python提取COCO数据集中特定的类

记录一下提取Coco自行车类别的过程

---

1.安装pycocotools github地址：https://github.com/philferriere/cocoapi

 pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI

2.提取其中的bicycle类的代码如下：

需要修改的地方

savepath

datasets_list

classes_names

dataDir

 使用的这篇博客中的代码

https://blog.csdn.net/weixin_38632246/article/details/97141364

from pycocotools.coco import COCO
import os
import shutil
from tqdm import tqdm
# import skimage.io as io
import matplotlib.pyplot as plt
import cv2
from PIL import Image, ImageDraw
 
#提取出的类别的保存路径
savepath="/media/deepnorth/14b6945d-9936-41a8-aeac-505b96fc2be8/COCO/"
  
img_dir=savepath+'images/'
anno_dir=savepath+'Annotations/'
# datasets_list=['train2014', 'val2014']
datasets_list=['train2014']

#这里填写需要提取的类别，本人此处提取bicycle  
classes_names = ['bicycle']  

#原coco数据集的目录
dataDir= '/media/deepnorth/14b6945d-9936-41a8-aeac-505b96fc2be8/COCO/'  
 
headstr = """\

    VOC
    %s
    
        My Database
        COCO
        flickr
        NULL
    
    
        NULL
        company
    
    
        %d
        %d
        %d
    
    0
"""
objstr = """\
    
"""
 
tailstr = '''\

'''
 
#if the dir is not exists,make it,else delete it
def mkr(path):
    if os.path.exists(path):
        shutil.rmtree(path)
        os.mkdir(path)
    else:
        os.mkdir(path)
mkr(img_dir)
mkr(anno_dir)
def id2name(coco):
    classes=dict()
    for cls in coco.dataset['categories']:
        classes[cls['id']]=cls['name']
    return classes
 
def write_xml(anno_path,head, objs, tail):
    f = open(anno_path, "w")
    f.write(head)
    for obj in objs:
        f.write(objstr%(obj[0],obj[1],obj[2],obj[3],obj[4]))
    f.write(tail)
 
 
def save_annotations_and_imgs(coco,dataset,filename,objs):
    #eg:COCO_train2014_000000196610.jpg-->COCO_train2014_000000196610.xml
    anno_path=anno_dir+filename[:-3]+'xml'
    img_path=dataDir+dataset+'/'+filename
    print(img_path)
    dst_imgpath=img_dir+filename
 
    img=cv2.imread(img_path)
    #if (img.shape[2] == 1):
    #    print(filename + " not a RGB image")
     #   return
    shutil.copy(img_path, dst_imgpath)
 
    head=headstr % (filename, img.shape[1], img.shape[0], img.shape[2])
    tail = tailstr
    write_xml(anno_path,head, objs, tail)
 
 
def showimg(coco,dataset,img,classes,cls_id,show=True):
    global dataDir
    I=Image.open('%s/%s/%s'%(dataDir,dataset,img['file_name']))
    #通过id，得到注释的信息
    annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None)
    # print(annIds)
    anns = coco.loadAnns(annIds)
    # print(anns)
    # coco.showAnns(anns)
    objs = []
    for ann in anns:
        class_name=classes[ann['category_id']]
        if class_name in classes_names:
            print(class_name)
            if 'bbox' in ann:
                bbox=ann['bbox']
                xmin = int(bbox[0])
                ymin = int(bbox[1])
                xmax = int(bbox[2] + bbox[0])
                ymax = int(bbox[3] + bbox[1])
                obj = [class_name, xmin, ymin, xmax, ymax]
                objs.append(obj)
                draw = ImageDraw.Draw(I)
                draw.rectangle([xmin, ymin, xmax, ymax])
    if show:
        plt.figure()
        plt.axis('off')
        plt.imshow(I)
        plt.show()
 
    return objs
 
for dataset in datasets_list:
    #./COCO/annotations/instances_train2014.json
    annFile='{}/annotations/instances_{}.json'.format(dataDir,dataset)
 
    #COCO API for initializing annotated data
    coco = COCO(annFile)

    #show all classes in coco
    classes = id2name(coco)
    print(classes)
    #[1, 2, 3, 4, 6, 8]
    classes_ids = coco.getCatIds(catNms=classes_names)
    print(classes_ids)
    for cls in classes_names:
        #Get ID number of this class
        cls_id=coco.getCatIds(catNms=[cls])
        img_ids=coco.getImgIds(catIds=cls_id)
        print(cls,len(img_ids))
        # imgIds=img_ids[0:10]
        for imgId in tqdm(img_ids):
            img = coco.loadImgs(imgId)[0]
            filename = img['file_name']
            # print(filename)
            objs=showimg(coco, dataset, img, classes,classes_ids,show=False)
            print(objs)
            save_annotations_and_imgs(coco, dataset, filename, objs)

 

COCO数据集2014

代码执行完之后会生成对应的  images文件夹和 Annotations（.xml）文件夹

 

有了这两个文件就可以利用voc的代码转换为yolo目标检测的txt标签文件

相关代码

需要修改的参数

classes

data_path

list_file

in_file

out_file

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join


classes = ["bicycle"]



def convert(size, box):
    dw = 1./(size[0])
    dh = 1./(size[1])
    x = (box[0] + box[1])/2.0 - 1
    y = (box[2] + box[3])/2.0 - 1
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return (x,y,w,h)

def convert_annotation(image_id):
    in_file = open('coco_voc_val/Annotations/%s.xml'%(image_id))
    out_file = open('coco_voc_val/labels/%s.txt'%(image_id), 'w')
    tree=ET.parse(in_file)
    root = tree.getroot()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)

    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        print(cls)
        if cls not in classes or int(difficult)==1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
        bb = convert((w,h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


data_path = '/media/COCO/coco_voc_val/images'
img_names = os.listdir(data_path)

list_file = open('2014_val.txt', 'w')
for img_name in img_names:
    if not os.path.exists('coco_voc_val/labels'):
        os.makedirs('coco_voc_val/labels')

    list_file.write('/media/COCO/coco_voc_val/images/%s\n'%img_name)
    image_id = img_name[:-4]
    convert_annotation(image_id)

list_file.close()