OpenCV getPerspectiveTransform和warpPerspective Java [英] OpenCV getPerspectiveTransform and warpPerspective Java

问题描述

我目前有一个 ArrayList< MatOfPoint> ，它存储图像的轮廓并检索出最大的轮廓，这是我对warpPerspective的目标。

I current have a ArrayList<MatOfPoint> which stores the contours of the image and have retrieved the largest contour which is my target for the warpPerspective.

List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(matOut, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);

sort(contours);
MatOfPoint2f  m2f = MatOfPoint2f(contours.get(0).toArray());
double arc = Imgproc.arcLength(m2f, true);
MatOfPoint2f approx = new MatOfPoint2f();
Imgproc.approxPolyDP(m2f, approx, arc*0.02, true);

我在如何使用getPerspectiveTransform然后应用warpPerspective更改图像方面遇到了麻烦它将适合450x450的图像。

I am having trouble on how to use the getPerspectiveTransform and then applying the warpPerspective to change the image so that it will fit within a 450x450 image.

我在python中找到了一个非常好的例子，不过我对它不是很熟悉有人可以解释我会如何纠正在java？

I found an pretty good example of this in python, however I am not very familiar with it can someone explain how I would do rectify in java?

def rectify(h):
    ''' this function put vertices of square we got, in clockwise order '''
    h = h.reshape((4,2))
    hnew = np.zeros((4,2),dtype = np.float32)

    add = h.sum(1)
    hnew[0] = h[np.argmin(add)]
    hnew[2] = h[np.argmax(add)]

    diff = np.diff(h,axis = 1)
    hnew[1] = h[np.argmin(diff)]
    hnew[3] = h[np.argmax(diff)]

    return hnew

这里是源链接： https://github.com/abidrahmank/OpenCV2-Python/blob/master/ OpenCV_Python_Blog / sudoku_v_0.0.6 / sudoku.py

推荐答案

简介：使用wrapPerspective我们需要使用getPerspectiveTransform来计算包裹垫：Mat src和Mat dst。那些是你的轮廓的src（左下角，右下角，左上角，右上角）的MarOfPoints2f和相应的mat dst（0,0; ​​0,449; 449,0; 449,449）（所以图像将是450x450 - 如果这是错误的，请更正我。）

Intro: to make use of wrapPerspective we need to calculate wrapping mat using getPerspectiveTransform takes: Mat src and Mat dst. Those are MarOfPoints2f with src(bottom-left, bottom-right, top-left, top-right) of your contour and corresponding mat dst( 0,0 ; 0,449 ; 449,0 ; 449,449) (So the image will be 450x450 - If this is wrong please correct me).

解决方案在Java中用于opencv 3将是：

Solution in Java for opencv 3 would be:

1. 获得点数
   矩形轮廓为 MatOfPoints2f ，我们需要按上面所述对点进行排序：

1. Get points sorted Having rectangular contour as MatOfPoints2f approx we need to sort the points as written above:

//calculate the center of mass of our contour image using moments
Moments moment = Imgproc.moments(approx.get(0));
int x = (int) (moment.get_m10() / moment.get_m00());
int y = (int) (moment.get_m01() / moment.get_m00());

//SORT POINTS RELATIVE TO CENTER OF MASS 
Point[] sortedPoints = new Point[4];

double[] data;
int count = 0;
for(int i=0; i<approx.get(0).rows(); i++){
    data = approx.get(0).get(i, 0);
    double datax = data[0];
    double datay = data[1];
    if(datax < x && datay < y){
        sortedPoints[0]=new Point(datax,datay);
        count++;
    }else if(datax > x && datay < y){
        sortedPoints[1]=new Point(datax,datay);
        count++;
    }else if (datax < x && datay > y){
        sortedPoints[2]=new Point(datax,datay);
        count++;
    }else if (datax > x && datay > y){
        sortedPoints[3]=new Point(datax,datay);
        count++;
    }
}

准备Mat src和det

Prepare Mat src and det

        MatOfPoint2f src = new MatOfPoint2f(
                sortedPoints[0],
                sortedPoints[1],
                sortedPoints[2],
                sortedPoints[3]);

        MatOfPoint2f dst = new MatOfPoint2f(
                new Point(0, 0),
                new Point(450-1,0),
                new Point(0,450-1),
                new Point(450-1,450-1)      
                );

使用getPerspectiveTransform和wrapPrespective

Use getPerspectiveTransform and wrapPrespective

    Mat warpMat = Imgproc.getPerspectiveTransform(src,dst);
    //This is you new image as Mat
    Mat destImage = new Mat();
    Imgproc.warpPerspective(originalImg, destImage, warpMat, originalImg.size());

我刚刚完成了类似的项目，所以我我相信这可以做得更好，但如果有人需要，这是一个有效的解决方案。

I have just finished doing similar project so I am sure this can be done better but it is a working solution if anyone needs one.

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