生成voc格式数据集

数据集存放格式：（Annotations文件夹放标注的xml文件，JPEGImages文件夹放标注的图片）

运行代码：

import os
import random
import xml.etree.ElementTree as ETimport numpy as npdef get_classes(classes_path):with open(classes_path, encoding='utf-8') as f:class_names = f.readlines()class_names = [c.strip() for c in class_names]return class_names, len(class_names)annotation_mode = 0
classes_path = ["person", "cat"]     # 对应类别名称
trainval_percent = 0.9
train_percent = 0.9VOCdevkit_path = 'VOCdevkit'VOCdevkit_sets = [('2007', 'train'), ('2007', 'val')]
classes, _ = get_classes(classes_path)photo_nums = np.zeros(len(VOCdevkit_sets))
nums = np.zeros(len(classes))def convert_annotation(year, image_id, list_file):in_file = open(os.path.join(VOCdevkit_path, 'VOC%s/Annotations/%s.xml' % (year, image_id)), encoding='utf-8')tree = ET.parse(in_file)root = tree.getroot()for obj in root.iter('object'):difficult = 0if obj.find('difficult') != None:difficult = obj.find('difficult').textcls = obj.find('name').textif cls not in classes or int(difficult) == 1:continuecls_id = classes.index(cls)xmlbox = obj.find('bndbox')b = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)),int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text)))list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))nums[classes.index(cls)] = nums[classes.index(cls)] + 1if __name__ == "__main__":random.seed(0)if " " in os.path.abspath(VOCdevkit_path):raise ValueError("数据集存放的文件夹路径与图片名称中不可以存在空格，否则会影响正常的模型训练，请注意修改。")if annotation_mode == 0 or annotation_mode == 1:print("Generate txt in ImageSets.")xmlfilepath = os.path.join(VOCdevkit_path, 'VOC2007/Annotations')saveBasePath = os.path.join(VOCdevkit_path, 'VOC2007/ImageSets/Main')temp_xml = os.listdir(xmlfilepath)total_xml = []for xml in temp_xml:if xml.endswith(".xml"):total_xml.append(xml)num = len(total_xml)list = range(num)tv = int(num * trainval_percent)tr = int(tv * train_percent)trainval = random.sample(list, tv)train = random.sample(trainval, tr)print("train and val size", tv)print("train size", tr)ftrainval = open(os.path.join(saveBasePath, 'trainval.txt'), 'w')ftest = open(os.path.join(saveBasePath, 'test.txt'), 'w')ftrain = open(os.path.join(saveBasePath, 'train.txt'), 'w')fval = open(os.path.join(saveBasePath, 'val.txt'), 'w')for i in list:name = total_xml[i][:-4] + '\n'if i in trainval:ftrainval.write(name)if i in train:ftrain.write(name)else:fval.write(name)else:ftest.write(name)ftrainval.close()ftrain.close()fval.close()ftest.close()print("Generate txt in ImageSets done.")if annotation_mode == 0 or annotation_mode == 2:print("Generate 2007_train.txt and 2007_val.txt for train.")type_index = 0for year, image_set in VOCdevkit_sets:image_ids = open(os.path.join(VOCdevkit_path, 'VOC%s/ImageSets/Main/%s.txt' % (year, image_set)),encoding='utf-8').read().strip().split()list_file = open('%s_%s.txt' % (year, image_set), 'w', encoding='utf-8')for image_id in image_ids:list_file.write('%s/VOC%s/JPEGImages/%s.jpg' % (os.path.abspath(VOCdevkit_path), year, image_id))convert_annotation(year, image_id, list_file)list_file.write('\n')photo_nums[type_index] = len(image_ids)type_index += 1list_file.close()print("Generate 2007_train.txt and 2007_val.txt for train done.")def printTable(List1, List2):for i in range(len(List1[0])):print("|", end=' ')for j in range(len(List1)):print(List1[j][i].rjust(int(List2[j])), end=' ')print("|", end=' ')print()str_nums = [str(int(x)) for x in nums]tableData = [classes, str_nums]colWidths = [0] * len(tableData)len1 = 0for i in range(len(tableData)):for j in range(len(tableData[i])):if len(tableData[i][j]) > colWidths[i]:colWidths[i] = len(tableData[i][j])printTable(tableData, colWidths)if photo_nums[0] <= 500:print("训练集数量小于500，属于较小的数据量，请注意设置较大的训练世代（Epoch）以满足足够的梯度下降次数（Step）。")if np.sum(nums) == 0:print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")print("（重要的事情说三遍）。")

生成的txt文件格式：