图片数据增强库imgaug的安装与使用

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图片数据增强库imgaug的安装与使用


pip3 install imgaug
Installing collected packages: Shapely, imgaug
Successfully installed Shapely-1.7.1 imgaug-0.4.0

1. 
   
2. import os
   
3. import glob
   
4. import cv2
   
5. import numpy as np
   
6. import imageio
   
7. import imgaug as ia
   
8. import imgaug.augmenters as iaa
   
9. 
   
10. input_dir = "/home/jiang/DataSets/bctc_face_special"
    
11. output_dir = "/home/jiang/DataSets/bctc_face_special_imgaug"
    
12. # output_dir = "/home/jiang/DataSets/bctc_guang_zhao/0_no_pass"
    
13. 
    
14. ia.seed(10)
    
15. 
    
16. sometimes = lambda aug: iaa.Sometimes(0.5, aug)
    
17. 
    
18. # Pipeline:
    
19. # (1) Crop images from each side by 1-16px, do not resize the results
    
20. #     images back to the input size. Keep them at the cropped size.
    
21. # (2) Horizontally flip 50% of the images.
    
22. # (3) Blur images using a gaussian kernel with sigma between 0.0 and 3.0.
    
23. seq_simple = iaa.Sequential([
    
24.     # iaa.Crop(px=(1, 16), keep_size=True),
    
25.     # iaa.Fliplr(0.5),
    
26.     # iaa.Affine(rotate=(-25, 25)),
    
27.     # iaa.GaussianBlur(sigma=(0, 3.0)),
    
28.     # iaa.AdditiveGaussianNoise(scale=(30, 90)),
    
29. ])
    
30. 
    
31. seq = iaa.Sequential(
    
32.     [
    
33.         # apply the following augmenters to most images
    
34.         iaa.Fliplr(0.5), # horizontally flip 50% of all images
    
35.         # crop images by -5% to 10% of their height/width
    
36.         sometimes(iaa.CropAndPad(
    
37.             percent=(-0.05, 0.1),
    
38.             pad_mode=ia.ALL,
    
39.             pad_cval=(0, 255)
    
40.         )),
    
41.         sometimes(iaa.Affine(
    
42.             scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis
    
43.             translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis)
    
44.             rotate=(-25, 25), # rotate by -45 to +45 degrees
    
45.             shear=(-16, 16), # shear by -16 to +16 degrees
    
46.             order=[0, 1], # use nearest neighbour or bilinear interpolation (fast)
    
47.             cval=(0, 255), # if mode is constant, use a cval between 0 and 255
    
48.             mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
    
49.         )),
    
50.         # execute 0 to 5 of the following (less important) augmenters per image
    
51.         # don't execute all of them, as that would often be way too strong
    
52.         iaa.SomeOf((0, 8),
    
53.             [
    
54.                 iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200)), # convert images into their superpixel representation
    
55.                 iaa.OneOf([
    
56.                     iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0
    
57.                     iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7
    
58.                     iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7
    
59.                 ]),
    
60.                 # search either for all edges or for directed edges,
    
61.                 # blend the result with the original image using a blobby mask
    
62. 
    
63.                 iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images
    
64.                 iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels
    
65.                 iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value)
    
66.                 iaa.AddToHueAndSaturation((-20, 20)), # change hue and saturation
    
67.                 # either change the brightness of the whole image (sometimes
    
68.                 # per channel) or change the brightness of subareas
    
69.                 iaa.LinearContrast((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast
    
70.                 iaa.Grayscale(alpha=(0.8, 1.0)),
    
71.                 sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths)
    
72.                 sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))), # sometimes move parts of the image around
    
73.                 sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1)))
    
74.             ],
    
75.             random_order=True
    
76.         )
    
77.     ],
    
78.     random_order=True
    
79. )
    
80. 
    
81. 
    
82. for img_path in glob.glob(os.path.join(input_dir, "*.png")):
    
83.     img_name = img_path.strip().split("/")[-1].split(".")[0]
    
84.     print("图片链接：", img_path)
    
85.     img_ori = imageio.imread(img_path)
    
86.     # print("原始图片的shape：", img_ori.shape)
    
87.     img_ori = img_ori[:, :, :3]
    
88.     assert img_ori.shape[2] == 3, "必须是3通道的图片才能增强!"
    
89.     # ia.imshow(img_ori)
    
90.     # 一张原始图片要 增强多少张？
    
91.     images = [img_ori] * 399
    
92.     images_aug = seq.augment_images(images=images)
    
93. 
    
94.     img_ori = cv2.resize(img_ori, dsize=(80, 80))
    
95.     imageio.imwrite(os.path.join(output_dir, "%s_ori.png" % img_name), img_ori)
    
96. 
    
97.     # ia.imshow(np.hstack(images_aug))
    
98.     # ia.imshow(ia.draw_grid(images_aug, cols=8, rows=5))
    
99.     for idx, image_aug in enumerate(images_aug):
    
100.         # 保存增强后的图片
     
101.         # print(type(image_aug))
     
102.         image_aug = cv2.resize(image_aug, dsize=(80, 80))
     
103.         imageio.imwrite(os.path.join(output_dir, "%s_aug_%d.png" % (img_name, idx)), image_aug)
     
104. 
     
105.     assert 0 == 1, "停"
     
106.     pass
     

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