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yuxue 5 years ago
parent bb3df46522
commit 28ed2a5565
4 changed files with 56 additions and 32 deletions
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56
src/main/java/com/yuxue/train/ANNTrain.java
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@ -45,7 +45,7 @@ public class ANNTrain {
// 训练模型文件保存位置
private static final String MODEL_PATH = DEFAULT_PATH + "ann.xml";
public static float[] projectedHistogram(final Mat img, Direction direction) {
int sz = 0;
switch (direction) {
@ -121,12 +121,10 @@ public class ANNTrain {
for (int i = 0; i < Constant.numCharacter; i++) {
String str = DEFAULT_PATH + "learn/" + Constant.strCharacters[i];
Vector<String> files = new Vector<String>();
FileUtil.getFiles(str, files);
int size = (int) files.size();
for (int j = 0; j < size; j++) {
Mat img = Imgcodecs.imread(files.get(j), 0);
// System.err.println(files.get(j)); // 文件名不能包含中文
FileUtil.getFiles(str, files); // 文件名不能包含中文
for (String filePath : files) {
Mat img = Imgcodecs.imread(filePath);
Mat f = features(img, _predictsize);
samples.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
@ -138,22 +136,18 @@ public class ANNTrain {
String str = DEFAULT_PATH + "learn/" + Constant.strChinese[i];
Vector<String> files = new Vector<String>();
FileUtil.getFiles(str, files);
int size = (int) files.size();
for (int j = 0; j < size; j++) {
Mat img = Imgcodecs.imread(files.get(j), 0);
// System.err.println(files.get(j)); // 文件名不能包含中文
for (String filePath : files) {
Mat img = Imgcodecs.imread(filePath);
Mat f = features(img, _predictsize);
samples.push_back(f);
trainingLabels.add(i + Constant.numCharacter);
trainingLabels.add(i + Constant.numCharacter); // 每一幅字符图片所对应的字符类别索引下标
}
}
//440 vhist.length + hhist.length + lowData.cols() * lowData.rows();
// CV_32FC1 CV_32SC1 CV_32F
Mat classes = new Mat(trainingLabels.size(), Constant.numAll, CvType.CV_32F);
float[] labels = new float[trainingLabels.size()];
for (int i = 0; i < labels.length; ++i) {
classes.put(i, trainingLabels.get(i), 1.f);
@ -167,8 +161,8 @@ public class ANNTrain {
layers.put(0, 0, samples.cols());
layers.put(0, 1, _neurons);
layers.put(0, 2, classes.cols());
System.out.println(layers);
// System.out.println(layers);
ann.setLayerSizes(layers);
ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1);
@ -183,32 +177,42 @@ public class ANNTrain {
// ann.write(fsto, "ann");
ann.save(MODEL_PATH);
}
public void predict() {
ann.clear();
ann = ANN_MLP.load(MODEL_PATH);
Vector<String> files = new Vector<String>();
FileUtil.getFiles(DEFAULT_PATH + "test/", files);
String plate = "";
for (String string : files) {
Mat img = Imgcodecs.imread(string, 0);
Mat f = features(img, Constant.predictSize);
// 140 predictSize = 10; vhist.length + hhist.length + lowData.cols() * lowData.rows();
// 440 predictSize = 20;
Mat output = new Mat(1, 140, CvType.CV_32F);
//ann.predict(f, output, 0); // 预测结果
System.err.println(string + "===>" + (int) ann.predict(f, output, 0));
// ann.predict(f, output, 0);
// System.err.println(string + "===>" + output.get(0, 0)[0]);
int index = (int) ann.predict(f, output, 0);
System.err.println(string + "===>" + index);
if (index < Constant.numCharacter) {
plate += String.valueOf(Constant.strCharacters[index]);
} else {
String s = Constant.strChinese[index - Constant.numCharacter];
plate += Constant.KEY_CHINESE_MAP.get(s);
}
}
System.err.println("===>" + plate);
}
public static void main(String[] args) {
ANNTrain annT = new ANNTrain();
// 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
// 可根据需要训练不同的predictSize或者neurons的ANN模型
@ -216,7 +220,7 @@ public class ANNTrain {
// annT.train(Constant.predictSize, Constant.neurons);
annT.predict();
System.out.println("The end.");
}

2
src/main/java/com/yuxue/train/ANNTrain1.java
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@ -128,7 +128,7 @@ public class ANNTrain1 {
int index = (int) ann.predict(f, output, 0);
String result = "";
if (index <= Constant.numCharacter) {
if (index < Constant.numCharacter) {
result = String.valueOf(Constant.strCharacters[index]);
} else {
String s = Constant.strChinese[index - Constant.numCharacter];

2
src/main/java/com/yuxue/util/ImageUtil.java
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@ -71,7 +71,7 @@ public class ImageUtil {
Instant start = Instant.now();
String tempPath = DEFAULT_BASE_TEST_PATH + "test/";
String filename = tempPath + "/100_yuantu.jpg";
filename = tempPath + "/100_yuantu2.jpg";
filename = tempPath + "/100_yuantu1.jpg";
// filename = tempPath + "/109_crop_0.png";
Mat src = Imgcodecs.imread(filename);

28
src/main/java/com/yuxue/util/PlateUtil.java
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@ -24,6 +24,7 @@ import com.google.common.collect.Lists;
import com.google.common.collect.Maps;
import com.yuxue.constant.Constant;
import com.yuxue.enumtype.PlateColor;
import com.yuxue.train.ANNTrain;
import com.yuxue.train.SVMTrain;
@ -58,7 +59,8 @@ public class PlateUtil {
entry.setValue(index);
index ++;
}
// 这个位置加载模型文件会报错，暂时没时间定位啥问题报错
/*loadSvmModel("D:/PlateDetect/train/plate_detect_svm/svm2.xml");
loadAnnModel("D:/PlateDetect/train/chars_recognise_ann/ann.xml");*/
}
@ -288,16 +290,34 @@ public class PlateUtil {
Vector<Rect> sorted = new Vector<Rect>();
sortRect(rt, sorted);
String plate = "";
Vector<Mat> dst = new Vector<Mat>();
ANNTrain annT = new ANNTrain();
for (int i = 0; i < sorted.size(); i++) {
Mat img_crop = new Mat(threshold, sorted.get(i));
img_crop = preprocessChar(img_crop);
dst.add(img_crop);
Imgcodecs.imwrite(tempPath + debugMap.get("plateCrop") + "_plateCrop_" + i + ".jpg", img_crop);
if(debug) {
Imgcodecs.imwrite(tempPath + debugMap.get("plateCrop") + "_plateCrop_" + i + ".jpg", img_crop);
}
Mat f = annT.features(img_crop, Constant.predictSize);
// 字符预测
Mat output = new Mat(1, 140, CvType.CV_32F);
int index = (int) ann.predict(f, output, 0);
if (index < Constant.numCharacter) {
plate += String.valueOf(Constant.strCharacters[index]);
} else {
String s = Constant.strChinese[index - Constant.numCharacter];
plate += Constant.KEY_CHINESE_MAP.get(s);
}
}
System.err.println("===>" + plate);
return;
}

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