Spaces:
Running
Running
File size: 4,739 Bytes
71c9afb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
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], :] |