因为随手拍项目想做成类似于美图秀秀那种底部有一排Menu实现不同效果的功能,这里先简介怎样通过Menu实现打开相冊中的图片、怀旧效果、浮雕效果、光照效果和素描效果.后面可能会讲述怎样通过PopupWindow实现自己定义的Menu效果. 希望文章对大家有所帮助,假设有错误或不足之处请海涵~
一. Menu效果展示
Android手机上有个Menu按键,点击他会弹出一个菜单,通常在屏幕底部或右上角,在选项菜单OptionsMenu中最多显示2排每排3个菜单项,能够包括自己定义的图片和文字.假设Menu菜单项多于6项时,第6项(Expanded Menus,扩展菜单)会变成More,点击它会显示后面所隐藏的全部选项. 以下讲述怎样在Android 4.0项目中实现简单的Menu功能.加入例如以下代码：
@Overridepublic boolean onCreateOptionsMenu(Menu menu) { //创建Menu //自己定义menu 加入图标(使用自带图标) menu.add(Menu.NONE, Menu.FIRST + 1 , 1, "打开"). setIcon(android.R.drawable.ic_menu_slideshow); menu.add(Menu.NONE, Menu.FIRST + 2 , 2, "怀旧"). 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, "锐化"). setIcon(android.R.drawable.ic_menu_view); return true;} 因为Android 4.0系统缺省UI风格有所变化,所以须要设置Activity的theme为Theme.Light.同一时候也能够在res/menu/main.xml设置菜单项.參考"恺风"博主关于Menu的介绍,很不错. 下图是设置前面的显示Menu不同效果,同一时候我调用的图标都是Android自带的图片,用户也能够自己定义.()
同一时候设置XML格式显示图片: 二. Menu实现打开图片
然后通过onOptionsItemSelected(MenuItem item)实现选择图片,通过调用自己定义函数实现各种功能.
@Overridepublic boolean onOptionsItemSelected(MenuItem item) { //选择Menu //选择id 相应Menu.add的參数Menu.FIRST+i int id = item.getItemId(); switch(id) { case Menu.FIRST+1: Toast.makeText(this, "打开图片", Toast.LENGTH_SHORT).show(); OpenImage(); break; case Menu.FIRST+2: Toast.makeText(this, "图片怀旧效果", Toast.LENGTH_SHORT).show(); OldRemeberImage(); break; case Menu.FIRST+3: Toast.makeText(this, "图片浮雕效果", Toast.LENGTH_SHORT).show(); ReliefImage(); break; case Menu.FIRST+4: Toast.makeText(this, "图片模糊效果", Toast.LENGTH_SHORT).show(); FuzzyImage(); break; case Menu.FIRST+5: Toast.makeText(this, "图片光照效果", Toast.LENGTH_SHORT).show(); SunshineImage(); break; case Menu.FIRST+6: Toast.makeText(this, "图片锐化效果", Toast.LENGTH_SHORT).show(); SharpenImage(); break; } return super.onOptionsItemSelected(item);} 当中打开图片函数实现方法例如以下,而上面的非常多自己定义函数都将在第三部分介绍,你此处能够凝视掉仅仅验证"打开图片".首先加入自己定义变量和获取ImageView布局. //自己定义变量private ImageView imageShow; //显示图片 private Bitmap bmp; //原始图片 private final int IMAGE_OPEN = 0; //打开图片 @Overrideprotected 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(); }} 然后通过自己定义函数OpenImage打开函数,与前面文章介绍的方法一样.
//自己定义函数 打开图片public void OpenImage(){ Intent intent = new Intent(Intent.ACTION_PICK, android.provider.MediaStore.Images.Media.EXTERNAL_CONTENT_URI); startActivityForResult(intent, IMAGE_OPEN);}//显示打开图片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; //手机屏幕水平分辨率 int height = dm.heightPixels; //手机屏幕垂直分辨率 try { //加载图片尺寸大小没加载图片本身 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表示图片占原图比例 1表示原图 if(heightRatio>1&&widthRatio>1) { if(heightRatio>widthRatio) { bmpFactoryOptions.inSampleSize = heightRatio; } else { bmpFactoryOptions.inSampleSize = widthRatio; } } //图像真正解码 false bmpFactoryOptions.inJustDecodeBounds = false; bmp = BitmapFactory.decodeStream(getContentResolver().openInputStream(imageFileUri), null, bmpFactoryOptions); imageShow.setImageBitmap(bmp); } catch(FileNotFoundException e) { e.printStackTrace(); } } //end if } 以下讲讲使用Options Menu的函数： onCreateOptionsMenu(Menu menu)创建options menu,这个函数仅仅会在menu第一次显示时调用. onOptionsItemSelected(MenuItem item)处理选中的菜单项. 在通过menu.add函数实现加入菜单项,如menu.add(Menu.NONE,Menu.FIRST+1,1,"打开"),第一个參数表示组别;第二个參数menu标志编号与onOptionsItemSelected函数中值相应;第三个參数是在菜单中出现的顺序,顺序由小到大,由左至右;第四个參数是显示的文字,同一时候setIcon能够设置图标.
三. 图像各种效果实现
最后讲讲各个效果实现过程,通过不同自己定义函数实现.当中各个效果主要參照《Android图像处理总结》那篇文章和eoeAndroid社区亚瑟的文章. 书籍下载地址：1.图片怀旧效果
//图片怀旧处理private void OldRemeberImage(){ /* * 怀旧处理算法即设置新的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); }
显示效果例如以下图所看到的：
2.图片浮雕效果
//图片浮雕处理//底片效果也很easy:将当前像素点的RGB值分别与255之差后的值作为当前点的RGB//灰度图像:通常使用的方法是gray=0.3*pixR+0.59*pixG+0.11*pixBprivate void ReliefImage(){ /* * 算法原理：(前一个像素点RGB-当前像素点RGB+127)作为当前像素点RGB值 * 在ABC中计算B点浮雕效果(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++) { //获取前一个像素颜色 pixColor = pixels[width * i + k]; pixR = Color.red(pixColor); pixG = Color.green(pixColor); pixB = Color.blue(pixColor); //获取当前像素 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);} 显示效果例如以下图所看到的： 
3.图像模糊效果
//图像模糊处理private void FuzzyImage(){ /* * 算法原理： * 简单算法将像素周围八个点包含自身共九个点RGB值分别相加后平均,当前像素点的RGB值 * 复杂算法採用高斯模糊 * 高斯矩阵 int[] gauss = new int[] { 1, 2, 1, 2, 4, 2, 1, 2, 1 }; * 将九个点的RGB值分别与高斯矩阵中的相应项相乘的和,再除以一个相应的值作为当前像素点的RGB */ int[] gauss = new int[] { 1, 2, 1, 2, 4, 2, 1, 2, 1 }; // 高斯矩阵 int delta = 16; // 除以值 值越小图片会越亮,越大则越暗 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); //循环赋值 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++) //宽不变 { for (int n = -1; n <= 1; n++) //高先变 { 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);} 该图显示效果不是非常理想,对高斯模糊理解还不够,建议大家看我收藏合集里面讲述模糊的超链接.4.图像光照效果
//图片光照效果private void SunshineImage(){ /* * 算法原理：(前一个像素点RGB-当前像素点RGB+127)作为当前像素点RGB值 * 在ABC中计算B点浮雕效果(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 * 光照中心取长宽较小值为半径,也能够自己定义从左上角射过来 */ 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; //环绕圆形光照 int centerX = width / 2; int centerY = height / 2; int radius = Math.min(centerX, centerY); float strength = 150F; //光照强度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++) { //获取前一个像素颜色 pixColor = pixels[width * i + k]; pixR = Color.red(pixColor); pixG = Color.green(pixColor); pixB = Color.blue(pixColor); newR = pixR; newG = pixG; newB = pixB; //计算当前点到光照中心的距离,平面坐标系中两点之间的距离 int distance = (int) (Math.pow((centerY-i), 2) + Math.pow((centerX-k), 2)); if(distance < radius*radius) { //依照距离大小计算增强的光照值 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);} 显示效果例如以下图所看到的 
5.图片锐化效果 本打算採用拉普拉斯算子或Sobel算子对图像进行锐化,在使用C++对24位bmp图像处理时能非常好的显示图像的轮廓,可是Android总是效果不是非常好啊,并且有虚线!网上一些锐化效果全然没有实现显示图像轮廓,与原图差别不大,感觉是错误的方法.研究ing //图像锐化处理 拉普拉斯算子处理private void SharpenImage(){ /* * 锐化基本思想是加强图像中景物的边缘和轮廓,使图像变得清晰 * 而图像平滑是使图像中边界和轮廓变得模糊 * * 拉普拉斯算子图像锐化 * 获取周围9个点的矩阵乘以模板9个的矩阵 卷积 */ //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; //图片透明度 int[] pixels = new int[width * height]; bmp.getPixels(pixels, 0, width, 0, 0, width, height); //图像处理 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++) //取出图像3*3领域像素 { for (int m = -1; m <= 1; m++) //n行数不变 m列变换 { pixColor = pixels[(i + n) * width + k + m]; //当前点(i,k) pixR = Color.red(pixColor); pixG = Color.green(pixColor); pixB = Color.blue(pixColor); //图像像素与相应摸板相乘 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)); //赋值 pixels[i * width + k] = Color.argb(255, newR, newG, newB); } } bitmap.setPixels(pixels, 0, width, 0, 0, width, height); imageShow.setImageBitmap(bitmap);} 作图是其显示效果,而右图是我曾经《数字图像处理》课用C++写的不同模版的锐化效果. 
以下再介绍些效果,以下这个效果是參考亚瑟BOY的冰冻效果. 源码地址： //图片冰冻效果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++) { //获取前一个像素颜色 pixColor = pixels[width * i + k]; pixR = Color.red(pixColor); pixG = Color.green(pixColor); pixB = Color.blue(pixColor); //红色 newColor = pixR - pixG - pixB; newColor = newColor * 3 / 2; if(newColor < 0) { newColor = -newColor; } if(newColor >255) { newColor = 255; } newR = newColor; //绿色 newColor = pixG - pixB - pixR; newColor = newColor * 3 / 2; if(newColor < 0) { newColor = -newColor; } if(newColor >255) { newColor = 255; } newG = newColor; //蓝色 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);} 以下这个代码是CSDN的xu_fu博主的素描处理,对我软件实用. 源码地址： 效果显演示样例如以下图所看到的,在Menu选择中调用函数IceImage或SuMiaoImage就可以实现. 
//素描效果private void SuMiaoImage(){ //创建新Bitmap int width = bmp.getWidth(); int height = bmp.getHeight(); int[] pixels = new int[width * height]; //存储变换图像 int[] linpix = new int[width * height]; //存储灰度图像 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; //灰度图像 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 { //获取前一个像素颜色 pixColor = pixels[width * i + j]; pixR = Color.red(pixColor); pixG = Color.green(pixColor); pixB = Color.blue(pixColor); //灰度图像 int gray=(int)(0.3*pixR+0.59*pixG+0.11*pixB); linpix[width * i + j] = Color.argb(255, gray, gray, gray); //图像反向 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 採用半径10 int[] result = colorDodge(linpix, copixels); //素描图像 颜色减淡 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;} //颜色减淡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; }
最后希望文章对大家有所帮助,感谢上面提到的作者,同一时候可能还有些如LOMO等效果可參考以下的文章,它是图像处理的一个集合超链接.后面会写PopupWindows实现美图秀秀的效果和对人脸进行处理. 源码下载： (By:Eastmount 2014-11-2 晚8点 http://blog.csdn.net/eastmount/)