o W h@s4ddlmZmZmZddlmZGdddeZdS)) BaseSolutionSolutionAnnotatorSolutionResults)colorscs(eZdZdZfddZddZZS) QueueManageraA Manages queue counting in real-time video streams based on object tracks. This class extends BaseSolution to provide functionality for tracking and counting objects within a specified region in video frames. Attributes: counts (int): The current count of objects in the queue. rect_color (Tuple[int, int, int]): RGB color tuple for drawing the queue region rectangle. region_length (int): The number of points defining the queue region. track_line (List[Tuple[int, int]]): List of track line coordinates. track_history (Dict[int, List[Tuple[int, int]]]): Dictionary storing tracking history for each object. Methods: initialize_region: Initializes the queue region. process: Processes a single frame for queue management. extract_tracks: Extracts object tracks from the current frame. store_tracking_history: Stores the tracking history for an object. display_output: Displays the processed output. Examples: >>> cap = cv2.VideoCapture("path/to/video.mp4") >>> queue_manager = QueueManager(region=[100, 100, 200, 200, 300, 300]) >>> while cap.isOpened(): >>> success, im0 = cap.read() >>> if not success: >>> break >>> results = queue_manager.process(im0) c s6tjdi||d|_d|_t|j|_dS)zaInitializes the QueueManager with parameters for tracking and counting objects in a video stream.r)rrN)super__init__initialize_regioncounts rect_colorlenregion region_length)selfkwargs __class__rZ/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/solutions/queue_management.pyr &s zQueueManager.__init__c Cs,d|_||t||jd}|j|j|j|jddt|j|j |j |j D]I\}}}}|j || |||t|dd||||j|g}d}t|dkrV|d }|jd krp|rp|j||jd rp|jd7_q'|jd t|j|j|jd d|} || t| |jt|j dS)a3 Process queue management for a single frame of video. Args: im0 (numpy.ndarray): Input image for processing, typically a frame from a video stream. Returns: (SolutionResults): Contains processed image `im0`, 'queue_count' (int, number of objects in the queue) and 'total_tracks' (int, total number of tracked objects). Examples: >>> queue_manager = QueueManager() >>> frame = cv2.imread("frame.jpg") >>> results = queue_manager.process(frame) r) line_width)reg_ptscolor thicknessT)labelrNzQueue Counts : )h)points region_color txt_color)plot_im queue_count total_tracks)r extract_tracksrr draw_regionrr zipboxes track_idsclssconfs box_labeladjust_box_labelrstore_tracking_history track_historygetrrr_scontainsPoint track_linequeue_counts_displaystrresultdisplay_outputr) rim0 annotatorboxtrack_idclsconfr3 prev_positionr&rrrprocess.s. "   & zQueueManager.process)__name__ __module__ __qualname____doc__r rD __classcell__rrrrrs rN)ultralytics.solutions.solutionsrrrultralytics.utils.plottingrrrrrrs