506 lines
20 KiB
Python
506 lines
20 KiB
Python
from collections import deque
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import os
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torchsummary import summary
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from core.mot.general import non_max_suppression_and_inds, non_max_suppression_jde, non_max_suppression, scale_coords
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from core.mot.torch_utils import intersect_dicts
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from models.mot.cstrack import Model
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from mot_online import matching
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from mot_online.kalman_filter import KalmanFilter
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from mot_online.log import logger
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from mot_online.utils import *
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from mot_online.basetrack import BaseTrack, TrackState
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class STrack(BaseTrack):
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shared_kalman = KalmanFilter()
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def __init__(self, tlwh, score):
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# wait activate
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self._tlwh = np.asarray(tlwh, dtype=np.float)
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self.kalman_filter = None
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self.mean, self.covariance = None, None
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self.is_activated = False
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self.score = score
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self.tracklet_len = 0
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def predict(self):
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mean_state = self.mean.copy()
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if self.state != TrackState.Tracked:
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mean_state[7] = 0
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self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
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@staticmethod
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def multi_predict(stracks):
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if len(stracks) > 0:
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multi_mean = np.asarray([st.mean.copy() for st in stracks])
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multi_covariance = np.asarray([st.covariance for st in stracks])
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for i, st in enumerate(stracks):
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if st.state != TrackState.Tracked:
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multi_mean[i][7] = 0
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multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
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for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
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stracks[i].mean = mean
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stracks[i].covariance = cov
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def activate(self, kalman_filter, frame_id):
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"""Start a new tracklet"""
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self.kalman_filter = kalman_filter
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self.track_id = self.next_id()
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self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
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self.tracklet_len = 0
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self.state = TrackState.Tracked
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#self.is_activated = True
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self.frame_id = frame_id
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self.start_frame = frame_id
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def re_activate(self, new_track, frame_id, new_id=False):
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self.mean, self.covariance = self.kalman_filter.update(
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self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
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)
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self.tracklet_len = 0
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self.state = TrackState.Tracked
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self.is_activated = True
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self.frame_id = frame_id
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if new_id:
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self.track_id = self.next_id()
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def update(self, new_track, frame_id):
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"""
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Update a matched track
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:type new_track: STrack
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:type frame_id: int
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:type update_feature: bool
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:return:
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"""
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self.frame_id = frame_id
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self.tracklet_len += 1
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new_tlwh = new_track.tlwh
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self.mean, self.covariance = self.kalman_filter.update(
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self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
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self.state = TrackState.Tracked
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self.is_activated = True
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self.score = new_track.score
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@property
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# @jit(nopython=True)
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def tlwh(self):
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"""Get current position in bounding box format `(top left x, top left y,
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width, height)`.
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"""
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if self.mean is None:
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return self._tlwh.copy()
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ret = self.mean[:4].copy()
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ret[2] *= ret[3]
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ret[:2] -= ret[2:] / 2
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return ret
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@property
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# @jit(nopython=True)
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def tlbr(self):
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"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
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`(top left, bottom right)`.
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"""
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ret = self.tlwh.copy()
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ret[2:] += ret[:2]
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return ret
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@staticmethod
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# @jit(nopython=True)
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def tlwh_to_xyah(tlwh):
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"""Convert bounding box to format `(center x, center y, aspect ratio,
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height)`, where the aspect ratio is `width / height`.
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"""
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ret = np.asarray(tlwh).copy()
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ret[:2] += ret[2:] / 2
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ret[2] /= ret[3]
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return ret
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def to_xyah(self):
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return self.tlwh_to_xyah(self.tlwh)
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@staticmethod
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# @jit(nopython=True)
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def tlbr_to_tlwh(tlbr):
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ret = np.asarray(tlbr).copy()
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ret[2:] -= ret[:2]
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return ret
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@staticmethod
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# @jit(nopython=True)
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def tlwh_to_tlbr(tlwh):
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ret = np.asarray(tlwh).copy()
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ret[2:] += ret[:2]
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return ret
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def __repr__(self):
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return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
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class BYTETracker(object):
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def __init__(self, opt, frame_rate=30):
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self.opt = opt
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if int(opt.gpus[0]) >= 0:
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opt.device = torch.device('cuda')
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else:
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opt.device = torch.device('cpu')
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print('Creating model...')
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ckpt = torch.load(opt.weights, map_location=opt.device) # load checkpoint
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self.model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=1).to(opt.device) # create
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exclude = ['anchor'] if opt.cfg else [] # exclude keys
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if type(ckpt['model']).__name__ == "OrderedDict":
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state_dict = ckpt['model']
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else:
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state_dict = ckpt['model'].float().state_dict() # to FP32
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state_dict = intersect_dicts(state_dict, self.model.state_dict(), exclude=exclude) # intersect
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self.model.load_state_dict(state_dict, strict=False) # load
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self.model.cuda().eval()
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total_params = sum(p.numel() for p in self.model.parameters())
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print(f'{total_params:,} total parameters.')
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self.tracked_stracks = [] # type: list[STrack]
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self.lost_stracks = [] # type: list[STrack]
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self.removed_stracks = [] # type: list[STrack]
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self.frame_id = 0
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self.det_thresh = opt.conf_thres
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self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer)
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self.max_time_lost = self.buffer_size
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self.mean = np.array(opt.mean, dtype=np.float32).reshape(1, 1, 3)
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self.std = np.array(opt.std, dtype=np.float32).reshape(1, 1, 3)
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self.kalman_filter = KalmanFilter()
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self.low_thres = 0.1
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self.high_thres = self.opt.conf_thres + 0.1
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def update(self, im_blob, img0,seq_num, save_dir):
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self.frame_id += 1
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activated_starcks = []
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refind_stracks = []
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lost_stracks = []
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removed_stracks = []
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dets = []
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''' Step 1: Network forward, get detections & embeddings'''
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with torch.no_grad():
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output = self.model(im_blob, augment=False)
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pred, train_out = output[1]
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pred = pred[pred[:, :, 4] > self.low_thres]
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detections = []
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if len(pred) > 0:
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dets,x_inds,y_inds = non_max_suppression_and_inds(pred[:,:6].unsqueeze(0), 0.1, self.opt.nms_thres,method='cluster_diou')
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dets = dets.numpy()
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if len(dets) != 0:
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scale_coords(self.opt.img_size, dets[:, :4], img0.shape).round()
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remain_inds = dets[:, 4] > self.opt.conf_thres
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inds_low = dets[:, 4] > self.low_thres
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inds_high = dets[:, 4] < self.opt.conf_thres
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inds_second = np.logical_and(inds_low, inds_high)
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dets_second = dets[inds_second]
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dets = dets[remain_inds]
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detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4]) for
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tlbrs in dets[:, :5]]
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else:
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detections = []
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dets_second = []
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else:
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detections = []
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dets_second = []
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''' Add newly detected tracklets to tracked_stracks'''
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unconfirmed = []
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tracked_stracks = [] # type: list[STrack]
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for track in self.tracked_stracks:
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if not track.is_activated:
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unconfirmed.append(track)
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else:
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tracked_stracks.append(track)
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''' Step 2: First association, with embedding'''
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strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
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# Predict the current location with KF
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STrack.multi_predict(strack_pool)
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dists = matching.iou_distance(strack_pool, detections)
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matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.8)
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for itracked, idet in matches:
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track = strack_pool[itracked]
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det = detections[idet]
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if track.state == TrackState.Tracked:
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track.update(detections[idet], self.frame_id)
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activated_starcks.append(track)
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else:
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track.re_activate(det, self.frame_id, new_id=False)
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refind_stracks.append(track)
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# vis
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track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track = [],[],[],[],[]
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if self.opt.vis_state == 1 and self.frame_id % 20 == 0:
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if len(dets) != 0:
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for i in range(0, dets.shape[0]):
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bbox = dets[i][0:4]
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cv2.rectangle(img0, (int(bbox[0]), int(bbox[1])),(int(bbox[2]), int(bbox[3])),(0, 255, 0), 2)
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track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track = matching.vis_id_feature_A_distance(strack_pool, detections)
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vis_feature(self.frame_id,seq_num,img0,track_features,
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det_features, cost_matrix, cost_matrix_det, cost_matrix_track, max_num=5, out_path=save_dir)
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''' Step 3: Second association, with IOU'''
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# association the untrack to the low score detections
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if len(dets_second) > 0:
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detections_second = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4]) for
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tlbrs in dets_second[:, :5]]
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else:
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detections_second = []
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r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
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dists = matching.iou_distance(r_tracked_stracks, detections_second)
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matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.4)
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for itracked, idet in matches:
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track = r_tracked_stracks[itracked]
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det = detections_second[idet]
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if track.state == TrackState.Tracked:
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track.update(det, self.frame_id)
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activated_starcks.append(track)
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else:
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track.re_activate(det, self.frame_id, new_id=False)
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refind_stracks.append(track)
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for it in u_track:
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track = r_tracked_stracks[it]
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if not track.state == TrackState.Lost:
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track.mark_lost()
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lost_stracks.append(track)
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'''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
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detections = [detections[i] for i in u_detection]
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dists = matching.iou_distance(unconfirmed, detections)
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matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
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for itracked, idet in matches:
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unconfirmed[itracked].update(detections[idet], self.frame_id)
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activated_starcks.append(unconfirmed[itracked])
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for it in u_unconfirmed:
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track = unconfirmed[it]
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track.mark_removed()
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removed_stracks.append(track)
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""" Step 4: Init new stracks"""
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for inew in u_detection:
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track = detections[inew]
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if track.score < self.high_thres:
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continue
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track.activate(self.kalman_filter, self.frame_id)
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activated_starcks.append(track)
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""" Step 5: Update state"""
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for track in self.lost_stracks:
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if self.frame_id - track.end_frame > self.max_time_lost:
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track.mark_removed()
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removed_stracks.append(track)
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# print('Ramained match {} s'.format(t4-t3))
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self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
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self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
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self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
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self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
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self.lost_stracks.extend(lost_stracks)
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self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
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self.removed_stracks.extend(removed_stracks)
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self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
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# get scores of lost tracks
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output_stracks = [track for track in self.tracked_stracks if track.is_activated]
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logger.debug('===========Frame {}=========='.format(self.frame_id))
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logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks]))
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logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks]))
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logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))
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logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))
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return output_stracks
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def joint_stracks(tlista, tlistb):
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exists = {}
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res = []
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for t in tlista:
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exists[t.track_id] = 1
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res.append(t)
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for t in tlistb:
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tid = t.track_id
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if not exists.get(tid, 0):
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exists[tid] = 1
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res.append(t)
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return res
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def sub_stracks(tlista, tlistb):
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stracks = {}
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for t in tlista:
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stracks[t.track_id] = t
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for t in tlistb:
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tid = t.track_id
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if stracks.get(tid, 0):
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del stracks[tid]
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return list(stracks.values())
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def remove_duplicate_stracks(stracksa, stracksb):
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pdist = matching.iou_distance(stracksa, stracksb)
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pairs = np.where(pdist < 0.15)
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dupa, dupb = list(), list()
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for p, q in zip(*pairs):
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timep = stracksa[p].frame_id - stracksa[p].start_frame
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timeq = stracksb[q].frame_id - stracksb[q].start_frame
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if timep > timeq:
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dupb.append(q)
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else:
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dupa.append(p)
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resa = [t for i, t in enumerate(stracksa) if not i in dupa]
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resb = [t for i, t in enumerate(stracksb) if not i in dupb]
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return resa, resb
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def vis_feature(frame_id,seq_num,img,track_features, det_features, cost_matrix, cost_matrix_det, cost_matrix_track,max_num=5, out_path='/home/XX/'):
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num_zero = ["0000","000","00","0"]
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img = cv2.resize(img, (778, 435))
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if len(det_features) != 0:
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max_f = det_features.max()
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min_f = det_features.min()
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det_features = np.round((det_features - min_f) / (max_f - min_f) * 255)
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det_features = det_features.astype(np.uint8)
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d_F_M = []
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cutpff_line = [40]*512
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for d_f in det_features:
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for row in range(45):
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d_F_M += [[40]*3+d_f.tolist()+[40]*3]
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for row in range(3):
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d_F_M += [[40]*3+cutpff_line+[40]*3]
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d_F_M = np.array(d_F_M)
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d_F_M = d_F_M.astype(np.uint8)
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det_features_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
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feature_img2 = cv2.resize(det_features_img, (435, 435))
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#cv2.putText(feature_img2, "det_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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else:
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feature_img2 = np.zeros((435, 435))
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feature_img2 = feature_img2.astype(np.uint8)
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feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
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#cv2.putText(feature_img2, "det_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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feature_img = np.concatenate((img, feature_img2), axis=1)
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if len(cost_matrix_det) != 0 and len(cost_matrix_det[0]) != 0:
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max_f = cost_matrix_det.max()
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min_f = cost_matrix_det.min()
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cost_matrix_det = np.round((cost_matrix_det - min_f) / (max_f - min_f) * 255)
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d_F_M = []
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cutpff_line = [40]*len(cost_matrix_det)*10
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for c_m in cost_matrix_det:
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add = []
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for row in range(len(c_m)):
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add += [255-c_m[row]]*10
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for row in range(10):
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d_F_M += [[40]+add+[40]]
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d_F_M = np.array(d_F_M)
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d_F_M = d_F_M.astype(np.uint8)
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cost_matrix_det_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
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feature_img2 = cv2.resize(cost_matrix_det_img, (435, 435))
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#cv2.putText(feature_img2, "cost_matrix_det", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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else:
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feature_img2 = np.zeros((435, 435))
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feature_img2 = feature_img2.astype(np.uint8)
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feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
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#cv2.putText(feature_img2, "cost_matrix_det", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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feature_img = np.concatenate((feature_img, feature_img2), axis=1)
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if len(track_features) != 0:
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max_f = track_features.max()
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min_f = track_features.min()
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track_features = np.round((track_features - min_f) / (max_f - min_f) * 255)
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track_features = track_features.astype(np.uint8)
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d_F_M = []
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cutpff_line = [40]*512
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for d_f in track_features:
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for row in range(45):
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d_F_M += [[40]*3+d_f.tolist()+[40]*3]
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for row in range(3):
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d_F_M += [[40]*3+cutpff_line+[40]*3]
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d_F_M = np.array(d_F_M)
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d_F_M = d_F_M.astype(np.uint8)
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track_features_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
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feature_img2 = cv2.resize(track_features_img, (435, 435))
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#cv2.putText(feature_img2, "track_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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else:
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feature_img2 = np.zeros((435, 435))
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feature_img2 = feature_img2.astype(np.uint8)
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feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
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#cv2.putText(feature_img2, "track_features", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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feature_img = np.concatenate((feature_img, feature_img2), axis=1)
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|
|
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if len(cost_matrix_track) != 0 and len(cost_matrix_track[0]) != 0:
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max_f = cost_matrix_track.max()
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min_f = cost_matrix_track.min()
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cost_matrix_track = np.round((cost_matrix_track - min_f) / (max_f - min_f) * 255)
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d_F_M = []
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|
cutpff_line = [40]*len(cost_matrix_track)*10
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for c_m in cost_matrix_track:
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add = []
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for row in range(len(c_m)):
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add += [255-c_m[row]]*10
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|
for row in range(10):
|
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d_F_M += [[40]+add+[40]]
|
|
d_F_M = np.array(d_F_M)
|
|
d_F_M = d_F_M.astype(np.uint8)
|
|
cost_matrix_track_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
|
|
feature_img2 = cv2.resize(cost_matrix_track_img, (435, 435))
|
|
#cv2.putText(feature_img2, "cost_matrix_track", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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|
else:
|
|
feature_img2 = np.zeros((435, 435))
|
|
feature_img2 = feature_img2.astype(np.uint8)
|
|
feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
|
|
#cv2.putText(feature_img2, "cost_matrix_track", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
|
|
feature_img = np.concatenate((feature_img, feature_img2), axis=1)
|
|
|
|
if len(cost_matrix) != 0 and len(cost_matrix[0]) != 0:
|
|
max_f = cost_matrix.max()
|
|
min_f = cost_matrix.min()
|
|
cost_matrix = np.round((cost_matrix - min_f) / (max_f - min_f) * 255)
|
|
d_F_M = []
|
|
cutpff_line = [40]*len(cost_matrix[0])*10
|
|
for c_m in cost_matrix:
|
|
add = []
|
|
for row in range(len(c_m)):
|
|
add += [255-c_m[row]]*10
|
|
for row in range(10):
|
|
d_F_M += [[40]+add+[40]]
|
|
d_F_M = np.array(d_F_M)
|
|
d_F_M = d_F_M.astype(np.uint8)
|
|
cost_matrix_img = cv2.applyColorMap(d_F_M, cv2.COLORMAP_JET)
|
|
feature_img2 = cv2.resize(cost_matrix_img, (435, 435))
|
|
#cv2.putText(feature_img2, "cost_matrix", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
|
|
else:
|
|
feature_img2 = np.zeros((435, 435))
|
|
feature_img2 = feature_img2.astype(np.uint8)
|
|
feature_img2 = cv2.applyColorMap(feature_img2, cv2.COLORMAP_JET)
|
|
#cv2.putText(feature_img2, "cost_matrix", (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
|
|
feature_img = np.concatenate((feature_img, feature_img2), axis=1)
|
|
|
|
dst_path = out_path + "/" + seq_num + "_" + num_zero[len(str(frame_id))-1] + str(frame_id) + '.png'
|
|
cv2.imwrite(dst_path, feature_img)
|