diff --git a/src/main/java/com/yuxue/train/ANNTrain.java b/src/main/java/com/yuxue/train/ANNTrain.java index d10a9502..315c3f43 100644 --- a/src/main/java/com/yuxue/train/ANNTrain.java +++ b/src/main/java/com/yuxue/train/ANNTrain.java @@ -1,30 +1,33 @@ package com.yuxue.train; import static org.bytedeco.javacpp.opencv_core.CV_32F; -import static org.bytedeco.javacpp.opencv_core.CV_32FC1; -import static org.bytedeco.javacpp.opencv_core.CV_32SC1; -import static org.bytedeco.javacpp.opencv_core.getTickCount; -import static org.bytedeco.javacpp.opencv_imgproc.resize; import static org.bytedeco.javacpp.opencv_ml.ROW_SAMPLE; import java.util.Vector; import org.bytedeco.javacpp.opencv_core.FileStorage; import org.bytedeco.javacpp.opencv_core.Mat; -import org.bytedeco.javacpp.opencv_core.Scalar; -import org.bytedeco.javacpp.opencv_core.Size; import org.bytedeco.javacpp.opencv_core.TermCriteria; +import org.bytedeco.javacpp.opencv_core; import org.bytedeco.javacpp.opencv_imgcodecs; import org.bytedeco.javacpp.opencv_ml.ANN_MLP; import org.bytedeco.javacpp.opencv_ml.TrainData; import com.yuxue.easypr.core.CoreFunc; -import com.yuxue.enumtype.Direction; import com.yuxue.util.Convert; import com.yuxue.util.FileUtil; /** + * 基于org.bytedeco.javacpp包实现的训练 + * + * 图片文字识别训练 + * 训练出来的库文件，用于判断切图是否包含车牌 + * + * 训练的svm.xml应用： + * 1、替换res/model/svm.xml文件 + * 2、修改com.yuxue.easypr.core.PlateJudge.plateJudge(Mat) 方法 + * 将样本处理方法切换一下，即将对应被注释掉的模块代码取消注释 * * @author yuxue * @date 2020-05-14 22:16 @@ -32,172 +35,111 @@ import com.yuxue.util.FileUtil; public class ANNTrain { private ANN_MLP ann = ANN_MLP.create(); + - // 中国车牌 - private final char strCharacters[] = { '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', /* 没有I */ - 'J', 'K', 'L', 'M', 'N', /* 没有O */'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' }; - private final int numCharacter = 34; /* 没有I和0,10个数字与24个英文字符之和 */ - - // 以下都是我训练时用到的中文字符数据，并不全面，有些省份没有训练数据所以没有字符 - // 有些后面加数字2的表示在训练时常看到字符的一种变形，也作为训练数据存储 - private final String strChinese[] = { "zh_cuan" /* 川 */, "zh_e" /* 鄂 */, "zh_gan" /* 赣 */, "zh_hei" /* 黑 */, "zh_hu" /* 沪 */, "zh_ji" /* 冀 */, "zh_jl" /* 吉 */, "zh_jin" /* 津 */, "zh_jing" /* 京 */, - "zh_shan" /* 陕 */, "zh_liao" /* 辽 */, "zh_lu" /* 鲁 */, "zh_min" /* 闽 */, "zh_ning" /* 宁 */, "zh_su" /* 苏 */, "zh_sx" /* 晋 */, "zh_wan" /* 皖 */, "zh_yu" /* 豫 */, "zh_yue" /* 粤 */, - "zh_zhe" /* 浙 */ }; - - private final int numAll = 54; /* 34+20=54 */ - - public Mat features(Mat in, int sizeData) { - // Histogram features - float[] vhist = CoreFunc.projectedHistogram(in, Direction.VERTICAL); - float[] hhist = CoreFunc.projectedHistogram(in, Direction.HORIZONTAL); - - // Low data feature - Mat lowData = new Mat(); - resize(in, lowData, new Size(sizeData, sizeData)); + // 中国车牌; 34个字符; 没有 字母I、字母O + private final char strCharacters[] = { '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', + 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', /* 没有I */ 'J', 'K', 'L', 'M', 'N', /* 没有O */'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' }; - // Last 10 is the number of moments components - int numCols = vhist.length + hhist.length + lowData.cols() * lowData.cols(); - - Mat out = Mat.zeros(1, numCols, CV_32F).asMat(); - // Asign values to feature,ANN的样本特征为水平、垂直直方图和低分辨率图像所组成的矢量 - int j = 0; - for (int i = 0; i < vhist.length; i++, ++j) { - out.ptr(j).put(Convert.getBytes(vhist[i])); - } - for (int i = 0; i < hhist.length; i++, ++j) { - out.ptr(j).put(Convert.getBytes(hhist[i])); - } - for (int x = 0; x < lowData.cols(); x++) { - for (int y = 0; y < lowData.rows(); y++, ++j) { - float val = lowData.ptr(x, y).get() & 0xFF; - out.ptr(j).put(Convert.getBytes(val)); - } - } - return out; - } + private final int numCharacter = strCharacters.length; + // 并不全面，有些省份没有训练数据所以没有字符 + // 有些后面加数字2的表示在训练时常看到字符的一种变形，也作为训练数据存储 + private final String strChinese[] = { + "zh_cuan", /*川*/ + "zh_e", /*鄂*/ + "zh_gan", /*赣*/ + "zh_gan1", /*甘*/ + "zh_gui", /*贵*/ + "zh_gui1", /*桂*/ + "zh_hei", /*黑*/ + "zh_hu", /*沪*/ + "zh_ji", /*冀*/ + "zh_jin", /*津*/ + "zh_jing", /*京*/ + "zh_jl", /*吉*/ + "zh_liao", /*辽*/ + "zh_lu", /*鲁*/ + "zh_meng", /*蒙*/ + "zh_min", /*闽*/ + "zh_ning", /*宁*/ + "zh_qing", /*青*/ + "zh_qiong", /*琼*/ + "zh_shan", /*陕*/ + "zh_su", /*苏*/ + "zh_sx", /*晋*/ + "zh_wan", /*皖*/ + "zh_xiang", /*湘*/ + "zh_xin", /*新*/ + "zh_yu", /*豫*/ + "zh_yu1", /*渝*/ + "zh_yue", /*粤*/ + "zh_yun", /*云*/ + "zh_zang", /*藏*/ + "zh_zhe" /*浙*/ + }; + + private final int numAll = strCharacters.length + strChinese.length; - /** - * - * @param TrainData 训练样本数据 - * @param classes 数据对应的标签 - * @param nNeruns - */ - public void annTrain(Mat trainingData, Mat classes, int nNeruns) { - ann.clear(); - Mat layers = new Mat(1, 3, CV_32SC1); - layers.ptr(0).put(Convert.getBytes(trainingData.cols())); - layers.ptr(1).put(Convert.getBytes(nNeruns)); - layers.ptr(2).put(Convert.getBytes(numAll)); + // 默认的训练操作的根目录 + private static final String DEFAULT_PATH = "D:/PlateDetect/train/chars_recognise_ann/"; - // Prepare trainClases - // Create a mat with n trained data by m classes - Mat trainClasses = new Mat(); - trainClasses.create(trainingData.rows(), numAll, CV_32FC1); - for (int i = 0; i < trainClasses.rows(); i++) { - for (int k = 0; k < trainClasses.cols(); k++) { - // If class of data i is same than a k class - if (k == Convert.toInt(classes.ptr(i))) - trainClasses.ptr(i, k).put(Convert.getBytes(1f)); - else - trainClasses.ptr(i, k).put(Convert.getBytes(0f)); - } - } - Mat weights = new Mat(1, trainingData.rows(), CV_32FC1, Scalar.all(1)); - // Learn classifier - - TrainData train_data = TrainData.create(trainingData, ROW_SAMPLE, trainClasses); - - /* - ann_->setTrainMethod(cv::ml::ANN_MLP::TrainingMethods::BACKPROP); - ann_->setTermCriteria(cvTermCriteria(CV_TERMCRIT_ITER, 30000, 0.0001)); - ann_->setBackpropWeightScale(0.1); - ann_->setBackpropMomentumScale(0.1);*/ - - ann.setLayerSizes(layers); - ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1); - ann.setTrainMethod(ANN_MLP.BACKPROP); - TermCriteria criteria = new TermCriteria(TermCriteria.MAX_ITER, 30000, 0.0001); - ann.setTermCriteria(criteria); - ann.setBackpropWeightScale(0.1); - ann.setBackpropMomentumScale(0.1); - ann.train(train_data); - } + // 训练模型文件保存位置 + private static final String DATA_PATH = DEFAULT_PATH + "ann_data.xml"; + private static final String MODEL_PATH = DEFAULT_PATH + "ann.xml"; + + + public int saveTrainData(int _predictsize) { - public int saveTrainData() { - - System.out.println("Begin saveTrainData"); Mat classes = new Mat(); - Mat trainingDataf5 = new Mat(); - Mat trainingDataf10 = new Mat(); - Mat trainingDataf15 = new Mat(); - Mat trainingDataf20 = new Mat(); + Mat trainingDataf = new Mat(); Vector trainingLabels = new Vector(); - String path = "res/train/data/chars_recognise_ann/chars2/chars2"; - + + // 加载数字及字母字符 for (int i = 0; i < numCharacter; i++) { - System.out.println("Character: " + strCharacters[i]); - String str = path + '/' + strCharacters[i]; + String str = DEFAULT_PATH + strCharacters[i]; + System.err.println(str); Vector files = new Vector(); FileUtil.getFiles(str, files); int size = (int) files.size(); for (int j = 0; j < size; j++) { - // System.out.println(files.get(j)); Mat img = opencv_imgcodecs.imread(files.get(j), 0); - Mat f5 = features(img, 5); - Mat f10 = features(img, 10); - Mat f15 = features(img, 15); - Mat f20 = features(img, 20); - - trainingDataf5.push_back(f5); - trainingDataf10.push_back(f10); - trainingDataf15.push_back(f15); - trainingDataf20.push_back(f20); + // System.err.println(files.get(j)); // 文件名不能包含中文 + Mat f = CoreFunc.features(img, _predictsize); + trainingDataf.push_back(f); trainingLabels.add(i); // 每一幅字符图片所对应的字符类别索引下标 } } - - path = "res/train/data/chars_recognise_ann/charsChinese/charsChinese"; - + + // 加载汉字字符 for (int i = 0; i < strChinese.length; i++) { - System.out.println("Character: " + strChinese[i]); - String str = path + '/' + strChinese[i]; + String str = DEFAULT_PATH + strChinese[i]; Vector files = new Vector(); FileUtil.getFiles(str, files); int size = (int) files.size(); for (int j = 0; j < size; j++) { - // System.out.println(files.get(j)); Mat img = opencv_imgcodecs.imread(files.get(j), 0); - Mat f5 = features(img, 5); - Mat f10 = features(img, 10); - Mat f15 = features(img, 15); - Mat f20 = features(img, 20); - - trainingDataf5.push_back(f5); - trainingDataf10.push_back(f10); - trainingDataf15.push_back(f15); - trainingDataf20.push_back(f20); + // System.err.println(files.get(j)); // 文件名不能包含中文 + Mat f = CoreFunc.features(img, _predictsize); + trainingDataf.push_back(f); trainingLabels.add(i + numCharacter); } } - - trainingDataf5.convertTo(trainingDataf5, CV_32FC1); - trainingDataf10.convertTo(trainingDataf10, CV_32FC1); - trainingDataf15.convertTo(trainingDataf15, CV_32FC1); - trainingDataf20.convertTo(trainingDataf20, CV_32FC1); + + // CV_32FC1 CV_32SC1 CV_32F + trainingDataf.convertTo(trainingDataf, opencv_core.CV_32F); + int[] labels = new int[trainingLabels.size()]; - for (int i = 0; i < labels.length; ++i) + for (int i = 0; i < labels.length; ++i) { labels[i] = trainingLabels.get(i).intValue(); + } new Mat(labels).copyTo(classes); - FileStorage fs = new FileStorage("res/train/ann_data.xml", FileStorage.WRITE); - fs.write("TrainingDataF5", trainingDataf5); - fs.write("TrainingDataF10", trainingDataf10); - fs.write("TrainingDataF15", trainingDataf15); - fs.write("TrainingDataF20", trainingDataf20); + FileStorage fs = new FileStorage(DATA_PATH, FileStorage.WRITE); + fs.write("TrainingDataF" + _predictsize, trainingDataf); fs.write("classes", classes); fs.release(); @@ -205,44 +147,68 @@ public class ANNTrain { return 0; } - public void saveModel(int _predictsize, int _neurons) { + public void train(int _predictsize, int _neurons) { - // 样本文件数据已经保存到xml - FileStorage fs = new FileStorage("res/train/ann_data.xml", FileStorage.READ); - String training = "TrainingDataF" + _predictsize; - Mat TrainingData = new Mat(fs.get(training)); - Mat Classes = new Mat(fs.get("classes")); - - long start = getTickCount(); - annTrain(TrainingData, Classes, _neurons); - long end = getTickCount(); - System.out.println("完成耗时: " + Long.valueOf((end - start) / 1000).toString()); + // 读取样本文件数据 + FileStorage fs = new FileStorage(DATA_PATH, FileStorage.READ); + Mat samples = new Mat(fs.get("TrainingDataF" + _predictsize)); + Mat classes = new Mat(fs.get("classes")); + + Mat trainClasses = new Mat(samples.rows(), numAll, CV_32F); + for (int i = 0; i < trainClasses.rows(); i++) { + for (int k = 0; k < trainClasses.cols(); k++) { + // If class of data i is same than a k class + if (k == Convert.toInt(classes.ptr(i))) { + trainClasses.ptr(i, k).put(Convert.getBytes(1f)); + + } else { + trainClasses.ptr(i, k).put(Convert.getBytes(0f)); + } + } + } - String model_name = "res/train/ann.xml"; + samples.convertTo(samples, CV_32F); + + System.out.println(samples.type()); + + // samples.type() == CV_32F || samples.type() == CV_32S + TrainData train_data = TrainData.create(samples, ROW_SAMPLE, trainClasses); - FileStorage fsto = new FileStorage(model_name, FileStorage.WRITE); + ann.clear(); + Mat layers = new Mat(1, 3, CV_32F); + layers.ptr(0).put(Convert.getBytes(samples.cols())); + layers.ptr(1).put(Convert.getBytes(_neurons)); + layers.ptr(2).put(Convert.getBytes(numAll)); + + ann.setLayerSizes(layers); + ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1); + ann.setTrainMethod(ANN_MLP.BACKPROP); + TermCriteria criteria = new TermCriteria(TermCriteria.MAX_ITER, 30000, 0.0001); + ann.setTermCriteria(criteria); + ann.setBackpropWeightScale(0.1); + ann.setBackpropMomentumScale(0.1); + ann.train(train_data); + + System.err.println("完成 "); + FileStorage fsto = new FileStorage(MODEL_PATH, FileStorage.WRITE); ann.write(fsto, "ann"); } - public int annMain() { - System.out.println("To be begin."); - - saveTrainData(); + public static void main(String[] args) { + ANNTrain annT = new ANNTrain(); // 可根据需要训练不同的predictSize或者neurons的ANN模型 - /*for (int i = 2; i <= 2; i++) { - int size = i * 5; - for (int j = 5; j <= 10; j++) { - int neurons = j * 10; - saveModel(size, neurons); - } - }*/ + int _predictsize = 10; + int _neurons = 40; + + // annT.saveTrainData(_predictsize); // 这里演示只训练model文件夹下的ann.xml，此模型是一个predictSize=10,neurons=40的ANN模型。 // 根据机器的不同，训练时间不一样，但一般需要10分钟左右，所以慢慢等一会吧。 - saveModel(10, 40); + annT.train(_predictsize, _neurons); System.out.println("To be end."); - return 0; } + + } \ No newline at end of file