Spaces:
Runtime error
Runtime error
File size: 3,479 Bytes
3975131 33d1544 9383270 33d1544 9383270 33d1544 9383270 33d1544 9383270 33d1544 3975131 9383270 33d1544 9383270 33d1544 9383270 33d1544 9383270 1cc103f 33d1544 33e1bb1 9383270 33d1544 33e1bb1 9383270 7039593 5efd316 33e1bb1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
"""Peak Signal-to-Noise Ratio metric."""
import datasets
import numpy as np
from skimage.metrics import peak_signal_noise_ratio
from typing import Dict, Optional
import evaluate
_DESCRIPTION = """
Compute the Peak Signal-to-Noise Ratio (PSNR) for an image.
Please pay attention to the `data_range` parameter with floating-point images.
"""
_KWARGS_DESCRIPTION = """
Args:
predictions (`list` of `np.array`): Predicted labels.
references (`list` of `np.array`): Ground truth labels.
sample_weight (`list` of `float`): Sample weights Defaults to None.
Returns:
psnr (`float`): Peak Signal-to-Noise Ratio. The SSIM values are positive. Typical
values for the PSNR in lossy image and video compression are between 30 and 50 dB,
provided the bit depth is 8 bits, where higher is better.
Examples:
Example 1-A simple example
>>> psnr = evaluate.load("jpxkqx/peak_signal_to_noise_ratio")
>>> results = psnr.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
>>> print(results)
{'psnr': 0.5}
Example 2-The same as Example 1, except with `sample_weight` set.
>>> psnr = evaluate.load("jpxkqx/peak_signal_to_noise_ratio")
>>> results = psnr.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
>>> print(results)
{'psnr': 0.8778625954198473}
"""
_CITATION = """
@article{boulogne2014scikit,
title={Scikit-image: Image processing in Python},
author={Boulogne, Fran{\c{c}}ois and Warner, Joshua D and Neil Yager, Emmanuelle},
journal={J. PeerJ},
volume={2},
pages={453},
year={2014}
}
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class PeakSignaltoNoiseRatio(evaluate.Metric):
def _info(self):
return evaluate.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(self._get_feature_types()),
reference_urls=["https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio"],
)
def _get_feature_types(self):
if self.config_name == "multilist":
return {
# 1st Seq - num_samples, 2nd Seq - Height, 3rd Seq - Width
"predictions": datasets.Sequence(
datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
),
"references": datasets.Sequence(
datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
),
}
else:
return {
# 1st Seq - Height, 2rd Seq - Width
"predictions": datasets.Sequence(
datasets.Sequence(datasets.Value("float32"))
),
"references": datasets.Sequence(
datasets.Sequence(datasets.Value("float32"))
),
}
def _compute(
self,
predictions,
references,
data_range: Optional[float] = None,
sample_weight=None,
) -> Dict[str, float]:
samples = zip(np.array(predictions), np.array(references))
psnrs = list(map(
lambda args: peak_signal_noise_ratio(*args, data_range=data_range), samples
))
return {"Peak Signal-to-Noise Ratio": np.average(psnrs, weights=sample_weight)}
|