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230 lines
8.5 KiB
230 lines
8.5 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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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 .kalman_filter import chi2inv95
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try:
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import lap # for linear_assignment
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assert lap.__version__ # verify package is not directory
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except (ImportError, AssertionError, AttributeError):
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from ultralytics.yolo.utils.checks import check_requirements
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check_requirements('lapx>=0.5.2') # update to lap package from https://github.com/rathaROG/lapx
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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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"""_indices_to_matches: 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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return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
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if use_lap:
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_, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
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matches = [[ix, mx] for ix, mx in enumerate(x) if mx >= 0]
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unmatched_a = np.where(x < 0)[0]
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unmatched_b = np.where(y < 0)[0]
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else:
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# Scipy linear sum assignment is NOT working correctly, DO NOT USE
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y, x = scipy.optimize.linear_sum_assignment(cost_matrix) # row y, col x
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matches = np.asarray([[i, x] for i, x in enumerate(x) if cost_matrix[i, x] <= thresh])
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unmatched = np.ones(cost_matrix.shape)
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for i, xi in matches:
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unmatched[i, xi] = 0.0
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unmatched_a = np.where(unmatched.all(1))[0]
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unmatched_b = np.where(unmatched.all(0))[0]
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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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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)) \
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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.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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return 1 - _ious # 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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_ious = ious(atlbrs, btlbrs)
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return 1 - _ious # 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.float32)
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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.float32)
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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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track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float32)
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cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Normalized features
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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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return cost_matrix
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iou_sim = 1 - cost_matrix
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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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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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