import tensorflow as tf
import matplotlib.pyplot as plt

def read_image(file_name):
 img = tf.read_file(filename=file_name)  # 默認(rèn)讀取格式為uint8
 print("img 的類型是",type(img));
 img = tf.image.decode_jpeg(img,channels=0) # channels 為1得到的是灰度圖，為0則按照?qǐng)D片格式來(lái)讀
 return img

def main( ):
 with tf.device("/cpu:0"):
　　　　  # img_path是文件所在地址包括文件名稱，地址用相對(duì)地址或者絕對(duì)地址都行 
   img_path='./1.jpg'
   img=read_image(img_path)
   with tf.Session() as sess:
   image_numpy=sess.run(img)
   print(image_numpy)
   print(image_numpy.dtype)
   print(image_numpy.shape)
   plt.imshow(image_numpy)
   plt.show()

if __name__=="__main__":
 main()

"""

def get_image_batch(data_file,batch_size):
 data_names=[os.path.join(data_file,k) for k in os.listdir(data_file)]
 
 #這個(gè)num_epochs函數(shù)在整個(gè)Graph是local Variable，所以在sess.run全局變量的時(shí)候也要加上局部變量。 
 filenames_queue=tf.train.string_input_producer(data_names,num_epochs=50,shuffle=True,capacity=512)
 reader=tf.WholeFileReader()
 _,img_bytes=reader.read(filenames_queue)
 image=tf.image.decode_png(img_bytes,channels=1) #讀取的是什么格式，就decode什么格式
 #解碼成單通道的，并且獲得的結(jié)果的shape是[?, ?,1]，也就是Graph不知道圖像的大小，需要set_shape
 image.set_shape([180,180,1]) #set到原本已知圖像的大小?；蛘咧苯油ㄟ^(guò)tf.image.resize_images，tf.reshape()
 image=tf.image.convert_image_dtype(image,tf.float32)
 #預(yù)處理 下面的一句代碼可以換成自己想使用的預(yù)處理方式
 #image=tf.divide(image,255.0) 
 return tf.train.batch([image],batch_size) 

import tensorflow as tf
import os
def read_image(data_file,batch_size):
 data_names=[os.path.join(data_file,k) for k in os.listdir(data_file)]
 filenames_queue=tf.train.string_input_producer(data_names,num_epochs=5,shuffle=True,capacity=30)
 reader=tf.WholeFileReader()
 _,img_bytes=reader.read(filenames_queue)
 image=tf.image.decode_jpeg(img_bytes,channels=1)
 image=tf.image.resize_images(image,(180,180))

 image=tf.image.convert_image_dtype(image,tf.float32)
 return tf.train.batch([image],batch_size)

def main( ):
 img_path=r'F:\dataSet\WIDER\WIDER_train\images\6--Funeral' #本地的一個(gè)數(shù)據(jù)集目錄，有足夠的圖像
 img=read_image(img_path,batch_size=10)
 image=img[0] #取出每個(gè)batch的第一個(gè)數(shù)據(jù)
 print(image)
 init=[tf.global_variables_initializer(),tf.local_variables_initializer()]
 with tf.Session() as sess:
  sess.run(init)
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(sess=sess,coord=coord)
  try:
   while not coord.should_stop():
    print(image.shape)
  except tf.errors.OutOfRangeError:
   print('read done')
  finally:
   coord.request_stop()
  coord.join(threads)


if __name__=="__main__":
 main()

"""

import tensorflow as tf
import matplotlib.pyplot as plt
import os
import cv2
import numpy as np
import glob

def read_image(data_file,batch_size):
 files_path=glob.glob(data_file)
 queue=tf.train.string_input_producer(files_path,num_epochs=None)
 reader = tf.TFRecordReader()
 print(queue)
 _, serialized_example = reader.read(queue)
 features = tf.parse_single_example(
  serialized_example,
  features={
   'image_raw': tf.FixedLenFeature([], tf.string),
   'label_raw': tf.FixedLenFeature([], tf.string),
  })
 image = tf.decode_raw(features['image_raw'], tf.uint8)
 image = tf.cast(image, tf.float32)
 image.set_shape((12*12*3))
 label = tf.decode_raw(features['label_raw'], tf.float32)
 label.set_shape((2))
 # 預(yù)處理部分省略，大家可以自己根據(jù)需要添加
 return tf.train.batch([image,label],batch_size=batch_size,num_threads=4,capacity=5*batch_size)

def main( ):
 img_path=r'F:\python\MTCNN_by_myself\prepare_data\pnet*.tfrecords' #本地的幾個(gè)tf文件
 img,label=read_image(img_path,batch_size=10)
 image=img[0]
 init=[tf.global_variables_initializer(),tf.local_variables_initializer()]
 with tf.Session() as sess:
  sess.run(init)
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(sess=sess,coord=coord)
  try:
   while not coord.should_stop():
    print(image.shape)
  except tf.errors.OutOfRangeError:
   print('read done')
  finally:
   coord.request_stop()
  coord.join(threads)


if __name__=="__main__":
 main()

reader=tf.TFRecordReader()
_,serialized_example=reader.read(file_name_queue)
features = tf.parse_single_example(serialized_example, features={
 'data': tf.FixedLenFeature([256,256], tf.float32), ###
 'label': tf.FixedLenFeature([], tf.int64),
 'id': tf.FixedLenFeature([], tf.int64)
})
img = features['data']
label =features['label']
id = features['id']