宜配屋

TensorFLow能够识别的图像文件，可以通过numpy，使用tf.Variable或者tf.placeholder加载进tensorflow；也可以通过自带函数（tf.read）读取，当图像文件过多时，一般使用pipeline通过队列的方法进行读取。下面我们介绍两种生成tensorflow的图像格式的方法，供给tensorflow的graph的输入与输出。

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))

先生成图像文件的路径：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自带函数读取

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读取方法

# 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()

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持【听图阁-专注于Python设计】。