94 lines
2.4 KiB
Python
94 lines
2.4 KiB
Python
import cv2
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import numpy as np
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def super_tresh_main(img):
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image = img
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# cv2.imshow("Image", image)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# cv2.imshow("gray", gray)
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blur = cv2.GaussianBlur(gray, (5, 5), 0)
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# cv2.imshow("blur", blur)
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thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
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# thresh = cv2.bitwise_not(thresh)
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# cv2.imshow("thresh", thresh)
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# cv2.waitKey()
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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max_area = 0
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c = 0
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for i in contours:
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area = cv2.contourArea(i)
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if area > 1000:
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if area > max_area:
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max_area = area
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best_cnt = i
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image = cv2.drawContours(image, contours, c, (0, 255, 0), 1)
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c += 1
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mask = np.zeros((gray.shape), np.uint8)
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cv2.drawContours(mask, [best_cnt], 0, 255, -1)
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cv2.drawContours(mask, [best_cnt], 0, 0, 1)
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# cv2.imshow("mask", mask)
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out = np.zeros_like(gray)
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out[mask == 255] = gray[mask == 255]
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# cv2.imshow("New image", out)
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blur = cv2.GaussianBlur(out, (5, 5), 0)
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# cv2.imshow("blur1", blur)
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thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
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return cv2.bitwise_not(thresh)
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def super_tresh_needle(img):
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image = img
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# cv2.imshow("Image", image)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# cv2.imshow("gray", gray)
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blur = cv2.GaussianBlur(gray, (5, 5), 0)
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# cv2.imshow("blur", blur)
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thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
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return cv2.bitwise_not(thresh)
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'''
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# cv2.imshow("thresh", thresh)
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# cv2.waitKey()
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contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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max_area = 0
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c = 0
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for i in contours:
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area = cv2.contourArea(i)
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if area > 1000:
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if area > max_area:
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max_area = area
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best_cnt = i
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image = cv2.drawContours(image, contours, c, (0, 255, 0), 1)
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c += 1
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mask = np.zeros((gray.shape), np.uint8)
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cv2.drawContours(mask, [best_cnt], 0, 255, -1)
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cv2.drawContours(mask, [best_cnt], 0, 0, 1)
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# cv2.imshow("mask", mask)
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out = np.zeros_like(gray)
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out[mask == 255] = gray[mask == 255]
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# cv2.imshow("New image", out)
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blur = cv2.GaussianBlur(out, (5, 5), 0)
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# cv2.imshow("blur1", blur)
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thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
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return cv2.bitwise_not(thresh)
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''' |