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101 lines
3.1 KiB
101 lines
3.1 KiB
import os
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import supervision as sv
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from tqdm import tqdm
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from ultralytics_example import YOLO
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from swarms.models.base_llm import AbstractLLM
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from swarms.utils.download_weights_from_url import (
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download_weights_from_url,
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)
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class Odin(AbstractLLM):
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"""
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Odin class represents an object detection and tracking model.
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Attributes:
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source_weights_path (str): The file path to the YOLO model weights.
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confidence_threshold (float): The confidence threshold for object detection.
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iou_threshold (float): The intersection over union (IOU) threshold for object detection.
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Example:
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>>> odin = Odin(
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... source_weights_path="yolo.weights",
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... confidence_threshold=0.3,
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... iou_threshold=0.7,
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... )
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>>> odin.run(video="input.mp4")
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"""
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def __init__(
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self,
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source_weights_path: str = "yolo.weights",
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confidence_threshold: float = 0.3,
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iou_threshold: float = 0.7,
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):
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super().__init__()
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self.source_weights_path = source_weights_path
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self.confidence_threshold = confidence_threshold
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self.iou_threshold = iou_threshold
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if not os.path.exists(self.source_weights_path):
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download_weights_from_url(
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url=source_weights_path,
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save_path=self.source_weights_path,
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)
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def run(self, video: str, *args, **kwargs):
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"""
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Runs the object detection and tracking algorithm on the specified video.
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Args:
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video (str): The path to the input video file.
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*args: Additional positional arguments.
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**kwargs: Additional keyword arguments.
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Returns:
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bool: True if the video was processed successfully, False otherwise.
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"""
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model = YOLO(self.source_weights_path)
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tracker = sv.ByteTrack()
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box_annotator = sv.BoxAnnotator()
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frame_generator = sv.get_video_frames_generator(
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source_path=self.source_video
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)
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video_info = sv.VideoInfo.from_video(video=video)
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with sv.VideoSink(
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target_path=self.target_video, video_info=video_info
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) as sink:
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for frame in tqdm(
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frame_generator, total=video_info.total_frames
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):
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results = model(
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frame,
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verbose=True,
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conf=self.confidence_threshold,
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iou=self.iou_threshold,
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)[0]
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detections = sv.Detections.from_ultranalytics(results)
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detections = tracker.update_with_detections(
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detections
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)
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labels = [
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f"#{tracker_id} {model.model.names[class_id]}"
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for _, _, _, class_id, tracker_id in detections
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]
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annotated_frame = box_annotator.annotate(
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scene=frame.copy(),
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detections=detections,
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labels=labels,
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)
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result = sink.write_frame(frame=annotated_frame)
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return result
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