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matlab绘图(六)-图像光滑&数据取点

过冷水

3年前浏览2701

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过冷水今天和大家分享一下读取图像数据点的小技巧：用cftool插值绘图得到拟合后的图像，然后正确获取拟合图像对应的数据。

有时候的实验室数据图像得到的如下图所示：

当然之前中过冷水多次有跟大家提多项式拟合、傅里叶级数拟合、高斯级数拟合，实际更加常见的操作是用matlab中图像拟合工具箱cftool灵活进行函数拟合。

这个过程大多数读者应该都有了解，问题在于：“how to get fit data”?首先我们需要先把函数工具拟合方法生成代形式：

function [fitresult, gof] = createFit(x1, y1)

%CREATEFIT(X1,Y1)

% Create a fit.

% Data for 'untitled fit 1' fit:

% X Input : x1

% Y Output: y1

% Output:

% fitresult : a fit object representing the fit.

% gof : structure with goodness-of fit info.

% 另请参阅 FIT, CFIT, SFIT.

% 由 MATLAB 于 06-Sep-2020 19:30:43 自动生成

%% Fit: 'untitled fit 1'.

[xData, yData] = prepareCurveData( x1, y1 );

% Set up fittype and options.

ft = fittype( 'smoothingspline' );

% Fit model to data.

[fitresult, gof] = fit( xData, yData, ft );

% Plot fit with data.

figure( 'Name', 'untitled fit 1' );

h = plot( fitresult, xData, yData );

legend( h, 'y1 vs. x1', 'untitled fit 1', 'Location', 'NorthEast' );

% Label axes

xlabel x1

ylabel y1

grid on

生成代码形式后，对代码进行简化，可以得到如下代码：

clear;

x1=[2,2.89795918367347,2.94897959183673,2.97959183673469,2.98979591836735,3.01020408163265,3.03061224489796,3.06122448979592,3.10204081632653,3.14285714285714,3.24489795918367,3.29591836734694,3.34693877551020,3.39795918367347,3.43877551020408,3.48979591836735,3.56122448979592,3.67346938775510,3.82653061224490,4.06122448979592,4.32653061224490,4.65306122448980,5,5.26530612244898,5.56122448979592,5.88775510204082,6.11224489795918,6.33673469387755,6.62244897959184,6.98979591836735,7.47959183673469,7.98979591836735];

y1=[0,0.524822695035461,0.978723404255319,1.31205673758865,1.57446808510638,1.82978723404255,2.04964539007092,2.31914893617021,2.63120567375887,2.85815602836879,2.82269503546099,2.59574468085106,2.34042553191489,2.08510638297872,1.87234042553192,1.64539007092199,1.38297872340426,1.11347517730496,0.936170212765958,0.858156028368794,0.900709219858156,0.964539007092199,0.929078014184397,0.829787234042553,0.787234042553191,0.872340425531915,1.02836879432624,1.17730496453901,1.20567375886525,1.07801418439716,0.964539007092199,0.921985815602837];

f=fit(x1',y1','smoothingspline');

x2=linspace(2,8,100)';

y2=f(x2);

figure1 = figure;

subplot1 = subplot(2,1,1,'Parent',figure1);

hold(subplot1,'on');

plot(x1,y1,'Parent',subplot1,'LineWidth',2);

xlabel('$x$','Interpreter','latex');

ylabel('$y$','Interpreter','latex');

title('原始数据','Interpreter','latex');

set(subplot1,'FontSize',12,'LineWidth',2);

subplot2 = subplot(2,1,2,'Parent',figure1);