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。
免责声明:本站文章均来自网站采集或用户投稿,网站不提供任何软件下载或自行开发的软件!
如有用户或公司发现本站内容信息存在侵权行为,请邮件告知! 858582#qq.com
白云城资源网 Copyright www.dyhadc.com
暂无“python生成tensorflow输入输出的图像格式的方法”评论...
更新日志
2025年04月02日
2025年04月02日
- 小骆驼-《草原狼2(蓝光CD)》[原抓WAV+CUE]
- 群星《欢迎来到我身边 电影原声专辑》[320K/MP3][105.02MB]
- 群星《欢迎来到我身边 电影原声专辑》[FLAC/分轨][480.9MB]
- 雷婷《梦里蓝天HQⅡ》 2023头版限量编号低速原抓[WAV+CUE][463M]
- 群星《2024好听新歌42》AI调整音效【WAV分轨】
- 王思雨-《思念陪着鸿雁飞》WAV
- 王思雨《喜马拉雅HQ》头版限量编号[WAV+CUE]
- 李健《无时无刻》[WAV+CUE][590M]
- 陈奕迅《酝酿》[WAV分轨][502M]
- 卓依婷《化蝶》2CD[WAV+CUE][1.1G]
- 群星《吉他王(黑胶CD)》[WAV+CUE]
- 齐秦《穿乐(穿越)》[WAV+CUE]
- 发烧珍品《数位CD音响测试-动向效果(九)》【WAV+CUE】
- 邝美云《邝美云精装歌集》[DSF][1.6G]
- 吕方《爱一回伤一回》[WAV+CUE][454M]