Delete box_annotator.py

box_annotator.py

@@ -1,262 +0,0 @@
-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