二、VOC可视化数据集

1、作用

在做目标检测时，首先要检查标注数据。一方面是要了解标注的情况，另一方面是检查数据集的标注和格式是否正确，只有正确的情况下才能进行下一步的训练。

2、代码实现

import os
# import sys
import cv2
import random
from tqdm import tqdm
# import numpy as np
import argparse
import xml.etree.ElementTree as ET


def xml_reader(filename):
    """ Parse a PASCAL VOC xml file """
    tree = ET.parse(filename)
    objects = []
    for obj in tree.findall('object'):
        obj_struct = {}
        obj_struct['name'] = obj.find('name').text
        bbox = obj.find('bndbox')
        obj_struct['bbox'] = [int(bbox.find('xmin').text),
                              int(bbox.find('ymin').text),
                              int(bbox.find('xmax').text),
                              int(bbox.find('ymax').text)]
        objects.append(obj_struct)

    return objects


def get_image_list(image_dir, suffix=['jpg', 'png']):
    '''get all image path ends with suffix'''
    if not os.path.exists(image_dir):
        print("PATH:%s not exists" % image_dir)
        return []
    imglist = []
    for root, sdirs, files in os.walk(image_dir):
        if not files:
            continue
        for filename in files:
            filepath = os.path.join(root, filename)
            if filename.split('.')[-1] in suffix:
                imglist.append(filepath)
    return imglist


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='check data')
    parser.add_argument('--input', dest='input', help='The input dir of images', type=str)
    parser.add_argument('--output', dest='output', default='temp', help='The output dir of images', type=str)
    parser.add_argument('--num', dest='num', default=50, help='The number of images you want to check', type=int)
    args = parser.parse_args()

    if not os.path.exists(args.output):
        os.makedirs(args.output)

    img_list = get_image_list(args.input)
    img_list = random.sample(img_list, args.num)

    for img_path in tqdm(img_list):
        img = cv2.imread(img_path)
        if img is None or not img.any():
            continue
        xml_path = img_path.replace("JPEGImages", "Annotations").replace(".jpg", ".xml").replace(".png", ".xml")
        objects = xml_reader(xml_path)
        if len(objects) == 0:
            continue

        # draw box and name
        for obj in objects:
            name = obj['name']
            box = obj['bbox']
            p1 = (box[0], box[1])
            p2 = (box[2], box[3])
            p3 = (max(box[0], 15), max(box[1], 15))
            cv2.rectangle(img, p1, p2, (0, 0, 255), 2)
            cv2.putText(img, name, p3, cv2.FONT_ITALIC, 1, (0, 255, 0), 2)

        img_name = os.path.basename(img_path)
        cv2.imwrite(os.path.join(args.output, img_name), img)

3、使用方法

python Visual_dataset.py --input VOCdevkit/JPEGImages --output ./Result_imgs --num 3408

python 上述代码的文件名称 --input 图片地址 --output 输出文件夹地址 --num 图片数量

4、常见报错

(python38) D:\pythontorch\VOC>python Visual_dataset.py --input VOCdevkit/ImageSets --output Result_imgs --num 3408
Traceback (most recent call last):
  File "Visual_dataset.py", line 55, in <module>
    img_list = random.sample(img_list, args.num)
  File "C:\ProgramData\Anaconda3\envs\python38\lib\random.py", line 363, in sample
    raise ValueError("Sample larger than population or is negative")
ValueError: Sample larger than population or is negative

原因 你的路径写错了