Py之pycocotools库的简介、安装、使用方法及说明

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2 年前

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pycocotools库的简介

pycocotools是什么？即python api tools of COCO。

COCO是一个大型的图像数据集，用于目标检测、分割、人的关键点检测、素材分割和标题生成。

这个包提供了Matlab、Python和luaapi，这些api有助于在COCO中加载、解析和可视化注释。

请访问COCO – Common Objects in Context，可以了解关于COCO的更多信息，包括数据、论文和教程。

COCO网站上也描述了注释的确切格式。

Matlab和PythonAPI是完整的，LuaAPI只提供基本功能。

除了这个API，请下载COCO图片和注释，以便运行演示和使用API。

两者都可以在项目网站上找到。

-请下载、解压缩并将图像放入：coco/images/
-请下载并将注释放在：coco/annotations中/

COCO API： http://cocodataset.org/

pycocotools库的安装

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

pycocotools库的使用方法

1、from pycocotools.coco import COCO

__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]

2、输出COCO数据集信息并进行图片可视化

from pycocotools.coco import COCO
import matplotlib.pyplot as plt
import cv2
import os
import numpy as np
import random
 
 
#1、定义数据集路径
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、为实例注释初始化COCO的API
coco=COCO(annFile)
 
 
#3、采用不同函数获取对应数据或类别
ids = coco.getCatIds('person')[0]    #采用getCatIds函数获取"person"类别对应的ID
print(f'"person" 对应的序号: {ids}') 
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
imgIds = coco.catToImgs[id]
print(f'包含dog的图片共有：{len(imgIds)}张, 分别是：',imgIds)
 
 
cats = coco.loadCats(1)               #采用loadCats函数获取序号对应的类别名称
print(f'"1" 对应的类别名称: {cats}')
 
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
print(f'包含person的图片共有：{len(imgIds)}张')
 
 
 
#4、将图片进行可视化
imgId = imgIds[10]
imgInfo = coco.loadImgs(imgId)[0]
print(f'图像{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'])      # 获取该图像对应的anns的Id
print(f'图像{imgInfo["id"]}包含{len(anns)}个ann对象，分别是:\n{annIds}')
anns = coco.loadAnns(annIds)
 
coco.showAnns(anns)
print(f'ann{annIds[3]}对应的mask如下：')
mask = coco.annToMask(anns[3])
plt.imshow(mask); plt.axis('off')

总结

以上为个人经验，希望能给大家一个参考，也希望大家多多支持aitechtogether.com。