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yuxue 5 years ago
parent 078f942bbc
commit d264764bea
4 changed files with 123 additions and 38 deletions
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4
README.md
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@ -60,10 +60,6 @@ debug_Contours：
![1.png](./res/doc_image/debug_Contours.jpg)
debug_result：
![1.png](./res/doc_image/debug_result.jpg)
debug_crop：
![1.png](./res/doc_image/debug_crop_1.jpg)

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21
src/main/java/com/yuxue/util/FileUtil.java
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@ -44,15 +44,32 @@ public class FileUtil {
return false;
}
/**
* 重命名文件
* @param file
* @param newName 可以是文件名，也可以是路径+文件名
* @return
*/
public static boolean renameFile(String filePath, String newName) {
File file = new File(filePath);
return renameFile(file, newName);
}
/**
* 重命名文件
* @param file
* @param newName 可以是文件名，也可以是路径+文件名
* @return
*/
public static boolean renameFile(File file, String newName) {
if(file.exists()) {
String targetPath = file.getParentFile().getAbsolutePath() + "/" + newName;
String targetPath = null;
if(newName.indexOf("/") >= 0 || newName.indexOf("\\\\") >= 0) {
targetPath = newName;
} else {
targetPath = file.getParentFile().getAbsolutePath() + "/" + newName;
}
File targetFile = new File(targetPath);
file.renameTo(targetFile);
return true;

136
src/main/java/com/yuxue/util/ImageUtil.java
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@ -1,6 +1,5 @@
package com.yuxue.util;
import java.util.Arrays;
import java.util.Map;
import java.util.Set;
@ -9,6 +8,7 @@ import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_core.Mat;
import org.bytedeco.javacpp.opencv_core.MatVector;
import org.bytedeco.javacpp.opencv_core.Scalar;
import org.bytedeco.javacpp.opencv_core.Size;
import org.bytedeco.javacpp.opencv_imgcodecs;
import org.bytedeco.javacpp.opencv_imgproc;
@ -25,31 +25,53 @@ public class ImageUtil {
private static String DEFAULT_BASE_TEST_PATH = "D:/PlateDetect/temp/";
// 车牌定位处理步骤，该map用于表示步骤图片的顺序
private static Map<String, Integer> debugMap = Maps.newLinkedHashMap();
static {
// debugMap.put("result", 99);
debugMap.put("gaussianBlur", 0); // 高斯模糊
debugMap.put("gray", 1); // 图像灰度化
debugMap.put("sobel", 2); // Sobel 算子
debugMap.put("threshold", 3); //图像二值化
debugMap.put("morphology", 4); // 图像闭操作
debugMap.put("contours", 5); // 提取外部轮廓
debugMap.put("result", 6); // 原图处理结果
debugMap.put("crop", 7); // 切图
debugMap.put("resize", 8); // 切图resize
debugMap.put("char_threshold", 9); //
// debugMap.put("char_clearLiuDing", 10); // 去除柳钉
// debugMap.put("specMat", 11);
// debugMap.put("chineseMat", 12);
// debugMap.put("char_auxRoi", 13);
}
public static void main(String[] args) {
String filename = DEFAULT_BASE_TEST_PATH + "test.jpg";
// String filename = DEFAULT_BASE_TEST_PATH + "test01.jpg";
String tempPath = DEFAULT_BASE_TEST_PATH + System.currentTimeMillis() + "/";
FileUtil.createDir(tempPath); // 创建文件夹
// String filename = DEFAULT_BASE_TEST_PATH + "test01.jpg";
String filename = DEFAULT_BASE_TEST_PATH + "test.png";
Mat inMat = opencv_imgcodecs.imread(filename);
FileUtil.renameFile(filename, tempPath + "000_yuantu.jpg");
Mat inMat = opencv_imgcodecs.imread(filename);
Boolean debug = true;
Mat gsMat = ImageUtil.gaussianBlur(inMat, debug, tempPath);
Mat grey = ImageUtil.grey(gsMat, debug, tempPath);
Mat sobel = ImageUtil.sobel(grey, debug, tempPath);
// ImageUtil.rgb2Hsv(inMat, debug, tempPath);
}
/**
* 高斯模糊
* @param inMat
@ -61,12 +83,12 @@ public class ImageUtil {
Mat dst = new Mat();
opencv_imgproc.GaussianBlur(inMat, dst, new Size(DEFAULT_GAUSSIANBLUR_SIZE, DEFAULT_GAUSSIANBLUR_SIZE), 0, 0, opencv_core.BORDER_DEFAULT);
if (debug) {
opencv_imgcodecs.imwrite(tempPath + "gaussianBlur.jpg", dst);
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("gaussianBlur") + 100) + "_gaussianBlur.jpg", dst);
}
return dst;
}
/**
* 将图像进行灰度化
* @param inMat
@ -78,12 +100,12 @@ public class ImageUtil {
Mat dst = new Mat();
opencv_imgproc.cvtColor(inMat, dst, opencv_imgproc.CV_RGB2GRAY);
if (debug) {
opencv_imgcodecs.imwrite(tempPath + "debugGray.jpg", dst);
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("gray") + 100) + "_gray.jpg", dst);
}
return dst;
}
/**
* 对图像进行Sobel 运算，得到图像的一阶水平方向导数
* @param inMat
@ -97,14 +119,14 @@ public class ImageUtil {
public static final int SOBEL_X_WEIGHT = 1;
public static final int SOBEL_Y_WEIGHT = 0;
public static Mat sobel(Mat inMat, Boolean debug, String tempPath) {
Mat dst = new Mat();
Mat grad_x = new Mat();
Mat grad_y = new Mat();
Mat abs_grad_x = new Mat();
Mat abs_grad_y = new Mat();
opencv_imgproc.Sobel(inMat, grad_x, SOBEL_DDEPTH, 1, 0, 3, SOBEL_SCALE, SOBEL_DELTA, opencv_core.BORDER_DEFAULT);
opencv_core.convertScaleAbs(grad_x, abs_grad_x);
@ -113,15 +135,15 @@ public class ImageUtil {
// Total Gradient (approximate)
opencv_core.addWeighted(abs_grad_x, SOBEL_X_WEIGHT, abs_grad_y, SOBEL_Y_WEIGHT, 0, dst);
if (debug) {
opencv_imgcodecs.imwrite(tempPath + "debugSobel.jpg", dst);
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("sobel") + 100) + "_sobel.jpg", dst);
}
return dst;
}
/**
* 对图像进行二值化。将灰度图像（每个像素点有256 个取值可能）转化为二值图像（每个像素点仅有1 和0 两个取值可能）
* @param inMat
@ -133,12 +155,63 @@ public class ImageUtil {
Mat dst = new Mat();
opencv_imgproc.threshold(inMat, dst, 0, 255, opencv_imgproc.CV_THRESH_OTSU + opencv_imgproc.CV_THRESH_BINARY);
if (debug) {
opencv_imgcodecs.imwrite(tempPath + "debugThreshold.jpg", dst);
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("threshold") + 100) + "_threshold.jpg", dst);
}
return dst;
}
/**
* 使用闭操作。对图像进行闭操作以后，可以看到车牌区域被连接成一个矩形装的区域
* @param inMat
* @param debug
* @param tempPath
* @return
*/
public static final int DEFAULT_MORPH_SIZE_WIDTH = 17;
public static final int DEFAULT_MORPH_SIZE_HEIGHT = 3;
public static Mat morphology(Mat inMat, Boolean debug, String tempPath) {
Mat dst = new Mat();
Size size = new Size(DEFAULT_MORPH_SIZE_WIDTH, DEFAULT_MORPH_SIZE_HEIGHT);
Mat element = opencv_imgproc.getStructuringElement(opencv_imgproc.MORPH_RECT, size);
opencv_imgproc.morphologyEx(inMat, dst, opencv_imgproc.MORPH_CLOSE, element);
if (debug) {
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("morphology") + 100) + "_morphology.jpg", dst);
}
return dst;
}
/**
* Find 轮廓 of possibles plates 求轮廓。求出图中所有的轮廓。
* 这个算法会把全图的轮廓都计算出来，因此要进行筛选。
* @param src 原图
* @param inMat morphology Mat
* @param debug
* @param tempPath
* @return
*/
public static MatVector contours(Mat src, Mat inMat, Boolean debug, String tempPath) {
MatVector contours = new MatVector();
opencv_imgproc.findContours(inMat, contours, // a vector of contours
opencv_imgproc.CV_RETR_EXTERNAL, // 提取外部轮廓
opencv_imgproc.CV_CHAIN_APPROX_NONE); // all pixels of each contours
if (debug) {
// 将轮廓描绘到原图
opencv_imgproc.drawContours(src, contours, -1, new Scalar(0, 0, 255, 255));
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("contours") + 100) + "_contours.jpg", src);
}
return contours;
}
/**
@ -157,13 +230,13 @@ public class ImageUtil {
// 直方图均衡化是一种常见的增强图像对比度的方法，使用该方法可以增强局部图像的对比度，尤其在数据较为相似的图像中作用更加明显
opencv_imgproc.equalizeHist(hsvSplit.get(2), hsvSplit.get(2));
opencv_core.merge(hsvSplit, dst);
if (debug) {
opencv_imgcodecs.imwrite(tempPath + "hsvMat_"+System.currentTimeMillis()+".jpg", dst);
}
return dst;
}
/**
* 获取HSV中各个颜色所对应的H的范围
@ -175,7 +248,7 @@ public class ImageUtil {
* @param debug
*/
public static void getHSVValue(Mat inMat, Boolean debug, String tempPath) {
int channels = inMat.channels();
int nRows = inMat.rows();
// 图像数据列需要考虑通道数的影响；
@ -200,7 +273,6 @@ public class ImageUtil {
} else {
map.put(H, 1);
}
}
}
@ -214,5 +286,5 @@ public class ImageUtil {
return;
}
}

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