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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])
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