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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。
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。
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 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]
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')
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