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load_data.py
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load_data.py
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import os
import numpy as np
from PIL import Image
from keras.utils import Sequence
#from skimage.io import imread
def load_data(nr_of_channels, batch_size=1, nr_target_train_imgs=None, nr_S1_train_imgs=None, nr_S2_train_imgs=None, nr_S3_train_imgs=None,
nr_target_test_imgs=None, nr_S1_test_imgs=None, nr_S2_test_imgs=None, nr_S3_test_imgs=None, subfolder='',
generator=False, D_model=None, use_multiscale_discriminator=False, use_supervised_learning=False, REAL_LABEL=1.0):
trainT_path = os.path.join('data', subfolder, 'trainT')
trainS1_path = os.path.join('data', subfolder, 'trainS1')
trainS2_path = os.path.join('data', subfolder, 'trainS2')
trainS3_path = os.path.join('data', subfolder, 'trainS3')
testT_path = os.path.join('data', subfolder, 'testT')
testS1_path = os.path.join('data', subfolder, 'testS1')
testS2_path = os.path.join('data', subfolder, 'testS2')
testS3_path = os.path.join('data', subfolder, 'testS3')
trainT_image_names = os.listdir(trainT_path)
if nr_target_train_imgs != None:
trainT_image_names = trainT_image_names[:nr_target_train_imgs]
trainS1_image_names = os.listdir(trainS1_path)
if nr_S1_train_imgs != None:
trainS1_image_names = trainS1_image_names[:nr_S1_train_imgs]
trainS2_image_names = os.listdir(trainS2_path)
if nr_S2_train_imgs != None:
trainS2_image_names = trainS2_image_names[:nr_S2_train_imgs]
trainS3_image_names = os.listdir(trainS3_path)
if nr_S3_train_imgs != None:
trainS3_image_names = trainS3_image_names[:nr_S3_train_imgs]
testT_image_names = os.listdir(testT_path)
if nr_target_test_imgs != None:
testT_image_names = testT_image_names[:nr_target_test_imgs]
testS1_image_names = os.listdir(testS1_path)
if nr_S1_test_imgs != None:
testS1_image_names = testS1_image_names[:nr_S1_test_imgs]
testS2_image_names = os.listdir(testS2_path)
if nr_S2_test_imgs != None:
testS2_image_names = testS2_image_names[:nr_S2_test_imgs]
testS3_image_names = os.listdir(testS3_path)
if nr_S3_test_imgs != None:
testS3_image_names = testS3_image_names[:nr_S3_test_imgs]
if generator:
return data_sequence(trainT_path, trainS1_path, trainS2_path, trainS3_path, trainT_image_names, trainS1_image_names, trainS2_image_names, trainS3_image_names, batch_size=batch_size) # D_model, use_multiscale_discriminator, use_supervised_learning, REAL_LABEL)
else:
trainT_images = create_image_array(trainT_image_names, trainT_path, nr_of_channels)
trainS1_images = create_image_array(trainS1_image_names, trainS1_path, nr_of_channels)
trainS2_images = create_image_array(trainS2_image_names, trainS2_path, nr_of_channels)
trainS3_images = create_image_array(trainS3_image_names, trainS3_path, nr_of_channels)
testT_images = create_image_array(testT_image_names, testT_path, nr_of_channels)
testS1_images = create_image_array(testS1_image_names, testS1_path, nr_of_channels)
testS2_images = create_image_array(testS2_image_names, testS2_path, nr_of_channels)
testS3_images = create_image_array(testS3_image_names, testS3_path, nr_of_channels)
return {"trainT_images": trainT_images,
"trainS1_images": trainS1_images,
"trainS2_images": trainS2_images,
"trainS3_images": trainS3_images,
"testT_images": testT_images,
"testS1_images": testS1_images,
"testS2_images": testS2_images,
"testS3_images": testS3_images,
"trainT_image_names": trainT_image_names,
"trainS1_image_names": trainS1_image_names,
"trainS2_image_names": trainS2_image_names,
"trainS3_image_names": trainS3_image_names,
"testT_image_names": testT_image_names,
"testS1_image_names": testS1_image_names,
"testS2_image_names": testS2_image_names,
"testS3_image_names": testS3_image_names}
def create_image_array(image_list, image_path, nr_of_channels):
image_array = []
for image_name in image_list:
if image_name[-1].lower() == 'g': # to avoid e.g. thumbs.db files
if nr_of_channels == 1: # Gray scale image -> MR image
image = np.array(Image.open(os.path.join(image_path, image_name)))
image = image[:, :, np.newaxis]
else: # RGB image -> 3 channels
image = np.array(Image.open(os.path.join(image_path, image_name)))
image = normalize_array(image)
image_array.append(image)
return np.array(image_array)
# If using 16 bit depth images, use the formula 'array = array / 32767.5 - 1' instead
def normalize_array(array):
array = array / 127.5 - 1
# array = array / 100
return array
class data_sequence(Sequence):
def __init__(self, trainT_path, trainS1_path, trainS2_path, trainS3_path, image_list_T, image_list_S1, image_list_S2, image_list_S3, batch_size=1): # , D_model, use_multiscale_discriminator, use_supervised_learning, REAL_LABEL):
self.batch_size = batch_size
self.train_T = []
self.train_S1 = []
self.train_S2 = []
self.train_S3 = []
for image_name in image_list_T:
if image_name[-1].lower() == 'g': # to avoid e.g. thumbs.db files
self.train_T.append(os.path.join(trainT_path, image_name))
for image_name in image_list_S1:
if image_name[-1].lower() == 'g': # to avoid e.g. thumbs.db files
self.train_S1.append(os.path.join(trainS1_path, image_name))
for image_name in image_list_S2:
if image_name[-1].lower() == 'g': # to avoid e.g. thumbs.db files
self.train_S2.append(os.path.join(trainS2_path, image_name))
for image_name in image_list_S3:
if image_name[-1].lower() == 'g': # to avoid e.g. thumbs.db files
self.train_S3.append(os.path.join(trainS3_path, image_name))
def __len__(self):
return int(max(len(self.train_T), len(self.train_S1), len(self.train_S2), len(self.train_S3)) / float(self.batch_size))
def __getitem__(self, idx): # , use_multiscale_discriminator, use_supervised_learning):if loop_index + batch_size >= min_nr_imgs:
# if idx >= min(len(self.train_A), len(self.train_B)):
# # If all images soon are used for one domain,
# # randomly pick from this domain
# if len(self.train_A) <= len(self.train_B):
# indexes_A = np.random.randint(len(self.train_A), size=self.batch_size)
# batch_A = []
# for i in indexes_A:
# batch_A.append(self.train_A[i])
# batch_B = self.train_B[idx * self.batch_size:(idx + 1) * self.batch_size]
# else:
# indexes_B = np.random.randint(len(self.train_B), size=self.batch_size)
# batch_B = []
# for i in indexes_B:
# batch_B.append(self.train_B[i])
# batch_A = self.train_A[idx * self.batch_size:(idx + 1) * self.batch_size]
# else:
batch_T = self.train_T[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_S1 = self.train_S1[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_S2 = self.train_S2[idx * self.batch_size:(idx + 1) * self.batch_size]
batch_S3 = self.train_S3[idx * self.batch_size:(idx + 1) * self.batch_size]
real_images_T = create_image_array(batch_T, '', 3)
real_images_S1 = create_image_array(batch_S1, '', 3)
real_images_S2 = create_image_array(batch_S2, '', 3)
real_images_S3 = create_image_array(batch_S3, '', 3)
return real_images_T, real_images_S1, real_images_S2, real_images_S3 # input_data, target_data
if __name__ == '__main__':
load_data()