036、imgaug图像增强之Bounding Boxes变换

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036、imgaug图像增强之Bounding Boxes变换

1. 
   
2. import imgaug as ia
   
3. import imgaug.augmenters as iaa
   
4. import numpy as np
   
5. import imageio
   
6. 
   
7. print(ia.__version__)
   
8. 
   
9. img_path = 'dfy_imgs/ai111.jpg'
   
10. image = imageio.imread(img_path)
    
11. print(type(image), image.shape)
    
12. ia.seed(666)
    
13. 
    
14. 
    
15. # 定义2个bounding box
    
16. bbs = ia.BoundingBoxesOnImage([
    
17.     ia.BoundingBox(x1=65, y1=100, x2=200, y2=150),
    
18.     ia.BoundingBox(x1=150, y1=80, x2=200, y2=130)
    
19. ], shape=image.shape)
    
20. 
    
21. seq = iaa.Sequential([
    
22.     iaa.Multiply((1.2, 1.5)),   # 改变亮度, 不影响bounding box
    
23.     iaa.Affine(translate_px={"x": 40, "y": 60}, scale=0.8)   # 平移后缩放,会影响bounding box
    
24. ])
    
25. 
    
26. # 固定变换
    
27. seq_det = seq.to_deterministic()
    
28. 
    
29. # 变换图像和bounding box
    
30. image_aug = seq_det.augment_images([image])[0]
    
31. bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]
    
32. 
    
33. # 打印坐标
    
34. # use .x1_int, .y_int, ... to get integer coordinates
    
35. for i in range(len(bbs.bounding_boxes)):
    
36.     before = bbs.bounding_boxes[i]
    
37.     after = bbs_aug.bounding_boxes[i]
    
38.     print("BB %d: (%.4f, %.4f, %.4f, %.4f) -> (%.4f, %.4f, %.4f, %.4f)" % (
    
39.         i,
    
40.         before.x1, before.y1, before.x2, before.y2,
    
41.         after.x1, after.y1, after.x2, after.y2)
    
42.     )
    
43. 
    
44. # image with BBs before/after augmentation (shown below)
    
45. image_before = bbs.draw_on_image(image, size=2)
    
46. image_after = bbs_aug.draw_on_image(image_aug, size=2, color=[0, 0, 255])
    
47. 
    
48. ia.imshow(np.hstack([image_before, image_after]))
    
49. 
    

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