90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
import numpy as np
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import cv2
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from matplotlib import pyplot as plt
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import os
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print('-----')
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dirname = os.path.dirname(__file__)
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filename = os.path.join(dirname,'data/LegosBlue/blue5.jpg')
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print(os.path.exists(filename))
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img = cv2.imread(filename)
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hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
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lwr = np.array([85,50,40])
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upr = np.array([135,255,255])
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msk = cv2.inRange(hsv, lwr, upr)
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img[msk>0]=(0,0,0)
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b,g,r = cv2.split(img)
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rgb_img = cv2.merge([r,g,b])
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gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
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# smooth = cv2.bilateralFilter(gray,5,150,150)
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# smooth = cv2.medianBlur(gray,5)
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smooth = cv2.GaussianBlur(gray,(5,5),0)
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# sobelx = cv2.Sobel(smooth,cv2.CV_8UC1,1,0,ksize=5)
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# sobely = cv2.Sobel(smooth,cv2.CV_8UC1,0,1,ksize=5)
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# ret, thresh = cv2.threshold(smooth,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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# thresh = cv2.adaptiveThreshold(smooth,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,21,5)
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threshLevel = np.mean(smooth)*1.6
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ret, thresh = cv2.threshold(smooth,threshLevel,255,cv2.THRESH_BINARY)
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#thresh = cv2.Canny(smooth,200,200)
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# noise removal
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kernel = np.ones((3,3),np.uint8)
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#thresh = cv2.dilate(thresh,kernel,iterations=1)
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opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 5)
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# closing = cv2.morphologyEx(thresh,cv2.MORPH_CLOSE,kernel, iterations = 2)
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# sure background area
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sure_bg = cv2.dilate(opening,kernel,iterations=3)
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# Finding sure foreground area
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dist_transform = cv2.distanceTransform(sure_bg,cv2.DIST_L2,3)
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# Threshold
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ret, sure_fg = cv2.threshold(dist_transform,0.1*dist_transform.max(),255,0)
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# Finding unknown region
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sure_fg = np.uint8(sure_fg)
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unknown = cv2.subtract(sure_bg,sure_fg)
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# Marker labelling
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ret, markers = cv2.connectedComponents(sure_fg)
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# Add one to all labels so that sure background is not 0, but 1
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markers = markers+1
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# Now, mark the region of unknown with zero
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markers[unknown==255] = 0
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markers = cv2.watershed(img,markers)
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img[markers == -1] = [255,0,0]
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plt.subplot(421),plt.imshow(rgb_img)
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plt.title('Input Image'), plt.xticks([]), plt.yticks([])
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# plt.subplot(422),plt.imshow(thresh, 'gray')
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# plt.title("Otsu's binary threshold"), plt.xticks([]), plt.yticks([])
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plt.subplot(422),plt.imshow(thresh, 'gray')
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plt.title("smoothing"), plt.xticks([]), plt.yticks([])
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plt.subplot(423),plt.imshow(opening, 'gray')
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plt.title("morphologyEx:Closing:2x2"), plt.xticks([]), plt.yticks([])
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plt.subplot(424),plt.imshow(sure_bg, 'gray')
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plt.title("Dilation"), plt.xticks([]), plt.yticks([])
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plt.subplot(425),plt.imshow(dist_transform, 'gray')
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plt.title("Distance Transform"), plt.xticks([]), plt.yticks([])
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plt.subplot(426),plt.imshow(sure_fg, 'gray')
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plt.title("Thresholding"), plt.xticks([]), plt.yticks([])
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plt.subplot(427),plt.imshow(unknown, 'gray')
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plt.title("Unknown"), plt.xticks([]), plt.yticks([])
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plt.subplot(428),plt.imshow(img, 'gray')
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plt.title("Result from Watershed"), plt.xticks([]), plt.yticks([])
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plt.tight_layout()
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plt.show() |