139 lines
4.3 KiB
Python
139 lines
4.3 KiB
Python
import argparse
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from typing import List
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import cv2
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import numpy as np
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from inference import get_model
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from utils.general import find_in_list, get_stream_frames_generator, load_zones_config
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from utils.timers import ClockBasedTimer
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import supervision as sv
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COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
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COLOR_ANNOTATOR = sv.ColorAnnotator(color=COLORS)
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LABEL_ANNOTATOR = sv.LabelAnnotator(
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color=COLORS, text_color=sv.Color.from_hex("#000000")
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)
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def main(
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rtsp_url: str,
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zone_configuration_path: str,
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model_id: str,
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confidence: float,
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iou: float,
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classes: List[int],
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) -> None:
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model = get_model(model_id=model_id)
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tracker = sv.ByteTrack(minimum_matching_threshold=0.5)
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frames_generator = get_stream_frames_generator(rtsp_url=rtsp_url)
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fps_monitor = sv.FPSMonitor()
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polygons = load_zones_config(file_path=zone_configuration_path)
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zones = [
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sv.PolygonZone(
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polygon=polygon,
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triggering_anchors=(sv.Position.CENTER,),
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)
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for polygon in polygons
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]
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timers = [ClockBasedTimer() for _ in zones]
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for frame in frames_generator:
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fps_monitor.tick()
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fps = fps_monitor.fps
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results = model.infer(frame, confidence=confidence, iou_threshold=iou)[0]
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detections = sv.Detections.from_inference(results)
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detections = detections[find_in_list(detections.class_id, classes)]
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detections = tracker.update_with_detections(detections)
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annotated_frame = frame.copy()
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annotated_frame = sv.draw_text(
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scene=annotated_frame,
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text=f"{fps:.1f}",
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text_anchor=sv.Point(40, 30),
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background_color=sv.Color.from_hex("#A351FB"),
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text_color=sv.Color.from_hex("#000000"),
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)
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for idx, zone in enumerate(zones):
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annotated_frame = sv.draw_polygon(
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scene=annotated_frame, polygon=zone.polygon, color=COLORS.by_idx(idx)
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)
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detections_in_zone = detections[zone.trigger(detections)]
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time_in_zone = timers[idx].tick(detections_in_zone)
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custom_color_lookup = np.full(detections_in_zone.class_id.shape, idx)
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annotated_frame = COLOR_ANNOTATOR.annotate(
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scene=annotated_frame,
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detections=detections_in_zone,
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custom_color_lookup=custom_color_lookup,
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)
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labels = [
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f"#{tracker_id} {int(time // 60):02d}:{int(time % 60):02d}"
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for tracker_id, time in zip(detections_in_zone.tracker_id, time_in_zone)
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]
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annotated_frame = LABEL_ANNOTATOR.annotate(
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scene=annotated_frame,
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detections=detections_in_zone,
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labels=labels,
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custom_color_lookup=custom_color_lookup,
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)
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cv2.imshow("Processed Video", annotated_frame)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Calculating detections dwell time in zones, using RTSP stream."
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)
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parser.add_argument(
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"--zone_configuration_path",
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type=str,
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required=True,
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help="Path to the zone configuration JSON file.",
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)
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parser.add_argument(
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"--rtsp_url",
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type=str,
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required=True,
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help="Complete RTSP URL for the video stream.",
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)
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parser.add_argument(
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"--model_id", type=str, default="yolov8s-640", help="Roboflow model ID."
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)
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parser.add_argument(
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"--confidence_threshold",
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type=float,
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default=0.3,
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help="Confidence level for detections (0 to 1). Default is 0.3.",
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)
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parser.add_argument(
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"--iou_threshold",
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default=0.7,
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type=float,
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help="IOU threshold for non-max suppression. Default is 0.7.",
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)
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parser.add_argument(
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"--classes",
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nargs="*",
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type=int,
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default=[],
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help="List of class IDs to track. If empty, all classes are tracked.",
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)
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args = parser.parse_args()
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main(
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rtsp_url=args.rtsp_url,
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zone_configuration_path=args.zone_configuration_path,
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model_id=args.model_id,
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confidence=args.confidence_threshold,
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iou=args.iou_threshold,
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classes=args.classes,
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)
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