038、imgaug图像增强之混合叠加图片

东方耀

038、imgaug图像增强之混合叠加图片

1. # -*- coding: utf-8 -*-
   
2. __author__ = u'东方耀 微信：dfy_88888'
   
3. __date__ = '2020/1/13 20:21'
   
4. __product__ = 'PyCharm'
   
5. __filename__ = 'Overlaying images'
   
6. 
   
7. '''
   
8. 混合叠加图片
   
9. augment会直接改变图片而把原图舍弃掉了。有时我们需要改变图像的局部,或者将原来的图片跟新变换的图片结合起来。
   
10. 这可以通过给变换前后的图片配上一定的权重(参数)或者使用一个pixel-wise的mask
    
11. '''
    
12. import imgaug as ia
    
13. import imgaug.augmenters as iaa
    
14. import numpy as np
    
15. 
    
16. 
    
17. image = ia.quokka(size=(256, 256))
    
18. print(type(image), image.shape)
    
19. ia.seed(666)
    
20. 
    
21. batch_images = [image] * 8
    
22. 
    
23. seq1 = iaa.Sequential(children=[
    
24.     # First row
    
25.     iaa.Alpha(
    
26.         (0.0, 1.0),
    
27.         first=iaa.MedianBlur(11),
    
28.         per_channel=True
    
29.     )
    
30. ])
    
31. 
    
32. batch_images_row1 = seq1.augment_images(batch_images)
    
33. batch_images_row1 = np.hstack(batch_images_row1)
    
34. # ia.imshow(batch_images_row1)
    
35. 
    
36. 
    
37. seq2 = iaa.Sequential(children=[
    
38.     # Second row
    
39.     iaa.SimplexNoiseAlpha(
    
40.         first=iaa.EdgeDetect(1.0),
    
41.         per_channel=False
    
42.     )
    
43. ])
    
44. 
    
45. batch_images_row2 = seq2.augment_images(batch_images)
    
46. batch_images_row2 = np.hstack(batch_images_row2)
    
47. 
    
48. seq3 = iaa.Sequential(children=[
    
49.     # Third row
    
50.     iaa.SimplexNoiseAlpha(
    
51.         first=iaa.EdgeDetect(1.0),
    
52.         # second=iaa.ContrastNormalization((0.5, 2.0)),
    
53.         second=iaa.contrast.LinearContrast(alpha=(0.5, 2.0)),
    
54.         per_channel=0.5
    
55.     )
    
56. ])
    
57. 
    
58. batch_images_row3 = seq3.augment_images(batch_images)
    
59. batch_images_row3 = np.hstack(batch_images_row3)
    
60. 
    
61. seq4 = iaa.Sequential(children=[
    
62.     # Forth row
    
63.     iaa.FrequencyNoiseAlpha(
    
64.         first=iaa.Affine(
    
65.             rotate=(-10, 10),
    
66.             translate_px={"x": (-4, 4), "y": (-4, 4)}
    
67.         ),
    
68.         second=iaa.AddToHueAndSaturation((-40, 40)),
    
69.         per_channel=0.5
    
70.     )
    
71. ])
    
72. 
    
73. batch_images_row4 = seq4.augment_images(batch_images)
    
74. batch_images_row4 = np.hstack(batch_images_row4)
    
75. 
    
76. seq5 = iaa.Sequential(children=[
    
77.     # Fifth row
    
78.     iaa.SimplexNoiseAlpha(
    
79.         first=iaa.SimplexNoiseAlpha(
    
80.             first=iaa.EdgeDetect(1.0),
    
81.             second=iaa.contrast.LinearContrast(alpha=(0.5, 2.0)),
    
82.             per_channel=True
    
83.         ),
    
84.         second=iaa.FrequencyNoiseAlpha(
    
85.             exponent=(-2.5, -1.0),
    
86.             first=iaa.Affine(
    
87.                 rotate=(-10, 10),
    
88.                 translate_px={"x": (-4, 4), "y": (-4, 4)}
    
89.             ),
    
90.             second=iaa.AddToHueAndSaturation((-40, 40)),
    
91.             per_channel=True
    
92.         ),
    
93.         per_channel=True,
    
94.         aggregation_method="max",
    
95.         sigmoid=False
    
96.     )
    
97. ])
    
98. 
    
99. batch_images_row5 = seq5.augment_images(batch_images)
    
100. batch_images_row5 = np.hstack(batch_images_row5)
     
101. 
     
102. 
     
103. ia.imshow(np.vstack([batch_images_row1, batch_images_row2, batch_images_row3, batch_images_row4, batch_images_row5]))
     
104. 
     

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