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In the C++ equivalent of this example a check is made whether the vector (here in Python we have a list) actually has any circles in it that is whether the Hough circles function has managed to find any in the given image. This check is missing for the Python example and if no circles are found the application breaks.
42 lines
1.2 KiB
Python
Executable File
42 lines
1.2 KiB
Python
Executable File
#!/usr/bin/python
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'''
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This example illustrates how to use cv2.HoughCircles() function.
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Usage:
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houghcircles.py [<image_name>]
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image argument defaults to ../data/board.jpg
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import cv2
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import numpy as np
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import sys
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if __name__ == '__main__':
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print(__doc__)
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try:
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fn = sys.argv[1]
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except IndexError:
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fn = "../data/board.jpg"
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src = cv2.imread(fn, 1)
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img = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
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img = cv2.medianBlur(img, 5)
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cimg = src.copy() # numpy function
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circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 10, np.array([]), 100, 30, 1, 30)
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if circles != None: # Check if circles have been found and only then iterate over these and add them to the image
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a, b, c = circles.shape
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for i in range(b):
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cv2.circle(cimg, (circles[0][i][0], circles[0][i][1]), circles[0][i][2], (0, 0, 255), 3, cv2.LINE_AA)
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cv2.circle(cimg, (circles[0][i][0], circles[0][i][1]), 2, (0, 255, 0), 3, cv2.LINE_AA) # draw center of circle
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cv2.imshow("source", src)
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cv2.imshow("detected circles", cimg)
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cv2.waitKey(0)
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