123 lines
3.7 KiB
Python
123 lines
3.7 KiB
Python
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import cv2
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import numpy as np
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import scipy
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from scipy.spatial.distance import cdist
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from sklearn.utils import linear_assignment_
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from utils.cython_bbox import bbox_ious
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from utils import kalman_filter
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import time
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def merge_matches(m1, m2, shape):
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O,P,Q = shape
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m1 = np.asarray(m1)
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m2 = np.asarray(m2)
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M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
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M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
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mask = M1*M2
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match = mask.nonzero()
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match = list(zip(match[0], match[1]))
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unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
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unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
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return match, unmatched_O, unmatched_Q
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def _indices_to_matches(cost_matrix, indices, thresh):
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matched_cost = cost_matrix[tuple(zip(*indices))]
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matched_mask = (matched_cost <= thresh)
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matches = indices[matched_mask]
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unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
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unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
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return matches, unmatched_a, unmatched_b
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def linear_assignment(cost_matrix, thresh):
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"""
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Simple linear assignment
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:type cost_matrix: np.ndarray
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:type thresh: float
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:return: matches, unmatched_a, unmatched_b
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"""
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if cost_matrix.size == 0:
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return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
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cost_matrix[cost_matrix > thresh] = thresh + 1e-4
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indices = linear_assignment_.linear_assignment(cost_matrix)
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return _indices_to_matches(cost_matrix, indices, thresh)
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def ious(atlbrs, btlbrs):
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"""
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Compute cost based on IoU
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:type atlbrs: list[tlbr] | np.ndarray
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:type atlbrs: list[tlbr] | np.ndarray
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:rtype ious np.ndarray
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"""
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ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
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if ious.size == 0:
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return ious
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ious = bbox_ious(
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np.ascontiguousarray(atlbrs, dtype=np.float),
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np.ascontiguousarray(btlbrs, dtype=np.float)
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)
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return ious
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def iou_distance(atracks, btracks):
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"""
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Compute cost based on IoU
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:type atracks: list[STrack]
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:type btracks: list[STrack]
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:rtype cost_matrix np.ndarray
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"""
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if (len(atracks)>0 and isinstance(atracks[0], np.ndarray)) or (len(btracks) > 0 and isinstance(btracks[0], np.ndarray)):
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atlbrs = atracks
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btlbrs = btracks
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else:
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atlbrs = [track.tlbr for track in atracks]
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btlbrs = [track.tlbr for track in btracks]
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_ious = ious(atlbrs, btlbrs)
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cost_matrix = 1 - _ious
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return cost_matrix
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def embedding_distance(tracks, detections, metric='cosine'):
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"""
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:param tracks: list[STrack]
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:param detections: list[BaseTrack]
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:param metric:
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:return: cost_matrix np.ndarray
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"""
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cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
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if cost_matrix.size == 0:
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return cost_matrix
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det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
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for i, track in enumerate(tracks):
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cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
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return cost_matrix
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def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
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if cost_matrix.size == 0:
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return cost_matrix
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gating_dim = 2 if only_position else 4
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gating_threshold = kalman_filter.chi2inv95[gating_dim]
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measurements = np.asarray([det.to_xyah() for det in detections])
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for row, track in enumerate(tracks):
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gating_distance = kf.gating_distance(
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track.mean, track.covariance, measurements, only_position)
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cost_matrix[row, gating_distance > gating_threshold] = np.inf
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return cost_matrix
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