（转）将cifar10数据集保存为可见图片

https://www.cnblogs.com/dudu1992/p/8908081.html

下载cifar10数据集：http://www.cs.toronto.edu/~kriz/cifar.html

选择cifar-10-python.tar.gz进行下载。

1 建立 main.py

import tensorflow as tf
import os
import scipy.misc
import cifar10_input
def inputs_origin(data_dir):filenames = [os.path.join(data_dir, 'data_batch_%d' % i) for i in range(1, 6)]for f in filenames:print(f)if not tf.gfile.Exists(f):raise ValueError('Failed to find file' + f)filenames_queue =tf.train.string_input_producer(filenames)read_input = cifar10_input.read_cifar10(filenames_queue)reshaped_image = tf.cast(read_input.uint8image,tf.float32)print(reshaped_image)return reshaped_imageif __name__ == '__main__':with tf.Session() as sess:reshaped_image = inputs_origin('cifar-10-batches-py')threads = tf.train.start_queue_runners(sess=sess)print(threads)sess.run(tf.global_variables_initializer())if not os.path.exists('cifar-10-batches-py/raw/'):os.makedirs('cifar-10-batches-py/raw/')for i in range(30):image = sess.run(reshaped_image)scipy.misc.toimage(image).save('cifar-10-batches-py/raw/%d.jpg' %i)

2 建立 cifar10_input.py


from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport osfrom six.moves import xrange  # pylint: disable=redefined-builtinimport tensorflow as tf# Process images of this size. Note that this differs from the original CIFAR# image size of 32 x 32. If one alters this number, then the entire model# architecture will change and any model would need to be retrained.IMAGE_SIZE = 24# Global constants describing the CIFAR-10 data set.NUM_CLASSES = 10NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000def read_cifar10(filename_queue):"""Reads and parses examples from CIFAR10 data files.Recommendation: if you want N-way read parallelism, call this functionN times.  This will give you N independent Readers reading differentfiles & positions within those files, which will give better mixing ofexamples.Args:filename_queue: A queue of strings with the filenames to read from.Returns:An object representing a single example, with the following fields:height: number of rows in the result (32)width: number of columns in the result (32)depth: number of color channels in the result (3)key: a scalar string Tensor describing the filename & record numberfor this example.label: an int32 Tensor with the label in the range 0..9.uint8image: a [height, width, depth] uint8 Tensor with the image data"""class CIFAR10Record(object):passresult = CIFAR10Record()# Dimensions of the images in the CIFAR-10 dataset.# See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the# input format.label_bytes = 1  # 2 for CIFAR-100result.height = 50result.width = 50result.depth = 3image_bytes = result.height * result.width * result.depth# Every record consists of a label followed by the image, with a# fixed number of bytes for each.record_bytes = label_bytes + image_bytes# Read a record, getting filenames from the filename_queue.  No# header or footer in the CIFAR-10 format, so we leave header_bytes# and footer_bytes at their default of 0.reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)result.key, value = reader.read(filename_queue)# Convert from a string to a vector of uint8 that is record_bytes long.record_bytes = tf.decode_raw(value, tf.uint8)# The first bytes represent the label, which we convert from uint8->int32.result.label = tf.cast(tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)# The remaining bytes after the label represent the image, which we reshape# from [depth * height * width] to [depth, height, width].depth_major = tf.reshape(tf.strided_slice(record_bytes, [label_bytes],[label_bytes + image_bytes]),[result.depth, result.height, result.width])# Convert from [depth, height, width] to [height, width, depth].result.uint8image = tf.transpose(depth_major, [1, 2, 0])return resultdef _generate_image_and_label_batch(image, label, min_queue_examples,batch_size, shuffle):"""Construct a queued batch of images and labels.Args:image: 3-D Tensor of [height, width, 3] of type.float32.label: 1-D Tensor of type.int32min_queue_examples: int32, minimum number of samples to retainin the queue that provides of batches of examples.batch_size: Number of images per batch.shuffle: boolean indicating whether to use a shuffling queue.Returns:images: Images. 4D tensor of [batch_size, height, width, 3] size.labels: Labels. 1D tensor of [batch_size] size."""# Create a queue that shuffles the examples, and then# read 'batch_size' images + labels from the example queue.num_preprocess_threads = 16if shuffle:images, label_batch = tf.train.shuffle_batch([image, label],batch_size=batch_size,num_threads=num_preprocess_threads,capacity=min_queue_examples + 3 * batch_size,min_after_dequeue=min_queue_examples)else:images, label_batch = tf.train.batch([image, label],batch_size=batch_size,num_threads=num_preprocess_threads,capacity=min_queue_examples + 3 * batch_size)# Display the training images in the visualizer.tf.summary.image('images', images)return images, tf.reshape(label_batch, [batch_size])def distorted_inputs(data_dir, batch_size):"""Construct distorted input for CIFAR training using the Reader ops.Args:data_dir: Path to the CIFAR-10 data directory.batch_size: Number of images per batch.Returns:images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.labels: Labels. 1D tensor of [batch_size] size."""filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)]for f in filenames:if not tf.gfile.Exists(f):raise ValueError('Failed to find file: ' + f)# Create a queue that produces the filenames to read.filename_queue = tf.train.string_input_producer(filenames)# Read examples from files in the filename queue.read_input = read_cifar10(filename_queue)reshaped_image = tf.cast(read_input.uint8image, tf.float32)height = IMAGE_SIZEwidth = IMAGE_SIZE# Image processing for training the network. Note the many random# distortions applied to the image.# Randomly crop a [height, width] section of the image.distorted_image = tf.random_crop(reshaped_image, [height, width, 3])# Randomly flip the image horizontally.distorted_image = tf.image.random_flip_left_right(distorted_image)# Because these operations are not commutative, consider randomizing# the order their operation.distorted_image = tf.image.random_brightness(distorted_image, max_delta=63)distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8)# Subtract off the mean and divide by the variance of the pixels.float_image = tf.image.per_image_standardization(distorted_image)# Set the shapes of tensors.float_image.set_shape([height, width, 3])read_input.label.set_shape([1])# Ensure that the random shuffling has good mixing properties.min_fraction_of_examples_in_queue = 0.4min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue)print('Filling queue with %d CIFAR images before starting to train. ''This will take a few minutes.' % min_queue_examples)# Generate a batch of images and labels by building up a queue of examples.return _generate_image_and_label_batch(float_image,read_input.label,min_queue_examples,batch_size,shuffle=True)def inputs(eval_data, data_dir, batch_size):"""Construct input for CIFAR evaluation using the Reader ops.Args:eval_data: bool, indicating if one should use the train or eval data set.data_dir: Path to the CIFAR-10 data directory.batch_size: Number of images per batch.Returns:images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.labels: Labels. 1D tensor of [batch_size] size."""if not eval_data:filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)]num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAINelse:filenames = [os.path.join(data_dir, 'test_batch.bin')]num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVALfor f in filenames:if not tf.gfile.Exists(f):raise ValueError('Failed to find file: ' + f)# Create a queue that produces the filenames to read.filename_queue = tf.train.string_input_producer(filenames)# Read examples from files in the filename queue.read_input = read_cifar10(filename_queue)reshaped_image = tf.cast(read_input.uint8image, tf.float32)height = IMAGE_SIZEwidth = IMAGE_SIZE# Image processing for evaluation.# Crop the central [height, width] of the image.resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, width, height)# Subtract off the mean and divide by the variance of the pixels.float_image = tf.image.per_image_standardization(resized_image)# Set the shapes of tensors.float_image.set_shape([height, width, 3])read_input.label.set_shape([1])# Ensure that the random shuffling has good mixing properties.min_fraction_of_examples_in_queue = 0.4min_queue_examples = int(num_examples_per_epoch * min_fraction_of_examples_in_queue)# Generate a batch of images and labels by building up a queue of examples.return _generate_image_and_label_batch(float_image,read_input.label,min_queue_examples,batch_size,shuffle=False)

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