File size: 8,488 Bytes
a64b7d4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
import cv2
import numpy as np
import torch
import torch.nn.functional as F

from basicsr.metrics.metric_util import reorder_image, to_y_channel
from basicsr.utils.color_util import rgb2ycbcr_pt
from basicsr.utils.registry import METRIC_REGISTRY


@METRIC_REGISTRY.register()
def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs):
    """Calculate PSNR (Peak Signal-to-Noise Ratio).

    Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio

    Args:
        img (ndarray): Images with range [0, 255].
        img2 (ndarray): Images with range [0, 255].
        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
        input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'.
        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.

    Returns:
        float: PSNR result.
    """

    assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')
    if input_order not in ['HWC', 'CHW']:
        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"')
    img = reorder_image(img, input_order=input_order)
    img2 = reorder_image(img2, input_order=input_order)

    if crop_border != 0:
        img = img[crop_border:-crop_border, crop_border:-crop_border, ...]
        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]

    if test_y_channel:
        img = to_y_channel(img)
        img2 = to_y_channel(img2)

    img = img.astype(np.float64)
    img2 = img2.astype(np.float64)

    mse = np.mean((img - img2)**2)
    if mse == 0:
        return float('inf')
    return 10. * np.log10(255. * 255. / mse)


@METRIC_REGISTRY.register()
def calculate_psnr_pt(img, img2, crop_border, test_y_channel=False, **kwargs):
    """Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version).

    Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio

    Args:
        img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
        img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.

    Returns:
        float: PSNR result.
    """

    assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')

    if crop_border != 0:
        img = img[:, :, crop_border:-crop_border, crop_border:-crop_border]
        img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border]

    if test_y_channel:
        img = rgb2ycbcr_pt(img, y_only=True)
        img2 = rgb2ycbcr_pt(img2, y_only=True)

    img = img.to(torch.float64)
    img2 = img2.to(torch.float64)

    mse = torch.mean((img - img2)**2, dim=[1, 2, 3])
    return 10. * torch.log10(1. / (mse + 1e-8))


@METRIC_REGISTRY.register()
def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs):
    """Calculate SSIM (structural similarity).

    ``Paper: Image quality assessment: From error visibility to structural similarity``

    The results are the same as that of the official released MATLAB code in
    https://ece.uwaterloo.ca/~z70wang/research/ssim/.

    For three-channel images, SSIM is calculated for each channel and then
    averaged.

    Args:
        img (ndarray): Images with range [0, 255].
        img2 (ndarray): Images with range [0, 255].
        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
        input_order (str): Whether the input order is 'HWC' or 'CHW'.
            Default: 'HWC'.
        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.

    Returns:
        float: SSIM result.
    """

    assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')
    if input_order not in ['HWC', 'CHW']:
        raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"')
    img = reorder_image(img, input_order=input_order)
    img2 = reorder_image(img2, input_order=input_order)

    if crop_border != 0:
        img = img[crop_border:-crop_border, crop_border:-crop_border, ...]
        img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]

    if test_y_channel:
        img = to_y_channel(img)
        img2 = to_y_channel(img2)

    img = img.astype(np.float64)
    img2 = img2.astype(np.float64)

    ssims = []
    for i in range(img.shape[2]):
        ssims.append(_ssim(img[..., i], img2[..., i]))
    return np.array(ssims).mean()


@METRIC_REGISTRY.register()
def calculate_ssim_pt(img, img2, crop_border, test_y_channel=False, **kwargs):
    """Calculate SSIM (structural similarity) (PyTorch version).

    ``Paper: Image quality assessment: From error visibility to structural similarity``

    The results are the same as that of the official released MATLAB code in
    https://ece.uwaterloo.ca/~z70wang/research/ssim/.

    For three-channel images, SSIM is calculated for each channel and then
    averaged.

    Args:
        img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
        img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
        crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation.
        test_y_channel (bool): Test on Y channel of YCbCr. Default: False.

    Returns:
        float: SSIM result.
    """

    assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.')

    if crop_border != 0:
        img = img[:, :, crop_border:-crop_border, crop_border:-crop_border]
        img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border]

    if test_y_channel:
        img = rgb2ycbcr_pt(img, y_only=True)
        img2 = rgb2ycbcr_pt(img2, y_only=True)

    img = img.to(torch.float64)
    img2 = img2.to(torch.float64)

    ssim = _ssim_pth(img * 255., img2 * 255.)
    return ssim


def _ssim(img, img2):
    """Calculate SSIM (structural similarity) for one channel images.

    It is called by func:`calculate_ssim`.

    Args:
        img (ndarray): Images with range [0, 255] with order 'HWC'.
        img2 (ndarray): Images with range [0, 255] with order 'HWC'.

    Returns:
        float: SSIM result.
    """

    c1 = (0.01 * 255)**2
    c2 = (0.03 * 255)**2
    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())

    mu1 = cv2.filter2D(img, -1, window)[5:-5, 5:-5]  # valid mode for window size 11
    mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
    mu1_sq = mu1**2
    mu2_sq = mu2**2
    mu1_mu2 = mu1 * mu2
    sigma1_sq = cv2.filter2D(img**2, -1, window)[5:-5, 5:-5] - mu1_sq
    sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
    sigma12 = cv2.filter2D(img * img2, -1, window)[5:-5, 5:-5] - mu1_mu2

    ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2))
    return ssim_map.mean()


def _ssim_pth(img, img2):
    """Calculate SSIM (structural similarity) (PyTorch version).

    It is called by func:`calculate_ssim_pt`.

    Args:
        img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).
        img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w).

    Returns:
        float: SSIM result.
    """
    c1 = (0.01 * 255)**2
    c2 = (0.03 * 255)**2

    kernel = cv2.getGaussianKernel(11, 1.5)
    window = np.outer(kernel, kernel.transpose())
    window = torch.from_numpy(window).view(1, 1, 11, 11).expand(img.size(1), 1, 11, 11).to(img.dtype).to(img.device)

    mu1 = F.conv2d(img, window, stride=1, padding=0, groups=img.shape[1])  # valid mode
    mu2 = F.conv2d(img2, window, stride=1, padding=0, groups=img2.shape[1])  # valid mode
    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1 * mu2
    sigma1_sq = F.conv2d(img * img, window, stride=1, padding=0, groups=img.shape[1]) - mu1_sq
    sigma2_sq = F.conv2d(img2 * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu2_sq
    sigma12 = F.conv2d(img * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu1_mu2

    cs_map = (2 * sigma12 + c2) / (sigma1_sq + sigma2_sq + c2)
    ssim_map = ((2 * mu1_mu2 + c1) / (mu1_sq + mu2_sq + c1)) * cs_map
    return ssim_map.mean([1, 2, 3])