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。
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import cv2 import numpy as np import h5py height = 460width = 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
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import cv2 import numpy as np image_path = './'list_file = 'list.txt'height = 48width = 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
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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
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# 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 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 = 28IMAGE_WIDTH = 28NUM_CHANNELS = 3BATCH_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服务器之家。
原文链接：http://blog.csdn.net/helei001/article/details/51354404
