Reference for ultralytics/solutions/region_counter.py

class RegionCounter(BaseSolution):
    """A class for real-time counting of objects within user-defined regions in a video stream.

    This class inherits from `BaseSolution` and provides functionality to define polygonal regions in a video frame,
    track objects, and count those objects that pass through each defined region. Useful for applications requiring
    counting in specified areas, such as monitoring zones or segmented sections.

    Attributes:
        region_template (dict): Template for creating new counting regions with default attributes including name,
            polygon coordinates, and display colors.
        counting_regions (list): List storing all defined regions, where each entry is based on `region_template` and
            includes specific region settings like name, coordinates, and color.
        region_counts (dict): Dictionary storing the count of objects for each named region.

    Methods:
        add_region: Add a new counting region with specified attributes.
        process: Process video frames to count objects in each region.
        initialize_regions: Initialize zones to count the objects in each one. Zones could be multiple as well.

    Examples:
        Initialize a RegionCounter and add a counting region
        >>> counter = RegionCounter()
        >>> counter.add_region("Zone1", [(100, 100), (200, 100), (200, 200), (100, 200)], (255, 0, 0), (255, 255, 255))
        >>> results = counter.process(frame)
        >>> print(f"Total tracks: {results.total_tracks}")
    """

    def __init__(self, **kwargs: Any) -> None:
        """Initialize the RegionCounter for real-time object counting in user-defined regions."""
        super().__init__(**kwargs)
        self.region_template = {
            "name": "Default Region",
            "polygon": None,
            "counts": 0,
            "region_color": (255, 255, 255),
            "text_color": (0, 0, 0),
        }
        self.region_counts = {}
        self.counting_regions = []
        self.initialize_regions()

def add_region(
    self,
    name: str,
    polygon_points: list[tuple],
    region_color: tuple[int, int, int],
    text_color: tuple[int, int, int],
) -> dict[str, Any]:
    """Add a new region to the counting list based on the provided template with specific attributes.

    Args:
        name (str): Name assigned to the new region.
        polygon_points (list[tuple]): List of (x, y) coordinates defining the region's polygon.
        region_color (tuple[int, int, int]): BGR color for region visualization.
        text_color (tuple[int, int, int]): BGR color for the text within the region.

    Returns:
        (dict[str, any]): Returns a dictionary including the region information i.e. name, region_color etc.
    """
    region = self.region_template.copy()
    region.update(
        {
            "name": name,
            "polygon": self.Polygon(polygon_points),
            "region_color": region_color,
            "text_color": text_color,
        }
    )
    self.counting_regions.append(region)
    return region

def initialize_regions(self):
    """Initialize regions only once."""
    if self.region is None:
        self.initialize_region()
    if not isinstance(self.region, dict):  # Ensure self.region is initialized and structured as a dictionary
        self.region = {"Region#01": self.region}
    for i, (name, pts) in enumerate(self.region.items()):
        region = self.add_region(name, pts, colors(i, True), (255, 255, 255))
        region["prepared_polygon"] = self.prep(region["polygon"])

def process(self, im0: np.ndarray) -> SolutionResults:
    """Process the input frame to detect and count objects within each defined region.

    Args:
        im0 (np.ndarray): Input image frame where objects and regions are annotated.

    Returns:
        (SolutionResults): Contains processed image `plot_im`, 'total_tracks' (int, total number of tracked
            objects), and 'region_counts' (dict, counts of objects per region).
    """
    self.extract_tracks(im0)
    annotator = SolutionAnnotator(im0, line_width=self.line_width)

    for box, cls, track_id, conf in zip(self.boxes, self.clss, self.track_ids, self.confs):
        annotator.box_label(box, label=self.adjust_box_label(cls, conf, track_id), color=colors(track_id, True))
        center = self.Point(((box[0] + box[2]) / 2, (box[1] + box[3]) / 2))
        for region in self.counting_regions:
            if region["prepared_polygon"].contains(center):
                region["counts"] += 1
                self.region_counts[region["name"]] = region["counts"]

    # Display region counts
    for region in self.counting_regions:
        poly = region["polygon"]
        pts = list(map(tuple, np.array(poly.exterior.coords, dtype=np.int32)))
        (x1, y1), (x2, y2) = [(int(poly.centroid.x), int(poly.centroid.y))] * 2
        annotator.draw_region(pts, region["region_color"], self.line_width * 2)
        annotator.adaptive_label(
            [x1, y1, x2, y2],
            label=str(region["counts"]),
            color=region["region_color"],
            txt_color=region["text_color"],
            margin=self.line_width * 4,
            shape="rect",
        )
        region["counts"] = 0  # Reset for next frame
    plot_im = annotator.result()
    self.display_output(plot_im)

    return SolutionResults(plot_im=plot_im, total_tracks=len(self.track_ids), region_counts=self.region_counts)