import tensorflow as tf
import numpy as np
from Image import Image
import SuperResolution
# np.set_printoptions(threshold=np.nan)
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)
filter,
output_shape=output_shape,
strides=stride,
padding=padding_type)
def Normalize_1D(images):
images_flatten = Images.Flatten(images)
return Image.Normalize(images_flatten)
def Normalize_2D(images):
return Image.Normalize(images)
X_grey = Image.LoadTrainingGreyImage(dataset_size, './Training/X2_grey/')
Y_grey = Image.LoadTrainingGreyImage(dataset_size, './Training/HR_grey/')
X_norm = Image.ExpandDims(Normalize_2D(X_grey))
Y_norm = Image.ExpandDims(Normalize_2D(Y_grey))
X_3dims = Image.ExpandDims(X_grey)
Y_3dims = Image.ExpandDims(Y_grey)
imsave('tmp.png', X_grey[0].astype(int))
imsave('tmp1.png', Y_grey[0].astype(int))
X_train = tf.placeholder(tf.float32)
Y_train = tf.placeholder(tf.float32)
# 1st Layer - Features Extraction
