You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
194 lines
6.7 KiB
194 lines
6.7 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
|
|
|
|
import lap
|
|
import numpy as np
|
|
import scipy
|
|
from scipy.spatial.distance import cdist
|
|
|
|
from .kalman_filter import chi2inv95
|
|
|
|
|
|
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)) - {i for i, j in match})
|
|
unmatched_Q = tuple(set(range(Q)) - {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):
|
|
if cost_matrix.size == 0:
|
|
return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
|
|
matches, unmatched_a, unmatched_b = [], [], []
|
|
|
|
# TODO: investigate scipy.optimize.linear_sum_assignment() for lap.lapjv()
|
|
cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
|
|
|
|
matches.extend([ix, mx] for ix, mx in enumerate(x) if mx >= 0)
|
|
unmatched_a = np.where(x < 0)[0]
|
|
unmatched_b = np.where(y < 0)[0]
|
|
matches = np.asarray(matches)
|
|
return matches, unmatched_a, unmatched_b
|
|
|
|
|
|
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.float32)
|
|
if ious.size == 0:
|
|
return ious
|
|
|
|
ious = bbox_ious(np.ascontiguousarray(atlbrs, dtype=np.float32), np.ascontiguousarray(btlbrs, dtype=np.float32))
|
|
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)
|
|
return 1 - _ious # cost matrix
|
|
|
|
|
|
def v_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.tlwh_to_tlbr(track.pred_bbox) for track in atracks]
|
|
btlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in btracks]
|
|
_ious = ious(atlbrs, btlbrs)
|
|
return 1 - _ious # 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.float32)
|
|
if cost_matrix.size == 0:
|
|
return cost_matrix
|
|
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float32)
|
|
# for i, track in enumerate(tracks):
|
|
# cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
|
|
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float32)
|
|
cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Normalized features
|
|
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 = 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 = 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, metric='maha')
|
|
cost_matrix[row, gating_distance > gating_threshold] = np.inf
|
|
cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_) * gating_distance
|
|
return cost_matrix
|
|
|
|
|
|
def fuse_iou(cost_matrix, tracks, detections):
|
|
if cost_matrix.size == 0:
|
|
return cost_matrix
|
|
reid_sim = 1 - cost_matrix
|
|
iou_dist = iou_distance(tracks, detections)
|
|
iou_sim = 1 - iou_dist
|
|
fuse_sim = reid_sim * (1 + iou_sim) / 2
|
|
# det_scores = np.array([det.score for det in detections])
|
|
# det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
|
|
return 1 - fuse_sim # fuse cost
|
|
|
|
|
|
def fuse_score(cost_matrix, detections):
|
|
if cost_matrix.size == 0:
|
|
return cost_matrix
|
|
iou_sim = 1 - cost_matrix
|
|
det_scores = np.array([det.score for det in detections])
|
|
det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
|
|
fuse_sim = iou_sim * det_scores
|
|
return 1 - fuse_sim # fuse_cost
|
|
|
|
|
|
def bbox_ious(box1, box2, eps=1e-7):
|
|
"""Boxes are x1y1x2y2
|
|
box1: np.array of shape(nx4)
|
|
box2: np.array of shape(mx4)
|
|
returns: np.array of shape(nxm)
|
|
"""
|
|
# Get the coordinates of bounding boxes
|
|
b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
|
|
b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
|
|
|
|
# Intersection area
|
|
inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * \
|
|
(np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
|
|
|
|
# box2 area
|
|
box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
|
|
box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
|
|
return inter_area / (box2_area + box1_area[:, None] - inter_area + eps)
|