viqa.nr_metrics.qmeasure.QMeasure

class viqa.nr_metrics.qmeasure.QMeasure(data_range=255, normalize=False, **kwargs)[source]

Class to calculate the Q-Measure [1] for an image.

score_val

Q-Measure value of the last calculation.

Type:

float

parameters

Dictionary containing the parameters for QMeasure calculation.

Type:

dict

Parameters:

  • data_range ({1, 255, 65535}, default=255) – Data range of the returned data in data loading. Is used for image loading when normalize is True. Passed to viqa.utils.load_data().

  • normalize (bool, default False) – If True, the input images are normalized to the data_range argument.

  • **kwargs (optional) – Additional parameters for data loading. The keyword arguments are passed to viqa.utils.load_data().

  • chromatic (bool, default False) –

    If True, the input images are expected to be RGB images.

    Note

    Currently not supported.

Notes

This metric will always be calculated with float32 precision.

References

print_score(decimals=2)[source]

Print the Q-Measure value of the last calculation.

Parameters:

decimals (int, default=2) – Number of decimal places to print the score value.

Warns:

RuntimeWarning – If score_val is not available.

export_results(path, filename)

Export the score to a csv file.

Parameters:

  • path (str) – The path where the csv file should be saved.

  • filename (str) – The name of the csv file.

Notes

The arguments get passed to viqa.utils.export_results().

load_images(img)

Load the image.

Uses the viqa.utils.load_data() function to load the image.

Parameters:

img (np.ndarray, viqa.ImageArray, torch.Tensor, str or os.PathLike) – The image to load.

Returns:

img – The loaded image as an viqa.utils.ImageArray.

Return type:

viqa.ImageArray

score(*args, **kwargs)

Calculate the score.

Deprecated since version 3.0.0: Call the metric directly instead, e.g. metric(img_m, img_r). score will be removed in the future. For full-reference metrics.