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322 | % Created by LI Xu
% Version 1.0
% May 31, 2024
% If you have any question about this code,
% please do not hesitate to contact me via E-mail:
% jeremy456@163.com
% Blog:
% http://blog.sciencenet.cn/u/lixujeremy
% http://lixuworld.blogspot.com/
clear;
clc;
timebegin=tic;
cur_data=date;
cur_time=fix(clock);
str1=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6));
fprintf('Time Begin: ');
fprintf(str1);
fprintf('\n');
% settings********************************************
% BackValue
BackVal=0;
fieldname='id';
cropname='canola';
% ***************************************************
% Source Directory
SouDir='input';
% Destination Directory
DesDir='output';
% All images
files=dir(fullfile(SouDir, "*.tif"));
txtpath=fullfile(DesDir, ['note.txt']);
fid=fopen(txtpath, 'w', 'n', 'US-ASCII');
% Loop
for ii=1:numel(files)
filename=files(ii).name;
filepath=fullfile(SouDir, filename);
[~, fname, ext]=fileparts(filename);
cr_folder=fullfile(DesDir, fname);
strname=strsplit(fname, '_');
strname=strname(end-2:end);
strname=strjoin(strname, '_');
if ~isfolder(cr_folder)
mkdir(cr_folder);
else
cmd_rmdir(cr_folder);
mkdir(cr_folder);
end
rows=['shapefiles\BL4rows_'];
plots=['shapefiles\weeds_grassy_'];
wfplots=['shapefiles\wf_grassy_'];
samples=['shapefiles\BL_'];
rows=[rows, strname, '.shp'];
plots=[plots, strname, '.shp'];
wfplots=[wfplots, strname, '.shp'];
samples=[samples, strname, '.tif'];
disp(['[', num2str(ii), '\', num2str(numel(files)), ']~', filename]);
fprintf(fid, '%s\r\n', ['[', num2str(ii), '\', num2str(numel(files)), ']~', filename]);
% Clip the BL/G community from the image
simagepath=fullfile(cr_folder, ['BL_', strname, '.tif']);
GenClip(rows, filepath, 0, simagepath);
% Convert to the ordinary image
tifpath=GenOrdTifImage(simagepath);
% Create the Mask
maskpaths=GenCompMark(simagepath, samples);
end
fprintf('Time Begin: ');
fprintf(str1);
fprintf('\n');
cur_data=date;
cur_time=fix(clock);
str2=sprintf('%s %.2d:%.2d:%.2d', cur_data, cur_time(4), cur_time(5), cur_time(6));
fprintf('Time End: ');
disp(str2);
timespan=toc(timebegin);
fprintf('Time Span: %.4f s\n', timespan);
disp('*******************************************************************');
function tifpath=GenOrdTifImage(inputpath)
[srcdir, fname, ~]=fileparts(inputpath);
tifpath=fullfile(srcdir, [fname, '_ordinary.tif']);
image=imread(inputpath);
imwrite(image, tifpath);
end
function GenClip(oneshp, filepath, BackVal, otpath)
[~, fname, ~]=fileparts(oneshp);
strcmd=['gdalwarp -of GTiff -cutline ', oneshp, ' -cl ', fname, ' -crop_to_cutline '];
strcmd=[strcmd, '-dstnodata ' num2str(BackVal),' ', filepath, ' ', otpath];
[~, cmdout]=system(strcmd);
end
function maskpaths=GenCompMark(filepath, samples)
xlspath='classes.sets.xlsx';
% Color plate
uniValues=readcell(xlspath, 'Sheet', 'colorplate');
uniValues(1, :)=[];
uniValues(:, 1)=[];
[soudir, fname, ~]=fileparts(filepath);
maskpaths=fullfile(soudir, [fname, '_mask.tif']);
sampimage=imread(samples);
try
[image, geo]=readgeoraster(filepath);
try
info=geotiffinfo(filepath);
catch
info=georasterinfo(filepath);
end
catch
image=imread(filepath);
end
sampimage=GenFalse(sampimage);
%% Use nearest neighbor classifier
mask=GenNearestNeighborClass(image, sampimage);
% rendering the mask
outMat=zeros(size(mask, 1), size(mask, 2), 3);
outMat=RenderUniValues(mask, uniValues, outMat);
showmat=[image; outMat];
% imshow(showmat);
knn_euclidean=fullfile(soudir, [fname, '_knn_euclidean.png']);
imwrite(showmat, knn_euclidean);
try
geotiffwrite(maskpaths, uint8(mask), geo);
catch
strcmd=['gdalinfo ', filepath];
[~, cmdout]=system(strcmd);
epsg=extractBetween(cmdout, "EPSG:"," got from GeoTIFF keys");
epsg=epsg{1};
% geotiffwrite(otpath, uint8(class_imag), geo, 'GeoKeyDirectoryTag', info.GeoTIFFTags.GeoKeyDirectoryTag);
geotiffwrite(maskpaths, uint8(mask), geo, 'CoordRefSysCode', ['EPSG:', epsg]);
end
end
function output=GenFalse(input)
output=[];
[rows, cols, ~]=size(input);
values=unique(input(:));
values(values>=100)=[];
sample_regions=false([rows, cols, numel(values)]);
% Loop to assign the matrixs
for ii=1:numel(values)
val=values(ii);
index=find(input==val);
band=sample_regions(:, :, ii);
band(index)=1;
sample_regions(:, :, ii)=band;
end
output=sample_regions;
end
function mask=GenNearestNeighborClass(image, sampimage)
[rows, cols, ~]=size(image);
% Enhance the image**************************
ycbcr=rgb2ycbcr(image);
ycbcr(:, :, 1)=0;
ycbcr=imadjust(ycbcr, stretchlim(ycbcr), []);
% imwrite(ycbcr, 'ycbcr.tif');
% *****************************************
% classes={'soil', 'canola', 'soybean'};
% nClasses=numel(classes);
nClasses=size(sampimage, 3);
% sample_regions=false([rows, cols, nClasses]);
sample_regions=sampimage;
mask=[];
% select each sample region
% figure;
% imshow(image);
% f=figure;
% for ii=1:nClasses
% set(f, 'name', ['Select sample region for ', classes{ii}]);
% sample_regions(:, :, ii)=roipoly(image);
% end
%
% close(f);
% Convert RGB to L*a*b colorspace
lab=rgb2lab(image);
% Calcualate the mean 'a*' and 'b*' value for each ROI area
% a=lab(:, :, 2);
% b=lab(:, :, 3);
a=ycbcr(:, :, 2);
b=ycbcr(:, :, 3);
color_markers=repmat(0, [nClasses, 2]);
for count=1:nClasses
color_markers(count, 1)=mean2(a(sample_regions(:, :, count)));
color_markers(count, 2)=mean2(b(sample_regions(:, :, count)));
end
% https://www.youtube.com/watch?v=3hEvcyCJNRc&list=PLEo-jHOqGNyUWoCSD3l3V-FjX9PnHvx5n&index=33
% Classify each pixel using the nearest neighbor rule
% Each class marker now has an 'a*' and 'b*' value.
% You can classify each pixel in the |lab_x| image by calculating the
% Euclidean distance bewteen that pixel and each marker. The smallest
% distance will tell you that the pixel most closely matched that
% marker. For example, if the distance between a pixel and the read
% color marker is the smallest, then the pixel would be labeled as a
% red pixel.
color_labels=0:nClasses-1;
a=double(a);
b=double(b);
distance=repmat(0, [size(a), nClasses]);
% Perform classification
for count=1:nClasses
distance(:, :, count)=((a-color_markers(count, 1)).^2+...
(b-color_markers(count, 2)).^2).^0.5;
end
% The other formulas as follows:
% https://www.saedsayad.com/k_nearest_neighbors.htm
[value, label]=min(distance, [], 3);
label=color_labels(label);
% clear value distance
colors=[0, 0, 0; 0, 255, 0; 255, 0, 0];
y=zeros(size(image));
l=double(label)+1;
for m=1:rows
for n=1:cols
y(m, n, :)=colors(l(m, n), :);
end
end
mask=uint8(label);
end
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