how to find features from '.txt' files?

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santhosh kumar buddepu
评论： santhosh kumar buddepu 2021-10-26
initially my data is like this :
Hy_r=h5read('D:\PSNR_survey\Plastic_tiffin_box_eps9\Plastic_tiffin_box_antennaX_Ey_eps9_merged.out','/rxs/rx1/Ey');
after some steps I have used:
imwrite (mat2gray(Gxx_t2), 'NEW IMAGE.png');
[X_raw,map] = imread ('NEW IMAGE.png');
m=mean(X_raw');
m=uint8(m);
X_avg=X_raw-m'; % removal of direct path or clutter removal done
imagesc(X,z_depth,X_avg)
The values X_avg are saved in excel sheet as ' text (tab delimited)'.
now I can recall X_avg directly by using:
y=dlmread('X_avg.txt', '\t', 0, 0);
figure,imagesc(X,z_depth,X_avg)
I need to use dlmread to read the data and i need to use imagesc instead of imread and imshow.
Like this I have created a database of around 40 text files for metal pipe, steel box, plastic box. I want to extract statistical features from all the files for that I have calculated mean, variance, skew, kurtosis for single file.
[pixelCounts GLs] = imhist(X_avg); % GL-gray levels
% Get the number of pixels in the histogram.
numberOfPixels = sum(pixelCounts);
% Get the mean gray lavel.
meanGL = sum(GLs .* pixelCounts) / numberOfPixels
% Get the variance, which is the second central moment.
varianceGL = sum((GLs - meanGL) .^ 2 .* pixelCounts) / (numberOfPixels-1)
% Get the standard deviation.
sd = sqrt(varianceGL);
% Get the skew.
skew = sum((GLs - meanGL) .^ 3 .* pixelCounts) / ((numberOfPixels - 1) * sd^3)
% Get the kurtosis.
kurtosis = sum((GLs - meanGL) .^ 4 .* pixelCounts) / ((numberOfPixels - 1) * sd^4)
How to calculate these features for all files using single code and how to store these features?

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KSSV
KSSV 2021-10-26
编辑：KSSV 2021-10-26
txtFiles = dir('*.txt') ;
N = length(txtFiles) ;
meanGL= zeros(N,1);
varianceGL = zeros(N,1) ;
sd = zeros(N,1) ;
skew = zeros(N,1);
kurtosis = zeros(N,1) ;
for i = 1N
y=dlmread(txtFiles(i).name, '\t', 0, 0);
figure,imagesc(X,z_depth,X_avg)
[pixelCounts GLs] = imhist(X_avg); % GL-gray levels
% Get the number of pixels in the histogram.
numberOfPixels = sum(pixelCounts);
% Get the mean gray lavel.
meanGL(i) = sum(GLs .* pixelCounts) / numberOfPixels
% Get the variance, which is the second central moment.
varianceGL(i) = sum((GLs - meanGL) .^ 2 .* pixelCounts) / (numberOfPixels-1)
% Get the standard deviation.
sd(i) = sqrt(varianceGL);
% Get the skew.
skew(i) = sum((GLs - meanGL) .^ 3 .* pixelCounts) / ((numberOfPixels - 1) * sd^3)
% Get the kurtosis.
kurtosis(i) = sum((GLs - meanGL) .^ 4 .* pixelCounts) / ((numberOfPixels - 1) * sd^4)
end
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santhosh kumar buddepu
santhosh kumar buddepu 2021-10-26
clc;
clear; close all;
% Specify the folder where the files live.
myFolder = 'D:\gpr_targets';
% Check to make sure that folder actually exists. Warn user if it doesn't.
if ~isdir(myFolder)
errorMessage = sprintf('Error: The following folder does not exist:\n%s', myFolder);
uiwait(warndlg(errorMessage));
return;
end
% Get a list of all files in the folder with the desired file name pattern.
filePattern = fullfile(myFolder, '**/*.txt'); % requires to read folders and its subfolders.
theFiles = dir(filePattern);
N = length(theFiles) ;
meanGL= zeros(N,1);
varianceGL = zeros(N,1) ;
sd = zeros(N,1) ;
skew = zeros(N,1);
kurtosis = zeros(N,1) ;
Ex = zeros(N, 4);
for i = 1 : N
baseFileName = theFiles(i).name;
fullFileName = fullfile(myFolder, baseFileName);
fprintf(1, 'Now reading %s\n', fullFileName);
% Now do whatever you want with this file name,
% such as reading it in as an image array with imread()
A= dlmread(theFiles(i).name, '\t', 0, 0);
[pixelCounts GLs] = imhist(A); % GL-gray levels
% Get the number of pixels in the histogram.
numberOfPixels = sum(pixelCounts);
% Get the mean gray lavel.
meanGL = sum(GLs .* pixelCounts) / numberOfPixels
% Get the variance, which is the second central moment.
varianceGL = sum((GLs - meanGL) .^ 2 .* pixelCounts) / (numberOfPixels-1)
% Get the standard deviation.
sd = sqrt(varianceGL);
% Get the skew.
skew = sum((GLs - meanGL) .^ 3 .* pixelCounts) / ((numberOfPixels - 1) * sd^3)
% Get the kurtosis.
kurtosis = sum((GLs - meanGL) .^ 4 .* pixelCounts) / ((numberOfPixels - 1) * sd^4)
% feature variables
thisFeatureVector = [meanGL varianceGL skew kurtosis];
Ex(i, 1 : length(thisFeatureVector)) = thisFeatureVector;
end
worksheetName = 'Results';
cellReference = sprintf('A%d', i);
xlswrite('D:\gpr_targets\featuresnew1.xls', Ex, worksheetName, cellReference);
I have done like this and I got features but features starts from 48th row and if I will keep xlswrite inside loop upto 48th row same feature is coming. please look into this issue sir.

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