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Java OpenCV - 使用霍夫变换进行矩形检测

转载 作者:塔克拉玛干 更新时间:2023-11-01 23:06:38 27 4
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我正在开发一个程序来检测矩形形状并将边界框绘制到检测到的区域。

对于边缘检测,我使用了 Canny 边缘检测。然后,我使用霍夫变换来提取线条。

这是原图 enter image description here

这是结果图 enter image description here

我的问题是我无法为检测到的区域绘制边界框。看来我的程序只能检测到一条水平线。如何检测矩形形状并将矩形线绘制到检测到的形状?

我看过类似的题,要求找到矩形的4个角点,检查点是否为90度,并找到交点。我真的很困惑如何在 Java opencv 中对其进行编码。其他检测形状并将边界框绘制到检测到的方法也可以。

这是代码

import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.imgcodecs.*;
import org.opencv.imgproc.Imgproc;

public class HoughTransformCV2 {

public static void main(String[] args) {
try {
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
Mat source = Imgcodecs.imread("rectangle.jpg", Imgcodecs.CV_LOAD_IMAGE_ANYCOLOR);
Mat destination = new Mat(source.rows(), source.cols(), source.type());

Imgproc.cvtColor(source, destination, Imgproc.COLOR_RGB2GRAY);
Imgproc.equalizeHist(destination, destination);
Imgproc.GaussianBlur(destination, destination, new Size(5, 5), 0, 0, Core.BORDER_DEFAULT);

Imgproc.Canny(destination, destination, 50, 100);
//Imgproc.adaptiveThreshold(destination, destination, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY, 15, 40);
Imgproc.threshold(destination, destination, 0, 255, Imgproc.THRESH_BINARY);

if (destination != null) {
Mat lines = new Mat();
Imgproc.HoughLinesP(destination, lines, 1, Math.PI / 180, 50, 30, 10);
Mat houghLines = new Mat();
houghLines.create(destination.rows(), destination.cols(), CvType.CV_8UC1);

//Drawing lines on the image
for (int i = 0; i < lines.cols(); i++) {
double[] points = lines.get(0, i);
double x1, y1, x2, y2;
x1 = points[0];
y1 = points[1];
x2 = points[2];
y2 = points[3];

Point pt1 = new Point(x1, y1);
Point pt2 = new Point(x2, y2);

//Drawing lines on an image
Imgproc.line(source, pt1, pt2, new Scalar(0, 0, 255), 4);
}

}

Imgcodecs.imwrite("rectangle_houghtransform.jpg", source);

} catch (Exception e) {
System.out.println("error: " + e.getMessage());
}
}
}

Java 方面的任何帮助将不胜感激:)非常感谢!

最佳答案

您可以按照以下步骤执行此操作:

  1. 将颜色转换为灰色后,执行 canny edge。

    int 阈值 = 100;

    Imgproc.Canny(grayImage, edges, threshold, threshold*3);

  2. 现在找到边缘图像中的轮廓。

Imgproc.findContours(edges, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);

  1. 然后在所有轮廓上循环

.....

MatOfPoint2f matOfPoint2f = new MatOfPoint2f();
MatOfPoint2f approxCurve = new MatOfPoint2f();

for (int idx = 0; idx >= 0; idx = (int) hierarchy.get(0, idx)[0]) {
MatOfPoint contour = contours.get(idx);
Rect rect = Imgproc.boundingRect(contour);
double contourArea = Imgproc.contourArea(contour);
matOfPoint2f.fromList(contour.toList());
Imgproc.approxPolyDP(matOfPoint2f, approxCurve, Imgproc.arcLength(matOfPoint2f, true) * 0.02, true);
long total = approxCurve.total();
if (total == 3) { // is triangle
// do things for triangle
}
if (total >= 4 && total <= 6) {
List<Double> cos = new ArrayList<>();
Point[] points = approxCurve.toArray();
for (int j = 2; j < total + 1; j++) {
cos.add(angle(points[(int) (j % total)], points[j - 2], points[j - 1]));
}
Collections.sort(cos);
Double minCos = cos.get(0);
Double maxCos = cos.get(cos.size() - 1);
boolean isRect = total == 4 && minCos >= -0.1 && maxCos <= 0.3;
boolean isPolygon = (total == 5 && minCos >= -0.34 && maxCos <= -0.27) || (total == 6 && minCos >= -0.55 && maxCos <= -0.45);
if (isRect) {
double ratio = Math.abs(1 - (double) rect.width / rect.height);
drawText(rect.tl(), ratio <= 0.02 ? "SQU" : "RECT");
}
if (isPolygon) {
drawText(rect.tl(), "Polygon");
}
}
}

辅助方法:

private double angle(Point pt1, Point pt2, Point pt0) {
double dx1 = pt1.x - pt0.x;
double dy1 = pt1.y - pt0.y;
double dx2 = pt2.x - pt0.x;
double dy2 = pt2.y - pt0.y;
return (dx1*dx2 + dy1*dy2)/Math.sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}

private void drawText(Point ofs, String text) {
Imgproc.putText(colorImage, text, ofs, Core.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(255,255,25));
}

希望对您有所帮助!!

关于Java OpenCV - 使用霍夫变换进行矩形检测,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/38748508/

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