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In a previous tutorial, we have seen how you can detect edges in an image. However, that's not usually enough in the image processing phase. In this tutorial, you will learn how you can detect shapes (mainly lines and circles) in images using Hough Transform technique in Python using OpenCV library.
The Hough Transform is a popular feature extraction technique to detect any shape within an image. It is mainly used in image analysis, computer vision and image recognition.
It is important to differentiate between hough transform and image segmentation. The hough transform is a feature extraction technique that identifies geometric shapes within an image, while image segmentation using transformers is a machine learning approach that partitions an image into different regions based on semantic or visual characteristics. Check this tutorial if you want to perform image segmentation using deep learning.
RELATED: How to Apply HOG Feature Extraction in Python.
Let's get started, installing the requirements:
pip3 install opencv-python numpy matplotlib
Importing the modules:
import numpy as np import matplotlib.pyplot as plt import cv2
I'm gonna use a photo of a computer monitor, make sure you have the photo monitor.jpg in your current directory (you're free to use any):
# read the image image = cv2.imread("monitor.jpg")
We need to convert this image to gray scale for edge detection:
# convert to grayscale grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
Let's detect the edges of the image:
# perform edge detection edges = cv2.Canny(grayscale, 30, 100)
If you're not sure what cv2.Canny is doing, refer to this tutorial.
Now we have detected the edges in the image, it is suited for us to use hough transform to detect the lines:
# detect lines in the image using hough lines technique lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array(), 50, 5)
cv2.HoughLinesP() function finds line segments in a binary image using the probabilistic Hough transform. For more information about its parameters, check this tutorial.
Let's draw the lines:
# iterate over the output lines and draw them for line in lines: for x1, y1, x2, y2 in line: cv2.line(image, (x1, y1), (x2, y2), (20, 220, 20), 3)
Showing the image:
# show the image plt.imshow(image) plt.show()
Here is my output:
The green lines are the lines we just drew, as you can see, most of the monitor is surrounded by green lines, feel free to tweak the parameters to get better results.
Here is the full code for detecting lines in your live camera:
import numpy as np import matplotlib.pyplot as plt import cv2 cap = cv2.VideoCapture(0) while True: _, image = cap.read() # convert to grayscale grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # perform edge detection edges = cv2.Canny(grayscale, 30, 100) # detect lines in the image using hough lines technique lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array(), 50, 5) # iterate over the output lines and draw them for line in lines: for x1, y1, x2, y2 in line: cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 3) cv2.line(edges, (x1, y1), (x2, y2), (255, 0, 0), 3) # show images cv2.imshow("image", image) cv2.imshow("edges", edges) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows()
Learn Also: Image Transformations using OpenCV in Python.
In order to detect circles, we gonna need to use
cv2.HoughCircles() method instead, I have coins.jpg image (which contains several coins) in the current directory, let's load it:
# load the image img = cv2.imread("coins.jpg")
Next, we gonna create a new copy of this image, in which we're going to draw the detected circles:
# convert BGR to RGB to be suitable for showing using matplotlib library img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # make a copy of the original image cimg = img.copy()
In order to pass the image to that method, we need to convert it to grayscale and blur the image,
cv2.medianBlur() does the job:
# convert image to grayscale img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # apply a blur using the median filter img = cv2.medianBlur(img, 5)
After that, let's detect the circles:
# finds the circles in the grayscale image using the Hough transform circles = cv2.HoughCircles(image=img, method=cv2.HOUGH_GRADIENT, dp=0.9, minDist=80, param1=110, param2=39, maxRadius=70)
In case you're wondering what does these parameters refer to, type
help(cv2.HoughCircles) and you'll find a good explanation.
Finally, let's draw and show the circles we justed detected:
for co, i in enumerate(circles[0, :], start=1): # draw the outer circle in green cv2.circle(cimg,(i,i),i,(0,255,0),2) # draw the center of the circle in red cv2.circle(cimg,(i,i),2,(0,0,255),3) # print the number of circles detected print("Number of circles detected:", co) # save the image, convert to BGR to save with proper colors # cv2.imwrite("coins_circles_detected.png", cimg) # show the image plt.imshow(cimg) plt.show()
Here is my result:
As you can see, it isn't perfect, as it doesn't detect all circles in the image, try to tune the parameters passed to
cv2.HoughCircles() method and see if you achieve better results.
Alright, that's it for now, here are the references of this tutorial:
Check the full code.
Finally, I've collected some useful resources and courses for you for further learning, I highly recommend the following courses:
Learn also: How to Perform YOLO Object Detection using OpenCV and PyTorch in Python.
Happy Coding ♥View Full Code