实例介绍
【实例简介】基于sift和SVM算法实现的手势识别程序，用MATLAB GUI编写的，附有手势库，可拷贝至任何磁盘运行不必担心路径问题，但可能要求版本高一点的MATLAB软件
Hand gesture Recognition based SIFT and SVM_matlab
【实例截图】
【核心代码】
%A - 1329
%B - 487
%C - 572
%Five - 654
%Point - 1395
%V - 435
%%
imgDir = './shp_marcel_train/Marcel-Train/';
outDir = './surfTrain';
mkdir(outDir);
% trainNumA = 1329;
% trainNumB = 487;
% trainNumC = 572;
% trainNumFive = 654;
% trainNumPoint = 1395;
% trainNumV = 435;
trainNumA = 10;
trainNumB = 10;
trainNumC = 10;
% trainNumFive = 200;
% trainNumPoint = 600;
% trainNumV = 200;
imgSize = 80;
patchSize = 16;
gridSpacing = patchSize/2;
gridRowNum = imgSize/gridSpacing - 1;
row_matOne = gridRowNum^2;
%% A
matDSift = [];
matOne = zeros(row_matOne, 128);
trainLabel = 'A';
for i = 1:trainNumA
% num = [num2str(floor(i/1000)), num2str(floor(mod(i,1000)/100)), num2str(floor(mod(i,100)/10)), num2str(floor(mod(i, 10)))];
num = [num2str(floor(mod(i,100)/10)), num2str(floor(mod(i, 10)))];
imgName = [trainLabel, '-uniform', num, '.ppm'];
imgPath = [imgDir, trainLabel, '/', imgName];
img = imread(imgPath);
img = imresize(img, [imgSize, imgSize]);
descriptor = dense_sift(img, patchSize, gridSpacing);
for j = 1:gridRowNum
for k = 1:gridRowNum
matOne((j-1)*gridRowNum k, :) = descriptor(j, k, :);
end
end
matDSift = cat(1, matDSift, matOne);
end
matOutFileName = [outDir, '/', 'dsift', trainLabel, '.txt'];
dlmwrite(matOutFileName, matDSift);
%% B
matDSift = [];
matOne = zeros(row_matOne, 128);
trainLabel = 'B';
for i = 1:trainNumB
% num = [num2str(floor(i/100)), num2str(floor(mod(i,100)/10)), num2str(floor(mod(i, 10)))];
num = [num2str(floor(mod(i,100)/10)), num2str(floor(mod(i, 10)))];
imgName = [trainLabel, '-uniform', num, '.ppm'];
imgPath = [imgDir, trainLabel, '/', imgName];
img = imread(imgPath);
img = imresize(img, [imgSize, imgSize]);
descriptor = dense_sift(img, patchSize, gridSpacing);
for j = 1:gridRowNum
for k = 1:gridRowNum
matOne((j-1)*gridRowNum k, :) = descriptor(j, k, :);
end
end
matDSift = cat(1, matDSift, matOne);
end
matOutFileName = [outDir, '/', 'dsift', trainLabel, '.txt'];
dlmwrite(matOutFileName, matDSift);
%% C
matDSift = [];
matOne = zeros(row_matOne, 128);
trainLabel = 'C';
for i = 1:trainNumC
% num = [num2str(floor(i/100)), num2str(floor(mod(i,100)/10)), num2str(floor(mod(i, 10)))];
num = [num2str(floor(mod(i,100)/10)), num2str(floor(mod(i, 10)))];
imgName = [trainLabel, '-uniform', num, '.ppm'];
imgPath = [imgDir, trainLabel, '/', imgName];
img = imread(imgPath);
img = imresize(img, [imgSize, imgSize]);
descriptor = dense_sift(img, patchSize, gridSpacing);
for j = 1:gridRowNum
for k = 1:gridRowNum
matOne((j-1)*gridRowNum k, :) = descriptor(j, k, :);
end
end
matDSift = cat(1, matDSift, matOne);
end
matOutFileName = [outDir, '/', 'dsift', trainLabel, '.txt'];
dlmwrite(matOutFileName, matDSift);
%
% matDSift = [];
% matOne = zeros(row_matOne, 128);
% trainLabel = 'Five';
% for i = 1:trainNumFive
% num = [num2str(floor(i/100)), num2str(floor(mod(i,100)/10)), num2str(floor(mod(i, 10)))];
% imgName = [trainLabel, '-train', num, '.ppm'];
% imgPath = [imgDir, trainLabel, '/', imgName];
% img = imread(imgPath);
% img = imresize(img, [imgSize, imgSize]);
% descriptor = dense_sift(img, patchSize, gridSpacing);
% for j = 1:gridRowNum
% for k = 1:gridRowNum
% matOne((j-1)*gridRowNum k, :) = descriptor(j, k, :);
% end
% end
% matDSift = cat(1, matDSift, matOne);
% end
% matOutFileName = [outDir, '/', 'dsift', trainLabel, '.txt'];
% dlmwrite(matOutFileName, matDSift);
%
% matDSift = [];
% matOne = zeros(row_matOne, 128);
% trainLabel = 'Point';
% for i = 1:trainNumPoint
% num = [num2str(floor(i/1000)), num2str(floor(mod(i,1000)/100)), num2str(floor(mod(i,100)/10)), num2str(floor(mod(i, 10)))];
% imgName = [trainLabel, '-train', num, '.ppm'];
% imgPath = [imgDir, trainLabel, '/', imgName];
% img = imread(imgPath);
% img = imresize(img, [imgSize, imgSize]);
% descriptor = dense_sift(img, patchSize, gridSpacing);
% for j = 1:gridRowNum
% for k = 1:gridRowNum
% matOne((j-1)*gridRowNum k, :) = descriptor(j, k, :);
% end
% end
% matDSift = cat(1, matDSift, matOne);
% end
% matOutFileName = [outDir, '/', 'dsift', trainLabel, '.txt'];
% dlmwrite(matOutFileName, matDSift);
%
% matDSift = [];
% matOne = zeros(row_matOne, 128);
% trainLabel = 'V';
% for i = 1:trainNumV
% num = [num2str(floor(i/100)), num2str(floor(mod(i,100)/10)), num2str(floor(mod(i, 10)))];
% imgName = [trainLabel, '-train', num, '.ppm'];
% imgPath = [imgDir, trainLabel, '/', imgName];
% img = imread(imgPath);
% img = imresize(img, [imgSize, imgSize]);
% descriptor = dense_sift(img, patchSize, gridSpacing);
% for j = 1:gridRowNum
% for k = 1:gridRowNum
% matOne((j-1)*gridRowNum k, :) = descriptor(j, k, :);
% end
% end
% matDSift = cat(1, matDSift, matOne);
% end
% matOutFileName = [outDir, '/', 'dsift', trainLabel, '.txt'];
% dlmwrite(matOutFileName, matDSift);
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