# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

from __future__ import annotations

from collections import defaultdict
from typing import Any

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_count (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.
        margin (int): Margin for background rectangle size to display counts properly.

    Methods:
        count_objects: Count objects within a polygonal or linear region based on their tracks.
        display_counts: Display object counts on the frame.
        process: Process input data and update counts.

    Examples:
        >>> counter = ObjectCounter()
        >>> frame = cv2.imread("frame.jpg")
        >>> results = counter.process(frame)
        >>> print(f"Inward count: {results.in_count}, Outward count: {results.out_count}")
    """

    def __init__(self, **kwargs: Any) -> None:
        """Initialize 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_count = defaultdict(lambda: {"IN": 0, "OUT": 0})  # Dictionary for counts, categorized by 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: tuple[float, float],
        track_id: int,
        prev_position: tuple[float, float] | None,
        cls: int,
    ) -> None:
        """Count 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], optional): 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)
            if self.r_s.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_count[self.names[cls]]["IN"] += 1
                    else:  # Moving left
                        self.out_count += 1
                        self.classwise_count[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_count[self.names[cls]]["IN"] += 1
                else:  # Moving upward
                    self.out_count += 1
                    self.classwise_count[self.names[cls]]["OUT"] += 1
                self.counted_ids.append(track_id)

        # An object fast enough to straddle the region leaves no centroid inside it, so count a crossing segment
        # too, but only from outside: a track already inside has had the containment check since it entered.
        elif len(self.region) > 2 and (
            self.r_s.contains(self.Point(current_centroid))
            or (
                not self.r_s.contains(self.Point(prev_position))
                and self.r_s.crosses(self.LineString([prev_position, current_centroid]))
            )
        ):
            # Judge direction by the object's dominant motion axis over its recent track, not by the
            # region's shape; a ~5-frame baseline is robust to tracker jitter where a 1-frame delta is not.
            # The baseline is the oldest recent point OUTSIDE the region, so the entry vector is not
            # polluted by an uncounted first frame that spawned inside (quick exit and re-entry).
            window = self.track_history[track_id][-5:] or [prev_position]
            baseline = next((p for p in window if not self.r_s.contains(self.Point(p))), window[0])
            dx = current_centroid[0] - baseline[0]
            dy = current_centroid[1] - baseline[1]
            moving_in = dx > 0 if abs(dx) > abs(dy) else dy > 0  # moving right or downward
            if moving_in:
                self.in_count += 1
                self.classwise_count[self.names[cls]]["IN"] += 1
            else:  # Moving left or upward
                self.out_count += 1
                self.classwise_count[self.names[cls]]["OUT"] += 1
            self.counted_ids.append(track_id)

    def display_counts(self, plot_im) -> None:
        """Display object counts on the input image or frame.

        Args:
            plot_im (np.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_count.items()
            if (value["IN"] != 0 and self.show_in) or (value["OUT"] != 0 and self.show_out)
        }
        if labels_dict:
            self.annotator.display_analytics(plot_im, labels_dict, (104, 31, 17), (255, 255, 255), self.margin)

    def process(self, im0) -> SolutionResults:
        """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 (np.ndarray): The input image or frame to be processed.

        Returns:
            (SolutionResults): Contains processed image `plot_im`, '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

            # 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_count),
            total_tracks=len(self.track_ids),
        )
