111 lines
4.8 KiB
Python
111 lines
4.8 KiB
Python
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from time import time
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import numpy as np
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from ultralytics.solutions.solutions import BaseSolution
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from ultralytics.utils.plotting import Annotator, colors
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class SpeedEstimator(BaseSolution):
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"""
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A class to estimate the speed of objects in a real-time video stream based on their tracks.
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This class extends the BaseSolution class and provides functionality for estimating object speeds using
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tracking data in video streams.
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Attributes:
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spd (Dict[int, float]): Dictionary storing speed data for tracked objects.
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trkd_ids (List[int]): List of tracked object IDs that have already been speed-estimated.
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trk_pt (Dict[int, float]): Dictionary storing previous timestamps for tracked objects.
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trk_pp (Dict[int, Tuple[float, float]]): Dictionary storing previous positions for tracked objects.
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annotator (Annotator): Annotator object for drawing on images.
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region (List[Tuple[int, int]]): List of points defining the speed estimation region.
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track_line (List[Tuple[float, float]]): List of points representing the object's track.
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r_s (LineString): LineString object representing the speed estimation region.
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Methods:
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initialize_region: Initializes the speed estimation region.
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estimate_speed: Estimates the speed of objects based on tracking data.
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store_tracking_history: Stores the tracking history for an object.
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extract_tracks: Extracts tracks from the current frame.
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display_output: Displays the output with annotations.
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Examples:
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>>> estimator = SpeedEstimator()
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>>> frame = cv2.imread("frame.jpg")
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>>> processed_frame = estimator.estimate_speed(frame)
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>>> cv2.imshow("Speed Estimation", processed_frame)
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"""
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def __init__(self, **kwargs):
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"""Initializes the SpeedEstimator object with speed estimation parameters and data structures."""
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super().__init__(**kwargs)
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self.initialize_region() # Initialize speed region
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self.spd = {} # set for speed data
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self.trkd_ids = [] # list for already speed_estimated and tracked ID's
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self.trk_pt = {} # set for tracks previous time
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self.trk_pp = {} # set for tracks previous point
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def estimate_speed(self, im0):
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"""
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Estimates the speed of objects based on tracking data.
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Args:
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im0 (np.ndarray): Input image for processing. Shape is typically (H, W, C) for RGB images.
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Returns:
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(np.ndarray): Processed image with speed estimations and annotations.
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Examples:
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>>> estimator = SpeedEstimator()
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>>> image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
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>>> processed_image = estimator.estimate_speed(image)
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"""
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self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator
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self.extract_tracks(im0) # Extract tracks
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self.annotator.draw_region(
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reg_pts=self.region, color=(104, 0, 123), thickness=self.line_width * 2
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) # Draw region
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for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
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self.store_tracking_history(track_id, box) # Store track history
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# Check if track_id is already in self.trk_pp or trk_pt initialize if not
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if track_id not in self.trk_pt:
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self.trk_pt[track_id] = 0
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if track_id not in self.trk_pp:
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self.trk_pp[track_id] = self.track_line[-1]
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speed_label = f"{int(self.spd[track_id])} km/h" if track_id in self.spd else self.names[int(cls)]
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self.annotator.box_label(box, label=speed_label, color=colors(track_id, True)) # Draw bounding box
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# Draw tracks of objects
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self.annotator.draw_centroid_and_tracks(
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self.track_line, color=colors(int(track_id), True), track_thickness=self.line_width
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)
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# Calculate object speed and direction based on region intersection
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if self.LineString([self.trk_pp[track_id], self.track_line[-1]]).intersects(self.r_s):
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direction = "known"
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else:
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direction = "unknown"
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# Perform speed calculation and tracking updates if direction is valid
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if direction == "known" and track_id not in self.trkd_ids:
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self.trkd_ids.append(track_id)
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time_difference = time() - self.trk_pt[track_id]
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if time_difference > 0:
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self.spd[track_id] = np.abs(self.track_line[-1][1] - self.trk_pp[track_id][1]) / time_difference
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self.trk_pt[track_id] = time()
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self.trk_pp[track_id] = self.track_line[-1]
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self.display_output(im0) # display output with base class function
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return im0 # return output image for more usage
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