編輯:關於Android編程
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~
@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)
@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:
//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:
//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:
//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.
//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
//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.
//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.
//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.
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