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Cleanup tracker and remove unused functions (#4374)
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@ -3,10 +3,6 @@
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import numpy as np
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import scipy.linalg
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# Table for the 0.95 quantile of the chi-square distribution with N degrees of freedom (contains values for N=1, ..., 9)
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# Taken from MATLAB/Octave's chi2inv function and used as Mahalanobis gating threshold.
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chi2inv95 = {1: 3.8415, 2: 5.9915, 3: 7.8147, 4: 9.4877, 5: 11.070, 6: 12.592, 7: 14.067, 8: 15.507, 9: 16.919}
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class KalmanFilterXYAH:
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"""
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@ -235,7 +231,7 @@ class KalmanFilterXYAH:
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raise ValueError('invalid distance metric')
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class KalmanFilterXYWH:
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class KalmanFilterXYWH(KalmanFilterXYAH):
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"""
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For BoT-SORT
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A simple Kalman filter for tracking bounding boxes in image space.
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@ -253,22 +249,6 @@ class KalmanFilterXYWH:
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"""
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def __init__(self):
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"""Initialize Kalman filter model matrices with motion and observation uncertainties."""
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ndim, dt = 4, 1.
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# Create Kalman filter model matrices.
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self._motion_mat = np.eye(2 * ndim, 2 * ndim)
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for i in range(ndim):
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self._motion_mat[i, ndim + i] = dt
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self._update_mat = np.eye(ndim, 2 * ndim)
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# Motion and observation uncertainty are chosen relative to the current
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# state estimate. These weights control the amount of uncertainty in
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# the model. This is a bit hacky.
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self._std_weight_position = 1. / 20
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self._std_weight_velocity = 1. / 160
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def initiate(self, measurement):
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"""Create track from unassociated measurement.
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@ -409,54 +389,4 @@ class KalmanFilterXYWH:
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Returns the measurement-corrected state distribution.
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"""
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projected_mean, projected_cov = self.project(mean, covariance)
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chol_factor, lower = scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)
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kalman_gain = scipy.linalg.cho_solve((chol_factor, lower),
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np.dot(covariance, self._update_mat.T).T,
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check_finite=False).T
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innovation = measurement - projected_mean
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new_mean = mean + np.dot(innovation, kalman_gain.T)
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new_covariance = covariance - np.linalg.multi_dot((kalman_gain, projected_cov, kalman_gain.T))
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return new_mean, new_covariance
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def gating_distance(self, mean, covariance, measurements, only_position=False, metric='maha'):
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"""Compute gating distance between state distribution and measurements.
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A suitable distance threshold can be obtained from `chi2inv95`. If
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`only_position` is False, the chi-square distribution has 4 degrees of
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freedom, otherwise 2.
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Parameters
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----------
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mean : ndarray
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Mean vector over the state distribution (8 dimensional).
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covariance : ndarray
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Covariance of the state distribution (8x8 dimensional).
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measurements : ndarray
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An Nx4 dimensional matrix of N measurements, each in
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format (x, y, a, h) where (x, y) is the bounding box center
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position, a the aspect ratio, and h the height.
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only_position : Optional[bool]
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If True, distance computation is done with respect to the bounding
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box center position only.
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Returns
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-------
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ndarray
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Returns an array of length N, where the i-th element contains the
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squared Mahalanobis distance between (mean, covariance) and
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`measurements[i]`.
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"""
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mean, covariance = self.project(mean, covariance)
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if only_position:
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mean, covariance = mean[:2], covariance[:2, :2]
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measurements = measurements[:, :2]
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d = measurements - mean
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if metric == 'gaussian':
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return np.sum(d * d, axis=1)
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elif metric == 'maha':
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cholesky_factor = np.linalg.cholesky(covariance)
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z = scipy.linalg.solve_triangular(cholesky_factor, d.T, lower=True, check_finite=False, overwrite_b=True)
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return np.sum(z * z, axis=0) # square maha
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else:
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raise ValueError('invalid distance metric')
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return super().update(mean, covariance, measurement)
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@ -4,7 +4,7 @@ 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 .kalman_filter import chi2inv95
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from ultralytics.utils.metrics import bbox_ioa
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try:
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import lap # for linear_assignment
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@ -17,36 +17,6 @@ except (ImportError, AssertionError, AttributeError):
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import lap
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def merge_matches(m1, m2, shape):
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"""Merge two sets of matches and return matched and unmatched indices."""
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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)) - {i for i, j in match})
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unmatched_Q = tuple(set(range(Q)) - {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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"""Return matched and unmatched indices given a cost matrix, indices, and a threshold."""
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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, use_lap=True):
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"""Linear assignment implementations with scipy and lap.lapjv."""
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if cost_matrix.size == 0:
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@ -70,26 +40,6 @@ def linear_assignment(cost_matrix, thresh, use_lap=True):
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return matches, unmatched_a, unmatched_b
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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.float32)
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if ious.size == 0:
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return ious
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ious = bbox_ious(np.ascontiguousarray(atlbrs, dtype=np.float32), np.ascontiguousarray(btlbrs, dtype=np.float32))
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# TODO: replace bbox_ious() with numpy-capable update of utils.metrics.box_iou
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# from ...utils.metrics import box_iou
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# ious = box_iou()
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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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@ -106,26 +56,13 @@ def iou_distance(atracks, 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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return 1 - ious(atlbrs, btlbrs) # cost matrix
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def v_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)) \
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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.tlwh_to_tlbr(track.pred_bbox) for track in atracks]
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btlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in btracks]
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return 1 - ious(atlbrs, btlbrs) # cost matrix
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ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
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if len(atlbrs) and len(btlbrs):
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ious = bbox_ioa(np.ascontiguousarray(atlbrs, dtype=np.float32),
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np.ascontiguousarray(btlbrs, dtype=np.float32),
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iou=True)
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return 1 - ious # cost matrix
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def embedding_distance(tracks, detections, metric='cosine'):
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@ -147,46 +84,6 @@ def embedding_distance(tracks, detections, metric='cosine'):
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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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"""Apply gating to the cost matrix based on predicted tracks and detected objects."""
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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 = 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(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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def fuse_motion(kf, cost_matrix, tracks, detections, only_position=False, lambda_=0.98):
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"""Fuse motion between tracks and detections with gating and Kalman filtering."""
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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 = 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(track.mean, track.covariance, measurements, only_position, metric='maha')
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cost_matrix[row, gating_distance > gating_threshold] = np.inf
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cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_) * gating_distance
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return cost_matrix
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def fuse_iou(cost_matrix, tracks, detections):
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"""Fuses ReID and IoU similarity matrices to yield a cost matrix for object tracking."""
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if cost_matrix.size == 0:
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return cost_matrix
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reid_sim = 1 - cost_matrix
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iou_dist = iou_distance(tracks, detections)
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iou_sim = 1 - iou_dist
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fuse_sim = reid_sim * (1 + iou_sim) / 2
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# det_scores = np.array([det.score for det in detections])
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# det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
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return 1 - fuse_sim # fuse cost
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def fuse_score(cost_matrix, detections):
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"""Fuses cost matrix with detection scores to produce a single similarity matrix."""
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if cost_matrix.size == 0:
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@ -196,36 +93,3 @@ def fuse_score(cost_matrix, detections):
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det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
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fuse_sim = iou_sim * det_scores
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return 1 - fuse_sim # fuse_cost
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def bbox_ious(box1, box2, eps=1e-7):
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"""
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Calculate the Intersection over Union (IoU) between pairs of bounding boxes.
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Args:
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box1 (np.array): A numpy array of shape (n, 4) representing 'n' bounding boxes.
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Each row is in the format (x1, y1, x2, y2).
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box2 (np.array): A numpy array of shape (m, 4) representing 'm' bounding boxes.
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Each row is in the format (x1, y1, x2, y2).
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eps (float, optional): A small constant to prevent division by zero. Defaults to 1e-7.
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Returns:
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(np.array): A numpy array of shape (n, m) representing the IoU scores for each pair
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of bounding boxes from box1 and box2.
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Note:
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The bounding box coordinates are expected to be in the format (x1, y1, x2, y2).
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"""
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# Get the coordinates of bounding boxes
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b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
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b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
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# Intersection area
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inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * \
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(np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
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# box2 area
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box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
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box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
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return inter_area / (box2_area + box1_area[:, None] - inter_area + eps)
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@ -21,13 +21,14 @@ def box_area(box):
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return (box[2] - box[0]) * (box[3] - box[1])
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def bbox_ioa(box1, box2, eps=1e-7):
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def bbox_ioa(box1, box2, iou=False, eps=1e-7):
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"""
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Calculate the intersection over box2 area given box1 and box2. Boxes are in x1y1x2y2 format.
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Args:
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box1 (np.array): A numpy array of shape (n, 4) representing n bounding boxes.
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box2 (np.array): A numpy array of shape (m, 4) representing m bounding boxes.
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iou (bool): Calculate the standard iou if True else return inter_area/box2_area.
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eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
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Returns:
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@ -43,10 +44,13 @@ def bbox_ioa(box1, box2, eps=1e-7):
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(np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
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# box2 area
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box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
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area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
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if iou:
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box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
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area = area + box1_area[:, None] - inter_area
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# Intersection over box2 area
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return inter_area / box2_area
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return inter_area / (area + eps)
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def box_iou(box1, box2, eps=1e-7):
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