tensorflow mobilenetssd训练自己的数据集

要训练tensorflow版的mobilenetssd，首先要进行搭建网络环境，本文使用的系统环境为ubuntu18.04，然后进行搭建，tensorflow环境，这里tensorflow-gpu版的环境搭建，进行简单介绍，还是很容易进行搭建的，不需要编译等复杂操作，只需要几个命令既可以，在这之前，是需要进行安装conda的。

一，环境搭建

安装conda，这个可以参考其他博客，然后进行安装以下软件包

#安装
conda install cudatoolkit = 10.0 cudnn=7.6  tensorflow-gpu=1.15

具体安装如图所示：

                  conda 安装的部分软件

tensorflow环境安装好后，进行制作自己的tfrecord数据集：

首先启动虚拟环境，source activate mobilenetssd

二，数据集制作

准备自己的voc数据集，即xml文件和图片文件，也就是标记好的样本

新建文件 train_test_split.py将样本进行切割划分，分别为train，test，validition三部分。 训练验证集占80%，测试集占20%。训练集占训练验证集的80% 。分别存到指定文件夹下：

import os  
import random  
import time  
import shutil
xmlfilepath=r'./Annotations'  
saveBasePath=r"./Annotations_save"
trainval_percent=0.8  
train_percent=0.8  
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)  
print("train and val size",tv)  
print("train size",tr) 
start = time.time()
test_num=0  
val_num=0  
train_num=0  
for i in list:  
    name=total_xml[i]
    if i in trainval:  #train and val set 
        if i in train: 
            directory="train"  
            train_num += 1  
            xml_path = os.path.join(os.getcwd(), 'Annotations_save/{}'.format(directory))  
            if(not os.path.exists(xml_path)):  
                os.mkdir(xml_path)  
            filePath=os.path.join(xmlfilepath,name) 
            print(filePath)      
            newfile=os.path.join(saveBasePath,os.path.join(directory,name))  
            shutil.copyfile(filePath, newfile)
        else:
            directory="validation"  
            xml_path = os.path.join(os.getcwd(), 'Annotations_save/{}'.format(directory))  
            if(not os.path.exists(xml_path)):  
                os.mkdir(xml_path)  
            val_num += 1    
            filePath=os.path.join(xmlfilepath,name)
            print(filePath)    
            newfile=os.path.join(saveBasePath,os.path.join(directory,name))  
            shutil.copyfile(filePath, newfile)
    else:
        directory="test"  
        xml_path = os.path.join(os.getcwd(), 'Annotations_save/{}'.format(directory))  
        if(not os.path.exists(xml_path)):  
                os.mkdir(xml_path)  
        test_num += 1  
        filePath=os.path.join(xmlfilepath,name)  
        newfile=os.path.join(saveBasePath,os.path.join(directory,name))  
        shutil.copyfile(filePath, newfile)
end = time.time()  
seconds=end-start  
print("train total : "+str(train_num))  
print("validation total : "+str(val_num))  
print("test total : "+str(test_num))  
total_num=train_num+val_num+test_num  
print("total number : "+str(total_num))  
print( "Time taken : {0} seconds".format(seconds))

                                分好的样本

如上图：Annotations为xml文件，Annotations_save，为分类保存的文件，其下面有test，train，validation三个文件夹。

新建xml_to_csv.py 再进行制作csv文件，

import os  
import glob  
import pandas as pd  
import xml.etree.ElementTree as ET 
def xml_to_csv(path):  
    xml_list = []  
    for xml_file in glob.glob(path + '/*.xml'):  
        tree = ET.parse(xml_file)  
        root = tree.getroot()
        
        print(root.find('filename').text)  
        for member in root.findall('object'): 
            value = (root.find('filename').text,  
                int(root.find('size')[0].text),   #width  
                int(root.find('size')[1].text),   #height  
                member[0].text,  
                int(member[4][0].text),  
                int(float(member[4][1].text)),  
                int(member[4][2].text),  
                int(member[4][3].text)  
                )  
            xml_list.append(value)
    column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']
    xml_df = pd.DataFrame(xml_list, columns=column_name)  
    return xml_df      
def main():  
    for directory in ['train','test','validation']:  
        xml_path = os.path.join(os.getcwd(), 'Annotations_save/{}'.format(directory))  
        xml_df = xml_to_csv(xml_path)  
        xml_df.to_csv('./my.csv', index=None)  
        #xml_df.to_csv('/home/lijingle/sdb/mobilenetssdtrafficlight_{}_labels.csv'.format(directory), index=None)  
        print('Successfully converted xml to csv.')
main()

最后制作tfcord文件，新建文件 generate_tfrecord.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar  5 15:28:55 2019
@author: z
"""
"""
Usage:
  # From tensorflow/models/
  # Create train data:
  python generate_tfrecord.py --csv_input=data/tv_vehicle_labels.csv  --output_path=train.record
  # Create test data:
  python generate_tfrecord.py --csv_input=data/test_labels.csv  --output_path=test.record
"""
import os
import io
import pandas as pd
import tensorflow as tf
from PIL import Image
from object_detection.utils import dataset_util
from collections import namedtuple, OrderedDict
/*保存文件路径*/
os.chdir('/home/lijingle/deep_work/2Dimage/mobilenetssd/models/research/')
flags = tf.app.flags
flags.DEFINE_string('csv_input', '', 'Path to the CSV input')
flags.DEFINE_string('output_path', '', 'Path to output TFRecord')
FLAGS = flags.FLAGS
# TO-DO replace this with label map
def class_text_to_int(row_label):
        # 你的所有类别
    if row_label == 'person':
            return 1
    else:
        return None
def split(df, group):
    data = namedtuple('data', ['filename', 'object'])
    gb = df.groupby(group)
    return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]
def create_tf_example(group, path):
    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
        encoded_jpg = fid.read()
    encoded_jpg_io = io.BytesIO(encoded_jpg)
    image = Image.open(encoded_jpg_io)
    width, height = image.size
    filename = group.filename.encode('utf8')
    image_format = b'jpg'
    xmins = []
    xmaxs = []
    ymins = []
    ymaxs = []
    classes_text = []
    classes = []
    for index, row in group.object.iterrows():
        xmins.append(row['xmin'] / width)
        xmaxs.append(row['xmax'] / width)
        ymins.append(row['ymin'] / height)
        ymaxs.append(row['ymax'] / height)
        classes_text.append(row['class'].encode('utf8'))
        print(classes_text)
        classes.append(class_text_to_int(row['class']))
        print(classes)
    tf_example = tf.train.Example(features=tf.train.Features(feature={
        'image/height': dataset_util.int64_feature(height),
        'image/width': dataset_util.int64_feature(width),
        'image/filename': dataset_util.bytes_feature(filename),
        'image/source_id': dataset_util.bytes_feature(filename),
        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
        'image/format': dataset_util.bytes_feature(image_format),
        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
        'image/object/class/label': dataset_util.int64_list_feature(classes),
    }))
    return tf_example
def main(_):
    writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
    path = os.path.join(os.getcwd(), '/home/lijingle/sdb/JPEGImages_p_train')
    examples = pd.read_csv(FLAGS.csv_input)
    grouped = split(examples, 'filename')
    num = 0
    for group in grouped:
        num += 1
        tf_example = create_tf_example(group, path)
        writer.write(tf_example.SerializeToString())
        if (num % 100 == 0):    # 每完成100个转换，打印一次
            print(num)
    writer.close()
    output_path = os.path.join(os.getcwd(), FLAGS.output_path)
    print('Successfully created the TFRecords: {}'.format(output_path))
if __name__ == '__main__':
    tf.app.run()

制作 _tfrecord 文件时需要用到一下命令：

python generate_tfrecord.py --csv_input=/home/lijingle/sdb/mobilenetssd/my.csv --output_path=./trafficlight_train.tfrecord
##注意csv文件路径为绝对路径
至此对tfrecord文件制作完成。
参考：https://www.cnblogs.com/gezhuangzhuang/p/10613468.html