select_feature_set_trainf_svm_protected — Select an optimal combination of features to classify OCR data from a (protected) training file.
select_feature_set_trainf_svm_protected selects an optimal combination of features to classify the data given in the training files TrainingFile with a support vector machine (SVM). Its functionality corresponds to the functionality of select_feature_set_trainf_svm, with the addition that select_feature_set_trainf_svm_protected can process unprotected and protected training files. Protected training files can be used only with the correct user password Password. If the number of passwords Password equals 1, then every input file TrainingFile is checked with that password, otherwise the number of passwords has to be equal to the number of input files and the input file at position n is checked with the password at position n. For unprotected training files the passwords are ignored.
For a more detailed description of the operator's functionality see select_feature_set_trainf_svm. The concept of protecting OCR training data in HALCON is described in protect_ocr_trainf.
This operator may take considerable time, depending on the size of the data set in the training file, and the number of features.
Please note, that this operator should not be called, if only a small set of training data is available. Due to the risk of overfitting the operator select_feature_set_trainf_svm_protected may deliver a classifier with a very high score. However, the classifier may perfom poorly when tested.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
Names of the training files.
Default value: ''
File extension: .trf, .otr
Passwords for protected training files.
List of features that should be considered for selection.
Default value: ['zoom_factor','ratio','width','height','foreground','foreground_grid_9','foreground_grid_16','anisometry','compactness','convexity','moments_region_2nd_invar','moments_region_2nd_rel_invar','moments_region_3rd_invar','moments_central','phi','num_connect','num_holes','projection_horizontal','projection_vertical','projection_horizontal_invar','projection_vertical_invar','chord_histo','num_runs','pixel','pixel_invar','pixel_binary','gradient_8dir','cooc','moments_gray_plane']
List of values: 'anisometry', 'chord_histo', 'compactness', 'convexity', 'cooc', 'default', 'foreground', 'foreground_grid_16', 'foreground_grid_9', 'gradient_8dir', 'height', 'moments_central', 'moments_gray_plane', 'moments_region_2nd_invar', 'moments_region_2nd_rel_invar', 'moments_region_3rd_invar', 'num_connect', 'num_holes', 'num_runs', 'phi', 'pixel', 'pixel_binary', 'pixel_invar', 'projection_horizontal', 'projection_horizontal_invar', 'projection_vertical', 'projection_vertical_invar', 'ratio', 'width', 'zoom_factor'
Method to perform the selection.
Default value: 'greedy'
List of values: 'greedy', 'greedy_oscillating'
Width of the rectangle to which the gray values of the segmented character are zoomed.
Default value: 15
Height of the rectangle to which the gray values of the segmented character are zoomed.
Default value: 16
Names of generic parameters to configure the selection process and the classifier.
Default value: 
List of values: 'gamma', 'nu'
Values of generic parameters to configure the selection process and the classifier.
Default value: 
Suggested values: 'auto', '0.1', '0.3'
Trained OCR-SVM Classifier.
Selected feature set, contains only entries from FeatureList.
Achieved score using tow-fold cross-validation.
If the parameters are valid, the operator select_feature_set_trainf_svm_protected returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.