pip install pycocotools==2.0.0

1. __author__ = 'tylin'
2. __version__ = '2.0'
3. # Interface for accessing the Microsoft COCO dataset.
4. 
5. # Microsoft COCO is a large image dataset designed for object detection,
6. # segmentation, and caption generation. pycocotools is a Python API that
7. # assists in loading, parsing and visualizing the annotations in COCO.
8. # Please visit http://mscoco.org/ for more information on COCO, including
9. # for the data, paper, and tutorials. The exact format of the annotations
10. # is also described on the COCO website. For example usage of the pycocotools
11. # please see pycocotools_demo.ipynb. In addition to this API, please download both
12. # the COCO images and annotations in order to run the demo.
13. 
14. # An alternative to using the API is to load the annotations directly
15. # into Python dictionary
16. # Using the API provides additional utility functions. Note that this API
17. # supports both *instance* and *caption* annotations. In the case of
18. # captions not all functions are defined (e.g. categories are undefined).
19. 
20. # The following API functions are defined:
21. #  COCO       - COCO api class that loads COCO annotation file and prepare data structures.
22. #  decodeMask - Decode binary mask M encoded via run-length encoding.
23. #  encodeMask - Encode binary mask M using run-length encoding.
24. #  getAnnIds  - Get ann ids that satisfy given filter conditions.
25. #  getCatIds  - Get cat ids that satisfy given filter conditions.
26. #  getImgIds  - Get img ids that satisfy given filter conditions.
27. #  loadAnns   - Load anns with the specified ids.
28. #  loadCats   - Load cats with the specified ids.
29. #  loadImgs   - Load imgs with the specified ids.
30. #  annToMask  - Convert segmentation in an annotation to binary mask.
31. #  showAnns   - Display the specified annotations.
32. #  loadRes    - Load algorithm results and create API for accessing them.
33. #  download   - Download COCO images from mscoco.org server.
34. # Throughout the API "ann"=annotation, "cat"=category, and "img"=image.
35. # Help on each functions can be accessed by: "help COCO>function".
36. 
37. # See also COCO>decodeMask,
38. # COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds,
39. # COCO>getImgIds, COCO>loadAnns, COCO>loadCats,
40. # COCO>loadImgs, COCO>annToMask, COCO>showAnns
41. 
42. # Microsoft COCO Toolbox.      version 2.0
43. # Data, paper, and tutorials available at:  http://mscoco.org/
44. # Code written by Piotr Dollar and Tsung-Yi Lin, 2014.
45. # Licensed under the Simplified BSD License [see bsd.txt]

1. from pycocotools.coco import COCO
2. import matplotlib.pyplot as plt
3. import cv2
4. import os
5. import numpy as np
6. import random
7. 
8. 
9. #1、定义数据集路径
10. cocoRoot = "F:/File_Python/Resources/image/COCO"
11. dataType = "val2017"
12. annFile = os.path.join(cocoRoot, f'annotations/instances_{dataType}.json')
13. print(f'Annotation file: {annFile}')
14. 
15. #2、为实例注释初始化COCO的API
16. coco=COCO(annFile)
17. 
18. 
19. #3、采用不同函数获取对应数据或类别
20. ids = coco.getCatIds('person')[0]    #采用getCatIds函数获取"person"类别对应的ID
21. print(f'"person" 对应的序号: {ids}') 
22. id = coco.getCatIds(['dog'])[0]      #获取某一类的所有图片，比如获取包含dog的所有图片
23. imgIds = coco.catToImgs[id]
24. print(f'包含dog的图片共有：{len(imgIds)}张, 分别是：',imgIds)
25. 
26. 
27. cats = coco.loadCats(1)               #采用loadCats函数获取序号对应的类别名称
28. print(f'"1" 对应的类别名称: {cats}')
29. 
30. imgIds = coco.getImgIds(catIds=[1])    #采用getImgIds函数获取满足特定条件的图片（交集），获取包含person的所有图片
31. print(f'包含person的图片共有：{len(imgIds)}张')
32. 
33. 
34. 
35. #4、将图片进行可视化
36. imgId = imgIds[10]
37. imgInfo = coco.loadImgs(imgId)[0]
38. print(f'图像{imgId}的信息如下：\n{imgInfo}')
39. 
40. imPath = os.path.join(cocoRoot, 'images', dataType, imgInfo['file_name'])                     
41. im = cv2.imread(imPath)
42. plt.axis('off')
43. plt.imshow(im)
44. plt.show()
45. 
46. 
47. plt.imshow(im); plt.axis('off')
48. annIds = coco.getAnnIds(imgIds=imgInfo['id'])      # 获取该图像对应的anns的Id
49. print(f'图像{imgInfo["id"]}包含{len(anns)}个ann对象，分别是:\n{annIds}')
50. anns = coco.loadAnns(annIds)
51. 
52. coco.showAnns(anns)
53. print(f'ann{annIds[3]}对应的mask如下：')
54. mask = coco.annToMask(anns[3])
55. plt.imshow(mask); plt.axis('off')