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Cleanup tracker and remove unused functions (#4374)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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3 changed files with 16 additions and 218 deletions
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74
ultralytics/trackers/utils/kalman_filter.py
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@ -3,10 +3,6 @@
import numpy as np
import scipy.linalg
# Table for the 0.95 quantile of the chi-square distribution with N degrees of freedom (contains values for N=1, ..., 9)
# Taken from MATLAB/Octave's chi2inv function and used as Mahalanobis gating threshold.
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}
class KalmanFilterXYAH:
"""
@ -235,7 +231,7 @@ class KalmanFilterXYAH:
raise ValueError('invalid distance metric')
class KalmanFilterXYWH:
class KalmanFilterXYWH(KalmanFilterXYAH):
"""
For BoT-SORT
A simple Kalman filter for tracking bounding boxes in image space.
@ -253,22 +249,6 @@ class KalmanFilterXYWH:
"""
def __init__(self):
"""Initialize Kalman filter model matrices with motion and observation uncertainties."""
ndim, dt = 4, 1.
# Create Kalman filter model matrices.
self._motion_mat = np.eye(2 * ndim, 2 * ndim)
for i in range(ndim):
self._motion_mat[i, ndim + i] = dt
self._update_mat = np.eye(ndim, 2 * ndim)
# Motion and observation uncertainty are chosen relative to the current
# state estimate. These weights control the amount of uncertainty in
# the model. This is a bit hacky.
self._std_weight_position = 1. / 20
self._std_weight_velocity = 1. / 160
def initiate(self, measurement):
"""Create track from unassociated measurement.
@ -409,54 +389,4 @@ class KalmanFilterXYWH:
Returns the measurement-corrected state distribution.
"""
projected_mean, projected_cov = self.project(mean, covariance)
chol_factor, lower = scipy.linalg.cho_factor(projected_cov, lower=True, check_finite=False)
kalman_gain = scipy.linalg.cho_solve((chol_factor, lower),
np.dot(covariance, self._update_mat.T).T,
check_finite=False).T
innovation = measurement - projected_mean
new_mean = mean + np.dot(innovation, kalman_gain.T)
new_covariance = covariance - np.linalg.multi_dot((kalman_gain, projected_cov, kalman_gain.T))
return new_mean, new_covariance
def gating_distance(self, mean, covariance, measurements, only_position=False, metric='maha'):
"""Compute gating distance between state distribution and measurements.
A suitable distance threshold can be obtained from `chi2inv95`. If
`only_position` is False, the chi-square distribution has 4 degrees of
freedom, otherwise 2.
Parameters
----------
mean : ndarray
Mean vector over the state distribution (8 dimensional).
covariance : ndarray
Covariance of the state distribution (8x8 dimensional).
measurements : ndarray
An Nx4 dimensional matrix of N measurements, each in
format (x, y, a, h) where (x, y) is the bounding box center
position, a the aspect ratio, and h the height.
only_position : Optional[bool]
If True, distance computation is done with respect to the bounding
box center position only.
Returns
-------
ndarray
Returns an array of length N, where the i-th element contains the
squared Mahalanobis distance between (mean, covariance) and
`measurements[i]`.
"""
mean, covariance = self.project(mean, covariance)
if only_position:
mean, covariance = mean[:2], covariance[:2, :2]
measurements = measurements[:, :2]
d = measurements - mean
if metric == 'gaussian':
return np.sum(d * d, axis=1)
elif metric == 'maha':
cholesky_factor = np.linalg.cholesky(covariance)
z = scipy.linalg.solve_triangular(cholesky_factor, d.T, lower=True, check_finite=False, overwrite_b=True)
return np.sum(z * z, axis=0) # square maha
else:
raise ValueError('invalid distance metric')
return super().update(mean, covariance, measurement)

150
ultralytics/trackers/utils/matching.py
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@ -4,7 +4,7 @@ import numpy as np
import scipy
from scipy.spatial.distance import cdist
from .kalman_filter import chi2inv95
from ultralytics.utils.metrics import bbox_ioa
try:
import lap # for linear_assignment
@ -17,36 +17,6 @@ except (ImportError, AssertionError, AttributeError):
import lap
def merge_matches(m1, m2, shape):
"""Merge two sets of matches and return matched and unmatched indices."""
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):
"""Return matched and unmatched indices given a cost matrix, indices, and a threshold."""
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, use_lap=True):
"""Linear assignment implementations with scipy and lap.lapjv."""
if cost_matrix.size == 0:
@ -70,26 +40,6 @@ def linear_assignment(cost_matrix, thresh, use_lap=True):
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))
# TODO: replace bbox_ious() with numpy-capable update of utils.metrics.box_iou
# from ...utils.metrics import box_iou
# ious = box_iou()
return ious
def iou_distance(atracks, btracks):
"""
Compute cost based on IoU
@ -106,26 +56,13 @@ def iou_distance(atracks, btracks):
else:
atlbrs = [track.tlbr for track in atracks]
btlbrs = [track.tlbr for track in btracks]
return 1 - ious(atlbrs, btlbrs) # 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]
return 1 - ious(atlbrs, btlbrs) # cost matrix
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
if len(atlbrs) and len(btlbrs):
ious = bbox_ioa(np.ascontiguousarray(atlbrs, dtype=np.float32),
np.ascontiguousarray(btlbrs, dtype=np.float32),
iou=True)
return 1 - ious # cost matrix
def embedding_distance(tracks, detections, metric='cosine'):
@ -147,46 +84,6 @@ def embedding_distance(tracks, detections, metric='cosine'):
return cost_matrix
def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
"""Apply gating to the cost matrix based on predicted tracks and detected objects."""
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):
"""Fuse motion between tracks and detections with gating and Kalman filtering."""
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):
"""Fuses ReID and IoU similarity matrices to yield a cost matrix for object tracking."""
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):
"""Fuses cost matrix with detection scores to produce a single similarity matrix."""
if cost_matrix.size == 0:
@ -196,36 +93,3 @@ def fuse_score(cost_matrix, 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):
"""
Calculate the Intersection over Union (IoU) between pairs of bounding boxes.
Args:
box1 (np.array): A numpy array of shape (n, 4) representing 'n' bounding boxes.
Each row is in the format (x1, y1, x2, y2).
box2 (np.array): A numpy array of shape (m, 4) representing 'm' bounding boxes.
Each row is in the format (x1, y1, x2, y2).
eps (float, optional): A small constant to prevent division by zero. Defaults to 1e-7.
Returns:
(np.array): A numpy array of shape (n, m) representing the IoU scores for each pair
of bounding boxes from box1 and box2.
Note:
The bounding box coordinates are expected to be in the format (x1, y1, x2, y2).
"""
# 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)

10
ultralytics/utils/metrics.py
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@ -21,13 +21,14 @@ def box_area(box):
return (box[2] - box[0]) * (box[3] - box[1])
def bbox_ioa(box1, box2, eps=1e-7):
def bbox_ioa(box1, box2, iou=False, eps=1e-7):
"""
Calculate the intersection over box2 area given box1 and box2. Boxes are in x1y1x2y2 format.
Args:
box1 (np.array): A numpy array of shape (n, 4) representing n bounding boxes.
box2 (np.array): A numpy array of shape (m, 4) representing m bounding boxes.
iou (bool): Calculate the standard iou if True else return inter_area/box2_area.
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
Returns:
@ -43,10 +44,13 @@ def bbox_ioa(box1, box2, eps=1e-7):
(np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
# box2 area
box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
if iou:
box1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
area = area + box1_area[:, None] - inter_area
# Intersection over box2 area
return inter_area / box2_area
return inter_area / (area + eps)
def box_iou(box1, box2, eps=1e-7):

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