def Normalize_2D(images):
return Image.Normalize(images)
path_gpu = '/data/ssd/public/kkwong6/Training/'
path_local = './Training/'
# Hyper params
learning_rate = 0.0002
epochs = 500
batch_size = 40
dataset_size = 100
X_grey = Image.LoadTrainingGreyImage(dataset_size, path_gpu + 'X2_grey/')
Y_grey = Image.LoadTrainingGreyImage(dataset_size, path_gpu + 'HR_grey/')
print('finish reading')
X_norm = Image.Normalize(X_grey)
Y_norm = Image.Normalize(Y_grey)
print('finish Normalize')
X_cropped = Image.Segment(X_norm, 256)
Y_cropped = Image.Segment(Y_norm, 512)
print('finish cropping')
X_final = Image.ExpandDims(X_cropped)
Y_final = Image.ExpandDims(Y_cropped)
print('finish ExpandDims')
SuperResolution.Train(X_final, Y_final, Image.ExpandDims(X_norm),
def Normalize_1D(images):
images_flatten = Images.Flatten(images)
return Image.Normalize(images_flatten)
