from collections import deque

import numpy as np
import torch

from ...appearance.reid_multibackend import ReIDDetectMultiBackend
from ...motion.adapters import BotSortKalmanFilterAdapter
from ...utils.gmc import GlobalMotionCompensation
from ...utils.matching import (embedding_distance, fuse_score,  # fuse_motion,
                               iou_distance, linear_assignment)
from ...utils.ops import xywh2xyxy, xyxy2xywh
from .basetrack import BaseTrack, TrackState


class STrack(BaseTrack):
    shared_kalman = BotSortKalmanFilterAdapter()

    def __init__(self, tlwh, score, cls, feat=None, feat_history=50):
        # wait activate
        self._tlwh = np.asarray(tlwh, dtype=np.float32)
        self.kalman_filter = None
        self.mean, self.covariance = None, None
        self.is_activated = False

        self.cls = -1
        self.cls_hist = []  # (cls id, freq)
        self.update_cls(cls, score)

        self.score = score
        self.tracklet_len = 0

        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):
        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 update_cls(self, cls, score):
        if len(self.cls_hist) > 0:
            max_freq = 0
            found = False
            for c in self.cls_hist:
                if cls == c[0]:
                    c[1] += score
                    found = True

                if c[1] > max_freq:
                    max_freq = c[1]
                    self.cls = c[0]
            if not found:
                self.cls_hist.append([cls, score])
                self.cls = cls
        else:
            self.cls_hist.append([cls, score])
            self.cls = cls

    def predict(self):
        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
        )

    @staticmethod
    def multi_predict(stracks):
        if len(stracks) > 0:
            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 = STrack.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

    @staticmethod
    def multi_gmc(stracks, H=np.eye(2, 3)):
        if len(stracks) > 0:
            multi_mean = np.asarray([st.mean.copy() for st in stracks])
            multi_covariance = np.asarray([st.covariance for st in stracks])

            R = H[:2, :2]
            R8x8 = np.kron(np.eye(4, dtype=float), R)
            t = H[:2, 2]

            for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
                mean = R8x8.dot(mean)
                mean[:2] += t
                cov = R8x8.dot(cov).dot(R8x8.transpose())

                stracks[i].mean = mean
                stracks[i].covariance = cov

    def activate(self, kalman_filter, frame_id):
        """Start a new tracklet"""
        self.kalman_filter = kalman_filter
        self.track_id = self.next_id()

        self.mean, self.covariance = self.kalman_filter.initiate(
            self.tlwh_to_xywh(self._tlwh)
        )

        self.tracklet_len = 0
        self.state = TrackState.Tracked
        if frame_id == 1:
            self.is_activated = True
        self.frame_id = frame_id
        self.start_frame = frame_id

    def re_activate(self, new_track, frame_id, new_id=False):
        self.mean, self.covariance = self.kalman_filter.update(
            self.mean, self.covariance, self.tlwh_to_xywh(new_track.tlwh)
        )
        if new_track.curr_feat is not None:
            self.update_features(new_track.curr_feat)
        self.tracklet_len = 0
        self.state = TrackState.Tracked
        self.is_activated = True
        self.frame_id = frame_id
        if new_id:
            self.track_id = self.next_id()
        self.score = new_track.score

        self.update_cls(new_track.cls, new_track.score)

    def update(self, new_track, frame_id):
        """
        Update a matched track
        :type new_track: STrack
        :type frame_id: int
        :type update_feature: bool
        :return:
        """
        self.frame_id = frame_id
        self.tracklet_len += 1

        new_tlwh = new_track.tlwh

        self.mean, self.covariance = self.kalman_filter.update(
            self.mean, self.covariance, self.tlwh_to_xywh(new_tlwh)
        )

        if new_track.curr_feat is not None:
            self.update_features(new_track.curr_feat)

        self.state = TrackState.Tracked
        self.is_activated = True

        self.score = new_track.score
        self.update_cls(new_track.cls, new_track.score)

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

    @property
    def tlbr(self):
        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
        `(top left, bottom right)`.
        """
        ret = self.tlwh.copy()
        ret[2:] += ret[:2]
        return ret

    @property
    def xywh(self):
        """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
        `(top left, bottom right)`.
        """
        ret = self.tlwh.copy()
        ret[:2] += ret[2:] / 2.0
        return ret

    @staticmethod
    def tlwh_to_xyah(tlwh):
        """Convert bounding box to format `(center x, center y, aspect ratio,
        height)`, where the aspect ratio is `width / height`.
        """
        ret = np.asarray(tlwh).copy()
        ret[:2] += ret[2:] / 2
        ret[2] /= ret[3]
        return ret

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

    def to_xywh(self):
        return self.tlwh_to_xywh(self.tlwh)

    @staticmethod
    def tlbr_to_tlwh(tlbr):
        ret = np.asarray(tlbr).copy()
        ret[2:] -= ret[:2]
        return ret

    @staticmethod
    def tlwh_to_tlbr(tlwh):
        ret = np.asarray(tlwh).copy()
        ret[2:] += ret[:2]
        return ret

    def __repr__(self):
        return "OT_{}_({}-{})".format(self.track_id, self.start_frame, self.end_frame)


class BoTSORT(object):
    def __init__(
        self,
        model_weights,
        device,
        fp16,
        track_high_thresh: float = 0.45,
        new_track_thresh: float = 0.6,
        track_buffer: int = 30,
        match_thresh: float = 0.8,
        proximity_thresh: float = 0.5,
        appearance_thresh: float = 0.25,
        cmc_method: str = "sparseOptFlow",
        frame_rate=30,
        lambda_=0.985,
    ):
        self.tracked_stracks = []  # type: list[STrack]
        self.lost_stracks = []  # type: list[STrack]
        self.removed_stracks = []  # type: list[STrack]
        BaseTrack.clear_count()

        self.frame_id = 0

        self.lambda_ = lambda_
        self.track_high_thresh = track_high_thresh
        self.new_track_thresh = new_track_thresh

        self.buffer_size = int(frame_rate / 30.0 * track_buffer)
        self.max_time_lost = self.buffer_size
        self.kalman_filter = BotSortKalmanFilterAdapter()

        # ReID module
        self.proximity_thresh = proximity_thresh
        self.appearance_thresh = appearance_thresh
        self.match_thresh = match_thresh

        self.model = ReIDDetectMultiBackend(
            weights=model_weights, device=device, fp16=fp16
        )

        self.gmc = GlobalMotionCompensation(method=cmc_method, verbose=[None, False])

    def update(self, dets, img):
        assert isinstance(
            dets, np.ndarray
        ), f"Unsupported 'dets' input format '{type(dets)}', valid format is np.ndarray"
        assert isinstance(
            img, np.ndarray
        ), f"Unsupported 'img_numpy' input format '{type(img)}', valid format is np.ndarray"
        assert (
            len(dets.shape) == 2
        ), "Unsupported 'dets' dimensions, valid number of dimensions is two"
        assert (
            dets.shape[1] == 6
        ), "Unsupported 'dets' 2nd dimension lenght, valid lenghts is 6"

        self.frame_id += 1
        activated_starcks = []
        refind_stracks = []
        lost_stracks = []
        removed_stracks = []

        xyxys = dets[:, 0:4]
        xywh = xyxy2xywh(xyxys)
        confs = dets[:, 4]
        clss = dets[:, 5]

        classes = clss
        xyxys = xyxys
        confs = confs

        remain_inds = confs > self.track_high_thresh
        inds_low = confs > 0.1
        inds_high = confs < self.track_high_thresh

        inds_second = np.logical_and(inds_low, inds_high)

        dets_second = xywh[inds_second]
        dets = xywh[remain_inds]

        scores_keep = confs[remain_inds]
        scores_second = confs[inds_second]

        classes_keep = classes[remain_inds]
        clss_second = classes[inds_second]

        self.height, self.width = img.shape[:2]

        """Extract embeddings """
        features_keep = self._get_features(dets, img)

        if len(dets) > 0:
            """Detections"""

            detections = [
                STrack(xyxy, s, c, f.cpu().numpy())
                for (xyxy, s, c, f) in zip(
                    dets, scores_keep, classes_keep, features_keep
                )
            ]
        else:
            detections = []

        """ Add newly detected tracklets to tracked_stracks"""
        unconfirmed = []
        tracked_stracks = []  # type: list[STrack]
        for track in self.tracked_stracks:
            if not track.is_activated:
                unconfirmed.append(track)
            else:
                tracked_stracks.append(track)

        """ Step 2: First association, with high score detection boxes"""
        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)

        # Predict the current location with KF
        STrack.multi_predict(strack_pool)

        # Fix camera motion
        warp = self.gmc.apply(img, dets)
        STrack.multi_gmc(strack_pool, warp)
        STrack.multi_gmc(unconfirmed, warp)

        # Associate with high score detection boxes
        ious_dists = iou_distance(strack_pool, detections)
        ious_dists_mask = ious_dists > self.proximity_thresh

        emb_dists = embedding_distance(strack_pool, detections) / 2.0
        emb_dists[emb_dists > self.appearance_thresh] = 1.0
        emb_dists[ious_dists_mask] = 1.0
        dists = np.minimum(ious_dists, emb_dists)

        matches, u_track, u_detection = linear_assignment(
            dists, thresh=self.match_thresh
        )

        for itracked, idet in matches:
            track = strack_pool[itracked]
            det = detections[idet]
            if track.state == TrackState.Tracked:
                track.update(detections[idet], self.frame_id)
                activated_starcks.append(track)
            else:
                track.re_activate(det, self.frame_id, new_id=False)
                refind_stracks.append(track)

        """ Step 3: Second association, with low score detection boxes"""
        # if len(scores):
        #     inds_high = scores < self.track_high_thresh
        #     inds_low = scores > self.track_low_thresh
        #     inds_second = np.logical_and(inds_low, inds_high)
        #     dets_second = bboxes[inds_second]
        #     scores_second = scores[inds_second]
        #     classes_second = classes[inds_second]
        # else:
        #     dets_second = []
        #     scores_second = []
        #     classes_second = []

        # association the untrack to the low score detections
        if len(dets_second) > 0:
            """Detections"""
            detections_second = [
                STrack(STrack.tlbr_to_tlwh(tlbr), s, c)
                for (tlbr, s, c) in zip(dets_second, scores_second, clss_second)
            ]
        else:
            detections_second = []

        r_tracked_stracks = [
            strack_pool[i]
            for i in u_track
            if strack_pool[i].state == TrackState.Tracked
        ]
        dists = iou_distance(r_tracked_stracks, detections_second)
        matches, u_track, u_detection_second = linear_assignment(dists, thresh=0.5)
        for itracked, idet in matches:
            track = r_tracked_stracks[itracked]
            det = detections_second[idet]
            if track.state == TrackState.Tracked:
                track.update(det, self.frame_id)
                activated_starcks.append(track)
            else:
                track.re_activate(det, self.frame_id, new_id=False)
                refind_stracks.append(track)

        for it in u_track:
            track = r_tracked_stracks[it]
            if not track.state == TrackState.Lost:
                track.mark_lost()
                lost_stracks.append(track)

        """Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
        detections = [detections[i] for i in u_detection]
        ious_dists = iou_distance(unconfirmed, detections)
        ious_dists_mask = ious_dists > self.proximity_thresh

        ious_dists = fuse_score(ious_dists, detections)

        emb_dists = embedding_distance(unconfirmed, detections) / 2.0
        emb_dists[emb_dists > self.appearance_thresh] = 1.0
        emb_dists[ious_dists_mask] = 1.0
        dists = np.minimum(ious_dists, emb_dists)

        matches, u_unconfirmed, u_detection = linear_assignment(dists, thresh=0.7)
        for itracked, idet in matches:
            unconfirmed[itracked].update(detections[idet], self.frame_id)
            activated_starcks.append(unconfirmed[itracked])
        for it in u_unconfirmed:
            track = unconfirmed[it]
            track.mark_removed()
            removed_stracks.append(track)

        """ Step 4: Init new stracks"""
        for inew in u_detection:
            track = detections[inew]
            if track.score < self.new_track_thresh:
                continue

            track.activate(self.kalman_filter, self.frame_id)
            activated_starcks.append(track)

        """ Step 5: Update state"""
        for track in self.lost_stracks:
            if self.frame_id - track.end_frame > self.max_time_lost:
                track.mark_removed()
                removed_stracks.append(track)

        """ Merge """
        self.tracked_stracks = [
            t for t in self.tracked_stracks if t.state == TrackState.Tracked
        ]
        self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
        self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
        self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
        self.lost_stracks.extend(lost_stracks)
        self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
        self.removed_stracks.extend(removed_stracks)
        self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(
            self.tracked_stracks, self.lost_stracks
        )

        # output_stracks = [track for track in self.tracked_stracks if track.is_activated]
        output_stracks = [track for track in self.tracked_stracks if track.is_activated]
        outputs = []
        for t in output_stracks:
            output = []
            tlwh = t.tlwh
            tid = t.track_id
            tlwh = np.expand_dims(tlwh, axis=0)
            xyxy = xywh2xyxy(tlwh)
            xyxy = np.squeeze(xyxy, axis=0)
            output.extend(xyxy)
            output.append(tid)
            output.append(t.score)
            output.append(t.cls)
            outputs.append(output)

        outputs = np.asarray(outputs)
        return outputs

    def _xywh_to_xyxy(self, bbox_xywh):
        x, y, w, h = bbox_xywh
        x1 = max(int(x - w / 2), 0)
        x2 = min(int(x + w / 2), self.width - 1)
        y1 = max(int(y - h / 2), 0)
        y2 = min(int(y + h / 2), self.height - 1)
        return x1, y1, x2, y2

    @torch.no_grad()
    def _get_features(self, bbox_xywh, ori_img):
        im_crops = []
        for box in bbox_xywh:
            x1, y1, x2, y2 = self._xywh_to_xyxy(box)
            im = ori_img[y1:y2, x1:x2]
            im_crops.append(im)
        if im_crops:
            features = self.model(im_crops)
        else:
            features = np.array([])
        return features


def joint_stracks(tlista, tlistb):
    exists = {}
    res = []
    for t in tlista:
        exists[t.track_id] = 1
        res.append(t)
    for t in tlistb:
        tid = t.track_id
        if not exists.get(tid, 0):
            exists[tid] = 1
            res.append(t)
    return res


def sub_stracks(tlista, tlistb):
    stracks = {}
    for t in tlista:
        stracks[t.track_id] = t
    for t in tlistb:
        tid = t.track_id
        if stracks.get(tid, 0):
            del stracks[tid]
    return list(stracks.values())


def remove_duplicate_stracks(stracksa, stracksb):
    pdist = iou_distance(stracksa, stracksb)
    pairs = np.where(pdist < 0.15)
    dupa, dupb = list(), list()
    for p, q in zip(*pairs):
        timep = stracksa[p].frame_id - stracksa[p].start_frame
        timeq = stracksb[q].frame_id - stracksb[q].start_frame
        if timep > timeq:
            dupb.append(q)
        else:
            dupa.append(p)
    resa = [t for i, t in enumerate(stracksa) if i not in dupa]
    resb = [t for i, t in enumerate(stracksb) if i not in dupb]
    return resa, resb
