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chenmingling/MRI_Deep_learning

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prepare_data.py 10.58 KB
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chris1992212 提交于 2018-10-11 15:00 . SR_crop
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import tensorflow as tf

import numpy as np

import h5py

from scipy.misc import imsave

import tensorlayer as tl



# ###data for one_channel

# crop_number = 100

# crop_patch_size = 48

# patch_size = 384

# X_data= np.zeros((crop_number*48*6*9,384,384,1))

# Y_data = np.zeros((crop_number*48*6*9,384,384,1))



# writer = tf.python_io.TFRecordWriter('Amp_all_echo_one_channel.tfrecord')

# s1 = h5py.File("train//Amp_all_echo_in_one_channel"+'/low_CompI.mat')

# X_dataa = s1['low_CompI'].value

# X_data = X_dataa[:,:,:]

# s2 = h5py.File("train//Amp_all_echo_in_one_channel"+'/CompI.mat')

# Y_dataa = s2['CompI'].value

# Y_data = Y_dataa[:, :, :]

# nb_images = Y_data.shape[0]

# # X_small_patch = np.zeros((nb_images*crop_number,crop_patch_size,crop_patch_size,1))

# # Y_small_patch = np.zeros((nb_images*crop_number,crop_patch_size,crop_patch_size,1))

# image_shape = (crop_patch_size, crop_patch_size,1)

# y_image_shape = (crop_patch_size, crop_patch_size,1)

# k=0

# for j in range(nb_images):

#     for i in range(crop_number):

#         X_input = X_data[j,:,:].reshape(1,patch_size,patch_size,1)

#         Y_input = Y_data[j,:,:].reshape(1,patch_size,patch_size,1)

#         Input = np.concatenate((X_input, Y_input))

#         Output = tl.prepro.crop_multi(Input,crop_patch_size,crop_patch_size,True)

#         X_small_patch = Output[0,:,:,0].tostring()

#         Y_small_patch = Output[1,:,:,0].tostring()

#         example = tf.train.Example(

#             features=tf.train.Features(

#                 feature={'low_CompI': tf.train.Feature(bytes_list=tf.train.BytesList(value=[X_small_patch])),

#                          'CompI': tf.train.Feature(bytes_list=tf.train.BytesList(value=[Y_small_patch]))

#                          }

#             )

#         )

#         # X_small_patch[k:,:,0],Y_small_patch[k,:,:,0]  = tl.prepro.crop_multi([X_input, Y_input],16,16,True)

#         serialized = example.SerializeToString()

#         writer.write(serialized)

# writer.close()





# ###data for six_channel

# crop_number = 50

# crop_patch_size = 80

# patch_size_PE = 288

# patch_size_FE = 384

# batch_x= np.zeros((1,patch_size_PE,patch_size_FE,6))

# batch_y = np.zeros((1,patch_size_PE,patch_size_FE,6))

# #

# writer = tf.python_io.TFRecordWriter('Amp_6channel.tfrecord')

# #

# # all echo in one mat

# s1 = h5py.File("train//Amp_6echo"  + '/low_CompI_final.mat')

# X_data1 = s1['low_CompI_final'].value

# X_data = X_data1[:,:,:,:]

#

# s1 = h5py.File("train//Amp_6echo"  + '/CompI_final.mat')

# Y_data1 = s1['CompI_final'].value

# Y_data = Y_data1[:,:,:,:]

# nb_images = Y_data.shape[1]

# # X_small_patch = np.zeros((nb_images*crop_number,crop_patch_size,crop_patch_size,1))

# # Y_small_patch = np.zeros((nb_images*crop_number,crop_patch_size,crop_patch_size,1))

# image_shape = (crop_patch_size, crop_patch_size,1)

# y_image_shape = (crop_patch_size, crop_patch_size,1)

# k=0

# for j in range(nb_images):

#     for i in range(crop_number):

#         batch_x[0, :, :, 0] = X_data[0,j, :, :].astype('float32')

#         batch_x[0, :, :, 1] = X_data[1,j, :, :].astype('float32')

#         batch_x[0, :, :, 2] = X_data[2,j, :, :].astype('float32')

#         batch_x[0, :, :, 3] = X_data[3,j, :, :].astype('float32')

#         batch_x[0, :, :, 4] = X_data[4,j, :, :].astype('float32')

#         batch_x[0, :, :, 5] = X_data[5,j, :, :].astype('float32')

#

#         batch_y[0, :, :, 0] = Y_data[0,j, :, :].astype('float32')

#         batch_y[0, :, :, 1] = Y_data[1,j, :, :].astype('float32')

#         batch_y[0, :, :, 2] = Y_data[2,j, :, :].astype('float32')

#         batch_y[0, :, :, 3] = Y_data[3,j, :, :].astype('float32')

#         batch_y[0, :, :, 4] = Y_data[4,j, :, :].astype('float32')

#         batch_y[0, :, :, 5] = Y_data[5,j, :, :].astype('float32')

#         Input = np.concatenate((batch_x, batch_y))

#         # ##### crop

#         Output = tl.prepro.crop_multi(Input,crop_patch_size,crop_patch_size,True)

#         X_small_patch = Output[0,:,:,0:6].tostring()

#         Y_small_patch = Output[1,:,:,0:6].tostring()

#         example = tf.train.Example(

#             features=tf.train.Features(

#                 feature={'low_CompI': tf.train.Feature(bytes_list=tf.train.BytesList(value=[X_small_patch])),

#                          'CompI': tf.train.Feature(bytes_list=tf.train.BytesList(value=[Y_small_patch]))

#                          }

#             )

#         )

#         # X_small_patch[k:,:,0],Y_small_patch[k,:,:,0]  = tl.prepro.crop_multi([X_input, Y_input],16,16,True)

#         serialized = example.SerializeToString()

#         writer.write(serialized)

# writer.close()

######### Address for 12channel data(complex)

crop_number = 50

crop_patch_size = 80

patch_size_PE = 288

patch_size_FE = 384

batch_x= np.zeros((1,patch_size_PE,patch_size_FE,12))

batch_y = np.zeros((1,patch_size_PE,patch_size_FE,12))

#

writer = tf.python_io.TFRecordWriter('Amp_12channel.tfrecord')

s1 = h5py.File("train//Amp_6echo_12channel"  + '/low_CompI_final.mat')

X_data1 = s1['low_CompI_final'].value

X_data = X_data1[:,:,:,:]



s1 = h5py.File("train//Amp_6echo_12channel"  + '/CompI_final.mat')

Y_data1 = s1['CompI_final'].value

Y_data = Y_data1[:,:,:,:]

nb_images = Y_data.shape[1]

# X_small_patch = np.zeros((nb_images*crop_number,crop_patch_size,crop_patch_size,1))

# Y_small_patch = np.zeros((nb_images*crop_number,crop_patch_size,crop_patch_size,1))

image_shape = (crop_patch_size, crop_patch_size,1)

y_image_shape = (crop_patch_size, crop_patch_size,1)

k=0

for j in range(nb_images):

    for i in range(crop_number):

        batch_x[0, :, :, 0] = X_data[0,j, :, :].astype('float32')

        batch_x[0, :, :, 1] = X_data[1,j, :, :].astype('float32')

        batch_x[0, :, :, 2] = X_data[2,j, :, :].astype('float32')

        batch_x[0, :, :, 3] = X_data[3,j, :, :].astype('float32')

        batch_x[0, :, :, 4] = X_data[4,j, :, :].astype('float32')

        batch_x[0, :, :, 5] = X_data[5,j, :, :].astype('float32')

        batch_x[0, :, :, 6] = X_data[6, j, :, :].astype('float32')

        batch_x[0, :, :, 7] = X_data[7, j, :, :].astype('float32')

        batch_x[0, :, :, 8] = X_data[8, j, :, :].astype('float32')

        batch_x[0, :, :, 9] = X_data[9, j, :, :].astype('float32')

        batch_x[0, :, :, 10] = X_data[10, j, :, :].astype('float32')

        batch_x[0, :, :, 11] = X_data[11, j, :, :].astype('float32')



        batch_y[0, :, :, 0] = Y_data[0,j, :, :].astype('float32')

        batch_y[0, :, :, 1] = Y_data[1,j, :, :].astype('float32')

        batch_y[0, :, :, 2] = Y_data[2,j, :, :].astype('float32')

        batch_y[0, :, :, 3] = Y_data[3,j, :, :].astype('float32')

        batch_y[0, :, :, 4] = Y_data[4,j, :, :].astype('float32')

        batch_y[0, :, :, 5] = Y_data[5,j, :, :].astype('float32')

        batch_y[0, :, :, 6] = Y_data[6, j, :, :].astype('float32')

        batch_y[0, :, :, 7] = Y_data[7, j, :, :].astype('float32')

        batch_y[0, :, :, 8] = Y_data[8, j, :, :].astype('float32')

        batch_y[0, :, :, 9] = Y_data[9, j, :, :].astype('float32')

        batch_y[0, :, :, 10] = Y_data[10, j, :, :].astype('float32')

        batch_y[0, :, :, 11] = Y_data[11, j, :, :].astype('float32')

        Input = np.concatenate((batch_x, batch_y))

        # ##### crop

        Output = tl.prepro.crop_multi(Input,crop_patch_size,crop_patch_size,True)

        X_small_patch = Output[0,:,:,0:12].tostring()

        Y_small_patch = Output[1,:,:,0:12].tostring()

        example = tf.train.Example(

            features=tf.train.Features(

                feature={'low_CompI': tf.train.Feature(bytes_list=tf.train.BytesList(value=[X_small_patch])),

                         'CompI': tf.train.Feature(bytes_list=tf.train.BytesList(value=[Y_small_patch]))

                         }

            )

        )

        # X_small_patch[k:,:,0],Y_small_patch[k,:,:,0]  = tl.prepro.crop_multi([X_input, Y_input],16,16,True)

        serialized = example.SerializeToString()

        writer.write(serialized)

writer.close()





# output file name string to a queue

# ## 测试单通道的tfrecord数据

# filename_queue = tf.train.string_input_producer(['Amp_all_echo_one_channel.tfrecord'], num_epochs=None)

# # create a reader from file queue

# reader = tf.TFRecordReader()

# _, serialized_example = reader.read(filename_queue)

# # get feature from serialized example

# features = tf.parse_single_example(serialized_example,

#                                    features={

#                                        'low_CompI': tf.FixedLenFeature([],tf.string),

#                                         'CompI': tf.FixedLenFeature([],tf.string)

#                                    })

# low = tf.decode_raw(features['low_CompI'],tf.float64)

# low = tf.reshape(low,[crop_patch_size,crop_patch_size])

#

# high = tf.decode_raw(features['CompI'], tf.float64)

# high = tf.reshape(high, [crop_patch_size, crop_patch_size])

# low_batch, high_batch = tf.train.shuffle_batch([low, high],batch_size =16, capacity= 50000, min_after_dequeue=20000, num_threads=1)

# high_batch = tf.reshape(high_batch, [16,crop_patch_size,crop_patch_size,1])

#

# sess = tf.Session()

# init = tf.initialize_all_variables()

# sess.run(init)

#

# tf.train.start_queue_runners(sess=sess)

# for i in range(100):

#     low_images, high_images = sess.run([low_batch, high_batch])

#     d = high_images[0,:,:,0]

#     imsave('high.bmp',d*255)





# ###测试六通道的tfrecord数据

filename_queue = tf.train.string_input_producer(['Amp_12channel.tfrecord'], num_epochs=None)

# create a reader from file queue

reader = tf.TFRecordReader()

_, serialized_example = reader.read(filename_queue)

# get feature from serialized example

features = tf.parse_single_example(serialized_example,

                                   features={

                                       'low_CompI': tf.FixedLenFeature([],tf.string),

                                        'CompI': tf.FixedLenFeature([],tf.string)

                                   })

low = tf.decode_raw(features['low_CompI'],tf.float64)

low = tf.reshape(low,[crop_patch_size,crop_patch_size,12])



high = tf.decode_raw(features['CompI'], tf.float64)

high = tf.reshape(high, [crop_patch_size, crop_patch_size,12])

low_batch, high_batch = tf.train.shuffle_batch([low, high],batch_size =16, capacity= 50000, min_after_dequeue=20000, num_threads=1)

high_batch = tf.reshape(high_batch, [16,crop_patch_size,crop_patch_size,12])



sess = tf.Session()

init = tf.initialize_all_variables()

sess.run(init)



tf.train.start_queue_runners(sess=sess)

for i in range(100):

    low_images, high_images = sess.run([low_batch, high_batch])

    d = high_images[0,:,:,0]

    imsave('high.bmp',d*255)
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