169 lines
6.6 KiB
Python
169 lines
6.6 KiB
Python
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from collections import defaultdict
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import cv2
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from ultralytics.utils.checks import check_imshow, check_requirements
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from ultralytics.utils.plotting import Annotator, colors
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check_requirements("shapely>=2.0.0")
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from shapely.geometry import Point, Polygon
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class QueueManager:
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"""A class to manage the queue in a real-time video stream based on object tracks."""
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def __init__(
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self,
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names,
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reg_pts=None,
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line_thickness=2,
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track_thickness=2,
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view_img=False,
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region_color=(255, 0, 255),
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view_queue_counts=True,
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draw_tracks=False,
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count_txt_color=(255, 255, 255),
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track_color=None,
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region_thickness=5,
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fontsize=0.7,
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):
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"""
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Initializes the QueueManager with specified parameters for tracking and counting objects.
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Args:
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names (dict): A dictionary mapping class IDs to class names.
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reg_pts (list of tuples, optional): Points defining the counting region polygon. Defaults to a predefined
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rectangle.
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line_thickness (int, optional): Thickness of the annotation lines. Defaults to 2.
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track_thickness (int, optional): Thickness of the track lines. Defaults to 2.
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view_img (bool, optional): Whether to display the image frames. Defaults to False.
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region_color (tuple, optional): Color of the counting region lines (BGR). Defaults to (255, 0, 255).
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view_queue_counts (bool, optional): Whether to display the queue counts. Defaults to True.
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draw_tracks (bool, optional): Whether to draw tracks of the objects. Defaults to False.
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count_txt_color (tuple, optional): Color of the count text (BGR). Defaults to (255, 255, 255).
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track_color (tuple, optional): Color of the tracks. If None, different colors will be used for different
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tracks. Defaults to None.
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region_thickness (int, optional): Thickness of the counting region lines. Defaults to 5.
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fontsize (float, optional): Font size for the text annotations. Defaults to 0.7.
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"""
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# Mouse events state
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self.is_drawing = False
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self.selected_point = None
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# Region & Line Information
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self.reg_pts = reg_pts if reg_pts is not None else [(20, 60), (20, 680), (1120, 680), (1120, 60)]
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self.counting_region = (
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Polygon(self.reg_pts) if len(self.reg_pts) >= 3 else Polygon([(20, 60), (20, 680), (1120, 680), (1120, 60)])
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)
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self.region_color = region_color
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self.region_thickness = region_thickness
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# Image and annotation Information
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self.im0 = None
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self.tf = line_thickness
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self.view_img = view_img
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self.view_queue_counts = view_queue_counts
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self.fontsize = fontsize
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self.names = names # Class names
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self.annotator = None # Annotator
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self.window_name = "Ultralytics YOLOv8 Queue Manager"
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# Object counting Information
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self.counts = 0
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self.count_txt_color = count_txt_color
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# Tracks info
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self.track_history = defaultdict(list)
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self.track_thickness = track_thickness
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self.draw_tracks = draw_tracks
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self.track_color = track_color
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# Check if environment supports imshow
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self.env_check = check_imshow(warn=True)
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def extract_and_process_tracks(self, tracks):
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"""Extracts and processes tracks for queue management in a video stream."""
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# Initialize annotator and draw the queue region
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self.annotator = Annotator(self.im0, self.tf, self.names)
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if tracks[0].boxes.id is not None:
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boxes = tracks[0].boxes.xyxy.cpu()
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clss = tracks[0].boxes.cls.cpu().tolist()
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track_ids = tracks[0].boxes.id.int().cpu().tolist()
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# Extract tracks
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for box, track_id, cls in zip(boxes, track_ids, clss):
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# Draw bounding box
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self.annotator.box_label(box, label=f"{self.names[cls]}#{track_id}", color=colors(int(track_id), True))
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# Update track history
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track_line = self.track_history[track_id]
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track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2)))
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if len(track_line) > 30:
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track_line.pop(0)
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# Draw track trails if enabled
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if self.draw_tracks:
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self.annotator.draw_centroid_and_tracks(
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track_line,
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color=self.track_color or colors(int(track_id), True),
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track_thickness=self.track_thickness,
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)
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prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None
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# Check if the object is inside the counting region
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if len(self.reg_pts) >= 3:
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is_inside = self.counting_region.contains(Point(track_line[-1]))
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if prev_position is not None and is_inside:
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self.counts += 1
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# Display queue counts
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label = f"Queue Counts : {str(self.counts)}"
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if label is not None:
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self.annotator.queue_counts_display(
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label,
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points=self.reg_pts,
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region_color=self.region_color,
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txt_color=self.count_txt_color,
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)
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self.counts = 0 # Reset counts after displaying
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self.display_frames()
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def display_frames(self):
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"""Displays the current frame with annotations."""
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if self.env_check and self.view_img:
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self.annotator.draw_region(reg_pts=self.reg_pts, thickness=self.region_thickness, color=self.region_color)
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cv2.namedWindow(self.window_name)
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cv2.imshow(self.window_name, self.im0)
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# Close window on 'q' key press
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if cv2.waitKey(1) & 0xFF == ord("q"):
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return
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def process_queue(self, im0, tracks):
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"""
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Main function to start the queue management process.
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Args:
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im0 (ndarray): Current frame from the video stream.
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tracks (list): List of tracks obtained from the object tracking process.
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"""
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self.im0 = im0 # Store the current frame
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self.extract_and_process_tracks(tracks) # Extract and process tracks
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if self.view_img:
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self.display_frames() # Display the frame if enabled
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return self.im0
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if __name__ == "__main__":
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classes_names = {0: "person", 1: "car"} # example class names
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queue_manager = QueueManager(classes_names)
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