SignalNoiseRatio¶

class mmeval.metrics.SignalNoiseRatio(crop_border: int = 0, input_order: str = 'CHW', convert_to: Optional[str] = None, channel_order: str = 'rgb', **kwargs)[source]¶

Signal-to-Noise Ratio.

Ref: https://en.wikipedia.org/wiki/Signal-to-noise_ratio

Parameters

crop_border (int) – Cropped pixels in each edges of an image. These pixels are not involved in the PeakSignalNoiseRatio calculation. Defaults to 0.
input_order (str) – Whether the input order is ‘HWC’ or ‘CHW’. Defaults to ‘HWC’.
convert_to (str, optional) – Whether to convert the images to other color models. If None, the images are not altered. When computing for ‘Y’, the images are assumed to be in BGR order. Options are ‘Y’ and None. Defaults to None.
channel_order (str) – The channel order of image. Choices are ‘rgb’ and ‘bgr’. Defaults to ‘rgb’.
**kwargs – Keyword parameters passed to BaseMetric.

Examples

>>> from mmeval import SignalNoiseRatio
>>> import numpy as np
>>>
>>> snr = SignalNoiseRatio(crop_border=1, input_order='CHW',
...                        convert_to='Y', channel_order='rgb')
>>> gts = np.random.randint(0, 255, size=(3, 32, 32))
>>> preds = np.random.randint(0, 255, size=(3, 32, 32))
>>> snr(preds, gts)  
{'snr': ...}

Calculate SignalNoiseRatio between 2 images:

>>> gts = np.ones((3, 32, 32)) * 2
>>> preds = np.ones((3, 32, 32))
>>> SignalNoiseRatio.compute_snr(preds, gts)
6.020599913279624

add(predictions: Sequence[numpy.ndarray], groundtruths: Sequence[numpy.ndarray], channel_order: Optional[str] = None) → None[source]¶

Add SignalNoiseRatio score of batch to self._results

Parameters

predictions (Sequence[np.ndarray]) – Predictions of the model.
groundtruths (Sequence[np.ndarray]) – The ground truth images.
channel_order (Optional[str]) – The channel order of the input samples. If not passed, will set as self.channel_order. Defaults to None.

compute_metric(results: List[numpy.float64]) → Dict[str, float][source]¶

Compute the SignalNoiseRatio metric.

This method would be invoked in BaseMetric.compute after distributed synchronization.

Parameters: results (List[np.float64]) – A list that consisting the SignalNoiseRatio score. This list has already been synced across all ranks.
Returns: The computed SignalNoiseRatio metric.
Return type: Dict[str, float]

static compute_snr(prediction: numpy.ndarray, groundtruth: numpy.ndarray) → numpy.float64[source]¶

Calculate SignalNoiseRatio (Signal-to-Noise Ratio).

Ref: https://en.wikipedia.org/wiki/Signal-to-noise_ratio

Parameters

prediction (np.ndarray) – Images with range [0, 255].
groundtruth (np.ndarray) – Images with range [0, 255].

Returns

SignalNoiseRatio result.

Return type

np.float64