Py's pycocotools library introduction, installation, usage and instructions

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 / 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 (. 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  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:  /
# Code written by Piotr Dollar and Tsung-Yi Lin, 2014.
# Licensed under the Simplified BSD License [see ]

from  import COCO
import  as plt
import cv2
import os
import numpy as np
import random
 
 
#1 Define the dataset path
cocoRoot = "F:/File_Python/Resources/image/COCO"
dataType = "val2017"
annFile = (cocoRoot, f'annotations/instances_{dataType}.json')
print(f'Annotation file: {annFile}')
 
#2. initialize the COCO API for example annotations
coco=COCO(annFile)
 
 
#3. Use different functions to get the corresponding data or categories
ids = ('person')[0]    #Use getCatIds function to get the IDs of the "person" category.
print(f'"person" Corresponding serial number: {ids}') 
id = (['dog'])[0]      # Get all the images in a category, e.g. get all the images that contain dog
imgIds = [id]
print(f'embodydogThe total number of images in the：{len(imgIds)}sheet of paper, were...：',imgIds)
 
 
cats = (1)               # Use the loadCats function to get the name of the category corresponding to the serial number.
print(f'"1" Corresponding category name: {cats}')
 
imgIds = (catIds=[1])    # Use the getImgIds function to get images that meet specific conditions (intersection), get all images that contain person
print(f'embodypersonThe total number of images in the：{len(imgIds)}sheet of paper')
 
 
 
#4 Visualize the pictures
imgId = imgIds[10]
imgInfo = (imgId)[0]
print(f'imagery{imgId}The information is as follows：\n{imgInfo}')
 
imPath = (cocoRoot, 'images', dataType, imgInfo['file_name'])                     
im = (imPath)
('off')
(im)
()
 
 
(im); ('off')
annIds = (imgIds=imgInfo['id'])      # Get the Id of the anns corresponding to this image
print(f'imagery{imgInfo["id"]}embody{len(anns)}classifier for individual things or people, general, catch-all classifierannboyfriend，were...:\n{annIds}')
anns = (annIds)
 
(anns)
print(f'ann{annIds[3]}correspondingmaskas below：')
mask = (anns[3])
(mask); ('off')