Integrate ByteTracker and BoT-SORT trackers (#788)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

ultralytics/tracker/utils/matching.py

@@ -0,0 +1,188 @@
+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 = [], [], []
+    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))  # Nomalized 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)