refocus.rar_refocus_聚焦函数_聚焦区域

/** \file ahd_bayer.c * * \brief Adaptive Homogeneity-Directed Bayer array conversion routine. * * \author Copyright March 12, 2008 Theodore Kilgore <kilgota@auburn.edu> * * \par * gp_ahd_interpolate() from Eero Salminen <esalmine@gmail.com> * and Theodore Kilgore. The work of Eero Salminen is for partial completion * of a Diploma in Information and Computer Science, * Helsinki University of Technology, Finland. * * \par * The algorithm is based upon the paper * * \par * Adaptive Homogeneity-Directed Democsaicing Algoritm, * Keigo Hirakawa and Thomas W. Parks, presented in the * IEEE Transactions on Image Processing, vol. 14, no. 3, March 2005. * * \par License * This library is free software; you can redistribute it and/or * modify it under the terms of the GNU Lesser General Public * License as published by the Free Software Foundation; either * version 2 of the License, or (at your option) any later version. * * \par * This library is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU * Lesser General Public License for more details. * * \par * You should have received a copy of the GNU Lesser General Public * License along with this library; if not, write to the * Free Software Foundation, Inc., 59 Temple Place - Suite 330, * Boston, MA 02111-1307, USA. */ #include <stdio.h> #include <stdlib.h> #include <string.h> #include <math.h> #include <time.h> /* #include "config.h" */ #include "bayer.h" /* #include <gphoto2/gphoto2-result.h> #include <gphoto2/gphoto2-port-log.h> */ #define GP_MODULE "ahd_bayer" #define MAX(x,y) ((x < y) ? (y) : (x)) #define MIN(x,y) ((x > y) ? (y) : (x)) #define CLAMP(x) MAX(MIN(x,0xff),0) #define RED 0 #define GREEN 1 #define BLUE 2 static int dRGB(int i1, int i2, unsigned char *RGB); static void do_rb_ctr_row(unsigned char *image_h, unsigned char *image_v, int w, int h, int y, int *pos_code); static void do_green_ctr_row(unsigned char *image, unsigned char *image_h, unsigned char *image_v, int w, int h, int y, int *pos_code); static void get_diffs_row2(unsigned char * hom_buffer_h, unsigned char *hom_buffer_v, unsigned char * buffer_h, unsigned char *buffer_v, int w); #define AD(x, y, w) ((y)*(w)*3+3*(x)) /** * \brief This function computes distance^2 between two sets of pixel data. * \param i1 location of a pixel * \param i2 location of another pixel * \param RGB some RGB data. */ static int dRGB(int i1, int i2, unsigned char *RGB) { int dR,dG,dB; dR=RGB[i1+RED]-RGB[i2+RED]; dG=RGB[i1+GREEN]-RGB[i2+GREEN]; dB=RGB[i1+BLUE]-RGB[i2+BLUE]; return dR*dR+dG*dG+dB*dB; } /** * \brief Missing reds and/or blues are reconstructed on a single row * \param image_h three-row window, horizontal interpolation of row 1 is done * \param image_v three-row window, vertical interpolation of row 1 is done * \param w width of image * \param h height of image. * \param y row number from image which is under construction * \param pos_code position code related to Bayer tiling in use */ static void do_rb_ctr_row(unsigned char *image_h, unsigned char *image_v, int w, int h, int y, int *pos_code) { int x, bayer; int value,value2,div,color; /* * pos_code[0] = red. green lrtb, blue diagonals * pos_code[1] = green. red lr, blue tb * pos_code[2] = green. blue lr, red tb * pos_code[3] = blue. green lrtb, red diagonals * * The Red channel reconstruction is R=G+L(Rs-Gs), in which * G = interpolated & known Green * Rs = known Red * Gs = values of G at the positions of Rs * L()= should be a 2D lowpass filter, now we'll check * them from a 3x3 square * L-functions' convolution matrix is * [1/4 1/2 1/4;1/2 1 1/2; 1/4 1/2 1/4] * * The Blue channel reconstruction uses exactly the same methods. */ for (x = 0; x < w; x++) { bayer = (x&1?0:1) + (y&1?0:2); for (color=0; color < 3; color+=2) { if ((color==RED && bayer == pos_code[3]) || (color==BLUE && bayer == pos_code[0])) { value=value2=div=0; if (x > 0 && y > 0) { value += image_h[AD(x-1,0,w)+color] -image_h[AD(x-1,0,w)+GREEN]; value2+= image_v[AD(x-1,0,w)+color] -image_v[AD(x-1,0,w)+GREEN]; div++; } if (x > 0 && y < h-1) { value += image_h[AD(x-1,2,w)+color] -image_h[AD(x-1,2,w)+GREEN]; value2+= image_v[AD(x-1,2,w)+color] -image_v[AD(x-1,2,w)+GREEN]; div++; } if (x < w-1 && y > 0) { value += image_h[AD(x+1,0,w)+color] -image_h[AD(x+1,0,w)+GREEN]; value2+= image_v[AD(x+1,0,w)+color] -image_v[AD(x+1,0,w)+GREEN]; div++; } if (x < w-1 && y < h-1) { value += image_h[AD(x+1,2,w)+color] -image_h[AD(x+1,2,w)+GREEN]; value2+= image_v[AD(x+1,2,w)+color] -image_v[AD(x+1,2,w)+GREEN]; div++; } image_h[AD(x,1,w)+color]= CLAMP( image_h[AD(x,1,w)+GREEN] +value/div); image_v[AD(x,1,w)+color]= CLAMP(image_v[AD(x,1,w)+GREEN] +value2/div); } else if ((color==RED && bayer == pos_code[2]) || (color==BLUE && bayer == pos_code[1])) { value=value2=div=0; if (y > 0) { value += image_h[AD(x,0,w)+color] -image_h[AD(x,0,w)+GREEN]; value2+= image_v[AD(x,0,w)+color] -image_v[AD(x,0,w)+GREEN]; div++; } if (y < h-1) { value += image_h[AD(x,2,w)+color] -image_h[AD(x,2,w)+GREEN]; value2+= image_v[AD(x,2,w)+color] -image_v[AD(x,2,w)+GREEN]; div++; } image_h[AD(x,1,w)+color]= CLAMP( image_h[AD(x,1,w)+GREEN] +value/div); image_v[AD(x,1,w)+color]= CLAMP( image_v[AD(x,1,w)+GREEN] +value2/div); } else if ((color==RED && bayer == pos_code[1]) || (color==BLUE && bayer == pos_code[2])) { value=value2=div=0; if (x > 0) { value += image_h[AD(x-1,1,w)+color] -image_h[AD(x-1,1,w)+GREEN]; value2+= image_v[AD(x-1,1,w)+color] -image_v[AD(x-1,1,w)+GREEN]; div++; } if (x < w-1) { value += image_h[AD(x+1,1,w)+color] -image_h[AD(x+1,1,w)+GREEN]; value2+= image_v[AD(x+1,1,w)+color] -image_v[AD(x+1,1,w)+GREEN]; div++; } image_h[AD(x,1,w)+color]= CLAMP( image_h[AD(x,1,w)+GREEN] +value/div); image_v[AD(x,1,w)+color]= CLAMP( image_v[AD(x,1,w)+GREEN] +value2/div); } } } } /** * \brief Missing greens are reconstructed on a single row * \param image the image which is being reconstructed * \param image_h three-row window, horizontal interpolation of row 1 is done * \param image_v three-row window, vertical interpolation of row 1 is done * \param w width of image * \param h height of image. * \param y row number from image which is under construction * \param pos_code position code related to Bayer tiling in use */ static void do_green_ctr_row(unsigned char *image, unsigned char *image_h, unsigned char *image_v, int w, int h, int y, int *pos_code) { int x, bayer; int value,div; /* * The horizontal green estimation on a red-green row is * G(x) = (2*R(x)+2*G(x+1)+2*G(x-1)-R(x-2)-R(x+2))/4 * The estimation on a green-blue row works in the same * way. */ for (x = 0; x < w; x++) { bayer = (x&1?0:1) + (y&1?0:2); /* pos_code[0] = red. green lrtb, blue diagonals */ /* pos_code[3] = blue. green lrtb, red diagonals */ if ( bayer == pos_code[0] || bayer == pos_code[3]) { div=value=0; if (bayer==pos_code[0]) value += 2*image[AD(x,y,w)+RED]; else value += 2*image[AD(x,y,w)+BLUE]; div+=2; if (x < (w-1)) { value += 2*image[AD(x+1,y,w)+GREEN]; div+=2; } if (x < (w-2)) { if (bayer==pos_code[0]) value -= image[AD(x+2,y,w)+RED]; else value -= image[AD(x+2,y,w)+BLUE]; div--; } if (x > 0) { value += 2*image[AD(x-1,y,w