import os import random import xml.etree.ElementTree as ET import numpy as np from utils.utils import get_classes # --------------------------------------------------------------------------------------------------------------------------------# # annotation_mode用于指定该文件运行时计算的内容 # annotation_mode为0代表整个标签处理过程，包括获得VOCdevkit/VOC2007/ImageSets里面的txt以及训练用的2007_train.txt、2007_val.txt # annotation_mode为1代表获得VOCdevkit/VOC2007/ImageSets里面的txt # annotation_mode为2代表获得训练用的2007_train.txt、2007_val.txt # --------------------------------------------------------------------------------------------------------------------------------# annotation_mode = 0 # -------------------------------------------------------------------# # 必须要修改，用于生成2007_train.txt、2007_val.txt的目标信息 # 与训练和预测所用的classes_path一致即可 # 如果生成的2007_train.txt里面没有目标信息 # 那么就是因为classes没有设定正确 # 仅在annotation_mode为0和2的时候有效 # -------------------------------------------------------------------# classes_path = 'model_data/voc_classes.txt' # --------------------------------------------------------------------------------------------------------------------------------# # trainval_percent用于指定(训练集+验证集)与测试集的比例，默认情况下 (训练集+验证集):测试集 = 9:1 # train_percent用于指定(训练集+验证集)中训练集与验证集的比例，默认情况下 训练集:验证集 = 9:1 # 仅在annotation_mode为0和1的时候有效 # --------------------------------------------------------------------------------------------------------------------------------# trainval_percent = 0.9 train_percent = 0.9 # -------------------------------------------------------# # 指向VOC数据集所在的文件夹 # 默认指向根目录下的VOC数据集 # -------------------------------------------------------# VOCdevkit_path = 'VOCdevkit' VOCdevkit_sets = [('2007', 'train'), ('2007', 'val')] classes, _ = get_classes(classes_path) # -------------------------------------------------------# # 统计目标数量 # -------------------------------------------------------# photo_nums = np.zeros(len(VOCdevkit_sets)) # 生成train的数目，val的数目 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 = 0 if obj.find('difficult') != None: 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 = (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)] + 1 # 统计各个类别的个数 if __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') 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) # 训练、验证集 总数 tr = int(tv * train_percent) # 训练、验证集中 训练集的总数 trainval = random.sample(list, tv) # 在总数里采样 train = random.sample(trainval, tr) # 在tv中采样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 = 0 for 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)) # 文件名字是拼出来的 convert_annotation(year, image_id, list_file) list_file.write('\n') photo_nums[type_index] = len(image_ids) type_index += 1 list_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 = 0 for 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）。")