|
|
#图片预处理
|
|
|
|
|
|
# 将原始图片转换成需要的大小，并将其保存
|
|
|
import os
|
|
|
import tensorflow as tf
|
|
|
from PIL import Image
|
|
|
|
|
|
# 原始图片的存储位置 /Users/leixinhong/PycharmProjects/classification/teethimg/train-data/
|
|
|
orig_picture = '/Users/leixinhong/PycharmProjects/classification/teethimg/train-data'
|
|
|
|
|
|
# 生成图片的存储位置 /Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/
|
|
|
gen_picture = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
|
|
|
|
|
|
# 需要的识别类型
|
|
|
classes = ['one', 'two', 'three', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']
|
|
|
|
|
|
# 样本总数
|
|
|
num_samples = 90
|
|
|
|
|
|
|
|
|
# 制作TFRecords数据
|
|
|
def create_record():
|
|
|
writer = tf.compat.v1.python_io.TFRecordWriter("dishes_train.tfrecords")
|
|
|
for index, name in list(enumerate(classes)):
|
|
|
# /Users/leixinhong/PycharmProjects/classification/teethimg/train-data/one/
|
|
|
class_path = orig_picture + "/" + name + "/"
|
|
|
# print(index)
|
|
|
# print(name)
|
|
|
# print(class_path)
|
|
|
# print(os.listdir(class_path))
|
|
|
# os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表。
|
|
|
for img_name in os.listdir(class_path):
|
|
|
img_path = class_path + img_name
|
|
|
#print(img_path)
|
|
|
img = Image.open(img_path)
|
|
|
#print(img)
|
|
|
img = img.resize((64, 64)) # 设置需要转换的图片大小
|
|
|
#print(img)
|
|
|
img_raw = img.tobytes() # 将图片转化为原生bytes
|
|
|
print(index, img_raw)
|
|
|
example = tf.train.Example(
|
|
|
features=tf.train.Features(feature={
|
|
|
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
|
|
|
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
|
|
|
}))
|
|
|
writer.write(example.SerializeToString())
|
|
|
writer.close()
|
|
|
|
|
|
|
|
|
def read_and_decode(filename):
|
|
|
# 创建文件队列,不限读取的数量
|
|
|
filename_queue = tf.compat.v1.train.string_input_producer([filename])
|
|
|
# create a reader from file queue
|
|
|
reader = tf.compat.v1.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.io.parse_single_example(
|
|
|
serialized_example,
|
|
|
features={
|
|
|
'label': tf.io.FixedLenFeature([], tf.int64),
|
|
|
'img_raw': tf.io.FixedLenFeature([], tf.string)
|
|
|
})
|
|
|
label = features['label']
|
|
|
img = features['img_raw']
|
|
|
img = tf.io.decode_raw(img, tf.uint8)
|
|
|
img = tf.reshape(img, [64, 64, 3])
|
|
|
# img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
|
|
|
label = tf.cast(label, tf.int32)
|
|
|
return img, label
|
|
|
|
|
|
|
|
|
if __name__ == '__main__': # 程序主入口
|
|
|
create_record()
|
|
|
batch = read_and_decode('dishes_train.tfrecords')
|
|
|
init_op = tf.group(tf.compat.v1.global_variables_initializer(), tf.compat.v1.local_variables_initializer())
|
|
|
|
|
|
with tf.compat.v1.Session() as sess: # 开始一个会话
|
|
|
sess.run(init_op)
|
|
|
coord = tf.train.Coordinator()
|
|
|
threads = tf.compat.v1.train.start_queue_runners(coord=coord)
|
|
|
|
|
|
for i in range(num_samples):
|
|
|
example, lab = sess.run(batch) # 在会话中取出image和label
|
|
|
img = Image.fromarray(example, 'RGB') # 这里Image是之前提到的
|
|
|
img.save(gen_picture + '/' + str(i) + 'samples' + str(lab) + '.jpg') # 存下图片;注意cwd后边加上‘/’
|
|
|
print(example, lab)
|
|
|
coord.request_stop()
|
|
|
coord.join(threads)
|
|
|
sess.close()
|
