Include SSIM from scikit-image

app.py

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+import evaluate
+from evaluate.utils import launch_gradio_widget
+
+module = evaluate.load("structural_similarity_index_measure")
+launch_gradio_widget(module)

requirements.txt

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+numpy
+scikit-image>=0.19

structural_similarity_index_measure.py

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+"""Accuracy metric."""
+
+import datasets
+import numpy as np
+
+from skimage.metrics import structural_similarity
+from typing import Dict, Optional
+import evaluate
+
+
+_DESCRIPTION = """
+Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
+Accuracy = (TP + TN) / (TP + TN + FP + FN)
+ Where:
+TP: True positive
+TN: True negative
+FP: False positive
+FN: False negative
+"""
+
+
+_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:
+    ssim (`float`): Structural Similarity Index Measure. The SSIM values are in range (-1, 1], when pixels are non-negative.
+Examples:
+    Example 1-A simple example
+        >>> accuracy_metric = evaluate.load("accuracy")
+        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
+        >>> print(results)
+        {'accuracy': 0.5}
+    Example 2-The same as Example 1, except with `sample_weight` set.
+        >>> accuracy_metric = evaluate.load("accuracy")
+        >>> results = accuracy_metric.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)
+        {'accuracy': 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 StructuralSimilarityIndexMeasure(evaluate.Metric):
+    def _info(self):
+        return evaluate.MetricInfo(
+            description=_DESCRIPTION,
+            citation=_CITATION,
+            inputs_description=_KWARGS_DESCRIPTION,
+            features=datasets.Features({
+                    "predictions": datasets.Sequence(datasets.Array2D("float32")),
+                    "references": datasets.Sequence(datasets.Array2D("float32")),
+            }),
+            reference_urls=["https://scikit-image.org/docs/dev/auto_examples/transform/plot_ssim.html"],
+        )
+
+    def _compute(
+        self, 
+        predictions, 
+        references, 
+        win_size: Optional[int] = None, 
+        gaussian_weights: Optional[bool] = False,
+        data_range: Optional[float] = None, 
+        multichannel: Optional[bool] = False,
+        sample_weight=None,
+        **kwargs
+    ) -> Dict[str, float]:
+        def func_ssim(x, y):
+            return structural_similarity(
+                x, 
+                y, 
+                win_size=win_size, 
+                gaussian_weights=gaussian_weights, 
+                data_range=data_range, 
+                multichannel=multichannel
+            )
+            
+        return {
+            "ssim": np.average(list(map()), weights=sample_weight))
+        }