Fi=FddlmZddlmZddlmZmZmZGddeZy)) defaultdict)Any) BaseSolutionSolutionAnnotatorSolutionResultsc8eZdZdZdeddffd ZdefdZxZS)AIGyma@A class to manage gym steps of people in a real-time video stream based on their poses. This class extends BaseSolution to monitor workouts using YOLO pose estimation models. It tracks and counts repetitions of exercises based on predefined angle thresholds for up and down positions. Attributes: states (dict[int, dict[str, float | int | str]]): Per-track angle, rep count, and stage for workout monitoring. up_angle (float): Angle threshold for considering the 'up' position of an exercise. down_angle (float): Angle threshold for considering the 'down' position of an exercise. kpts (list[int]): Indices of keypoints used for angle calculation. Methods: process: Process a frame to detect poses, calculate angles, and count repetitions. Examples: >>> gym = AIGym(model="yolo26n-pose.pt") >>> image = cv2.imread("gym_scene.jpg") >>> results = gym.process(image) >>> processed_image = results.plot_im >>> cv2.imshow("Processed Image", processed_image) >>> cv2.waitKey(0) kwargsreturnNc |jdd|d<t|di|td|_t |j d|_t |j d|_|j d|_ y) aInitialize AIGym for workout monitoring using pose estimation and predefined angles. Args: **kwargs (Any): Keyword arguments passed to the parent class constructor including: - model (str): Model name or path, defaults to "yolo26n-pose.pt". modelzyolo26n-pose.ptcddddS)Nr-)anglecountstager^/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/solutions/ai_gym.pyz AIGym.__init__..*s AC*Prup_angle down_anglekptsNr) getsuper__init__rstatesfloatCFGrrr)selfr __class__s rrzAIGym.__init__!ss!**W.?@w "6"!"PQ dhhz23  67HHV$ rc t||j}|j|t|jr:|j j j}t|D] \}}|j|j|}|j|jDcgc]}|t|c}|d<|j||j|jdz|d|jkr|ddk(r |dxxdz cc<d |d<n|d|j kDrd|d<|j"s|j%|d|d|d|t|jd |j'}|j)|t+||jj-D cgc]} | d c} |jj-D cgc]} | d c} |jj-D cgc]} | d c} t|j Scc}wcc} wcc} wcc} w) a[Monitor workouts using Ultralytics YOLO Pose Model. This function processes an input image to track and analyze human poses for workout monitoring. It uses the YOLO Pose model to detect keypoints, estimate angles, and count repetitions based on predefined angle thresholds. Args: im0 (np.ndarray): Input image for processing. Returns: (SolutionResults): Contains processed image `plot_im`, 'workout_count' (list of completed reps), 'workout_stage' (list of current stages), 'workout_angle' (list of angles), and 'total_tracks' (total number of tracked individuals). Examples: >>> gym = AIGym() >>> image = cv2.imread("workout.jpg") >>> results = gym.process(image) >>> processed_image = results.plot_im ) line_widthr)radiusruprdown) angle_text count_text stage_text center_kpt)plot_im workout_count workout_stage workout_angle total_tracks)rr#extract_trackslenboxestracks keypointsdata enumerater track_idsestimate_pose_anglerintdraw_specific_kptsrr show_labelsplot_angle_and_count_and_stageresultdisplay_outputrvalues) r im0 annotatorkpt_dataikstateidxr-vs rprocessz AIGym.process1s(&cdooF  C tzz?{{,,11H!(+ 1 DNN1$56!>!>!>X\XaXa@bQT3s8@b!cg,,Q $//TUBU,V>DOO3W~-g!+%+E'N7^dmm3%)E'N##<<#(>#(>#(>#$S1%6#7 =! ,""$ G$/3{{/A/A/CD!1W:D/3{{/A/A/CD!1W:D/3{{/A/A/CD!1W:DT^^,   /Ac2EDDs'H8 < H= & I  I) __name__ __module__ __qualname____doc__rrrrJ __classcell__)r!s@rr r s'.%%% ; o; rr N) collectionsrtypingrultralytics.solutions.solutionsrrrr rrrrSs!$\\c Lc r