Reference for ultralytics/solutions/object_counter.py

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 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

    is_obb = getattr(self.tracks[0], "obb", None) is not None  # True if OBB results exist
    if is_obb and self.track_data and self.track_data.id is not None:
        self.boxes = self.track_data.xyxyxyxy.reshape(-1, 4, 2).cpu()

    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

        # 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_history[track_id][-1], track_id, prev_position, cls)  # 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=dict(self.classwise_counts),
        total_tracks=len(self.track_ids),
    )