基于TensorFlow的MTCNN人脸检测算法（通过合并的数据集为PNet生成tfrecord文件 代码注解）

代码源自Github:https://github.com/AITTSMD/MTCNN-Tensorflow
该阶段代码取自 Git主 代码目录下 MTCNN-Tensorflow-master / prepare_data / gen_PNet_tfrecords.py

#coding:utf-8
#导入各种库
import os
import random
import sys
import time
import tensorflow as tf

from prepare_data.tfrecord_utils import _process_image_withoutcoder, _convert_to_example_simple

#从图片和注释文件里加载数据并将其添加到TFRecord里
#参数（变量）：filename:存有数据的字典；tfrecord_writer:用来写入TFRecord的writer

def _add_to_tfrecord(filename, image_example, tfrecord_writer):
    #print('---', filename)
    
    #imaga_data:转化为字符串的图片
    #height:图片原始高度
    #width:图片原始宽度
    #image_example：包含图片信息的字典
    image_data, height, width = _process_image_withoutcoder(filename)
    example = _convert_to_example_simple(image_example, image_data)
    tfrecord_writer.write(example.SerializeToString())						#将imaga_data转化到image_example中并写入tfrecord


def _get_output_filename(output_dir, name, net):
	#定义一下输出的文件名
    
    #return '%s/%s_%s_%s.tfrecord' % (output_dir, name, net, st)
    #st = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
    #time.strftime() 函数接收以时间元组，并返回以可读字符串表示的当地时间，格式由参数format决定:time.strftime(format[, t]),用来输出当前时间
    #返回的是'../../DATA/imglists/PNet/train_PNet_landmark.tfrecord'
    return '%s/train_PNet_landmark.tfrecord' % (output_dir)
    

def run(dataset_dir, net, output_dir, name='MTCNN', shuffling=False):
    """
    运行转换操作
    Args:
      dataset_dir: 数据集所在的数据集目录
      output_dir: 输出目录
    """
    
    #tfrecord name 
    tf_filename = _get_output_filename(output_dir, name, net)	#'../../DATA/imglists/PNet/train_PNet_landmark.tfrecord'
    if tf.gfile.Exists(tf_filename):							#tf.gfile模块提供了文件操作的API,包括文件的读取、写入、删除、复制等等
        print('Dataset files already exist. Exiting without re-creating them.')	#判断是否存在同名文件
        return
    
    
    # 获得数据集，并打乱顺序
    dataset = get_dataset(dataset_dir, net=net)
    # filenames = dataset['filename']
    if shuffling:
        tf_filename = tf_filename + '_shuffle'
        #random.seed(12345454)
        random.shuffle(dataset)												#打乱dataset数据集的顺序
   	
    # Process dataset files.
    # write the data to tfrecord
    print('lala')
    with tf.python_io.TFRecordWriter(tf_filename) as tfrecord_writer:
        for i, image_example in enumerate(dataset):												#读取dataset的索引和内容
            if (i+1) % 100 == 0:
                sys.stdout.write('\r>> %d/%d images has been converted' % (i+1, len(dataset)))	#输出“x00/ len(dataset) images has been converted”
            sys.stdout.flush()																	#以一定间隔时间刷新输出
            filename = image_example['filename']												#赋值
            _add_to_tfrecord(filename, image_example, tfrecord_writer)
    # 最后，编写标签文件
    # labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
    # dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
    print('\nFinished converting the MTCNN dataset!')


def get_dataset(dir, net='PNet'):
    #获取文件名字，标签和注释
    item = 'imglists/PNet/train_%s_landmark.txt' % net
    
    dataset_dir = os.path.join(dir, item)			#dataset_dir = '../../DATA/imglists/PNet/train_PNet_landmark.txt'
    #print(dataset_dir)
    imagelist = open(dataset_dir, 'r')				#以只读的形式打开train_PNet_landmark.txt，并传入imagelist里面

    dataset = []									#新建列表
    for line in imagelist.readlines():				#按行读取imagelist里面的内容
        info = line.strip().split(' ')				#.strip().split()去除每一行首尾空格并且以空格为分隔符读取内容到info里面
        data_example = dict()						#新建字典
        bbox = dict()
        data_example['filename'] = info[0]			#filename=info[0]
        #print(data_example['filename'])
        data_example['label'] = int(info[1])		#label=info[1]，info[1]的值有四种可能，1，0，-1，-2；分别对应着正、负、无关、关键点样本
        bbox['xmin'] = 0							#初始化bounding box的值
        bbox['ymin'] = 0
        bbox['xmax'] = 0
        bbox['ymax'] = 0
        bbox['xlefteye'] = 0						#初始化人脸坐标的值
        bbox['ylefteye'] = 0
        bbox['xrighteye'] = 0
        bbox['yrighteye'] = 0
        bbox['xnose'] = 0
        bbox['ynose'] = 0
        bbox['xleftmouth'] = 0
        bbox['yleftmouth'] = 0
        bbox['xrightmouth'] = 0
        bbox['yrightmouth'] = 0        
        if len(info) == 6:							#当info的长度等于6时，表示此时的info是正样本或者无关样本
            bbox['xmin'] = float(info[2])
            bbox['ymin'] = float(info[3])
            bbox['xmax'] = float(info[4])
            bbox['ymax'] = float(info[5])
        if len(info) == 12:							#当info的长度等于12时，表示此时的info是landmark样本
            bbox['xlefteye'] = float(info[2])
            bbox['ylefteye'] = float(info[3])
            bbox['xrighteye'] = float(info[4])
            bbox['yrighteye'] = float(info[5])
            bbox['xnose'] = float(info[6])
            bbox['ynose'] = float(info[7])
            bbox['xleftmouth'] = float(info[8])
            bbox['yleftmouth'] = float(info[9])
            bbox['xrightmouth'] = float(info[10])
            bbox['yrightmouth'] = float(info[11])
            
        data_example['bbox'] = bbox					#将bounding box值传入字典
        dataset.append(data_example)				#将data_example字典内容传入列表dataset

    return dataset									#返回的是dataset，datase是个列表，但里面每个元素都是一个字典，每个字典都含有3个key，分别是filename、label和bounding box


if __name__ == '__main__':
    dir = '../../DATA/'
    net = 'PNet'
    output_directory = '../../DATA/imglists/PNet'
    run(dir, net, output_directory, shuffling=True)