各机器学习方法代码（OpenCV2）

  1 #include <iostream>
  2 #include <math.h>
  3 #include <string>
  4 #include "cv.h"
  5 #include "ml.h"
  6 #include "highgui.h"
  7 
  8 using namespace cv;
  9 using namespace std;
 10 
 11 bool plotSupportVectors=true;
 12 int numTrainingPoints=200;
 13 int numTestPoints=2000;
 14 int size=200;
 15 int eq=0;
 16 
 17 // accuracy
 18 float evaluate(cv::Mat& predicted, cv::Mat& actual) {
 19     assert(predicted.rows == actual.rows);
 20     int t = 0;
 21     int f = 0;
 22     for(int i = 0; i < actual.rows; i++) {
 23         float p = predicted.at<float>(i,0);
 24         float a = actual.at<float>(i,0);
 25         if((p >= 0.0 && a >= 0.0) || (p <= 0.0 &&  a <= 0.0)) {
 26             t++;
 27         } else {
 28             f++;
 29         }
 30     }
 31     return (t * 1.0) / (t + f);
 32 }
 33 
 34 // plot data and class
 35 void plot_binary(cv::Mat& data, cv::Mat& classes, string name) {
 36     cv::Mat plot(size, size, CV_8UC3);
 37     plot.setTo(cv::Scalar(255.0,255.0,255.0));
 38     for(int i = 0; i < data.rows; i++) {
 39 
 40         float x = data.at<float>(i,0) * size;
 41         float y = data.at<float>(i,1) * size;
 42 
 43         if(classes.at<float>(i, 0) > 0) {
 44             cv::circle(plot, Point(x,y), 2, CV_RGB(255,0,0),1);
 45         } else {
 46             cv::circle(plot, Point(x,y), 2, CV_RGB(0,255,0),1);
 47         }
 48     }
 49     cv::imshow(name, plot);
 50 }
 51 
 52 // function to learn
 53 int f(float x, float y, int equation) {
 54     switch(equation) {
 55     case 0:
 56         return y > sin(x*10) ? -1 : 1;
 57         break;
 58     case 1:
 59         return y > cos(x * 10) ? -1 : 1;
 60         break;
 61     case 2:
 62         return y > 2*x ? -1 : 1;
 63         break;
 64     case 3:
 65         return y > tan(x*10) ? -1 : 1;
 66         break;
 67     default:
 68         return y > cos(x*10) ? -1 : 1;
 69     }
 70 }
 71 
 72 // label data with equation
 73 cv::Mat labelData(cv::Mat points, int equation) {
 74     cv::Mat labels(points.rows, 1, CV_32FC1);
 75     for(int i = 0; i < points.rows; i++) {
 76              float x = points.at<float>(i,0);
 77              float y = points.at<float>(i,1);
 78              labels.at<float>(i, 0) = f(x, y, equation);
 79         }
 80     return labels;
 81 }
 82 
 83 void svm(cv::Mat& trainingData, cv::Mat& trainingClasses, cv::Mat& testData, cv::Mat& testClasses) {
 84     CvSVMParams param = CvSVMParams();
 85 
 86     param.svm_type = CvSVM::C_SVC;
 87     param.kernel_type = CvSVM::RBF; //CvSVM::RBF, CvSVM::LINEAR ...
 88     param.degree = 0; // for poly
 89     param.gamma = 20; // for poly/rbf/sigmoid
 90     param.coef0 = 0; // for poly/sigmoid
 91 
 92     param.C = 7; // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
 93     param.nu = 0.0; // for CV_SVM_NU_SVC, CV_SVM_ONE_CLASS, and CV_SVM_NU_SVR
 94     param.p = 0.0; // for CV_SVM_EPS_SVR
 95 
 96     param.class_weights = NULL; // for CV_SVM_C_SVC
 97     param.term_crit.type = CV_TERMCRIT_ITER +CV_TERMCRIT_EPS;
 98     param.term_crit.max_iter = 1000;
 99     param.term_crit.epsilon = 1e-6;
100 
101     // SVM training (use train auto for OpenCV>=2.0)
102     CvSVM svm(trainingData, trainingClasses, cv::Mat(), cv::Mat(), param);
103 
104     cv::Mat predicted(testClasses.rows, 1, CV_32F);
105 
106     for(int i = 0; i < testData.rows; i++) {
107         cv::Mat sample = testData.row(i);
108 
109         float x = sample.at<float>(0,0);
110         float y = sample.at<float>(0,1);
111 
112         predicted.at<float>(i, 0) = svm.predict(sample);
113     }
114 
115     cout << "Accuracy_{SVM} = " << evaluate(predicted, testClasses) << endl;
116     plot_binary(testData, predicted, "Predictions SVM");
117 
118     // plot support vectors
119     if(plotSupportVectors) {
120         cv::Mat plot_sv(size, size, CV_8UC3);
121         plot_sv.setTo(cv::Scalar(255.0,255.0,255.0));
122 
123         int svec_count = svm.get_support_vector_count();
124         for(int vecNum = 0; vecNum < svec_count; vecNum++) {
125             const float* vec = svm.get_support_vector(vecNum);
126             cv::circle(plot_sv, Point(vec[0]*size, vec[1]*size), 3 , CV_RGB(0, 0, 0));
127         }
128     cv::imshow("Support Vectors", plot_sv);
129     }
130 }
131 
132 void mlp(cv::Mat& trainingData, cv::Mat& trainingClasses, cv::Mat& testData, cv::Mat& testClasses) {
133 
134     cv::Mat layers = cv::Mat(4, 1, CV_32SC1);
135 
136     layers.row(0) = cv::Scalar(2);
137     layers.row(1) = cv::Scalar(10);
138     layers.row(2) = cv::Scalar(15);
139     layers.row(3) = cv::Scalar(1);
140 
141     CvANN_MLP mlp;
142     CvANN_MLP_TrainParams params;
143     CvTermCriteria criteria;
144     criteria.max_iter = 100;
145     criteria.epsilon = 0.00001f;
146     criteria.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
147     params.train_method = CvANN_MLP_TrainParams::BACKPROP;
148     params.bp_dw_scale = 0.05f;
149     params.bp_moment_scale = 0.05f;
150     params.term_crit = criteria;
151 
152     mlp.create(layers);
153 
154     // train
155     mlp.train(trainingData, trainingClasses, cv::Mat(), cv::Mat(), params);
156 
157     cv::Mat response(1, 1, CV_32FC1);
158     cv::Mat predicted(testClasses.rows, 1, CV_32F);
159     for(int i = 0; i < testData.rows; i++) {
160         cv::Mat response(1, 1, CV_32FC1);
161         cv::Mat sample = testData.row(i);
162 
163         mlp.predict(sample, response);
164         predicted.at<float>(i,0) = response.at<float>(0,0);
165 
166     }
167 
168     cout << "Accuracy_{MLP} = " << evaluate(predicted, testClasses) << endl;
169     plot_binary(testData, predicted, "Predictions Backpropagation");
170 }
171 
172 void knn(cv::Mat& trainingData, cv::Mat& trainingClasses, cv::Mat& testData, cv::Mat& testClasses, int K) {
173 
174     CvKNearest knn(trainingData, trainingClasses, cv::Mat(), false, K);
175     cv::Mat predicted(testClasses.rows, 1, CV_32F);
176     for(int i = 0; i < testData.rows; i++) {
177             const cv::Mat sample = testData.row(i);
178             predicted.at<float>(i,0) = knn.find_nearest(sample, K);
179     }
180 
181     cout << "Accuracy_{KNN} = " << evaluate(predicted, testClasses) << endl;
182     plot_binary(testData, predicted, "Predictions KNN");
183 
184 }
185 
186 void bayes(cv::Mat& trainingData, cv::Mat& trainingClasses, cv::Mat& testData, cv::Mat& testClasses) {
187 
188     CvNormalBayesClassifier bayes(trainingData, trainingClasses);
189     cv::Mat predicted(testClasses.rows, 1, CV_32F);
190     for (int i = 0; i < testData.rows; i++) {
191         const cv::Mat sample = testData.row(i);
192         predicted.at<float> (i, 0) = bayes.predict(sample);
193     }
194 
195     cout << "Accuracy_{BAYES} = " << evaluate(predicted, testClasses) << endl;
196     plot_binary(testData, predicted, "Predictions Bayes");
197 
198 }
199 
200 void decisiontree(cv::Mat& trainingData, cv::Mat& trainingClasses, cv::Mat& testData, cv::Mat& testClasses) {
201 
202     CvDTree dtree;
203     cv::Mat var_type(3, 1, CV_8U);
204 
205     // define attributes as numerical
206     var_type.at<unsigned int>(0,0) = CV_VAR_NUMERICAL;
207     var_type.at<unsigned int>(0,1) = CV_VAR_NUMERICAL;
208     // define output node as numerical
209     var_type.at<unsigned int>(0,2) = CV_VAR_NUMERICAL;
210 
211     dtree.train(trainingData,CV_ROW_SAMPLE, trainingClasses, cv::Mat(), cv::Mat(), var_type, cv::Mat(), CvDTreeParams());
212     cv::Mat predicted(testClasses.rows, 1, CV_32F);
213     for (int i = 0; i < testData.rows; i++) {
214         const cv::Mat sample = testData.row(i);
215         CvDTreeNode* prediction = dtree.predict(sample);
216         predicted.at<float> (i, 0) = prediction->value;
217     }
218 
219     cout << "Accuracy_{TREE} = " << evaluate(predicted, testClasses) << endl;
220     plot_binary(testData, predicted, "Predictions tree");
221 
222 }
223 
224 
225 int main() {
226 
227     cv::Mat trainingData(numTrainingPoints, 2, CV_32FC1);
228     cv::Mat testData(numTestPoints, 2, CV_32FC1);
229 
230     cv::randu(trainingData,0,1);
231     cv::randu(testData,0,1);
232 
233     cv::Mat trainingClasses = labelData(trainingData, eq);
234     cv::Mat testClasses = labelData(testData, eq);
235 
236     plot_binary(trainingData, trainingClasses, "Training Data");
237     plot_binary(testData, testClasses, "Test Data");
238 
239     svm(trainingData, trainingClasses, testData, testClasses);
240     mlp(trainingData, trainingClasses, testData, testClasses);
241     knn(trainingData, trainingClasses, testData, testClasses, 3);
242     bayes(trainingData, trainingClasses, testData, testClasses);
243     decisiontree(trainingData, trainingClasses, testData, testClasses);
244 
245     cv::waitKey();
246 
247     return 0;
248 }
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各机器学习方法代码（OpenCV2）

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