Ctrl+K

自动聚焦

自动聚焦#

实验介绍#

相机的自动对焦要求相机根据拍摄环境和场景的变化,通过相机内部的微型驱动马达,自动调节相机镜头和CMOS之间的距离,保证像平面正好投影到CMOS的成像表面上。这时候物体的成像比较清晰,图像细节信息丰富。 图像清晰度评价算法有很多种,在空域中,主要思路是考察图像的领域对比度,即相邻像素间的灰度特征的梯度差；在频域中,主要思路是考察图像的频率分量,对焦清晰的图像高频分量较多,对焦模糊的图像低频分量较多。 本实验选用Tenengrad梯度方法,利用Sobel算子分别计算水平和垂直方向的梯度,同一场景下梯度值越高,图像越清晰。 我们将焦点由远及近调整,并比较各个焦点位置的Sobel结果,值越大则表示该点离最佳聚焦点越近。 因为有的镜头不支持自动调整焦点,对于不支持调焦的镜头,需要手动调整焦点,通过评估值的显示,来确定最佳焦点。

代码#

#include "sobel_focus.h"

void ExtractPixel(XF_TNAME(XF_8UC3,XF_NPPC1)&src,ap_uint<8>src_value[3])
{
#pragma HLS INLINE off
      unsigned int i,j=0;
      for(i=0;i<24;i+=8)
      {
#pragma HLS UNROLL
              src_value[j]=src.range(i+7,i);
              j++;
      }
}

template<int ROWS, int COLS>
void sum_of_stream(xf::cv::Mat<XF_8UC1,ROWS,COLS,XF_NPPC1>    &_src, unsigned int &average)
{
      XF_TNAME(XF_8UC1,XF_NPPC1)pix;
      unsigned int sum=0;

      for(int v=0; v<ROWS; v++)
      {
              for(int h=0; h<COLS; h++)
              {
#pragma HLS pipeline
                      pix = _src.read(v*COLS+h);
                      sum += pix.range(7,0);
              }
      }
      average = sum;
}

template<int ROWS,int COLS>
void xfrgb2gray(xf::cv::Mat<XF_8UC3,ROWS,COLS,XF_NPPC1> &src,xf::cv::Mat<XF_8UC1,ROWS,COLS,XF_NPPC1> &dst)
{
      XF_TNAME(XF_8UC3,XF_NPPC1)rgb_packed;
      XF_TNAME(XF_8UC1,XF_NPPC1)gray_packed;
      ap_uint<8>rgb[3];
      ap_uint<8>gray;
      unsigned int i,j=0;
      for(i=0;i<ROWS;i++)
      {
              for(j=0;j<COLS;j++)
              {
#pragma HLS PIPELINE
                      rgb_packed=src.read(i*COLS+j);
                      ExtractPixel(rgb_packed,rgb);
                      gray=CalculateGRAY(rgb[0],rgb[1],rgb[2]);
                      gray_packed.range(7,0)=gray;
                      dst.write(i*COLS+j,gray_packed);
              }
      }
}

template<int ROWS,int COLS>
void AddWeightedKernel(xf::cv::Mat<XF_8UC1,ROWS,COLS,XF_NPPC1>&src1,
                                         float alpha,
                                         xf::cv::Mat<XF_8UC1,ROWS,COLS,XF_NPPC1>&src2,
                                         float beta,
                                         float gamma,
                                         xf::cv::Mat<XF_8UC1,ROWS,COLS,XF_NPPC1>&dst
                                      )
{
      ap_fixed<16,8,AP_RND>value_src1=alpha;
      ap_fixed<16,8,AP_RND>value_src2=beta;
      ap_fixed<16,8,AP_RND>value_src3=gamma;
      XF_TNAME(XF_8UC1,XF_NPPC1)pixel1;
      XF_TNAME(XF_8UC1,XF_NPPC1)pixel2;
      XF_TNAME(XF_8UC1,XF_NPPC1)pixel3;
      ap_int<24>firstcmp;
      ap_int<24>secondcmp;
      ap_int<16>thirdcmp;
      ap_uint<8>value;
      ap_uint<8>value_cmp1;
      ap_uint<8>value_cmp2;
      unsigned int i,j=0;
      for(i=0;i<ROWS;i++)
      {
              for(j=0;j<COLS;j++)
              {
#pragma HLS pipeline
                      pixel1=src1.read(i*COLS+j);
                      pixel2=src2.read(i*COLS+j);
                      value_cmp1=pixel1.range(7,0);
                      value_cmp2=pixel2.range(7,0);
                      firstcmp=(ap_int<24>)value_cmp1*value_src1;
                      secondcmp=(ap_int<24>)value_cmp2*value_src2;
                      thirdcmp=(ap_int<16>)firstcmp+secondcmp+value_src3;
                      if(thirdcmp>255)
                      {
                              thirdcmp=255;
                      }
                      else if(thirdcmp<0)
                      {
                              thirdcmp=0;
                      }
                      value=thirdcmp;
                      pixel3.range(7,0)=value;
                      dst.write(i*COLS+j,pixel3);
              }
      }
}

template<int ROWS,int COLS>
void duplicate(xf::cv::Mat<XF_8UC3,ROWS,COLS,XF_NPPC1>&src,xf::cv::Mat<XF_8UC3,ROWS,COLS,XF_NPPC1>&dst1,xf::cv::Mat<XF_8UC3,ROWS,COLS,XF_NPPC1>&dst2)
{
      unsigned int i,j=0;
      XF_TNAME(XF_8UC3,XF_NPPC1)pixel_src;
      for(i=0;i<ROWS;i++)
      {
              for(j=0;j<COLS;j++)
              {
#pragma HLS PIPELINE
                      pixel_src=src.read(i*COLS+j);
                      dst1.write(i*COLS+j,pixel_src);
                      dst2.write(i*COLS+j,pixel_src);
              }
      }
}


void sobel_focus(ustream_t &src, ustream_t &dst, unsigned int &average)
{
#pragma HLS INTERFACE axis port=src
#pragma HLS INTERFACE axis port=dst
#pragma HLS INTERFACE s_axilite port=average
#pragma HLS INTERFACE ap_ctrl_none port=return

      xf::cv::Mat<XF_8UC3,IMG_MAX_ROWS,IMG_MAX_COLS,XF_NPPC1> srcImg, split0, split1;
#pragma HLS STREAM depth=1920 type=fifo variable=split1
#pragma HLS STREAM depth=1920 type=fifo variable=split0
#pragma HLS STREAM depth=1920 type=fifo variable=srcImg
      xf::cv::Mat<XF_8UC1,IMG_MAX_ROWS,IMG_MAX_COLS,XF_NPPC1>grayImg,sobelImg_x,sobelImg_y,sobelImg;
#pragma HLS STREAM depth=1920 type=fifo variable=sobelImg
#pragma HLS STREAM depth=1920 type=fifo variable=sobelImg_y
#pragma HLS STREAM depth=1920 type=fifo variable=sobelImg_x
#pragma HLS STREAM depth=1920 type=fifo variable=grayImg
#pragma HLS DATAFLOW
      xf::cv::AXIvideo2xfMat(src, srcImg);
      duplicate<IMG_MAX_ROWS,IMG_MAX_COLS>(srcImg, split0, split1);
      xfrgb2gray<IMG_MAX_ROWS,IMG_MAX_COLS>(split0, grayImg);
    xf::cv::xFSobelFilter3x3<XF_8UC1, XF_8UC1,IMG_MAX_ROWS, IMG_MAX_COLS, XF_CHANNELS(XF_8UC1,XF_NPPC1), XF_DEPTH(XF_8UC1,XF_NPPC1), XF_DEPTH(XF_8UC1,XF_NPPC1),
                    XF_NPPC1, _XFCVDEPTH_DEFAULT,_XFCVDEPTH_DEFAULT,_XFCVDEPTH_DEFAULT,XF_WORDWIDTH(XF_8UC1,XF_NPPC1), XF_WORDWIDTH(XF_8UC1,XF_NPPC1), (IMG_MAX_COLS >> XF_BITSHIFT(XF_NPPC1)),false>(
        grayImg,sobelImg_x,sobelImg_y,grayImg.rows,grayImg.cols>>XF_BITSHIFT(XF_NPPC1));
      AddWeightedKernel<IMG_MAX_ROWS,IMG_MAX_COLS>(sobelImg_x,0.5f,sobelImg_y,0.5f,0.0f,sobelImg);
      sum_of_stream(sobelImg, average);
      xf::cv::xfMat2AXIvideo(split1, dst);
}

工程路径#

名称

路径

vivado 工程

vivado/auto_focus

HLS工程

vivado/sobel_focus

HLS工程

hls/mem2stream

HLS工程

hls/stream2mem

BOOT.bin文件

bootimage

实验结果#

默认焦点在最远处

../_images/image602.png

焦点拉近至最佳点

../_images/image6111.png
目录