helios.metrics.functional
=========================

.. py:module:: helios.metrics.functional


Functions
---------

.. autoapisummary::

   helios.metrics.functional.calculate_psnr
   helios.metrics.functional.calculate_psnr_torch
   helios.metrics.functional.calculate_ssim
   helios.metrics.functional.calculate_ssim_torch
   helios.metrics.functional.calculate_mAP
   helios.metrics.functional.calculate_mae
   helios.metrics.functional.calculate_mae_torch


Module Contents
---------------

.. py:function:: calculate_psnr(img: numpy.typing.NDArray, img2: numpy.typing.NDArray, crop_border: int, input_order: str = 'HWC', test_y_channel: bool = False) -> float

   Calculate PSNR (Peak Signal-to-Noise Ratio).

   Implementation follows: `<https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`__

   :param img: Images with range :math:`[0, 255]`.
   :param img2: Images with range :math:`[0, 255]`.
   :param crop_border: Cropped pixels in each edge of an image. These pixels are not
                       involved in the calculation.
   :param input_order: Whether the input order is "HWC" or "CHW". Defaults to "HWC".
   :param test_y_channel: Test on Y channel of YCbCr. Defaults to false.

   :returns: PSNR value.


.. py:function:: calculate_psnr_torch(img: torch.Tensor, img2: torch.Tensor, crop_border: int, test_y_channel: bool = False) -> float

   Calculate PSNR (Peak Signal-to-Noise Ratio).

   Implementation follows: `<https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`__

   :param img: Images with range :math:`[0, 255]`.
   :param img2: Images with range :math:`[0, 255]`.
   :param crop_border: Cropped pixels in each edge of an image. These pixels are not
                       involved in the calculation.
   :param test_y_channel: Test on Y channel of YCbCr. Defaults to false.

   :returns: PSNR value.


.. py:function:: calculate_ssim(img: numpy.typing.NDArray, img2: numpy.typing.NDArray, crop_border: int, input_order: str = 'HWC', test_y_channel: bool = False) -> float

   Calculate SSIM (structural similarity).

   Implementation follows: 'Image quality assesment: From error visibility to structural
   similarity'. Results are identical to those of the official MATLAB code in
   `<https://ece.uwaterloo.ca/~z70wang/research/ssim/>`__.
   For three-channel images, SSIM is calculated for each channel and then
   averaged.

   :param img: Images with range :math:`[0, 255]`.
   :param img2: Images with range :math:`[0, 255]`.
   :param crop_border: Cropped pixels in each edge of an image. These pixels are not
                       involved in the calculation.
   :param input_order: Whether the input order is "HWC" or "CHW". Defaults to "HWC"
   :param test_y_channel: Test on Y channel of YCbCr. Defaults to false.

   :returns: SSIM.


.. py:function:: calculate_ssim_torch(img: torch.Tensor, img2: torch.Tensor, crop_border: int, test_y_channel: bool = False) -> float

   Calculate SSIM (structural similarity).

   Implementation follows: 'Image quality assesment: From error visibility to structural
   similarity'. Results are identical to those of the official MATLAB code in
   `<https://ece.uwaterloo.ca/~z70wang/research/ssim/>`__.
   For three-channel images, SSIM is calculated for each channel and then
   averaged.

   :param img: Images with range :math:`[0, 255]`.
   :param img2: Images with range :math:`[0, 255]`.
   :param crop_border: Cropped pixels in each edge of an image. These pixels are not
                       involved in the calculation.
   :param test_y_channel: Test on Y channel of YCbCr. Defaults to false.

   :returns: SSIM.


.. py:function:: calculate_mAP(targs: numpy.typing.NDArray, preds: numpy.typing.NDArray) -> float

   Calculate the mAP (Mean Average Precision).

   Implementation follows:
   `<https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Mean_average_precision>`__

   :param targs: target (inferred) labels in range :math:`[0, 1]`.
   :param preds: predicate labels in range :math:`[0, 1]`.

   :returns: The mAP score


.. py:function:: calculate_mae(pred: numpy.typing.NDArray, gt: numpy.typing.NDArray, scale: float = 1.0) -> float

   Compute the MAE (Mean-Average Precision) score.

   Implementation follows: `<https://en.wikipedia.org/wiki/Mean_absolute_error>`__.
   The scale argument is used in the event that the input arrays are not in the range
   :math:`[0, 1]` but instead have been scaled to be in the range :math:`[0, N]` where
   :math:`N` is the factor. For example, if the arrays are images in the range
   :math:`[0, 255]`, then the scaling factor should be set to 255. If the arrays are
   already in the range :math:`[0, 1]`, then the scale can be omitted.

   :param pred: predicate (inferred) array
   :param gt: ground-truth array
   :param scale: scaling factor that was used on the input arrays. Defaults to 1.

   :returns: The MAE score.


.. py:function:: calculate_mae_torch(pred: torch.Tensor, gt: torch.Tensor, scale: float = 1.0) -> float

   Compute the MAE (Mean-Average Precision) score.

   Implementation follows: `<https://en.wikipedia.org/wiki/Mean_absolute_error>`__.
   The scale argument is used in the event that the input arrays are not in the range
   :math:`[0, 1]` but instead have been scaled to be in the range :math:`[0, N]` where
   :math:`N` is the factor. For example, if the arrays are images in the range
   :math:`[0, 255]`, then the scaling factor should be set to 255. If the arrays are
   already in the range :math:`[0, 1]`, then the scale can be omitted.

   :param pred: predicate (inferred) tensor
   :param gt: ground-truth tensor
   :param scale: scaling factor that was used on the input tensors. Defaults to 1.

   :returns: The MAE score.