o W h@s(ddlmZmZmZGdddeZdS)) BaseSolutionSolutionAnnotatorSolutionResultscs(eZdZdZfddZddZZS)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: count (List[int]): Repetition counts for each detected person. angle (List[float]): Current angle of the tracked body part for each person. stage (List[str]): Current exercise stage ('up', 'down', or '-') for each person. initial_stage (str | None): Initial stage of the exercise. 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: Processes a frame to detect poses, calculate angles, and count repetitions. Examples: >>> gym = AIGym(model="yolo11n-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) c sj|dd|d<tjdi|g|_g|_g|_d|_t|jd|_ t|jd|_ |jd|_ dS)a Initialize AIGym for workout monitoring using pose estimation and predefined angles. Args: **kwargs (Any): Keyword arguments passed to the parent class constructor. model (str): Model name or path, defaults to "yolo11n-pose.pt". modelzyolo11n-pose.ptNup_angle down_anglekpts) getsuper__init__countanglestage initial_stagefloatCFGrrr )selfkwargs __class__r P/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/solutions/ai_gym.pyr "szAIGym.__init__c st|jd}|jd}|jjdurt|tjkrGt|tj}jdg|7_jdg|7_j dg|7_ t t |j j D]k\}fddtdD}|j|j|<|jjjddj|jkrj |d krj|d 7<d j |<n j|jkrd j |<jr|jj|j|j |tjd d qO|}|t|jj jtjd S)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_widthrN-cs"g|] }tj|qSr )intr cpu).0ikrr r ]s"z!AIGym.process..)radiusupdown) angle_text count_text stage_text center_kpt)plot_im workout_count workout_stage workout_angle total_tracks)rrextract_trackstracksboxesidlenrrr enumeratereversed keypointsdatarangeestimate_pose_angledraw_specific_kptsr rr show_labelsplot_angle_and_count_and_stagerresultdisplay_outputr track_ids)rim0 annotatorr1 new_humanindr r+r rrprocess6sH      z AIGym.process)__name__ __module__ __qualname____doc__r rE __classcell__r r rrrs rN)ultralytics.solutions.solutionsrrrrr r r rs