import tensorflow as tf
import os
import random
import math
import sys
from PIL import Image
import numpy as np
# In[2]:
#验证集数量
_NUM_TEST = 500
#随机种子
_RANDOM_SEED = 0
#数据集路径 需要自己改动
DATASET_DIR = "D:/PycharmProjects/test/captcha/images"
#tfrecord文件存放路径 需要自己改动
TFRECORD_DIR = "D:/PycharmProjects/test/captcha"
#判断tfrecord文件是否存在
def _dataset_exists(dataset_dir):
for split_name in ['train', 'test']:
output_filename = os.path.join(dataset_dir,split_name + '.tfrecords')
if not tf.gfile.Exists(output_filename):
return False
return True
#获取所有验证码图片
def _get_filenames_and_classes(dataset_dir):
photo_filenames = []
for filename in os.listdir(dataset_dir):
#获取文件路径
path = os.path.join(dataset_dir, filename)
photo_filenames.append(path)
return photo_filenames
def int64_feature(values):
if not isinstance(values, (tuple, list)):
values = [values]
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
def bytes_feature(values):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
def image_to_tfexample(image_data, label0, label1, label2, label3):
#Abstract base class for protocol messages.
return tf.train.Example(features=tf.train.Features(feature={
'image': bytes_feature(image_data),
'label0': int64_feature(label0),
'label1': int64_feature(label1),
'label2': int64_feature(label2),
'label3': int64_feature(label3),
}))
#把数据转为TFRecord格式
def _convert_dataset(split_name, filenames, dataset_dir):
assert split_name in ['train', 'test']
with tf.Session() as sess:
#定义tfrecord文件的路径+名字
output_filename = os.path.join(TFRECORD_DIR,split_name + '.tfrecords')
with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
for i,filename in enumerate(filenames):
try:
sys.stdout.write('\r>> Converting image %d/%d' % (i+1, len(filenames)))
sys.stdout.flush()
#读取图片
image_data = Image.open(filename)
#根据模型的结构resize 需要自己改 动
image_data = image_data.resize((224, 224))
#灰度化
image_data = np.array(image_data.convert('L'))
#将图片转化为bytes
image_data = image_data.tobytes()
#获取label
labels = filename.split('/')[-1][0:4]
num_labels = []
for j in range(4):
num_labels.append(int(labels[j]))
#生成protocol数据类型 下面行 为了多任务学习拆成了4行
example = image_to_tfexample(image_data, num_labels[0], num_labels[1], num_labels[2], num_labels[3])
tfrecord_writer.write(example.SerializeToString())
except IOError as e:
print('Could not read:',filename)
print('Error:',e)
print('Skip it\n')
sys.stdout.write('\n')
sys.stdout.flush()
#判断tfrecord文件是否存在
if _dataset_exists(TFRECORD_DIR):
print('tfcecord文件已存在')
else:
#获得所有图片
photo_filenames = _get_filenames_and_classes(DATASET_DIR)
#把数据切分为训练集和测试集,并打乱
random.seed(_RANDOM_SEED)
random.shuffle(photo_filenames)
training_filenames = photo_filenames[_NUM_TEST:]
testing_filenames = photo_filenames[:_NUM_TEST]
#数据转换
_convert_dataset('train', training_filenames, DATASET_DIR)
_convert_dataset('test', testing_filenames, DATASET_DIR)
print('生成tfcecord文件')
D:\Anaconda3\python.exe D:/PycharmProjects/test/10-2生成tfrecord文件.py
>> Converting image 1/5811Traceback (most recent call last):
File "D:/PycharmProjects/test/10-2生成tfrecord文件.py", line 118, in <module>
_convert_dataset('train', training_filenames, DATASET_DIR)
File "D:/PycharmProjects/test/10-2生成tfrecord文件.py", line 91, in _convert_dataset
num_labels.append(int(labels[j]))
ValueError: invalid literal for int() with base 10: 'i'
我可以微信红包