Merge pull request #3636 from kaingwade:ml_to_contrib

Move ml to opencv_contrib #3636 Main PR: opencv/opencv#25017
2025-10-23 00:49:38 +08:00 · 2024-02-27 20:54:59 +08:00
parent c5d22ddf14
commit cf63a7f71f
101 changed files with 23882 additions and 0 deletions
--- a/modules/ml/samples/logistic_regression.cpp
+++ b/modules/ml/samples/logistic_regression.cpp
@@ -0,0 +1,127 @@
+// Logistic Regression sample
+// AUTHOR: Rahul Kavi rahulkavi[at]live[at]com
+
+#include <iostream>
+
+#include <opencv2/core.hpp>
+#include <opencv2/ml.hpp>
+#include <opencv2/highgui.hpp>
+
+using namespace std;
+using namespace cv;
+using namespace cv::ml;
+
+static void showImage(const Mat &data, int columns, const String &name)
+{
+    Mat bigImage;
+    for(int i = 0; i < data.rows; ++i)
+    {
+        bigImage.push_back(data.row(i).reshape(0, columns));
+    }
+    imshow(name, bigImage.t());
+}
+
+static float calculateAccuracyPercent(const Mat &original, const Mat &predicted)
+{
+    return 100 * (float)countNonZero(original == predicted) / predicted.rows;
+}
+
+int main()
+{
+    const String filename = samples::findFile("data01.xml");
+    cout << "**********************************************************************" << endl;
+    cout << filename
+         << " contains digits 0 and 1 of 20 samples each, collected on an Android device" << endl;
+    cout << "Each of the collected images are of size 28 x 28 re-arranged to 1 x 784 matrix"
+         << endl;
+    cout << "**********************************************************************" << endl;
+
+    Mat data, labels;
+    {
+        cout << "loading the dataset...";
+        FileStorage f;
+        if(f.open(filename, FileStorage::READ))
+        {
+            f["datamat"] >> data;
+            f["labelsmat"] >> labels;
+            f.release();
+        }
+        else
+        {
+            cerr << "file can not be opened: " << filename << endl;
+            return 1;
+        }
+        data.convertTo(data, CV_32F);
+        labels.convertTo(labels, CV_32F);
+        cout << "read " << data.rows << " rows of data" << endl;
+    }
+
+    Mat data_train, data_test;
+    Mat labels_train, labels_test;
+    for(int i = 0; i < data.rows; i++)
+    {
+        if(i % 2 == 0)
+        {
+            data_train.push_back(data.row(i));
+            labels_train.push_back(labels.row(i));
+        }
+        else
+        {
+            data_test.push_back(data.row(i));
+            labels_test.push_back(labels.row(i));
+        }
+    }
+    cout << "training/testing samples count: " << data_train.rows << "/" << data_test.rows << endl;
+
+    // display sample image
+    showImage(data_train, 28, "train data");
+    showImage(data_test, 28, "test data");
+
+    // simple case with batch gradient
+    cout << "training...";
+    //! [init]
+    Ptr<LogisticRegression> lr1 = LogisticRegression::create();
+    lr1->setLearningRate(0.001);
+    lr1->setIterations(10);
+    lr1->setRegularization(LogisticRegression::REG_L2);
+    lr1->setTrainMethod(LogisticRegression::BATCH);
+    lr1->setMiniBatchSize(1);
+    //! [init]
+    lr1->train(data_train, ROW_SAMPLE, labels_train);
+    cout << "done!" << endl;
+
+    cout << "predicting...";
+    Mat responses;
+    lr1->predict(data_test, responses);
+    cout << "done!" << endl;
+
+    // show prediction report
+    cout << "original vs predicted:" << endl;
+    labels_test.convertTo(labels_test, CV_32S);
+    cout << labels_test.t() << endl;
+    cout << responses.t() << endl;
+    cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses) << "%" << endl;
+
+    // save the classifier
+    const String saveFilename = "NewLR_Trained.xml";
+    cout << "saving the classifier to " << saveFilename << endl;
+    lr1->save(saveFilename);
+
+    // load the classifier onto new object
+    cout << "loading a new classifier from " << saveFilename << endl;
+    Ptr<LogisticRegression> lr2 = StatModel::load<LogisticRegression>(saveFilename);
+
+    // predict using loaded classifier
+    cout << "predicting the dataset using the loaded classifier...";
+    Mat responses2;
+    lr2->predict(data_test, responses2);
+    cout << "done!" << endl;
+
+    // calculate accuracy
+    cout << labels_test.t() << endl;
+    cout << responses2.t() << endl;
+    cout << "accuracy: " << calculateAccuracyPercent(labels_test, responses2) << "%" << endl;
+
+    waitKey(0);
+    return 0;
+}