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annotate.py
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annotate.py
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from typing import List, Optional, Union
import cv2
import numpy as np
from supervision.detection.core import Detections
from supervision.draw.color import Color, ColorPalette
class BoxAnnotator:
"""
A class for drawing bounding boxes on an image using detections provided.
Attributes:
color (Union[Color, ColorPalette]): The color to draw the bounding box, can be a single color or a color palette
thickness (int): The thickness of the bounding box lines, default is 2
text_color (Color): The color of the text on the bounding box, default is white
text_scale (float): The scale of the text on the bounding box, default is 0.5
text_thickness (int): The thickness of the text on the bounding box, default is 1
text_padding (int): The padding around the text on the bounding box, default is 5
"""
def __init__(
self,
color: Union[Color, ColorPalette] = ColorPalette.default(),
thickness: int = 2,
text_color: Color = Color.black(),
text_scale: float = 0.5,
text_thickness: int = 1,
text_padding: int = 10,
):
self.color: Union[Color, ColorPalette] = color
self.thickness: int = thickness
self.text_color: Color = text_color
self.text_scale: float = text_scale
self.text_thickness: int = text_thickness
self.text_padding: int = text_padding
def annotate(
self,
scene: np.ndarray,
detections: Detections,
labels: Optional[List[str]] = None,
skip_label: bool = False,
) -> np.ndarray:
"""
Draws bounding boxes on the frame using the detections provided.
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` 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)):
x1, y1, x2, y2 = detections.xyxy[i].astype(int)
class_id = (
detections.class_id[i] if detections.class_id is not None else None
)
idx = class_id if class_id is not None else i
color = (
self.color.by_idx(idx)
if isinstance(self.color, ColorPalette)
else self.color
)
cv2.rectangle(
img=scene,
pt1=(x1, y1),
pt2=(x2, y2),
color=color.as_bgr(),
thickness=self.thickness,
)
if skip_label:
continue
text = (
f"{class_id}"
if (labels is None or len(detections) != len(labels))
else labels[i]
)
text_width, text_height = cv2.getTextSize(
text=text,
fontFace=font,
fontScale=self.text_scale,
thickness=self.text_thickness,
)[0]
text_x = x1 + self.text_padding
text_y = y1 - self.text_padding
text_background_x1 = x1
text_background_y1 = y1 - 2 * self.text_padding - text_height
text_background_x2 = x1 + 2 * self.text_padding + text_width
text_background_y2 = y1
cv2.rectangle(
img=scene,
pt1=(text_background_x1, text_background_y1),
pt2=(text_background_x2, text_background_y2),
color=color.as_bgr(),
thickness=cv2.FILLED,
)
cv2.putText(
img=scene,
text=text,
org=(text_x, text_y),
fontFace=font,
fontScale=self.text_scale,
color=self.text_color.as_rgb(),
thickness=self.text_thickness,
lineType=cv2.LINE_AA,
)
return scene
class MaskAnnotator:
"""
A class for overlaying masks on an image using detections provided.
Attributes:
color (Union[Color, ColorPalette]): The color to fill the mask, can be a single color or a color palette
"""
def __init__(
self,
color: Union[Color, ColorPalette] = ColorPalette.default(),
):
self.color: Union[Color, ColorPalette] = color
def annotate(
self, scene: np.ndarray, detections: Detections, opacity: float = 0.5
) -> np.ndarray:
"""
Overlays the masks on the given image based on the provided detections, with a specified opacity.
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
Returns:
np.ndarray: The image with the masks overlaid
"""
if detections.mask is None:
return scene
for i in np.flip(np.argsort(detections.area)):
class_id = (
detections.class_id[i] if detections.class_id is not None else None
)
idx = class_id if class_id is not None else i
color = (
self.color.by_idx(idx)
if isinstance(self.color, ColorPalette)
else self.color
)
mask = detections.mask[i]
colored_mask = np.zeros_like(scene, dtype=np.uint8)
colored_mask[:] = color.as_bgr()
scene = np.where(
np.expand_dims(mask, axis=-1),
np.uint8(opacity * colored_mask + (1 - opacity) * scene),
scene,
)
return scene