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Referenz fĂŒr ultralytics/solutions/ai_gym.py

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ultralytics.solutions.ai_gym.AIGym

Eine Klasse, die die Schritte von Personen in einem Echtzeit-Videostream auf der Grundlage ihrer Posen verwaltet.

Quellcode in ultralytics/solutions/ai_gym.py
class AIGym:
    """A class to manage the gym steps of people in a real-time video stream based on their poses."""

    def __init__(
        self,
        kpts_to_check,
        line_thickness=2,
        view_img=False,
        pose_up_angle=145.0,
        pose_down_angle=90.0,
        pose_type="pullup",
    ):
        """
        Initializes the AIGym class with the specified parameters.

        Args:
            kpts_to_check (list): Indices of keypoints to check.
            line_thickness (int, optional): Thickness of the lines drawn. Defaults to 2.
            view_img (bool, optional): Flag to display the image. Defaults to False.
            pose_up_angle (float, optional): Angle threshold for the 'up' pose. Defaults to 145.0.
            pose_down_angle (float, optional): Angle threshold for the 'down' pose. Defaults to 90.0.
            pose_type (str, optional): Type of pose to detect ('pullup', 'pushup', 'abworkout'). Defaults to "pullup".
        """

        # Image and line thickness
        self.im0 = None
        self.tf = line_thickness

        # Keypoints and count information
        self.keypoints = None
        self.poseup_angle = pose_up_angle
        self.posedown_angle = pose_down_angle
        self.threshold = 0.001

        # Store stage, count and angle information
        self.angle = None
        self.count = None
        self.stage = None
        self.pose_type = pose_type
        self.kpts_to_check = kpts_to_check

        # Visual Information
        self.view_img = view_img
        self.annotator = None

        # Check if environment supports imshow
        self.env_check = check_imshow(warn=True)

    def start_counting(self, im0, results, frame_count):
        """
        Function used to count the gym steps.

        Args:
            im0 (ndarray): Current frame from the video stream.
            results (list): Pose estimation data.
            frame_count (int): Current frame count.
        """

        self.im0 = im0

        # Initialize count, angle, and stage lists on the first frame
        if frame_count == 1:
            self.count = [0] * len(results[0])
            self.angle = [0] * len(results[0])
            self.stage = ["-" for _ in results[0]]

        self.keypoints = results[0].keypoints.data
        self.annotator = Annotator(im0, line_width=2)

        for ind, k in enumerate(reversed(self.keypoints)):
            # Estimate angle and draw specific points based on pose type
            if self.pose_type in {"pushup", "pullup", "abworkout"}:
                self.angle[ind] = self.annotator.estimate_pose_angle(
                    k[int(self.kpts_to_check[0])].cpu(),
                    k[int(self.kpts_to_check[1])].cpu(),
                    k[int(self.kpts_to_check[2])].cpu(),
                )
                self.im0 = self.annotator.draw_specific_points(k, self.kpts_to_check, shape=(640, 640), radius=10)

                # Check and update pose stages and counts based on angle
                if self.pose_type in {"abworkout", "pullup"}:
                    if self.angle[ind] > self.poseup_angle:
                        self.stage[ind] = "down"
                    if self.angle[ind] < self.posedown_angle and self.stage[ind] == "down":
                        self.stage[ind] = "up"
                        self.count[ind] += 1

                elif self.pose_type == "pushup":
                    if self.angle[ind] > self.poseup_angle:
                        self.stage[ind] = "up"
                    if self.angle[ind] < self.posedown_angle and self.stage[ind] == "up":
                        self.stage[ind] = "down"
                        self.count[ind] += 1

                self.annotator.plot_angle_and_count_and_stage(
                    angle_text=self.angle[ind],
                    count_text=self.count[ind],
                    stage_text=self.stage[ind],
                    center_kpt=k[int(self.kpts_to_check[1])],
                )

            # Draw keypoints
            self.annotator.kpts(k, shape=(640, 640), radius=1, kpt_line=True)

        # Display the image if environment supports it and view_img is True
        if self.env_check and self.view_img:
            cv2.imshow("Ultralytics YOLOv8 AI GYM", self.im0)
            if cv2.waitKey(1) & 0xFF == ord("q"):
                return

        return self.im0

__init__(kpts_to_check, line_thickness=2, view_img=False, pose_up_angle=145.0, pose_down_angle=90.0, pose_type='pullup')

Initialisiert die AIGym-Klasse mit den angegebenen Parametern.

Parameter:

Name Typ Beschreibung Standard
kpts_to_check list

Indizes der zu prĂŒfenden Keypoints.

erforderlich
line_thickness int

Die Dicke der gezeichneten Linien. Der Standardwert ist 2.

2
view_img bool

Flagge zum Anzeigen des Bildes. Der Standardwert ist False.

False
pose_up_angle float

Schwellenwert fĂŒr den Winkel fĂŒr die Pose "oben". Der Standardwert ist 145,0.

145.0
pose_down_angle float

Schwellenwert fĂŒr den Winkel fĂŒr die Pose "unten". Der Standardwert ist 90,0.

90.0
pose_type str

Art der zu erkennenden Pose ("pullup", "pushup", "abworkout"). Der Standardwert ist "pullup".

'pullup'
Quellcode in ultralytics/solutions/ai_gym.py
def __init__(
    self,
    kpts_to_check,
    line_thickness=2,
    view_img=False,
    pose_up_angle=145.0,
    pose_down_angle=90.0,
    pose_type="pullup",
):
    """
    Initializes the AIGym class with the specified parameters.

    Args:
        kpts_to_check (list): Indices of keypoints to check.
        line_thickness (int, optional): Thickness of the lines drawn. Defaults to 2.
        view_img (bool, optional): Flag to display the image. Defaults to False.
        pose_up_angle (float, optional): Angle threshold for the 'up' pose. Defaults to 145.0.
        pose_down_angle (float, optional): Angle threshold for the 'down' pose. Defaults to 90.0.
        pose_type (str, optional): Type of pose to detect ('pullup', 'pushup', 'abworkout'). Defaults to "pullup".
    """

    # Image and line thickness
    self.im0 = None
    self.tf = line_thickness

    # Keypoints and count information
    self.keypoints = None
    self.poseup_angle = pose_up_angle
    self.posedown_angle = pose_down_angle
    self.threshold = 0.001

    # Store stage, count and angle information
    self.angle = None
    self.count = None
    self.stage = None
    self.pose_type = pose_type
    self.kpts_to_check = kpts_to_check

    # Visual Information
    self.view_img = view_img
    self.annotator = None

    # Check if environment supports imshow
    self.env_check = check_imshow(warn=True)

start_counting(im0, results, frame_count)

Funktion zum ZĂ€hlen der Schritte im Fitnessstudio.

Parameter:

Name Typ Beschreibung Standard
im0 ndarray

Aktuelles Bild aus dem Videostream.

erforderlich
results list

Daten zur PosenschÀtzung.

erforderlich
frame_count int

Aktuelle Frameanzahl.

erforderlich
Quellcode in ultralytics/solutions/ai_gym.py
def start_counting(self, im0, results, frame_count):
    """
    Function used to count the gym steps.

    Args:
        im0 (ndarray): Current frame from the video stream.
        results (list): Pose estimation data.
        frame_count (int): Current frame count.
    """

    self.im0 = im0

    # Initialize count, angle, and stage lists on the first frame
    if frame_count == 1:
        self.count = [0] * len(results[0])
        self.angle = [0] * len(results[0])
        self.stage = ["-" for _ in results[0]]

    self.keypoints = results[0].keypoints.data
    self.annotator = Annotator(im0, line_width=2)

    for ind, k in enumerate(reversed(self.keypoints)):
        # Estimate angle and draw specific points based on pose type
        if self.pose_type in {"pushup", "pullup", "abworkout"}:
            self.angle[ind] = self.annotator.estimate_pose_angle(
                k[int(self.kpts_to_check[0])].cpu(),
                k[int(self.kpts_to_check[1])].cpu(),
                k[int(self.kpts_to_check[2])].cpu(),
            )
            self.im0 = self.annotator.draw_specific_points(k, self.kpts_to_check, shape=(640, 640), radius=10)

            # Check and update pose stages and counts based on angle
            if self.pose_type in {"abworkout", "pullup"}:
                if self.angle[ind] > self.poseup_angle:
                    self.stage[ind] = "down"
                if self.angle[ind] < self.posedown_angle and self.stage[ind] == "down":
                    self.stage[ind] = "up"
                    self.count[ind] += 1

            elif self.pose_type == "pushup":
                if self.angle[ind] > self.poseup_angle:
                    self.stage[ind] = "up"
                if self.angle[ind] < self.posedown_angle and self.stage[ind] == "up":
                    self.stage[ind] = "down"
                    self.count[ind] += 1

            self.annotator.plot_angle_and_count_and_stage(
                angle_text=self.angle[ind],
                count_text=self.count[ind],
                stage_text=self.stage[ind],
                center_kpt=k[int(self.kpts_to_check[1])],
            )

        # Draw keypoints
        self.annotator.kpts(k, shape=(640, 640), radius=1, kpt_line=True)

    # Display the image if environment supports it and view_img is True
    if self.env_check and self.view_img:
        cv2.imshow("Ultralytics YOLOv8 AI GYM", self.im0)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            return

    return self.im0





Erstellt am 2023-12-02, Aktualisiert am 2024-05-18
Autoren: glenn-jocher (1), Burhan-Q (1), RizwanMunawar (1)