ML: The code of K-Nearest Neighbors

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322

% 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