Reference for ultralytics/solutions/object_counter.py

def count(self, im0):
    """
    Processes input data (frames or object tracks) and updates 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:
        (numpy.ndarray): The processed image with annotations and count information.

    Examples:
        >>> counter = ObjectCounter()
        >>> frame = cv2.imread("path/to/image.jpg")
        >>> processed_frame = counter.count(frame)
    """
    if not self.region_initialized:
        self.initialize_region()
        self.region_initialized = True

    self.annotator = Annotator(im0, line_width=self.line_width)  # Initialize annotator
    self.extract_tracks(im0)  # Extract tracks

    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 in zip(self.boxes, self.track_ids, self.clss):
        # Draw bounding box and counting region
        self.annotator.box_label(box, label=self.names[cls], color=colors(cls, True))
        self.store_tracking_history(track_id, box)  # Store track history
        self.store_classwise_counts(cls)  # store classwise counts in dict

        # Draw tracks of objects
        self.annotator.draw_centroid_and_tracks(
            self.track_line, color=colors(int(cls), True), track_thickness=self.line_width
        )

        # 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(self.track_line, box, track_id, prev_position, cls)  # Perform object counting

    self.display_counts(im0)  # Display the counts on the frame
    self.display_output(im0)  # display output with base class function

    return im0  # return output image for more usage

def count_objects(self, track_line, box, track_id, prev_position, cls):
    """
    Counts objects within a polygonal or linear region based on their tracks.

    Args:
        track_line (Dict): Last 30 frame track record for the object.
        box (List[float]): Bounding box coordinates [x1, y1, x2, y2] for the specific track 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 = 1
        >>> prev_position = (120, 220)
        >>> cls = 0
        >>> counter.count_objects(track_line, box, track_id, prev_position, cls)
    """
    if prev_position is None or track_id in self.counted_ids:
        return

    centroid = self.r_s.centroid
    dx = (box[0] - prev_position[0]) * (centroid.x - prev_position[0])
    dy = (box[1] - prev_position[1]) * (centroid.y - prev_position[1])

    if len(self.region) >= 3 and self.r_s.contains(self.Point(track_line[-1])):
        self.counted_ids.append(track_id)
        # For polygon region
        if dx > 0:
            self.in_count += 1
            self.classwise_counts[self.names[cls]]["IN"] += 1
        else:
            self.out_count += 1
            self.classwise_counts[self.names[cls]]["OUT"] += 1

    elif len(self.region) < 3 and self.LineString([prev_position, box[:2]]).intersects(self.r_s):
        self.counted_ids.append(track_id)
        # For linear region
        if dx > 0 and dy > 0:
            self.in_count += 1
            self.classwise_counts[self.names[cls]]["IN"] += 1
        else:
            self.out_count += 1
            self.classwise_counts[self.names[cls]]["OUT"] += 1

def display_counts(self, im0):
    """
    Displays object counts on the input image or frame.

    Args:
        im0 (numpy.ndarray): The input 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(im0, labels_dict, (104, 31, 17), (255, 255, 255), 10)

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.

    This method ensures that the 'classwise_counts' dictionary contains an entry for the specified class,
    initializing 'IN' and 'OUT' counts to zero if the class is not already present.

    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}