Reference for ultralytics/solutions/distance_calculation.py

@staticmethod
def calculate_centroid(box):
    """
    Calculates the centroid of a bounding box.

    Args:
        box (list): Bounding box coordinates [x1, y1, x2, y2].

    Returns:
        (tuple): Centroid coordinates (x, y).
    """
    return int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2)

def calculate_distance(self, centroid1, centroid2):
    """
    Calculates the distance between two centroids.

    Args:
        centroid1 (tuple): Coordinates of the first centroid (x, y).
        centroid2 (tuple): Coordinates of the second centroid (x, y).

    Returns:
        (tuple): Distance in meters and millimeters.
    """
    pixel_distance = math.sqrt((centroid1[0] - centroid2[0]) ** 2 + (centroid1[1] - centroid2[1]) ** 2)
    distance_m = pixel_distance / self.pixel_per_meter
    distance_mm = distance_m * 1000
    return distance_m, distance_mm

def display_frames(self):
    """Displays the current frame with annotations."""
    cv2.namedWindow("Ultralytics Distance Estimation")
    cv2.setMouseCallback("Ultralytics Distance Estimation", self.mouse_event_for_distance)
    cv2.imshow("Ultralytics Distance Estimation", self.im0)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        return

def extract_tracks(self, tracks):
    """
    Extracts tracking results from the provided data.

    Args:
        tracks (list): List of tracks obtained from the object tracking process.
    """
    self.boxes = tracks[0].boxes.xyxy.cpu()
    self.clss = tracks[0].boxes.cls.cpu().tolist()
    self.trk_ids = tracks[0].boxes.id.int().cpu().tolist()

def mouse_event_for_distance(self, event, x, y, flags, param):
    """
    Handles mouse events to select regions in a real-time video stream.

    Args:
        event (int): Type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN, etc.).
        x (int): X-coordinate of the mouse pointer.
        y (int): Y-coordinate of the mouse pointer.
        flags (int): Flags associated with the event (e.g., cv2.EVENT_FLAG_CTRLKEY, cv2.EVENT_FLAG_SHIFTKEY, etc.).
        param (dict): Additional parameters passed to the function.
    """
    if event == cv2.EVENT_LBUTTONDOWN:
        self.left_mouse_count += 1
        if self.left_mouse_count <= 2:
            for box, track_id in zip(self.boxes, self.trk_ids):
                if box[0] < x < box[2] and box[1] < y < box[3] and track_id not in self.selected_boxes:
                    self.selected_boxes[track_id] = box

    elif event == cv2.EVENT_RBUTTONDOWN:
        self.selected_boxes = {}
        self.left_mouse_count = 0

def start_process(self, im0, tracks):
    """
    Processes the video frame and calculates the distance between two bounding boxes.

    Args:
        im0 (ndarray): The image frame.
        tracks (list): List of tracks obtained from the object tracking process.

    Returns:
        (ndarray): The processed image frame.
    """
    self.im0 = im0
    if tracks[0].boxes.id is None:
        if self.view_img:
            self.display_frames()
        return im0

    self.extract_tracks(tracks)
    self.annotator = Annotator(self.im0, line_width=self.line_thickness)

    for box, cls, track_id in zip(self.boxes, self.clss, self.trk_ids):
        self.annotator.box_label(box, color=colors(int(cls), True), label=self.names[int(cls)])

        if len(self.selected_boxes) == 2:
            for trk_id in self.selected_boxes.keys():
                if trk_id == track_id:
                    self.selected_boxes[track_id] = box

    if len(self.selected_boxes) == 2:
        self.centroids = [self.calculate_centroid(self.selected_boxes[trk_id]) for trk_id in self.selected_boxes]

        distance_m, distance_mm = self.calculate_distance(self.centroids[0], self.centroids[1])
        self.annotator.plot_distance_and_line(
            distance_m, distance_mm, self.centroids, self.line_color, self.centroid_color
        )

    self.centroids = []

    if self.view_img and self.env_check:
        self.display_frames()

    return im0