util folder created

util/box_annotator.py

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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