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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is based on https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py
"""
try:
import lap
except:
print(
'Warning: Unable to use JDE/FairMOT/ByteTrack, please install lap, for example: `pip install lap`, see https://github.com/gatagat/lap'
)
pass
import scipy
import numpy as np
from scipy.spatial.distance import cdist
from ..motion import kalman_filter
import warnings
warnings.filterwarnings("ignore")
__all__ = [
'merge_matches',
'linear_assignment',
'bbox_ious',
'iou_distance',
'embedding_distance',
'fuse_motion',
]
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 linear_assignment(cost_matrix, thresh):
try:
import lap
except Exception as e:
raise RuntimeError(
'Unable to use JDE/FairMOT/ByteTrack, please install lap, for example: `pip install lap`, see https://github.com/gatagat/lap'
)
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 = [], [], []
cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
for ix, mx in enumerate(x):
if mx >= 0:
matches.append([ix, mx])
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 bbox_ious(atlbrs, btlbrs):
boxes = np.ascontiguousarray(atlbrs, dtype=np.float32)
query_boxes = np.ascontiguousarray(btlbrs, dtype=np.float32)
N = boxes.shape[0]
K = query_boxes.shape[0]
ious = np.zeros((N, K), dtype=boxes.dtype)
if N * K == 0:
return ious
for k in range(K):
box_area = ((query_boxes[k, 2] - query_boxes[k, 0] + 1) *
(query_boxes[k, 3] - query_boxes[k, 1] + 1))
for n in range(N):
iw = (min(boxes[n, 2], query_boxes[k, 2]) - max(
boxes[n, 0], query_boxes[k, 0]) + 1)
if iw > 0:
ih = (min(boxes[n, 3], query_boxes[k, 3]) - max(
boxes[n, 1], query_boxes[k, 1]) + 1)
if ih > 0:
ua = float((boxes[n, 2] - boxes[n, 0] + 1) * (boxes[
n, 3] - boxes[n, 1] + 1) + box_area - iw * ih)
ious[n, k] = iw * ih / ua
return ious
def iou_distance(atracks, btracks):
"""
Compute cost based on IoU between two list[STrack].
"""
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 = bbox_ious(atlbrs, btlbrs)
cost_matrix = 1 - _ious
return cost_matrix
def embedding_distance(tracks, detections, metric='euclidean'):
"""
Compute cost based on features between two list[STrack].
"""
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)
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)) # Nomalized features
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,
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
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