在上篇中我们已经实现了相机打开和实时图像信息的获取，那么接下来我们可以尝试在获取的图像信息进行一些处理，然后实时显示出来，在这里我们要完成的的几种处理：
灰化、Canny边缘检测、Hist直方图计算、Sobel边缘检测、SEPIA(色调变换)、ZOOM放大镜、PIXELIZE像素化
一、修改布局界面：
由于这里我们需要切换不同的图像处理模式，所以这里我们需要在界面上放置一个按钮，我们可以放置很多个按钮，每个按钮对应一种处理模式，但是这里我们也可以只放置一个按钮，每次点击按钮就切换一次，循环切换模式：
activity_main.xml文件：
<FrameLayout查看预览图：
xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:tools="http://schemas.android.com/tools"
xmlns:opencv="http://schemas.android.com/apk/res-auto"
android:layout_width="match_parent"
android:layout_height="match_parent">
<org.opencv.android.JavaCameraView
android:layout_width="fill_parent"
android:layout_height="fill_parent"
android:id="@+id/camera_view"
opencv:show_fps="true"
opencv:camera_id="any"/>
<RelativeLayout
android:layout_width="fill_parent"
android:layout_height="fill_parent"
android:gravity="bottom|center_horizontal">
<Button
android:id="@+id/deal_btn"
android:layout_width="100dp"
android:layout_height="40dp"
android:layout_marginBottom="20dp"
android:text="处理"/>
</RelativeLayout>
</FrameLayout>
二、获取按钮组件并监听按钮点击：
1.声明一个Button对象用于绑定上面的按钮组件和一个状态标志位用于存储当前状态：
//按钮组件
private Button mButton;
//当前处理状态
private static int Cur_State = 0;
mButton = (Button) findViewById(R.id.deal_btn);这里的状态标志位Cur_State与图像处理的每个类型对应，0对应默认状态，也就是显示原图，1-7分别对应： 灰化、Canny边缘检测、Hist直方图计算、Sobel边缘检测、SEPIA(色调变换)、ZOOM放大镜、PIXELIZE像素化
mButton.setOnClickListener(new OnClickListener(){
@Override
public void onClick(View v) {
if(Cur_State<8){
//切换状态
Cur_State ++;
}else{
//恢复初始状态
Cur_State = 0;
}
}
});
三、图像信息获取保存、处理和显示：
1.在OpenCV中一般都是使用Mat类型来存储图像等矩阵信息，所以我们可以声明一个Mat对象用来作为实时帧图像的缓存对象：
//缓存相机每帧输入的数据
private Mat mRgba;
public void onCameraViewStarted(int width, int height) {
// TODO Auto-generated method stub
mRgba = new Mat(height, width, CvType.CV_8UC4);
}
/**
* 图像处理都写在此处
*/
@Override
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
switch (Cur_State) {
case 1:
//灰化处理
Imgproc.cvtColor(inputFrame.gray(), mRgba, Imgproc.COLOR_GRAY2RGBA,4);
break;
default:
//显示原图
mRgba = inputFrame.rgba();
break;
}
//返回处理后的结果数据
return mRgba;
}
4.由于用对象存储图像数据的话，数据会保存到内存中，所以结束的时候需要进行数据释放，不然可能导致崩溃：
@Override
public void onCameraViewStopped() {
// TODO Auto-generated method stub
mRgba.release();
}
5.运行查看效果：
正常模式：
灰化图：
四、其他处理及结果：
在以上的例子中我们已经完成了预览图的灰化处理，那么接下来我们把其他处理都添加到代码中，查看效果。由于在2.x版本中使用到的部分方法已经发生了变化，如：在OpenCV 3.1.0中org.opencv.core.Core类中的方法line和rectangle都已失效，可以用org.opencv.imgproc.Imgproc中的line和rectangle来代替：
1. MainActivity.java源码：
package com.linsh.opencv_test;
import java.util.Arrays;
import org.opencv.android.BaseLoaderCallback;
import org.opencv.android.CameraBridgeViewBase;
import org.opencv.android.OpenCVLoader;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewFrame;
import org.opencv.android.CameraBridgeViewBase.CvCameraViewListener2;
import org.opencv.android.LoaderCallbackInterface;
import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfInt;
import org.opencv.core.Point;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.imgproc.Imgproc;
import android.R.string;
import android.app.Activity;
import android.os.Bundle;
import android.util.Log;
import android.widget.Button;
import android.view.View;
import android.view.View.OnClickListener;
public class MainActivity extends Activity implements CvCameraViewListener2{
private String TAG = "OpenCV_Test";
//OpenCV的相机接口
private CameraBridgeViewBase mCVCamera;
//缓存相机每帧输入的数据
private Mat mRgba,mTmp;
//按钮组件
private Button mButton;
//当前处理状态
private static int Cur_State = 0;
private Size mSize0;
private Mat mIntermediateMat;
private MatOfInt mChannels[];
private MatOfInt mHistSize;
private int mHistSizeNum = 25;
private Mat mMat0;
private float[] mBuff;
private MatOfFloat mRanges;
private Point mP1;
private Point mP2;
private Scalar mColorsRGB[];
private Scalar mColorsHue[];
private Scalar mWhilte;
private Mat mSepiaKernel;
/**
* 通过OpenCV管理Android服务，异步初始化OpenCV
*/
BaseLoaderCallback mLoaderCallback = new BaseLoaderCallback(this) {
@Override
public void onManagerConnected(int status){
switch (status) {
case LoaderCallbackInterface.SUCCESS:
Log.i(TAG,"OpenCV loaded successfully");
mCVCamera.enableView();
break;
default:
break;
}
}
};
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
mCVCamera = (CameraBridgeViewBase) findViewById(R.id.camera_view);
mCVCamera.setCvCameraViewListener(this);
mButton = (Button) findViewById(R.id.deal_btn);
mButton.setOnClickListener(new OnClickListener(){
@Override
public void onClick(View v) {
if(Cur_State<8){
//切换状态
Cur_State ++;
}else{
//恢复初始状态
Cur_State = 0;
}
}
});
}
@Override
public void onResume() {
super.onResume();
if (!OpenCVLoader.initDebug()) {
Log.d(TAG,"OpenCV library not found!");
} else {
Log.d(TAG, "OpenCV library found inside package. Using it!");
mLoaderCallback.onManagerConnected(LoaderCallbackInterface.SUCCESS);
}
};
@Override
public void onDestroy() {
if(mCVCamera!=null){
mCVCamera.disableView();
}
};
@Override
public void onCameraViewStarted(int width, int height) {
// TODO Auto-generated method stub
mRgba = new Mat(height, width, CvType.CV_8UC4);
mTmp = new Mat(height, width, CvType.CV_8UC4);
mIntermediateMat = new Mat();
mSize0 = new Size();
mChannels = new MatOfInt[] { new MatOfInt(0), new MatOfInt(1), new MatOfInt(2) };
mBuff = new float[mHistSizeNum];
mHistSize = new MatOfInt(mHistSizeNum);
mRanges = new MatOfFloat(0f, 256f);
mMat0 = new Mat();
mColorsRGB = new Scalar[] { new Scalar(200, 0, 0, 255), new Scalar(0, 200, 0, 255), new Scalar(0, 0, 200, 255) };
mColorsHue = new Scalar[] {
new Scalar(255, 0, 0, 255), new Scalar(255, 60, 0, 255), new Scalar(255, 120, 0, 255), new Scalar(255, 180, 0, 255), new Scalar(255, 240, 0, 255),
new Scalar(215, 213, 0, 255), new Scalar(150, 255, 0, 255), new Scalar(85, 255, 0, 255), new Scalar(20, 255, 0, 255), new Scalar(0, 255, 30, 255),
new Scalar(0, 255, 85, 255), new Scalar(0, 255, 150, 255), new Scalar(0, 255, 215, 255), new Scalar(0, 234, 255, 255), new Scalar(0, 170, 255, 255),
new Scalar(0, 120, 255, 255), new Scalar(0, 60, 255, 255), new Scalar(0, 0, 255, 255), new Scalar(64, 0, 255, 255), new Scalar(120, 0, 255, 255),
new Scalar(180, 0, 255, 255), new Scalar(255, 0, 255, 255), new Scalar(255, 0, 215, 255), new Scalar(255, 0, 85, 255), new Scalar(255, 0, 0, 255)
};
mWhilte = Scalar.all(255);
mP1 = new Point();
mP2 = new Point();
// Fill sepia kernel
mSepiaKernel = new Mat(4, 4, CvType.CV_32F);
mSepiaKernel.put(0, 0, /* R */0.189f, 0.769f, 0.393f, 0f);
mSepiaKernel.put(1, 0, /* G */0.168f, 0.686f, 0.349f, 0f);
mSepiaKernel.put(2, 0, /* B */0.131f, 0.534f, 0.272f, 0f);
mSepiaKernel.put(3, 0, /* A */0.000f, 0.000f, 0.000f, 1f);
}
@Override
public void onCameraViewStopped() {
// TODO Auto-generated method stub
mRgba.release();
mTmp.release();
}
/**
* 图像处理都写在此处
*/
@Override
public Mat onCameraFrame(CvCameraViewFrame inputFrame) {
mRgba = inputFrame.rgba();
Size sizeRgba = mRgba.size();
int rows = (int) sizeRgba.height;
int cols = (int) sizeRgba.width;
Mat rgbaInnerWindow;
int left = cols / 8;
int top = rows / 8;
int width = cols * 3 / 4;
int height = rows * 3 / 4;
switch (Cur_State) {
case 1:
//灰化处理
Imgproc.cvtColor(inputFrame.gray(), mRgba, Imgproc.COLOR_GRAY2RGBA,4);
break;
case 2:
//Canny边缘检测
mRgba = inputFrame.rgba();
Imgproc.Canny(inputFrame.gray(), mTmp, 80, 100);
Imgproc.cvtColor(mTmp, mRgba, Imgproc.COLOR_GRAY2RGBA, 4);
break;
case 3:
//Hist直方图计算
Mat hist = new Mat();
int thikness = (int) (sizeRgba.width / (mHistSizeNum + 10) / 5);
if(thikness > 5) thikness = 5;
int offset = (int) ((sizeRgba.width - (5*mHistSizeNum + 4*10)*thikness)/2);
// RGB
for(int c=0; c<3; c++) {
Imgproc.calcHist(Arrays.asList(mRgba), mChannels[c], mMat0, hist, mHistSize, mRanges);
Core.normalize(hist, hist, sizeRgba.height/2, 0, Core.NORM_INF);
hist.get(0, 0, mBuff);
for(int h=0; h<mHistSizeNum; h++) {
mP1.x = mP2.x = offset + (c * (mHistSizeNum + 10) + h) * thikness;
mP1.y = sizeRgba.height-1;
mP2.y = mP1.y - 2 - (int)mBuff[h];
Imgproc.line(mRgba, mP1, mP2, mColorsRGB[c], thikness);
}
}
// Value and Hue
Imgproc.cvtColor(mRgba, mTmp, Imgproc.COLOR_RGB2HSV_FULL);
// Value
Imgproc.calcHist(Arrays.asList(mTmp), mChannels[2], mMat0, hist, mHistSize, mRanges);
Core.normalize(hist, hist, sizeRgba.height/2, 0, Core.NORM_INF);
hist.get(0, 0, mBuff);
for(int h=0; h<mHistSizeNum; h++) {
mP1.x = mP2.x = offset + (3 * (mHistSizeNum + 10) + h) * thikness;
mP1.y = sizeRgba.height-1;
mP2.y = mP1.y - 2 - (int)mBuff[h];
Imgproc.line(mRgba, mP1, mP2, mWhilte, thikness);
}
break;
case 4:
//Sobel边缘检测
Mat gray = inputFrame.gray();
Mat grayInnerWindow = gray.submat(top, top + height, left, left + width);
rgbaInnerWindow = mRgba.submat(top, top + height, left, left + width);
Imgproc.Sobel(grayInnerWindow, mIntermediateMat, CvType.CV_8U, 1, 1);
Core.convertScaleAbs(mIntermediateMat, mIntermediateMat, 10, 0);
Imgproc.cvtColor(mIntermediateMat, rgbaInnerWindow, Imgproc.COLOR_GRAY2BGRA, 4);
grayInnerWindow.release();
rgbaInnerWindow.release();
break;
case 5:
//SEPIA(色调变换)
rgbaInnerWindow = mRgba.submat(top, top + height, left, left + width);
Core.transform(rgbaInnerWindow, rgbaInnerWindow, mSepiaKernel);
rgbaInnerWindow.release();
break;
case 6:
//ZOOM放大镜
Mat zoomCorner = mRgba.submat(0, rows / 2 - rows / 10, 0, cols / 2 - cols / 10);
Mat mZoomWindow = mRgba.submat(rows / 2 - 9 * rows / 100, rows / 2 + 9 * rows / 100, cols / 2 - 9 * cols / 100, cols / 2 + 9 * cols / 100);
Imgproc.resize(mZoomWindow, zoomCorner, zoomCorner.size());
Size wsize = mZoomWindow.size();
Imgproc.rectangle(mZoomWindow, new Point(1, 1), new Point(wsize.width - 2, wsize.height - 2), new Scalar(255, 0, 0, 255), 2);
zoomCorner.release();
mZoomWindow.release();
break;
case 7:
//PIXELIZE像素化
rgbaInnerWindow = mRgba.submat(top, top + height, left, left + width);
Imgproc.resize(rgbaInnerWindow, mIntermediateMat, mSize0, 0.1, 0.1, Imgproc.INTER_NEAREST);
Imgproc.resize(mIntermediateMat, rgbaInnerWindow, rgbaInnerWindow.size(), 0., 0., Imgproc.INTER_NEAREST);
rgbaInnerWindow.release();
break;
default:
//显示原图
mRgba = inputFrame.rgba();
break;
}
//返回处理后的结果数据
return mRgba;
}
}
Canny边缘检测：
Hist直方图计算：
Sobel边缘检测：
SEPIA(色调变换)：
ZOOM放大镜：
PIXELIZE像素化：
