pip install pycocotools==2.0.0
or
pip install pycocotools-windows

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

from pycocotools.coco import COCO
import matplotlib.pyplot as plt
import cv2
import os
import numpy as np
import random
 
 
#1、定義數(shù)據(jù)集路徑
cocoRoot = "F:/File_Python/Resources/image/COCO"
dataType = "val2017"
annFile = os.path.join(cocoRoot, f'annotations/instances_{dataType}.json')
print(f'Annotation file: {annFile}')
 
#2、為實例注釋初始化COCO的API
coco=COCO(annFile)
 
 
#3、采用不同函數(shù)獲取對應數(shù)據(jù)或類別
ids = coco.getCatIds('person')[0]    #采用getCatIds函數(shù)獲取"person"類別對應的ID
print(f'"person" 對應的序號: {ids}') 
id = coco.getCatIds(['dog'])[0]      #獲取某一類的所有圖片，比如獲取包含dog的所有圖片
imgIds = coco.catToImgs[id]
print(f'包含dog的圖片共有：{len(imgIds)}張, 分別是：',imgIds)
 
 
cats = coco.loadCats(1)               #采用loadCats函數(shù)獲取序號對應的類別名稱
print(f'"1" 對應的類別名稱: {cats}')
 
imgIds = coco.getImgIds(catIds=[1])    #采用getImgIds函數(shù)獲取滿足特定條件的圖片（交集），獲取包含person的所有圖片
print(f'包含person的圖片共有：{len(imgIds)}張')
 
 
 
#4、將圖片進行可視化
imgId = imgIds[10]
imgInfo = coco.loadImgs(imgId)[0]
print(f'圖像{imgId}的信息如下：\n{imgInfo}')
 
imPath = os.path.join(cocoRoot, 'images', dataType, imgInfo['file_name'])                     
im = cv2.imread(imPath)
plt.axis('off')
plt.imshow(im)
plt.show()
 
 
plt.imshow(im); plt.axis('off')
annIds = coco.getAnnIds(imgIds=imgInfo['id'])      # 獲取該圖像對應的anns的Id
print(f'圖像{imgInfo["id"]}包含{len(anns)}個ann對象，分別是:\n{annIds}')
anns = coco.loadAnns(annIds)
 
coco.showAnns(anns)
print(f'ann{annIds[3]}對應的mask如下：')
mask = coco.annToMask(anns[3])
plt.imshow(mask); plt.axis('off')