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()

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