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DataGenTFRecord.py

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#!/usr/bin/env python

# -*- coding:utf-8 -*-

import tensorflow as tf

import numpy as np

import os

import cv2

from PIL import Image

def _int64_feature(value):

return tf.train.Feature(int64_list = tf.train.Int64List(value = [value]))

def _bytes_feature(value):

return tf.train.Feature(bytes_list = tf.train.BytesList(value = [value]))

DEPTH = 3

def convert_to(data_path, name):

rows = 256

cols = 256

depth = DEPTH

writer = tf.python_io.TFRecordWriter(name + '.tfrecords')

for img_name in os.listdir(data_path):

img_path = data_path + '\\' + img_name

img = Image.open(img_path)

img = img.resize((256, 256))

img_raw = img.tobytes()

label = img_name[:-4]

label = int(label)

example = tf.train.Example(features = tf.train.Features(feature = {

'image_label': _int64_feature(label),

'image_raw':_bytes_feature(img_raw)

}))

writer.write(example.SerializeToString())

writer.close()

data_path = r'E:\work\Python\shoes\train\images'

name = 'shoes_images_train'

convert_to(data_path, name)

data_path = r'E:\work\Python\shoes\train\sketches'

name = 'shoes_sketches_train'

convert_to(data_path, name)