8d7d1fe390
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: RizwanMunawar <chr043416@gmail.com> Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
109 lines
4.8 KiB
Python
109 lines
4.8 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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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 QueueManager(BaseSolution):
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"""
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Manages queue counting in real-time video streams based on object tracks.
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This class extends BaseSolution to provide functionality for tracking and counting objects within a specified
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region in video frames.
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Attributes:
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counts (int): The current count of objects in the queue.
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rect_color (Tuple[int, int, int]): RGB color tuple for drawing the queue region rectangle.
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region_length (int): The number of points defining the queue region.
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annotator (Annotator): An instance of the Annotator class for drawing on frames.
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track_line (List[Tuple[int, int]]): List of track line coordinates.
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track_history (Dict[int, List[Tuple[int, int]]]): Dictionary storing tracking history for each object.
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Methods:
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initialize_region: Initializes the queue region.
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process_queue: Processes a single frame for queue management.
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extract_tracks: Extracts object tracks from the current frame.
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store_tracking_history: Stores the tracking history for an object.
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display_output: Displays the processed output.
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Examples:
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>>> queue_manager = QueueManager(source="video.mp4", region=[100, 100, 200, 200, 300, 300])
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>>> for frame in video_stream:
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... processed_frame = queue_manager.process_queue(frame)
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... cv2.imshow("Queue Management", processed_frame)
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"""
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def __init__(self, **kwargs):
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"""Initializes the QueueManager with parameters for tracking and counting objects in a video stream."""
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super().__init__(**kwargs)
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self.initialize_region()
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self.counts = 0 # Queue counts Information
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self.rect_color = (255, 255, 255) # Rectangle color
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self.region_length = len(self.region) # Store region length for further usage
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def process_queue(self, im0):
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"""
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Processes the queue management for a single frame of video.
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Args:
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im0 (numpy.ndarray): Input image for processing, typically a frame from a video stream.
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Returns:
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(numpy.ndarray): Processed image with annotations, bounding boxes, and queue counts.
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This method performs the following steps:
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1. Resets the queue count for the current frame.
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2. Initializes an Annotator object for drawing on the image.
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3. Extracts tracks from the image.
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4. Draws the counting region on the image.
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5. For each detected object:
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- Draws bounding boxes and labels.
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- Stores tracking history.
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- Draws centroids and tracks.
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- Checks if the object is inside the counting region and updates the count.
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6. Displays the queue count on the image.
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7. Displays the processed output.
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Examples:
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>>> queue_manager = QueueManager()
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>>> frame = cv2.imread("frame.jpg")
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>>> processed_frame = queue_manager.process_queue(frame)
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"""
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self.counts = 0 # Reset counts every frame
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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=self.rect_color, 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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# Draw bounding box and counting region
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self.annotator.box_label(box, label=self.names[cls], color=colors(track_id, True))
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self.store_tracking_history(track_id, box) # Store track history
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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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# Cache frequently accessed attributes
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track_history = self.track_history.get(track_id, [])
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# store previous position of track and check if the object is inside the counting region
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prev_position = None
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if len(track_history) > 1:
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prev_position = track_history[-2]
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if self.region_length >= 3 and prev_position and self.r_s.contains(self.Point(self.track_line[-1])):
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self.counts += 1
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# Display queue counts
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self.annotator.queue_counts_display(
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f"Queue Counts : {str(self.counts)}",
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points=self.region,
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region_color=self.rect_color,
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txt_color=(104, 31, 17),
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)
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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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