140 lines
4.6 KiB
Python
140 lines
4.6 KiB
Python
import cv2
|
|
import numpy as np
|
|
import scipy
|
|
from scipy.spatial.distance import cdist
|
|
from sklearn.utils import linear_assignment_
|
|
|
|
from cython_bbox import bbox_overlaps as 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.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
|
|
|
|
|
|
def fuse_motion(kf, cost_matrix, tracks, detections, only_position=False, lambda_=0.98):
|
|
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
|
|
#print(cost_matrix[row])
|
|
#print(gating_distance)
|
|
#print('-'*90)
|
|
cost_matrix[row] = lambda_ * cost_matrix[row] + (1-lambda_)* gating_distance
|
|
return cost_matrix
|