b1af683d7a
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
124 lines
5.4 KiB
Python
124 lines
5.4 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import math
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import cv2
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from ultralytics.solutions.solutions import BaseSolution
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from ultralytics.utils.plotting import Annotator, colors
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class DistanceCalculation(BaseSolution):
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"""
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A class to calculate distance between two objects in a real-time video stream based on their tracks.
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This class extends BaseSolution to provide functionality for selecting objects and calculating the distance
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between them in a video stream using YOLO object detection and tracking.
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Attributes:
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left_mouse_count (int): Counter for left mouse button clicks.
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selected_boxes (Dict[int, List[float]]): Dictionary to store selected bounding boxes and their track IDs.
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annotator (Annotator): An instance of the Annotator class for drawing on the image.
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boxes (List[List[float]]): List of bounding boxes for detected objects.
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track_ids (List[int]): List of track IDs for detected objects.
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clss (List[int]): List of class indices for detected objects.
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names (List[str]): List of class names that the model can detect.
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centroids (List[List[int]]): List to store centroids of selected bounding boxes.
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Methods:
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mouse_event_for_distance: Handles mouse events for selecting objects in the video stream.
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calculate: Processes video frames and calculates the distance between selected objects.
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Examples:
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>>> distance_calc = DistanceCalculation()
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>>> frame = cv2.imread("frame.jpg")
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>>> processed_frame = distance_calc.calculate(frame)
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>>> cv2.imshow("Distance Calculation", processed_frame)
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>>> cv2.waitKey(0)
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"""
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def __init__(self, **kwargs):
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"""Initializes the DistanceCalculation class for measuring object distances in video streams."""
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super().__init__(**kwargs)
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# Mouse event information
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self.left_mouse_count = 0
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self.selected_boxes = {}
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self.centroids = [] # Initialize empty list to store centroids
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def mouse_event_for_distance(self, event, x, y, flags, param):
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"""
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Handles mouse events to select regions in a real-time video stream for distance calculation.
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Args:
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event (int): Type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN).
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x (int): X-coordinate of the mouse pointer.
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y (int): Y-coordinate of the mouse pointer.
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flags (int): Flags associated with the event (e.g., cv2.EVENT_FLAG_CTRLKEY, cv2.EVENT_FLAG_SHIFTKEY).
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param (Dict): Additional parameters passed to the function.
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Examples:
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>>> # Assuming 'dc' is an instance of DistanceCalculation
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>>> cv2.setMouseCallback("window_name", dc.mouse_event_for_distance)
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"""
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if event == cv2.EVENT_LBUTTONDOWN:
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self.left_mouse_count += 1
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if self.left_mouse_count <= 2:
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for box, track_id in zip(self.boxes, self.track_ids):
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if box[0] < x < box[2] and box[1] < y < box[3] and track_id not in self.selected_boxes:
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self.selected_boxes[track_id] = box
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elif event == cv2.EVENT_RBUTTONDOWN:
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self.selected_boxes = {}
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self.left_mouse_count = 0
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def calculate(self, im0):
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"""
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Processes a video frame and calculates the distance between two selected bounding boxes.
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This method extracts tracks from the input frame, annotates bounding boxes, and calculates the distance
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between two user-selected objects if they have been chosen.
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Args:
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im0 (numpy.ndarray): The input image frame to process.
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Returns:
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(numpy.ndarray): The processed image frame with annotations and distance calculations.
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Examples:
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>>> import numpy as np
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>>> from ultralytics.solutions import DistanceCalculation
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>>> dc = DistanceCalculation()
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>>> frame = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
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>>> processed_frame = dc.calculate(frame)
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"""
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self.annotator = Annotator(im0, line_width=self.line_width) # Initialize annotator
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self.extract_tracks(im0) # Extract tracks
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# Iterate over bounding boxes, track ids and classes index
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for box, track_id, cls in zip(self.boxes, self.track_ids, self.clss):
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self.annotator.box_label(box, color=colors(int(cls), True), label=self.names[int(cls)])
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if len(self.selected_boxes) == 2:
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for trk_id in self.selected_boxes.keys():
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if trk_id == track_id:
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self.selected_boxes[track_id] = box
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if len(self.selected_boxes) == 2:
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# Store user selected boxes in centroids list
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self.centroids.extend(
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[[int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2)] for box in self.selected_boxes.values()]
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)
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# Calculate pixels distance
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pixels_distance = math.sqrt(
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(self.centroids[0][0] - self.centroids[1][0]) ** 2 + (self.centroids[0][1] - self.centroids[1][1]) ** 2
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)
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self.annotator.plot_distance_and_line(pixels_distance, self.centroids)
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self.centroids = []
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self.display_output(im0) # display output with base class function
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cv2.setMouseCallback("Ultralytics Solutions", self.mouse_event_for_distance)
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return im0 # return output image for more usage
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