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This commit is contained in:
kirilllzaitsev 2023-07-06 22:43:43 +02:00
parent 85c4d5a644
commit 9c5c296fb6
1 changed files with 17 additions and 6 deletions
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23
supervision/metrics/detection.py
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@ -27,17 +27,15 @@ class ConfusionMatrix:
iou_threshold: float = 0.5,
) -> "ConfusionMatrix":
"""
Calculate confusion matrix based on ground-true and detected objects across all images in concerned dataset.
Calculate confusion matrix based on predicted and ground-truth detections.
Args:
target: representing ground-truth objects across all images in concerned dataset. Each element of `target` list describe single image and has `shape = (N, 5)` where `N` is number of ground-truth objects. Each row is an sv.Detections object
predictions: representing detected objects across all images in concerned dataset. Each element of `detection_batches` list describe single image and has `shape = (M, 1)` where `M` is number of detected objects. Each row is an sv.Detections object
target: Detections objects from ground-truth.
predictions: Detections objects predicted by the model.
classes: all known classes.
conf_threshold: detection confidence threshold between 0 and 1. Detections with lower confidence will be excluded.
iou_threshold: detection iou threshold between 0 and 1. Detections with lower iou will be classified as FP.
Returns:
confusion_matrix: `ConfusionMatrix` object raw confusion matrix 2d `np.ndarray`.
Example:
```
@ -116,6 +114,15 @@ class ConfusionMatrix:
conf_threshold: float,
iou_threshold: float,
) -> np.ndarray:
"""
Calculate confusion matrix for a batch of detections for a single image.
Args:
See ConfusionMatrix.from_detections
Returns:
confusion matrix based on a single image.
"""
result_matrix = np.zeros((num_classes + 1, num_classes + 1))
detection_batch_filtered = pred_detections[
pred_detections.confidence > conf_threshold
@ -159,6 +166,10 @@ class ConfusionMatrix:
@staticmethod
def _drop_extra_matches(matches: np.ndarray) -> np.ndarray:
"""
Deduplicate matches. If there are multiple matches for the same true or predicted box,
only the one with the highest IoU is kept.
"""
if matches.shape[0] > 0:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
@ -211,7 +222,7 @@ class ConfusionMatrix:
Create confusion matrix plot and save it at selected location.
Args:
target_path: save location of confusion matrix plot.
save_path: save location of confusion matrix plot.
title: title displayed at the top of the confusion matrix plot. Default `None`.
class_names: list of class names detected my model. If non given class indexes will be used. Default `None`.
normalize: chart will display absolute number of detections falling into given category. Otherwise percentage of detections will be displayed.