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# Untitled

a guest Mar 21st, 2019 52 Never
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1. import cv2
2. import numpy as np
3.
5.
6. height, width = image.shape
7.
8. # Extract Sobel Edges
9. sobel_x = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=5)
10. sobel_y = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=5)
11.
12. cv2.imshow('Original', image)
13. cv2.waitKey(0)
14. cv2.imshow('Sobel X', sobel_x)
15. cv2.waitKey(0)
16. cv2.imshow('Sobel Y', sobel_y)
17. cv2.waitKey(0)
18.
19. sobel_OR = cv2.bitwise_or(sobel_x, sobel_y)
20. cv2.imshow('sobel_OR', sobel_OR)
21. cv2.waitKey(0)
22.
23. laplacian = cv2.Laplacian(image, cv2.CV_64F)
24. cv2.imshow('Laplacian', laplacian)
25. cv2.waitKey(0)
26.
27.
28.
29.
30.
31. ##  Then, we need to provide two values: threshold1 and threshold2. Any gradient value larger than threshold2
32. # is considered to be an edge. Any value below threshold1 is considered not to be an edge.
33. #Values in between threshold1 and threshold2 are either classiﬁed as edges or non-edges based on how their
34. #intensities are “connected”. In this case, any gradient values below 60 are considered non-edges
35. #whereas any values above 120 are considered edges.
36.
37.
38. # Canny Edge Detection uses gradient values as thresholds
39. # The first threshold gradient
40. canny = cv2.Canny(image, 50, 120)
41. cv2.imshow('Canny', canny)
42. cv2.waitKey(0)
43.
44. cv2.destroyAllWindows()
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