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util_calculate_psnr_ssim.py
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util_calculate_psnr_ssim.py
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import cv2
import torch
import numpy as np
def calculate_psnr(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.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"')
img1 = reorder_image(img1, input_order=input_order)
img2 = reorder_image(img2, input_order=input_order)
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
if crop_border != 0:
img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
if test_y_channel:
img1 = to_y_channel(img1)
img2 = to_y_channel(img2)
mse = np.mean((img1 - img2) ** 2)
if mse == 0:
return float('inf')
return 20. * np.log10(255. / np.sqrt(mse))
def _ssim(img1, img2):
C1 = (0.01 * 255) ** 2
C2 = (0.03 * 255) ** 2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = cv2.getGaussianKernel(11, 1.5)
window = np.outer(kernel, kernel.transpose())
mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
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(img1 ** 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(img1 * 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 calculate_ssim(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.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"')
img1 = reorder_image(img1, input_order=input_order)
img2 = reorder_image(img2, input_order=input_order)
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
if crop_border != 0:
img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
if test_y_channel:
img1 = to_y_channel(img1)
img2 = to_y_channel(img2)
ssims = []
for i in range(img1.shape[2]):
ssims.append(_ssim(img1[..., i], img2[..., i]))
return np.array(ssims).mean()
def _blocking_effect_factor(im):
block_size = 8
block_horizontal_positions = torch.arange(7, im.shape[3] - 1, 8)
block_vertical_positions = torch.arange(7, im.shape[2] - 1, 8)
horizontal_block_difference = (
(im[:, :, :, block_horizontal_positions] - im[:, :, :, block_horizontal_positions + 1]) ** 2).sum(
3).sum(2).sum(1)
vertical_block_difference = (
(im[:, :, block_vertical_positions, :] - im[:, :, block_vertical_positions + 1, :]) ** 2).sum(3).sum(
2).sum(1)
nonblock_horizontal_positions = np.setdiff1d(torch.arange(0, im.shape[3] - 1), block_horizontal_positions)
nonblock_vertical_positions = np.setdiff1d(torch.arange(0, im.shape[2] - 1), block_vertical_positions)
horizontal_nonblock_difference = (
(im[:, :, :, nonblock_horizontal_positions] - im[:, :, :, nonblock_horizontal_positions + 1]) ** 2).sum(
3).sum(2).sum(1)
vertical_nonblock_difference = (
(im[:, :, nonblock_vertical_positions, :] - im[:, :, nonblock_vertical_positions + 1, :]) ** 2).sum(
3).sum(2).sum(1)
n_boundary_horiz = im.shape[2] * (im.shape[3] // block_size - 1)
n_boundary_vert = im.shape[3] * (im.shape[2] // block_size - 1)
boundary_difference = (horizontal_block_difference + vertical_block_difference) / (
n_boundary_horiz + n_boundary_vert)
n_nonboundary_horiz = im.shape[2] * (im.shape[3] - 1) - n_boundary_horiz
n_nonboundary_vert = im.shape[3] * (im.shape[2] - 1) - n_boundary_vert
nonboundary_difference = (horizontal_nonblock_difference + vertical_nonblock_difference) / (
n_nonboundary_horiz + n_nonboundary_vert)
scaler = np.log2(block_size) / np.log2(min([im.shape[2], im.shape[3]]))
bef = scaler * (boundary_difference - nonboundary_difference)
bef[boundary_difference <= nonboundary_difference] = 0
return bef
def calculate_psnrb(img1, img2, crop_border, input_order='HWC', test_y_channel=False):
assert img1.shape == img2.shape, (f'Image shapes are differnet: {img1.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"')
img1 = reorder_image(img1, input_order=input_order)
img2 = reorder_image(img2, input_order=input_order)
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
if crop_border != 0:
img1 = img1[crop_border:-crop_border, crop_border:-crop_border, ...]
img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
if test_y_channel:
img1 = to_y_channel(img1)
img2 = to_y_channel(img2)
img1 = torch.from_numpy(img1).permute(2, 0, 1).unsqueeze(0) / 255.
img2 = torch.from_numpy(img2).permute(2, 0, 1).unsqueeze(0) / 255.
total = 0
for c in range(img1.shape[1]):
mse = torch.nn.functional.mse_loss(img1[:, c:c + 1, :, :], img2[:, c:c + 1, :, :], reduction='none')
bef = _blocking_effect_factor(img1[:, c:c + 1, :, :])
mse = mse.view(mse.shape[0], -1).mean(1)
total += 10 * torch.log10(1 / (mse + bef))
return float(total) / img1.shape[1]
def reorder_image(img, input_order='HWC'):
if input_order not in ['HWC', 'CHW']:
raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are ' "'HWC' and 'CHW'")
if len(img.shape) == 2:
img = img[..., None]
if input_order == 'CHW':
img = img.transpose(1, 2, 0)
return img
def to_y_channel(img):
img = img.astype(np.float32) / 255.
if img.ndim == 3 and img.shape[2] == 3:
img = bgr2ycbcr(img, y_only=True)
img = img[..., None]
return img * 255.
def _convert_input_type_range(img):
img_type = img.dtype
img = img.astype(np.float32)
if img_type == np.float32:
pass
elif img_type == np.uint8:
img /= 255.
else:
raise TypeError('The img type should be np.float32 or np.uint8, ' f'but got {img_type}')
return img
def _convert_output_type_range(img, dst_type):
if dst_type not in (np.uint8, np.float32):
raise TypeError('The dst_type should be np.float32 or np.uint8, ' f'but got {dst_type}')
if dst_type == np.uint8:
img = img.round()
else:
img /= 255.
return img.astype(dst_type)
def bgr2ycbcr(img, y_only=False):
img_type = img.dtype
img = _convert_input_type_range(img)
if y_only:
out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0
else:
out_img = np.matmul(
img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128]
out_img = _convert_output_type_range(out_img, img_type)
return out_img