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from typing import List, Optional, Union, Tuple
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 = 3, # 1 for seeclick 2 for mind2web and 3 for demo
text_color: Color = Color.BLACK,
text_scale: float = 0.5, # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web
text_thickness: int = 2, #1, # 2 for demo
text_padding: int = 10,
avoid_overlap: bool = True,
):
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
self.avoid_overlap: bool = avoid_overlap
def annotate(
self,
scene: np.ndarray,
detections: Detections,
labels: Optional[List[str]] = None,
skip_label: bool = False,
image_size: Optional[Tuple[int, int]] = None,
) -> 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]
if not self.avoid_overlap:
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
# text_x = x1 - self.text_padding - text_width
# text_y = y1 + self.text_padding + text_height
# text_background_x1 = x1 - 2 * self.text_padding - text_width
# text_background_y1 = y1
# text_background_x2 = x1
# text_background_y2 = y1 + 2 * self.text_padding + text_height
else:
text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2 = get_optimal_label_pos(self.text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size)
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,
)
# import pdb; pdb.set_trace()
box_color = color.as_rgb()
luminance = 0.299 * box_color[0] + 0.587 * box_color[1] + 0.114 * box_color[2]
text_color = (0,0,0) if luminance > 160 else (255,255,255)
cv2.putText(
img=scene,
text=text,
org=(text_x, text_y),
fontFace=font,
fontScale=self.text_scale,
# color=self.text_color.as_rgb(),
color=text_color,
thickness=self.text_thickness,
lineType=cv2.LINE_AA,
)
return scene
def box_area(box):
return (box[2] - box[0]) * (box[3] - box[1])
def intersection_area(box1, box2):
x1 = max(box1[0], box2[0])
y1 = max(box1[1], box2[1])
x2 = min(box1[2], box2[2])
y2 = min(box1[3], box2[3])
return max(0, x2 - x1) * max(0, y2 - y1)
def IoU(box1, box2, return_max=True):
intersection = intersection_area(box1, box2)
union = box_area(box1) + box_area(box2) - intersection
if box_area(box1) > 0 and box_area(box2) > 0:
ratio1 = intersection / box_area(box1)
ratio2 = intersection / box_area(box2)
else:
ratio1, ratio2 = 0, 0
if return_max:
return max(intersection / union, ratio1, ratio2)
else:
return intersection / union
def get_optimal_label_pos(text_padding, text_width, text_height, x1, y1, x2, y2, detections, image_size):
""" check overlap of text and background detection box, and get_optimal_label_pos,
pos: str, position of the text, must be one of 'top left', 'top right', 'outer left', 'outer right' TODO: if all are overlapping, return the last one, i.e. outer right
Threshold: default to 0.3
"""
def get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size):
is_overlap = False
for i in range(len(detections)):
detection = detections.xyxy[i].astype(int)
if IoU([text_background_x1, text_background_y1, text_background_x2, text_background_y2], detection) > 0.3:
is_overlap = True
break
# check if the text is out of the image
if text_background_x1 < 0 or text_background_x2 > image_size[0] or text_background_y1 < 0 or text_background_y2 > image_size[1]:
is_overlap = True
return is_overlap
# if pos == 'top left':
text_x = x1 + text_padding
text_y = y1 - text_padding
text_background_x1 = x1
text_background_y1 = y1 - 2 * text_padding - text_height
text_background_x2 = x1 + 2 * text_padding + text_width
text_background_y2 = y1
is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size)
if not is_overlap:
return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2
# elif pos == 'outer left':
text_x = x1 - text_padding - text_width
text_y = y1 + text_padding + text_height
text_background_x1 = x1 - 2 * text_padding - text_width
text_background_y1 = y1
text_background_x2 = x1
text_background_y2 = y1 + 2 * text_padding + text_height
is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size)
if not is_overlap:
return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2
# elif pos == 'outer right':
text_x = x2 + text_padding
text_y = y1 + text_padding + text_height
text_background_x1 = x2
text_background_y1 = y1
text_background_x2 = x2 + 2 * text_padding + text_width
text_background_y2 = y1 + 2 * text_padding + text_height
is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size)
if not is_overlap:
return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2
# elif pos == 'top right':
text_x = x2 - text_padding - text_width
text_y = y1 - text_padding
text_background_x1 = x2 - 2 * text_padding - text_width
text_background_y1 = y1 - 2 * text_padding - text_height
text_background_x2 = x2
text_background_y2 = y1
is_overlap = get_is_overlap(detections, text_background_x1, text_background_y1, text_background_x2, text_background_y2, image_size)
if not is_overlap:
return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2
return text_x, text_y, text_background_x1, text_background_y1, text_background_x2, text_background_y2
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