Py之pycocotools庫的簡介、安裝、使用方法及說明

更新時間：2023年02月22日 09:13:06 投稿：jingxian

這篇文章主要介紹了Py之pycocotools庫的簡介、安裝、使用方法及說明，具有很好的參考價值，希望對大家有所幫助。如有錯誤或未考慮完全的地方，望不吝賜教

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(shù)據(jù)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(shù)據(jù)、論文和教程。

COCO網(wǎng)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(wǎng)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
or
pip install pycocotools-windows

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(shù)據(jù)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(shù)據(jù)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(shù)獲取對應數(shù)據(jù)或類別
ids = coco.getCatIds('person')[0]    #采用getCatIds函數(shù)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(shù)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(shù)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')