Reference for `ultralytics/solutions/ai_gym.py`

Note

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ultralytics.solutions.ai_gym.AIGym

AIGym(**kwargs)

Bases: BaseSolution

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:

Name	Type	Description
`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.
`lw`	`int`	Line width for drawing annotations.
`annotator`	`Annotator`	Object for drawing annotations on the image.

Methods:

Name	Description
`monitor`	Processes a frame to detect poses, calculate angles, and count repetitions.

Examples:

>>> gym = AIGym(model="yolov8n-pose.pt")
>>> image = cv2.imread("gym_scene.jpg")
>>> processed_image = gym.monitor(image)
>>> cv2.imshow("Processed Image", processed_image)
>>> cv2.waitKey(0)

Source code in ultralytics/solutions/ai_gym.py

def __init__(self, **kwargs):
    """Initializes AIGym for workout monitoring using pose estimation and predefined angles."""
    # Check if the model name ends with '-pose'
    if "model" in kwargs and "-pose" not in kwargs["model"]:
        kwargs["model"] = "yolo11n-pose.pt"
    elif "model" not in kwargs:
        kwargs["model"] = "yolo11n-pose.pt"

    super().__init__(**kwargs)
    self.count = []  # List for counts, necessary where there are multiple objects in frame
    self.angle = []  # List for angle, necessary where there are multiple objects in frame
    self.stage = []  # List for stage, necessary where there are multiple objects in frame

    # Extract details from CFG single time for usage later
    self.initial_stage = None
    self.up_angle = float(self.CFG["up_angle"])  # Pose up predefined angle to consider up pose
    self.down_angle = float(self.CFG["down_angle"])  # Pose down predefined angle to consider down pose
    self.kpts = self.CFG["kpts"]  # User selected kpts of workouts storage for further usage
    self.lw = self.CFG["line_width"]  # Store line_width for usage

monitor

monitor(im0)

Monitors 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.

Parameters:

Name	Type	Description	Default
`im0`	`ndarray`	Input image for processing.	required

Returns:

Type	Description
`ndarray`	Processed image with annotations for workout monitoring.

Examples:

>>> gym = AIGym()
>>> image = cv2.imread("workout.jpg")
>>> processed_image = gym.monitor(image)

Source code in ultralytics/solutions/ai_gym.py

def monitor(self, im0):
    """
    Monitors 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 (ndarray): Input image for processing.

    Returns:
        (ndarray): Processed image with annotations for workout monitoring.

    Examples:
        >>> gym = AIGym()
        >>> image = cv2.imread("workout.jpg")
        >>> processed_image = gym.monitor(image)
    """
    # Extract tracks
    tracks = self.model.track(source=im0, persist=True, classes=self.CFG["classes"])[0]

    if tracks.boxes.id is not None:
        # Extract and check keypoints
        if len(tracks) > len(self.count):
            new_human = len(tracks) - len(self.count)
            self.angle += [0] * new_human
            self.count += [0] * new_human
            self.stage += ["-"] * new_human

        # Initialize annotator
        self.annotator = Annotator(im0, line_width=self.lw)

        # Enumerate over keypoints
        for ind, k in enumerate(reversed(tracks.keypoints.data)):
            # Get keypoints and estimate the angle
            kpts = [k[int(self.kpts[i])].cpu() for i in range(3)]
            self.angle[ind] = self.annotator.estimate_pose_angle(*kpts)
            im0 = self.annotator.draw_specific_points(k, self.kpts, radius=self.lw * 3)

            # Determine stage and count logic based on angle thresholds
            if self.angle[ind] < self.down_angle:
                if self.stage[ind] == "up":
                    self.count[ind] += 1
                self.stage[ind] = "down"
            elif self.angle[ind] > self.up_angle:
                self.stage[ind] = "up"

            # Display angle, count, and stage text
            self.annotator.plot_angle_and_count_and_stage(
                angle_text=self.angle[ind],  # angle text for display
                count_text=self.count[ind],  # count text for workouts
                stage_text=self.stage[ind],  # stage position text
                center_kpt=k[int(self.kpts[1])],  # center keypoint for display
            )

    self.display_output(im0)  # Display output image, if environment support display
    return im0  # return an image for writing or further usage

📅 Created 10 months ago ✏️ Updated 1 month ago

Reference for ultralytics/solutions/ai_gym.py

ultralytics.solutions.ai_gym.AIGym

monitor

Reference for `ultralytics/solutions/ai_gym.py`