Py之pycocotools：pycocotools庫的簡介、安裝、使用方法之詳細攻略

目錄

pycocotools庫的簡介

pycocotools庫的安裝

pycocotools庫的使用方法

1、from pycocotools.coco import COCO

2、輸出COCO數據集信息并進行圖片可視化

---

pycocotools庫的簡介

? ? ? ?pycocotools是什么？即python api tools of COCO。COCO是一個大型的圖像數據集，用于目標檢測、分割、人的關鍵點檢測、素材分割和標題生成。這個包提供了Matlab、Python和luaapi，這些api有助于在COCO中加載、解析和可視化注釋。請訪問COCO - Common Objects in Context，可以了解關于COCO的更多信息，包括數據、論文和教程。COCO網站上也描述了注釋的確切格式。Matlab和PythonAPI是完整的，LuaAPI只提供基本功能。
? ? ? ?除了這個API，請下載COCO圖片和注釋，以便運行演示和使用API。兩者都可以在項目網站上找到。

• -請下載、解壓縮并將圖像放入：coco/images/
• -請下載并將注釋放在：coco/annotations中/

COCO API：?http://cocodataset.org/

pycocotools庫的安裝

pip install pycocotools==2.0.0

pycocotools庫的使用方法

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、輸出COCO數據集信息并進行圖片可視化

from pycocotools.coco import COCO import matplotlib.pyplot as plt import cv2 import os import numpy as np import random#1、定義數據集路徑 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、為實例注釋初始化COCO的API coco=COCO(annFile)#3、采用不同函數獲取對應數據或類別 ids = coco.getCatIds('person')[0] #采用getCatIds函數獲取"person"類別對應的ID print(f'"person" 對應的序號: {ids}') id = coco.getCatIds(['dog'])[0] #獲取某一類的所有圖片，比如獲取包含dog的所有圖片 imgIds = coco.catToImgs[id] print(f'包含dog的圖片共有：{len(imgIds)}張, 分別是：',imgIds)cats = coco.loadCats(1) #采用loadCats函數獲取序號對應的類別名稱 print(f'"1" 對應的類別名稱: {cats}')imgIds = coco.getImgIds(catIds=[1]) #采用getImgIds函數獲取滿足特定條件的圖片（交集），獲取包含person的所有圖片 print(f'包含person的圖片共有：{len(imgIds)}張')#4、將圖片進行可視化 imgId = imgIds[10] imgInfo = coco.loadImgs(imgId)[0] print(f'圖像{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']) # 獲取該圖像對應的anns的Id print(f'圖像{imgInfo["id"]}包含{len(anns)}個ann對象，分別是:\n{annIds}') anns = coco.loadAnns(annIds)coco.showAnns(anns) print(f'ann{annIds[3]}對應的mask如下：') mask = coco.annToMask(anns[3]) plt.imshow(mask); plt.axis('off')

總結

以上是生活随笔為你收集整理的Py之pycocotools：pycocotools库的简介、安装、使用方法之详细攻略的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。