import cv2 
import numpy as np 
import h5py 
 
height = 460 
width = 345 
 
with h5py.File('make3d_dataset_f460.mat','r') as f: 
  images = f['images'][:] 
   
image_num = len(images) 
 
data = np.zeros((image_num, height, width, 3), np.uint8) 
data = images.transpose((0,3,2,1)) 

import cv2 
import numpy as np 
 
image_path = './' 
list_file = 'list.txt' 
height = 48 
width = 48 
 
image_name_list = [] # read image 
with open(image_path + list_file) as fid: 
  image_name_list = [x.strip() for x in fid.readlines()] 
image_num = len(image_name_list) 
 
data = np.zeros((image_num, height, width, 3), np.uint8) 
 
for idx in range(image_num): 
  img = cv2.imread(image_name_list[idx]) 
  img = cv2.resize(img, (height, width)) 
  data[idx, :, :, :] = img 

def get_image(image_path): 
  """Reads the jpg image from image_path. 
  Returns the image as a tf.float32 tensor 
  Args: 
    image_path: tf.string tensor 
  Reuturn: 
    the decoded jpeg image casted to float32 
  """ 
  return tf.image.convert_image_dtype( 
    tf.image.decode_jpeg( 
      tf.read_file(image_path), channels=3), 
    dtype=tf.uint8) 

# Example on how to use the tensorflow input pipelines. The explanation can be found here ischlag.github.io. 
import tensorflow as tf 
import random 
from tensorflow.python.framework import ops 
from tensorflow.python.framework import dtypes 
 
dataset_path   = "/path/to/your/dataset/mnist/" 
test_labels_file = "test-labels.csv" 
train_labels_file = "train-labels.csv" 
 
test_set_size = 5 
 
IMAGE_HEIGHT = 28 
IMAGE_WIDTH  = 28 
NUM_CHANNELS = 3 
BATCH_SIZE  = 5 
 
def encode_label(label): 
 return int(label) 
 
def read_label_file(file): 
 f = open(file, "r") 
 filepaths = [] 
 labels = [] 
 for line in f: 
  filepath, label = line.split(",") 
  filepaths.append(filepath) 
  labels.append(encode_label(label)) 
 return filepaths, labels 
 
# reading labels and file path 
train_filepaths, train_labels = read_label_file(dataset_path + train_labels_file) 
test_filepaths, test_labels = read_label_file(dataset_path + test_labels_file) 
 
# transform relative path into full path 
train_filepaths = [ dataset_path + fp for fp in train_filepaths] 
test_filepaths = [ dataset_path + fp for fp in test_filepaths] 
 
# for this example we will create or own test partition 
all_filepaths = train_filepaths + test_filepaths 
all_labels = train_labels + test_labels 
 
all_filepaths = all_filepaths[:20] 
all_labels = all_labels[:20] 
 
# convert string into tensors 
all_images = ops.convert_to_tensor(all_filepaths, dtype=dtypes.string) 
all_labels = ops.convert_to_tensor(all_labels, dtype=dtypes.int32) 
 
# create a partition vector 
partitions = [0] * len(all_filepaths) 
partitions[:test_set_size] = [1] * test_set_size 
random.shuffle(partitions) 
 
# partition our data into a test and train set according to our partition vector 
train_images, test_images = tf.dynamic_partition(all_images, partitions, 2) 
train_labels, test_labels = tf.dynamic_partition(all_labels, partitions, 2) 
 
# create input queues 
train_input_queue = tf.train.slice_input_producer( 
                  [train_images, train_labels], 
                  shuffle=False) 
test_input_queue = tf.train.slice_input_producer( 
                  [test_images, test_labels], 
                  shuffle=False) 
 
# process path and string tensor into an image and a label 
file_content = tf.read_file(train_input_queue[0]) 
train_image = tf.image.decode_jpeg(file_content, channels=NUM_CHANNELS) 
train_label = train_input_queue[1] 
 
file_content = tf.read_file(test_input_queue[0]) 
test_image = tf.image.decode_jpeg(file_content, channels=NUM_CHANNELS) 
test_label = test_input_queue[1] 
 
# define tensor shape 
train_image.set_shape([IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS]) 
test_image.set_shape([IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS]) 
 
 
# collect batches of images before processing 
train_image_batch, train_label_batch = tf.train.batch( 
                  [train_image, train_label], 
                  batch_size=BATCH_SIZE 
                  #,num_threads=1 
                  ) 
test_image_batch, test_label_batch = tf.train.batch( 
                  [test_image, test_label], 
                  batch_size=BATCH_SIZE 
                  #,num_threads=1 
                  ) 
 
print "input pipeline ready" 
 
with tf.Session() as sess: 
  
 # initialize the variables 
 sess.run(tf.initialize_all_variables()) 
  
 # initialize the queue threads to start to shovel data 
 coord = tf.train.Coordinator() 
 threads = tf.train.start_queue_runners(coord=coord) 
 
 print "from the train set:" 
 for i in range(20): 
  print sess.run(train_label_batch) 
 
 print "from the test set:" 
 for i in range(10): 
  print sess.run(test_label_batch) 
 
 # stop our queue threads and properly close the session 
 coord.request_stop() 
 coord.join(threads) 
 sess.close()