Hugging Face's logo

Spaces:
jordonpeter01
/
Face-Real-ESRGAN
Runtime error

App Files Community
Face-Real-ESRGAN
File size: 4,916 Bytes 
53d095b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
134
135
136
137
138
139
140
141
 
import numpy as np
import torch
from PIL import Image
import os
import io
import imageio

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 jpegBlur(im,q):
    buf = io.BytesIO()
    imageio.imwrite(buf,im,format='jpg',quality=q)
    s = buf.getbuffer()
    return imageio.imread(s,format='jpg')


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], :]