80 lines
1.9 KiB
Python
80 lines
1.9 KiB
Python
import cv2 as cv
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import numpy as np
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from skimage import measure, morphology, color, segmentation
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import matplotlib.pyplot as plt
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file = 'proj-area-3.jpg'
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original = cv.imread(file)
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original = cv.cvtColor(original, cv.COLOR_BGR2RGB)
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img = original
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img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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retval, thresh_gray = cv.threshold(img, 200, 255, cv.THRESH_BINARY)
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def remove_dirt(image):
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image = morphology.area_closing(image, area_threshold=250, connectivity=1)
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# image = morphology.opening(image, morphology.square(5))
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return image
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def calculate_area(countour):
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c = np.expand_dims(countour.astype(np.float32), 1)
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c = cv.UMat(c)
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return cv.contourArea(c)
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def center_of_mass(X):
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x = X[:,0]
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y = X[:,1]
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g = (x[:-1]*y[1:] - x[1:]*y[:-1])
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A = 0.5*g.sum()
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cx = ((x[:-1] + x[1:])*g).sum()
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cy = ((y[:-1] + y[1:])*g).sum()
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return 1./(6*A)*np.array([cx,cy])
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img = remove_dirt(thresh_gray)
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# alpha = 1 # Contrast control (1.0-3.0)
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# beta = 1 # Brightness control (0-100)
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# img = cv.convertScaleAbs(img, alpha=alpha, beta=beta)
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# img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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# img = cv.medianBlur(img, 5)
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# retval, thresh_gray = cv.threshold(img, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
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# img = cv.medianBlur(img, 5)
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# img = cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV, 51, 15)
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contours = measure.find_contours(array=img, level=100)
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fig, ax = plt.subplots()
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ax.imshow(img, cmap=plt.cm.gray, alpha=0)
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index = 1
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for contour in contours:
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if calculate_area(contour) > 300:
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ax.plot(contour[:, 1], contour[:, 0], linewidth=0.5, color='orangered')
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cX, cY = center_of_mass(contour)
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plt.text(cY, cX, index, color='black', fontsize=6)
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index += 1
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ax.axis('image')
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ax.set_xticks([])
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ax.set_yticks([])
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plt.savefig('output.png', dpi=300)
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# cv.imwrite('proj-area-1-processed.jpg', remove_dirt(img)) |