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comparison scripts/dltrack.py @ 1250:77fbd0e2ba7d

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dltrack works with moving average filtering
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 15 Feb 2024 22:04:35 -0500
parents 2aa56b101041
children dff5b678a33a
comparison
equal deleted inserted replaced
1249:2aa56b101041 1250:77fbd0e2ba7d
57 lastFrameNum = params.lastFrameNum 57 lastFrameNum = params.lastFrameNum
58 else: 58 else:
59 lastFrameNum = args.lastFrameNum 59 lastFrameNum = args.lastFrameNum
60 if args.maskFilename is not None: 60 if args.maskFilename is not None:
61 mask = cv2.imread(args.maskFilename, cv2.IMREAD_GRAYSCALE) 61 mask = cv2.imread(args.maskFilename, cv2.IMREAD_GRAYSCALE)
62 elif params.maskFilename is not None: 62 elif params is not None and params.maskFilename is not None:
63 mask = cv2.imread(params.maskFilename, cv2.IMREAD_GRAYSCALE) 63 mask = cv2.imread(params.maskFilename, cv2.IMREAD_GRAYSCALE)
64 else: 64 else:
65 mask = None 65 mask = None
66 if params is not None:
67 smoothingHalfWidth = params.smoothingHalfWidth
68 else:
69 smoothingHalfWidth = None
66 70
67 # TODO use mask, remove short objects, smooth 71 # TODO use mask, remove short objects, smooth
68 72
69 # TODO add option to refine position with mask for vehicles, to save different positions 73 # TODO add option to refine position with mask for vehicles, to save different positions
70 # TODO work with optical flow (farneback or RAFT) https://pytorch.org/vision/main/models/raft.html 74 # TODO work with optical flow (farneback or RAFT) https://pytorch.org/vision/main/models/raft.html
133 results = model.track(source=frame, persist=True) 137 results = model.track(source=frame, persist=True)
134 capture.release() 138 capture.release()
135 cv2.destroyAllWindows() 139 cv2.destroyAllWindows()
136 140
137 # classification 141 # classification
142 shortObjectNumbers = []
138 for num, obj in objects.items(): 143 for num, obj in objects.items():
139 obj.setUserType(utils.mostCommon(obj.userTypes)) # improve? mix with speed? 144 if obj.length() < 3:
140 145 shortObjectNumbers.append(num)
146 else:
147 obj.setUserType(utils.mostCommon(obj.userTypes)) # improve? mix with speed?
148 for num in shortObjectNumbers:
149 del objects[num]
141 # TODO add quality control: avoid U-turns 150 # TODO add quality control: avoid U-turns
142 151
143 # merge bikes and people 152 # merge bikes and people
144 twowheels = [num for num, obj in objects.items() if obj.getUserType() in (moving.userType2Num['motorcyclist'],moving.userType2Num['cyclist'])] 153 twowheels = [num for num, obj in objects.items() if obj.getUserType() in (moving.userType2Num['motorcyclist'],moving.userType2Num['cyclist'])]
145 pedestrians = [num for num, obj in objects.items() if obj.getUserType() == moving.userType2Num['pedestrian']] 154 pedestrians = [num for num, obj in objects.items() if obj.getUserType() == moving.userType2Num['pedestrian']]
240 #t = (moving.Trajectory.add(t1, t2)*0.5).asArray() 249 #t = (moving.Trajectory.add(t1, t2)*0.5).asArray()
241 projected = cvutils.imageToWorldProject(np.array(t).T, intrinsicCameraMatrix, distortionCoefficients, homography) 250 projected = cvutils.imageToWorldProject(np.array(t).T, intrinsicCameraMatrix, distortionCoefficients, homography)
242 featureNum = features[0].getNum() 251 featureNum = features[0].getNum()
243 obj.features=[moving.MovingObject(featureNum, obj.getTimeInterval(), moving.Trajectory(projected.tolist()))] 252 obj.features=[moving.MovingObject(featureNum, obj.getTimeInterval(), moving.Trajectory(projected.tolist()))]
244 obj.featureNumbers = [featureNum] 253 obj.featureNumbers = [featureNum]
254 if smoothingHalfWidth is not None: # smoothing
255 for num, obj in objects.items():
256 for f in obj.getFeatures():
257 f.positions = f.getPositions().filterMovingWindow(smoothingHalfWidth)
245 storage.saveTrajectoriesToSqlite(args.databaseFilename, list(objects.values()), 'object') 258 storage.saveTrajectoriesToSqlite(args.databaseFilename, list(objects.values()), 'object')
259
260
246 261
247 # todo save bbox and mask to study localization / representation 262 # todo save bbox and mask to study localization / representation
248 # apply quality checks deviation and acceleration bounds? 263 # apply quality checks deviation and acceleration bounds?
249 264
250 # def mergeBBoxes(tw, ped): 265 # def mergeBBoxes(tw, ped):