# Ultralytics YOLO 🚀, AGPL-3.0 license from collections import defaultdict import cv2 from ultralytics.utils.checks import check_imshow, check_requirements from ultralytics.utils.plotting import Annotator, colors check_requirements("shapely>=2.0.0") from shapely.geometry import LineString, Point, Polygon class ObjectCounter: """A class to manage the counting of objects in a real-time video stream based on their tracks.""" def __init__(self): """Initializes the Counter with default values for various tracking and counting parameters.""" # Mouse events self.is_drawing = False self.selected_point = None # Region & Line Information self.reg_pts = [(20, 400), (1260, 400)] self.line_dist_thresh = 15 self.counting_region = None self.region_color = (255, 0, 255) self.region_thickness = 5 # Image and annotation Information self.im0 = None self.tf = None self.view_img = False self.view_in_counts = True self.view_out_counts = True self.names = None # Classes names self.annotator = None # Annotator self.window_name = "Ultralytics YOLOv8 Object Counter" # Object counting Information self.in_counts = 0 self.out_counts = 0 self.counting_dict = {} self.count_txt_thickness = 0 self.count_txt_color = (0, 0, 0) self.count_color = (255, 255, 255) # Tracks info self.track_history = defaultdict(list) self.track_thickness = 2 self.draw_tracks = False self.track_color = (0, 255, 0) # Check if environment support imshow self.env_check = check_imshow(warn=True) def set_args( self, classes_names, reg_pts, count_reg_color=(255, 0, 255), line_thickness=2, track_thickness=2, view_img=False, view_in_counts=True, view_out_counts=True, draw_tracks=False, count_txt_thickness=2, count_txt_color=(0, 0, 0), count_color=(255, 255, 255), track_color=(0, 255, 0), region_thickness=5, line_dist_thresh=15, ): """ Configures the Counter's image, bounding box line thickness, and counting region points. Args: line_thickness (int): Line thickness for bounding boxes. view_img (bool): Flag to control whether to display the video stream. view_in_counts (bool): Flag to control whether to display the incounts on video stream. view_out_counts (bool): Flag to control whether to display the outcounts on video stream. reg_pts (list): Initial list of points defining the counting region. classes_names (dict): Classes names track_thickness (int): Track thickness draw_tracks (Bool): draw tracks count_txt_thickness (int): Text thickness for object counting display count_txt_color (RGB color): count text color value count_color (RGB color): count text background color value count_reg_color (RGB color): Color of object counting region track_color (RGB color): color for tracks region_thickness (int): Object counting Region thickness line_dist_thresh (int): Euclidean Distance threshold for line counter """ self.tf = line_thickness self.view_img = view_img self.view_in_counts = view_in_counts self.view_out_counts = view_out_counts self.track_thickness = track_thickness self.draw_tracks = draw_tracks # Region and line selection if len(reg_pts) == 2: print("Line Counter Initiated.") self.reg_pts = reg_pts self.counting_region = LineString(self.reg_pts) elif len(reg_pts) >= 3: print("Region Counter Initiated.") self.reg_pts = reg_pts self.counting_region = Polygon(self.reg_pts) else: print("Invalid Region points provided, region_points must be 2 for lines or >= 3 for polygons.") print("Using Line Counter Now") self.counting_region = LineString(self.reg_pts) self.names = classes_names self.track_color = track_color self.count_txt_thickness = count_txt_thickness self.count_txt_color = count_txt_color self.count_color = count_color self.region_color = count_reg_color self.region_thickness = region_thickness self.line_dist_thresh = line_dist_thresh def mouse_event_for_region(self, event, x, y, flags, params): """ This function is designed to move region with mouse events in a real-time video stream. Args: event (int): The type of mouse event (e.g., cv2.EVENT_MOUSEMOVE, cv2.EVENT_LBUTTONDOWN, etc.). x (int): The x-coordinate of the mouse pointer. y (int): The y-coordinate of the mouse pointer. flags (int): Any flags associated with the event (e.g., cv2.EVENT_FLAG_CTRLKEY, cv2.EVENT_FLAG_SHIFTKEY, etc.). params (dict): Additional parameters you may want to pass to the function. """ if event == cv2.EVENT_LBUTTONDOWN: for i, point in enumerate(self.reg_pts): if ( isinstance(point, (tuple, list)) and len(point) >= 2 and (abs(x - point[0]) < 10 and abs(y - point[1]) < 10) ): self.selected_point = i self.is_drawing = True break elif event == cv2.EVENT_MOUSEMOVE: if self.is_drawing and self.selected_point is not None: self.reg_pts[self.selected_point] = (x, y) self.counting_region = Polygon(self.reg_pts) elif event == cv2.EVENT_LBUTTONUP: self.is_drawing = False self.selected_point = None def extract_and_process_tracks(self, tracks): """Extracts and processes tracks for object counting in a video stream.""" # Annotator Init and region drawing self.annotator = Annotator(self.im0, self.tf, self.names) if tracks[0].boxes.id is not None: boxes = tracks[0].boxes.xyxy.cpu() clss = tracks[0].boxes.cls.cpu().tolist() track_ids = tracks[0].boxes.id.int().cpu().tolist() # Extract tracks for box, track_id, cls in zip(boxes, track_ids, clss): # Draw bounding box self.annotator.box_label(box, label=f"{track_id}:{self.names[cls]}", color=colors(int(track_id), True)) # Draw Tracks track_line = self.track_history[track_id] track_line.append((float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2))) if len(track_line) > 30: track_line.pop(0) # Draw track trails if self.draw_tracks: self.annotator.draw_centroid_and_tracks( track_line, color=self.track_color, track_thickness=self.track_thickness ) prev_position = self.track_history[track_id][-2] if len(self.track_history[track_id]) > 1 else None centroid = Point((box[:2] + box[2:]) / 2) # Count objects if len(self.reg_pts) >= 3: # any polygon is_inside = self.counting_region.contains(centroid) current_position = "in" if is_inside else "out" if prev_position is not None: if self.counting_dict[track_id] != current_position and is_inside: self.in_counts += 1 self.counting_dict[track_id] = "in" elif self.counting_dict[track_id] != current_position and not is_inside: self.out_counts += 1 self.counting_dict[track_id] = "out" else: self.counting_dict[track_id] = current_position else: self.counting_dict[track_id] = current_position elif len(self.reg_pts) == 2: if prev_position is not None: is_inside = (box[0] - prev_position[0]) * ( self.counting_region.centroid.x - prev_position[0] ) > 0 current_position = "in" if is_inside else "out" if self.counting_dict[track_id] != current_position and is_inside: self.in_counts += 1 self.counting_dict[track_id] = "in" elif self.counting_dict[track_id] != current_position and not is_inside: self.out_counts += 1 self.counting_dict[track_id] = "out" else: self.counting_dict[track_id] = current_position else: self.counting_dict[track_id] = None incount_label = f"In Count : {self.in_counts}" outcount_label = f"OutCount : {self.out_counts}" # Display counts based on user choice counts_label = None if not self.view_in_counts and not self.view_out_counts: counts_label = None elif not self.view_in_counts: counts_label = outcount_label elif not self.view_out_counts: counts_label = incount_label else: counts_label = f"{incount_label} {outcount_label}" if counts_label is not None: self.annotator.count_labels( counts=counts_label, count_txt_size=self.count_txt_thickness, txt_color=self.count_txt_color, color=self.count_color, ) def display_frames(self): """Display frame.""" if self.env_check: self.annotator.draw_region(reg_pts=self.reg_pts, color=self.region_color, thickness=self.region_thickness) cv2.namedWindow(self.window_name) if len(self.reg_pts) == 4: # only add mouse event If user drawn region cv2.setMouseCallback(self.window_name, self.mouse_event_for_region, {"region_points": self.reg_pts}) cv2.imshow(self.window_name, self.im0) # Break Window if cv2.waitKey(1) & 0xFF == ord("q"): return def start_counting(self, im0, tracks): """ Main function to start the object counting process. Args: im0 (ndarray): Current frame from the video stream. tracks (list): List of tracks obtained from the object tracking process. """ self.im0 = im0 # store image self.extract_and_process_tracks(tracks) # draw region even if no objects if self.view_img: self.display_frames() return self.im0 if __name__ == "__main__": ObjectCounter()