Py之pycocotools：pycocotools库的简介、安装、使用方法之详细攻略

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

http://cocodataset.org/

，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：

pycocotools库的安装

pip install pycocotools==2.0.0

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:  

# 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')