《数字图像处理原理与实践（MATLAB版）》一书之代码Part4

时间：2022-11-04 19:01:39

本文系《数字图像处理原理与实践（MATLAB版）》一书之代码系列的Part4（由于之前发布顺序调整，请读者注意页码标注而不要仅仅依据系列文章的标题编号），辑录该书第186至第280页之代码，供有需要读者下载研究使用。至此全书代码发布已经过半。代码执行结果请参见原书配图。

-------------------------------------------

P186


A = rgb2gray(imread('circle.png'));
B = edge(A, 'canny');
[centers, radii, metric] = imfindcircles(B,[22 65]);
imshow(A);
viscircles(centers, radii,'EdgeColor','b');

P195

BW = imread('contour.bmp');
imshow(BW,[]);
hold on

s=size(BW);
for row = 2:55:s(1)
   for col=1:s(2)
      if BW(row,col),
         break;
      end
   end

   contour = bwtraceboundary(BW, [row, col], 'W', 8);
   if(~isempty(contour))
      plot(contour(:,2),contour(:,1),'g','LineWidth',2);
   end
end


P197 

I = im2bw(imread('penguins.bmp'), 0.38);
BW = 1-I;
B = bwboundaries(BW,8,'noholes');
imshow(I)
hold on

for k = 1:length(B)
    boundary = B{k};
    plot(boundary(:,2), boundary(:,1), 'g', 'LineWidth', 2)
end


-------------------------------------------
补充一点小技巧。先前在写Demo的时候没想过这个问题，后来是因为要为图书做插图，所以就需要把处理结果的白边去掉，下面这段代码实现了这种结果。与图像处理无关，这种方法也没有出现在书里，只是关于MATLAB保存图像时的一点小技巧而已。 


I = im2bw(imread('penguins.bmp'), 0.38);
BW = 1-I;
B = bwboundaries(BW,8,'noholes');
imshow(I,'border','tight');
hold on

for k = 1:length(B)
    boundary = B{k};
    plot(boundary(:,2), boundary(:,1), 'g', 'LineWidth', 2)
end

saveas(gcf,'pengs3.bmp');


-------------------------------------------
P203

I = imread('nums.bmp');
locs =[57 64;47 103;81 224;94 274;11 365;85 461;86 540];
BW = imfill(I, locs, 4);
imshow(BW);


P204 

I = imread('nums.bmp');
BW2 = imfill(I,'holes');
imshow(BW2);


P205 

I = imread('tire.tif');
I2 = imfill(I);
figure, imshow(I), figure, imshow(I2)


P206 

I = imread('eight.tif');
c = [222 272 300 270 221 194];
r = [21 21 75 121 121 75];
J = roifill(I,c,r);
imshow(I)
figure, imshow(J)


P207 

function J = regiongrowing(I,x,y,threshold)

if(exist('threshold','var')==0), threshold=0.2; end
J = zeros(size(I)); % 用来标记输出结果的二值矩阵
[m n] = size(I); % 输入图像的尺寸
reg_mean = I(x,y); % 被分割区域的灰度均值
reg_size = 1; % 区域中像素的数目
% 用以存储被分割出来的区域的邻域点的堆栈
neg_free = 10000; neg_pos=0;
neg_list = zeros(neg_free,3);
delta=0; % 最新被引入的像素与区域灰度均值的差值

% 区域生长直至满足终止条件
while(delta<threshold && reg_size<numel(I))

    % 检测邻域像素，并判读是否将其划入区域
    for i = -1:1
        for j = -1:1
            xn = x + i; yn = y + j; % 计算邻域点的坐标
            % 检查邻域像素是否越界
            indicator = (xn >= 1)&&(yn >= 1)&&(xn <= m)&&(yn <= n);
        
            % 如果邻域像素还不属于被分割区域则加入堆栈
            if(indicator && (J(xn,yn)==0))
                neg_pos = neg_pos+1;
                neg_list(neg_pos,:) = [xn yn I(xn,yn)]; J(xn,yn)=1;
            end
        end
    end
    
    if(neg_pos+10>neg_free), % 如果堆栈空间不足，则对其进行扩容
        neg_free=neg_free+10000;
        neg_list((neg_pos+1):neg_free,:)=0;
    end
    
    % 将那些灰度值最接近区域均值的像素加入到区域中去
    dist = abs(neg_list(1:neg_pos,3)-reg_mean);
    [delta, index] = min(dist);
    J(x,y)=2; reg_size=reg_size+1;
    
    % 计算新区域的均值
    reg_mean = (reg_mean*reg_size + neg_list(index,3))/(reg_size+1);
    % 保存像素坐标，然后将像素从堆栈中移除
    x = neg_list(index,1); y = neg_list(index,2);
    neg_list(index,:)=neg_list(neg_pos,:); neg_pos=neg_pos-1;
end

% 将由区域生长得到的分割区域以二值矩阵的形式返回
J=J>1;

P208 

I = im2double(rgb2gray(imread('penguins.bmp')));
x = 244; y = 679;
J = regiongrowing(I,x,y,0.2);
figure, imshow(I+J);

P213 

I = imread('liftingbody.png');
S = qtdecomp(I,.27);
blocks = repmat(uint8(0),size(S));

for dim = [512 256 128 64 32 16 8 4 2 1];    
  numblocks = length(find(S==dim));    
  if (numblocks > 0)        
    values = repmat(uint8(1),[dim dim numblocks]);
    values(2:dim,2:dim,:) = 0;
    blocks = qtsetblk(blocks,S,dim,values);
  end
end

blocks(end,1:end) = 1;
blocks(1:end,end) = 1;
imshow(I), figure, imshow(blocks,[])

P219 

rgb = imread('potatos.jpg');
I = rgb2gray(rgb);

hy = fspecial('sobel');
hx = hy';
Iy = imfilter(double(I), hy, 'replicate');
Ix = imfilter(double(I), hx, 'replicate');
gradmag = sqrt(Ix.^2 + Iy.^2);

L = watershed(gradmag);
Lrgb = label2rgb(L);
figure
subplot(1, 2, 1); imshow(gradmag,[]), title('梯度幅值图像')
subplot(1, 2, 2); imshow(Lrgb); title('对梯度图直接做分水岭分割')


P221-P224 

rgb = imread('potatos.jpg');
I = rgb2gray(rgb);

hy = fspecial('sobel');
hx = hy';
Iy = imfilter(double(I), hy, 'replicate');
Ix = imfilter(double(I), hx, 'replicate');
gradmag = sqrt(Ix.^2 + Iy.^2);

se = strel('disk', 12);
Ie = imerode(I, se);
Iobr = imreconstruct(Ie, I);

Iobrd = imdilate(Iobr, se);
Iobrcbr = imreconstruct(imcomplement(Iobrd), imcomplement(Iobr));
Iobrcbr = imcomplement(Iobrcbr);

fgm = imregionalmax(Iobrcbr);
It1 = rgb(:, :, 1);
It2 = rgb(:, :, 2);
It3 = rgb(:, :, 3);
It1(fgm) = 255; It2(fgm) = 0; It3(fgm) = 0;
I2 = cat(3, It1, It2, It3);

figure
subplot(1, 2, 1); imshow(fgm, []); title('局部极大值图像');
subplot(1, 2, 2); imshow(I2); title('局部极大值叠加图像');

se2 = strel(ones(15,15));
fgm2 = imclose(fgm, se2);
fgm3 = imerode(fgm2, se2);
fgm4 = bwareaopen(fgm3, 400);

bw = im2bw(Iobrcbr, graythresh(Iobrcbr));
D = bwdist(bw);
DL = watershed(D);
bgm = DL == 0;
gradmag2 = imimposemin(gradmag, bgm | fgm4);

L = watershed(gradmag2);
%第一种显示方法
Lrgb = label2rgb(L, 'jet', 'w', 'shuffle');
figure
subplot(1,2,1), imshow(Lrgb), title('分水岭分割结果显示1');
%第二种显示方法
subplot(1, 2, 2); imshow(rgb, []), title('分水岭分割结果显示2');
hold on;
himage = imshow(Lrgb);
set(himage, 'AlphaData', 0.3);

P245

I = imread('lena.png');
fcoef=fft2(double(I));            %FFT变换
tmp1 =log(1+abs(fcoef));
spectrum = fftshift(fcoef);        %调整中心
tmp2 = log(1+abs(spectrum));
ifcoef = ifft2(fcoef);            %逆变换

figure                            %显示处理结果
subplot(2,2,1), imshow(I), title('source image');
subplot(2,2,2), imshow(tmp1,[]), title('FFT image');
subplot(2,2,3), imshow(tmp2,[]), title('shift FFT image');
subplot(2,2,4), imshow(ifcoef,[]), title('IFFT image');

P251 
J= double(imread('lena.bmp'));
K = dct2(J);
figure, imshow(K,[0 255])


P252-1 
J= double(imread('lena.bmp'));
K = dct2(J);
figure, imshow(K,[0 255]);
K_i = idct2(K);
figure, imshow(K_i,[0 255])


P252-2 

J= double(imread('lena.bmp'));
A = J(1:8,1:8);
D = dctmtx(8);
dct_1 = D*A;
dct_2 = D'*dct_1;

P252-3 

J= double(imread('lena.bmp'));
A = J(1:8,1:8);
D = dctmtx(8);
dct_1 = D*A*D';
dct_2 = dct2(A);

P253 
I = imread('cameraman.tif');
I = im2double(I);
T = dctmtx(8);
dct = @(block_struct) T * block_struct.data * T';
B = blockproc(I,[8 8],dct);
mask = [1   1   1   1   0   0   0   0
        1   1   1   0   0   0   0   0
        1   1   0   0   0   0   0   0
        1   0   0   0   0   0   0   0
        0   0   0   0   0   0   0   0
        0   0   0   0   0   0   0   0
        0   0   0   0   0   0   0   0
        0   0   0   0   0   0   0   0];
B2 = blockproc(B,[8 8],@(block_struct) mask .* block_struct.data);
invdct = @(block_struct) T' * block_struct.data * T;
I2 = blockproc(B2,[8 8],invdct);
imshow(I), figure, imshow(I2)

P262 
a = [0 0 1 1 0 0 1 1];
b = fwht(a);

P263 

I = imread('baboon.bmp');
I1 = double(I);
T = hadamard(8);
myFun1 = @(block_struct)T*block_struct.data*T/64;
H = blockproc(I1, [8 8], myFun1);
H(abs(H)<3.5)=0;
myFun2 = @(block_struct)T*block_struct.data*T;
I2 = blockproc(H, [8 8], myFun2);
subplot(121), imshow(I1,[]), title('original image');
subplot(122), imshow(I2,[]), title('zipped image');

P264 

I = imread('baboon.bmp');
I1 = double(I);
[m n] =size(I);
sizi = 8;
num = 16;
%分块进行离散沃尔什变换
T = hadamard(sizi);
myFun1 = @(block_struct)T*block_struct.data*T/(sizi.^2);
hdcoe = blockproc(I1, [sizi, sizi], myFun1);
%重新排列系数
coe = im2col(hdcoe,  [sizi, sizi], 'distinct');
coe_t = abs(coe);
[Y, ind] = sort(coe_t);
%舍去绝对值较小的系数
[m_c, n_c] = size(coe);
for i = 1:n_c
coe(ind(1:num, i), i)=0;
end
%重建图像
re_hdcoe = col2im(coe, [sizi, sizi], [m, n], 'distinct');
myFun2 = @(block_struct)T*block_struct.data*T;
re_s = blockproc(re_hdcoe, [sizi, sizi], myFun2);
subplot(121), imshow(I1,[]), title('original image');
subplot(122), imshow(re_s,[]), title('compressed image');

P268 
dim1 = [1 1 1 2 2 2 3 3 3];
dim2 = [1 2 3 1 2 3 1 2 3];
dim3 = [63 75 78 50 56 65 70 71 80];

sum( (dim1-mean(dim1)) .* (dim2-mean(dim2)) ) / ( 9-1 ) % 0
sum( (dim1-mean(dim1)) .* (dim3-mean(dim3)) ) / ( 9-1 ) % 0.625
sum( (dim2-mean(dim2)) .* (dim3-mean(dim3)) ) / (9-1 ) % 5

std(dim1)^2  %  0.75
std(dim2)^2  %  0.75
std(dim3)^2  %  100.7778

P274 
X = [2 2; 2 3; 3 4; 4 3; 5 4; 5 5];
[COEFF,SCORE,latent,tsquare] = princomp(X);

P275-1 
X0=X-repmat(mean(X),6,1);
SCORE_1 = X0*COEFF;

P275-2 
X = [2 2; 2 3; 3 4; 4 3; 5 4; 5 5];
V = cov(X);
[COEFF,latent] = pcacov(V)


P277

I = imread('baboon.bmp');
x = double(I)/255;
[m,n]=size(x);
y =[];
%拆解图像
for i = 1:m/8;
    for j = 1:n/8;
        ii = (i-1)*8+1;
        jj = (j-1)*8+1;
        y_app = reshape(x(ii:ii+7,jj:jj+7),1,64);
        y=[y;y_app];
    end
end

%KL变换
[COEFF,SCORE,latent] = princomp(y);
kl = y * COEFF;

kl1 = kl;
kl2 = kl;
kl3 = kl;

%置零压缩过程
kl1(:, 33:64)=0;
kl2(:, 17:64)=0;
kl3(:, 9:64)=0;

%KL逆变换
kl_i = kl*COEFF';
kl1_i = kl1*COEFF';
kl2_i = kl2*COEFF';
kl3_i = kl3*COEFF';

image = ones(256,256);
image1 = ones(256,256);
image2 = ones(256,256);
image3 = ones(256,256);

k=1;
%重组图像
for i = 1:m/8;
    for j = 1:n/8;

        y = reshape(kl_i(k, 1:64),8,8);
        y1 = reshape(kl1_i(k, 1:64),8,8);
        y2 = reshape(kl2_i(k, 1:64),8,8);
        y3 = reshape(kl3_i(k, 1:64),8,8);

        ii = (i-1)*8+1;
        jj = (j-1)*8+1;

        image(ii:ii+7,jj:jj+7) = y;
        image1(ii:ii+7,jj:jj+7) = y1;
        image2(ii:ii+7,jj:jj+7) = y2;
        image3(ii:ii+7,jj:jj+7) = y3;

        k=k+1;
    end
end


（代码发布未完，请待后续...）


《数字图像处理原理与实践（MATLAB版）》一书之代码Part4
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