目标检测小脚本：提取训练集图片与标签并更新索引

Posted 2022-08-11 zstar-_

问题场景

在做目标检测任务时，我想提取训练集的图片单独进行外部数据增强。因此，需要根据划分出的train.txt来提取训练集图片与标签。

需求实现

我使用VOC数据集进行测试，实现比较简单。

import shutil


if __name__ == '__main__':
    img_src = r"D:\\Dataset\\VOC2007\\images"
    xml_src = r"D:\\Dataset\\VOC2007\\Annotations"
    img_out = "image_out/"
    xml_out = "xml_out/"
    txt_path = r"D:\\Dataset\\VOC2007\\ImageSets\\Segmentation\\train.txt"
    # 读取txt文件
    with open(txt_path, 'r') as f:
        line_list = f.readlines()
    for line in line_list:
        line_new = line.replace('\\n', '')  # 将换行符替换为空('')
        shutil.copy(img_src + '/' + line_new + ".jpg", img_out)
        shutil.copy(xml_src + '/' + line_new + ".xml", xml_out)

效果：

更新训练集索引

使用数据增强之后，把生成的图片和标签丢到VOC里面，混在一起。

然后再写个脚本，将生成好的图片名称添加到train.txt文件中。

import os

if __name__ == '__main__':
    xml_src = r"C:\\Users\\xy\\Desktop\\read_train\\xml_out_af"
    txt_path = r"D:\\Dataset\\VOC2007\\ImageSets\\Segmentation\\train.txt"
    for name in os.listdir(xml_src):
        with open(txt_path, 'a') as f:
            f.write(name[:-4] + "\\n")

效果：

最后，再运行之前在VOC博文里面写过的xml2txt脚本：

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

sets = ['train', 'test', 'val']

Imgpath = r'D:\\Dataset\\VOC2007\\images'  # 图片文件夹
xmlfilepath = r'D:\\Dataset\\VOC2007\\Annotations'  # xml文件存放地址
ImageSets_path = r'D:\\Dataset\\VOC2007\\ImageSets\\Segmentation'
Label_path = r'D:\\Dataset\\VOC2007'
classes = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
         'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']


def convert(size, box):
    dw = 1. / size[0]
    dh = 1. / size[1]
    x = (box[0] + box[1]) / 2.0
    y = (box[2] + box[3]) / 2.0
    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(xmlfilepath + '/%s.xml' % (image_id))
    out_file = open(Label_path + '/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
        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')


for image_set in sets:
    if not os.path.exists(Label_path + 'labels/'):
        os.makedirs(Label_path + 'labels/')
    image_ids = open(ImageSets_path + '/%s.txt' % (image_set)).read().strip().split()
    list_file = open(Label_path + '%s.txt' % (image_set), 'w')
    for image_id in image_ids:
        # print(image_id)  # DJI_0013_00360
        list_file.write(Imgpath + '/%s.jpg\\n' % (image_id))
        convert_annotation(image_id)
    list_file.close()

运行之后，就可以看到生成的数据增强样本被完美得添加到了原始数据集中。