Android——Nv21高效率转Bitmap

查找问题

最近在项目中遇到将摄像头数据处理后转Bitmap的内存溢出问题,大概运行到七八个小时后,就出现了内存溢出,后来看了一下错误提示发现

bitmap = BitmapFactory.decodeByteArray(stream.toByteArray(), 0, stream.size());

这个地方会导致出现问题,故对此需要进行优化。

优化之前

首先看一下原先的处理方式

private static Bitmap nv21ToBitmap(byte[] nv21, int width, int height) {
 Bitmap bitmap = null;
 try {
 YuvImage image = new YuvImage(nv21, ImageFormat.NV21, width, height, null);
 ByteArrayOutputStream stream = new ByteArrayOutputStream();
 image.compressToJpeg(new Rect(0, 0, width, height), 80, stream);
 bitmap = BitmapFactory.decodeByteArray(stream.toByteArray(), 0, stream.size());
 stream.close();
 } catch (IOException e) {
 e.printStackTrace();
 }
 return bitmap;
 }

优化之后

优化后的处理如下:

package com.cdigi.facedep.util;
import android.content.Context;
import android.graphics.Bitmap;
import android.renderscript.Allocation;
import android.renderscript.Element;
import android.renderscript.RenderScript;
import android.renderscript.ScriptIntrinsicYuvToRGB;
import android.renderscript.Type;
/**
 * Created by caydencui on 2018/12/7.
 */
public class NV21ToBitmap {
 private RenderScript rs;
 private ScriptIntrinsicYuvToRGB yuvToRgbIntrinsic;
 private Type.Builder yuvType, rgbaType;
 private Allocation in, out;
 public NV21ToBitmap(Context context) {
 rs = RenderScript.create(context);
 yuvToRgbIntrinsic = ScriptIntrinsicYuvToRGB.create(rs, Element.U8_4(rs));
 }
 public Bitmap nv21ToBitmap(byte[] nv21, int width, int height){
 if (yuvType == null){
 yuvType = new Type.Builder(rs, Element.U8(rs)).setX(nv21.length);
 in = Allocation.createTyped(rs, yuvType.create(), Allocation.USAGE_SCRIPT);
 rgbaType = new Type.Builder(rs, Element.RGBA_8888(rs)).setX(width).setY(height);
 out = Allocation.createTyped(rs, rgbaType.create(), Allocation.USAGE_SCRIPT);
 }
 in.copyFrom(nv21);
 yuvToRgbIntrinsic.setInput(in);
 yuvToRgbIntrinsic.forEach(out);
 Bitmap bmpout = Bitmap.createBitmap(width, height, Bitmap.Config.ARGB_8888);
 out.copyTo(bmpout);
 return bmpout;
 }
}

优化对比

对帧率要求不高时,一直使用BitmapFactory.decodeByteArray来进行处理,耗时非常可观,耗时达到60-80ms,在新方法下,仅仅3~4ms就可完成对图像的处理,需要使用Renderscript内联函数,可以更快的转换为YUV图像,从而提供了性能,增加了程序的稳定性.

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