OpenCV 3.3中給出了決策樹Decision Tres算法的實現，即cv::ml::DTrees類，此類的聲明在include/opencv2/ml.hpp文件中，實現在modules/ml/src/tree.cpp文件中。其中：

(1)、cv::ml::DTrees類：繼承自cv::ml::StateModel，而cv::ml::StateModel又繼承自cv::Algorithm；

(2)、create函數：為static，new一個DTreesImpl對象用來創建一個DTrees對象；

(3)、setMaxCategories/getMaxCategories函數：設置/獲取最大的類別數，默認值為10；

(4)、setMaxDepth/getMaxDepth函數：設置/獲取樹的最大深度，默認值為INT_MAX；

(5)、setMinSampleCount/getMinSampleCount函數：設置/獲取最小訓練樣本數，默認值為10；

(6)、setCVFolds/getCVFolds函數：設置/獲取CVFolds(thenumber of cross-validation folds)值，默認值為10，如果此值大于1，用于修剪構建的決策樹；

(7)、setUseSurrogates/getUseSurrogates函數：設置/獲取是否使用surrogatesplits方法，默認值為false；

(8)、setUse1SERule/getUse1SERule函數：設置/獲取是否使用1-SE規則，默認值為true；

(9)、setTruncatePrunedTree/getTruncatedTree函數：設置/獲取是否進行剪枝后移除操作，默認值為true；

(10)、setRegressionAccuracy/getRegressionAccuracy函數：設置/獲取回歸時用于終止的標準，默認值為0.01；

(11)、setPriors/getPriors函數：設置/獲取先驗概率數值，用于調整決策樹的偏好，默認值為空的Mat；

(12)、getRoots函數：獲取根節點索引；

(13)、getNodes函數：獲取所有節點索引；

(14)、getSplits函數：獲取所有拆分索引；

(15)、getSubsets函數：獲取分類拆分的所有bitsets；

(16)、load函數：load已序列化的model文件。

關于決策樹算法的簡介可以參考：http://blog.csdn.net/fengbingchun/article/details/78880934

以下是從數據集MNIST中提取的40幅圖像，0,1,2,3四類各20張，每類的前10幅來自于訓練樣本，用于訓練，后10幅來自測試樣本，用于測試，如下圖：

關于MNIST的介紹可以參考：http://blog.csdn.net/fengbingchun/article/details/49611549

測試代碼如下：

#include "opencv.hpp"
#include <string>
#include <vector>
#include <memory>
#include <algorithm>
#include <opencv2/opencv.hpp>
#include <opencv2/ml.hpp>
#include "common.hpp"/ Decision Tree 
int test_opencv_decision_tree_train()
{const std::string image_path{ "E:/GitCode/NN_Test/data/images/digit/handwriting_0_and_1/" };cv::Mat tmp = cv::imread(image_path + "0_1.jpg", 0);CHECK(tmp.data != nullptr);const int train_samples_number{ 40 };const int every_class_number{ 10 };cv::Mat train_data(train_samples_number, tmp.rows * tmp.cols, CV_32FC1);cv::Mat train_labels(train_samples_number, 1, CV_32FC1);float* p = (float*)train_labels.data;for (int i = 0; i < 4; ++i) {std::for_each(p + i * every_class_number, p + (i + 1)*every_class_number, [i](float& v){v = (float)i; });}// train datafor (int i = 0; i < 4; ++i) {static const std::vector<std::string> digit{ "0_", "1_", "2_", "3_" };static const std::string suffix{ ".jpg" };for (int j = 1; j <= every_class_number; ++j) {std::string image_name = image_path + digit[i] + std::to_string(j) + suffix;cv::Mat image = cv::imread(image_name, 0);CHECK(!image.empty() && image.isContinuous());image.convertTo(image, CV_32FC1);image = image.reshape(0, 1);tmp = train_data.rowRange(i * every_class_number + j - 1, i * every_class_number + j);image.copyTo(tmp);}}cv::Ptr<cv::ml::DTrees> dtree = cv::ml::DTrees::create();dtree->setMaxCategories(4);dtree->setMaxDepth(10);dtree->setMinSampleCount(10);dtree->setCVFolds(0);dtree->setUseSurrogates(false);dtree->setUse1SERule(false);dtree->setTruncatePrunedTree(false);dtree->setRegressionAccuracy(0);dtree->setPriors(cv::Mat());dtree->train(train_data, cv::ml::ROW_SAMPLE, train_labels);const std::string save_file{ "E:/GitCode/NN_Test/data/decision_tree_model.xml" }; // .xml, .yaml, .jsonsdtree->save(save_file);return 0;
}int test_opencv_decision_tree_predict()
{const std::string image_path{ "E:/GitCode/NN_Test/data/images/digit/handwriting_0_and_1/" };const std::string load_file{ "E:/GitCode/NN_Test/data/decision_tree_model.xml" }; // .xml, .yaml, .jsonsconst int predict_samples_number{ 40 };const int every_class_number{ 10 };cv::Mat tmp = cv::imread(image_path + "0_1.jpg", 0);CHECK(tmp.data != nullptr);// predict dattacv::Mat predict_data(predict_samples_number, tmp.rows * tmp.cols, CV_32FC1);for (int i = 0; i < 4; ++i) {static const std::vector<std::string> digit{ "0_", "1_", "2_", "3_" };static const std::string suffix{ ".jpg" };for (int j = 11; j <= every_class_number + 10; ++j) {std::string image_name = image_path + digit[i] + std::to_string(j) + suffix;cv::Mat image = cv::imread(image_name, 0);CHECK(!image.empty() && image.isContinuous());image.convertTo(image, CV_32FC1);image = image.reshape(0, 1);tmp = predict_data.rowRange(i * every_class_number + j - 10 - 1, i * every_class_number + j - 10);image.copyTo(tmp);}}cv::Mat result;cv::Ptr<cv::ml::DTrees> dtrees = cv::ml::DTrees::load(load_file);dtrees->predict(predict_data, result);CHECK(result.rows == predict_samples_number);cv::Mat predict_labels(predict_samples_number, 1, CV_32FC1);float* p = (float*)predict_labels.data;for (int i = 0; i < 4; ++i) {std::for_each(p + i * every_class_number, p + (i + 1)*every_class_number, [i](float& v){v = (float)i; });}int count{ 0 };for (int i = 0; i < predict_samples_number; ++i) {float value1 = ((float*)predict_labels.data)[i];float value2 = ((float*)result.data)[i];fprintf(stdout, "expected value: %f, actual value: %f\n", value1, value2);if (int(value1) == int(value2)) ++count;}fprintf(stdout, "accuracy: %f\n", count * 1.f / predict_samples_number);return 0;
}

執行結果如下：由于訓練樣本數量少，所以識別率只有72.5%，為了提高識別率，可以增加訓練樣本數。

GitHub：?https://github.com/fengbingchun/NN_Test

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