# Author Qian Chenglong
# -*-coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import os
import cv2
import matplotlib.pyplot as plt
import random
from PIL import Image
##########################################################################
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
# 生成字符串型的属性
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
# 生成实数型的属性
def float_list_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def get_example_nums(tf_records_filenames):
'''
统计tf_records图像的个数(example)个数
:param tf_records_filenames: tf_records文件路径
:return:
'''
nums = 0
for record in tf.python_io.tf_record_iterator(tf_records_filenames):
nums += 1
return nums
def show_image(title, image):
'''
显示图片
:param title: 图像标题
:param image: 图像的数据
:return:
'''
# plt.figure("show_image")
# print(image.dtype)
plt.imshow(image)
plt.axis('on') # 关掉坐标轴为 off
plt.title(title) # 图像题目
plt.show()
def load_labels_file(filename, labels_num=1, shuffle=False):
'''
载图txt文件，文件中每行为一个图片信息，且以空格隔开：图像路径 标签1 标签2，如：test_image/1.jpg 0 2
:param filename:
:param labels_num :labels个数
:param shuffle :是否打乱顺序
:return:images type->list
:return:labels type->list
'''
images = []
labels = []
with open(filename) as f:
lines_list = f.readlines()
if shuffle:
random.shuffle(lines_list)
for lines in lines_list:
line = lines.rstrip().split(' ')
label = []
for i in range(labels_num):
label.append(int(line[i + 1]))
images.append(line[0])
labels.append(label)
return images, labels
def read_image(filename, resize_height, resize_width, normalization=False):
'''
读取图片数据,默认返回的是uint8,[0,255]
:param filename:
:param resize_height:
:param resize_
:param normalization:是否归一化到[0.,1.0]
:return: 返回的图片数据
'''
bgr_image = cv2.imread(filename)
if len(bgr_image.shape) == 2: # 若是灰度图则转为三通道
print("Warning:gray image", filename)
bgr_image = cv2.cvtColor(bgr_image, cv2.COLOR_GRAY2BGR)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB) # 将BGR转为RGB
# show_image(filename,rgb_image)
# rgb_image=Image.open(filename)
if resize_height > 0 and resize_width > 0:
rgb_image = cv2.resize(rgb_image, (resize_width, resize_height))
rgb_image = np.asanyarray(rgb_image)
if normalization:
# 不能写成:rgb_image=rgb_image/255
rgb_image = rgb_image / 255.0
# show_image("src resize image",image)
return rgb_image
def get_batch_images(images, labels, batch_size, labels_nums, one_hot=False, shuffle=False, num_threads=1):
'''
:param images:图像
:param labels:标签
:param batch_size:
:param labels_nums:标签个数
:param one_hot:是否将labels转为one_hot的形式
:param shuffle:是否打乱顺序,一般train时shuffle=True,验证时shuffle=False
:return:返回batch的images和labels
'''
min_after_dequeue = 200
capacity = min_after_dequeue + 3 * batch_size # 保证capacity必须大于min_after_dequeue参数值
if shuffle:
images_batch, labels_batch = tf.train.shuffle_batch([images, labels],
batch_size=batch_size,
capacity=capacity,
min_after_dequeue=min_after_dequeue,
num_threads=num_threads)
else:
images_batch, labels_batch = tf.train.batch([images, labels],
batch_size=batch_size,
capacity=capacity,
num_threads=num_threads)
if one_hot:
labels_batch = tf.one_hot(labels_batch, labels_nums, 1, 0)
return images_batch, labels_batch
def read_records(filename, resize_height, resize_width, type=None):
'''
解析record文件:源文件的图像数据是RGB,uint8,[0,255],一般作为训练数据时,需要归一化到[0,1]
:param filename:
:param resize_height:
:param resize_
:param type:选择图像数据的返回类型
None:默认将uint8-[0,255]转为float32-[0,255]
normalization:归一化float32-[0,1]
standardization:标准化float32-[0,1],再减均值中心化
:return:
'''
# 创建文件队列,不限读取的数量
filename_queue = tf.train.string_input_producer([filename])
# create a reader from file queue
reader = tf.TFRecordReader()
# reader从文件队列中读入一个序列化的样本
_, serialized_example = reader.read(filename_queue)
# get feature from serialized example
# 解析符号化的样本
features = tf.parse_single_example(
serialized_example,
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'depth': tf.FixedLenFeature([], tf.int64),
'label': tf.FixedLenFeature([], tf.int64)
}
)
tf_image = tf.decode_raw(features['image_raw'], tf.uint8) # 获得图像原始的数据
tf_height = features['height']
tf_width = features['width']
tf_depth = features['depth']
tf_label = tf.cast(features['label'], tf.int32)
# PS:恢复原始图像数据,reshape的大小必须与保存之前的图像shape一致,否则出错
# tf_image=tf.reshape(tf_image, [-1]) # 转换为行向量
tf_image = tf.reshape(tf_image, [resize_height, resize_width, 3]) # 设置图像的维度
# 恢复数据后,才可以对图像进行resize_images:输入uint->输出float32
# tf_image=tf.image.resize_images(tf_image,[224, 224])
# [3]数据类型处理
# 存储的图像类型为uint8,tensorflow训练时数据必须是tf.float32
if type is None:
tf_image = tf.cast(tf_image, tf.float32)
elif type == 'normalization': # [1]若需要归一化请使用:
# 仅当输入数据是uint8,才会归一化[0,255]
# tf_image = tf.cast(tf_image, dtype=tf.uint8)
# tf_image = tf.image.convert_image_dtype(tf_image, tf.float32)
tf_image = tf.cast(tf_image, tf.float32) * (1. / 255.0) # 归一化
elif type == 'standardization': # 标准化
# tf_image = tf.cast(tf_image, dtype=tf.uint8)
# tf_image = tf.image.per_image_standardization(tf_image) # 标准化（减均值除方差）
# 若需要归一化,且中心化,假设均值为0.5,请使用:
tf_image = tf.cast(tf_image, tf.float32) * (1. / 255) - 0.5 # 中心化
# 这里仅仅返回图像和标签
# return tf_image, tf_height,tf_width,tf_depth,tf_label
return tf_image, tf_label
def create_records(image_dir, file, output_record_dir, resize_height, resize_width, shuffle, log=5):
'''
实现将图像原始数据,label,长,宽等信息保存为record文件
注意:读取的图像数据默认是uint8,再转为tf的字符串型BytesList保存,解析请需要根据需要转换类型
:param image_dir:原始图像的目录
:param file:输入保存图片信息的txt文件(image_dir+file构成图片的路径)
:param output_record_dir:保存record文件的路径
:param resize_height:
:param resize_
PS:当resize_height或者resize_width=0是,不执行resize
:param shuffle:是否打乱顺序
:param log:log信息打印间隔
'''
# 加载文件,仅获取一个label
images_list, labels_list = load_labels_file(file, 1, shuffle)
writer = tf.python_io.TFRecordWriter(output_record_dir)
for i, [image_name, labels] in enumerate(zip(images_list, labels_list)):
image_path = os.path.join(image_dir, images_list[i])
if not os.path.exists(image_path):
print('Err:no image', image_path)
continue
image = read_image(image_path, resize_height, resize_width)
image_raw = image.tostring()
if i % log == 0 or i == len(images_list) - 1:
print('------------processing:%d-th------------' % (i))
print('current image_path=%s' % (image_path), 'shape:{}'.format(image.shape), 'labels:{}'.format(labels))
# 这里仅保存一个label,多label适当增加"'label': _int64_feature(label)"项
label = labels[0]
example = tf.train.Example(features=tf.train.Features(feature={
'image_raw': _bytes_feature(image_raw),
'height': _int64_feature(image.shape[0]),
'width': _int64_feature(image.shape[1]),
'depth': _int64_feature(image.shape[2]),
'label': _int64_feature(label)
}))
writer.write(example.SerializeToString())
writer.close()
def disp_records(record_file, resize_height, resize_width, show_nums=4):
'''
解析record文件，并显示show_nums张图片，主要用于验证生成record文件是否成功
:param tfrecord_file: record文件路径
:return:
'''
# 读取record函数
tf_image, tf_label = read_records(record_file, resize_height, resize_width, type='normalization')
# 显示前4个图片
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(show_nums):
image, label = sess.run([tf_image, tf_label]) # 在会话中取出image和label
# image = tf_image.eval()
# 直接从record解析的image是一个向量,需要reshape显示
# image = image.reshape([height,width,depth])
print('shape:{},tpye:{},labels:{}'.format(image.shape, image.dtype, label))
# pilimg = Image.fromarray(np.asarray(image_eval_reshape))
# pilimg.show()
show_image("image:%d" % (label), image)
coord.request_stop()
coord.join(threads)
def batch_test(record_file, resize_height, resize_width):
'''
:param record_file: record文件路径
:param resize_height:
:param resize_
:return:
:PS:image_batch, label_batch一般作为网络的输入
'''
# 读取record函数
tf_image, tf_label = read_records(record_file, resize_height, resize_width, type='normalization')
image_batch, label_batch = get_batch_images(tf_image, tf_label, batch_size=4, labels_nums=5, one_hot=False,
shuffle=False)
init = tf.global_variables_initializer()
with tf.Session() as sess: # 开始一个会话
sess.run(init)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(4):
# 在会话中取出images和labels
images, labels = sess.run([image_batch, label_batch])
# 这里仅显示每个batch里第一张图片
show_image("image", images[0, :, :, :])
print('shape:{},tpye:{},labels:{}'.format(images.shape, images.dtype, labels))
# 停止所有线程
coord.request_stop()
coord.join(threads)
if __name__ == '__main__':
# 参数设置
resize_height = 224 # 指定存储图片高度
resize_width = 224 # 指定存储图片宽度
shuffle = True
log = 5
# 产生train.record文件
image_dir = 'dataset/train'
train_labels = 'dataset/train.txt' # 图片路径
train_record_output = 'dataset/record/train.tfrecords'
create_records(image_dir, train_labels, train_record_output, resize_height, resize_width, shuffle, log)
train_nums = get_example_nums(train_record_output)
print("save train example nums={}".format(train_nums))
# 产生val.record文件
image_dir = 'dataset/val'
val_labels = 'dataset/val.txt' # 图片路径
val_record_output = 'dataset/record/val.tfrecords'
create_records(image_dir, val_labels, val_record_output, resize_height, resize_width, shuffle, log)
val_nums = get_example_nums(val_record_output)
print("save val example nums={}".format(val_nums))
# 测试显示函数
# disp_records(train_record_output,resize_height, resize_width)
batch_test(train_record_output, resize_height, resize_width)