[Android] 通過Menu實現圖片懷舊、浮雕、模糊、光照和素描效果

由於隨手拍項目想做成類似於美圖秀秀那種底部有一排Menu實現不同效果的功能,這裡先簡單介紹如何通過Menu實現打開相冊中的圖片、懷舊效果、浮雕效果、光照效果和素描效果.後面可能會講述如何通過PopupWindow實現自定義的Menu效果.
希望文章對大家有所幫助,如果有錯誤或不足之處請海涵~

一. Menu效果展示

Android手機上有個Menu按鍵,點擊他會彈出一個菜單,通常在屏幕底部或右上角,在選項菜單OptionsMenu中最多顯示2排每排3個菜單項,可以包含自定義的圖片和文字.如果Menu菜單項多於6項時,第6項(Expanded Menus,擴展菜單)會變成More,點擊它會顯示後面所隱藏的所有選項.
下面講述如何在Android 4.0項目中實現簡單的Menu功能.添加如下代碼：

@Override
public boolean onCreateOptionsMenu(Menu menu) { //創建Menu
	//自定義menu 添加圖標(使用自帶圖標)
	menu.add(Menu.NONE, Menu.FIRST + 1 , 1, 打開).
		setIcon(android.R.drawable.ic_menu_slideshow);
	menu.add(Menu.NONE, Menu.FIRST + 2 , 2, 懷舊).
		setIcon(android.R.drawable.ic_menu_edit);
	menu.add(Menu.NONE, Menu.FIRST + 3 , 3, 浮雕).
		setIcon(android.R.drawable.ic_menu_gallery);
	menu.add(Menu.NONE, Menu.FIRST + 4 , 4, 模糊).
		setIcon(android.R.drawable.ic_menu_crop);
	menu.add(Menu.NONE, Menu.FIRST + 5 , 5, 光照).
		setIcon(android.R.drawable.ic_menu_camera);
	menu.add(Menu.NONE, Menu.FIRST + 6  , 6, 銳化).
		setIcon(android.R.drawable.ic_menu_view);
    return true;
}

由於Android 4.0系統缺省UI風格有所變化,所以需要設置Activity的theme為Theme.Light.同時也可以在res/menu/main.xml設置菜單項.參考恺風博主關於Menu的介紹,非常不錯.http://blog.csdn.net/flowingflying/article/details/11967301

下圖是設置前面的顯示Menu不同效果,同時我調用的圖標都是Android自帶的圖片,用戶也可以自定義.(android默認圖標列表)

同時設置XML格式顯示圖片:

二. Menu實現打開圖片

然後通過onOptionsItemSelected(MenuItem item)實現選擇圖片,通過調用自定義函數實現各種功能.

@Override
public boolean onOptionsItemSelected(MenuItem item) { //選擇Menu
	//選擇id 對應Menu.add的參數Menu.FIRST+i
    int id = item.getItemId();
    switch(id) {
    case Menu.FIRST+1: 
    	Toast.makeText(this, 打開圖片, Toast.LENGTH_SHORT).show();
    	OpenImage();
    	break;
    case Menu.FIRST+2: 
    	Toast.makeText(this, 圖片懷舊效果, Toast.LENGTH_SHORT).show();
    	OldRemeberImage();
    	break;
    case Menu.FIRST+3: 
    	Toast.makeText(this, 圖片浮雕效果, Toast.LENGTH_SHORT).show();
    	ReliefImage();
    	break;
    case Menu.FIRST+4: 
    	Toast.makeText(this, 圖片模糊效果, Toast.LENGTH_SHORT).show();
    	FuzzyImage();
    	break;
    case Menu.FIRST+5: 
    	Toast.makeText(this, 圖片光照效果, Toast.LENGTH_SHORT).show();
    	SunshineImage();
    	break;
    case Menu.FIRST+6: 
    	Toast.makeText(this, 圖片銳化效果, Toast.LENGTH_SHORT).show();
    	SharpenImage();
    	break;
    }
    
    return super.onOptionsItemSelected(item);
}

其中打開圖片函數實現方法如下,而上面的很多自定義函數都將在第三部分介紹,你此處可以注釋掉只驗證打開圖片.首先添加自定義變量和獲取ImageView布局.

//自定義變量
private ImageView imageShow;           //顯示圖片  
private Bitmap bmp;                    //原始圖片  
private final int IMAGE_OPEN = 0;      //打開圖片 
@Override
protected void onCreate(Bundle savedInstanceState) {
    super.onCreate(savedInstanceState);
    setContentView(R.layout.activity_main);
    imageShow = (ImageView) findViewById(R.id.imageView1); 
    if (savedInstanceState == null) {
        getFragmentManager().beginTransaction()
                .add(R.id.container, new PlaceholderFragment())
                .commit();
    }
}

然後通過自定義函數OpenImage打開函數,與前面文章介紹的方法一樣.

//自定義函數 打開圖片
public void OpenImage()
{
	Intent intent = new Intent(Intent.ACTION_PICK,   
            android.provider.MediaStore.Images.Media.EXTERNAL_CONTENT_URI);  
    startActivityForResult(intent, IMAGE_OPEN);
}
//顯示打開圖片
protected void onActivityResult(int requestCode, int resultCode, Intent data) {   
    super.onActivityResult(requestCode, resultCode, data);    
    if(resultCode==RESULT_OK && requestCode==IMAGE_OPEN) {    
       Uri imageFileUri = data.getData();  
       DisplayMetrics dm = new DisplayMetrics();  
       getWindowManager().getDefaultDisplay().getMetrics(dm);    
       int width = dm.widthPixels;    //手機屏幕水平分辨率     
       int height = dm.heightPixels;  //手機屏幕垂直分辨率  
       try {    
           //載入圖片尺寸大小沒載入圖片本身 true  
           BitmapFactory.Options bmpFactoryOptions = new BitmapFactory.Options();    
           bmpFactoryOptions.inJustDecodeBounds = true;    
           bmp = BitmapFactory.decodeStream(getContentResolver().openInputStream(imageFileUri), null, bmpFactoryOptions);    
           int heightRatio = (int)Math.ceil(bmpFactoryOptions.outHeight/(float)height);    
           int widthRatio = (int)Math.ceil(bmpFactoryOptions.outWidth/(float)width);      
           //inSampleSize表示圖片占原圖比例 1表示原圖  
           if(heightRatio>1&&widthRatio>1) {    
               if(heightRatio>widthRatio) {    
                   bmpFactoryOptions.inSampleSize = heightRatio;    
               }    
               else {    
                   bmpFactoryOptions.inSampleSize = widthRatio;    
               }    
           }                     
           //圖像真正解碼 false  
           bmpFactoryOptions.inJustDecodeBounds = false;                   
           bmp = BitmapFactory.decodeStream(getContentResolver().openInputStream(imageFileUri), null, bmpFactoryOptions);      
           imageShow.setImageBitmap(bmp);  
       }  
       catch(FileNotFoundException e) {   
           e.printStackTrace();    
       }   
   }  //end if  
}  

下面講講使用Options Menu的函數：
onCreateOptionsMenu(Menu menu)創建options menu,這個函數只會在menu第一次顯示時調用.
onOptionsItemSelected(MenuItem item)處理選中的菜單項.
在通過menu.add函數實現添加菜單項,如menu.add(Menu.NONE,Menu.FIRST+1,1,打開),第一個參數表示組別;第二個參數menu標志編號與onOptionsItemSelected函數中值對應;第三個參數是在菜單中出現的順序,順序由小到大,由左至右;第四個參數是顯示的文字,同時setIcon可以設置圖標.

三. 圖像各種效果實現

最後講講各個效果實現過程,通過不同自定義函數實現.其中各個效果主要參照《Android圖像處理總結》那篇文章和eoeAndroid社區亞瑟的文章.
書籍下載地址：
1.圖片懷舊效果

//圖片懷舊處理
private void OldRemeberImage()
{
	/*
	 * 懷舊處理算法即設置新的RGB
	 * R=0.393r+0.769g+0.189b
	 * G=0.349r+0.686g+0.168b
	 * B=0.272r+0.534g+0.131b
	 */
	int width = bmp.getWidth();
	int height = bmp.getHeight();
	Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);
	int pixColor = 0;
	int pixR = 0;
	int pixG = 0;
	int pixB = 0;
	int newR = 0;
	int newG = 0;
	int newB = 0;
	int[] pixels = new int[width * height];
	bmp.getPixels(pixels, 0, width, 0, 0, width, height);
	for (int i = 0; i < height; i++)
	{
		for (int k = 0; k < width; k++)
		{
			pixColor = pixels[width * i + k];
			pixR = Color.red(pixColor);
			pixG = Color.green(pixColor);
			pixB = Color.blue(pixColor);
			newR = (int) (0.393 * pixR + 0.769 * pixG + 0.189 * pixB);
			newG = (int) (0.349 * pixR + 0.686 * pixG + 0.168 * pixB);
			newB = (int) (0.272 * pixR + 0.534 * pixG + 0.131 * pixB);
			int newColor = Color.argb(255, newR > 255 ? 255 : newR, newG > 255 ? 255 : newG, newB > 255 ? 255 : newB);
			pixels[width * i + k] = newColor;
		}
	}
	bitmap.setPixels(pixels, 0, width, 0, 0, width, height);
	imageShow.setImageBitmap(bitmap);
}

顯示效果如下圖所示：

2.圖片浮雕效果

//圖片浮雕處理
//底片效果也非常簡單:將當前像素點的RGB值分別與255之差後的值作為當前點的RGB
//灰度圖像:通常使用的方法是gray=0.3*pixR+0.59*pixG+0.11*pixB
private void ReliefImage()
{
	/*
	 * 算法原理：(前一個像素點RGB-當前像素點RGB+127)作為當前像素點RGB值
	 * 在ABC中計算B點浮雕效果(RGB值在0~255)
	 * B.r = C.r - B.r + 127
	 * B.g = C.g - B.g + 127
	 * B.b = C.b - B.b + 127
	 */
	int width = bmp.getWidth();
	int height = bmp.getHeight();
	Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);
	int pixColor = 0;
	int pixR = 0;
	int pixG = 0;
	int pixB = 0;
	int newR = 0;
	int newG = 0;
	int newB = 0;
	int[] pixels = new int[width * height];
	bmp.getPixels(pixels, 0, width, 0, 0, width, height);
	for (int i = 1; i < height-1; i++)
	{
		for (int k = 1; k < width-1; k++)
		{
			//獲取前一個像素顏色
			pixColor = pixels[width * i + k];   
			pixR = Color.red(pixColor);
			pixG = Color.green(pixColor);
			pixB = Color.blue(pixColor);
			//獲取當前像素
			pixColor = pixels[(width * i + k) + 1];
			newR = Color.red(pixColor) - pixR +127;
			newG = Color.green(pixColor) - pixG +127;
			newB = Color.blue(pixColor) - pixB +127;
			newR = Math.min(255, Math.max(0, newR));
			newG = Math.min(255, Math.max(0, newG));
			newB = Math.min(255, Math.max(0, newB));
			pixels[width * i + k] = Color.argb(255, newR, newG, newB);
		}
	}
	bitmap.setPixels(pixels, 0, width, 0, 0, width, height);
	imageShow.setImageBitmap(bitmap);
}

顯示效果如下圖所示：

3.圖像模糊效果

//圖像模糊處理
private void FuzzyImage()
{
	/*
	 * 算法原理：
	 * 簡單算法將像素周圍八個點包括自身共九個點RGB值分別相加後平均,當前像素點的RGB值
	 * 復雜算法采用高斯模糊
	 * 高斯矩陣 int[] gauss = new int[] { 1, 2, 1, 2, 4, 2, 1, 2, 1 };
	 * 將九個點的RGB值分別與高斯矩陣中的對應項相乘的和,再除以一個相應的值作為當前像素點的RGB
	 */
    int[] gauss = new int[] { 1, 2, 1, 2, 4, 2, 1, 2, 1 };  // 高斯矩陣
    int delta = 16; // 除以值 值越小圖片會越亮,越大則越暗
	int width = bmp.getWidth();
	int height = bmp.getHeight();
	Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);
	int pixColor = 0;
	int pixR = 0; 
    int pixG = 0; 
    int pixB = 0; 
	int newR, newG, newB;
	int pos = 0;    //位置
	int[] pixels = new int[width * height]; 
    bmp.getPixels(pixels, 0, width, 0, 0, width, height); 
	//循環賦值
	for (int i = 1; i < height-1; i++)
	{
		for (int k = 1; k < width-1; k++)
		{
			pos = 0;
			newR = 0; 
            newG = 0; 
            newB = 0; 
			for (int m = -1; m <= 1; m++)  //寬不變
            { 
                for (int n = -1; n <= 1; n++) //高先變
                { 
                    pixColor = pixels[(i + m) * width + k + n]; 
                    pixR = Color.red(pixColor); 
                    pixG = Color.green(pixColor); 
                    pixB = Color.blue(pixColor); 
                    //3*3像素相加
                    newR = newR + (int) (pixR * gauss[pos]); 
                    newG = newG + (int) (pixG * gauss[pos]); 
                    newB = newB + (int) (pixB * gauss[pos]); 
                    pos++; 
                } 
            } 			
			newR /= delta; 
            newG /= delta; 
            newB /= delta; 
            newR = Math.min(255, Math.max(0, newR)); 
            newG = Math.min(255, Math.max(0, newG)); 
            newB = Math.min(255, Math.max(0, newB));      
            pixels[i * width + k] = Color.argb(255, newR, newG, newB);  
		}
	}
	bitmap.setPixels(pixels, 0, width, 0, 0, width, height);
	imageShow.setImageBitmap(bitmap);
}

該圖顯示效果不是很理想,對高斯模糊理解還不夠,建議大家看我收藏合集裡面講述模糊的超鏈接.
4.圖像光照效果

//圖片光照效果
private void SunshineImage()
{
	/*
	 * 算法原理：(前一個像素點RGB-當前像素點RGB+127)作為當前像素點RGB值
	 * 在ABC中計算B點浮雕效果(RGB值在0~255)
	 * B.r = C.r - B.r + 127
	 * B.g = C.g - B.g + 127
	 * B.b = C.b - B.b + 127
	 * 光照中心取長寬較小值為半徑,也可以自定義從左上角射過來
	 */		
	int width = bmp.getWidth();
	int height = bmp.getHeight();
	Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);
	int pixColor = 0;
	int pixR = 0;
	int pixG = 0;
	int pixB = 0;
	int newR = 0;
	int newG = 0;
	int newB = 0;
	//圍繞圓形光照
	int centerX = width / 2;
	int centerY = height / 2;
	int radius = Math.min(centerX, centerY);
	float strength = 150F;  //光照強度100-150
	int[] pixels = new int[width * height];
	bmp.getPixels(pixels, 0, width, 0, 0, width, height);
	for (int i = 1; i < height-1; i++)
	{
		for (int k = 1; k < width-1; k++)
		{
			//獲取前一個像素顏色
			pixColor = pixels[width * i + k];   
			pixR = Color.red(pixColor);
			pixG = Color.green(pixColor);
			pixB = Color.blue(pixColor);
			newR = pixR;
			newG = pixG;
			newB = pixB;
			//計算當前點到光照中心的距離,平面坐標系中兩點之間的距離
			int distance = (int) (Math.pow((centerY-i), 2) + Math.pow((centerX-k), 2));
			if(distance < radius*radius)
			{
				//按照距離大小計算增強的光照值
				int result = (int)(strength*( 1.0-Math.sqrt(distance) / radius ));
				newR = pixR + result;
				newG = newG + result;
				newB = pixB + result;
			}
			newR = Math.min(255, Math.max(0, newR));
			newG = Math.min(255, Math.max(0, newG));
			newB = Math.min(255, Math.max(0, newB));
			pixels[width * i + k] = Color.argb(255, newR, newG, newB);
		}
	}
	bitmap.setPixels(pixels, 0, width, 0, 0, width, height);
	imageShow.setImageBitmap(bitmap);
}

顯示效果如下圖所示

5.圖片銳化效果
本打算采用拉普拉斯算子或Sobel算子對圖像進行銳化,在使用C++對24位bmp圖像處理時能非常好的顯示圖像的輪廓,但是Android總是效果不是很好啊,而且有虛線!網上一些銳化效果完全沒有實現顯示圖像輪廓,與原圖區別不大,感覺是錯誤的方法.研究ing

//圖像銳化處理 拉普拉斯算子處理
private void SharpenImage()
{
	/*
	 * 銳化基本思想是加強圖像中景物的邊緣和輪廓,使圖像變得清晰
	 * 而圖像平滑是使圖像中邊界和輪廓變得模糊
	 * 
	 * 拉普拉斯算子圖像銳化
	 * 獲取周圍9個點的矩陣乘以模板9個的矩陣 卷積
	 */
	//拉普拉斯算子模板 { 0, -1, 0, -1, -5, -1, 0, -1, 0 } { -1, -1, -1, -1, 9, -1, -1, -1, -1 }
	int[] laplacian = new int[] {  -1, -1, -1, -1, 9, -1, -1, -1, -1 }; 
	int width = bmp.getWidth();  
    int height = bmp.getHeight();  
    Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);  
    int pixR = 0;  
    int pixG = 0;  
    int pixB = 0;  
    int pixColor = 0;  
    int newR = 0;  
    int newG = 0;  
    int newB = 0;  
    int idx = 0;  
    float alpha = 0.3F;  //圖片透明度
    int[] pixels = new int[width * height];  
    bmp.getPixels(pixels, 0, width, 0, 0, width, height);  
    //圖像處理
    for (int i = 1; i < height - 1; i++)  
    {  
        for (int k = 1; k < width - 1; k++)  
        {  
        	idx = 0;
        	newR = 0;  
            newG = 0;  
            newB = 0;  
            for (int n = -1; n <= 1; n++)   //取出圖像3*3領域像素 
            {  
                for (int m = -1; m <= 1; m++)  //n行數不變 m列變換
                {  
                    pixColor = pixels[(i + n) * width + k + m];  //當前點(i,k)
                    pixR = Color.red(pixColor);  
                    pixG = Color.green(pixColor);  
                    pixB = Color.blue(pixColor);  
                    //圖像像素與對應摸板相乘   
                    newR = newR + (int) (pixR * laplacian[idx] * alpha);  
                    newG = newG + (int) (pixG * laplacian[idx] * alpha);  
                    newB = newB + (int) (pixB * laplacian[idx] * alpha);  
                    idx++; 
                }  
            }
            newR = Math.min(255, Math.max(0, newR));  
            newG = Math.min(255, Math.max(0, newG));  
            newB = Math.min(255, Math.max(0, newB));  
            //賦值  
            pixels[i * width + k] = Color.argb(255, newR, newG, newB);   
        }
    }
    bitmap.setPixels(pixels, 0, width, 0, 0, width, height);  
    imageShow.setImageBitmap(bitmap);
}

作圖是其顯示效果,而右圖是我以前《數字圖像處理》課用C++寫的不同模版的銳化效果.

下面再介紹些效果,下面這個效果是參考亞瑟BOY的冰凍效果.
源代碼地址：http://www.eoeandroid.com/thread-176490-1-1.html

//圖片冰凍效果
private void IceImage()
{
	int width = bmp.getWidth();
	int height = bmp.getHeight();
	Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);
	int pixColor = 0;
	int pixR = 0;
	int pixG = 0;
	int pixB = 0;
	int newColor = 0;
	int newR = 0;
	int newG = 0;
	int newB =0;
	int[] pixels = new int[width * height];
	bmp.getPixels(pixels, 0, width, 0, 0, width, height);
	for (int i = 0; i < height; i++)
	{
		for (int k = 0; k < width; k++)
		{
			//獲取前一個像素顏色
			pixColor = pixels[width * i + k];   
			pixR = Color.red(pixColor);
			pixG = Color.green(pixColor);
			pixB = Color.blue(pixColor);
			//紅色
			newColor = pixR - pixG - pixB;
			newColor = newColor * 3 / 2;
			if(newColor < 0) {
				newColor = -newColor;
			}
			if(newColor >255) {
				newColor = 255;
			}
			newR = newColor;
			//綠色
			newColor = pixG - pixB - pixR;
			newColor = newColor * 3 / 2;
			if(newColor < 0) {
				newColor = -newColor;
			}
			if(newColor >255) {
				newColor = 255;
			}
			newG = newColor;
			//藍色
			newColor = pixB - pixG - pixR;
			newColor = newColor * 3 / 2;
			if(newColor < 0) {
				newColor = -newColor;
			}
			if(newColor >255) {
				newColor = 255;
			}
			newB = newColor;
			pixels[width * i + k] = Color.argb(255, newR, newG, newB);
		}
	}
	bitmap.setPixels(pixels, 0, width, 0, 0, width, height);
	imageShow.setImageBitmap(bitmap);
}

下面這個代碼是CSDN的xu_fu博主的素描處理,對我軟件有用.
源代碼地址：http://blog.csdn.net/xu_fu/article/details/21485461
效果顯示如下圖所示,在Menu選擇中調用函數IceImage或SuMiaoImage即可實現.

//素描效果
private void SuMiaoImage()
{
	//創建新Bitmap
	int width = bmp.getWidth();  
    int height = bmp.getHeight();  
    int[] pixels = new int[width * height];    //存儲變換圖像
    int[] linpix = new int[width * height];     //存儲灰度圖像
    Bitmap bitmap = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);  
    bmp.getPixels(pixels, 0, width, 0, 0, width, height);
    int pixColor = 0;
	int pixR = 0;
	int pixG = 0;
	int pixB = 0;
    int newR = 0;  
    int newG = 0;  
    int newB = 0;
    //灰度圖像
    for (int i = 1; i < width - 1; i++)  
    {  
        for (int j = 1; j < height - 1; j++)   //拉普拉斯算子模板 { 0, -1, 0, -1, -5, -1, 0, -1, 0 
        {  
        	//獲取前一個像素顏色
			pixColor = pixels[width * i + j];   
			pixR = Color.red(pixColor);
			pixG = Color.green(pixColor);
			pixB = Color.blue(pixColor);
        	//灰度圖像
        	int gray=(int)(0.3*pixR+0.59*pixG+0.11*pixB);
        	linpix[width * i + j] = Color.argb(255, gray, gray, gray);
        	//圖像反向
        	gray=255-gray;
        	pixels[width * i + j] = Color.argb(255, gray, gray, gray);
        }
    }
    int radius = Math.min(width/2, height/2);
    int[] copixels = gaussBlur(pixels, width, height, 10, 10/3);   //高斯模糊 采用半徑10
    int[] result = colorDodge(linpix, copixels);   //素描圖像 顏色減淡
    bitmap.setPixels(result, 0, width, 0, 0, width, height);
    imageShow.setImageBitmap(bitmap);
}

//高斯模糊
public static int[] gaussBlur(int[] data, int width, int height, int radius,  
        float sigma) {  
  
    float pa = (float) (1 / (Math.sqrt(2 * Math.PI) * sigma));  
    float pb = -1.0f / (2 * sigma * sigma);  
  
    // generate the Gauss Matrix   
    float[] gaussMatrix = new float[radius * 2 + 1];  
    float gaussSum = 0f;  
    for (int i = 0, x = -radius; x <= radius; ++x, ++i) {  
        float g = (float) (pa * Math.exp(pb * x * x));  
        gaussMatrix[i] = g;  
        gaussSum += g;  
    }  
  
    for (int i = 0, length = gaussMatrix.length; i < length; ++i) {  
        gaussMatrix[i] /= gaussSum;  
    }  
  
    // x direction   
    for (int y = 0; y < height; ++y) {  
        for (int x = 0; x < width; ++x) {  
            float r = 0, g = 0, b = 0;  
            gaussSum = 0;  
            for (int j = -radius; j <= radius; ++j) {  
                int k = x + j;  
                if (k >= 0 && k < width) {  
                    int index = y * width + k;  
                    int color = data[index];  
                    int cr = (color & 0x00ff0000) >> 16;  
                    int cg = (color & 0x0000ff00) >> 8;  
                    int cb = (color & 0x000000ff);  
  
                    r += cr * gaussMatrix[j + radius];  
                    g += cg * gaussMatrix[j + radius];  
                    b += cb * gaussMatrix[j + radius];  
  
                    gaussSum += gaussMatrix[j + radius];  
                }  
            }  
  
            int index = y * width + x;  
            int cr = (int) (r / gaussSum);  
            int cg = (int) (g / gaussSum);  
            int cb = (int) (b / gaussSum);  
              
            data[index] = cr << 16 | cg << 8 | cb | 0xff000000;  
        }  
    }  
  
    // y direction   
    for (int x = 0; x < width; ++x) {  
        for (int y = 0; y < height; ++y) {  
            float r = 0, g = 0, b = 0;  
            gaussSum = 0;  
            for (int j = -radius; j <= radius; ++j) {  
                int k = y + j;  
                if (k >= 0 && k < height) {  
                    int index = k * width + x;  
                    int color = data[index];  
                    int cr = (color & 0x00ff0000) >> 16;  
                    int cg = (color & 0x0000ff00) >> 8;  
                    int cb = (color & 0x000000ff);  
  
                    r += cr * gaussMatrix[j + radius];  
                    g += cg * gaussMatrix[j + radius];  
                    b += cb * gaussMatrix[j + radius];  
  
                    gaussSum += gaussMatrix[j + radius];  
                }  
            }  
  
            int index = y * width + x;  
            int cr = (int) (r / gaussSum);  
            int cg = (int) (g / gaussSum);  
            int cb = (int) (b / gaussSum);  
            data[index] = cr << 16 | cg << 8 | cb | 0xff000000;  
        }  
    }  
    
    return data;
}  

//顏色減淡
public static int[] colorDodge(int[] baseColor, int[] mixColor) {  
	  
    for (int i = 0, length = baseColor.length; i < length; ++i) {  
        int bColor = baseColor[i];  
        int br = (bColor & 0x00ff0000) >> 16;  
        int bg = (bColor & 0x0000ff00) >> 8;  
        int bb = (bColor & 0x000000ff);  
  
        int mColor = mixColor[i];  
        int mr = (mColor & 0x00ff0000) >> 16;  
        int mg = (mColor & 0x0000ff00) >> 8;  
        int mb = (mColor & 0x000000ff);  
  
        int nr = colorDodgeFormular(br, mr);  
        int ng = colorDodgeFormular(bg, mg);  
        int nb = colorDodgeFormular(bb, mb);  
  
        baseColor[i] = nr << 16 | ng << 8 | nb | 0xff000000;  
    }  
    return baseColor;
}  
  
private static int colorDodgeFormular(int base, int mix) {  
      
    int result = base + (base * mix) / (255 - mix);  
    result = result > 255 ? 255 : result;  
    return result;  
      
}

最後希望文章對大家有所幫助,感謝上面提到的作者,同時可能還有些如LOMO等效果可參考下面的文章,它是圖像處理的一個集合超鏈接.後面會寫PopupWindows實現美圖秀秀的效果和對人臉進行處理.