首先，我非常感谢博客http://opencv-code.com/tutorials/automatic-perspective-correction-for-quadrilateral-objects/
我正在关注此事，但我没有处理image (Used in the blog above)，而是我试图实时检测任何尺寸的纸张（A4，合法或任何普通矩形纸张尺寸）相机预览。
问题我得到的是“扩大霍夫线段以适应图像”我得到了大量的霍夫线，因此获得了超过4个交叉点（不是== 4）。
像这样http://s17.postimg.org/i0a57fb8v/device_2015_04_07_171351.png
如何删除剩余的无效点，我只需要4个角点？我正在使用OpenCV Library for Android
请主要关注detectPaperSheet()方法。这是我的代码：
package org.opencv.samples.tutorial1;
import java.util.ArrayList;
import org.opencv.android.BaseLoaderCallback;
import org.opencv.android.CameraBridgeViewBase;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewFrame;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewListener2;
import org.opencv.android.LoaderCallbackInterface;
import org.opencv.android.OpenCVLoader;
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.imgproc.Imgproc;
import android.app.Activity;
import android.os.Bundle;
import android.util.Log;
import android.view.Menu;
import android.view.MenuItem;
import android.view.SurfaceView;
import android.view.WindowManager;
import android.widget.Toast;
public class Tutorial1Activity extends Activity implements
CvCameraViewListener2 {
private static final String TAG = "OCVSample::Activity";
private CameraBridgeViewBase mOpenCvCameraView;
private boolean mIsJavaCamera = true;
private MenuItem mItemSwitchCamera = null;
private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
@Override
public void onManagerConnected(int status) {
switch (status) {
case LoaderCallbackInterface.SUCCESS: {
Log.i(TAG, "OpenCV loaded successfully");
mOpenCvCameraView.enableView();
}
break;
default: {
super.onManagerConnected(status);
}
break;
}
}
};
public Tutorial1Activity() {
Log.i(TAG, "Instantiated new " + this.getClass());
}
/** Called when the activity is first created. */
@Override
public void onCreate(Bundle savedInstanceState) {
Log.i(TAG, "called onCreate");
super.onCreate(savedInstanceState);
getWindow().addFlags(WindowManager.LayoutParams.FLAG_KEEP_SCREEN_ON);
setContentView(R.layout.tutorial1_surface_view);
if (mIsJavaCamera)
mOpenCvCameraView = (CameraBridgeViewBase) findViewById(R.id.tutorial1_activity_java_surface_view);
else
mOpenCvCameraView = (CameraBridgeViewBase) findViewById(R.id.tutorial1_activity_native_surface_view);
mOpenCvCameraView.setVisibility(SurfaceView.VISIBLE);
mOpenCvCameraView.setCvCameraViewListener(this);
}
@Override
public void onPause() {
super.onPause();
if (mOpenCvCameraView != null)
mOpenCvCameraView.disableView();
}
@Override
public void onResume() {
super.onResume();
OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_9, this,
mLoaderCallback);
}
public void onDestroy() {
super.onDestroy();
if (mOpenCvCameraView != null)
mOpenCvCameraView.disableView();
}
@Override
public boolean onCreateOptionsMenu(Menu menu) {
Log.i(TAG, "called onCreateOptionsMenu");
mItemSwitchCamera = menu.add("Toggle Native/Java camera");
return true;
}
@Override
public boolean onOptionsItemSelected(MenuItem item) {
String toastMesage = new String();
Log.i(TAG, "called onOptionsItemSelected; selected item: " + item);
if (item == mItemSwitchCamera) {
mOpenCvCameraView.setVisibility(SurfaceView.GONE);
mIsJavaCamera = !mIsJavaCamera;
if (mIsJavaCamera) {
mOpenCvCameraView = (CameraBridgeViewBase) findViewById(R.id.tutorial1_activity_java_surface_view);
toastMesage = "Java Camera";
} else {
mOpenCvCameraView = (CameraBridgeViewBase) findViewById(R.id.tutorial1_activity_native_surface_view);
toastMesage = "Native Camera";
}
mOpenCvCameraView.setVisibility(SurfaceView.VISIBLE);
mOpenCvCameraView.setCvCameraViewListener(this);
mOpenCvCameraView.enableView();
Toast toast = Toast.makeText(this, toastMesage, Toast.LENGTH_LONG);
toast.show();
}
return true;
}
public void onCameraViewStarted(int width, int height) {
}
public void onCameraViewStopped() {
}
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
return detectPaperSheet(inputFrame.rgba());
}
private Mat detectPaperSheet(Mat original_image) {
Mat imgSource = original_image;
Mat untouched = original_image.clone();
// Converting to grayscale
Mat mHsvMat = new Mat(imgSource.rows(), imgSource.cols(),
CvType.CV_8UC1, new Scalar(0));
Imgproc.cvtColor(imgSource, mHsvMat, Imgproc.COLOR_BGRA2GRAY, 4);
// apply gaussian blur to smoothen lines of dots
Imgproc.GaussianBlur(imgSource, imgSource, new Size(11, 11), 0);
// Applying Canny
Imgproc.Canny(mHsvMat, mHsvMat, 80, 100);
Mat lines = new Mat();
int threshold = 100;
int minLineSize = 150;
int lineGap = 40;
Imgproc.HoughLinesP(mHsvMat, lines, 1, Math.PI / 180, threshold,
minLineSize, lineGap);
// Expanding the Lines To Image Width and Height
ArrayList<Point> corners = new ArrayList<Point>();
for (int x = 0; x < lines.cols(); x++) {
double[] vec = lines.get(0, x);
double[] val = new double[4];
val[0] = 0;
val[1] = ((float) vec[1] - vec[3]) / (vec[0] - vec[2]) * -vec[0]
+ vec[1];
val[2] = imgSource.cols();
val[3] = ((float) vec[1] - vec[3]) / (vec[0] - vec[2])
* (imgSource.cols() - vec[2]) + vec[3];
lines.put(0, x, val);
}
for (int x = 0; x < lines.cols(); x++) {
double[] vec = lines.get(0, x);
double x1 = vec[0], y1 = vec[1], x2 = vec[2], y2 = vec[3];
Point start = new Point(x1, y1);
Point end = new Point(x2, y2);
Core.line(imgSource, start, end, new Scalar(255, 0, 0), 1);
}
for (int i = 0; i < lines.cols(); i++) {
for (int j = i + 1; j < lines.cols(); j++) {
Point pt = computeIntersect(lines.get(0, i), lines.get(0, j));
if (pt.x >= 0 && pt.y >= 0)
corners.add(pt);
}
}
if (corners.size() < 4) {
Log.e("Corner < 4", corners.size() + " |");
return untouched;
} else {
Log.e("Corner > 4", corners.size() + " |");
}
//Mat cornerPoints = new Mat();
for (int j = 0; j < corners.size(); j++) {
Core.circle(imgSource,
new Point(corners.get(j).x, corners.get(j).y), 20,
new Scalar(0, 0, 255), 2);
}
return imgSource;
}
private static Point computeIntersect(double[] a, double[] b) {
double x1 = a[0], y1 = a[1], x2 = a[2], y2 = a[3], x3 = b[0], y3 = b[1], x4 = b[2], y4 = b[3];
double denom = ((x1 - x2) * (y3 - y4)) - ((y1 - y2) * (x3 - x4));
Point pt = new Point();
if (denom != 0) {
pt.x = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2)
* (x3 * y4 - y3 * x4))
/ denom;
pt.y = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2)
* (x3 * y4 - y3 * x4))
/ denom;
return pt;
} else
return new Point(-1, -1);
}
}
答案 0 :(得分：2)
我记得poly approximation在这种情况下对我有所帮助（检测矩形）。但是我使用findContours方法（使用CV_CHAIN_APPROX_SIMPLE模式），因为它似乎比Hough线产生更好的结果。
[编辑] 我使用JNI在C ++中完成它，但在Java中我认为它看起来应该是这样的：
Mat srcImg; //you may want to apply Canny or some threshold before searching for contours
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy;
Imgproc.findContours(srcImg, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
MatOfPoint2f mat2fsrc, mat2fdst;
Scalar color = new Scalar(250, 250, 255);
for (int i = 0; i < contours.size(); i++) {
contours.get(i).convertTo(mat2fsrc, CvType.CV_32FC2);
Imgproc.approxPolyDP(mat2fsrc, mat2fdst, 0.01 * Imgproc.arcLength(mat2fsrc, true), true);
mat2fdst.convertTo(contours.get(i), CvType.CV_32S);
Imgproc.drawContours(srcImg, contours, i, color, 2, 8, hierarchy, 0, new Point());
}