Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/classify-objects.py @ 946:e5970606066f

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bug fix on list filtering (cannot remove while iterating) and motion prediction keeping the same features
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 21 Jul 2017 11:25:20 -0400
parents 0e63a918a1ca
children 053484e08947
comparison
equal deleted inserted replaced
945:05d4302bf67e 946:e5970606066f
100 # img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) 100 # img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR)
101 for obj in objects: 101 for obj in objects:
102 if obj.getFirstInstant() <= frameNum: # if images are skipped 102 if obj.getFirstInstant() <= frameNum: # if images are skipped
103 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds, invHomography, intrinsicCameraMatrix, distortionCoefficients) 103 obj.initClassifyUserTypeHoGSVM(speedAggregationFunc, pedBikeCarSVM, bikeCarSVM, classifierParams.maxPedestrianSpeed, classifierParams.maxCyclistSpeed, classifierParams.nFramesIgnoreAtEnds, invHomography, intrinsicCameraMatrix, distortionCoefficients)
104 currentObjects.append(obj) 104 currentObjects.append(obj)
105 objects.remove(obj) 105 objects[:] = [obj for obj in objects if obj.getFirstInstant() > frameNum]
106 106
107 for obj in currentObjects: 107 for obj in currentObjects:
108 if obj.getLastInstant() <= frameNum: # if images are skipped 108 if obj.getLastInstant() <= frameNum: # if images are skipped
109 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown) 109 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
110 pastObjects.append(obj) 110 pastObjects.append(obj)
111 currentObjects.remove(obj)
112 else: 111 else:
113 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm) 112 obj.classifyUserTypeHoGSVMAtInstant(img, frameNum, width, height, classifierParams.percentIncreaseCrop, classifierParams.percentIncreaseCrop, classifierParams.minNPixels, classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogNCellsPerBlock, classifierParams.hogBlockNorm)
113 currentObjects[:] = [obj for obj in objects if obj.getLastInstant() > frameNum]
114 frameNum += 1 114 frameNum += 1
115 115
116 for obj in currentObjects: 116 for obj in currentObjects:
117 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown) 117 obj.classifyUserTypeHoGSVM(minSpeedEquiprobable = classifierParams.minSpeedEquiprobable, speedProbabilities = speedProbabilities, maxPercentUnknown = classifierParams.maxPercentUnknown)
118 pastObjects.append(obj) 118 pastObjects.append(obj)