Source code for kornia.metrics.psnr

import torch
from torch.nn.functional import mse_loss as mse

[docs]def psnr(input: torch.Tensor, target: torch.Tensor, max_val: float) -> torch.Tensor: r"""Create a function that calculates the PSNR between 2 images. PSNR is Peek Signal to Noise Ratio, which is similar to mean squared error. Given an m x n image, the PSNR is: .. math:: \text{PSNR} = 10 \log_{10} \bigg(\frac{\text{MAX}_I^2}{MSE(I,T)}\bigg) where .. math:: \text{MSE}(I,T) = \frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1} [I(i,j) - T(i,j)]^2 and :math:`\text{MAX}_I` is the maximum possible input value (e.g for floating point images :math:`\text{MAX}_I=1`). Args: input: the input image with arbitrary shape :math:`(*)`. labels: the labels image with arbitrary shape :math:`(*)`. max_val: The maximum value in the input tensor. Return: the computed loss as a scalar. Examples: >>> ones = torch.ones(1) >>> psnr(ones, 1.2 * ones, 2.) # 10 * log(4/((1.2-1)**2)) / log(10) tensor(20.0000) Reference: """ if not isinstance(input, torch.Tensor): raise TypeError(f"Expected torch.Tensor but got {type(input)}.") if not isinstance(target, torch.Tensor): raise TypeError(f"Expected torch.Tensor but got {type(target)}.") if input.shape != target.shape: raise TypeError(f"Expected tensors of equal shapes, but got {input.shape} and {target.shape}") return 10.0 * torch.log10(max_val**2 / mse(input, target, reduction='mean'))