[Android]-图片JNI(C++\Java)高斯模糊的实现与比较
转自:http://blog.csdn.net/qiujuer/article/details/24282047
前几天一直在弄android上的图片模糊效果的实现!
一直找不到方法,看别人说都是调用JNI,但是JNI这个东西我还真不熟悉啊!
只好从零开始了!这里不讲JNI的平台搭建,只讲JNI的关键代码,具体的项目我会共享出来给大家!
对于JNI下使用C++来模糊图片这个我真的没找到,只好自己写C++的来实现了。
在国外的一个项目中找到了一个”堆栈模糊效果“,原型如下:
// Stack Blur v1.0
//
// Author: Mario Klingemann <mario@quasimondo.com>
// http://incubator.quasimondo.com
// created Feburary 29, 2004
// 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>
PImage a;
PImage b;
void setup()
{
a=loadImage("dog.jpg");
size(a.width, a.height);
b=new PImage(a.width, a.height);
fill(255);
noStroke();
frameRate(25);
}
void draw()
{
System.arraycopy(a.pixels,0,b.pixels,0,a.pixels.length);
fastblur(b,mouseY/4);
image(b, 0, 0);
}
void fastblur(PImage img,int radius){
if (radius<1){
return;
}
int\[\] pix=img.pixels;
int w=img.width;
int h=img.height;
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\[max(w,h)\];
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\[yi+min(wm,max(i,0))\];
sir=stack\[i+radius\];
sir\[0\]=(p & 0xff0000)>>16;
sir\[1\]=(p & 0x00ff00)>>8;
sir\[2\]=(p & 0x0000ff);
rbs=r1-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\[x\]=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=max(0,yp)+x;
sir=stack\[i+radius\];
sir\[0\]=r\[yi\];
sir\[1\]=g\[yi\];
sir\[2\]=b\[yi\];
rbs=r1-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++){
pix\[yi\]=0xff000000 | (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\[y\]=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;
}
}
img.updatePixels();
}
同时找到一个借鉴这个所改进后成为Java的代码,具体如下:
public static Bitmap doBlur(Bitmap sentBitmap, int radius, boolean canReuseInBitmap) {
// 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>
Bitmap bitmap;
if (canReuseInBitmap) {
bitmap = sentBitmap;
} else {
bitmap = sentBitmap.copy(sentBitmap.getConfig(), true);
}
if (radius < 1) {
return (null);
}
int w = bitmap.getWidth();
int h = bitmap.getHeight();
int\[\] pix = new int\[w * h\];
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;
}
}
bitmap.setPixels(pix, 0, w, 0, 0, w, h);
return (bitmap);
}
借鉴于此我弄了一个C的代码,基本上的整体过程都没有变化,只是改变成了C(C++也可已)的而已:
文件名:ImageBlur.c
/*************************************************
Copyright: Copyright QIUJUER 2013.
Author: Qiujuer
Date: 2014-04-18
Description:实现图片模糊处理
**************************************************/
#include<malloc.h>
#define ABS(a) ((a)<(0)?(-a):(a))
#define MAX(a,b) ((a)>(b)?(a):(b))
#define MIN(a,b) ((a)<(b)?(a):(b))
/*************************************************
Function: StackBlur(堆栈模糊)
Description: 使用堆栈方式进行图片像素模糊处理
Calls: malloc
Table Accessed: NULL
Table Updated: NULL
Input: 像素点集合,图片宽,图片高,模糊半径
Output: 返回模糊后的像素点集合
Return: 返回模糊后的像素点集合
Others: NULL
*************************************************/
static int* StackBlur(int* pix, int w, int h, int radius) {
int wm = w - 1;
int hm = h - 1;
int wh = w * h;
int div = radius + radius + 1;
int *r = (int *)malloc(wh * sizeof(int));
int *g = (int *)malloc(wh * sizeof(int));
int *b = (int *)malloc(wh * sizeof(int));
int rsum, gsum, bsum, x, y, i, p, yp, yi, yw;
int *vmin = (int *)malloc(MAX(w,h) * sizeof(int));
int divsum = (div + 1) >> 1;
divsum *= divsum;
int *dv = (int *)malloc(256 * divsum * sizeof(int));
for (i = 0; i < 256 * divsum; i++) {
dv\[i\] = (i / divsum);
}
yw = yi = 0;
int(*stack)\[3\] = (int(*)\[3\])malloc(div * 3 * sizeof(int));
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\[yi + (MIN(wm, MAX(i, 0)))\];
sir = stack\[i + radius\];
sir\[0\] = (p & 0xff0000) >> 16;
sir\[1\] = (p & 0x00ff00) >> 8;
sir\[2\] = (p & 0x0000ff);
rbs = r1 - 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\[x\] = 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 = MAX(0, yp) + x;
sir = stack\[i + radius\];
sir\[0\] = r\[yi\];
sir\[1\] = g\[yi\];
sir\[2\] = b\[yi\];
rbs = r1 - 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\[y\] = 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;
}
}
free(r);
free(g);
free(b);
free(vmin);
free(dv);
free(stack);
return(pix);
}
在改为这个的过程中还遇到 了一个很喜剧的问题,我发现我使用这个来进行调用后结果程序内存一直增大,直到500多M,直接卡死。我知道是我写的有内存泄漏了!
然后找了一下,发现果然是。只好进行free了。然后一下就好了,发现内存占用的确比Java的要少,速度也是要快一些!
在JNI中的实现我使用了两种方案,一种是直接传递文件,一直是传递像素点集合进行模糊!分别如下:
/*
* Class: com_accumulation_imageblurring_app_jni_ImageBlur
* Method: blurIntArray
* Signature: (\[IIII)V
*/
JNIEXPORT void JNICALL Java_com_accumulation_imageblurring_app_jni_ImageBlur_blurIntArray
(JNIEnv *, jclass, jintArray, jint, jint, jint);
/*
* Class: com_accumulation_imageblurring_app_jni_ImageBlur
* Method: blurBitMap
* Signature: (Landroid/graphics/Bitmap;I)V
*/
JNIEXPORT void JNICALL Java_com_accumulation_imageblurring_app_jni_ImageBlur_blurBitMap
(JNIEnv *, jclass, jobject, jint);
对应的Java调用:
public class ImageBlur {
public static native void blurIntArray(int\[\] pImg, int w, int h, int r);
public static native void blurBitMap(Bitmap bitmap, int r);
static {
System.loadLibrary("JNI_ImageBlur");
}
}
///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
此时我做了3种测试,一种是直接在Java层实现,一种是传递像素点集合模糊,还有就是直接传递图片进行模糊,结果如下:
通过上面的比较我们可以得出这样的结论:
1.Java的确最慢,但是其实也慢不了多少,虚拟机优化好了一样猛。
2.C中直接传递像素集合的速度最快(第一次启动)
3.在我多次切换界面后发现,直接传递像素点集合的耗时会增加,从60多到120多。
4.多次切换后发现,其实直接传递像素点的速度与传递图片过去的速度几乎一样。
5.多次操作后发现传递文件的波动较小,在100~138之间,其次是传递像素点集合的波动较大,java的波动最大!
以上就是我的结论,可能有些不正确,但是在我的机器上的确是这样!
注:勾选选择框“Downscale before blur”会先压缩图片后模糊然后放大图片,这样的情况下,模糊效果会稍微损失一些效果,但是其速度确实无法比拟的。
其耗时在:1~10ms内可运算完成。当然与你要模糊的大小有关系!
最后:项目地址:GitHub