import numpy as np
import cv2
import os

from . import kalman_filter, linear_assignment, iou_matching
from .detection import Detection
from .track import Track


def _cosine_distance(a, b, data_is_normalized=False):
    if not data_is_normalized:
        a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
        b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
    return 1.0 - np.dot(a, b.T)


def _nn_cosine_distance(x, y):
    distances = _cosine_distance(x, y)
    return distances.min(axis=0)


class Tracker:
    def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3):
        self.metric = metric
        self.max_iou_distance = max_iou_distance
        self.max_age = max_age
        self.n_init = n_init

        self.kf = kalman_filter.KalmanFilter()
        self.tracks = []
        self._next_id = 1

    def predict(self):
        """Propagate track state distributions one time step forward.
        This function should be called once every time step, before `update`.
        """
        for track in self.tracks:
            track.predict(self.kf)

    def increment_ages(self):
        for track in self.tracks:
            track.increment_age()
            track.mark_missed()

    def update(self, detections, classes):
        """Perform measurement update and track management.
        Parameters
        ----------
        detections : List[deep_sort.detection.Detection]
            A list of detections at the current time step.
        """
        # Run matching cascade.
        matches, unmatched_tracks, unmatched_detections = self._match(detections)

        # Update track set.
        for track_idx, detection_idx in matches:
            self.tracks[track_idx].update(self.kf, detections[detection_idx])
        for track_idx in unmatched_tracks:
            self.tracks[track_idx].mark_missed()
        for detection_idx in unmatched_detections:
            self._initiate_track(detections[detection_idx], classes[detection_idx].item())
        self.tracks = [t for t in self.tracks if not t.is_deleted()]

        # Update distance metric.
        active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
        features, targets = [], []
        for track in self.tracks:
            if not track.is_confirmed():
                continue
            features += track.features
            targets += [track.track_id for _ in track.features]
            track.features = []
        self.metric.partial_fit(np.asarray(features), np.asarray(targets), active_targets)

    def _match(self, detections):
        def gated_metric(tracks, dets, track_indices, detection_indices):
            features = np.array([dets[i].feature for i in detection_indices])
            targets = np.array([tracks[i].track_id for i in track_indices])
            cost_matrix = self.metric.distance(features, targets)
            cost_matrix = linear_assignment.gate_cost_matrix(
                self.kf, cost_matrix, tracks, dets, track_indices, detection_indices
            )

            return cost_matrix

        # Split track set into confirmed and unconfirmed tracks.
        confirmed_tracks = [i for i, t in enumerate(self.tracks) if t.is_confirmed()]
        unconfirmed_tracks = [i for i, t in enumerate(self.tracks) if not t.is_confirmed()]

        # Associate confirmed tracks using appearance features.
        (matches_a, unmatched_tracks_a, unmatched_detections,) = linear_assignment.matching_cascade(
            gated_metric,
            self.metric.matching_threshold,
            self.max_age,
            self.tracks,
            detections,
            confirmed_tracks,
        )

        # Associate remaining tracks together with unconfirmed tracks using IOU.
        iou_track_candidates = unconfirmed_tracks + [
            k for k in unmatched_tracks_a if self.tracks[k].time_since_update == 1
        ]
        unmatched_tracks_a = [k for k in unmatched_tracks_a if self.tracks[k].time_since_update != 1]
        (matches_b, unmatched_tracks_b, unmatched_detections,) = linear_assignment.min_cost_matching(
            iou_matching.iou_cost,
            self.max_iou_distance,
            self.tracks,
            detections,
            iou_track_candidates,
            unmatched_detections,
        )

        matches = matches_a + matches_b
        unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
        return matches, unmatched_tracks, unmatched_detections

    def _initiate_track(self, detection, class_id):
        mean, covariance = self.kf.initiate(detection.to_xyah())
        self.tracks.append(
            Track(
                mean,
                covariance,
                self._next_id,
                class_id,
                self.n_init,
                self.max_age,
                detection.feature,
            )
        )
        self._next_id += 1


class NearestNeighborDistanceMetric(object):
    def __init__(self, metric, matching_threshold, budget=None):

        if metric == "cosine":
            self._metric = _nn_cosine_distance
        else:
            raise ValueError("Invalid metric; must be either 'euclidean' or 'cosine'")
        self.matching_threshold = matching_threshold
        self.budget = budget
        self.samples = {}

    def partial_fit(self, features, targets, active_targets):
        for feature, target in zip(features, targets):
            self.samples.setdefault(target, []).append(feature)
            if self.budget is not None:
                self.samples[target] = self.samples[target][-self.budget :]
        self.samples = {k: self.samples[k] for k in active_targets}

    def distance(self, features, targets):
        cost_matrix = np.zeros((len(targets), len(features)))
        for i, target in enumerate(targets):
            cost_matrix[i, :] = self._metric(self.samples[target], features)
        return cost_matrix


class DeepSort(object):
    def __init__(
        self,
        max_dist=0.1,
        min_confidence=0.3,
        nms_max_overlap=1.0,
        max_iou_distance=0.7,
        max_age=30,
        n_init=3,
        nn_budget=100,
        use_cuda=True,
    ):
        self.min_confidence = min_confidence
        self.nms_max_overlap = nms_max_overlap

        # self.extractor = Extractor(model_path, use_cuda=use_cuda)

        max_cosine_distance = max_dist
        metric = NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
        self.tracker = Tracker(metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init)

    def update(self, fdets, img):
        #####
        remain_inds = fdets[:, 4] > self.min_confidence
        dets, id_feature = fdets[remain_inds, 0:5], fdets[remain_inds, 5:]
        dets[:, 2:4] = dets[:, 2:4] - dets[:, 0:2]
        #####
        detections = [
            Detection(dets[i, 0:4], conf, id_feature[i])
            for i, conf in enumerate(dets[:, 4])
        ]
        classes = np.zeros((len(detections),))

        # update tracker
        self.tracker.predict()
        self.tracker.update(detections, classes)

        # output bbox identities
        outputs = []
        for track in self.tracker.tracks:
            if not track.is_confirmed() or track.time_since_update > 1:
                continue
            track.tlwh = track.to_tlwh()
            outputs.append(track)
        return outputs

    """
    TODO:
        Convert bbox from xc_yc_w_h to xtl_ytl_w_h
    Thanks JieChen91@github.com for reporting this bug!
    """

    @staticmethod
    def _xywh_to_tlwh(bbox_xywh):
        if isinstance(bbox_xywh, np.ndarray):
            bbox_tlwh = bbox_xywh.copy()
        elif isinstance(bbox_xywh, torch.Tensor):
            bbox_tlwh = bbox_xywh.clone()
        bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2.0
        bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2.0
        return bbox_tlwh

    @staticmethod
    def _xyxy_to_tlwh_array(bbox_xyxy):
        if isinstance(bbox_xyxy, np.ndarray):
            bbox_tlwh = bbox_xyxy.copy()
        elif isinstance(bbox_xyxy, torch.Tensor):
            bbox_tlwh = bbox_xyxy.clone()
        bbox_tlwh[:, 2] = bbox_xyxy[:, 2] - bbox_xyxy[:, 0]
        bbox_tlwh[:, 3] = bbox_xyxy[:, 3] - bbox_xyxy[:, 1]
        return bbox_tlwh

    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

    def _tlwh_to_xyxy(self, bbox_tlwh):
        """
        TODO:
            Convert bbox from xtl_ytl_w_h to xc_yc_w_h
        Thanks JieChen91@github.com for reporting this bug!
        """
        x, y, w, h = bbox_tlwh
        x1 = max(int(x), 0)
        x2 = min(int(x + w), self.width - 1)
        y1 = max(int(y), 0)
        y2 = min(int(y + h), self.height - 1)
        return x1, y1, x2, y2

    def _tlwh_to_xyxy_noclip(self, bbox_tlwh):
        """
        TODO:
            Convert bbox from xtl_ytl_w_h to xc_yc_w_h
        Thanks JieChen91@github.com for reporting this bug!
        """
        x, y, w, h = bbox_tlwh
        x1 = x
        x2 = x + w
        y1 = y
        y2 = y + h
        return x1, y1, x2, y2

    def increment_ages(self):
        self.tracker.increment_ages()

    def _xyxy_to_tlwh(self, bbox_xyxy):
        x1, y1, x2, y2 = bbox_xyxy

        t = x1
        l = y1
        w = int(x2 - x1)
        h = int(y2 - y1)
        return t, l, w, h

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