Shape Detection Using Opencv B Log If you have only these regular shapes, there is a simple procedure as follows : find contours in the image (image should be binary as given in your question) approximate each contour using approxpolydp function. check number of elements in the approximated contours of all shapes to recognize shape. for eg, square will have 4, pentagon will have. Tip: we can draw boundaries by getting corner points also using line function in opencv for contour in contours : area = cv2 . contourarea ( contour ) # gives area of contour perimeter = cv2 . arclength ( contour , closed = true ) # gives perimeter of contour borders = cv2 . approxpolydp ( curve = contour , epsilon = 0.05 * perimeter , closed.
Shape Detection Using Opencv B Log 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, and so on. let. The first step in building our shape detector is to write some code to encapsulate the shape identification logic. let’s go ahead and define our shapedetector. open up the shapedetector.py file and insert the following code: # import the necessary packages. import cv2. class shapedetector: def init (self): pass. We need to convert this image to grayscale 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. In this loop draw a outline of shapes (using drawcontours() ) and find out center point of shape. classify the detected shape on the basis of a number of contour points it has and put the detected shape name at the center point of shape. function used . cv2.findcontours(): basically this method find outs all the boundary points of shape in image.
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