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432 lines
18 KiB
432 lines
18 KiB
6 months ago
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"""
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This script is adopted from the SORT script by Alex Bewley alex@bewley.ai
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"""
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from __future__ import print_function
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import numpy as np
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from .association import *
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def k_previous_obs(observations, cur_age, k):
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if len(observations) == 0:
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return [-1, -1, -1, -1, -1]
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for i in range(k):
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dt = k - i
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if cur_age - dt in observations:
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return observations[cur_age-dt]
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max_age = max(observations.keys())
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return observations[max_age]
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def convert_bbox_to_z(bbox):
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"""
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Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
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[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
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the aspect ratio
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"""
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w = bbox[2] - bbox[0]
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h = bbox[3] - bbox[1]
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x = bbox[0] + w/2.
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y = bbox[1] + h/2.
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s = w * h # scale is just area
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r = w / float(h+1e-6)
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return np.array([x, y, s, r]).reshape((4, 1))
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def convert_x_to_bbox(x, score=None):
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"""
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Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
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[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
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"""
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w = np.sqrt(x[2] * x[3])
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h = x[2] / w
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if(score == None):
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return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2.]).reshape((1, 4))
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else:
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return np.array([x[0]-w/2., x[1]-h/2., x[0]+w/2., x[1]+h/2., score]).reshape((1, 5))
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def speed_direction(bbox1, bbox2):
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cx1, cy1 = (bbox1[0]+bbox1[2]) / 2.0, (bbox1[1]+bbox1[3])/2.0
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cx2, cy2 = (bbox2[0]+bbox2[2]) / 2.0, (bbox2[1]+bbox2[3])/2.0
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speed = np.array([cy2-cy1, cx2-cx1])
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norm = np.sqrt((cy2-cy1)**2 + (cx2-cx1)**2) + 1e-6
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return speed / norm
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class KalmanBoxTracker(object):
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"""
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This class represents the internal state of individual tracked objects observed as bbox.
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"""
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count = 0
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def __init__(self, bbox, delta_t=3, orig=False):
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"""
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Initialises a tracker using initial bounding box.
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"""
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# define constant velocity model
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if not orig:
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from .kalmanfilter import KalmanFilterNew as KalmanFilter
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self.kf = KalmanFilter(dim_x=7, dim_z=4)
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else:
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from filterpy.kalman import KalmanFilter
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self.kf = KalmanFilter(dim_x=7, dim_z=4)
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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], [
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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]])
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self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0],
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[0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]])
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self.kf.R[2:, 2:] *= 10.
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self.kf.P[4:, 4:] *= 1000. # give high uncertainty to the unobservable initial velocities
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self.kf.P *= 10.
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self.kf.Q[-1, -1] *= 0.01
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self.kf.Q[4:, 4:] *= 0.01
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self.kf.x[:4] = convert_bbox_to_z(bbox)
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self.time_since_update = 0
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self.id = KalmanBoxTracker.count
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KalmanBoxTracker.count += 1
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self.history = []
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self.hits = 0
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self.hit_streak = 0
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self.age = 0
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"""
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NOTE: [-1,-1,-1,-1,-1] is a compromising placeholder for non-observation status, the same for the return of
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function k_previous_obs. It is ugly and I do not like it. But to support generate observation array in a
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fast and unified way, which you would see below k_observations = np.array([k_previous_obs(...]]), let's bear it for now.
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"""
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self.last_observation = np.array([-1, -1, -1, -1, -1]) # placeholder
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self.observations = dict()
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self.history_observations = []
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self.velocity = None
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self.delta_t = delta_t
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def update(self, bbox):
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"""
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Updates the state vector with observed bbox.
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"""
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if bbox is not None:
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if self.last_observation.sum() >= 0: # no previous observation
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previous_box = None
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for i in range(self.delta_t):
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dt = self.delta_t - i
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if self.age - dt in self.observations:
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previous_box = self.observations[self.age-dt]
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break
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if previous_box is None:
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previous_box = self.last_observation
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"""
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Estimate the track speed direction with observations \Delta t steps away
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"""
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self.velocity = speed_direction(previous_box, bbox)
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"""
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Insert new observations. This is a ugly way to maintain both self.observations
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and self.history_observations. Bear it for the moment.
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"""
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self.last_observation = bbox
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self.observations[self.age] = bbox
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self.history_observations.append(bbox)
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self.time_since_update = 0
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self.history = []
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self.hits += 1
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self.hit_streak += 1
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self.kf.update(convert_bbox_to_z(bbox))
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else:
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self.kf.update(bbox)
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def predict(self):
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"""
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Advances the state vector and returns the predicted bounding box estimate.
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"""
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if((self.kf.x[6]+self.kf.x[2]) <= 0):
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self.kf.x[6] *= 0.0
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self.kf.predict()
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self.age += 1
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if(self.time_since_update > 0):
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self.hit_streak = 0
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self.time_since_update += 1
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self.history.append(convert_x_to_bbox(self.kf.x))
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return self.history[-1]
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def get_state(self):
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"""
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Returns the current bounding box estimate.
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"""
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return convert_x_to_bbox(self.kf.x)
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"""
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We support multiple ways for association cost calculation, by default
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we use IoU. GIoU may have better performance in some situations. We note
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that we hardly normalize the cost by all methods to (0,1) which may not be
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the best practice.
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"""
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ASSO_FUNCS = { "iou": iou_batch,
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"giou": giou_batch,
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"ciou": ciou_batch,
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"diou": diou_batch,
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"ct_dist": ct_dist}
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class OCSort(object):
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def __init__(self, det_thresh, max_age=30, min_hits=3,
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iou_threshold=0.3, delta_t=3, asso_func="iou", inertia=0.2, use_byte=False):
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"""
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Sets key parameters for SORT
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"""
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self.max_age = max_age
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self.min_hits = min_hits
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self.iou_threshold = iou_threshold
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self.trackers = []
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self.frame_count = 0
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self.det_thresh = det_thresh
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self.delta_t = delta_t
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self.asso_func = ASSO_FUNCS[asso_func]
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self.inertia = inertia
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self.use_byte = use_byte
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KalmanBoxTracker.count = 0
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def update(self, output_results, img_info, img_size):
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"""
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Params:
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dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
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Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
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Returns the a similar array, where the last column is the object ID.
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NOTE: The number of objects returned may differ from the number of detections provided.
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"""
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if output_results is None:
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return np.empty((0, 5))
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self.frame_count += 1
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# post_process detections
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if output_results.shape[1] == 5:
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scores = output_results[:, 4]
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bboxes = output_results[:, :4]
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else:
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output_results = output_results.cpu().numpy()
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scores = output_results[:, 4] * output_results[:, 5]
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bboxes = output_results[:, :4] # x1y1x2y2
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img_h, img_w = img_info[0], img_info[1]
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scale = min(img_size[0] / float(img_h), img_size[1] / float(img_w))
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bboxes /= scale
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dets = np.concatenate((bboxes, np.expand_dims(scores, axis=-1)), axis=1)
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inds_low = scores > 0.1
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inds_high = scores < self.det_thresh
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inds_second = np.logical_and(inds_low, inds_high) # self.det_thresh > score > 0.1, for second matching
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dets_second = dets[inds_second] # detections for second matching
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remain_inds = scores > self.det_thresh
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dets = dets[remain_inds]
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# get predicted locations from existing trackers.
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trks = np.zeros((len(self.trackers), 5))
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to_del = []
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ret = []
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for t, trk in enumerate(trks):
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pos = self.trackers[t].predict()[0]
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trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
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if np.any(np.isnan(pos)):
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to_del.append(t)
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trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
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for t in reversed(to_del):
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self.trackers.pop(t)
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velocities = np.array(
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[trk.velocity if trk.velocity is not None else np.array((0, 0)) for trk in self.trackers])
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last_boxes = np.array([trk.last_observation for trk in self.trackers])
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k_observations = np.array(
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[k_previous_obs(trk.observations, trk.age, self.delta_t) for trk in self.trackers])
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"""
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First round of association
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"""
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matched, unmatched_dets, unmatched_trks = associate(
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dets, trks, self.iou_threshold, velocities, k_observations, self.inertia)
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for m in matched:
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self.trackers[m[1]].update(dets[m[0], :])
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"""
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Second round of associaton by OCR
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"""
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# BYTE association
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if self.use_byte and len(dets_second) > 0 and unmatched_trks.shape[0] > 0:
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u_trks = trks[unmatched_trks]
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iou_left = self.asso_func(dets_second, u_trks) # iou between low score detections and unmatched tracks
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iou_left = np.array(iou_left)
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if iou_left.max() > self.iou_threshold:
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"""
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NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
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get a higher performance especially on MOT17/MOT20 datasets. But we keep it
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uniform here for simplicity
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"""
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matched_indices = linear_assignment(-iou_left)
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to_remove_trk_indices = []
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for m in matched_indices:
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det_ind, trk_ind = m[0], unmatched_trks[m[1]]
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if iou_left[m[0], m[1]] < self.iou_threshold:
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continue
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self.trackers[trk_ind].update(dets_second[det_ind, :])
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to_remove_trk_indices.append(trk_ind)
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unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))
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if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:
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left_dets = dets[unmatched_dets]
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left_trks = last_boxes[unmatched_trks]
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iou_left = self.asso_func(left_dets, left_trks)
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iou_left = np.array(iou_left)
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if iou_left.max() > self.iou_threshold:
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"""
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NOTE: by using a lower threshold, e.g., self.iou_threshold - 0.1, you may
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get a higher performance especially on MOT17/MOT20 datasets. But we keep it
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uniform here for simplicity
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"""
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rematched_indices = linear_assignment(-iou_left)
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to_remove_det_indices = []
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to_remove_trk_indices = []
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for m in rematched_indices:
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det_ind, trk_ind = unmatched_dets[m[0]], unmatched_trks[m[1]]
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if iou_left[m[0], m[1]] < self.iou_threshold:
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continue
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self.trackers[trk_ind].update(dets[det_ind, :])
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to_remove_det_indices.append(det_ind)
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to_remove_trk_indices.append(trk_ind)
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unmatched_dets = np.setdiff1d(unmatched_dets, np.array(to_remove_det_indices))
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unmatched_trks = np.setdiff1d(unmatched_trks, np.array(to_remove_trk_indices))
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for m in unmatched_trks:
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self.trackers[m].update(None)
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# create and initialise new trackers for unmatched detections
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for i in unmatched_dets:
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trk = KalmanBoxTracker(dets[i, :], delta_t=self.delta_t)
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self.trackers.append(trk)
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i = len(self.trackers)
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for trk in reversed(self.trackers):
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if trk.last_observation.sum() < 0:
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d = trk.get_state()[0]
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else:
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"""
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this is optional to use the recent observation or the kalman filter prediction,
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we didn't notice significant difference here
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"""
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d = trk.last_observation[:4]
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if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
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# +1 as MOT benchmark requires positive
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ret.append(np.concatenate((d, [trk.id+1])).reshape(1, -1))
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i -= 1
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# remove dead tracklet
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if(trk.time_since_update > self.max_age):
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self.trackers.pop(i)
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|
if(len(ret) > 0):
|
||
|
|
return np.concatenate(ret)
|
||
|
|
return np.empty((0, 5))
|
||
|
|
|
||
|
|
def update_public(self, dets, cates, scores):
|
||
|
|
self.frame_count += 1
|
||
|
|
|
||
|
|
det_scores = np.ones((dets.shape[0], 1))
|
||
|
|
dets = np.concatenate((dets, det_scores), axis=1)
|
||
|
|
|
||
|
|
remain_inds = scores > self.det_thresh
|
||
|
|
|
||
|
|
cates = cates[remain_inds]
|
||
|
|
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()[0]
|
||
|
|
cat = self.trackers[t].cate
|
||
|
|
trk[:] = [pos[0], pos[1], pos[2], pos[3], cat]
|
||
|
|
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])
|
||
|
|
|
||
|
|
matched, unmatched_dets, unmatched_trks = associate_kitti\
|
||
|
|
(dets, trks, cates, self.iou_threshold, velocities, k_observations, self.inertia)
|
||
|
|
|
||
|
|
for m in matched:
|
||
|
|
self.trackers[m[1]].update(dets[m[0], :])
|
||
|
|
|
||
|
|
if unmatched_dets.shape[0] > 0 and unmatched_trks.shape[0] > 0:
|
||
|
|
"""
|
||
|
|
The re-association stage by OCR.
|
||
|
|
NOTE: at this stage, adding other strategy might be able to continue improve
|
||
|
|
the performance, such as BYTE association by ByteTrack.
|
||
|
|
"""
|
||
|
|
left_dets = dets[unmatched_dets]
|
||
|
|
left_trks = last_boxes[unmatched_trks]
|
||
|
|
left_dets_c = left_dets.copy()
|
||
|
|
left_trks_c = left_trks.copy()
|
||
|
|
|
||
|
|
iou_left = self.asso_func(left_dets_c, left_trks_c)
|
||
|
|
iou_left = np.array(iou_left)
|
||
|
|
det_cates_left = cates[unmatched_dets]
|
||
|
|
trk_cates_left = trks[unmatched_trks][:,4]
|
||
|
|
num_dets = unmatched_dets.shape[0]
|
||
|
|
num_trks = unmatched_trks.shape[0]
|
||
|
|
cate_matrix = np.zeros((num_dets, num_trks))
|
||
|
|
for i in range(num_dets):
|
||
|
|
for j in range(num_trks):
|
||
|
|
if det_cates_left[i] != trk_cates_left[j]:
|
||
|
|
"""
|
||
|
|
For some datasets, such as KITTI, there are different categories,
|
||
|
|
we have to avoid associate them together.
|
||
|
|
"""
|
||
|
|
cate_matrix[i][j] = -1e6
|
||
|
|
iou_left = iou_left + cate_matrix
|
||
|
|
if iou_left.max() > self.iou_threshold - 0.1:
|
||
|
|
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 - 0.1:
|
||
|
|
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 i in unmatched_dets:
|
||
|
|
trk = KalmanBoxTracker(dets[i,:])
|
||
|
|
trk.cate = cates[i]
|
||
|
|
self.trackers.append(trk)
|
||
|
|
i = len(self.trackers)
|
||
|
|
|
||
|
|
for trk in reversed(self.trackers):
|
||
|
|
if trk.last_observation.sum() > 0:
|
||
|
|
d = trk.last_observation[:4]
|
||
|
|
else:
|
||
|
|
d = trk.get_state()[0]
|
||
|
|
if (trk.time_since_update < 1):
|
||
|
|
if (self.frame_count <= self.min_hits) or (trk.hit_streak >= self.min_hits):
|
||
|
|
# id+1 as MOT benchmark requires positive
|
||
|
|
ret.append(np.concatenate((d, [trk.id+1], [trk.cate], [0])).reshape(1,-1))
|
||
|
|
if trk.hit_streak == self.min_hits:
|
||
|
|
# Head Padding (HP): recover the lost steps during initializing the track
|
||
|
|
for prev_i in range(self.min_hits - 1):
|
||
|
|
prev_observation = trk.history_observations[-(prev_i+2)]
|
||
|
|
ret.append((np.concatenate((prev_observation[:4], [trk.id+1], [trk.cate],
|
||
|
|
[-(prev_i+1)]))).reshape(1,-1))
|
||
|
|
i -= 1
|
||
|
|
if (trk.time_since_update > self.max_age):
|
||
|
|
self.trackers.pop(i)
|
||
|
|
|
||
|
|
if(len(ret)>0):
|
||
|
|
return np.concatenate(ret)
|
||
|
|
return np.empty((0, 7))
|
||
|
|
|
||
|
|
|