Convert supervision/metrics/detection.py docstrings to doctests
--- Co-authored-by: copilot-swe-agent[bot] <198982749+copilot@users.noreply.github.com>
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jirkaandcopilot-swe-agent[bot] authored and Jirka Borovec committed
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@@ -115,34 +115,30 @@ class ConfusionMatrix:
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Returns:
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ConfusionMatrix: New instance of ConfusionMatrix.
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Example:
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```python
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import supervision as sv
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targets = [
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sv.Detections(...),
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sv.Detections(...)
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]
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predictions = [
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sv.Detections(...),
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sv.Detections(...)
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]
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confusion_matrix = sv.ConfusionMatrix.from_detections(
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predictions=predictions,
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targets=target,
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classes=['person', ...]
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)
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print(confusion_matrix.matrix)
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# np.array([
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# [0., 0., 0., 0.],
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# [0., 1., 0., 1.],
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# [0., 1., 1., 0.],
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# [1., 1., 0., 0.]
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# ])
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```
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Examples:
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>>> import numpy as np
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>>> import supervision as sv
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>>> targets = [
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... sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0])
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... )
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... ]
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>>> predictions = [
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... sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0]),
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... confidence=np.array([0.9])
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... )
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... ]
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>>> confusion_matrix = sv.ConfusionMatrix.from_detections(
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... predictions=predictions,
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... targets=targets,
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... classes=['person']
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... )
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>>> confusion_matrix.matrix
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array([[1., 0.],
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[0., 0.]])
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"""
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prediction_tensors = []
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@@ -190,50 +186,32 @@ class ConfusionMatrix:
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Returns:
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ConfusionMatrix: New instance of ConfusionMatrix.
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Example:
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```python
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import supervision as sv
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import numpy as np
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targets = (
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[
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np.array(
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[
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[0.0, 0.0, 3.0, 3.0, 1],
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[2.0, 2.0, 5.0, 5.0, 1],
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[6.0, 1.0, 8.0, 3.0, 2],
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]
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),
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np.array([1.0, 1.0, 2.0, 2.0, 2]),
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]
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)
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predictions = [
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np.array(
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[
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[0.0, 0.0, 3.0, 3.0, 1, 0.9],
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[0.1, 0.1, 3.0, 3.0, 0, 0.9],
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[6.0, 1.0, 8.0, 3.0, 1, 0.8],
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[1.0, 6.0, 2.0, 7.0, 1, 0.8],
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]
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),
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np.array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
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]
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confusion_matrix = sv.ConfusionMatrix.from_tensors(
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predictions=predictions,
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targets=targets,
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classes=['person', ...]
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)
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print(confusion_matrix.matrix)
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# np.array([
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# [0., 0., 0., 0.],
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# [0., 1., 0., 1.],
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# [0., 1., 1., 0.],
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# [1., 1., 0., 0.]
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# ])
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```
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Examples:
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>>> import supervision as sv
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>>> import numpy as np
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>>> targets = [
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... np.array([
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... [0.0, 0.0, 3.0, 3.0, 0],
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... [2.0, 2.0, 5.0, 5.0, 0],
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... [6.0, 1.0, 8.0, 3.0, 1],
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... ])
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... ]
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>>> predictions = [
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... np.array([
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... [0.0, 0.0, 3.0, 3.0, 0, 0.9],
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... [0.1, 0.1, 3.0, 3.0, 0, 0.9],
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... [6.0, 1.0, 8.0, 3.0, 1, 0.8],
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... ])
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... ]
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>>> confusion_matrix = sv.ConfusionMatrix.from_tensors(
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... predictions=predictions,
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... targets=targets,
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... classes=['person', 'dog']
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... )
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>>> confusion_matrix.matrix
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array([[1., 0., 1.],
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[0., 1., 0.],
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[1., 0., 0.]])
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"""
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validate_input_tensors(predictions, targets)
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@@ -546,28 +524,28 @@ class MeanAveragePrecision:
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Returns:
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MeanAveragePrecision: New instance of ConfusionMatrix.
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Example:
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```python
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import supervision as sv
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targets = [
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sv.Detections(...),
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sv.Detections(...)
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]
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predictions = [
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sv.Detections(...),
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sv.Detections(...)
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]
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mean_average_precision = sv.MeanAveragePrecision.from_detections(
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predictions=predictions,
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targets=target,
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)
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print(mean_average_precison.map50_95)
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# 0.2899
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```
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Examples:
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>>> import numpy as np
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>>> import supervision as sv
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>>> targets = [
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... sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0])
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... )
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... ]
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>>> predictions = [
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... sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0]),
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... confidence=np.array([0.9])
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... )
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... ]
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>>> mAP = sv.MeanAveragePrecision.from_detections(
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... predictions=predictions,
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... targets=targets,
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... )
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>>> round(float(mAP.map50), 2)
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0.99
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"""
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prediction_tensors = []
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target_tensors = []
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@@ -650,44 +628,29 @@ class MeanAveragePrecision:
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Returns:
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MeanAveragePrecision: New instance of MeanAveragePrecision.
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Example:
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```python
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import supervision as sv
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import numpy as np
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targets = (
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[
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np.array(
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[
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[0.0, 0.0, 3.0, 3.0, 1],
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[2.0, 2.0, 5.0, 5.0, 1],
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[6.0, 1.0, 8.0, 3.0, 2],
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]
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),
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np.array([[1.0, 1.0, 2.0, 2.0, 2]]),
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]
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)
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predictions = [
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np.array(
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[
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[0.0, 0.0, 3.0, 3.0, 1, 0.9],
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[0.1, 0.1, 3.0, 3.0, 0, 0.9],
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[6.0, 1.0, 8.0, 3.0, 1, 0.8],
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[1.0, 6.0, 2.0, 7.0, 1, 0.8],
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]
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),
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np.array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
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]
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mean_average_precision = sv.MeanAveragePrecision.from_tensors(
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predictions=predictions,
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targets=targets,
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)
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print(mean_average_precision.map50_95)
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# 0.6649
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```
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Examples:
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>>> import supervision as sv
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>>> import numpy as np
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>>> targets = [
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... np.array([
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... [0.0, 0.0, 3.0, 3.0, 0],
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... [2.0, 2.0, 5.0, 5.0, 0],
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... [6.0, 1.0, 8.0, 3.0, 1],
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... ])
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... ]
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>>> predictions = [
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... np.array([
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... [0.0, 0.0, 3.0, 3.0, 0, 0.9],
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... [0.1, 0.1, 3.0, 3.0, 0, 0.9],
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... [6.0, 1.0, 8.0, 3.0, 1, 0.8],
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... ])
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... ]
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>>> mAP = sv.MeanAveragePrecision.from_tensors(
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... predictions=predictions,
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... targets=targets,
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... )
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>>> round(float(mAP.map50), 2)
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0.81
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"""
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validate_input_tensors(predictions, targets)
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iou_thresholds = np.linspace(0.5, 0.95, 10)
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