache-2.0)

Open Source Computer Vision Library

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            opencvby opencv

            C++ doticon star image 69456 doticonVersion:4.7.0doticon License: Permissive (Apache-2.0)

            Open Source Computer Vision Library
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                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
                                Support
                                  Quality
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                                          If you do not have the OpenCV and numpy libraries that are required to run this code, you can install them by clicking on the above link.

                                          You can search for any dependent library on Kandi like opencv and Numpy.

                                          FAQ:   

                                          1. What is OpenCV Line Detection, and how does it work?   

                                          OpenCV uses many algorithms for line detection, including HoughLines and HoughLinesP. HoughLines is a standard Hough transform algorithm. It returns an array of lines in the image. HoughLinesP is a probabilistic Hough transform algorithm. It is faster and more accurate than HoughLines.   

                                             

                                          2. What is the Probabilistic Hough Line Transform algorithm used for?   

                                          To define features like lines and curves in an image, we use the standard Hough Transform (SHT). We use a binary image as input. Each active pixel is a part of an edge feature. The SHT maps each of these pixels to many points in Hough (or parameter) space.   

                                             

                                          3. Can you explain in detail the Probabilistic Line Transform algorithm?   

                                          Estimating the Hough parameter space represents the transform. It is into finite intervals or accumulator cells. The discretized (r,0) curve transforms each (X, Y). We increment the accumulator (2D array) cells along this curve.   

                                             

                                          4. What are some of the advantages of using the HoughLine method?   

                                          The Hough transform is a simple technique for detecting geometric shapes in images. They have done this even when people distort, incomplete, or partially obscure it. It is a versatile technique. You can apply it to detect lines, circles, ellipses, and other simple shapes.   

                                             

                                          5. What types of applications can benefit from using opencv houghline?   

                                          OpenCV is a huge open-source library. It is for computer vision, machine learning, and image processing. It plays a major role in real-time operations. It is very important in today's systems. One can process images and videos to identify objects, faces, or even the handwriting of a human by using it.  

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