Reference for ultralytics/solutions/distance_calculation.py

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

    Args:
        event (int): Type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN).
        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).
        param (Any): Additional parameters passed to the function.

    Examples:
        >>> # Assuming 'dc' is an instance of DistanceCalculation
        >>> cv2.setMouseCallback("window_name", dc.mouse_event_for_distance)
    """
    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.track_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 process(self, im0):
    """
    Processes a video frame and calculates the distance between two selected bounding boxes.

    This method extracts tracks from the input frame, annotates bounding boxes, and calculates the distance
    between two user-selected objects if they have been chosen.

    Args:
        im0 (numpy.ndarray): The input image frame to process.

    Returns:
        (SolutionResults): Contains processed image `plot_im`, `total_tracks` (int) representing the total number
            of tracked objects, and `pixels_distance` (float) representing the distance between selected objects
            in pixels.

    Examples:
        >>> import numpy as np
        >>> from ultralytics.solutions import DistanceCalculation
        >>> dc = DistanceCalculation()
        >>> frame = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
        >>> results = dc.process(frame)
        >>> print(f"Distance: {results.pixels_distance:.2f} pixels")
    """
    self.extract_tracks(im0)  # Extract tracks
    annotator = SolutionAnnotator(im0, line_width=self.line_width)  # Initialize annotator

    pixels_distance = 0
    # Iterate over bounding boxes, track ids and classes index
    for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
        annotator.box_label(box, color=colors(int(cls), True), label=self.names[int(cls)])

        # Update selected boxes if they're being tracked
        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:
        # Calculate centroids of selected boxes
        self.centroids.extend(
            [[int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2)] for box in self.selected_boxes.values()]
        )
        # Calculate Euclidean distance between centroids
        pixels_distance = math.sqrt(
            (self.centroids[0][0] - self.centroids[1][0]) ** 2 + (self.centroids[0][1] - self.centroids[1][1]) ** 2
        )
        annotator.plot_distance_and_line(pixels_distance, self.centroids)

    self.centroids = []  # Reset centroids for next frame
    plot_im = annotator.result()
    self.display_output(plot_im)  # Display output with base class function
    cv2.setMouseCallback("Ultralytics Solutions", self.mouse_event_for_distance)

    # Return SolutionResults with processed image and calculated metrics
    return SolutionResults(plot_im=plot_im, pixels_distance=pixels_distance, total_tracks=len(self.track_ids))