# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license from ultralytics.solutions.solutions import BaseSolution, SolutionAnnotator, SolutionResults from ultralytics.utils.plotting import colors class ObjectCounter(BaseSolution): """ A class to manage the counting of objects in a real-time video stream based on their tracks. This class extends the BaseSolution class and provides functionality for counting objects moving in and out of a specified region in a video stream. It supports both polygonal and linear regions for counting. Attributes: in_count (int): Counter for objects moving inward. out_count (int): Counter for objects moving outward. counted_ids (List[int]): List of IDs of objects that have been counted. classwise_counts (Dict[str, Dict[str, int]]): Dictionary for counts, categorized by object class. region_initialized (bool): Flag indicating whether the counting region has been initialized. show_in (bool): Flag to control display of inward count. show_out (bool): Flag to control display of outward count. Methods: count_objects: Counts objects within a polygonal or linear region. store_classwise_counts: Initializes class-wise counts if not already present. display_counts: Displays object counts on the frame. process: Processes input data (frames or object tracks) and updates counts. Examples: >>> counter = ObjectCounter() >>> frame = cv2.imread("frame.jpg") >>> results = counter.process(frame) >>> print(f"Inward count: {counter.in_count}, Outward count: {counter.out_count}") """ def __init__(self, **kwargs): """Initializes the ObjectCounter class for real-time object counting in video streams.""" super().__init__(**kwargs) self.in_count = 0 # Counter for objects moving inward self.out_count = 0 # Counter for objects moving outward self.counted_ids = [] # List of IDs of objects that have been counted self.classwise_counts = {} # Dictionary for counts, categorized by object class self.region_initialized = False # Flag indicating whether the region has been initialized self.show_in = self.CFG["show_in"] self.show_out = self.CFG["show_out"] self.margin = self.line_width * 2 # Scales the background rectangle size to display counts properly def count_objects(self, current_centroid, track_id, prev_position, cls): """ Counts objects within a polygonal or linear region based on their tracks. Args: current_centroid (Tuple[float, float]): Current centroid coordinates (x, y) in the current frame. track_id (int): Unique identifier for the tracked object. prev_position (Tuple[float, float]): Last frame position coordinates (x, y) of the track. cls (int): Class index for classwise count updates. Examples: >>> counter = ObjectCounter() >>> track_line = {1: [100, 200], 2: [110, 210], 3: [120, 220]} >>> box = [130, 230, 150, 250] >>> track_id_num = 1 >>> previous_position = (120, 220) >>> class_to_count = 0 # In COCO model, class 0 = person >>> counter.count_objects((140, 240), track_id_num, previous_position, class_to_count) """ if prev_position is None or track_id in self.counted_ids: return if len(self.region) == 2: # Linear region (defined as a line segment) line = self.LineString(self.region) # Check if the line intersects the trajectory of the object if line.intersects(self.LineString([prev_position, current_centroid])): # Determine orientation of the region (vertical or horizontal) if abs(self.region[0][0] - self.region[1][0]) < abs(self.region[0][1] - self.region[1][1]): # Vertical region: Compare x-coordinates to determine direction if current_centroid[0] > prev_position[0]: # Moving right self.in_count += 1 self.classwise_counts[self.names[cls]]["IN"] += 1 else: # Moving left self.out_count += 1 self.classwise_counts[self.names[cls]]["OUT"] += 1 # Horizontal region: Compare y-coordinates to determine direction elif current_centroid[1] > prev_position[1]: # Moving downward self.in_count += 1 self.classwise_counts[self.names[cls]]["IN"] += 1 else: # Moving upward self.out_count += 1 self.classwise_counts[self.names[cls]]["OUT"] += 1 self.counted_ids.append(track_id) elif len(self.region) > 2: # Polygonal region polygon = self.Polygon(self.region) if polygon.contains(self.Point(current_centroid)): # Determine motion direction for vertical or horizontal polygons region_width = max(p[0] for p in self.region) - min(p[0] for p in self.region) region_height = max(p[1] for p in self.region) - min(p[1] for p in self.region) if ( region_width < region_height and current_centroid[0] > prev_position[0] or region_width >= region_height and current_centroid[1] > prev_position[1] ): # Moving right or downward self.in_count += 1 self.classwise_counts[self.names[cls]]["IN"] += 1 else: # Moving left or upward self.out_count += 1 self.classwise_counts[self.names[cls]]["OUT"] += 1 self.counted_ids.append(track_id) def store_classwise_counts(self, cls): """ Initialize class-wise counts for a specific object class if not already present. Args: cls (int): Class index for classwise count updates. Examples: >>> counter = ObjectCounter() >>> counter.store_classwise_counts(0) # Initialize counts for class index 0 >>> print(counter.classwise_counts) {'person': {'IN': 0, 'OUT': 0}} """ if self.names[cls] not in self.classwise_counts: self.classwise_counts[self.names[cls]] = {"IN": 0, "OUT": 0} def display_counts(self, plot_im): """ Display object counts on the input image or frame. Args: plot_im (numpy.ndarray): The image or frame to display counts on. Examples: >>> counter = ObjectCounter() >>> frame = cv2.imread("image.jpg") >>> counter.display_counts(frame) """ labels_dict = { str.capitalize(key): f"{'IN ' + str(value['IN']) if self.show_in else ''} " f"{'OUT ' + str(value['OUT']) if self.show_out else ''}".strip() for key, value in self.classwise_counts.items() if value["IN"] != 0 or value["OUT"] != 0 } if labels_dict: self.annotator.display_analytics(plot_im, labels_dict, (104, 31, 17), (255, 255, 255), self.margin) def process(self, im0): """ Process input data (frames or object tracks) and update object counts. This method initializes the counting region, extracts tracks, draws bounding boxes and regions, updates object counts, and displays the results on the input image. Args: im0 (numpy.ndarray): The input image or frame to be processed. Returns: (SolutionResults): Contains processed image `im0`, 'in_count' (int, count of objects entering the region), 'out_count' (int, count of objects exiting the region), 'classwise_count' (dict, per-class object count), and 'total_tracks' (int, total number of tracked objects). Examples: >>> counter = ObjectCounter() >>> frame = cv2.imread("path/to/image.jpg") >>> results = counter.process(frame) """ if not self.region_initialized: self.initialize_region() self.region_initialized = True self.extract_tracks(im0) # Extract tracks self.annotator = SolutionAnnotator(im0, line_width=self.line_width) # Initialize annotator self.annotator.draw_region( reg_pts=self.region, color=(104, 0, 123), thickness=self.line_width * 2 ) # Draw region # 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): # Draw bounding box and counting region self.annotator.box_label(box, label=self.adjust_box_label(cls, conf, track_id), color=colors(cls, True)) self.store_tracking_history(track_id, box) # Store track history self.store_classwise_counts(cls) # Store classwise counts in dict current_centroid = ((box[0] + box[2]) / 2, (box[1] + box[3]) / 2) # Store previous position of track for object counting prev_position = None if len(self.track_history[track_id]) > 1: prev_position = self.track_history[track_id][-2] self.count_objects(current_centroid, track_id, prev_position, cls) # Perform object counting plot_im = self.annotator.result() self.display_counts(plot_im) # Display the counts on the frame self.display_output(plot_im) # Display output with base class function # Return SolutionResults return SolutionResults( plot_im=plot_im, in_count=self.in_count, out_count=self.out_count, classwise_count=self.classwise_counts, total_tracks=len(self.track_ids), )