OpenCV with Python By Example

2.0

1. import cv2
   
2. import numpy as np
   
3. 
   
4. img = cv2.imread('../images/input_sharp_edges.jpg')
   
5. rows, cols = img.shape[:2]
   
6. 
   
7. kernel_identity = np.array([[0,0,0], [0,1,0], [0,0,0]])
   
8. kernel_3x3 = np.ones((3,3), np.float32) / 9.0
   
9. kernel_5x5 = np.ones((5,5), np.float32) / 25.0
   
10. 
    
11. cv2.imshow('Original', img)
    
12. 
    
13. output = cv2.filter2D(img, -1, kernel_identity)
    
14. cv2.imshow('Identity filter', output)
    
15. 
    
16. output = cv2.filter2D(img, -1, kernel_3x3)
    
17. cv2.imshow('3x3 filter', output)
    
18. 
    
19. output = cv2.filter2D(img, -1, kernel_5x5)
    
20. cv2.imshow('5x5 filter', output)
    
21. 
    
22. cv2.waitKey()

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1. import cv2
   
2. import numpy as np
   
3. 
   
4. img = cv2.imread('../images/input_train.jpg', cv2.IMREAD_GRAYSCALE)
   
5. rows, cols = img.shape
   
6. 
   
7. sobel_horizontal_1 = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)
   
8. sobel_horizontal_2 = cv2.Sobel(img, cv2.CV_64F, 2, 0, ksize=5)
   
9. sobel_horizontal_3 = cv2.Sobel(img, cv2.CV_64F, 3, 0, ksize=5)
   
10. sobel_vertical = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)
    
11. laplacian = cv2.Laplacian(img, cv2.CV_64F)
    
12. canny = cv2.Canny(img, 50, 240)
    
13. 
    
14. cv2.imshow('Original', img)
    
15. #cv2.imshow('Sobel horizontal 1', sobel_horizontal_1)
    
16. #cv2.imshow('Sobel horizontal 2', sobel_horizontal_2)
    
17. #cv2.imshow('Sobel horizontal 3', sobel_horizontal_3)
    
18. #cv2.imshow('Sobel vertical', sobel_vertical)
    
19. cv2.imshow('Laplacian', laplacian)
    
20. cv2.imshow('Canny', canny)
    
21. 
    
22. cv2.waitKey()

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1. import cv2
   
2. import numpy as np
   
3. 
   
4. img = cv2.imread('../images/input_histogram.jpg', 0)
   
5. histeq = cv2.equalizeHist(img)
   
6. 
   
7. #cv2.imshow('Input', img)
   
8. #cv2.imshow('Histogram equalized', histeq)
   
9. 
   
10. ##################
    
11. # Histogram equalization of color images
    
12. 
    
13. img = cv2.imread('../images/input_histogram_color.jpg')
    
14. 
    
15. img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
    
16. img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])
    
17. 
    
18. img_output = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)
    
19. 
    
20. cv2.imshow('Color input image', img)
    
21. cv2.imshow('Histogram equalized', img_output)
    
22. 
    
23. cv2.waitKey()

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1. # http://lodev.org/cgtutor/filtering.html
   
2. 
   
3. import cv2
   
4. import numpy as np
   
5. 
   
6. #img = cv2.imread('../images/input_sharp_edges.jpg', cv2.IMREAD_GRAYSCALE)
   
7. img = cv2.imread('../images/input_tree.jpg')
   
8. rows, cols = img.shape[:2]
   
9. #cv2.imshow('Original', img)
   
10. 
    
11. ###################
    
12. # Motion Blur
    
13. size = 15
    
14. kernel_motion_blur = np.zeros((size, size))
    
15. kernel_motion_blur[int((size-1)/2), :] = np.ones(size)
    
16. kernel_motion_blur = kernel_motion_blur / size
    
17. output = cv2.filter2D(img, -1, kernel_motion_blur)
    
18. #cv2.imshow('Motion Blur', output)
    
19. 
    
20. ###################
    
21. # Sharpening
    
22. kernel_sharpen_1 = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
    
23. kernel_sharpen_2 = np.array([[1,1,1], [1,-7,1], [1,1,1]])
    
24. kernel_sharpen_3 = np.array([[-1,-1,-1,-1,-1],
    
25.                              [-1,2,2,2,-1],
    
26.                              [-1,2,8,2,-1],
    
27.                              [-1,2,2,2,-1],
    
28.                              [-1,-1,-1,-1,-1]]) / 8.0
    
29. output_1 = cv2.filter2D(img, -1, kernel_sharpen_1)
    
30. output_2 = cv2.filter2D(img, -1, kernel_sharpen_2)
    
31. output_3 = cv2.filter2D(img, -1, kernel_sharpen_3)
    
32. #cv2.imshow('Sharpening', output_1)
    
33. #cv2.imshow('Excessive Sharpening', output_2)
    
34. #cv2.imshow('Edge Enhancement', output_3)
    
35. 
    
36. ###################
    
37. # Embossing
    
38. img_emboss_input = cv2.imread('../images/input_house.jpg')
    
39. kernel_emboss_1 = np.array([[0,-1,-1],
    
40.                             [1,0,-1],
    
41.                             [1,1,0]])
    
42. kernel_emboss_2 = np.array([[-1,-1,0],
    
43.                             [-1,0,1],
    
44.                             [0,1,1]])
    
45. kernel_emboss_3 = np.array([[1,0,0],
    
46.                             [0,0,0],
    
47.                             [0,0,-1]])
    
48. gray_img = cv2.cvtColor(img_emboss_input,cv2.COLOR_BGR2GRAY)
    
49. output_1 = cv2.filter2D(gray_img, -1, kernel_emboss_1)
    
50. output_2 = cv2.filter2D(gray_img, -1, kernel_emboss_2)
    
51. output_3 = cv2.filter2D(gray_img, -1, kernel_emboss_3)
    
52. cv2.imshow('Input', img_emboss_input)
    
53. cv2.imshow('Embossing - South West', output_1 + 128)
    
54. cv2.imshow('Embossing - South East', output_2 + 128)
    
55. cv2.imshow('Embossing - North West', output_3 + 128)
    
56. 
    
57. ###################
    
58. # Erosion and dilation
    
59. 
    
60. img = cv2.imread('../images/input_morphology.png',0)
    
61. kernel = np.ones((5,5), np.uint8)
    
62. img_erosion = cv2.erode(img, kernel, iterations=1)
    
63. img_dilation = cv2.dilate(img, kernel, iterations=1)
    
64. #cv2.imshow('Input', img)
    
65. #cv2.imshow('Erosion', img_erosion)
    
66. #cv2.imshow('Dilation', img_dilation)
    
67. 
    
68. cv2.waitKey()

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1. import cv2
   
2. import numpy as np
   
3. 
   
4. img = cv2.imread('../images/input_flowers.jpg')
   
5. rows, cols = img.shape[:2]
   
6. 
   
7. kernel_x = cv2.getGaussianKernel(cols,200)
   
8. kernel_y = cv2.getGaussianKernel(rows,200)
   
9. kernel = kernel_y * kernel_x.T
   
10. mask = 255 * kernel / np.linalg.norm(kernel)
    
11. output = np.copy(img)
    
12. 
    
13. for i in range(3):
    
14.     output[:,:,i] = output[:,:,i] * mask
    
15. 
    
16. #cv2.imshow('Original', img)
    
17. #cv2.imshow('Vignette', output)
    
18. 
    
19. ################
    
20. # Shifting the focus
    
21. 
    
22. kernel_x = cv2.getGaussianKernel(int(1.5*cols),200)
    
23. kernel_y = cv2.getGaussianKernel(int(1.5*rows),200)
    
24. kernel = kernel_y * kernel_x.T
    
25. mask = 255 * kernel / np.linalg.norm(kernel)
    
26. mask = mask[int(0.5*rows):, int(0.5*cols):]
    
27. output = np.copy(img)
    
28. 
    
29. for i in range(3):
    
30.     output[:,:,i] = output[:,:,i] * mask
    
31. 
    
32. cv2.imshow('Input', img)
    
33. cv2.imshow('Vignette with shifted focus', output)
    
34. 
    
35. 
    
36. cv2.waitKey()

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1. import cv2
   
2. import numpy as np
   
3. 
   
4. img = cv2.imread('../images/input_bilateral_filter.jpg')
   
5. img = cv2.resize(img, None, fx=0.4, fy=0.4, interpolation=cv2.INTER_AREA)
   
6. 
   
7. img_gaussian = cv2.GaussianBlur(img, (13,13), 0)
   
8. img_bilateral = cv2.bilateralFilter(img, 13, 70, 50)
   
9. 
   
10. cv2.imshow('Input', img)
    
11. cv2.imshow('Gaussian filter', img_gaussian)
    
12. cv2.imshow('Bilateral filter', img_bilateral)
    
13. cv2.waitKey()

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1. import cv2
   
2. import numpy as np
   
3. 
   
4. img = cv2.imread('../images/input_median_filter.png')
   
5. output = cv2.medianBlur(img, 7)
   
6. cv2.imshow('Input', img)
   
7. cv2.imshow('Median filter', output)
   
8. cv2.waitKey()

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1. import cv2
   
2. import numpy as np
   
3. 
   
4. left_ear_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_mcs_leftear.xml')
   
5. right_ear_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_mcs_rightear.xml')
   
6. 
   
7. if left_ear_cascade.empty():
   
8.         raise IOError('Unable to load the left ear cascade classifier xml file')
   
9. 
   
10. if right_ear_cascade.empty():
    
11.         raise IOError('Unable to load the right ear cascade classifier xml file')
    
12. 
    
13. img = cv2.imread('../images/input_ear_3.jpg')
    
14. 
    
15. gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
16. 
    
17. left_ear = left_ear_cascade.detectMultiScale(gray, 1.3, 5)
    
18. right_ear = right_ear_cascade.detectMultiScale(gray, 1.3, 5)
    
19. 
    
20. for (x,y,w,h) in left_ear:
    
21.     cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 3)
    
22. 
    
23. for (x,y,w,h) in right_ear:
    
24.     cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 3)
    
25. 
    
26. cv2.imshow('Ear Detector', img)
    
27. cv2.waitKey()
    
28. cv2.destroyAllWindows()

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1. import cv2
   
2. import numpy as np
   
3. 
   
4. face_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_frontalface_alt.xml')
   
5. eye_cascade = cv2.CascadeClassifier('cascade_files/haarcascade_eye.xml')
   
6. 
   
7. if face_cascade.empty():
   
8.         raise IOError('Unable to load the face cascade classifier xml file')
   
9. 
   
10. if eye_cascade.empty():
    
11.         raise IOError('Unable to load the eye cascade classifier xml file')
    
12. 
    
13. cap = cv2.VideoCapture(0)
    
14. ds_factor = 0.5
    
15. 
    
16. while True:
    
17.     ret, frame = cap.read()
    
18.     frame = cv2.resize(frame, None, fx=ds_factor, fy=ds_factor, interpolation=cv2.INTER_AREA)
    
19.     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
20. 
    
21.     faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    
22.     for (x,y,w,h) in faces:
    
23.         roi_gray = gray[y:y+h, x:x+w]
    
24.         roi_color = frame[y:y+h, x:x+w]
    
25.         eyes = eye_cascade.detectMultiScale(roi_gray)
    
26.         for (x_eye,y_eye,w_eye,h_eye) in eyes:
    
27.             center = (int(x_eye + 0.5*w_eye), int(y_eye + 0.5*h_eye))
    
28.             radius = int(0.3 * (w_eye + h_eye))
    
29.             color = (0, 255, 0)
    
30.             thickness = 3
    
31.             cv2.circle(roi_color, center, radius, color, thickness)
    
32. 
    
33.     cv2.imshow('Eye Detector', frame)
    
34. 
    
35.     c = cv2.waitKey(1)
    
36.     if c == 27:
    
37.         break
    
38. 
    
39. cap.release()
    
40. cv2.destroyAllWindows()

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