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

目录

pycocotools库的简介

pycocotools库的安装

pycocotools库的使用方法

1、from pycocotools.coco import COCO

2、输出COCO数据集信息并进行图片可视化

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

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_-subst">{dataType}.json')
print(f'Annotation file: -subst">{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: -subst">{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：-subst">{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: -subst">{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：-subst">{len(imgIds)}张')
 
 
 
4、将图片进行可视化
imgId = imgIds[10]
imgInfo = coco.loadImgs(imgId)[0]
print(f'图像-subst">{imgId}的信息如下：\n-subst">{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-subst">{imgInfo["id"]}包含-subst">{len(anns)}个ann对象，分别是:\n-subst">{annIds}')
anns = coco.loadAnns(annIds)
 
coco.showAnns(anns)
print(f'ann-subst">{annIds[3]}对应的mask如下：')
mask = coco.annToMask(anns[3])
plt.imshow(mask); plt.axis('off')