Update `queue-management` solution (#16772)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>pull/16751/head
Muhammad Rizwan Munawar
2 months ago
committed by
GitHub
parent
094faeb722
commit
6509757879
5 changed files with 75 additions and 147 deletions
@ -1,127 +1,64 @@ |
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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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from collections import defaultdict |
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from shapely.geometry import Point |
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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.solutions.solutions import BaseSolution # Import a parent class |
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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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class QueueManager(BaseSolution): |
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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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view_img=False, |
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draw_tracks=False, |
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): |
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def __init__(self, **kwargs): |
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"""Initializes the QueueManager with specified parameters for tracking and counting objects.""" |
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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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Initializes the QueueManager with specified parameters for tracking and counting objects. |
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Main function to start the queue management process. |
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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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view_img (bool, optional): Whether to display the image frames. Defaults to False. |
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draw_tracks (bool, optional): Whether to draw tracks of the objects. Defaults to False. |
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im0 (ndarray): The input image that will be used for processing |
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Returns |
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im0 (ndarray): The processed image for more usage |
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""" |
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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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# annotation Information |
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self.tf = line_thickness |
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self.view_img = view_img |
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self.names = names # Class names |
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# Object counting Information |
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self.counts = 0 |
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# Tracks info |
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self.track_history = defaultdict(list) |
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self.draw_tracks = draw_tracks |
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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, im0): |
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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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annotator = Annotator(im0, self.tf, self.names) |
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self.counts = 0 # Reset counts every frame |
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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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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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# 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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annotator.box_label(box, label=self.names[cls], color=colors(int(track_id), True)) |
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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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# 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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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 track trails if enabled |
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if self.draw_tracks: |
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annotator.draw_centroid_and_tracks( |
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track_line, |
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color=colors(int(track_id), True), |
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track_thickness=self.line_thickness, |
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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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prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None |
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# Cache frequently accessed attributes |
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track_history = self.track_history.get(track_id, []) |
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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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# store previous position of track and check if the object is inside the counting region |
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prev_position = track_history[-2] if len(track_history) > 1 else None |
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if self.region_length >= 3 and prev_position and self.r_s.contains(Point(self.track_line[-1])): |
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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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annotator.queue_counts_display( |
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label, |
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points=self.reg_pts, |
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region_color=(255, 0, 255), |
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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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if self.env_check and self.view_img: |
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annotator.draw_region(reg_pts=self.reg_pts, thickness=self.tf * 2, color=(255, 0, 255)) |
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cv2.imshow("Ultralytics YOLOv8 Queue Manager", 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.extract_and_process_tracks(tracks, im0) # Extract and process tracks |
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return 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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return im0 # return output image for more usage |
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