目前,在我正在开发的Android应用程序中,我正在遍历图像的像素以使其模糊。在640x480的图片上,这大约需要30秒。
在Android Market中浏览应用程序时,我碰到了一个包含模糊功能的应用程序,它们的模糊速度非常快(例如5秒),因此它们必须使用另一种模糊方法。
除了遍历像素之外,有人知道更快的方法吗?
目前,在我正在开发的Android应用程序中,我正在遍历图像的像素以使其模糊。在640x480的图片上,这大约需要30秒。
在Android Market中浏览应用程序时,我碰到了一个包含模糊功能的应用程序,它们的模糊速度非常快(例如5秒),因此它们必须使用另一种模糊方法。
除了遍历像素之外,有人知道更快的方法吗?
Answers:
这是在黑暗中拍摄的,但是您可以尝试缩小图像,然后再次放大。可以使用完成此操作Bitmap.createScaledBitmap(Bitmap src, int dstWidth, int dstHeight, boolean filter)
。确保并将filter参数设置为true。它会以本机代码运行,因此可能会更快。
对于将来的Google员工,这是我从Quasimondo移植的算法。它是盒子模糊和高斯模糊之间的一种混合,它也非常漂亮而且非常快。
遇到ArrayIndexOutOfBoundsException问题的人的更新: @anthonycr在评论中提供了以下信息:
我发现通过用StrictMath.abs或其他一些Abs实现替换Math.abs,不会发生崩溃。
/**
* Stack Blur v1.0 from
* http://www.quasimondo.com/StackBlurForCanvas/StackBlurDemo.html
* Java Author: Mario Klingemann <mario at quasimondo.com>
* http://incubator.quasimondo.com
*
* created Feburary 29, 2004
* Android port : Yahel Bouaziz <yahel at kayenko.com>
* http://www.kayenko.com
* ported april 5th, 2012
*
* This is a compromise between Gaussian Blur and Box blur
* It creates much better looking blurs than Box Blur, but is
* 7x faster than my Gaussian Blur implementation.
*
* I called it Stack Blur because this describes best how this
* filter works internally: it creates a kind of moving stack
* of colors whilst scanning through the image. Thereby it
* just has to add one new block of color to the right side
* of the stack and remove the leftmost color. The remaining
* colors on the topmost layer of the stack are either added on
* or reduced by one, depending on if they are on the right or
* on the left side of the stack.
*
* If you are using this algorithm in your code please add
* the following line:
* Stack Blur Algorithm by Mario Klingemann <mario@quasimondo.com>
*/
public Bitmap fastblur(Bitmap sentBitmap, float scale, int radius) {
int width = Math.round(sentBitmap.getWidth() * scale);
int height = Math.round(sentBitmap.getHeight() * scale);
sentBitmap = Bitmap.createScaledBitmap(sentBitmap, width, height, false);
Bitmap bitmap = sentBitmap.copy(sentBitmap.getConfig(), true);
if (radius < 1) {
return (null);
}
int w = bitmap.getWidth();
int h = bitmap.getHeight();
int[] pix = new int[w * h];
Log.e("pix", w + " " + h + " " + pix.length);
bitmap.getPixels(pix, 0, w, 0, 0, w, h);
int wm = w - 1;
int hm = h - 1;
int wh = w * h;
int div = radius + radius + 1;
int r[] = new int[wh];
int g[] = new int[wh];
int b[] = new int[wh];
int rsum, gsum, bsum, x, y, i, p, yp, yi, yw;
int vmin[] = new int[Math.max(w, h)];
int divsum = (div + 1) >> 1;
divsum *= divsum;
int dv[] = new int[256 * divsum];
for (i = 0; i < 256 * divsum; i++) {
dv[i] = (i / divsum);
}
yw = yi = 0;
int[][] stack = new int[div][3];
int stackpointer;
int stackstart;
int[] sir;
int rbs;
int r1 = radius + 1;
int routsum, goutsum, boutsum;
int rinsum, ginsum, binsum;
for (y = 0; y < h; y++) {
rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;
for (i = -radius; i <= radius; i++) {
p = pix[yi + Math.min(wm, Math.max(i, 0))];
sir = stack[i + radius];
sir[0] = (p & 0xff0000) >> 16;
sir[1] = (p & 0x00ff00) >> 8;
sir[2] = (p & 0x0000ff);
rbs = r1 - Math.abs(i);
rsum += sir[0] * rbs;
gsum += sir[1] * rbs;
bsum += sir[2] * rbs;
if (i > 0) {
rinsum += sir[0];
ginsum += sir[1];
binsum += sir[2];
} else {
routsum += sir[0];
goutsum += sir[1];
boutsum += sir[2];
}
}
stackpointer = radius;
for (x = 0; x < w; x++) {
r[yi] = dv[rsum];
g[yi] = dv[gsum];
b[yi] = dv[bsum];
rsum -= routsum;
gsum -= goutsum;
bsum -= boutsum;
stackstart = stackpointer - radius + div;
sir = stack[stackstart % div];
routsum -= sir[0];
goutsum -= sir[1];
boutsum -= sir[2];
if (y == 0) {
vmin[x] = Math.min(x + radius + 1, wm);
}
p = pix[yw + vmin[x]];
sir[0] = (p & 0xff0000) >> 16;
sir[1] = (p & 0x00ff00) >> 8;
sir[2] = (p & 0x0000ff);
rinsum += sir[0];
ginsum += sir[1];
binsum += sir[2];
rsum += rinsum;
gsum += ginsum;
bsum += binsum;
stackpointer = (stackpointer + 1) % div;
sir = stack[(stackpointer) % div];
routsum += sir[0];
goutsum += sir[1];
boutsum += sir[2];
rinsum -= sir[0];
ginsum -= sir[1];
binsum -= sir[2];
yi++;
}
yw += w;
}
for (x = 0; x < w; x++) {
rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;
yp = -radius * w;
for (i = -radius; i <= radius; i++) {
yi = Math.max(0, yp) + x;
sir = stack[i + radius];
sir[0] = r[yi];
sir[1] = g[yi];
sir[2] = b[yi];
rbs = r1 - Math.abs(i);
rsum += r[yi] * rbs;
gsum += g[yi] * rbs;
bsum += b[yi] * rbs;
if (i > 0) {
rinsum += sir[0];
ginsum += sir[1];
binsum += sir[2];
} else {
routsum += sir[0];
goutsum += sir[1];
boutsum += sir[2];
}
if (i < hm) {
yp += w;
}
}
yi = x;
stackpointer = radius;
for (y = 0; y < h; y++) {
// Preserve alpha channel: ( 0xff000000 & pix[yi] )
pix[yi] = ( 0xff000000 & pix[yi] ) | ( dv[rsum] << 16 ) | ( dv[gsum] << 8 ) | dv[bsum];
rsum -= routsum;
gsum -= goutsum;
bsum -= boutsum;
stackstart = stackpointer - radius + div;
sir = stack[stackstart % div];
routsum -= sir[0];
goutsum -= sir[1];
boutsum -= sir[2];
if (x == 0) {
vmin[y] = Math.min(y + r1, hm) * w;
}
p = x + vmin[y];
sir[0] = r[p];
sir[1] = g[p];
sir[2] = b[p];
rinsum += sir[0];
ginsum += sir[1];
binsum += sir[2];
rsum += rinsum;
gsum += ginsum;
bsum += binsum;
stackpointer = (stackpointer + 1) % div;
sir = stack[stackpointer];
routsum += sir[0];
goutsum += sir[1];
boutsum += sir[2];
rinsum -= sir[0];
ginsum -= sir[1];
binsum -= sir[2];
yi += w;
}
}
Log.e("pix", w + " " + h + " " + pix.length);
bitmap.setPixels(pix, 0, w, 0, 0, w, h);
return (bitmap);
}
for
循环立即坏解引用之前,无论是计算rbs
变量或计算gsum
,rsum
或bsum
变量不是这样做的权利。我发现通过替换Math.abs
为StrictMath.abs
或其他abs
实现,不会发生崩溃。由于StrictMath.abs
其本身委托给Math.abs
,因此似乎必须进行错误的优化。
与Showcase / Benchmark App和Github上的Source一起使用。 还要检查我目前正在使用的Blur框架:Dali。
经过大量实验后,我现在可以安全地为您提供一些可靠的建议,这些建议将使您在使用Android Framework时在Android中的生活更加轻松。
切勿使用位图的完整大小。图像越大,越需要模糊,并且模糊半径也需要更大,通常,模糊半径越大,算法花费的时间越长。
final BitmapFactory.Options options = new BitmapFactory.Options();
options.inSampleSize = 8;
Bitmap blurTemplate = BitmapFactory.decodeResource(getResources(), R.drawable.myImage, options);
这将向位图加载inSampleSize
8,因此仅加载原始图像的1/64。测试inSampleSize
适合您需求的内容,但将其保留为2 ^ n(2,4,8,...),以避免因缩放而降低质量。有关更多信息,请参阅Google文档
另一个真正的大优点是位图加载非常快。在我的早期模糊测试中,我认为整个模糊过程中最长的时间是图像加载。因此,要从磁盘加载1920x1080图像,我的Nexus 5需要500毫秒,而模糊仅需250毫秒左右。
Renderscript提供ScriptIntrinsicBlur
了一个高斯模糊滤镜。它具有良好的视觉效果,是您在Android上实际获得的最快速度。Google声称“通常比多线程C实现快2-3倍,通常比Java实现快10倍以上”。Renderscript确实非常复杂(使用最快的处理设备(GPU,ISP等),等等),并且还具有v8支持库,使其兼容至2.2。至少从理论上讲,通过我自己的测试和其他开发人员的报告,似乎不可能盲目使用Renderscript,因为硬件/驱动程序碎片似乎会导致某些设备出现问题,即使使用更高的sdk lvl也是如此(例如, 4.1 Nexus S出现问题),因此请小心并在许多设备上进行测试。这是一个简单的示例,可以帮助您入门:
//define this only once if blurring multiple times
RenderScript rs = RenderScript.create(context);
(...)
//this will blur the bitmapOriginal with a radius of 8 and save it in bitmapOriginal
final Allocation input = Allocation.createFromBitmap(rs, bitmapOriginal); //use this constructor for best performance, because it uses USAGE_SHARED mode which reuses memory
final Allocation output = Allocation.createTyped(rs, input.getType());
final ScriptIntrinsicBlur script = ScriptIntrinsicBlur.create(rs, Element.U8_4(rs));
script.setRadius(8f);
script.setInput(input);
script.forEach(output);
output.copyTo(bitmapOriginal);
当Google特别推荐Google对Gradle使用v8支持时,“因为它们包含了最新的改进”,因此您只需向构建脚本添加2行,并android.support.v8.renderscript
与当前的构建工具一起使用(适用于Android Gradle插件v14 +的更新语法)
android {
...
defaultConfig {
...
renderscriptTargetApi 19
renderscriptSupportModeEnabled true
}
}
Nexus 5的简单基准测试-比较RenderScript与其他不同的Java和Renderscript实现:
不同图片大小下每次模糊的平均运行时间
每秒百万像素,可以被模糊
每个值是250轮的平均值。RS_GAUSS_FAST
是ScriptIntrinsicBlur
(并且几乎总是最快的),开始的其他RS_
大多数都是使用简单内核进行的卷积实现。算法的详细信息可以在这里找到。这并不是纯粹的模糊,因为很大一部分是被测量的垃圾收集。可以在这里看到(ScriptIntrinsicBlur
在100x100的图像上,约有500发回合)
峰值是gc。
您可以自己检查一下,基准测试应用程序在playstore中:BlurBenchmark
如果您需要多个模糊来进行实时模糊或类似的模糊处理,并且您的内存允许它不要多次从可绘制对象中加载位图,而是将其“缓存”在成员变量中。在这种情况下,请始终尝试使用相同的变量,以尽量减少垃圾收集。
从文件或可绘制对象inBitmap
加载时,还要签出新选项,这将重新使用位图内存并节省垃圾回收时间。
简单而幼稚的方法只是使用2 ImageViews
,一个模糊,然后使用alpha淡入淡出。但是,如果您想要更精致的外观,从尖锐到模糊的平滑过渡,请查看Roman Nurik的帖子,了解如何像在他的Muzei应用程序中那样进行操作。
基本上,他解释说,他预模糊了具有不同模糊程度的某些帧,并将其用作看起来非常平滑的动画中的关键帧。
renderscriptSupportModeEnabled true
否则将无法构建!我一直搜寻!
编辑(2014年4月):这是一个问题/答案页面,看起来仍然很受欢迎。我知道我总是对此文章赞不绝口。但是,如果您正在阅读本文,则需要意识到此处发布的答案(我的和接受的答案)都已过时。如果要今天实现有效的模糊处理,则应使用RenderScript而不是NDK或Java。RenderScript在Android 2.2+(使用Android支持库)上运行,因此没有理由不使用它。
遵循旧的答案,但要提防,因为它已经过时了。
对于未来的Google员工,以下是我从Yahel的Quasimondo算法端口移植的算法,但使用了NDK。当然,这是基于Yahel的答案。但这正在运行本机C代码,因此速度更快。快多了。就像,快40倍。
我发现使用NDK是应该在Android上完成所有图像操作的方式……一开始实施起来有点烦人(在此处阅读有关使用JNI和NDK的出色教程),但是更好,而且几乎实时。很多东西。
作为参考,使用Yahel的Java函数花了10秒钟来模糊我的480x532像素的图像,其模糊半径为10。但是使用本机C版本花了250毫秒。而且我很确定它仍然可以进一步优化...我只是对Java代码进行了愚蠢的转换,可能有些操作可以缩短,不想花太多时间重构整个东西。
#include <jni.h>
#include <string.h>
#include <math.h>
#include <stdio.h>
#include <android/log.h>
#include <android/bitmap.h>
#define LOG_TAG "libbitmaputils"
#define LOGI(...) __android_log_print(ANDROID_LOG_INFO,LOG_TAG,__VA_ARGS__)
#define LOGE(...) __android_log_print(ANDROID_LOG_ERROR,LOG_TAG,__VA_ARGS__)
typedef struct {
uint8_t red;
uint8_t green;
uint8_t blue;
uint8_t alpha;
} rgba;
JNIEXPORT void JNICALL Java_com_insert_your_package_ClassName_functionToBlur(JNIEnv* env, jobject obj, jobject bitmapIn, jobject bitmapOut, jint radius) {
LOGI("Blurring bitmap...");
// Properties
AndroidBitmapInfo infoIn;
void* pixelsIn;
AndroidBitmapInfo infoOut;
void* pixelsOut;
int ret;
// Get image info
if ((ret = AndroidBitmap_getInfo(env, bitmapIn, &infoIn)) < 0 || (ret = AndroidBitmap_getInfo(env, bitmapOut, &infoOut)) < 0) {
LOGE("AndroidBitmap_getInfo() failed ! error=%d", ret);
return;
}
// Check image
if (infoIn.format != ANDROID_BITMAP_FORMAT_RGBA_8888 || infoOut.format != ANDROID_BITMAP_FORMAT_RGBA_8888) {
LOGE("Bitmap format is not RGBA_8888!");
LOGE("==> %d %d", infoIn.format, infoOut.format);
return;
}
// Lock all images
if ((ret = AndroidBitmap_lockPixels(env, bitmapIn, &pixelsIn)) < 0 || (ret = AndroidBitmap_lockPixels(env, bitmapOut, &pixelsOut)) < 0) {
LOGE("AndroidBitmap_lockPixels() failed ! error=%d", ret);
}
int h = infoIn.height;
int w = infoIn.width;
LOGI("Image size is: %i %i", w, h);
rgba* input = (rgba*) pixelsIn;
rgba* output = (rgba*) pixelsOut;
int wm = w - 1;
int hm = h - 1;
int wh = w * h;
int whMax = max(w, h);
int div = radius + radius + 1;
int r[wh];
int g[wh];
int b[wh];
int rsum, gsum, bsum, x, y, i, yp, yi, yw;
rgba p;
int vmin[whMax];
int divsum = (div + 1) >> 1;
divsum *= divsum;
int dv[256 * divsum];
for (i = 0; i < 256 * divsum; i++) {
dv[i] = (i / divsum);
}
yw = yi = 0;
int stack[div][3];
int stackpointer;
int stackstart;
int rbs;
int ir;
int ip;
int r1 = radius + 1;
int routsum, goutsum, boutsum;
int rinsum, ginsum, binsum;
for (y = 0; y < h; y++) {
rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;
for (i = -radius; i <= radius; i++) {
p = input[yi + min(wm, max(i, 0))];
ir = i + radius; // same as sir
stack[ir][0] = p.red;
stack[ir][1] = p.green;
stack[ir][2] = p.blue;
rbs = r1 - abs(i);
rsum += stack[ir][0] * rbs;
gsum += stack[ir][1] * rbs;
bsum += stack[ir][2] * rbs;
if (i > 0) {
rinsum += stack[ir][0];
ginsum += stack[ir][1];
binsum += stack[ir][2];
} else {
routsum += stack[ir][0];
goutsum += stack[ir][1];
boutsum += stack[ir][2];
}
}
stackpointer = radius;
for (x = 0; x < w; x++) {
r[yi] = dv[rsum];
g[yi] = dv[gsum];
b[yi] = dv[bsum];
rsum -= routsum;
gsum -= goutsum;
bsum -= boutsum;
stackstart = stackpointer - radius + div;
ir = stackstart % div; // same as sir
routsum -= stack[ir][0];
goutsum -= stack[ir][1];
boutsum -= stack[ir][2];
if (y == 0) {
vmin[x] = min(x + radius + 1, wm);
}
p = input[yw + vmin[x]];
stack[ir][0] = p.red;
stack[ir][1] = p.green;
stack[ir][2] = p.blue;
rinsum += stack[ir][0];
ginsum += stack[ir][1];
binsum += stack[ir][2];
rsum += rinsum;
gsum += ginsum;
bsum += binsum;
stackpointer = (stackpointer + 1) % div;
ir = (stackpointer) % div; // same as sir
routsum += stack[ir][0];
goutsum += stack[ir][1];
boutsum += stack[ir][2];
rinsum -= stack[ir][0];
ginsum -= stack[ir][1];
binsum -= stack[ir][2];
yi++;
}
yw += w;
}
for (x = 0; x < w; x++) {
rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;
yp = -radius * w;
for (i = -radius; i <= radius; i++) {
yi = max(0, yp) + x;
ir = i + radius; // same as sir
stack[ir][0] = r[yi];
stack[ir][1] = g[yi];
stack[ir][2] = b[yi];
rbs = r1 - abs(i);
rsum += r[yi] * rbs;
gsum += g[yi] * rbs;
bsum += b[yi] * rbs;
if (i > 0) {
rinsum += stack[ir][0];
ginsum += stack[ir][1];
binsum += stack[ir][2];
} else {
routsum += stack[ir][0];
goutsum += stack[ir][1];
boutsum += stack[ir][2];
}
if (i < hm) {
yp += w;
}
}
yi = x;
stackpointer = radius;
for (y = 0; y < h; y++) {
output[yi].red = dv[rsum];
output[yi].green = dv[gsum];
output[yi].blue = dv[bsum];
rsum -= routsum;
gsum -= goutsum;
bsum -= boutsum;
stackstart = stackpointer - radius + div;
ir = stackstart % div; // same as sir
routsum -= stack[ir][0];
goutsum -= stack[ir][1];
boutsum -= stack[ir][2];
if (x == 0) vmin[y] = min(y + r1, hm) * w;
ip = x + vmin[y];
stack[ir][0] = r[ip];
stack[ir][1] = g[ip];
stack[ir][2] = b[ip];
rinsum += stack[ir][0];
ginsum += stack[ir][1];
binsum += stack[ir][2];
rsum += rinsum;
gsum += ginsum;
bsum += binsum;
stackpointer = (stackpointer + 1) % div;
ir = stackpointer; // same as sir
routsum += stack[ir][0];
goutsum += stack[ir][1];
boutsum += stack[ir][2];
rinsum -= stack[ir][0];
ginsum -= stack[ir][1];
binsum -= stack[ir][2];
yi += w;
}
}
// Unlocks everything
AndroidBitmap_unlockPixels(env, bitmapIn);
AndroidBitmap_unlockPixels(env, bitmapOut);
LOGI ("Bitmap blurred.");
}
int min(int a, int b) {
return a > b ? b : a;
}
int max(int a, int b) {
return a > b ? a : b;
}
然后像这样使用它(考虑一个名为com.insert.your.package.ClassName的类和一个名为functionToBlur的本机函数,如上面的代码所述):
// Create a copy
Bitmap bitmapOut = bitmapIn.copy(Bitmap.Config.ARGB_8888, true);
// Blur the copy
functionToBlur(bitmapIn, bitmapOut, __radius);
期望RGB_8888位图!
要使用RGB_565位图,请在传递参数(yuck)之前创建转换后的副本,或者将函数更改为使用新rgb565
类型而不是rgba
:
typedef struct {
uint16_t byte0;
} rgb565;
问题在于,如果这样做,您将无法再读取.red
,.green
并且不再读取.blue
像素中的一个,则需要正确读取字节duh。当我之前需要它时,我这样做了:
r = (pixels[x].byte0 & 0xF800) >> 8;
g = (pixels[x].byte0 & 0x07E0) >> 3;
b = (pixels[x].byte0 & 0x001F) << 3;
但是可能有一些更简单的方法。恐怕我不是底层C编码员。
r[wh]
,g[wh]
然后更改b[wh]
为uint8_t
。
pastebin.com
该代码对我来说是完美的
Bitmap tempbg = BitmapFactory.decodeResource(getResources(),R.drawable.b1); //Load a background.
Bitmap final_Bitmap = BlurImage(tempbg);
@SuppressLint("NewApi")
Bitmap BlurImage (Bitmap input)
{
try
{
RenderScript rsScript = RenderScript.create(getApplicationContext());
Allocation alloc = Allocation.createFromBitmap(rsScript, input);
ScriptIntrinsicBlur blur = ScriptIntrinsicBlur.create(rsScript, Element.U8_4(rsScript));
blur.setRadius(21);
blur.setInput(alloc);
Bitmap result = Bitmap.createBitmap(input.getWidth(), input.getHeight(), Bitmap.Config.ARGB_8888);
Allocation outAlloc = Allocation.createFromBitmap(rsScript, result);
blur.forEach(outAlloc);
outAlloc.copyTo(result);
rsScript.destroy();
return result;
}
catch (Exception e) {
// TODO: handle exception
return input;
}
}
现在,您可以使用RenderScript库中的ScriptIntrinsicBlur快速模糊。这是访问RenderScript API的方法。以下是我制作的用于模糊视图和位图的类:
public class BlurBuilder {
private static final float BITMAP_SCALE = 0.4f;
private static final float BLUR_RADIUS = 7.5f;
public static Bitmap blur(View v) {
return blur(v.getContext(), getScreenshot(v));
}
public static Bitmap blur(Context ctx, Bitmap image) {
int width = Math.round(image.getWidth() * BITMAP_SCALE);
int height = Math.round(image.getHeight() * BITMAP_SCALE);
Bitmap inputBitmap = Bitmap.createScaledBitmap(image, width, height, false);
Bitmap outputBitmap = Bitmap.createBitmap(inputBitmap);
RenderScript rs = RenderScript.create(ctx);
ScriptIntrinsicBlur theIntrinsic = ScriptIntrinsicBlur.create(rs, Element.U8_4(rs));
Allocation tmpIn = Allocation.createFromBitmap(rs, inputBitmap);
Allocation tmpOut = Allocation.createFromBitmap(rs, outputBitmap);
theIntrinsic.setRadius(BLUR_RADIUS);
theIntrinsic.setInput(tmpIn);
theIntrinsic.forEach(tmpOut);
tmpOut.copyTo(outputBitmap);
return outputBitmap;
}
private static Bitmap getScreenshot(View v) {
Bitmap b = Bitmap.createBitmap(v.getWidth(), v.getHeight(), Bitmap.Config.ARGB_8888);
Canvas c = new Canvas(b);
v.draw(c);
return b;
}
}
这对我来说效果很好:如何使用Android的RenderScript有效地模糊图像
public class BlurBuilder {
private static final float BITMAP_SCALE = 0.4f;
private static final float BLUR_RADIUS = 7.5f;
@SuppressLint("NewApi")
public static Bitmap blur(Context context, Bitmap image) {
int width = Math.round(image.getWidth() * BITMAP_SCALE);
int height = Math.round(image.getHeight() * BITMAP_SCALE);
Bitmap inputBitmap = Bitmap.createScaledBitmap(image, width, height,
false);
Bitmap outputBitmap = Bitmap.createBitmap(inputBitmap);
RenderScript rs = RenderScript.create(context);
ScriptIntrinsicBlur theIntrinsic = ScriptIntrinsicBlur.create(rs,
Element.U8_4(rs));
Allocation tmpIn = Allocation.createFromBitmap(rs, inputBitmap);
Allocation tmpOut = Allocation.createFromBitmap(rs, outputBitmap);
theIntrinsic.setRadius(BLUR_RADIUS);
theIntrinsic.setInput(tmpIn);
theIntrinsic.forEach(tmpOut);
tmpOut.copyTo(outputBitmap);
return outputBitmap;
}
}
这适用于所有需要增加半径ScriptIntrinsicBlur
以获得更硬的高斯模糊的人。
您可以缩小图像并获得相同的结果,而不必将半径设置为大于25。我写了一个叫的课程GaussianBlur
。在下面您可以看到如何使用以及整个类的实现。
用法:
GaussianBlur gaussian = new GaussianBlur(context);
gaussian.setMaxImageSize(60);
gaussian.setRadius(25); //max
Bitmap output = gaussian.render(<your bitmap>,true);
Drawable d = new BitmapDrawable(getResources(),output);
类:
public class GaussianBlur {
private final int DEFAULT_RADIUS = 25;
private final float DEFAULT_MAX_IMAGE_SIZE = 400;
private Context context;
private int radius;
private float maxImageSize;
public GaussianBlur(Context context) {
this.context = context;
setRadius(DEFAULT_RADIUS);
setMaxImageSize(DEFAULT_MAX_IMAGE_SIZE);
}
public Bitmap render(Bitmap bitmap, boolean scaleDown) {
RenderScript rs = RenderScript.create(context);
if (scaleDown) {
bitmap = scaleDown(bitmap);
}
Bitmap output = Bitmap.createBitmap(bitmap.getWidth(), bitmap.getHeight(), Config.ARGB_8888);
Allocation inAlloc = Allocation.createFromBitmap(rs, bitmap, Allocation.MipmapControl.MIPMAP_NONE, Allocation.USAGE_GRAPHICS_TEXTURE);
Allocation outAlloc = Allocation.createFromBitmap(rs, output);
ScriptIntrinsicBlur script = ScriptIntrinsicBlur.create(rs, inAlloc.getElement()); // Element.U8_4(rs));
script.setRadius(getRadius());
script.setInput(inAlloc);
script.forEach(outAlloc);
outAlloc.copyTo(output);
rs.destroy();
return output;
}
public Bitmap scaleDown(Bitmap input) {
float ratio = Math.min((float) getMaxImageSize() / input.getWidth(), (float) getMaxImageSize() / input.getHeight());
int width = Math.round((float) ratio * input.getWidth());
int height = Math.round((float) ratio * input.getHeight());
return Bitmap.createScaledBitmap(input, width, height, true);
}
public int getRadius() {
return radius;
}
public void setRadius(int radius) {
this.radius = radius;
}
public float getMaxImageSize() {
return maxImageSize;
}
public void setMaxImageSize(float maxImageSize) {
this.maxImageSize = maxImageSize;
}
}
感谢@Yahel提供的代码。发布具有alpha通道模糊支持的相同方法,因为我花了一些时间才能使其正常工作,因此可以节省某人的时间:
/**
* Stack Blur v1.0 from
* http://www.quasimondo.com/StackBlurForCanvas/StackBlurDemo.html
* Java Author: Mario Klingemann <mario at quasimondo.com>
* http://incubator.quasimondo.com
* <p/>
* created Feburary 29, 2004
* Android port : Yahel Bouaziz <yahel at kayenko.com>
* http://www.kayenko.com
* ported april 5th, 2012
* <p/>
* This is a compromise between Gaussian Blur and Box blur
* It creates much better looking blurs than Box Blur, but is
* 7x faster than my Gaussian Blur implementation.
* <p/>
* I called it Stack Blur because this describes best how this
* filter works internally: it creates a kind of moving stack
* of colors whilst scanning through the image. Thereby it
* just has to add one new block of color to the right side
* of the stack and remove the leftmost color. The remaining
* colors on the topmost layer of the stack are either added on
* or reduced by one, depending on if they are on the right or
* on the left side of the stack.
* <p/>
* If you are using this algorithm in your code please add
* the following line:
* Stack Blur Algorithm by Mario Klingemann <mario@quasimondo.com>
*/
public static Bitmap fastblur(Bitmap sentBitmap, float scale, int radius) {
int width = Math.round(sentBitmap.getWidth() * scale);
int height = Math.round(sentBitmap.getHeight() * scale);
sentBitmap = Bitmap.createScaledBitmap(sentBitmap, width, height, false);
Bitmap bitmap = sentBitmap.copy(sentBitmap.getConfig(), true);
if (radius < 1) {
return (null);
}
int w = bitmap.getWidth();
int h = bitmap.getHeight();
int[] pix = new int[w * h];
Log.e("pix", w + " " + h + " " + pix.length);
bitmap.getPixels(pix, 0, w, 0, 0, w, h);
int wm = w - 1;
int hm = h - 1;
int wh = w * h;
int div = radius + radius + 1;
int r[] = new int[wh];
int g[] = new int[wh];
int b[] = new int[wh];
int a[] = new int[wh];
int rsum, gsum, bsum, asum, x, y, i, p, yp, yi, yw;
int vmin[] = new int[Math.max(w, h)];
int divsum = (div + 1) >> 1;
divsum *= divsum;
int dv[] = new int[256 * divsum];
for (i = 0; i < 256 * divsum; i++) {
dv[i] = (i / divsum);
}
yw = yi = 0;
int[][] stack = new int[div][4];
int stackpointer;
int stackstart;
int[] sir;
int rbs;
int r1 = radius + 1;
int routsum, goutsum, boutsum, aoutsum;
int rinsum, ginsum, binsum, ainsum;
for (y = 0; y < h; y++) {
rinsum = ginsum = binsum = ainsum = routsum = goutsum = boutsum = aoutsum = rsum = gsum = bsum = asum = 0;
for (i = -radius; i <= radius; i++) {
p = pix[yi + Math.min(wm, Math.max(i, 0))];
sir = stack[i + radius];
sir[0] = (p & 0xff0000) >> 16;
sir[1] = (p & 0x00ff00) >> 8;
sir[2] = (p & 0x0000ff);
sir[3] = 0xff & (p >> 24);
rbs = r1 - Math.abs(i);
rsum += sir[0] * rbs;
gsum += sir[1] * rbs;
bsum += sir[2] * rbs;
asum += sir[3] * rbs;
if (i > 0) {
rinsum += sir[0];
ginsum += sir[1];
binsum += sir[2];
ainsum += sir[3];
} else {
routsum += sir[0];
goutsum += sir[1];
boutsum += sir[2];
aoutsum += sir[3];
}
}
stackpointer = radius;
for (x = 0; x < w; x++) {
r[yi] = dv[rsum];
g[yi] = dv[gsum];
b[yi] = dv[bsum];
a[yi] = dv[asum];
rsum -= routsum;
gsum -= goutsum;
bsum -= boutsum;
asum -= aoutsum;
stackstart = stackpointer - radius + div;
sir = stack[stackstart % div];
routsum -= sir[0];
goutsum -= sir[1];
boutsum -= sir[2];
aoutsum -= sir[3];
if (y == 0) {
vmin[x] = Math.min(x + radius + 1, wm);
}
p = pix[yw + vmin[x]];
sir[0] = (p & 0xff0000) >> 16;
sir[1] = (p & 0x00ff00) >> 8;
sir[2] = (p & 0x0000ff);
sir[3] = 0xff & (p >> 24);
rinsum += sir[0];
ginsum += sir[1];
binsum += sir[2];
ainsum += sir[3];
rsum += rinsum;
gsum += ginsum;
bsum += binsum;
asum += ainsum;
stackpointer = (stackpointer + 1) % div;
sir = stack[(stackpointer) % div];
routsum += sir[0];
goutsum += sir[1];
boutsum += sir[2];
aoutsum += sir[3];
rinsum -= sir[0];
ginsum -= sir[1];
binsum -= sir[2];
ainsum -= sir[3];
yi++;
}
yw += w;
}
for (x = 0; x < w; x++) {
rinsum = ginsum = binsum = ainsum = routsum = goutsum = boutsum = aoutsum = rsum = gsum = bsum = asum = 0;
yp = -radius * w;
for (i = -radius; i <= radius; i++) {
yi = Math.max(0, yp) + x;
sir = stack[i + radius];
sir[0] = r[yi];
sir[1] = g[yi];
sir[2] = b[yi];
sir[3] = a[yi];
rbs = r1 - Math.abs(i);
rsum += r[yi] * rbs;
gsum += g[yi] * rbs;
bsum += b[yi] * rbs;
asum += a[yi] * rbs;
if (i > 0) {
rinsum += sir[0];
ginsum += sir[1];
binsum += sir[2];
ainsum += sir[3];
} else {
routsum += sir[0];
goutsum += sir[1];
boutsum += sir[2];
aoutsum += sir[3];
}
if (i < hm) {
yp += w;
}
}
yi = x;
stackpointer = radius;
for (y = 0; y < h; y++) {
pix[yi] = (dv[asum] << 24) | (dv[rsum] << 16) | (dv[gsum] << 8) | dv[bsum];
rsum -= routsum;
gsum -= goutsum;
bsum -= boutsum;
asum -= aoutsum;
stackstart = stackpointer - radius + div;
sir = stack[stackstart % div];
routsum -= sir[0];
goutsum -= sir[1];
boutsum -= sir[2];
aoutsum -= sir[3];
if (x == 0) {
vmin[y] = Math.min(y + r1, hm) * w;
}
p = x + vmin[y];
sir[0] = r[p];
sir[1] = g[p];
sir[2] = b[p];
sir[3] = a[p];
rinsum += sir[0];
ginsum += sir[1];
binsum += sir[2];
ainsum += sir[3];
rsum += rinsum;
gsum += ginsum;
bsum += binsum;
asum += ainsum;
stackpointer = (stackpointer + 1) % div;
sir = stack[stackpointer];
routsum += sir[0];
goutsum += sir[1];
boutsum += sir[2];
aoutsum += sir[3];
rinsum -= sir[0];
ginsum -= sir[1];
binsum -= sir[2];
ainsum -= sir[3];
yi += w;
}
}
Log.e("pix", w + " " + h + " " + pix.length);
bitmap.setPixels(pix, 0, w, 0, 0, w, h);
return (bitmap);
}
我以前用过
public static Bitmap myblur(Bitmap image, Context context) {
final float BITMAP_SCALE = 0.4f;
final float BLUR_RADIUS = 7.5f;
int width = Math.round(image.getWidth() * BITMAP_SCALE);
int height = Math.round(image.getHeight() * BITMAP_SCALE);
Bitmap inputBitmap = Bitmap.createScaledBitmap(image, width, height, false);
Bitmap outputBitmap = Bitmap.createBitmap(inputBitmap);
RenderScript rs = RenderScript.create(context);
ScriptIntrinsicBlur theIntrinsic = ScriptIntrinsicBlur.create(rs, Element.U8_4(rs));
Allocation tmpIn = Allocation.createFromBitmap(rs, inputBitmap);
Allocation tmpOut = Allocation.createFromBitmap(rs, outputBitmap);
theIntrinsic.setRadius(BLUR_RADIUS);
theIntrinsic.setInput(tmpIn);
theIntrinsic.forEach(tmpOut);
tmpOut.copyTo(outputBitmap);
return outputBitmap;
}
对于选择NDK方法的未来Google员工-我找到了可靠的提到的stackblur算法。我在这里找到了不依赖SSE的C ++实现-http: //www.antigrain.com/__code/include/agg_blur.h.html#stack_blur_rgba32,其中包含一些使用静态表的优化,例如:
static unsigned short const stackblur_mul[255] =
{
512,512,456,512,328,456,335,512,405,328,271,456,388,335,292,512,
454,405,364,328,298,271,496,456,420,388,360,335,312,292,273,512,
482,454,428,405,383,364,345,328,312,298,284,271,259,496,475,456,
437,420,404,388,374,360,347,335,323,312,302,292,282,273,265,512,
497,482,468,454,441,428,417,405,394,383,373,364,354,345,337,328,
320,312,305,298,291,284,278,271,265,259,507,496,485,475,465,456,
446,437,428,420,412,404,396,388,381,374,367,360,354,347,341,335,
329,323,318,312,307,302,297,292,287,282,278,273,269,265,261,512,
505,497,489,482,475,468,461,454,447,441,435,428,422,417,411,405,
399,394,389,383,378,373,368,364,359,354,350,345,341,337,332,328,
324,320,316,312,309,305,301,298,294,291,287,284,281,278,274,271,
268,265,262,259,257,507,501,496,491,485,480,475,470,465,460,456,
451,446,442,437,433,428,424,420,416,412,408,404,400,396,392,388,
385,381,377,374,370,367,363,360,357,354,350,347,344,341,338,335,
332,329,326,323,320,318,315,312,310,307,304,302,299,297,294,292,
289,287,285,282,280,278,275,273,271,269,267,265,263,261,259
};
static unsigned char const stackblur_shr[255] =
{
9, 11, 12, 13, 13, 14, 14, 15, 15, 15, 15, 16, 16, 16, 16, 17,
17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19,
19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20,
20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21,
21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22,
22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22,
22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23,
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24,
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24
};
我对多核系统进行了stackblur算法的修改-可以在这里找到http://vitiy.info/stackblur-algorithm-multi-threaded-blur-for-cpp/ 随着越来越多的设备具有4个核心-优化为4倍的速度优势。
Nicolas POMEPUY的建议。我认为此链接会有所帮助:Android设计的模糊效果
github上的示例项目
@TargetApi(Build.VERSION_CODES.JELLY_BEAN_MR1)
private static Bitmap fastblur16(Bitmap source, int radius, Context ctx) {
Bitmap bitmap = source.copy(source.getConfig(), true);
RenderScript rs = RenderScript.create(ctx);
Allocation input = Allocation.createFromBitmap(rs, source, Allocation.MipmapControl.MIPMAP_NONE, Allocation.USAGE_SCRIPT);
Allocation output = Allocation.createTyped(rs, input.getType());
ScriptIntrinsicBlur script = ScriptIntrinsicBlur.create(rs, Element.U8_4(rs));
script.setRadius(radius);
script.setInput(input);
script.forEach(output);
output.copyTo(bitmap);
return bitmap;
}
我们尝试在不同的答案中实现上述RenderScript模糊。我们只能使用v8 RenderScript版本,这给我们带来了很多麻烦。
我想分享我们的肮脏的纯Java版本,它很慢,应该在一个单独的线程上完成,如果可能的话,应在使用前完成,因此可以持久使用。
private final Paint mPaint = new Paint();
public Bitmap blur(final String pathToBitmap) {
final BitmapFactory.Options options = new BitmapFactory.Options();
final Bitmap normalOne = BitmapFactory.decodeFile(pathToBitmap, options);
final Bitmap resultBitmap = Bitmap.createBitmap(options.outWidth, options.outHeight, Bitmap.Config.ARGB_8888);
Canvas canvas = new Canvas(resultBitmap);
mPaint.setAlpha(180);
canvas.drawBitmap(normalOne, 0, 0, mPaint);
int blurRadius = 12;
for (int row = -blurRadius; row < blurRadius; row += 2) {
for (int col = -blurRadius; col < blurRadius; col += 2) {
if (col * col + row * row <= blurRadius * blurRadius) {
mPaint.setAlpha((blurRadius * blurRadius) / ((col * col + row * row) + 1) * 2);
canvas.drawBitmap(normalOne, row, col, mPaint);
}
}
}
normalOne.recycle();
return resultBitmap;
}
该解决方案远非完美,但基于以下事实创建了合理的模糊效果:它在几乎不透明的“清晰”版本上绘制同一图像的高度透明版本。Alpha取决于到原点的距离。
您可以根据需要调整一些“魔术数字”。我只是想向所有对RenderScript v8支持版本有疑问的人共享该“解决方案”。
对于那些仍然对x86芯片组上的Renderscript支持库有疑问的人,请查看该库的创建者的这篇文章。看来他准备的修复程序未对Build Tools v20.0.0有所帮助,因此他提供了手动修复文件,并简要说明了如何进行修复。
来自Mario Viviani博客的人可以使用17个Android版本的此解决方案:
https://plus.google.com/+MarioViviani/posts/fhuzYkji9zz
要么
这是使用RenderScript进行的实时模糊叠加,似乎足够快。
我发现降低对比度,亮度和饱和度会使模糊的图像更美观,因此我结合了堆栈溢出的各种方法,使这个模糊类 可以处理模糊的图像,改变亮度,饱和度,对比度和模糊图像的大小。它还可以将图像从可绘制图像转换为位图图像,反之亦然。
在I / O 2019上,提出了以下解决方案:
/**
* Blurs the given Bitmap image
* @param bitmap Image to blur
* @param applicationContext Application context
* @return Blurred bitmap image
*/
@WorkerThread
fun blurBitmap(bitmap: Bitmap, applicationContext: Context): Bitmap {
lateinit var rsContext: RenderScript
try {
// Create the output bitmap
val output = Bitmap.createBitmap(
bitmap.width, bitmap.height, bitmap.config)
// Blur the image
rsContext = RenderScript.create(applicationContext, RenderScript.ContextType.DEBUG)
val inAlloc = Allocation.createFromBitmap(rsContext, bitmap)
val outAlloc = Allocation.createTyped(rsContext, inAlloc.type)
val theIntrinsic = ScriptIntrinsicBlur.create(rsContext, Element.U8_4(rsContext))
theIntrinsic.apply {
setRadius(10f)
theIntrinsic.setInput(inAlloc)
theIntrinsic.forEach(outAlloc)
}
outAlloc.copyTo(output)
return output
} finally {
rsContext.finish()
}
}