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Py之pycocotools库的简介、安装、使用方法及说明

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
  • 总结

    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 toTZUxQOgd 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 phpusing 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   -编程客栈 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 bjavascripty: "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}的信息编程客栈如下:\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,希望能给大家一个开发者_JS学习参考,也希望大家多多支持我们。

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