生成tensorflow輸入輸出的圖像格式——使用pipeline通過隊列的方法

轉載自：http://blog.csdn.net/helei001/article/details/51354404

TensorFLow能夠識別的圖像文件，可以通過numpy，使用tf.Variable或者tf.placeholder加載進tensorflow；也可以通過自帶函數（tf.read）讀取，當圖像文件過多時，一般使用pipeline通過隊列的方法進行讀取。下面我們介紹兩種生成tensorflow的圖像格式的方法，供給tensorflow的graph的輸入與輸出。

1

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))

先生成圖像文件的路徑：ls *.jpg> list.txt

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

2 Tensorflow自帶函數讀取

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)

 pipeline讀取方法

# 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()