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import numpy as np
import torch
from PIL import Image
import os
import io
def pad_reflect(image, pad_size):
imsize = image.shape
height, width = imsize[:2]
new_img = np.zeros([height+pad_size*2, width+pad_size*2, imsize[2]]).astype(np.uint8)
new_img[pad_size:-pad_size, pad_size:-pad_size, :] = image
new_img[0:pad_size, pad_size:-pad_size, :] = np.flip(image[0:pad_size, :, :], axis=0) #top
new_img[-pad_size:, pad_size:-pad_size, :] = np.flip(image[-pad_size:, :, :], axis=0) #bottom
new_img[:, 0:pad_size, :] = np.flip(new_img[:, pad_size:pad_size*2, :], axis=1) #left
new_img[:, -pad_size:, :] = np.flip(new_img[:, -pad_size*2:-pad_size, :], axis=1) #right
return new_img
def unpad_image(image, pad_size):
return image[pad_size:-pad_size, pad_size:-pad_size, :]
def process_array(image_array, expand=True):
""" Process a 3-dimensional array into a scaled, 4 dimensional batch of size 1. """
image_batch = image_array / 255.0
if expand:
image_batch = np.expand_dims(image_batch, axis=0)
return image_batch
def process_output(output_tensor):
""" Transforms the 4-dimensional output tensor into a suitable image format. """
sr_img = output_tensor.clip(0, 1) * 255
sr_img = np.uint8(sr_img)
return sr_img
def pad_patch(image_patch, padding_size, channel_last=True):
""" Pads image_patch with with padding_size edge values. """
if channel_last:
return np.pad(
image_patch,
((padding_size, padding_size), (padding_size, padding_size), (0, 0)),
'edge',
)
else:
return np.pad(
image_patch,
((0, 0), (padding_size, padding_size), (padding_size, padding_size)),
'edge',
)
def unpad_patches(image_patches, padding_size):
return image_patches[:, padding_size:-padding_size, padding_size:-padding_size, :]
def split_image_into_overlapping_patches(image_array, patch_size, padding_size=2):
""" Splits the image into partially overlapping patches.
The patches overlap by padding_size pixels.
Pads the image twice:
- first to have a size multiple of the patch size,
- then to have equal padding at the borders.
Args:
image_array: numpy array of the input image.
patch_size: size of the patches from the original image (without padding).
padding_size: size of the overlapping area.
"""
xmax, ymax, _ = image_array.shape
x_remainder = xmax % patch_size
y_remainder = ymax % patch_size
# modulo here is to avoid extending of patch_size instead of 0
x_extend = (patch_size - x_remainder) % patch_size
y_extend = (patch_size - y_remainder) % patch_size
# make sure the image is divisible into regular patches
extended_image = np.pad(image_array, ((0, x_extend), (0, y_extend), (0, 0)), 'edge')
# add padding around the image to simplify computations
padded_image = pad_patch(extended_image, padding_size, channel_last=True)
xmax, ymax, _ = padded_image.shape
patches = []
x_lefts = range(padding_size, xmax - padding_size, patch_size)
y_tops = range(padding_size, ymax - padding_size, patch_size)
for x in x_lefts:
for y in y_tops:
x_left = x - padding_size
y_top = y - padding_size
x_right = x + patch_size + padding_size
y_bottom = y + patch_size + padding_size
patch = padded_image[x_left:x_right, y_top:y_bottom, :]
patches.append(patch)
return np.array(patches), padded_image.shape
def stich_together(patches, padded_image_shape, target_shape, padding_size=4):
""" Reconstruct the image from overlapping patches.
After scaling, shapes and padding should be scaled too.
Args:
patches: patches obtained with split_image_into_overlapping_patches
padded_image_shape: shape of the padded image contructed in split_image_into_overlapping_patches
target_shape: shape of the final image
padding_size: size of the overlapping area.
"""
xmax, ymax, _ = padded_image_shape
patches = unpad_patches(patches, padding_size)
patch_size = patches.shape[1]
n_patches_per_row = ymax // patch_size
complete_image = np.zeros((xmax, ymax, 3))
row = -1
col = 0
for i in range(len(patches)):
if i % n_patches_per_row == 0:
row += 1
col = 0
complete_image[
row * patch_size: (row + 1) * patch_size, col * patch_size: (col + 1) * patch_size,:
] = patches[i]
col += 1
return complete_image[0: target_shape[0], 0: target_shape[1], :] |