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Java调用opencv实现图片矫正功能

作者:冒泡的肥皂

这篇文章主要为大家详细介绍了Java如何调用opencv实现图片矫正功能,文中的示例代码简洁易懂,感兴趣的小伙伴可以跟随小编一起学习一下

1.通过霍夫线矫正土图片

public static void getCorrect1(Mat image) {
Mat clone=image.clone();
Mat src=image.clone();
int width = image.width();
int height = image.height();
int pointCount = width * height;
Mat points=image.reshape(3, pointCount);
points.convertTo(points,  CvType.CV_32F);
Imgproc.GaussianBlur(clone, clone, new Size(3, 3), 0, 0);
HighGui.imshow("GaussianBlur", clone);
Imgproc.cvtColor(clone, clone,Imgproc.COLOR_BGR2GRAY);
HighGui.imshow("GRY", clone);
//一般来说,高阈值maxVal推荐是低阈值minVal的2~3倍
int lowThresh=20;
//边缘检测
Imgproc.Canny(clone, clone,lowThresh, lowThresh*3,3);
HighGui.imshow("Canny", clone);
Mat storage = new Mat();
/**
	 * HoughLines(Mat image, Mat lines, double rho, double theta, int threshold, double srn, double stn, double min_theta, double max_theta)
	 * image 原图
	 * lines 霍夫线变换检测到线条的输出矢量,由(ρ,θ)表示
	 * rho   以像素为单位的距离精度(直线搜索时的进步尺寸的单位半径)
	 * theta 以弧度为单位的角度精度(直线搜索时的进步尺寸的角度单位)
	 * threshold 累加平面的阈值参数(直线被识别时它在累加平面中必须达到的值)
	 * srn    对于多尺度霍夫变换,这是第三个参数进步尺寸的除数距离。
        *        粗略累加器进步尺寸直接是rho,精确的累加器进步尺寸为rho/srn
	 * min_theta 检测到的直线的最小角度
	 * max_theta 测到的直线的最大角度
	 */
double sum = 0;
double angle=0;
Imgproc.HoughLines(clone, storage, 1, Math.PI/ 180.0, 200, 0, 0);
for (int x = 0; x < storage.rows(); x++) {
	double[] vec = storage.get(x, 0);
	double rho = vec[0];
	double theta = vec[1];
	Point pt1 = new Point();
	Point pt2 = new Point();
	double a = Math.cos(theta);
	double b = Math.sin(theta);
	double x0 = a * rho;
	double y0 = b * rho;
	pt1.x = Math.round(x0 + 1000 * (-b));
	pt1.y = Math.round(y0 + 1000 * (a));
	pt2.x = Math.round(x0 - 1000 * (-b));
	pt2.y = Math.round(y0 - 1000 * (a));
	sum += theta;
	Imgproc.line(clone, pt1, pt2, new Scalar(255, 255, 255, 255), 1, Imgproc.LINE_4, 0);
}
HighGui.imshow("houghLines", clone);
double average = sum / storage.rows(); //对所有角度求平均,这样做旋转效果会更好
angle = average/ Math.PI * 180 - 90;
System.out.println("average:"+angle);
Point center=new Point();
center.x=image.cols()/2;
center.y=image.rows()/2;
// 得到旋转矩阵算子
Mat matrix = Imgproc.getRotationMatrix2D(center, angle, 1);
Imgproc.warpAffine(src, src, matrix,src.size(), 1, 0, new Scalar(0, 0, 0));
HighGui.imshow("rotation", src);
}

2.通过轮廓检测矫正土图片

public static Mat getCorrect2(Mat image) {
Mat clone=image.clone();
Mat src=image.clone();
int width = image.width();
int height = image.height();
int pointCount = width * height;
Mat points=image.reshape(3, pointCount);
points.convertTo(points,  CvType.CV_32F);
Imgproc.GaussianBlur(clone, clone, new Size(3, 3), 0, 0);
HighGui.imshow("GaussianBlur", clone);
Imgproc.cvtColor(clone, clone,Imgproc.COLOR_BGR2GRAY);
HighGui.imshow("GRY", clone);
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
// 寻找轮廓
Imgproc.findContours(clone, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_NONE,
        new Point(0, 0));
// 找出匹配到的最大轮廓
double area = Imgproc.boundingRect(contours.get(0)).area();
int index = 0;
// 找出匹配到的最大轮廓
for (int i = 0; i < contours.size(); i++) {
    double tempArea = Imgproc.boundingRect(contours.get(i)).area();
    if (tempArea > area) {
        area = tempArea;
        index = i;
    }
}
MatOfPoint2f matOfPoint2f = new MatOfPoint2f(contours.get(index).toArray());
RotatedRect rect = Imgproc.minAreaRect(matOfPoint2f);
 // 获取矩形的四个顶点
Point[] rectpoint = new Point[4];
rect.points(rectpoint);
double line1 = Math.sqrt((rectpoint[1].y - rectpoint[0].y)*(rectpoint[1].y - rectpoint[0].y) + (rectpoint[1].x - rectpoint[0].x)*(rectpoint[1].x - rectpoint[0].x));
double line2 = Math.sqrt((rectpoint[3].y - rectpoint[0].y)*(rectpoint[3].y - rectpoint[0].y) + (rectpoint[3].x - rectpoint[0].x)*(rectpoint[3].x - rectpoint[0].x));
double angle = rect.angle;
if (line1 > line2) 
{
	angle = 90 + angle;
}
Point center = rect.center;
Mat CorrectImg = new Mat(clone.size(), clone.type());
clone.copyTo(CorrectImg);
// 得到旋转矩阵算子
Mat matrix = Imgproc.getRotationMatrix2D(center, angle, 0.8);
Imgproc.warpAffine(src, src, matrix, CorrectImg.size(), 1, 0, new Scalar(0, 0, 0));
HighGui.imshow("rotation", src);
return src;
}

3.两个算法的应用场景

基于轮廓提取的矫正算法更适用于车牌、身份证、人民币、书本、发票一类矩形形状而且边界明显的物体矫正。

基于直线探测的矫正算法更适用于文本类的矫正。

到此这篇关于Java调用opencv实现图片矫正功能的文章就介绍到这了,更多相关Java opencv图片矫正内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家!

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