OPENCV图像边缘查找与分割技术在android中使用汇总

图像分割技术或者叫抠图技术，是一种根据需要对图像进行截取分离的技术，在一般的图像处理和视频处理中应用十分广泛，是图像查找，图像识别，图像特效的基础。经常被人们使用在相机美颜，自动人脸马赛克，车牌识别，图像查找，人脸查找，人脸识别，机器视觉，AR等领域。

图像分割分有标注和无标注两种情况，一种是自动根据分割，自选阀值，区域自动分割，一种是在给定条件下分割，比如分割人脸，人身体，给定区域分割，前一种由于准确度的问题，应用很少。

主要图像分割方法包括阈值分割，边缘分割（查找边缘），区域分割（种子区域生长法、区域分裂合并法和分水岭法等），图论分割，能量泛函（参数活动轮廓模型，几何活动轮廓模型）等。利用机器学习自动对像素分类也能达到某些分割 目的。

OPENCV封装的分割算法非常多，而且又能根据需要组合使用，以提升分割 精度，这使得用法灵活性大增，掌握的难度比较 大。比较重要的有阀值分割Imgproc.threshold， 边缘分割Imgproc.findContours，也可以使用Roberts 算子、Laplace 算子、Prewitt 算子、Sobel 算子、Rosonfeld算子、Kirsch 算子以及Canny 算子等实现，区域分割HSV亮度， 图论分割GraphCut，GrabCut和Random Walk，还可以根据颜色分割等。有些精度高，耗时长，有些分割粗糙，但速度快，需要根据需要自由组合使用：

import java.util.ArrayList;import java.util.List;import org.opencv.core.Core;import org.opencv.core.CvType;import org.opencv.core.Mat;import org.opencv.core.MatOfPoint;import org.opencv.core.MatOfPoint2f;import org.opencv.core.Point;import org.opencv.core.Rect;import org.opencv.core.Scalar;import org.opencv.core.Size;import org.opencv.imgcodecs.Imgcodecs;import org.opencv.imgproc.Imgproc;public class ImageOpencv {public static void main(String[] args) {System.loadLibrary(Core.NATIVE_LIBRARY_NAME);Mat src = Imgcodecs.imread("E:/work/qqq/a9.jpg");    Imgcodecs.imwrite("E:/work/qqq/hh1.jpg", removeBackground(src));        Mat src2 = Imgcodecs.imread("E:/work/qqq/a3.jpg");    Imgcodecs.imwrite("E:/work/qqq/hh2.jpg", MyThresholdHsv(src2));            Mat src3 = Imgcodecs.imread("E:/work/qqq/e1.jpg");        Imgcodecs.imwrite("E:/work/qqq/hh3.jpg", myGrabCut(src3,  new Point(50,0),new Point(300, 250)));            Mat src4 = Imgcodecs.imread("E:/work/qqq/dd.jpg");        Imgcodecs.imwrite("E:/work/qqq/hh4.jpg", MyFindLargestRectangle(src4));                Mat src5 = Imgcodecs.imread("E:/work/qqq/dd.jpg");        Imgcodecs.imwrite("E:/work/qqq/hh5.jpg", MyWatershed(src5));               Mat src6 = Imgcodecs.imread("E:/work/qqq/e1.jpg");        Imgcodecs.imwrite("E:/work/qqq/hh6.jpg", MyCanny(src6, 100));            SkinDetection sd= new SkinDetection(Imgcodecs.imread("E:/work/qqq/e1.jpg"));    Imgcodecs.imwrite("E:/work/qqq/hh7.jpg",sd.GetSkin());        Mat src7 = Imgcodecs.imread("E:/work/qqq/ee.jpg");     Imgcodecs.imwrite("E:/work/qqq/hh8.jpg", MyFloodFill(src7));}// threshold根据反差去掉深色单色背景public static Mat removeBackground(Mat nat) {Mat m = new Mat();Imgproc.cvtColor(nat, m, Imgproc.COLOR_BGR2GRAY);double threshold = Imgproc.threshold(m, m, 0, 255, Imgproc.THRESH_OTSU);Mat pre = new Mat(nat.size(), CvType.CV_8UC3, new Scalar(0, 0, 0));Mat fin = new Mat(nat.size(), CvType.CV_8UC3, new Scalar(0, 0, 0));for (int i = 0; i < m.rows(); i++) {for (int j = 0; j < m.cols(); j++) {double[] ds = m.get(i, j);double[] data = { ds[0] / 255, ds[0] / 255, ds[0] / 255 };pre.put(i, j, data);}}for (int i = 0; i < pre.rows(); i++) {for (int j = 0; j < pre.cols(); j++) {double[] pre_ds = pre.get(i, j);double[] nat_ds = nat.get(i, j);double[] data = { pre_ds[0] * nat_ds[0], pre_ds[1] * nat_ds[1], pre_ds[2] * nat_ds[2] };fin.put(i, j, data);}}return fin;}// threshold根据亮度去除背景private static Mat MyThresholdHsv(Mat frame) {Mat hsvImg = new Mat();List<Mat> hsvPlanes = new ArrayList<>();Mat thresholdImg = new Mat();// threshold the image with the average hue valuehsvImg.create(frame.size(), CvType.CV_8U);Imgproc.cvtColor(frame, hsvImg, Imgproc.COLOR_BGR2HSV);Core.split(hsvImg, hsvPlanes);// get the average hue value of the imageScalar average = Core.mean(hsvPlanes.get(0));double threshValue = average.val[0];Imgproc.threshold(hsvPlanes.get(0), thresholdImg, threshValue, 179.0, Imgproc.THRESH_BINARY_INV);Imgproc.blur(thresholdImg, thresholdImg, new Size(15, 15));// dilate to fill gaps, erode to smooth edgesImgproc.dilate(thresholdImg, thresholdImg, new Mat(), new Point(-1, -1), 1);Imgproc.erode(thresholdImg, thresholdImg, new Mat(), new Point(-1, -1), 3);Imgproc.threshold(thresholdImg, thresholdImg, threshValue, 179.0, Imgproc.THRESH_BINARY);// create the new imageMat foreground = new Mat(frame.size(), CvType.CV_8UC3, new Scalar(0, 0, 0));thresholdImg.convertTo(thresholdImg, CvType.CV_8U);frame.copyTo(foreground, thresholdImg);return foreground;}    //grabCut分割技术public static Mat myGrabCut(Mat in, Point tl, Point br) {Mat mask = new Mat();Mat image = in;mask.create(image.size(), CvType.CV_8UC1);mask.setTo(new Scalar(0));Mat bgdModel = new Mat();// Mat.eye(1, 13 * 5, CvType.CV_64FC1);Mat fgdModel = new Mat();// Mat.eye(1, 13 * 5, CvType.CV_64FC1);Mat source = new Mat(1, 1, CvType.CV_8U, new Scalar(3));Rect rectangle = new Rect(tl, br);Imgproc.grabCut(image, mask, rectangle, bgdModel, fgdModel, 3, Imgproc.GC_INIT_WITH_RECT);Core.compare(mask, source, mask, Core.CMP_EQ);Mat foreground = new Mat(image.size(), CvType.CV_8UC1, new Scalar(0, 0, 0));image.copyTo(foreground, mask);return foreground;}//findContours分割技术private static Mat MyFindLargestRectangle(Mat original_image) {Mat imgSource = original_image;Imgproc.cvtColor(imgSource, imgSource, Imgproc.COLOR_BGR2GRAY);Imgproc.Canny(imgSource, imgSource, 50, 50);Imgproc.GaussianBlur(imgSource, imgSource, new Size(5, 5), 5);List<MatOfPoint> contours = new ArrayList<MatOfPoint>();Imgproc.findContours(imgSource, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);double maxArea = 0;int maxAreaIdx = -1;MatOfPoint largest_contour = contours.get(0); MatOfPoint2f approxCurve = new MatOfPoint2f();for (int idx = 0; idx < contours.size(); idx++) {MatOfPoint temp_contour = contours.get(idx);double contourarea = Imgproc.contourArea(temp_contour);if (contourarea - maxArea > 1) {maxArea = contourarea;largest_contour = temp_contour;maxAreaIdx = idx;MatOfPoint2f new_mat = new MatOfPoint2f(temp_contour.toArray());int contourSize = (int) temp_contour.total();Imgproc.approxPolyDP(new_mat, approxCurve, contourSize * 0.05, true);}}Imgproc.drawContours(imgSource, contours, -1, new Scalar(255, 0, 0), 1);Imgproc.fillConvexPoly(imgSource, largest_contour, new Scalar(255, 255, 255));Imgproc.drawContours(imgSource, contours, maxAreaIdx, new Scalar(0, 0, 255), 3);return imgSource;}//watershed分水岭分割技术    public static Mat MyWatershed(Mat img)    {        Mat threeChannel = new Mat();        Imgproc.cvtColor(img, threeChannel, Imgproc.COLOR_BGR2GRAY);        //Imgproc.threshold(threeChannel, threeChannel, 200, 255, Imgproc.THRESH_BINARY);        Imgproc.threshold(threeChannel, threeChannel, 0, 255, Imgproc.THRESH_OTSU);                Mat fg = new Mat(img.size(),CvType.CV_8U);        Imgproc.erode(threeChannel,fg,new Mat());        Mat bg = new Mat(img.size(),CvType.CV_8U);        Imgproc.dilate(threeChannel,bg,new Mat());        Imgproc.threshold(bg,bg,1, 128,Imgproc.THRESH_BINARY_INV);        Mat markers = new Mat(img.size(),CvType.CV_8U, new Scalar(0));        Core.add(fg, bg, markers);        Mat result=new Mat();        markers.convertTo(result, CvType.CV_32SC1);        Imgproc.watershed(img, result);        result.convertTo(result,CvType.CV_8U);                        return result;    }    //Canny分割技术    public static Mat MyCanny(Mat img, int threshold) {        Imgproc.cvtColor(img, img, Imgproc.COLOR_BGR2GRAY);        Imgproc.Canny(img, img, threshold, threshold * 3, 3, true);        return img;    }   //漫水填充 public static Mat MyFloodFill(Mat img) {        Rect ccomp = new Rect();        Mat mask = new Mat();        Imgproc.floodFill(img, mask, new Point(50,20), new Scalar(0, 0,0), ccomp,new Scalar(10, 10, 10), new Scalar(10, 10, 10), 0);                return img;    }  }

上图：

removeBackground，较深单色背景时

MyThresholdHsv，较浅亮色背景

myGrabCut去除复杂背景

查找边缘轮廓，提取mask用于分割

MyWatershed

MyCanny和基于肤色分割

参考 ：

http://blog.csdn.net/zouxy09/article/details/8532106
http://blog.csdn.net/vast_sea/article/details/8196507
http://blog.csdn.net/bless2015/article/details/52805875
http://www.07net01.com/2016/04/1458778.html

http://blog.csdn.net/dcrmg/article/details/52498440

http://blog.csdn.net/liang_dun/article/details/45198911