Mercurial Hosting > traffic-intelligence
comparison scripts/classify-objects.py @ 935:0e63a918a1ca
updated classify-objects
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 14 Jul 2017 16:30:57 -0400 |
parents | 063d1267585d |
children | e5970606066f |
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934:39691b460fca | 935:0e63a918a1ca |
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72 | 72 |
73 capture = cv2.VideoCapture(videoFilename) | 73 capture = cv2.VideoCapture(videoFilename) |
74 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) | 74 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) |
75 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) | 75 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) |
76 | 76 |
77 if undistort: # setup undistortion | 77 #if undistort: # setup undistortion |
78 # [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) | 78 # [map1, map2] = cvutils.computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) |
79 # height, width = map1.shape | 79 # height, width = map1.shape |
80 newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication))) | 80 # newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication))) |
81 newCameraMatrix = cv2.getDefaultNewCameraMatrix(intrinsicCameraMatrix, newImgSize, True) | 81 # newCameraMatrix = cv2.getDefaultNewCameraMatrix(intrinsicCameraMatrix, newImgSize, True) |
82 else: | 82 #else: |
83 newCameraMatrix = None | 83 # newCameraMatrix = None |
84 | 84 |
85 pastObjects = [] | 85 pastObjects = [] |
86 currentObjects = [] | 86 currentObjects = [] |
87 if capture.isOpened(): | 87 if capture.isOpened(): |
88 ret = True | 88 ret = True |
98 print('frame number: {}'.format(frameNum)) | 98 print('frame number: {}'.format(frameNum)) |
99 #if undistort: | 99 #if undistort: |
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, newCameraMatrix, 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.remove(obj) |
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 |