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yuxue 5 years ago
parent ed3bb45a90
commit 5aab00224e
1 changed files with 114 additions and 48 deletions
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src/main/java/com/yuxue/util/ImageUtil.java
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@ -6,6 +6,7 @@ import java.util.Set;
import java.util.Vector;
import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.IntPointer;
import org.bytedeco.javacpp.opencv_core;
import org.bytedeco.javacpp.opencv_core.Mat;
import org.bytedeco.javacpp.opencv_core.MatVector;
@ -79,7 +80,7 @@ public class ImageUtil {
String tempPath = DEFAULT_BASE_TEST_PATH + "test/";
String filename = tempPath + "/100_yuantu.jpg";
// filename = tempPath + "/100_yuantu1.jpg";
filename = tempPath + "/100_yuantu1.jpg";
Mat src = opencv_imgcodecs.imread(filename);
@ -90,12 +91,12 @@ public class ImageUtil {
Mat grey = ImageUtil.grey(gsMat, debug, tempPath);
Mat sobel = ImageUtil.sobel(grey, debug, tempPath);
// Mat sobel = ImageUtil.scharr(grey, debug, tempPath);
Mat threshold = ImageUtil.threshold(sobel, debug, tempPath);
// Mat threshold = ImageUtil.threshold(sobel, debug, tempPath);
Mat morphology = ImageUtil.morphology(threshold, debug, tempPath);
Mat morphology = ImageUtil.morphology(ImageUtil.threshold(sobel, debug, tempPath), debug, tempPath);
MatVector contours = ImageUtil.contours(src, morphology, debug, tempPath);
@ -139,6 +140,7 @@ public class ImageUtil {
if (debug) {
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("gray") + 100) + "_gray.jpg", dst);
}
inMat.release();
return dst;
}
@ -174,14 +176,14 @@ public class ImageUtil {
opencv_core.addWeighted(abs_grad_x, SOBEL_X_WEIGHT, abs_grad_y, SOBEL_Y_WEIGHT, 0, dst);
abs_grad_x.release();
abs_grad_y.release();
if (debug) {
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("sobel") + 100) + "_sobel.jpg", dst);
}
return dst;
}
/**
* 对图像进行scharr 运算，得到图像的一阶水平方向导数
* @param inMat
@ -197,7 +199,7 @@ public class ImageUtil {
Mat grad_y = new Mat();
Mat abs_grad_x = new Mat();
Mat abs_grad_y = new Mat();
//注意求梯度的时候我们使用的是Scharr算法，sofia算法容易收到图像细节的干扰
//所谓梯度运算就是对图像中的像素点进行就导数运算，从而得到相邻两个像素点的差异值 by:Tantuo
opencv_imgproc.Scharr(inMat, grad_x, opencv_core.CV_32F, 1, 0);
@ -217,7 +219,7 @@ public class ImageUtil {
}
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）
@ -230,18 +232,10 @@ public class ImageUtil {
public static Mat threshold(Mat inMat, Boolean debug, String tempPath) {
Mat dst = new Mat();
opencv_imgproc.threshold(inMat, dst, 0, 255, opencv_imgproc.CV_THRESH_OTSU + opencv_imgproc.CV_THRESH_BINARY);
/*for (int i = 0; i < dst.rows(); i++) {
for (int j = 0; j < dst.cols(); j++) {
if(dst.ptr(i, j).getInt() !=0 ) {
System.err.println(i + "\t" + j + "\t" +dst.ptr(i, j).getInt());
}
}
}*/
if (debug) {
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("threshold") + 100) + "_threshold.jpg", dst);
}
inMat.release();
return dst;
}
@ -258,25 +252,21 @@ public class ImageUtil {
public static final int DEFAULT_MORPH_SIZE_WIDTH = 9;
public static final int DEFAULT_MORPH_SIZE_HEIGHT = 3;
public static Mat morphology(Mat inMat, Boolean debug, String tempPath) {
Mat dst = new Mat();
Mat dst = new Mat(inMat.size(), opencv_core.CV_8UC1);
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) + "_morphology0.jpg", dst);
}
// 去除小连通区域
removeSmallRegion(dst, dst, 100, 1, 1, debug, tempPath);
// removeSmallRegion(dst, dst, 100, 1, 1, debug, tempPath);
// 去除孔洞
removeSmallRegion(dst, dst, 100, 0, 0, debug, tempPath);
if (debug) {
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("morphology") + 100) + "_morphology1.jpg", dst);
}
// removeSmallRegion(dst, dst, 100, 0, 0, debug, tempPath);
clearHole(dst, 136/10, 36/10, debug, tempPath);
return dst;
}
@ -303,7 +293,7 @@ public class ImageUtil {
for (int i = 0; i < contours.size(); i++) {
retContour.put(contours.get(i));
}*/
if (debug) {
Mat result = new Mat();
src.copyTo(result); // 复制一张图，不在原图上进行操作，防止后续需要使用原图
@ -338,23 +328,23 @@ public class ImageUtil {
// RotatedRect 该类表示平面上的旋转矩形，有三个属性： 矩形中心点(质心); 边长(长和宽); 旋转角度
// boundingRect()得到包覆此轮廓的最小正矩形， minAreaRect()得到包覆轮廓的最小斜矩形
RotatedRect mr = opencv_imgproc.minAreaRect(contours.get(i));
float angle = Math.abs(mr.angle());
if (checkPlateSize(mr) && angle <= DEFAULT_ANGLE) { // 判断尺寸及旋转角度 ±30°，排除不合法的图块
mv.put(contours.get(i));
Size rect_size = new Size((int) mr.size().width(), (int) mr.size().height());
if (mr.size().width() / mr.size().height() < 1) { // 宽度小于高度
angle = 90 + angle; // 旋转90°
rect_size = new Size(rect_size.height(), rect_size.width());
}
// 旋转角度，根据需要是否进行角度旋转
Mat img_rotated = new Mat();
Mat rotmat = opencv_imgproc.getRotationMatrix2D(mr.center(), angle, 1); // 旋转
opencv_imgproc.warpAffine(src, img_rotated, rotmat, src.size()); // 仿射变换
// 切图
Mat img_crop = new Mat();
opencv_imgproc.getRectSubPix(src, rect_size, mr.center(), img_crop);
@ -417,6 +407,7 @@ public class ImageUtil {
}
return min <= area && area <= max && rmin <= r && r <= rmax;
}
@ -499,9 +490,9 @@ public class ImageUtil {
return;
}
/**
* 计算最大内接矩形
* https://blog.csdn.net/cfqcfqcfqcfqcfq/article/details/53084090
@ -514,7 +505,7 @@ public class ImageUtil {
edge[1] = (int) point2f.x() + 1;//right
edge[2] = (int) point2f.y() - 1;//bottom
edge[3] = (int) point2f.x() - 1;//left
boolean[] expand = { true, true, true, true};//扩展标记位
int n = 0;
while (expand[0] || expand[1] || expand[2] || expand[3]){
@ -530,15 +521,15 @@ public class ImageUtil {
return null;
}
/**
* @brief expandEdge 扩展边界函数
* @param img:输入图像，单通道二值图，深度为8
* @param edge 边界数组，存放4条边界值
* @param edgeID 当前边界号
* @return 布尔值 确定当前边界是否可以扩展
*/
/**
* @brief expandEdge 扩展边界函数
* @param img:输入图像，单通道二值图，深度为8
* @param edge 边界数组，存放4条边界值
* @param edgeID 当前边界号
* @return 布尔值 确定当前边界是否可以扩展
*/
public static boolean expandEdge(Mat img, int edge[], int edgeID) {
int nc = img.cols();
int nr = img.rows();
@ -590,7 +581,7 @@ public class ImageUtil {
}
}
/**
* 对于二值图，0代表黑色，255代表白色。
* 去除小连通区域与孔洞，小连通区域用8邻域，孔洞用4邻域
@ -663,7 +654,7 @@ public class ImageUtil {
}
}
}
if (GrowBuffer.size() > AreaLimit) { // 判断结果（是否超出限定的大小），1为未超出，2为超出
CheckResult = 2;
} else {
@ -691,4 +682,79 @@ public class ImageUtil {
}
}
/**
* 清除二值图像的黑洞
* 按矩形清理
* @param inMat 二值图像
* @param rowLimit 宽度半径限制
* @param colsLimit 高度半径限制
* @param debug
* @param tempPath
*/
public static void clearHole(Mat inMat, int rowLimit, int colsLimit, Boolean debug, String tempPath) {
int uncheck = 0, checking = 1, black = 2, white = 3;
Mat dst = new Mat(inMat.size(), opencv_core.CV_8UC1);
inMat.copyTo(dst);
rowLimit = 2;
colsLimit = 2;
// 初始化的图像全部为0，未检查; 全黑图像
Mat label = new Mat(inMat.size(), opencv_core.CV_8UC1);
// 标记所有的白色区域
for (int i = 0; i < inMat.rows(); i++) {
for (int j = 0; j < inMat.cols(); j++) {
if (inMat.ptr(i, j).getInt() != 0) { // 对于二值图，0代表黑色，255代表白色
label.ptr(i, j).putInt(white);
int x1 = i - rowLimit < 0 ? 0 : i - rowLimit;
int x2 = i + rowLimit > inMat.rows() ? inMat.rows() : i + rowLimit;
int y1 = j - colsLimit < 0 ? 0 : j - colsLimit ;
int y2 = j + colsLimit > inMat.cols() ? inMat.cols() : j + colsLimit ;
/*IntPointer p1 = new IntPointer(x1, y1); // 左上角
IntPointer p2 = new IntPointer(x1, y2); // 左下角
IntPointer p3 = new IntPointer(x2, y1); // 右上角
IntPointer p4 = new IntPointer(x2, y2); // 右下角
*/
// System.out.println(x1 + "," + x2 + "\t" + y1 + "," + y2 + "\n");
// System.out.println(inMat.ptr(x1, y1).getInt() + "," + inMat.ptr(x1, y2).getInt() + "," + inMat.ptr(x2, y1).getInt() + "," + inMat.ptr(x2, y2).getInt());
//if(inMat.ptr(x1, y1).getInt() > 10 && inMat.ptr(x1, y2).getInt() > 10 && inMat.ptr(x2, y1).getInt() > 10 && inMat.ptr(x2, y2).getInt() > 10 ) {
for (int n = x1; n < x2; n++) {
for (int m = y1; m < y2; m++) {
//System.err.println(n + "," + m);
if (/*inMat.ptr(n, m).getInt() < 10 && */label.ptr(n, m).getInt() == uncheck) {
// System.err.println(n + "," + m);
label.ptr(n, m).putInt(black);
}
}
}
// }
}
}
}
// 1184 //1550
int count = 0;
// 替换颜色
for (int i = 0; i < inMat.rows(); i++) {
for (int j = 0; j < inMat.cols(); j++) {
if(label.ptr(i, j).getInt() == black) {
dst.ptr(i, j).putInt(255);
count ++;
}
}
}
System.err.println(count);
if (debug) {
opencv_imgcodecs.imwrite(tempPath + (debugMap.get("morphology") + 100) + "_morphology1.jpg", dst);
}
}
}

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