OpenCV Java：从图像中提取卡片
我正在尝试使用OpenCV和Java实现一些图像处理，以从图像中提取出一张图像。
以下是我的方法：
在步骤8，我面临一些问题，因为我没有得到适当的角落/顶点。 以下示例图像显示了该场景：
原始图像 
边缘检测和扩张后。 （要做什么才能获得合适的边缘？在这里，我已经断了边缘，无法让Hough变换工作） 
找到顶点后。 （以绿色显示）
以下是代码：
System.loadLibrary( Core.NATIVE_LIBRARY_NAME );
//load Image
File input = new File("card4.png");
BufferedImage image = ImageIO.read(input);
byte[] data = ((DataBufferByte) image.getRaster().getDataBuffer()).getData();
//put read image to Mat
mat = new Mat(image.getHeight(), image.getWidth(), CvType.CV_8UC3); //original Mat
mat.put(0, 0, data);
mat_f = new Mat(image.getHeight(), image.getWidth(), CvType.CV_8UC3); //for storing manipulated Mat
//conversion to grayscale, blurring and edge detection
Imgproc.cvtColor(mat, mat_f, Imgproc.COLOR_RGB2BGR);
Imgproc.cvtColor(mat_f, mat_f, Imgproc.COLOR_RGB2GRAY);
Imgproc.GaussianBlur(mat_f, mat_f, new Size(13,13), 0);
Imgproc.Canny(mat_f, mat_f, 300, 600, 5, true);
Imgproc.dilate(mat_f, mat_f, new Mat(), new Point(-1, -1), 2);
Imgcodecs.imwrite("D:JAVAImage_ProcCVTest1.jpg",mat_f);
//finding contours
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(mat_f, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
double maxArea=0;
int maxAreaIdx=0;
//finding largest contour
for (int idx = 0; idx != contours.size(); ++idx)
{
Mat contour = contours.get(idx);
double contourarea = Imgproc.contourArea(contour);
if (contourarea > maxArea)
{
maxArea = contourarea;
maxAreaIdx = idx;
}
}
//Rect rect = Imgproc.boundingRect(contours.get(maxAreaIdx));
//Imgproc.rectangle(mat, new Point(rect.x,rect.y), new Point(rect.x+rect.width,rect.y+rect.height),new Scalar(0,0,255),7);
// mat = mat.submat(rect.y, rect.y + rect.height, rect.x, rect.x + rect.width);
//Polygon approximation
MatOfPoint2f approxCurve = new MatOfPoint2f();
MatOfPoint2f oriCurve = new MatOfPoint2f(contours.get(maxAreaIdx).toArray());
Imgproc.approxPolyDP(oriCurve, approxCurve, 6.0, true);
//drawing red markers at vertices
Point [] array = approxCurve.toArray();
for(int i=0; i < array.length;i++) {
Imgproc.circle(mat, array[i], 2, new Scalar(0, 255, 0), 5);
}
Imgcodecs.imwrite("D:JAVAImage_ProcCVTest.jpg",mat);
寻求帮助获得适当的角落顶点......在此先感谢..
为了使用你的方法归档好结果，你的卡片必须包含4个角落。 但我更喜欢使用HoughLine方法来完成这项任务。
第1步：调整图像以获得更高性能
步骤2：边缘检测
您可以使用扩大使下一步的白色变大
第3步：找到卡的角落
这里是Java中的示例代码
// STEP 1: Resize input image to img_proc to reduce computation
double ratio = DOWNSCALE_IMAGE_SIZE / Math.max(frame.width(), frame.height());
Size downscaledSize = new Size(frame.width() * ratio, frame.height() * ratio);
Mat dst = new Mat(downscaledSize, frame.type());
Imgproc.resize(frame, dst, downscaledSize);
Mat grayImage = new Mat();
Mat detectedEdges = new Mat();
// STEP 2: convert to grayscale
Imgproc.cvtColor(dst, grayImage, Imgproc.COLOR_BGR2GRAY);
// STEP 3: try to filter text inside document
Imgproc.medianBlur(grayImage, detectedEdges, 9);
// STEP 4: Edge detection
Mat edges = new Mat();
// Imgproc.erode(edges, edges, new Mat());
// Imgproc.dilate(edges, edges, new Mat(), new Point(-1, -1), 1); // 1
// canny detector, with ratio of lower:upper threshold of 3:1
Imgproc.Canny(detectedEdges, edges, this.threshold.getValue(), this.threshold.getValue() * 3, 3, true);
// STEP 5: makes the object in white bigger to join nearby lines
Imgproc.dilate(edges, edges, new Mat(), new Point(-1, -1), 1); // 1
Image imageToShow = Utils.mat2Image(edges);
updateImageView(cannyFrame, imageToShow);
// STEP 6: Compute the contours
List<MatOfPoint> contours = new ArrayList<>();
Imgproc.findContours(edges, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
// STEP 7: Sort the contours by length and only keep the largest one
MatOfPoint largestContour = getMaxContour(contours);
// STEP 8: Generate the convex hull of this contour
Mat convexHullMask = Mat.zeros(frame.rows(), frame.cols(), frame.type());
MatOfInt hullInt = new MatOfInt();
Imgproc.convexHull(largestContour, hullInt);
MatOfPoint hullPoint = OpenCVUtil.getNewContourFromIndices(largestContour, hullInt);
// STEP 9: Use approxPolyDP to simplify the convex hull (this should give a quadrilateral)
MatOfPoint2f polygon = new MatOfPoint2f();
Imgproc.approxPolyDP(OpenCVUtil.convert(hullPoint), polygon, 20, true);
List<MatOfPoint> tmp = new ArrayList<>();
tmp.add(OpenCVUtil.convert(polygon));
restoreScaleMatOfPoint(tmp, ratio);
Imgproc.drawContours(convexHullMask, tmp, 0, new Scalar(25, 25, 255), 2);
// Image extractImageToShow = Utils.mat2Image(convexHullMask);
// updateImageView(extractFrame, extractImageToShow);
MatOfPoint2f finalCorners = new MatOfPoint2f();
Point[] tmpPoints = polygon.toArray();
for (Point point : tmpPoints) {
point.x = point.x / ratio;
point.y = point.y / ratio;
}
finalCorners.fromArray(tmpPoints);
boolean clockwise = true;
double currentThreshold = this.threshold.getValue();
if (finalCorners.toArray().length == 4) {
Size size = getRectangleSize(finalCorners);
Mat result = Mat.zeros(size, frame.type());
// STEP 10: Homography: Use findHomography to find the affine transformation of your paper sheet
Mat homography = new Mat();
MatOfPoint2f dstPoints = new MatOfPoint2f();
Point[] arrDstPoints = { new Point(result.cols(), result.rows()), new Point(0, result.rows()), new Point(0, 0), new Point(result.cols(), 0) };
dstPoints.fromArray(arrDstPoints);
homography = Calib3d.findHomography(finalCorners, dstPoints);
// STEP 11: Warp the input image using the computed homography matrix
Imgproc.warpPerspective(frame, result, homography, size);
}