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# Untitled

a guest Mar 20th, 2019 71 Never
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1. import cv2
2. import numpy as np
3.
5. cv2.imshow('Original Image', image)
6. cv2.waitKey(0)
7.
8. # Creating our 3 x 3 kernel
9. kernel_3x3 = np.ones((3, 3), np.float32) / 9
10.
11. # We use the cv2.fitler2D to conovlve the kernal with an image
12. blurred = cv2.filter2D(image, -1, kernel_3x3)
13. cv2.imshow('3x3 Kernel Blurring', blurred)
14. cv2.waitKey(0)
15.
16. # Creating our 7 x 7 kernel
17. kernel_7x7 = np.ones((7, 7), np.float32) / 49
18.
19. blurred2 = cv2.filter2D(image, -1, kernel_7x7)
20. cv2.imshow('7x7 Kernel Blurring', blurred2)
21. cv2.waitKey(0)
22.
23. cv2.destroyAllWindows()
24.
25.
26.
27. import cv2
28. import numpy as np
29.
31.
32. # Averaging done by convolving the image with a normalized box filter.
33. # This takes the pixels under the box and replaces the central element
34. # Box size needs to odd and positive
35. blur = cv2.blur(image, (3,3))
36. cv2.imshow('Averaging', blur)
37. cv2.waitKey(0)
38.
39. # Instead of box filter, gaussian kernel
40. Gaussian = cv2.GaussianBlur(image, (7,7), 0)
41. cv2.imshow('Gaussian Blurring', Gaussian)
42. cv2.waitKey(0)
43.
44. # Takes median of all the pixels under kernel area and central
45. # element is replaced with this median value
46. median = cv2.medianBlur(image, 5)
47. cv2.imshow('Median Blurring', median)
48. cv2.waitKey(0)
49.
50. # Bilateral is very effective in noise removal while keeping edges sharp
51. bilateral = cv2.bilateralFilter(image, 9, 75, 75)
52. cv2.imshow('Bilateral Blurring', bilateral)
53. cv2.waitKey(0)
54. cv2.destroyAllWindows()
55.
56.
57.
58. import numpy as np
59. import cv2
60.
62.
63. # Parameters, after None are - the filter strength 'h' (5-10 is a good range)
64. # Next is hForColorComponents, set as same value as h again
65. #
66. dst = cv2.fastNlMeansDenoisingColored(image, None, 6, 6, 7, 21)
67.
68. cv2.imshow('Fast Means Denoising', dst)
69. cv2.waitKey(0)
70.
71. cv2.destroyAllWindows()
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