📚 Improve BoxAnnotator.annotate documentation and add example

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SkalskiP committed 2023-05-09 12:14:37 +02:00
1 parent e16cca4349
commit 3063dfb1f5
1 file changed
+25 -4
+25 -4
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@@ -47,13 +47,34 @@ class BoxAnnotator:
"""
Draws bounding boxes on the frame using the detections provided.
Parameters:
Args:
scene (np.ndarray): The image on which the bounding boxes will be drawn
detections (Detections): The detections for which the bounding boxes will be drawn
labels (Optional[List[str]]): An optional list of labels corresponding to each detection. If labels is provided, the confidence score of the detection will be replaced with the label.
skip_label (bool): Is set to True, skips bounding box label annotation.
labels (Optional[List[str]]): An optional list of labels corresponding to each detection. If `labels` are not provided, corresponding `class_id` will be used as label.
skip_label (bool): Is set to `True`, skips bounding box label annotation.
Returns:
np.ndarray: The image with the bounding boxes drawn on it
Example:
```python
>>> import supervision as sv
>>> classes = ['person', ...]
>>> image = ...
>>> detections = sv.Detections(...)
>>> box_annotator = sv.BoxAnnotator()
>>> labels = [
... f"{classes[class_id]} {confidence:0.2f}"
... for _, _, confidence, class_id, _
... in detections
... ]
>>> annotated_frame = box_annotator.annotate(
... scene=image.copy(),
... detections=detections,
... labels=labels
... )
```
"""
font = cv2.FONT_HERSHEY_SIMPLEX
for i in range(len(detections)):
@@ -139,7 +160,7 @@ class MaskAnnotator:
"""
Overlays the masks on the given image based on the provided detections, with a specified opacity.
Parameters:
Args:
scene (np.ndarray): The image on which the masks will be overlaid
detections (Detections): The detections for which the masks will be overlaid
opacity (float): The opacity of the masks, between 0 and 1, default is 0.5