Approach : The approach we would be used to detect the shape of a given polygon will be based on classifying the detected shape on the basis of a number of sides it has. For example, if the detected polynomial has 3 sides, then it could be considered as a triangle, if the polynomial has 4 sides then it could be classified as a square or a rectangle.
Prerequisite :
- Make sure you have Python3, OpenCV, numpy already installed on your computer.
- Basic knowledge about OpenCV would be helpful – Basics of OpenCV
- Make sure to save the image in which shapes is to be detected in your local directory
Implementation : In the following code, we will be detecting an arrow-shaped object from the image ‘arrow.jpg’. The shape will be detected on the basis of the number of sides it has
Code: Python program to detect polygons in an image
# Python code to detect an arrow (seven-sided shape) from an image. import numpy as np import cv2 # Reading image img2 = cv2.imread( 'arrow.jpg' , cv2.IMREAD_COLOR) # Reading same image in another variable and # converting to gray scale. img = cv2.imread( 'arrow.jpg' , cv2.IMREAD_GRAYSCALE) # Converting image to a binary image # (black and white only image). _,threshold = cv2.threshold(img, 110 , 255 , cv2.THRESH_BINARY) # Detecting shapes in image by selecting region # with same colors or intensity. contours,_ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Searching through every region selected to # find the required polygon. for cnt in contours : area = cv2.contourArea(cnt) # Shortlisting the regions based on there area. if area > 400 : approx = cv2.approxPolyDP(cnt, 0.009 * cv2.arcLength(cnt, True ), True ) # Checking if the no. of sides of the selected region is 7. if ( len (approx) = = 7 ): cv2.drawContours(img2, [approx], 0 , ( 0 , 0 , 255 ), 5 ) # Showing the image along with outlined arrow. cv2.imshow( 'image2' , img2) # Exiting the window if 'q' is pressed on the keyboard. if cv2.waitKey( 0 ) & 0xFF = = ord ( 'q' ): cv2.destroyAllWindows() |
Note : The parameter ‘110’ in threshold could be adjusted as per need if the object is of different colour and is based on trial and error.
Result :
Image with arrow
Binary Image
Outlined Arrow
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