o W h5@s@ddlmZddlmZmZmZddlmZGdddeZdS))time) BaseSolutionSolutionAnnotatorSolutionResults)colorscs(eZdZdZfddZddZZS)SpeedEstimatora A class to estimate the speed of objects in a real-time video stream based on their tracks. This class extends the BaseSolution class and provides functionality for estimating object speeds using tracking data in video streams. Attributes: spd (Dict[int, float]): Dictionary storing speed data for tracked objects. trkd_ids (List[int]): List of tracked object IDs that have already been speed-estimated. trk_pt (Dict[int, float]): Dictionary storing previous timestamps for tracked objects. trk_pp (Dict[int, Tuple[float, float]]): Dictionary storing previous positions for tracked objects. region (List[Tuple[int, int]]): List of points defining the speed estimation region. track_line (List[Tuple[float, float]]): List of points representing the object's track. r_s (LineString): LineString object representing the speed estimation region. Methods: initialize_region: Initializes the speed estimation region. process: Processes input frames to estimate object speeds. store_tracking_history: Stores the tracking history for an object. extract_tracks: Extracts tracks from the current frame. display_output: Displays the output with annotations. Examples: >>> estimator = SpeedEstimator() >>> frame = cv2.imread("frame.jpg") >>> results = estimator.process(frame) >>> cv2.imshow("Speed Estimation", results.plot_im) c s6tjdi||i|_g|_i|_i|_dS)z Initialize the SpeedEstimator object with speed estimation parameters and data structures. Args: **kwargs (Any): Additional keyword arguments passed to the parent class. N)super__init__initialize_regionspdtrkd_idstrk_pttrk_pp)selfkwargs __class__rZ/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/solutions/speed_estimation.pyr 's  zSpeedEstimator.__init__c Cs||t||jd}|j|jd|jddt|j|j|j|j D]\}}}}| ||||j vr9d|j |<||j vrF|j d|j |<||jvrX|jrXt|j|dn||||}|j||t|dd ||j ||j dg|jr~d }nd }|d kr||jvr|j|t|j |} | dkrt|j dd |j |d | |j|<t|j |<|j d|j |<q#|} || t| t|jd S)a Process an input frame to estimate object speeds based on tracking data. Args: im0 (np.ndarray): Input image for processing with shape (H, W, C) for RGB images. Returns: (SolutionResults): Contains processed image `plot_im` and `total_tracks` (number of tracked objects). Examples: >>> estimator = SpeedEstimator() >>> image = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8) >>> results = estimator.process(image) ) line_width)hr{)reg_ptscolor thicknessrz km/hT)labelrknownunknown)plot_im total_tracks)extract_tracksrr draw_regionregionzipboxes track_idsclssconfsstore_tracking_historyrr track_liner show_labelsintadjust_box_label box_labelr LineString intersectsr_sr appendrabsresultdisplay_outputrlen) rim0 annotatorboxtrack_idclsconf speed_label directiontime_differencer!rrrprocess7s6 "     " *  zSpeedEstimator.process)__name__ __module__ __qualname____doc__r rB __classcell__rrrrr s rN)rultralytics.solutions.solutionsrrrultralytics.utils.plottingrrrrrrs