数字图像处理

南京工程学院通信工程学院实验报告课程名称数字图像处理实验项目名称彩色图像处理实验学生班级实验学生姓名同组学生姓名实验时间实验地点一．实验目的1.掌握彩色图像表示的方法；2.掌握彩色图像处理的基本原理；3.使用 RGB和 Gray 图像间转换函数；4.彩色图像的分割；二．实验设备安装有MA TLAB 软件的计算机三．实验原理RGB色彩模式是工业界的一种颜色标准，是通过对红(R)、绿(G)、蓝(B)三个颜色通道的变化以及它们相互之间的叠加来得到各式各样的颜色的，RGB即是代表红、绿、蓝三个通道的颜色，这个标准几乎包括了人类视力所能感知的所有颜色，是目前运用最广的颜色系统之一。

HSI模型是美国色彩学家孟塞尔(H.A.Munseu)于1915年提出的，它反映了人的视觉系统感知彩色的方式，以色调、饱和度和强度三种基本特征量来感知颜色。

色调H(Hue)：与光波的波长有关，它表示人的感官对不同颜色的感受，如红色、绿色、蓝色等，它也可表示一定范围的颜色，如暖色、冷色等。

饱和度S(Saturation)：表示颜色的纯度，纯光谱色是完全饱和的，加入白光会稀释饱和度。

饱和度越大，颜色看起来就会越鲜艳，反之亦然。

强度I(Intensity)：对应成像亮度和图像灰度，是颜色的明亮程度。

使用RGB彩色向量进行彩色区域分割是很简单的，假设我们的目的是在RGB图像中分割一个特定彩色范围的物体，给定一组感兴趣的彩色或彩色范围描述的彩色样本点，我们获得一个“平均”的颜色估计，它是我们希望分割的那种颜色，让这种平均颜色用RGB列向量来定义，分割的目的是对图像中的每一个RGB像素进行分类，使其在指定范围内有一种或没有颜色，为执行这一比较，我们需要一个相似性度量。

四．实验内容和步骤1.图像从RGB转换到HSI格式的一个M函数rgb = imread('g.jpg');figure,imshow(rgb);function hsi = rgb2hsi(rgb)% hsi = rgb2hsi(rgb)把一幅RGB图像转换为HSI图像，% 输入图像是一个彩色像素的M×N×3的数组，% 其中每一个彩色像素都在特定空间位置的彩色图像中对应红、绿、蓝三个分量。

% 假如所有的RGB分量是均衡的，那么HSI转换就是未定义的。

% 输入图像可能是double（取值范围是[0, 1]），uint8或 uint16。

% 输出HSI图像是double，% 其中hsi(:, :, 1)是色度分量，它的范围是除以2*pi后的[0, 1]；% hsi(:, :, 2)是饱和度分量，范围是[0, 1]；% hsi(:, :, 3)是亮度分量，范围是[0, 1]。

% 抽取图像分量rgb = im2double(rgb);r = rgb(:, :, 1);g = rgb(:, :, 2);b = rgb(:, :, 3);% 执行转换方程num = 0.5*((r - g) + (r - b));den = sqrt((r - g).^2 + (r - b).*(g - b));theta = acos(num./(den + eps)); %防止除数为0H = theta;H(b > g) = 2*pi - H(b > g);H = H/(2*pi);num = min(min(r, g), b);den = r + g + b;den(den == 0) = eps; %防止除数为0S = 1 - 3.* num./den;H(S == 0) = 0;I = (r + g + b)/3;% 将3个分量联合成为一个HSI图像hsi = cat(3, H, S, I);figure,imshow(hsi);HSI图像：转换后的RGB图像：2.从HSI转换到RGB的一个M函数。

Function rgb=hsi2rgb(hsi)％ HSI2RGB的HSI图像转换为RGB。

％ HSI2RGB的HSI图像转换为RGB ，其中HSI被假定为％ HSI （：，：，1）=色调图像归一化值到范围[0,1]通过％ 2 * PI除以所有的角度值。

％HSI（：，：， 2 ） =饱和度图像，在区间[ 0,1] 。

％HSI（：，：， 3 ） =强度图像，在区间[ 0,1] 。

％输出图像的组成部分是：％ RGB （：，：， 1）=红色;％ RGB （：，：，2）=绿色。

％ RGB （：，：， 3）=蓝色。

％提取individaulHSI分量图像。

H=hsi(:,:,1)*2*pi;S=hsi(:,:,2);I=hsi(:,:,3);％实施转换方程。

R=zeros(size(hsi,1),size(hsi,2));G=zeros(size(hsi,1),size(hsi,2));B=zeros(size(hsi,1),size(hsi,2));％ RG扇区（ 0 < = H < 2 * PI / 3 ）。

idx=find((0<=H)&(H<2*pi/3));B(idx)=I(idx).*(1-S(idx));R(idx)=I(idx).*(1+S(idx).*cos(H(idx))./cos(pi/3-H(idx)));G(idx)=3*I(idx)-(R(idx)+B(idx));％ BG扇区（ 2 * PI / 3 < = H < 4 * PI / 3 ）。

idx=find((2*pi/3<=H)&(H<4*pi/3));R(idx)=I(idx).*(1-S(idx));G(idx)=I(idx).*(1+S(idx).*cos(H(idx)-2*pi/3)./cos(pi-H(idx)));B(idx)=3*I(idx)-(R(idx)+G(idx));％ BR扇区。

idx=find((4*pi/3<=H)&(H<=2*pi));G(idx)=I(idx).*(1-S(idx));B(idx)=I(idx).*(1+S(idx).*cos(H(idx)-4*pi/3)./cos(5*pi/3-H(idx))); R(idx)=3*I(idx)-(G(idx)+B(idx));％将所有三种结果为RGB图像。

剪裁到[0,1] ，以弥补浮点算术舍入的效果。

rgb=cat(3,R,G,B);rgb=max(min(rgb,1),0);figure,imshow(rgb);原图：hsi2rgb函数转换后的图形3.RGB彩色图像分割f=imread('b.jpg');%首先我们获得表示待分割彩色图像的样本，用roiploy函数产生一个交互选择的区域的二%值掩模，其中mask是一幅二值图像(大小与f相同，)其背景为0，交互选择区域为1. mask=roipoly(f);red=immultiply(mask,f(:,:,1));green=immultiply(mask,f(:,:,2));blue=immultiply(mask,f(:,:,3));g=cat(3,red,green,blue);figure,imshow(g)%接着我们计算ROI中的点的均值向量和协方差矩阵，但ROI中的点坐标必须首先提取出来 [M,N,K]=size(g);I=reshape(g,M*N,3);%将g中的彩色像素重新排列为I行idx=find(mask); %找出非黑彩色像素的行索引I=double(I(idx,1:3));%[C,M]=covmatrix(I);[K, n] = size(I);I = double(I);if n == 1 % Handle special case.C = 0;m = I;else% Compute an unbiased estimate of m.m = sum(I, 1)/K;% Subtract the mean from each row of X.I = I - m(ones(K, 1), :);% Compute an unbiased estimate of C. Note that the product is % X'*X because the vectors are rows of X.C = (I'*I)/(K - 1);m = m'; % Convert to a column vector.end%最后的初步计算决定T的值，首先让T变为一个彩色分量的标准偏差的倍数。

C的主对角%线包括RGB分量的方差，所以必须提取这些元素并计算它们的平方根d=diag(C);sd=sqrt(d)'%现对图像分割，T值取120的倍数E=colorseg1('euclidean',f,120,m);figure;imshow(E)原图：使用roipoly函数交互的提取的感兴趣区域T=120时的分割图像五．实验感想通过此次彩色图像处理的实验，对彩色图像表示的方法和使用 RGB和 HSI 图像间转换的理解更为深入，对彩色图像的分割有了新的认识，感想老师的指导和同学的帮住。

附录：调用colorseg(varargin)函数原型：function I = colorseg(varargin)%COLORSEG Performs segmentation of a color image.% S = COLORSEG('EUCLIDEAN', F, T, M) performs segmentation of color % image F using a Euclidean measure of similarity. M is a 1-by-3 % vector representing the average color used for segmentation (this % is the center of the sphere in Fig. 6.26 of DIPUM). T is the% threshold against which the distances are compared.%% S = COLORSEG('MAHALANOBIS', F, T, M, C) performs segmentation of % color image F using the Mahalanobis distance as a measure of% similarity. C is the 3-by-3 covariance matrix of the sample color % vectors of the class of interest. See function covmatrix for the % computation of C and M.%% S is the segmented image (a binary matrix) in which 0s denote the % background.% Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins % Digital Image Processing Using MATLAB, Prentice-Hall, 2004% $Revision: 1.5 $ $Date: 2003/11/21 14:28:34 $% Preliminaries.% Recall that varargin is a cell array.f = varargin{2};if (ndims(f) ~= 3) | (size(f, 3) ~= 3)error('Input image must be RGB.');endM = size(f, 1); N = size(f, 2);% Convert f to vector format using function imstack2vectors.[f, L] = imstack2vectors(f);f = double(f);% Initialize I as a column vector. It will be reshaped later% into an image.I = zeros(M*N, 1);T = varargin{3};m = varargin{4};m = m(:)'; % Make sure that m is a row vector.if length(varargin) == 4method = 'euclidean';elseif length(varargin) == 5method = 'mahalanobis';elseerror('Wrong number of inputs.');endswitch methodcase'euclidean'% Compute the Euclidean distance between all rows of X and m. See % Section 12.2 of DIPUM for an explanation of the following% expression. D(i) is the Euclidean distance between vector X(i,:) % and vector m.p = length(f);D = sqrt(sum(abs(f - repmat(m, p, 1)).^2, 2));case'mahalanobis'C = varargin{5};D = mahalanobis(f, C, m);otherwiseerror('Unknown segmentation method.')end% D is a vector of size MN-by-1 containing the distance computations % from all the color pixels to vector m. Find the distances <= T.J = find(D <= T);% Set the values of I(J) to 1. These are the segmented% color pixels.I(J) = 1;% Reshape I into an M-by-N image.I = reshape(I, M, N);调用imstack2vectors(S, MASK)函数原型function [X, R] = imstack2vectors(S, MASK)%IMSTACK2VECTORS Extracts vectors from an image stack.% [X, R] = imstack2vectors(S, MASK) extracts vectors from S, which % is an M-by-N-by-n stack array of n registered images of size% M-by-N each (see Fig. 11.24). The extracted vectors are arranged % as the rows of array X. Input MASK is an M-by-N logical or% numeric image with nonzero values (1s if it is a logical array) % in the locations where elements of S are to be used in forming X % and 0s in locations to be ignored. The number of row vectors in X% is equal to the number of nonzero elements of MASK. If MASK is % omitted, all M*N locations are used in forming X. A simple way % to obtain MASK interactively is to use function roipoly. Finally, % R is an array whose rows are the 2-D coordinates containing the% region locations in MASK from which the vectors in S were% extracted to form X.% Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins % Digital Image Processing Using MATLAB, Prentice-Hall, 2004% $Revision: 1.6 $ $Date: 2003/11/21 14:37:21 $% Preliminaries.[M, N, n] = size(S);if nargin == 1MASK = true(M, N);elseMASK = MASK ~= 0;end% Find the set of locations where the vectors will be kept before % MASK is changed later in the program.[I, J] = find(MASK);R = [I, J];% Now find X.% First reshape S into X by turning each set of n values along the third% dimension of S so that it becomes a row of X. The order is from top to% bottom along the first column, the second column, and so on.Q = M*N;X = reshape(S, Q, n);% Now reshape MASK so that it corresponds to the right locations % vertically along the elements of X.MASK = reshape(MASK, Q, 1);% Keep the rows of X at locations where MASK is not 0.X = X(MASK, :);。