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License: GNU General Public License v3.0
example of SVM classification using PCL library
License: GNU General Public License v3.0
Hi,
I want to train with my own data. Can I input the point cloud data (kitti) to train, or input the data for the extracted features, such as sift?
Hello,
I have some question about training my own model, could you give me some afvice
1.how can I create the test.dat and train.dat with my LiDAR?
2.which point cloud feature you use?
thank you so much
These are the errors I receive while trying to build (have you run into these before?):
Scanning dependencies of target pcl_SVM_train_classify
[ 50%] Building CXX object CMakeFiles/pcl_SVM_train_classify.dir/pcl_SVM_train_classify.cpp.o
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp: In function ‘int main(int, char**)’:
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:117:45: error: ‘class pcl::SVMClassify’ has no member named ‘getParameters’
my_svm_parameters = my_svm_classifier.getParameters();
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:130:30: error: ‘class pcl::SVMClassify’ has no member named ‘hasLabelledTrainingSet’
if ( my_svm_classifier.hasLabelledTrainingSet())
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:176:7: error: ‘SVMtestReport’ is not a member of ‘pcl’
pcl::SVMtestReport svm_test_report = my_svm_classifier.getClassificationTestReport();
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:176:26: error: expected ‘;’ before ‘svm_test_report’
pcl::SVMtestReport svm_test_report = my_svm_classifier.getClassificationTestReport();
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:179:36: error: ‘svm_test_report’ was not declared in this scope
svm_test_report.accuracy,
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:211:23: error: ‘class pcl::SVMTrain’ has no member named ‘adaptProbToInput’
my_svm_trainer.adaptProbToInput();
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:267:67: error: could not convert ‘my_svm_trainer.pcl::SVMTrain::.pcl::SVM::saveClassifierModel(((const char*)"./model_out.dat"))’ from ‘void’ to ‘bool’
if ( my_svm_trainer.saveClassifierModel("./model_out.dat") ) {
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:352:31: error: ‘class pcl::SVMClassify’ has no member named ‘hasLabelledTrainingSet’
if ( my_svm_classifier.hasLabelledTrainingSet())
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:388:8: error: ‘SVMtestReport’ is not a member of ‘pcl’
pcl::SVMtestReport svm_test_report = my_svm_classifier.getClassificationTestReport();
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:388:27: error: expected ‘;’ before ‘svm_test_report’
pcl::SVMtestReport svm_test_report = my_svm_classifier.getClassificationTestReport();
^
/home/kdean/projects/src/SVM_pcl_demo/pcl_SVM_train_classify.cpp:391:36: error: ‘svm_test_report’ was not declared in this scope
svm_test_report.accuracy,
^
make[2]: *** [CMakeFiles/pcl_SVM_train_classify.dir/pcl_SVM_train_classify.cpp.o] Error 1
make[1]: *** [CMakeFiles/pcl_SVM_train_classify.dir/all] Error 2
Hi Mauro,
Thanks for the code available for public.
I have a question regarding multiple classes, can we use it for classify the multiple classes.
Hi Mauro,
I didn't understand the labeling format, can you please explain the format?
how can we save tested results?
Thanks again
1 1:-0.893290 2:-0.109680 3:0.000000 4:-0.879160 5:-0.092400 6:0.000000 7:-0.874660 8:-0.076520 9:0.000000 10:-0.861900 11:-0.060270 12:0.000000 13:-0.861820 14:-0.045170 15:0.000000 16:-0.853480 17:-0.029800 18:0.000000 19:-0.846870 20:-0.014780 21:0.000000 22:-0.834000 23:-0.000000 24:0.000000 25:-0.827870 26:0.014450 27:0.000000 28:-0.814500 29:0.028440 30:0.000000 31:-0.810890 32:0.042500 33:0.000000 34:-0.803040 35:0.056150 36:0.000000 37:-0.799940 38:0.069990 39:0.000000 40:-0.811530 41:0.085300 42:0.000000 43:-0.833740 44:0.102370 45:0.000000 46:-0.838760 47:0.117880 48:0.000000 49:-0.860280 50:0.136250 51:0.000000 52:-0.868600 53:0.153160 54:0.000000 55:-0.880520 56:0.171160 57:0.000000 58:-0.884250 59:0.187950 60:0.000000 61:-0.883750 62:0.204030 63:0.000000 64:-0.901400 65:0.224750 66:0.000000
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