151 lines
4.9 KiB
Python
151 lines
4.9 KiB
Python
from enum import Enum
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from functools import wraps
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from typing import Optional, Union
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import cv2
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import numpy as np
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from PIL import Image
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from supervision.annotators.base import ImageType
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from supervision.detection.core import Detections
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from supervision.draw.color import Color, ColorPalette
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from supervision.geometry.core import Position
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class ColorLookup(Enum):
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"""
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Enumeration class to define strategies for mapping colors to annotations.
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This enum supports three different lookup strategies:
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- `INDEX`: Colors are determined by the index of the detection within the scene.
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- `CLASS`: Colors are determined by the class label of the detected object.
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- `TRACK`: Colors are determined by the tracking identifier of the object.
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"""
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INDEX = "index"
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CLASS = "class"
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TRACK = "track"
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@classmethod
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def list(cls):
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return list(map(lambda c: c.value, cls))
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def resolve_color_idx(
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detections: Detections,
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detection_idx: int,
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color_lookup: Union[ColorLookup, np.ndarray] = ColorLookup.CLASS,
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) -> int:
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if detection_idx >= len(detections):
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raise ValueError(
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f"Detection index {detection_idx}"
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f"is out of bounds for detections of length {len(detections)}"
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)
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if isinstance(color_lookup, np.ndarray):
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if len(color_lookup) != len(detections):
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raise ValueError(
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f"Length of color lookup {len(color_lookup)}"
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f"does not match length of detections {len(detections)}"
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)
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return color_lookup[detection_idx]
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elif color_lookup == ColorLookup.INDEX:
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return detection_idx
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elif color_lookup == ColorLookup.CLASS:
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if detections.class_id is None:
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raise ValueError(
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"Could not resolve color by class because"
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"Detections do not have class_id"
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)
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return detections.class_id[detection_idx]
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elif color_lookup == ColorLookup.TRACK:
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if detections.tracker_id is None:
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raise ValueError(
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"Could not resolve color by track because"
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"Detections do not have tracker_id"
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)
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return detections.tracker_id[detection_idx]
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def get_color_by_index(color: Union[Color, ColorPalette], idx: int) -> Color:
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if isinstance(color, ColorPalette):
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return color.by_idx(idx)
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return color
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def resolve_color(
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color: Union[Color, ColorPalette],
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detections: Detections,
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detection_idx: int,
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color_lookup: Union[ColorLookup, np.ndarray] = ColorLookup.CLASS,
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) -> Color:
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idx = resolve_color_idx(
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detections=detections,
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detection_idx=detection_idx,
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color_lookup=color_lookup,
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)
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return get_color_by_index(color=color, idx=idx)
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class Trace:
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def __init__(
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self,
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max_size: Optional[int] = None,
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start_frame_id: int = 0,
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anchor: Position = Position.CENTER,
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) -> None:
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self.current_frame_id = start_frame_id
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self.max_size = max_size
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self.anchor = anchor
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self.frame_id = np.array([], dtype=int)
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self.xy = np.empty((0, 2), dtype=np.float32)
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self.tracker_id = np.array([], dtype=int)
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def put(self, detections: Detections) -> None:
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frame_id = np.full(len(detections), self.current_frame_id, dtype=int)
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self.frame_id = np.concatenate([self.frame_id, frame_id])
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self.xy = np.concatenate(
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[self.xy, detections.get_anchors_coordinates(self.anchor)]
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)
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self.tracker_id = np.concatenate([self.tracker_id, detections.tracker_id])
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unique_frame_id = np.unique(self.frame_id)
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if 0 < self.max_size < len(unique_frame_id):
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max_allowed_frame_id = self.current_frame_id - self.max_size + 1
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filtering_mask = self.frame_id >= max_allowed_frame_id
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self.frame_id = self.frame_id[filtering_mask]
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self.xy = self.xy[filtering_mask]
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self.tracker_id = self.tracker_id[filtering_mask]
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self.current_frame_id += 1
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def get(self, tracker_id: int) -> np.ndarray:
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return self.xy[self.tracker_id == tracker_id]
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def scene_to_annotator_img_type(annotate_func):
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"""
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Decorates `BaseAnnotator.annotate` implementations, converts scene to
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an image type used internally by the annotators, converts back when annotation
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is complete.
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"""
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@wraps(annotate_func)
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def wrapper(self, scene: ImageType, *args, **kwargs):
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if isinstance(scene, np.ndarray):
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return annotate_func(self, scene, *args, **kwargs)
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if isinstance(scene, Image.Image):
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scene = np.array(scene)
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scene = cv2.cvtColor(scene, cv2.COLOR_RGB2BGR)
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annotated = annotate_func(self, scene, *args, **kwargs)
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annotated = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
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annotated = Image.fromarray(annotated)
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return annotated
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raise ValueError(f"Unsupported image type: {type(scene)}")
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return wrapper
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