Mercurial Hosting > traffic-intelligence
view scripts/train-object-classification.py @ 1203:7b3384a8e409
second version of code loading kitti data, to clean
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 22 Mar 2023 15:40:33 -0400 |
parents | 933670761a57 |
children | d478d3122804 |
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#! /usr/bin/env python3 import numpy as np import argparse from cv2.ml import SVM_RBF, SVM_C_SVC, ROW_SAMPLE # row_sample for layout in cv2.ml.SVM_load import cvutils, moving, ml, storage parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True) parser.add_argument('--kernel', dest = 'kernelType', help = 'kernel type for the support vector machine (SVM)', default = SVM_RBF, type = long) parser.add_argument('--svm', dest = 'svmType', help = 'SVM type', default = SVM_C_SVC, type = long) parser.add_argument('--deg', dest = 'degree', help = 'SVM degree', default = 0, type = int) parser.add_argument('--gamma', dest = 'gamma', help = 'SVM gamma', default = 1, type = int) parser.add_argument('--coef0', dest = 'coef0', help = 'SVM coef0', default = 0, type = int) parser.add_argument('--cvalue', dest = 'cvalue', help = 'SVM Cvalue', default = 1, type = int) parser.add_argument('--nu', dest = 'nu', help = 'SVM nu', default = 0, type = int) parser.add_argument('--svmp', dest = 'svmP', help = 'SVM p', default = 0, type = int) parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the classifier configuration file', required = True) parser.add_argument('--confusion-matrix', dest = 'computeConfusionMatrix', help = 'compute the confusion matrix on the training data', action = 'store_true') args = parser.parse_args() classifierParams = storage.ClassifierParameters(args.configFilename) imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/", 'bicycle': args.directoryName + "/Cyclists/", 'car': args.directoryName + "/Vehicles/"} trainingSamplesPBV = {} trainingLabelsPBV = {} trainingSamplesBV = {} trainingLabelsBV = {} trainingSamplesPB = {} trainingLabelsPB = {} trainingSamplesPV = {} trainingLabelsPV = {} for k, v in imageDirectories.items(): print('Loading {} samples'.format(k)) trainingSamples, trainingLabels = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], classifierParams.hogRescaleSize, classifierParams.hogNOrientations, classifierParams.hogNPixelsPerCell, classifierParams.hogBlockNorm, classifierParams.hogNCellsPerBlock) trainingSamplesPBV[k], trainingLabelsPBV[k] = trainingSamples, trainingLabels if k != 'pedestrian': trainingSamplesBV[k], trainingLabelsBV[k] = trainingSamples, trainingLabels if k != 'car': trainingSamplesPB[k], trainingLabelsPB[k] = trainingSamples, trainingLabels if k != 'bicycle': trainingSamplesPV[k], trainingLabelsPV[k] = trainingSamples, trainingLabels # Training the Support Vector Machine print("Training Pedestrian-Cyclist-Vehicle Model") model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) classifications = model.train(np.concatenate(list(trainingSamplesPBV.values())), ROW_SAMPLE, np.concatenate(list(trainingLabelsPBV.values())), True) if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelPBV.xml") print("Training Cyclist-Vehicle Model") model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) classifications = model.train(np.concatenate(list(trainingSamplesBV.values())), ROW_SAMPLE, np.concatenate(list(trainingLabelsBV.values())), True) if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelBV.xml") print("Training Pedestrian-Cyclist Model") model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) classifications = model.train(np.concatenate(list(trainingSamplesPB.values())), ROW_SAMPLE, np.concatenate(list(trainingLabelsPB.values())), True) if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelPB.xml") print("Training Pedestrian-Vehicle Model") model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) classifications = model.train(np.concatenate(list(trainingSamplesPV.values())), ROW_SAMPLE, np.concatenate(list(trainingLabelsPV.values())), True) if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelPV.xml")