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added docstring and minor reformatting

This commit is contained in:
Hardik Dava 2023-08-06 12:30:13 +02:00
parent 0fcc17ee11
commit e0ca4ca5e7
1 changed files with 65 additions and 63 deletions
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128
supervision/metrics/detection.py
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@ -12,6 +12,65 @@ from supervision.detection.core import Detections
from supervision.detection.utils import box_iou_batch
def detections_to_tensor(
detections: Detections, with_confidence: bool = False
) -> np.ndarray:
"""
Convert Supervision Detections to numpy tensors for further computation
Args:
detections (sv.Detections): Detections/Targets in the format of sv.Detections
with_confidence (bool): Whether to include confidence in the tensor
Returns:
(np.ndarray): Detections as numpy tensors as in (xyxy, class_id, confidence) order
"""
if len(detections) == 0:
if with_confidence:
return np.zeros((0, 6))
else:
return np.zeros((0, 5))
if detections.class_id is None:
raise ValueError(
"ConfusionMatrix can only be calculated for Detections with class_id"
)
arrays_to_concat = [detections.xyxy, np.expand_dims(detections.class_id, 1)]
if with_confidence:
if detections.confidence is None:
raise ValueError(
"ConfusionMatrix can only be calculated for Detections with confidence"
)
arrays_to_concat.append(np.expand_dims(detections.confidence, 1))
return np.concatenate(arrays_to_concat, axis=1)
def _validate_input_tensors(predictions: List[np.ndarray], targets: List[np.ndarray]):
"""
Checks for shape consistency of input tensors.
"""
if len(predictions) != len(targets):
raise ValueError(
f"Number of predictions ({len(predictions)}) and targets ({len(targets)}) must be equal."
)
if len(predictions) > 0:
if not isinstance(predictions[0], np.ndarray) or not isinstance(
targets[0], np.ndarray
):
raise ValueError(
f"Predictions and targets must be lists of numpy arrays. Got {type(predictions[0])} and {type(targets[0])} instead."
)
if predictions[0].shape[1] != 6:
raise ValueError(
f"Predictions must have shape (N, 6). Got {predictions[0].shape} instead."
)
if targets[0].shape[1] != 5:
raise ValueError(
f"Targets must have shape (N, 5). Got {targets[0].shape} instead."
)
@dataclass
class ConfusionMatrix:
"""
@ -85,11 +144,9 @@ class ConfusionMatrix:
target_tensors = []
for prediction, target in zip(predictions, targets):
prediction_tensors.append(
ConfusionMatrix.detections_to_tensor(prediction, with_confidence=True)
)
target_tensors.append(
ConfusionMatrix.detections_to_tensor(target, with_confidence=False)
detections_to_tensor(prediction, with_confidence=True)
)
target_tensors.append(detections_to_tensor(target, with_confidence=False))
return cls.from_tensors(
predictions=prediction_tensors,
targets=target_tensors,
@ -98,32 +155,6 @@ class ConfusionMatrix:
iou_threshold=iou_threshold,
)
@staticmethod
def detections_to_tensor(
detections: Detections, with_confidence: bool = False
) -> np.ndarray:
if len(detections) == 0:
if with_confidence:
return np.zeros((0, 6))
else:
return np.zeros((0, 5))
if detections.class_id is None:
raise ValueError(
"ConfusionMatrix can only be calculated for Detections with class_id"
)
arrays_to_concat = [detections.xyxy, np.expand_dims(detections.class_id, 1)]
if with_confidence:
if detections.confidence is None:
raise ValueError(
"ConfusionMatrix can only be calculated for Detections with confidence"
)
arrays_to_concat.append(np.expand_dims(detections.confidence, 1))
return np.concatenate(arrays_to_concat, axis=1)
@classmethod
def from_tensors(
cls,
@ -190,7 +221,7 @@ class ConfusionMatrix:
])
```
"""
cls._validate_input_tensors(predictions, targets)
_validate_input_tensors(predictions, targets)
num_classes = len(classes)
matrix = np.zeros((num_classes + 1, num_classes + 1))
@ -209,33 +240,6 @@ class ConfusionMatrix:
iou_threshold=iou_threshold,
)
@classmethod
def _validate_input_tensors(
cls, predictions: List[np.ndarray], targets: List[np.ndarray]
):
"""
Checks for shape consistency of input tensors.
"""
if len(predictions) != len(targets):
raise ValueError(
f"Number of predictions ({len(predictions)}) and targets ({len(targets)}) must be equal."
)
if len(predictions) > 0:
if not isinstance(predictions[0], np.ndarray) or not isinstance(
targets[0], np.ndarray
):
raise ValueError(
f"Predictions and targets must be lists of numpy arrays. Got {type(predictions[0])} and {type(targets[0])} instead."
)
if predictions[0].shape[1] != 6:
raise ValueError(
f"Predictions must have shape (N, 6). Got {predictions[0].shape} instead."
)
if targets[0].shape[1] != 5:
raise ValueError(
f"Targets must have shape (N, 5). Got {targets[0].shape} instead."
)
@staticmethod
def evaluate_detection_batch(
predictions: np.ndarray,
@ -523,11 +527,9 @@ class MeanAveragePrecision:
target_tensors = []
for prediction, target in zip(predictions, targets):
prediction_tensors.append(
ConfusionMatrix.detections_to_tensor(prediction, with_confidence=True)
)
target_tensors.append(
ConfusionMatrix.detections_to_tensor(target, with_confidence=False)
detections_to_tensor(prediction, with_confidence=True)
)
target_tensors.append(detections_to_tensor(target, with_confidence=False))
return cls.from_tensors(
predictions=prediction_tensors,
targets=target_tensors,
@ -633,7 +635,7 @@ class MeanAveragePrecision:
0.2899
```
"""
ConfusionMatrix._validate_input_tensors(predictions, targets)
_validate_input_tensors(predictions, targets)
map, map50, map75 = 0, 0, 0
class_index = 4