| """
|
| This script is adopted from the SORT script by Alex Bewley alex@bewley.ai
|
| """
|
| from __future__ import print_function
|
|
|
| import numpy as np
|
| from .association import *
|
| from .cmc import GMC
|
|
|
|
|
| def k_previous_obs(observations, cur_age, k):
|
| if len(observations) == 0:
|
| return [-1, -1, -1, -1, -1]
|
| for i in range(k):
|
| dt = k - i
|
| if cur_age - dt in observations:
|
| return observations[cur_age - dt]
|
| max_age = max(observations.keys())
|
| return observations[max_age]
|
|
|
|
|
| def convert_bbox_to_z(bbox):
|
| """
|
| Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
|
| [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
|
| the aspect ratio
|
| """
|
| w = bbox[2] - bbox[0]
|
| h = bbox[3] - bbox[1]
|
| x = bbox[0] + w / 2.
|
| y = bbox[1] + h / 2.
|
| s = w * h
|
| r = w / float(h + 1e-6)
|
| return np.array([x, y, s, r]).reshape((4, 1))
|
|
|
|
|
| def convert_x_to_bbox(x, score=None):
|
| """
|
| Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
|
| [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
|
| """
|
| w = np.sqrt(x[2] * x[3])
|
| h = x[2] / w
|
| if (score == None):
|
| return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4))
|
| else:
|
| return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5))
|
|
|
|
|
| def speed_direction(bbox1, bbox2):
|
| cx1, cy1 = (bbox1[0] + bbox1[2]) / 2.0, (bbox1[1] + bbox1[3]) / 2.0
|
| cx2, cy2 = (bbox2[0] + bbox2[2]) / 2.0, (bbox2[1] + bbox2[3]) / 2.0
|
| speed = np.array([cy2 - cy1, cx2 - cx1])
|
| norm = np.sqrt((cy2 - cy1) ** 2 + (cx2 - cx1) ** 2) + 1e-6
|
| return speed / norm
|
|
|
|
|
| class KalmanBoxTracker(object):
|
| """
|
| This class represents the internal state of individual tracked objects observed as bbox.
|
| """
|
| count = 0
|
|
|
| def __init__(self, bbox, delta_t=3, orig=False, use_gmc=True):
|
| """
|
| Initialises a tracker using initial bounding box.
|
|
|
| """
|
|
|
| if not orig:
|
| from .kalmanfilter import KalmanFilterNew as KalmanFilter
|
| self.kf = KalmanFilter(dim_x=7, dim_z=4)
|
| else:
|
| from filterpy.kalman import KalmanFilter
|
| self.kf = KalmanFilter(dim_x=7, dim_z=4)
|
| self.kf.F = np.array([[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1], [
|
| 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1]])
|
| self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0],
|
| [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]])
|
|
|
| self.kf.R[2:, 2:] *= 10.
|
| self.kf.P[4:, 4:] *= 1000.
|
| self.kf.P *= 10.
|
| self.kf.Q[-1, -1] *= 0.01
|
| self.kf.Q[4:, 4:] *= 0.01
|
|
|
| self.kf.x[:4] = convert_bbox_to_z(bbox)
|
| self.time_since_update = 0
|
| self.id = KalmanBoxTracker.count
|
| KalmanBoxTracker.count += 1
|
| self.history = []
|
| self.hits = 0
|
| self.hit_streak = 0
|
| self.age = 0
|
| """
|
| NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of
|
| function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a
|
| fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]), let's bear it for now.
|
| """
|
| self.last_observation = np.array([-1, -1, -1, -1, -1])
|
| self.observations = dict()
|
| self.history_observations = []
|
| self.velocity = None
|
| self.delta_t = delta_t
|
| self.use_gmc = use_gmc
|
|
|
| def update(self, bbox):
|
| """
|
| Updates the state vector with observed bbox.
|
| """
|
| if bbox is not None:
|
| if self.last_observation.sum() >= 0:
|
| previous_box = None
|
| for i in range(self.delta_t):
|
| dt = self.delta_t - i
|
| if self.age - dt in self.observations:
|
| previous_box = self.observations[self.age - dt]
|
| break
|
| if previous_box is None:
|
| previous_box = self.last_observation
|
| """
|
| Estimate the track speed direction with observations \Delta t steps away
|
| """
|
| self.velocity = speed_direction(previous_box, bbox)
|
|
|
| """
|
| Insert new observations. This is a ugly way to maintain both self.observations
|
| and self.history_observations. Bear it for the moment.
|
| """
|
| self.last_observation = bbox
|
| self.observations[self.age] = bbox
|
| self.history_observations.append(bbox)
|
|
|
| self.time_since_update = 0
|
| self.history = []
|
| self.hits += 1
|
| self.hit_streak += 1
|
| self.kf.update(convert_bbox_to_z(bbox))
|
| else:
|
| self.kf.update(bbox)
|
|
|
| def predict(self, H=np.eye(2, 3)):
|
| """
|
| Advances the state vector and returns the predicted bounding box estimate.
|
| """
|
| if ((self.kf.x[6] + self.kf.x[2]) <= 0):
|
| self.kf.x[6] *= 0.0
|
|
|
| self.kf.predict()
|
| if self.use_gmc:
|
|
|
| state_org = self.kf.x
|
| P = self.kf.P
|
| a11, a12, tx = H[0]
|
| a21, a22, ty = H[1]
|
|
|
| J = np.array([
|
| [a11, a12, 0, 0, 0, 0, 0],
|
| [a21, a22, 0, 0, 0, 0, 0],
|
| [0, 0, abs(a11 * a22 - a21 * a12), 0, 0, 0, 0],
|
| [0, 0, 0, 1, 0, 0, 0],
|
| [0, 0, 0, 0, a11, a12, 0],
|
| [0, 0, 0, 0, a21, a22, 0],
|
| [0, 0, 0, 0, 0, 0, abs(a11 * a22 - a21 * a12)]
|
| ])
|
| self.kf.x = np.dot(J, state_org) + np.array([[tx], [ty], [0], [0], [0], [0], [0]])
|
| self.kf.P = J @ P @ J.T
|
|
|
| self.age += 1
|
| if (self.time_since_update > 0):
|
| self.hit_streak = 0
|
| self.time_since_update += 1
|
| self.history.append(convert_x_to_bbox(self.kf.x))
|
| return self.history[-1]
|
|
|
| def get_state(self):
|
| """
|
| Returns the current bounding box estimate.
|
| """
|
| return convert_x_to_bbox(self.kf.x)
|
|
|
|
|
| """
|
| We support multiple ways for association cost calculation, by default
|
| we use IoU. GIoU may have better performance in some situations. We note
|
| that we hardly normalize the cost by all methods to (0,1) which may not be
|
| the best practice.
|
| """
|
| ASSO_FUNCS = {"iou": iou_batch,
|
| "giou": giou_batch,
|
| "ciou": ciou_batch,
|
| "diou": diou_batch,
|
| "ct_dist": ct_dist}
|
|
|
|
|
| class Track:
|
| def __init__(self, track_id, tlrb, score):
|
| self.track_id = track_id
|
| self.tlwh = [tlrb[0], tlrb[1], tlrb[2] - tlrb[0], tlrb[3] - tlrb[1]]
|
| self.score = score
|
|
|
|
|
| class OCSort(object):
|
| def __init__(self, det_thresh, max_age=30, min_hits=3,
|
| iou_threshold=0.3, delta_t=3, asso_func="iou", inertia=0.2, use_byte=True, use_gmc=True):
|
| """
|
| Sets key parameters for SORT
|
| """
|
| self.max_age = max_age
|
| self.min_hits = min_hits
|
| self.iou_threshold = iou_threshold
|
| self.trackers = []
|
| self.frame_count = 0
|
| self.det_thresh = det_thresh
|
| self.delta_t = delta_t
|
| self.asso_func = ASSO_FUNCS[asso_func]
|
| self.inertia = inertia
|
| self.use_byte = use_byte
|
| KalmanBoxTracker.count = 0
|
| self.gmc = GMC(method="cmc", verbose=None)
|
| self.use_gmc = use_gmc
|
|
|
| def update(self, fdets, frame):
|
| """
|
| Params:
|
| dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
|
| Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
|
| Returns the a similar array, where the last column is the object ID.
|
| NOTE: The number of objects returned may differ from the number of detections provided.
|
| """
|
|
|
| bboxes, scores = fdets[:, 0:4], fdets[:, 4]
|
| dets = np.concatenate((bboxes, np.expand_dims(scores, axis=-1)), axis=1)
|
| inds_low = scores > 0.1
|
| if len(scores) > 1:
|
| det_thresh = scores[np.argmin(np.diff(scores))]
|
| if det_thresh < self.det_thresh:
|
| det_thresh = self.det_thresh
|
| else:
|
| det_thresh = self.det_thresh
|
|
|
| H = None
|
| if self.use_gmc:
|
| H = self.gmc.applyCMC(frame)
|
| inds_high = scores <= det_thresh
|
| inds_second = np.logical_and(inds_low, inds_high)
|
| dets_second = dets[inds_second]
|
| remain_inds = scores > det_thresh
|
| dets = dets[remain_inds]
|
|
|
|
|
| trks = np.zeros((len(self.trackers), 5))
|
| to_del = []
|
| ret = []
|
| for t, trk in enumerate(trks):
|
| pos = self.trackers[t].predict(H)[0]
|
| trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
|
| if np.any(np.isnan(pos)):
|
| to_del.append(t)
|
|
|
| trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
|
| for t in reversed(to_del):
|
| self.trackers.pop(t)
|
|
|
| velocities = np.array(
|
| [trk.velocity if trk.velocity is not None else np.array((0, 0)) for trk in self.trackers])
|
| last_boxes = np.array([trk.last_observation for trk in self.trackers])
|
| k_observations = np.array(
|
| [k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])
|
|
|
| """
|
| First round of association
|
| """
|
| matched, unmatched_dets, unmatched_trks = associate(
|
| self.asso_func, dets, trks, self.iou_threshold, velocities, k_observations, self.inertia)
|
| for m in matched:
|
| self.trackers[m[1]].update(dets[m[0], :])
|
|
|
| """
|
| Second round of associaton by OCR
|
| """
|
|
|
| if self.use_byte and len(dets_second) > 0 and unmatched_trks.shape[0] > 0:
|
| u_trks = trks[unmatched_trks]
|
| iou_left = self.asso_func(dets_second, u_trks)
|
| iou_left = np.array(iou_left)
|
| if iou_left.max() > self.iou_threshold:
|
| """
|
| NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
|
| get a higher performance especially on MOT17/MOT20 datasets. But we keep it
|
| uniform here for simplicity
|
| """
|
| matched_indices = linear_assignment(-iou_left)
|
| to_remove_trk_indices = []
|
| for m in matched_indices:
|
| det_ind, trk_ind = m[0], unmatched_trks[m[1]]
|
| if iou_left[m[0], m[1]] < self.iou_threshold:
|
| continue
|
| self.trackers[trk_ind].update(dets_second[det_ind, :])
|
| to_remove_trk_indices.append(trk_ind)
|
| unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))
|
|
|
| if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:
|
| left_dets = dets[unmatched_dets]
|
| left_trks = last_boxes[unmatched_trks]
|
| iou_left = self.asso_func(left_dets, left_trks)
|
| iou_left = np.array(iou_left)
|
| if iou_left.max() > self.iou_threshold:
|
| """
|
| NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
|
| get a higher performance especially on MOT17/MOT20 datasets. But we keep it
|
| uniform here for simplicity
|
| """
|
| rematched_indices = linear_assignment(-iou_left)
|
| to_remove_det_indices = []
|
| to_remove_trk_indices = []
|
| for m in rematched_indices:
|
| det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]
|
| if iou_left[m[0], m[1]] < self.iou_threshold:
|
| continue
|
| self.trackers[trk_ind].update(dets[det_ind, :])
|
| to_remove_det_indices.append(det_ind)
|
| to_remove_trk_indices.append(trk_ind)
|
| unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))
|
| unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))
|
|
|
| for m in unmatched_trks:
|
| self.trackers[m].update(None)
|
|
|
|
|
| for i in unmatched_dets:
|
| trk = KalmanBoxTracker(dets[i, :], delta_t=self.delta_t, use_gmc=self.use_gmc)
|
| self.trackers.append(trk)
|
| i = len(self.trackers)
|
| for trk in reversed(self.trackers):
|
| if trk.last_observation.sum() < 0:
|
| d = trk.get_state()[0]
|
| else:
|
| """
|
| this is optional to use the recent observation or the kalman filter prediction,
|
| we didn't notice significant difference here
|
| """
|
| d = trk.last_observation[:4]
|
| if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
|
|
|
| ret.append(Track(trk.id + 1, d, 1))
|
| i -= 1
|
|
|
| if (trk.time_since_update > self.max_age):
|
| self.trackers.pop(i)
|
| return ret
|
|
|
