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

from collections import deque

import numpy as np

from .basetrack import TrackState
from .byte_tracker import BYTETracker, STrack
from .utils import matching
from .utils.gmc import GMC
from .utils.kalman_filter import KalmanFilterXYWH


class BOTrack(STrack):
    """
    An extended version of the STrack class for YOLOv8, adding object tracking features.

    This class extends the STrack class to include additional functionalities for object tracking, such as feature
    smoothing, Kalman filter prediction, and reactivation of tracks.

    Attributes:
        shared_kalman (KalmanFilterXYWH): A shared Kalman filter for all instances of BOTrack.
        smooth_feat (np.ndarray): Smoothed feature vector.
        curr_feat (np.ndarray): Current feature vector.
        features (deque): A deque to store feature vectors with a maximum length defined by `feat_history`.
        alpha (float): Smoothing factor for the exponential moving average of features.
        mean (np.ndarray): The mean state of the Kalman filter.
        covariance (np.ndarray): The covariance matrix of the Kalman filter.

    Methods:
        update_features(feat): Update features vector and smooth it using exponential moving average.
        predict(): Predicts the mean and covariance using Kalman filter.
        re_activate(new_track, frame_id, new_id): Reactivates a track with updated features and optionally new ID.
        update(new_track, frame_id): Update the YOLOv8 instance with new track and frame ID.
        tlwh: Property that gets the current position in tlwh format `(top left x, top left y, width, height)`.
        multi_predict(stracks): Predicts the mean and covariance of multiple object tracks using shared Kalman filter.
        convert_coords(tlwh): Converts tlwh bounding box coordinates to xywh format.
        tlwh_to_xywh(tlwh): Convert bounding box to xywh format `(center x, center y, width, height)`.

    Examples:
        Create a BOTrack instance and update its features
        >>> bo_track = BOTrack(tlwh=[100, 50, 80, 40], score=0.9, cls=1, feat=np.random.rand(128))
        >>> bo_track.predict()
        >>> new_track = BOTrack(tlwh=[110, 60, 80, 40], score=0.85, cls=1, feat=np.random.rand(128))
        >>> bo_track.update(new_track, frame_id=2)
    """

    shared_kalman = KalmanFilterXYWH()

    def __init__(self, tlwh, score, cls, feat=None, feat_history=50):
        """
        Initialize a BOTrack object with temporal parameters, such as feature history, alpha, and current features.

        Args:
            tlwh (np.ndarray): Bounding box coordinates in tlwh format (top left x, top left y, width, height).
            score (float): Confidence score of the detection.
            cls (int): Class ID of the detected object.
            feat (np.ndarray | None): Feature vector associated with the detection.
            feat_history (int): Maximum length of the feature history deque.

        Examples:
            Initialize a BOTrack object with bounding box, score, class ID, and feature vector
            >>> tlwh = np.array([100, 50, 80, 120])
            >>> score = 0.9
            >>> cls = 1
            >>> feat = np.random.rand(128)
            >>> bo_track = BOTrack(tlwh, score, cls, feat)
        """
        super().__init__(tlwh, score, cls)

        self.smooth_feat = None
        self.curr_feat = None
        if feat is not None:
            self.update_features(feat)
        self.features = deque([], maxlen=feat_history)
        self.alpha = 0.9

    def update_features(self, feat):
        """Update the feature vector and apply exponential moving average smoothing."""
        feat /= np.linalg.norm(feat)
        self.curr_feat = feat
        if self.smooth_feat is None:
            self.smooth_feat = feat
        else:
            self.smooth_feat = self.alpha * self.smooth_feat + (1 - self.alpha) * feat
        self.features.append(feat)
        self.smooth_feat /= np.linalg.norm(self.smooth_feat)

    def predict(self):
        """Predicts the object's future state using the Kalman filter to update its mean and covariance."""
        mean_state = self.mean.copy()
        if self.state != TrackState.Tracked:
            mean_state[6] = 0
            mean_state[7] = 0

        self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)

    def re_activate(self, new_track, frame_id, new_id=False):
        """Reactivates a track with updated features and optionally assigns a new ID."""
        if new_track.curr_feat is not None:
            self.update_features(new_track.curr_feat)
        super().re_activate(new_track, frame_id, new_id)

    def update(self, new_track, frame_id):
        """Updates the YOLOv8 instance with new track information and the current frame ID."""
        if new_track.curr_feat is not None:
            self.update_features(new_track.curr_feat)
        super().update(new_track, frame_id)

    @property
    def tlwh(self):
        """Returns the current bounding box position in `(top left x, top left y, width, height)` format."""
        if self.mean is None:
            return self._tlwh.copy()
        ret = self.mean[:4].copy()
        ret[:2] -= ret[2:] / 2
        return ret

    @staticmethod
    def multi_predict(stracks):
        """Predicts the mean and covariance for multiple object tracks using a shared Kalman filter."""
        if len(stracks) <= 0:
            return
        multi_mean = np.asarray([st.mean.copy() for st in stracks])
        multi_covariance = np.asarray([st.covariance for st in stracks])
        for i, st in enumerate(stracks):
            if st.state != TrackState.Tracked:
                multi_mean[i][6] = 0
                multi_mean[i][7] = 0
        multi_mean, multi_covariance = BOTrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
        for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
            stracks[i].mean = mean
            stracks[i].covariance = cov

    def convert_coords(self, tlwh):
        """Converts tlwh bounding box coordinates to xywh format."""
        return self.tlwh_to_xywh(tlwh)

    @staticmethod
    def tlwh_to_xywh(tlwh):
        """Convert bounding box from tlwh (top-left-width-height) to xywh (center-x-center-y-width-height) format."""
        ret = np.asarray(tlwh).copy()
        ret[:2] += ret[2:] / 2
        return ret


class BOTSORT(BYTETracker):
    """
    An extended version of the BYTETracker class for YOLOv8, designed for object tracking with ReID and GMC algorithm.

    Attributes:
        proximity_thresh (float): Threshold for spatial proximity (IoU) between tracks and detections.
        appearance_thresh (float): Threshold for appearance similarity (ReID embeddings) between tracks and detections.
        encoder (Any): Object to handle ReID embeddings, set to None if ReID is not enabled.
        gmc (GMC): An instance of the GMC algorithm for data association.
        args (Any): Parsed command-line arguments containing tracking parameters.

    Methods:
        get_kalmanfilter(): Returns an instance of KalmanFilterXYWH for object tracking.
        init_track(dets, scores, cls, img): Initialize track with detections, scores, and classes.
        get_dists(tracks, detections): Get distances between tracks and detections using IoU and (optionally) ReID.
        multi_predict(tracks): Predict and track multiple objects with YOLOv8 model.

    Examples:
        Initialize BOTSORT and process detections
        >>> bot_sort = BOTSORT(args, frame_rate=30)
        >>> bot_sort.init_track(dets, scores, cls, img)
        >>> bot_sort.multi_predict(tracks)

    Note:
        The class is designed to work with the YOLOv8 object detection model and supports ReID only if enabled via args.
    """

    def __init__(self, args, frame_rate=30):
        """
        Initialize YOLOv8 object with ReID module and GMC algorithm.

        Args:
            args (object): Parsed command-line arguments containing tracking parameters.
            frame_rate (int): Frame rate of the video being processed.

        Examples:
            Initialize BOTSORT with command-line arguments and a specified frame rate:
            >>> args = parse_args()
            >>> bot_sort = BOTSORT(args, frame_rate=30)
        """
        super().__init__(args, frame_rate)
        # ReID module
        self.proximity_thresh = args.proximity_thresh
        self.appearance_thresh = args.appearance_thresh

        if args.with_reid:
            # Haven't supported BoT-SORT(reid) yet
            self.encoder = None
        self.gmc = GMC(method=args.gmc_method)

    def get_kalmanfilter(self):
        """Returns an instance of KalmanFilterXYWH for predicting and updating object states in the tracking process."""
        return KalmanFilterXYWH()

    def init_track(self, dets, scores, cls, img=None):
        """Initialize object tracks using detection bounding boxes, scores, class labels, and optional ReID features."""
        if len(dets) == 0:
            return []
        if self.args.with_reid and self.encoder is not None:
            features_keep = self.encoder.inference(img, dets)
            return [BOTrack(xyxy, s, c, f) for (xyxy, s, c, f) in zip(dets, scores, cls, features_keep)]  # detections
        else:
            return [BOTrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)]  # detections

    def get_dists(self, tracks, detections):
        """Calculates distances between tracks and detections using IoU and optionally ReID embeddings."""
        dists = matching.iou_distance(tracks, detections)
        dists_mask = dists > self.proximity_thresh

        if self.args.fuse_score:
            dists = matching.fuse_score(dists, detections)

        if self.args.with_reid and self.encoder is not None:
            emb_dists = matching.embedding_distance(tracks, detections) / 2.0
            emb_dists[emb_dists > self.appearance_thresh] = 1.0
            emb_dists[dists_mask] = 1.0
            dists = np.minimum(dists, emb_dists)
        return dists

    def multi_predict(self, tracks):
        """Predicts the mean and covariance of multiple object tracks using a shared Kalman filter."""
        BOTrack.multi_predict(tracks)

    def reset(self):
        """Resets the BOTSORT tracker to its initial state, clearing all tracked objects and internal states."""
        super().reset()
        self.gmc.reset_params()
