Towards-Realtime-MOT/tracker/matching.py

import cv2
import numpy as np
import scipy
from scipy.spatial.distance import cdist
from sklearn.utils import linear_assignment_

from utils.cython_bbox import bbox_ious
from utils import kalman_filter
import time

def merge_matches(m1, m2, shape):
    O,P,Q = shape
    m1 = np.asarray(m1)
    m2 = np.asarray(m2)

    M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
    M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))

    mask = M1*M2
    match = mask.nonzero()
    match = list(zip(match[0], match[1]))
    unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
    unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))

    return match, unmatched_O, unmatched_Q




def _indices_to_matches(cost_matrix, indices, thresh):
    matched_cost = cost_matrix[tuple(zip(*indices))]
    matched_mask = (matched_cost <= thresh)

    matches = indices[matched_mask]
    unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
    unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))

    return matches, unmatched_a, unmatched_b


def linear_assignment(cost_matrix, thresh):
    """
    Simple linear assignment
    :type cost_matrix: np.ndarray
    :type thresh: float
    :return: matches, unmatched_a, unmatched_b
    """
    if cost_matrix.size == 0:
        return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))

    cost_matrix[cost_matrix > thresh] = thresh + 1e-4
    indices = linear_assignment_.linear_assignment(cost_matrix)

    return _indices_to_matches(cost_matrix, indices, thresh)


def ious(atlbrs, btlbrs):
    """
    Compute cost based on IoU
    :type atlbrs: list[tlbr] | np.ndarray
    :type atlbrs: list[tlbr] | np.ndarray

    :rtype ious np.ndarray
    """
    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
    if ious.size == 0:
        return ious

    ious = bbox_ious(
        np.ascontiguousarray(atlbrs, dtype=np.float),
        np.ascontiguousarray(btlbrs, dtype=np.float)
    )

    return ious


def iou_distance(atracks, btracks):
    """
    Compute cost based on IoU
    :type atracks: list[STrack]
    :type btracks: list[STrack]

    :rtype cost_matrix np.ndarray
    """

    if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
        atlbrs = atracks
        btlbrs = btracks
    else:
        atlbrs = [track.tlbr for track in atracks]
        btlbrs = [track.tlbr for track in btracks]
    _ious = ious(atlbrs, btlbrs)
    cost_matrix = 1 - _ious

    return cost_matrix

#def embedding_distance(tracks, detections, metric='cosine'):
#    """
#    :param tracks: list[STrack]
#    :param detections: list[BaseTrack]
#    :param metric:
#    :return: cost_matrix np.ndarray
#    """
#
#    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
#    if cost_matrix.size == 0:
#        return cost_matrix
#    det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
#    for i, track in enumerate(tracks):
#        #cost_matrix[i, :] = np.maximum(0.0, cdist(track.features, det_features, metric).min(axis=0))
#        cost_matrix[i, :] = np.maximum(0.0, cdist(track.features, det_features, metric).min(axis=0))
#    return cost_matrix

def embedding_distance(tracks, detections, metric='cosine'):
    """
    :param tracks: list[STrack]
    :param detections: list[BaseTrack]
    :param metric:
    :return: cost_matrix np.ndarray
    """

    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
    if cost_matrix.size == 0:
        return cost_matrix
    det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
    for i, track in enumerate(tracks):
        cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
    return cost_matrix


def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
    if cost_matrix.size == 0:
        return cost_matrix
    gating_dim = 2 if only_position else 4
    gating_threshold = kalman_filter.chi2inv95[gating_dim]
    measurements = np.asarray([det.to_xyah() for det in detections])
    for row, track in enumerate(tracks):
        gating_distance = kf.gating_distance(
            track.mean, track.covariance, measurements, only_position)
        cost_matrix[row, gating_distance > gating_threshold] = np.inf
    return cost_matrix