Python Image.ExpandDims Examples

Example #1

File: backup.py Project: TloAndy/FYP

                                  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
print('1st layer')
weight_conv1 = weight_variable(
    [filter_size_conv1, filter_size_conv1, n_channels_conv1, n_filters_conv1])

Example #2

File: main.py Project: TloAndy/FYP

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),
                      learning_rate, epochs, batch_size)

Example #3

File: Predict.py Project: TloAndy/FYP

import tensorflow as tf
import numpy as np
from skimage.io import imread, imsave
from Image import Image
import SuperResolution
# np.set_printoptions(threshold=np.nan)

batch_size = 30
dataset_size = 1

model_path = './Models/128_32_300_30_100_ssim/'

X_grey = Image.LoadTrainingGreyImage(dataset_size, './Training/X2_grey/')
Y_grey = Image.LoadTrainingGreyImage(dataset_size, './Training/HR_grey/')

X_norm = Image.Normalize(X_grey)
Y_norm = Image.Normalize(Y_grey)

X_cropped = Image.Segment(X_norm, 256)
Y_cropped = Image.Segment(Y_norm, 512)

# print(X_cropped[0])

X_shuffle, Y_shuffle = Image.Shuffle(X_cropped, Y_cropped)

X_final = Image.ExpandDims(X_shuffle)
Y_final = Image.ExpandDims(Y_shuffle)

Image.SaveOutput([Y_final[0]], './y.png')
# print(Y_final[0])
SuperResolution.Test(X_final[0], model_path, './')