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ultralytics/solutions/distance_calculation.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import math
+from typing import Any
+
+import cv2
+
+from ultralytics.solutions.solutions import BaseSolution, SolutionAnnotator, SolutionResults
+from ultralytics.utils.plotting import colors
+
+
+class DistanceCalculation(BaseSolution):
+    """
+    A class to calculate distance between two objects in a real-time video stream based on their tracks.
+
+    This class extends BaseSolution to provide functionality for selecting objects and calculating the distance
+    between them in a video stream using YOLO object detection and tracking.
+
+    Attributes:
+        left_mouse_count (int): Counter for left mouse button clicks.
+        selected_boxes (dict[int, list[float]]): Dictionary to store selected bounding boxes and their track IDs.
+        centroids (list[list[int]]): List to store centroids of selected bounding boxes.
+
+    Methods:
+        mouse_event_for_distance: Handle mouse events for selecting objects in the video stream.
+        process: Process video frames and calculate the distance between selected objects.
+
+    Examples:
+        >>> distance_calc = DistanceCalculation()
+        >>> frame = cv2.imread("frame.jpg")
+        >>> results = distance_calc.process(frame)
+        >>> cv2.imshow("Distance Calculation", results.plot_im)
+        >>> cv2.waitKey(0)
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        """Initialize the DistanceCalculation class for measuring object distances in video streams."""
+        super().__init__(**kwargs)
+
+        # Mouse event information
+        self.left_mouse_count = 0
+        self.selected_boxes: dict[int, list[float]] = {}
+        self.centroids: list[list[int]] = []  # Store centroids of selected objects
+
+    def mouse_event_for_distance(self, event: int, x: int, y: int, flags: int, param: Any) -> None:
+        """
+        Handle 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) -> SolutionResults:
+        """
+        Process a video frame and calculate 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 (np.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, conf in zip(self.boxes, self.track_ids, self.clss, self.confs):
+            annotator.box_label(box, color=colors(int(cls), True), label=self.adjust_box_label(cls, conf, track_id))
+
+            # 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
+        if self.CFG.get("show") and self.env_check:
+            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))