OpenCV：使用OpenCV3随机森林进行统计特征多类分析

时间：2022-08-23 15:11:14

原文链接：在opencv3中的机器学习算法练习：对OCR进行分类

本文贴出的代码为自己的训练集所用，作为参考。可运行demo程序请拜访原作者。

CNN作为图像识别和检测器，在分析物体结构分布的多类识别中具有绝对的优势。通多多层卷积核Pooling实现对物体表面分布的模板学习，以卷积核的形式存储在网络中。而对于统计特征，暂时没有明确的指导规则。

opencv3中的ml类与opencv2中发生了变化，下面列举opencv3的机器学习类方法实例，以随机森林为例。

代码：

	//使用OpenCV随机森林训练模型//使用训练好的样本-TXT文件

	int RTreesTrain( int argc, char* argv[] )

	{

		if (argc < 9) {

			std::cout << "argc<9";

			return 0;

		}

		std::string fileFeatureTrain(argv[1]);

		std::string  fileFeatureTest(argv[2]);

		std::string        fileTrees(argv[3]);

		int lenF   = atoi(argv[4]);//特征长度 32

		int numF = atoi(argv[5]);//使用特征个数 1000

		int nsample = atoi(argv[6]);//总样本数 大于numF

		int nTrees = atoi(argv[7]);

		int nClass = atoi(argv[8]);

		//载入特征

		cv::Mat data;

		cv::Mat responses;

		const string data_filename = fileFeatureTrain;

		read_num_class_data( data_filename, numF, lenF, &data, &responses );

		cv::Ptr<cv::ml::RTrees>  StyleModelHSV;

		StyleModelHSV = cv::ml::RTrees::create();

		StyleModelHSV->setMaxDepth(10);

		StyleModelHSV->setMinSampleCount(10);

		StyleModelHSV->setRegressionAccuracy(0);

		StyleModelHSV->setUseSurrogates(false);

		StyleModelHSV->setMaxCategories(nClass);

		StyleModelHSV->setPriors(cv::Mat());

		StyleModelHSV->setCalculateVarImportance(true);

		StyleModelHSV->setActiveVarCount(4);

		StyleModelHSV->setTermCriteria(TC(10000, 0.01f));

		int nsamples_all = nsample;// data.rows;

		int ntrain_samples = numF;// (int)(nsamples_all*0.8);

		cv::Ptr<cv::ml::TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);

		cout << "The Model is training....." << endl;

		StyleModelHSV->train(tdata);

		StyleModelHSV->save(fileTrees);

		return 1;

	}

	// 读取文件数据

	bool read_num_class_data( const string& fileFeatureTrain, int numF,int fLen, cv::Mat* _data, cv::Mat* _responses)

	{

		using namespace cv;

		Mat el_ptr(1, numF, CV_32F);

		vector<int>  responses(0);

		_data->release();

		_responses->release();

		freopen(fileFeatureTrain.c_str(), "r", stdin);

		cout << "The feature is loading....." << endl;

		int i = 0;

		int label = 0;

		for (int i = 0; i < numF; ++i) {

			StyleFeature  aFeat;aFeat.second.resize(fLen);

			std::string sline;getline(cin, sline);

			//以空格分开

			int idxBlank = sline.find_first_of(" ");

			std::string sLabel = sline;//获取标签；

			sLabel.erase(idxBlank, sLabel.length());

			responses.push_back(label);//aFeat.first = label = atoi(sLabel.c_str());

			std::string sFV = sline;

			sFV.erase(0, idxBlank + 1);//获取一行，特征

			int idxFv = 0;

			float fV = 0.0;

			while (sFV.length() > 0 && idxFv < fLen) {

				int idxColon = sFV.find_first_of(":");

				std::string sv = sFV;

				std::strstream ssv;

				sv = sv.substr(idxColon + 1, sv.find_first_of(" ") - 2);

				ssv << sv;ssv >> fV;

				el_ptr.at<float>(i) = fV;//aFeat.second[idxFv] = fV;

				++idxFv;

				sFV.erase(0, sFV.find_first_of(" ") + 1);

			}

			_data->push_back(el_ptr);//trainData.push_back(aFeat);

		}

		fclose(stdin); cout << "The feature load over....." << endl;

		Mat(responses).copyTo(*_responses);

		return true;

	}


	//准备训练数据

	cv::Ptr<cv::ml::TrainData> prepare_train_data( const cv::Mat& data, const cv::Mat& responses, int ntrain_samples )

	{

		using namespace cv;

		Mat sample_idx = Mat::zeros(1, data.rows, CV_8U);

		Mat train_samples = sample_idx.colRange(0, ntrain_samples);

		train_samples.setTo(Scalar::all(1));

		int nvars = data.cols;

		Mat var_type(nvars + 1, 1, CV_8U);

		var_type.setTo(Scalar::all(ml::VAR_ORDERED));

		var_type.at<uchar>(nvars) = ml::VAR_CATEGORICAL;

		return ml::TrainData::create(data, ml::ROW_SAMPLE, responses, noArray(), sample_idx, noArray(), var_type);

	}

样本结构：

0 1:211946 2:0 3:0 4:0 5:105 6:5693 7:34 8:0 9:0 10:0 11:25 12:12697 13:226916 14:1826 15:497 16:282 17:105 18:15 19:104 20:18 21:0 22:737 23:46979 24:17889 25:7121 26:6970 27:9441 28:12679 29:20890 30:37498 31:43568 32:27465

0 1:23544 2:210 3:11663 4:158 5:310 6:166 7:591 8:6131 9:193297 10:1985 11:1136 12:809 13:149069 14:33036 15:20045 16:11525 17:6552 18:2928 19:2590 20:1844 21:1305 22:11106 23:81817 24:29063 25:6654 26:5015 27:4916 28:8862 29:34762 30:44044 31:17409 32:7458

0 1:254596 2:0 3:65361 4:0 5:0 6:0 7:0 8:0 9:0 10:0 11:10 12:14033 13:333347 14:330 15:75 16:80 17:25 18:0 19:42 20:0 21:0 22:101 23:31990 24:66583 25:49191 26:59149 27:35800 28:25089 29:21463 30:18022 31:18409 32:8304

0 1:11697 2:2431 3:228 4:9 5:0 6:1 7:150 8:28 9:8413 10:9673 11:6345 12:6025 13:7695 14:8080 15:5689 16:6175 17:5146 18:4358 19:3246 20:2170 21:1478 22:963 23:2192 24:6866 25:7082 26:4273 27:3100 28:2733 29:2833 30:3265 31:3835 32:8821

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