OpenCV Android Studio环境搭建与边缘检测、霍夫直线识别
使用OpenCV 进行Android 开发需要集成Android相关的OpenCV SDK
OpenCV官网
demo git地址 OpenCVDemo
https://opencv.org/
点击Release 进入 SDK下载页面，目前最新的版本是OpenCV – 4.4.0
新建好工程之后，导入下载好的SDK(import module),导入的是sdk。如果有不清楚的，可以看下SDK中的samples例子
git中关于sdk的代码最好自己从新引用自己的sdk moudle
此代码主要是介绍https://blog.csdn.net/weixin_36340947/article/details/77095924中的代码
package com.wqx.opencv;
import androidx.appcompat.app.AppCompatActivity;
import android.os.Bundle;
/*
功能介绍：深入OpenCV Android应用开发第二章代码，检测图像的基本特征
包括了Canny边缘检测法Sobel边缘检测法等
实现步骤：1.从手机中取出一张图片作为原始图片,通过点击menu对应的按钮开始选择图片
2.通过menu按钮选择要对照片进行的图像处理
*/
import android.content.Intent;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.net.Uri;
import android.util.Log;
import android.view.Menu;
import android.view.MenuItem;
import android.widget.ImageView;
import android.widget.Toast;
import org.opencv.android.BaseLoaderCallback;
import org.opencv.android.LoaderCallbackInterface;
import org.opencv.android.OpenCVLoader;
import org.opencv.android.Utils;
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.imgproc.Imgproc;
import java.io.FileNotFoundException;
import java.io.InputStream;
import java.util.Random;
public class MainActivity extends AppCompatActivity {
private final static int CANNY = 0;
private final static int HARRIS = 1;
private final static int HOUGH = 2;
private final static int HOUGH2 = 3;
private final static String TAG = "infor";
private Mat src = null;//定义一个Mat型类用于临时存放选择的图片
private Mat image = null;//用于存放得到的图片
private Mat des = null;//用于临时存放Mat型类的图片
private Bitmap resultBitmap;
private ImageView pictureView = null;//定义一个ImageView类视图用于存放选择的图片
private BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
@Override
public void onManagerConnected(int status) {
switch (status) {
case LoaderCallbackInterface.SUCCESS:
/*在这里执行自己的语句*/
break;
default:
super.onManagerConnected(status);
break;
}
}
};
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
pictureView = (ImageView) findViewById(R.id.Picture);
}
/*启动openCV*/
@Override
protected void onResume() {
super.onResume();
if (!OpenCVLoader.initDebug()) {
Log.d(TAG, "Internal OpenCV library not found. Using OpenCV Manager for initialization");
OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION, this, mLoaderCallback);
} else {
Log.d(TAG, "OpenCV library found inside package. Using it!");
mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS);
}
}
@Override
public boolean onCreateOptionsMenu(Menu menu) {
// Inflate the menu; this adds items to the action bar if it is present.
getMenuInflater().inflate(R.menu.menu_main, menu);
return true;
}
/*在这里选取要进行的操作*/
@Override
public boolean onOptionsItemSelected(MenuItem item) {
// Handle action bar item clicks here. The action bar will
// automatically handle clicks on the Home/Up button, so long
// as you specify a parent activity in AndroidManifest.xml.
int id = item.getItemId();
//对应Canny边缘检测的按钮
if (id == R.id.Canny) {
/*下面对通过Intent对象得到选择图片的Activity，最后返回图片的信息，得到图片*/
Intent pictureSelectIntent = new Intent(Intent.ACTION_PICK);//设置Action
pictureSelectIntent.setType("image/");//设置数据的类型
startActivityForResult(pictureSelectIntent, CANNY);
return true;
}
//对应Harris边缘检测的按钮
if (R.id.Harris == id) {
Intent pictureSelectIntent = new Intent(Intent.ACTION_PICK);
pictureSelectIntent.setType("image/");
startActivityForResult(pictureSelectIntent, HARRIS);
return true;
}
//对应Hough的直线检测按钮
if (R.id.Hough == id) {
Intent pictureSelectIntent = new Intent(Intent.ACTION_PICK);
pictureSelectIntent.setType("image/");
startActivityForResult(pictureSelectIntent, HOUGH);
return true;
}
//对应Hough的直线检测按钮
if (R.id.Hough2 == id) {
Intent pictureSelectIntent = new Intent(Intent.ACTION_PICK);
pictureSelectIntent.setType("image/");
startActivityForResult(pictureSelectIntent, HOUGH2);
return true;
}
return super.onOptionsItemSelected(item);
}
/*调用StartActivityForResult后的回调函数
* 在这个函数里面得到图片然后进行相应的处理
* */
@Override
protected void onActivityResult(int requestCode, int resultCode, Intent data) {
super.onActivityResult(requestCode, resultCode, data);
if (RESULT_OK == resultCode) {
switch (requestCode) {
case CANNY:
try {
image = getPicture(data);
Toast.makeText(MainActivity.this, "图片选取成功", Toast.LENGTH_SHORT).show();
resultBitmap = canny(image);
pictureView.setImageBitmap(resultBitmap);
} catch (FileNotFoundException e) {
e.printStackTrace();
}
break;
case HARRIS:
try {
image = getPicture(data);//得到图片
Toast.makeText(MainActivity.this, "图片选取成功", Toast.LENGTH_SHORT).show();
resultBitmap = harris(image);//角点检测的图像处理
pictureView.setImageBitmap(resultBitmap);
} catch (FileNotFoundException e) {
e.printStackTrace();
}
break;
case HOUGH:
try {
image = getPicture(data);//得到图片
Toast.makeText(MainActivity.this, "图片选取成功", Toast.LENGTH_SHORT).show();
resultBitmap = houghLine(image);
pictureView.setImageBitmap(resultBitmap);
} catch (FileNotFoundException e) {
e.printStackTrace();
}
break;
case HOUGH2:
try {
image = getPicture(data);//得到图片
Toast.makeText(MainActivity.this, "图片选取成功", Toast.LENGTH_SHORT).show();
resultBitmap = MyHoughLine2(image);
pictureView.setImageBitmap(resultBitmap);
} catch (FileNotFoundException e) {
e.printStackTrace();
}
break;
}
}
}
/*得到图片*/
public Mat getPicture(Intent data) throws FileNotFoundException {
/*下面的代码是获得手机内的图片*/
final Uri imageUri = data.getData();//得到图片的路径
final InputStream imageStream = getContentResolver().openInputStream(imageUri);//得到基于路径的流文件
final Bitmap selectImage = BitmapFactory.decodeStream(imageStream);//得到了图片的位图
/*下面将位图转换成Mat型，可以进行图片的处理*/
src = new Mat(selectImage.getHeight(), selectImage.getWidth(), CvType.CV_8UC4);
Utils.bitmapToMat(selectImage, src);
return src;
}
/* 下面进行图片的处理
*
* Canny边缘处理
*/
public Bitmap canny(Mat src) {
Bitmap result;
Mat grayMat = new Mat();
Mat cannyEdges = new Mat();
/*将图片转换成灰度图*/
Imgproc.cvtColor(src, grayMat, Imgproc.COLOR_BGR2GRAY);
/*得到边缘图,这里最后两个参数控制着选择边缘的阀值上限和下限*/
Imgproc.Canny(grayMat, cannyEdges, 50, 300);
/*将Mat图转换成位图*/
result = Bitmap.createBitmap(src.cols(), src.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(cannyEdges, result);
return result;
}
/*Harris角点检测*/
public Bitmap harris(Mat src) {
Bitmap resultHarris;
Mat grayMat = new Mat();
Mat corners = new Mat();
/*将图片转换成灰度图*/
Imgproc.cvtColor(src, grayMat, Imgproc.COLOR_BGR2GRAY);
/*找出角点*/
Mat tempDst = new Mat();
Imgproc.cornerHarris(grayMat, tempDst, 2, 3, 0.04);
/*归一化Harris角点的输出*/
Mat tempDstNorm = new Mat();
Core.normalize(tempDst, tempDstNorm, 0, 255, Core.NORM_MINMAX);
Core.convertScaleAbs(tempDstNorm, corners);
/*在新的图片上绘制角点*/
Random r = new Random();
for (int i = 0; i < tempDstNorm.cols(); i++) {
for (int j = 0; j < tempDstNorm.rows(); j++) {
double[] value = tempDstNorm.get(j, i);
if (value[0] > 250) {//决定了画出哪些角点，值越大选择画出的点就越少。如果程序跑的比较慢，就是由于值选取的太小，导致画的点过多
Imgproc.circle(corners, new Point(i, j), 5, new Scalar(r.nextInt(255)), 2);
}
}
}
/*将Mat图转换成位图*/
resultHarris = Bitmap.createBitmap(src.cols(), src.rows(), Bitmap.Config.ARGB_8888);//这一步至关重要，必须初始化Bitmap对象的大小
Utils.matToBitmap(corners, resultHarris);
return resultHarris;
}
/*Hough直线检测*/
public Bitmap houghLine(Mat src) {
Bitmap resultHough;
Mat grayMat = new Mat();
Mat cannyEdges = new Mat();
Mat lines = new Mat();
Mat origination = new Mat(src.size(), CvType.CV_8UC4);
src.copyTo(origination);//拷贝
/*通过Canny得到边缘图*/
Imgproc.cvtColor(origination, grayMat, Imgproc.COLOR_BGR2GRAY);//灰度图片
//Imgproc.Canny(grayMat, cannyEdges, 50, 300);
Imgproc.Canny(grayMat, cannyEdges, 50, 150, 3);
//Mat cannyEdges = new Mat(resultHough.getHeight(),resultHough.getWidth(),CvType.CV_8UC1);
/*获得直线图*/
//Imgproc.HoughLinesP(cannyEdges, lines, 1, Math.PI / 180, 10, 0, 50);
//maxLineGap 点的间隔
//minLineLength 最小线长
Imgproc.HoughLinesP(cannyEdges, lines, 1, Math.PI / 180, 100, 0, 100);
Mat houghLines = new Mat();
houghLines.create(cannyEdges.rows(), cannyEdges.cols(), CvType.CV_8UC3);//背景色 CvType.CV_8UC4 白底，CV_8UC1 黑底，CV_8UC3 直线的颜色才起作用
/*在图线的上绘制直线*/
for (int i = 0; i < lines.rows(); i++) {
double[] points = lines.get(i, 0);
if (null != points) {
double x1, y1, x2, y2;
x1 = points[0];
y1 = points[1];
x2 = points[2];
y2 = points[3];
Point pt1 = new Point(x1, y1);
Point pt2 = new Point(x2, y2);
/*在一幅图像上绘制直线*/
//Imgproc.line(houghLines, pt1, pt2, new Scalar(55, 100, 195), 3);// thickness 画线的宽度
Imgproc.line(houghLines, pt1, pt2, new Scalar(255, 255, 0), 3);// thickness 画线的宽度
}
}
resultHough = Bitmap.createBitmap(src.cols(), src.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(houghLines, resultHough);
return resultHough;
}
/*Hough直线检测*/
public Bitmap MyHoughLine2(Mat src) {
// System.loadLibrary( Core.NATIVE_LIBRARY_NAME );
// // Reading the Image from the file and storing it in to a Matrix object
// String file = "F:/worksp/opencv/images/hough_input.jpg";
// // Reading the image
// Mat src = Imgcodecs.imread(file,0);
// 商业请保留原文链接：https://www.yiibai.com/opencv/opencv_hough_line_transform.html
// Detecting edges of it
Mat canny = new Mat();
Imgproc.Canny(src, canny, 50, 200, 3, false);
Bitmap resultHough;
// Changing the color of the canny
Mat cannyColor = new Mat();
Imgproc.cvtColor(canny, cannyColor, Imgproc.COLOR_GRAY2BGR);
// Detecting the hough lines from (canny)
Mat lines = new Mat();
Imgproc.HoughLines(canny, lines, 1, Math.PI / 180, 100);
// Drawing lines on the image
double[] data;
double rho, theta;
Point pt1 = new Point();
Point pt2 = new Point();
double a, b;
double x0, y0;
for (int i = 0; i < lines.cols(); i++) {
data = lines.get(0, i);
rho = data[0];
theta = data[1];
a = Math.cos(theta);
b = Math.sin(theta);
x0 = a * rho;
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));
Imgproc.line(cannyColor, pt1, pt2, new Scalar(0, 100, 255), 6);
}
// Writing the image
//Imgcodecs.imwrite("F:/worksp/opencv/images/hough_output.jpg", cannyColor);//原文出自【易百教程】，商业转载请联系作者获得授权，非商业请保留原文链接：https://www.yiibai.com/opencv/opencv_hough_line_transform.html
resultHough = Bitmap.createBitmap(src.cols(), src.rows(), Bitmap.Config.ARGB_8888);
Utils.matToBitmap(canny, resultHough);
return resultHough;
}
}
参考
opencv line 绘制直线
android openCV检测图像的基本特征，包括Canny边缘检测、Harris角点检测、霍夫直线检测-基于Android studio
OpenCV霍夫变换系列（中篇）-霍夫圆变换
OpenCV-霍夫变换
【OpenCV学习笔记】之霍夫变换（Hough Transform）
opencv学习之路（18）、霍夫变换
OpenCV霍夫变换系列（前篇）-经典霍夫线变换