1 2 // 3 // This file is auto-generated. Please don't modify it! 4 // 5 package org.opencv.ml; 6 7 import org.opencv.core.Algorithm; 8 import org.opencv.core.Mat; 9 10 // C++: class StatModel 11 //javadoc: StatModel 12 public class StatModel extends Algorithm { 13 14 protected StatModel(long addr) { super(addr); } 15 16 17 public static final int 18 UPDATE_MODEL = 1, 19 RAW_OUTPUT = 1, 20 COMPRESSED_INPUT = 2, 21 PREPROCESSED_INPUT = 4; 22 23 24 // 25 // C++: int getVarCount() 26 // 27 28 //javadoc: StatModel::getVarCount() 29 public int getVarCount() 30 { 31 32 int retVal = getVarCount_0(nativeObj); 33 34 return retVal; 35 } 36 37 38 // 39 // C++: bool empty() 40 // 41 42 //javadoc: StatModel::empty() 43 public boolean empty() 44 { 45 46 boolean retVal = empty_0(nativeObj); 47 48 return retVal; 49 } 50 51 52 // 53 // C++: bool isTrained() 54 // 55 56 //javadoc: StatModel::isTrained() 57 public boolean isTrained() 58 { 59 60 boolean retVal = isTrained_0(nativeObj); 61 62 return retVal; 63 } 64 65 66 // 67 // C++: bool isClassifier() 68 // 69 70 //javadoc: StatModel::isClassifier() 71 public boolean isClassifier() 72 { 73 74 boolean retVal = isClassifier_0(nativeObj); 75 76 return retVal; 77 } 78 79 80 // 81 // C++: bool train(Ptr_TrainData trainData, int flags = 0) 82 // 83 84 // Unknown type 'Ptr_TrainData' (I), skipping the function 85 86 87 // 88 // C++: bool train(Mat samples, int layout, Mat responses) 89 // 90 91 //javadoc: StatModel::train(samples, layout, responses) 92 public boolean train(Mat samples, int layout, Mat responses) 93 { 94 95 boolean retVal = train_0(nativeObj, samples.nativeObj, layout, responses.nativeObj); 96 97 return retVal; 98 } 99 100 101 // 102 // C++: float calcError(Ptr_TrainData data, bool test, Mat& resp) 103 // 104 105 // Unknown type 'Ptr_TrainData' (I), skipping the function 106 107 108 // 109 // C++: float predict(Mat samples, Mat& results = Mat(), int flags = 0) 110 // 111 112 //javadoc: StatModel::predict(samples, results, flags) 113 public float predict(Mat samples, Mat results, int flags) 114 { 115 116 float retVal = predict_0(nativeObj, samples.nativeObj, results.nativeObj, flags); 117 118 return retVal; 119 } 120 121 //javadoc: StatModel::predict(samples) 122 public float predict(Mat samples) 123 { 124 125 float retVal = predict_1(nativeObj, samples.nativeObj); 126 127 return retVal; 128 } 129 130 131 @Override 132 protected void finalize() throws Throwable { 133 delete(nativeObj); 134 } 135 136 137 138 // C++: int getVarCount() 139 private static native int getVarCount_0(long nativeObj); 140 141 // C++: bool empty() 142 private static native boolean empty_0(long nativeObj); 143 144 // C++: bool isTrained() 145 private static native boolean isTrained_0(long nativeObj); 146 147 // C++: bool isClassifier() 148 private static native boolean isClassifier_0(long nativeObj); 149 150 // C++: bool train(Mat samples, int layout, Mat responses) 151 private static native boolean train_0(long nativeObj, long samples_nativeObj, int layout, long responses_nativeObj); 152 153 // C++: float predict(Mat samples, Mat& results = Mat(), int flags = 0) 154 private static native float predict_0(long nativeObj, long samples_nativeObj, long results_nativeObj, int flags); 155 private static native float predict_1(long nativeObj, long samples_nativeObj); 156 157 // native support for java finalize() 158 private static native void delete(long nativeObj); 159 160 } 161
