VEC-C之直方图使用案例源码

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一，不使用VEC-C直方图适量优化，用纯C++写的参考源码，作用是求取一张图片的直方图：

#include <fstream>
#include <sstream>
#include <iostream>
#include <vec-c.h>
using namespace std;
unsigned char source1[512*512];
int main()
{
	int hist[256];
	for(int i=0;i<256;i++)
		hist[i]=0;

	const char* txt = "E:\\test\\lena.txt";
	const char* histtxt = "E:\\test\\lena_hist_nocv.txt";
	ifstream inf;
	std::ofstream ofh(histtxt);
	inf.open(txt, ifstream::in);
	string line;
	int i = 0;
	while (!inf.eof())
	{
		getline(inf, line);
		int t= atoi(line.c_str());
		unsigned char tt = (unsigned char)(t & 0xff);
		source1[i++] =tt;
	}

	int width = 512;//图片宽度
	int height = 512;//图片高度

	unsigned char* ptr=(unsigned char*)source1;
	//ptr[0]=0;
	for(int h=0;h<height;h++)
	{
		for(int w=0;w<width;w++)
		{
			int intensity=ptr[h*width+w];
			hist[intensity]+=1;
			//cout<<intensity<<",";
		}
		//of <<"\n";

	}

	int maxx=hist[0];
	for(int i=1;i<256;i++)
	{
	
		ofh << i<<","<<hist[i]<<"\n";
		if(maxx<hist[i])
		maxx=hist[i];
	}

	ofh.flush();
	ofh.close();
	inf.close();
	return 0;
}

二，修改为VEC-C矢量优化版：

#include <fstream>
#include <sstream>
#include <iostream>
#include <vec-c.h>
using namespace std;
unsigned char source[512*512];
int histv[256];
unsigned short p_u16DstB[256*16];

void sum_histogram2_nocv(unsigned short* p_u16SrcB0, unsigned int* p_u32Dst);
void histogram_nocv(unsigned char* p_u8Src, unsigned short* p_u16DstB, int s32N, int s32M);
int main()
{

	int width = 512;//图片宽度
	int height = 512;//图片高度
	const char* txt = "E:\\test\\lena.txt";
	const char* histtxt = "E:\\test\\lena_hist_vc_nocv.txt";
	ifstream inf;
	std::ofstream ofh(histtxt);
	inf.open(txt, ifstream::in);
	string line;
	int i = 0;
	while (!inf.eof())
	{
		getline(inf, line);
		int t= atoi(line.c_str());
		unsigned char tt = (unsigned char)(t & 0xff);
		source[i++] =tt;
	}

	unsigned char *src=(unsigned char*)source;
	//src[15]=255;
	//TODO vec-c progress
	histogram_nocv(src, p_u16DstB, height, width);
	//histogram_syj(src, p_u16DstB , p_u16DstB1, 16, height, width);
	sum_histogram2_nocv(p_u16DstB,(unsigned int*) histv);

	for(int i=1;i<256;i++)
	{
		ofh << i<<","<<histv[i]<<"\n";
	}

	ofh.flush();
	ofh.close();
	inf.close();
	
	return 0;
}

void sum_histogram2_nocv(unsigned short* p_u16SrcB0, unsigned int* p_u32Dst)
{

	short16 inN,inM;
	short in[16]={0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30};
	inN=*(short16*)in;
	inM = vadd(inN, (short)1);
	unsigned short* src=p_u16SrcB0;

	for(int i=0;i<256;i+=2)
	{
		short16 v0=(short16)vpld(src,inN);
		short8 vec_lo = vunpack_lo(v0);
		short8 vec_hi = vunpack_hi(v0);
		int sum=vintrasum((int8)vec_lo);
		sum+=vintrasum((int8)vec_hi);
		p_u32Dst[i]=sum;

		short16 v1=(short16)vpld(src,inM);
		short8 vec_lo1 = vunpack_lo(v1);
		short8 vec_hi1 = vunpack_hi(v1);
		int sum1=vintrasum((int8)vec_lo1);
		sum1+=vintrasum((int8)vec_hi1);
		p_u32Dst[i+1]=sum1;

		src+=32;
	}

	//vst(vec_lo,(short*)tmp,(short)0xffff);
	/*for(int i=0;i<256;i++)
	{
		if(p_u32Dst[i]!=0)
		cout<<i<<":"<<p_u32Dst[i]<<",";
	}*/
}

void histogram_nocv(unsigned char* p_u8Src, unsigned short* p_u16DstB, int s32N, int s32M)
{
	unsigned char* src=p_u8Src;
	unsigned short mask=0xffff;
	for(int i=0;i<s32N*s32M;i+=16)
	{
		if(i+16>s32N*s32M)
		{
			mask = (1 << ((s32N*s32M-i) & 15)) - 1;
			short16 v0=(short16)*(uchar16*)src;
			vhist((short*)p_u16DstB,v0,mask); 
		}
		else
		{
			short16 v0=(short16)*(uchar16*)src;
			vhist((short*)p_u16DstB,v0); 
			src+=16;
		}
		
	}

}

这两版在cycle数上肯定是不一样的，VEC-C版本要快很多，看它们生成的汇编代码上VEC-C会有更多的双竖线，也就是与操作，这就是VEC-C版把很多过程都并行起来了，这就是矢量优化技术。