89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
from datetime import datetime
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from typing import Dict
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import numpy as np
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import supervision as sv
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class FPSBasedTimer:
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"""
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A timer that calculates the duration each object has been detected based on frames
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per second (FPS).
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Attributes:
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fps (int): The frame rate of the video stream, used to calculate time durations.
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frame_id (int): The current frame number in the sequence.
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tracker_id2frame_id (Dict[int, int]): Maps each tracker's ID to the frame number
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at which it was first detected.
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"""
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def __init__(self, fps: int = 30) -> None:
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"""Initializes the FPSBasedTimer with the specified frames per second rate.
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Args:
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fps (int, optional): The frame rate of the video stream. Defaults to 30.
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"""
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self.fps = fps
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self.frame_id = 0
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self.tracker_id2frame_id: Dict[int, int] = {}
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def tick(self, detections: sv.Detections) -> np.ndarray:
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"""Processes the current frame, updating time durations for each tracker.
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Args:
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detections: The detections for the current frame, including tracker IDs.
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Returns:
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np.ndarray: Time durations (in seconds) for each detected tracker, since
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their first detection.
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"""
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self.frame_id += 1
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times = []
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for tracker_id in detections.tracker_id:
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self.tracker_id2frame_id.setdefault(tracker_id, self.frame_id)
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start_frame_id = self.tracker_id2frame_id[tracker_id]
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time_duration = (self.frame_id - start_frame_id) / self.fps
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times.append(time_duration)
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return np.array(times)
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class ClockBasedTimer:
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"""
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A timer that calculates the duration each object has been detected based on the
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system clock.
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Attributes:
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tracker_id2start_time (Dict[int, datetime]): Maps each tracker's ID to the
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datetime when it was first detected.
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"""
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def __init__(self) -> None:
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"""Initializes the ClockBasedTimer."""
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self.tracker_id2start_time: Dict[int, datetime] = {}
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def tick(self, detections: sv.Detections) -> np.ndarray:
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"""Processes the current frame, updating time durations for each tracker.
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Args:
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detections: The detections for the current frame, including tracker IDs.
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Returns:
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np.ndarray: Time durations (in seconds) for each detected tracker, since
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their first detection.
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"""
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current_time = datetime.now()
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times = []
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for tracker_id in detections.tracker_id:
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self.tracker_id2start_time.setdefault(tracker_id, current_time)
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start_time = self.tracker_id2start_time[tracker_id]
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time_duration = (current_time - start_time).total_seconds()
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times.append(time_duration)
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return np.array(times)
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