Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/classify-objects.py @ 788:5b970a5bc233 dev

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updated classifying code to OpenCV 3.x (bug in function to load classification models)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 24 Mar 2016 16:37:37 -0400
parents de278c5e65f6
children 1158a6e2d28e
comparison
equal deleted inserted replaced
787:0a428b449b80 788:5b970a5bc233
2 2
3 import cvutils, moving, ml, storage 3 import cvutils, moving, ml, storage
4 4
5 import numpy as np 5 import numpy as np
6 import sys, argparse 6 import sys, argparse
7 #from cv2 import SVM_RBF, SVM_C_SVC 7 from cv2 import SVM_RBF, SVM_C_SVC
8 import cv2 8 import cv2
9 from scipy.stats import norm, lognorm 9 from scipy.stats import norm, lognorm
10 10
11 # TODO add mode detection live 11 # TODO add mode detection live, add choice of kernel and svm type (to be saved in future classifier format)
12 12
13 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') 13 parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene')
14 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) 14 parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True)
15 parser.add_argument('--kernel', dest = 'kernelType', help = 'kernel type for the support vector machine (SVM)', default = SVM_RBF, type = long)
16 parser.add_argument('--svm', dest = 'svmType', help = 'SVM type', default = SVM_C_SVC, type = long)
15 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)') 17 parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file (overrides the configuration file)')
16 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)') 18 parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file (overrides the configuration file)')
17 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None) 19 parser.add_argument('-n', dest = 'nObjects', help = 'number of objects to classify', type = int, default = None)
18 parser.add_argument('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true') 20 parser.add_argument('--plot-speed-distributions', dest = 'plotSpeedDistribution', help = 'simply plots the distributions used for each user type', action = 'store_true')
19 parser.add_argument('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display', type = float, default = 50.) 21 parser.add_argument('--max-speed-distribution-plot', dest = 'maxSpeedDistributionPlot', help = 'if plotting the user distributions, the maximum speed to display', type = float, default = 50.)
42 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile) 44 speedAggregationFunc = lambda speeds: np.percentile(speeds, args.speedAggregationQuantile)
43 else: 45 else:
44 print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod)) 46 print('Unknown speed aggregation method: {}. Exiting'.format(params.speedAggregationMethod))
45 sys.exit() 47 sys.exit()
46 48
47 pedBikeCarSVM = ml.SVM() 49 pedBikeCarSVM = ml.SVM(args.svmType, args.kernelType)
48 pedBikeCarSVM.load(params.pedBikeCarSVMFilename) 50 pedBikeCarSVM.load(params.pedBikeCarSVMFilename)
49 bikeCarSVM = ml.SVM() 51 bikeCarSVM = ml.SVM(args.svmType, args.kernelType)
50 bikeCarSVM.load(params.bikeCarSVMFilename) 52 bikeCarSVM.load(params.bikeCarSVMFilename)
51 53
52 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.) 54 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
53 speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s), 55 speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s),
54 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s), 56 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s),
73 #obj.setFeatures(features) 75 #obj.setFeatures(features)
74 intervals.append(obj.getTimeInterval()) 76 intervals.append(obj.getTimeInterval())
75 timeInterval = moving.unionIntervals(intervals) 77 timeInterval = moving.unionIntervals(intervals)
76 78
77 capture = cv2.VideoCapture(videoFilename) 79 capture = cv2.VideoCapture(videoFilename)
78 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) 80 width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
79 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) 81 height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
80 82
81 pastObjects = [] 83 pastObjects = []
82 if params.undistort: # setup undistortion 84 if params.undistort: # setup undistortion
83 [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) 85 [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients)
84 if capture.isOpened(): 86 if capture.isOpened():
85 ret = True 87 ret = True
86 frameNum = timeInterval.first 88 frameNum = timeInterval.first
87 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum) 89 capture.set(cv2.CAP_PROP_POS_FRAMES, frameNum)
88 lastFrameNum = timeInterval.last 90 lastFrameNum = timeInterval.last
89 91
90 while ret and frameNum <= lastFrameNum: 92 while ret and frameNum <= lastFrameNum:
91 ret, img = capture.read() 93 ret, img = capture.read()
92 if ret: 94 if ret: