Image processing based on Visual C + + programming Learning notes (3) displaying grayscale images and two-valued images

Grayscale Image Imggray

With the base above, the grayscale image is fairly simple.

Color graph to Grayscale method:

1. Floating-point algorithm: gray=r*0.3+g*0.59+b*0.11
2. Integer method: gray= (r*30+g*59+b*11)/100
3. Shift Method: Gray = (r*28+g*151+b*77) >>8; ？？
4. Average method: gray= (R+G+B)/3;
5. Take green only: gray=g;

Floating point algorithm is used here

voidImggray::showgray (CDC *PDC, CRect RC)    {Readbitdata (); unsignedinti,j;  for(i=0; I<vimageheight (); i++){         for(j=0; J<vimagewidth (); j + +) {unsignedCharR = img[3* (I*vimagewidth () +j)]; unsignedCharG = img[3* (I*vimagewidth () + j) +1]; unsignedCharB = img[3* (I*vimagewidth () + j) +2]; unsignedChargray= (unsignedChar)(0.30*r+0.59*g+0.11*B); R=g=b=GRAY; PDC->setpixel (RC. Width ()-vimagewidth ())/2+j, (RC. Height ()-vimageheight ())/2+I,rgb (r,g,b)); }    }}voidImggray::showgray2 (CDC *PDC, CRect RC)//Quick Display{unsignedint_img_w = Vimagewidth () *3; if(_img_w%4!=0) _img_w= (_img_w/4+1)*4; unsignedintI,j,x,y; Y=vimageheight (); X=vimagewidth ();  for(i=0; i<y;i++){         for(j=0; j<x;j++) {unsignedCharb=raw_img[i*_img_w+j*3]; unsignedCharg=raw_img[i*_img_w+j*3+1]; unsignedCharr=raw_img[i*_img_w+j*3+2]; unsignedChargray= (unsignedChar)(0.30*r+0.59*g+0.11*c); Raw_img[i*_img_w+j*3]   =GRAY; Raw_img[i*_img_w+j*3+1] =GRAY; Raw_img[i*_img_w+j*3+2] =GRAY; }} imshow2 (PDC, RC);}

Grayscale Equalization:

According to the distribution of gray scale, the gray-scale pixels are distributed evenly between 0-255 (establish gray level table), and then assign the pixels of each point according to the table.

voidImggray::grayequalize (unsignedChar*_img) {unsignedCharmap[ the]; inti,cnt[ the],scnt[ the];  for(i=0;i< the; i++) {Cnt[i]=0; Scnt[i]=0; }     for(i=0; i<data_size;i++) {Cnt[_img[i]]++; } scnt[0]=cnt[0];  for(i=1;i< the; i++) {Scnt[i]=scnt[i-1]+Cnt[i]; }     for(i=0;i< the; i++) {Map[i]=scnt[i]*255*1.0/data_size; }     for(i=0; i<data_size;i++) {_img[i]=Map[_img[i]]; }}

：

Imgbinary of binary image

Two value based on histogram equalization (mean method)

voidImgbinary::binary ()//Histogram based{    inti,sum=0;  for(i=0; i<data_size;i++) sum+=Img_g[i]; intavg=sum/data_size;  for(i=0; i<data_size;i++)    {        if(IMG_G[I]&GT;AVG) img_b[i]=true; Elseimg_b[i]=false; }}

Image processing based on Visual C + + programming Learning notes (3) displaying grayscale images and two-valued images