SSD-tensorflow-2 制作自己的数据集

VOC2007数据集格式：

VOC2007详细介绍在这里，提供给大家有兴趣作了解。而制作自己的数据集只需用到前三个文件夹，所以请事先建好这三个文件夹放入同一文件夹内，同时ImageSets文件夹内包含Main文件夹

JPEGImages：用于存放训练、测试的图片（图片格式最好为.jpg）
Annatations：用于存放.xml格式的文件，也就是图片对应的标签，每个.xml文件都对应于JPEGImages文件夹的一张图片
ImageSets：内含Main文件夹，在…/ImageSets/Main文件夹下包含test.txt、train.txt、val.txt、trainval.txt四个文件，生成的方式第二步有详细说明

第一步

下载图片，存入JPEGImages文件夹——你可以直接从各种渠道下载得到所需要的图片集，存入到JPEGImages文件夹下，命名格式统一为“00xxxx.jpg”，如下图：

第二步

使用labelImg工具给图片打标签——这是最重要的一步。如果你的python已经pip install lxml下载了lxml，

labelImg工具简单的使用步骤就是：

打开单个文件，或者打开一个图片文件夹
给目标物体建立box边框
对box边框内的物体贴上标签
把一张图片内所有目标物都打上各自标签后，再保存生成.xml文件，注意存入Annatations文件夹，文件名也要与当前图片保存一致
然后next下一张图片继续打标签，直到所有图片内物体都打上了标签，最后exit

第三步

生成Main文件夹下的.txt文件——在主目录下运行以下代码既可生成test.txt、train.txt、val.txt、trainval.txt四个文件，请注意每一个path地址是否正确（其实这四个txt文件在后续并没有什么用处）

 # -*- coding：utf-8 -*-
 # -*- author：zzZ_CMing  CSDN address:https://blog.csdn.net/zzZ_CMing
 # -*- 2018/07/18; 15:19
 # -*- python3.5
 import os
 import random  

 trainval_percent = 0.7
 train_percent = 0.8
 xmlfilepath = 'Annotations'
 txtsavepath = 'ImageSets/Main'
 total_xml = os.listdir(xmlfilepath)  

 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)  

 ftrainval = open(txtsavepath+'/trainval.txt', 'w')
 ftest = open(txtsavepath+'/test.txt', 'w')
 ftrain = open(txtsavepath+'/train.txt', 'w')
 fval = open(txtsavepath+'/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('Well Done！！！')

运行完成，得到如下文件：可以打开看一看，内容就是各个图片的索引，意味着哪些图片用做训练，哪些用做测试。

第四步

用.xml标签，生成.tfrecord文件

说明：SSD框架所用到的标签文件并不直接是.xml格式文件，而是.tfrecord文件

特别注意：要在主目录提前建好tfrecords_文件夹，不然会报错找不到目标文件夹

 # -*- coding：utf-8 -*-
 # -*- author：zzZ_CMing  CSDN address:https://blog.csdn.net/zzZ_CMing
 # -*- 2018/07/17; 13:18
 # -*- python3.5
 """
 特别注意: path地址是否正确、要在主目录下提前创建“tfrecords_”文件夹
 """

 import os
 import sys
 import random
 import numpy as np
 import tensorflow as tf
 import xml.etree.ElementTree as ET  # 操作xml文件

 # 我的标签定义只有两类，要根据自己的图片而定
 VOC_LABELS = {
     'none': (0, 'Background'),
     'aiaitie': (1, 'Product')
 }

 # 图片和标签存放的文件夹.
 DIRECTORY_ANNOTATIONS = 'Annotations/'
 DIRECTORY_IMAGES = 'JPEGImages/'

 # 随机种子.
 RANDOM_SEED = 4242
 SAMPLES_PER_FILES = 3  # 每个.tfrecords文件包含几个.xml样本

 # 生成整数型，浮点型和字符串型的属性
 def int64_feature(value):
     if not isinstance(value, list):
         value = [value]
     return tf.train.Feature(int64_list=tf.train.Int64List(value=value))

 def float_feature(value):
     if not isinstance(value, list):
         value = [value]
     return tf.train.Feature(float_list=tf.train.FloatList(value=value))

 def bytes_feature(value):
     if not isinstance(value, list):
         value = [value]
     return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))

 # 图片处理
 def _process_image(directory, name):
     # Read the image file.
     filename = directory + DIRECTORY_IMAGES + name + '.jpg'
     image_data = tf.gfile.FastGFile(filename, 'rb').read()

     # Read the XML annotation file.
     filename = os.path.join(directory, DIRECTORY_ANNOTATIONS, name + '.xml')
     tree = ET.parse(filename)
     root = tree.getroot()

     # Image shape.
     size = root.find('size')
     shape = [int(size.find('height').text),
              int(size.find('width').text),
              int(size.find('depth').text)]
     # Find annotations.
     bboxes = []
     labels = []
     labels_text = []
     difficult = []
     truncated = []
     for obj in root.findall('object'):
         label = obj.find('name').text
         labels.append(int(VOC_LABELS[label][0]))
         labels_text.append(label.encode('ascii'))  # 变为ascii格式

         if obj.find('difficult'):
             difficult.append(int(obj.find('difficult').text))
         else:
             difficult.append(0)
         if obj.find('truncated'):
             truncated.append(int(obj.find('truncated').text))
         else:
             truncated.append(0)

         bbox = obj.find('bndbox')
         a = float(bbox.find('ymin').text) / shape[0]
         b = float(bbox.find('xmin').text) / shape[1]
         a1 = float(bbox.find('ymax').text) / shape[0]
         b1 = float(bbox.find('xmax').text) / shape[1]
         a_e = a1 - a
         b_e = b1 - b
         if abs(a_e) < 1 and abs(b_e) < 1:
             bboxes.append((a, b, a1, b1))

     return image_data, shape, bboxes, labels, labels_text, difficult, truncated

 # 转化样例
 def _convert_to_example(image_data, labels, labels_text, bboxes, shape,
                         difficult, truncated):
     xmin = []
     ymin = []
     xmax = []
     ymax = []
     for b in bboxes:
         assert len(b) == 4
         # pylint: disable=expression-not-assigned
         [l.append(point) for l, point in zip([ymin, xmin, ymax, xmax], b)]
         # pylint: enable=expression-not-assigned

     image_format = b'JPEG'
     example = tf.train.Example(features=tf.train.Features(feature={
         'image/height': int64_feature(shape[0]),
         'image/width': int64_feature(shape[1]),
         'image/channels': int64_feature(shape[2]),
         'image/shape': int64_feature(shape),
         'image/object/bbox/xmin': float_feature(xmin),
         'image/object/bbox/xmax': float_feature(xmax),
         'image/object/bbox/ymin': float_feature(ymin),
         'image/object/bbox/ymax': float_feature(ymax),
         'image/object/bbox/label': int64_feature(labels),
         'image/object/bbox/label_text': bytes_feature(labels_text),
         'image/object/bbox/difficult': int64_feature(difficult),
         'image/object/bbox/truncated': int64_feature(truncated),
         'image/format': bytes_feature(image_format),
         'image/encoded': bytes_feature(image_data)}))
     return example

 # 增加到tfrecord
 def _add_to_tfrecord(dataset_dir, name, tfrecord_writer):
     image_data, shape, bboxes, labels, labels_text, difficult, truncated = \
         _process_image(dataset_dir, name)
     example = _convert_to_example(image_data, labels, labels_text,
                                   bboxes, shape, difficult, truncated)
     tfrecord_writer.write(example.SerializeToString())

 # name为转化文件的前缀
 def _get_output_filename(output_dir, name, idx):
     return '%s/%s_%03d.tfrecord' % (output_dir, name, idx)

 def run(dataset_dir, output_dir, name='voc_train', shuffling=False):
     if not tf.gfile.Exists(dataset_dir):
         tf.gfile.MakeDirs(dataset_dir)

     path = os.path.join(dataset_dir, DIRECTORY_ANNOTATIONS)
     filenames = sorted(os.listdir(path))  # 排序
     if shuffling:
         random.seed(RANDOM_SEED)
         random.shuffle(filenames)

     i = 0
     fidx = 0
     while i < len(filenames):
         # Open new TFRecord file.
         tf_filename = _get_output_filename(output_dir, name, fidx)
         with tf.python_io.TFRecordWriter(tf_filename) as tfrecord_writer:
             j = 0
             while i < len(filenames) and j < SAMPLES_PER_FILES:
                 sys.stdout.write(' Converting image %d/%d \n' % (i + 1, len(filenames)))  # 终端打印，类似print
                 sys.stdout.flush()  # 缓冲

                 filename = filenames[i]
                 img_name = filename[:-4]
                 _add_to_tfrecord(dataset_dir, img_name, tfrecord_writer)
                 i += 1
                 j += 1
             fidx += 1

     print('\nFinished converting the Pascal VOC dataset!')

 # 原数据集路径，输出路径以及输出文件名，要根据自己实际做改动
 dataset_dir = "C:/Users/Admin/Desktop/"
 output_dir = "./tfrecords_"
 name = "voc_train"

 def main(_):
     run(dataset_dir, output_dir, name)

 if __name__ == '__main__':
     tf.app.run()

得到的.tfrecords文件如下：