from __future__ import absolute_import
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from . import kalman_filter


INFTY_COST = 1e+5


def min_cost_matching(
        distance_metric, max_distance, tracks, detections, track_indices=None,
        detection_indices=None):
    if track_indices is None:
        track_indices = np.arange(len(tracks))
    if detection_indices is None:
        detection_indices = np.arange(len(detections))

    if len(detection_indices) == 0 or len(track_indices) == 0:
        return [], track_indices, detection_indices  # Nothing to match.

    cost_matrix = distance_metric(
        tracks, detections, track_indices, detection_indices)
    cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5 
    indices = linear_assignment(cost_matrix)

    matches, unmatched_tracks, unmatched_detections = [], [], []
    for col, detection_idx in enumerate(detection_indices):
        if col not in indices[:, 1]:
            unmatched_detections.append(detection_idx)
    for row, track_idx in enumerate(track_indices):
        if row not in indices[:, 0]:
            unmatched_tracks.append(track_idx)
    for row, col in indices:
        track_idx = track_indices[row]
        detection_idx = detection_indices[col]
        if cost_matrix[row, col] > max_distance:
            unmatched_tracks.append(track_idx)
            unmatched_detections.append(detection_idx)
        else:
            matches.append((track_idx, detection_idx))
    return matches, unmatched_tracks, unmatched_detections

def matching_cascade(
        distance_metric, max_distance, cascade_depth, tracks, detections,
        track_indices=None, detection_indices=None):
    if track_indices is None:
        track_indices = list(range(len(tracks)))
    if detection_indices is None:
        detection_indices = list(range(len(detections)))

    unmatched_detections = detection_indices
    matches = []
    for level in range(cascade_depth):
        if len(unmatched_detections) == 0:  
            break

        track_indices_l = [
            k for k in track_indices
            if tracks[k].time_since_update == 1 + level
        ]
        if len(track_indices_l) == 0:  
            continue

        matches_l, _, unmatched_detections = \
            min_cost_matching(
                distance_metric, max_distance, tracks, detections,
                track_indices_l, unmatched_detections)
        matches += matches_l
    unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))
    return matches, unmatched_tracks, unmatched_detections

def gate_cost_matrix(
        kf, cost_matrix, tracks, detections, track_indices, detection_indices,
        gated_cost=INFTY_COST, only_position=False):
    gating_dim = 2 if only_position else 4
    gating_threshold = kalman_filter.chi2inv95[gating_dim]
    measurements = np.asarray(
        [detections[i].to_xyah() for i in detection_indices])
    for row, track_idx in enumerate(track_indices):
        track = tracks[track_idx]
        gating_distance = kf.gating_distance(
            track.mean, track.covariance, measurements, only_position)
        cost_matrix[row, gating_distance > gating_threshold] = gated_cost  # 设置为inf ,距离远的直接扩大到无穷
    return cost_matrix