39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import cv2 as cv
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import numpy as np
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from matplotlib import pyplot as plt
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img = cv.imread('screen.jpg', cv.IMREAD_GRAYSCALE)
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assert img is not None, "file could not be read, check with os.path.exists()"
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img2 = img.copy()
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template = cv.imread('bobber.jpg', cv.IMREAD_GRAYSCALE)
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assert template is not None, "file could not be read, check with os.path.exists()"
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w, h = template.shape[::-1]
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# All the 6 methods for comparison in a list
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methods = ['TM_CCOEFF', 'TM_CCOEFF_NORMED', 'TM_CCORR',
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'TM_CCORR_NORMED', 'TM_SQDIFF', 'TM_SQDIFF_NORMED']
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for meth in methods:
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img = img2.copy()
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method = getattr(cv, meth)
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# Apply template Matching
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res = cv.matchTemplate(img,template,method)
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min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
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# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
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if method in [cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED]:
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top_left = min_loc
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else:
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top_left = max_loc
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bottom_right = (top_left[0] + w, top_left[1] + h)
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cv.rectangle(img,top_left, bottom_right, 255, 2)
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plt.subplot(121),plt.imshow(res,cmap = 'gray')
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plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
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plt.subplot(122),plt.imshow(img,cmap = 'gray')
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plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
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plt.suptitle(meth)
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plt.show() |