159 lines
8.5 KiB
Python
159 lines
8.5 KiB
Python
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import os
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import random
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import xml.etree.ElementTree as ET
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import numpy as np
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from utils.utils import get_classes
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# --------------------------------------------------------------------------------------------------------------------------------#
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# annotation_mode用于指定该文件运行时计算的内容
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# 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
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# annotation_mode为1代表获得VOCdevkit/VOC2007/ImageSets里面的txt
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# annotation_mode为2代表获得训练用的2007_train.txt、2007_val.txt
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# --------------------------------------------------------------------------------------------------------------------------------#
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annotation_mode = 0
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# -------------------------------------------------------------------#
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# 必须要修改，用于生成2007_train.txt、2007_val.txt的目标信息
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# 与训练和预测所用的classes_path一致即可
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# 如果生成的2007_train.txt里面没有目标信息
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# 那么就是因为classes没有设定正确
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# 仅在annotation_mode为0和2的时候有效
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# -------------------------------------------------------------------#
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classes_path = 'model_data/voc_classes.txt' # 这里定义的名字是xml的物体的名字，出现的顺序是训练时的onehot顺序。
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# --------------------------------------------------------------------------------------------------------------------------------#
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# 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
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# 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
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# 仅在annotation_mode为0和1的时候有效
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# --------------------------------------------------------------------------------------------------------------------------------#
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trainval_percent = 0.9
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train_percent = 0.9
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# -------------------------------------------------------#
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# 指向VOC数据集所在的文件夹
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# 默认指向根目录下的VOC数据集
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# -------------------------------------------------------#
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VOCdevkit_path = 'VOCdevkit'
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VOCdevkit_sets = [('2007', 'train'), ('2007', 'val')]
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classes, _ = get_classes(classes_path)
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# -------------------------------------------------------#
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# 统计目标数量
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# -------------------------------------------------------#
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photo_nums = np.zeros(len(VOCdevkit_sets)) # 生成train的数目，val的数目
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nums = np.zeros(len(classes)) # 统计各个类别的数量
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def convert_annotation(year, image_id, list_file):
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in_file = open(os.path.join(VOCdevkit_path, 'VOC%s/Annotations/%s.xml' % (year, image_id)), encoding='utf-8') # 'VOCdevkit\\VOC2007/Annotations/000001.xml'
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tree = ET.parse(in_file)
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root = tree.getroot()
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for obj in root.iter('object'):
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difficult = 0
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if obj.find('difficult') != None:
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difficult = obj.find('difficult').text
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cls = obj.find('name').text
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if cls not in classes or int(difficult) == 1: # 不在classes里或者difficult为1，跳过当前类别
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continue
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cls_id = classes.index(cls) # 类别对应于classes文件的下标，是类别的id属性
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xmlbox = obj.find('bndbox')
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b = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)),
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int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text)))
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list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))
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# list_file的每一行，前面先写了图片的全路径，接着一个空格，依次写各个物体的 以,分隔的坐标，和id
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nums[classes.index(cls)] = nums[classes.index(cls)] + 1 # 统计各个类别的个数
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if __name__ == "__main__":
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random.seed(0)
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if " " in os.path.abspath(VOCdevkit_path):
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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。")
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if annotation_mode == 0 or annotation_mode == 1:
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print("Generate txt in ImageSets.")
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xmlfilepath = os.path.join(VOCdevkit_path, 'VOC2007/Annotations')
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saveBasePath = os.path.join(VOCdevkit_path, 'VOC2007/ImageSets/Main')
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temp_xml = os.listdir(xmlfilepath)
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total_xml = [xml for xml in temp_xml if xml.endswith(".xml")]
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num = len(total_xml) # 取得原始数据集中的总数，从总数中划分数据集
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list = range(num)
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tv = int(num * trainval_percent) # 训练+验证集 总数
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tr = int(tv * train_percent) # 训练+验证集中 训练集的总数
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trainval = random.sample(list, tv) # 在总数里采样
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train = random.sample(trainval, tr) # 在tv中采样tr
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print("train and val size", tv)
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print("train size", tr)
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ftrainval = open(os.path.join(saveBasePath, 'trainval.txt'), 'w')
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ftest = open(os.path.join(saveBasePath, 'test.txt'), 'w')
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ftrain = open(os.path.join(saveBasePath, 'train.txt'), 'w')
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fval = open(os.path.join(saveBasePath, 'val.txt'), 'w')
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for i in list:
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name = total_xml[i][:-4] + '\n' # 取出除了后缀的文件名字
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if i in trainval:
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ftrainval.write(name)
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if i in train:
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ftrain.write(name)
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else:
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fval.write(name)
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else:
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ftest.write(name)
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ftrainval.close()
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ftrain.close()
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fval.close()
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ftest.close()
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print("Generate txt in ImageSets done.")
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if annotation_mode == 0 or annotation_mode == 2:
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print("Generate 2007_train.txt and 2007_val.txt for train.")
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type_index = 0
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for year, image_set in VOCdevkit_sets:
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image_ids = open(os.path.join(VOCdevkit_path, 'VOC%s/ImageSets/Main/%s.txt' % (year, image_set)), # 'VOCdevkit\\VOC2007/ImageSets/Main/train.txt'
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encoding='utf-8').read().strip().split()
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list_file = open('%s_%s.txt' % (year, image_set), 'w', encoding='utf-8') # '2007_train.txt'
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for image_id in image_ids:
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list_file.write( # 'C:\\my_code\\a_python\\YOLO_all\\yolo_v3\\VOCdevkit/VOC2007/JPEGImages/000001.jpg'
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'%s/VOC%s/JPEGImages/%s.jpg' % (os.path.abspath(VOCdevkit_path), year, image_id)) # 文件全路径名字是拼出来的
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convert_annotation(year, image_id, list_file)
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list_file.write('\n')
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photo_nums[type_index] = len(image_ids) # 记录训练集总数和验证集总数
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type_index += 1 # 用来标记是操作 训练集还是验证集
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list_file.close()
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print("Generate 2007_train.txt and 2007_val.txt for train done.")
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def printTable(List1, List2):
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# for i in range(len(List1[0])):
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for i, _ in enumerate(List1[0]):
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print("|", end=' ')
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for j in range(len(List1)): # len(List1)为2
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print(List1[j][i].rjust(int(List2[j])), end=' ')
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print("|", end=' ')
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print()
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str_nums = [str(int(x)) for x in nums] # 每个类别的数目
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tableData = [
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classes, str_nums # 类别与数目对应
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]
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colWidths = [0] * len(tableData) # 计算列宽，共有len(tableData)列，这里是2
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len1 = 0
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for i in range(len(tableData)):
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for j in range(len(tableData[i])):
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if len(tableData[i][j]) > colWidths[i]:
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colWidths[i] = len(tableData[i][j]) # 每列中，每个元素的最大长度赋值给colWidths
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printTable(tableData, colWidths)
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if photo_nums[0] <= 500:
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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）。")
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if np.sum(nums) == 0:
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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！")
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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！")
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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！")
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print("（重要的事情说三遍）。")
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