<pre name="code" class="cpp">function HOGFeature = ImgHOGFeature( imgPathName, cell_size, nblock,...,overlap, angle, bin_num) % 計算輸入圖像的特征描述子 % imgPathName：圖片路徑 % cell_size: cell的長寬 % nblock: block的width、height包括的cell的個數(shù) % overlap: block重疊的比例 % angle: 180\360 % bin_num: 方向bin的數(shù)目if nargin<2% default parametercell_size=8;nblock=2;overlap=0.5;angle=180;bin_num=9; elseif nargin<6error('Input parameters are not enough.'); endImg = imread(imgPathName); if size(Img,3) == 3% 簡化計算，直接轉(zhuǎn)換成灰度圖像G = rgb2gray(Img); elseG = Img; end[height, width] = size(G);% 計算x、y方向的梯度 hx = [-1,0,1]; hy = -hx'; grad_x = imfilter(double(G),hx); grad_y = imfilter(double(G),hy);% 計算梯度的模長 grad_mag=sqrt(grad_x.^2+grad_y.^2);% 計算梯度的方向 index= grad_x==0; grad_x(index)=1e-5; YX=grad_y./grad_x; if angle==180grad_angle= ((atan(YX)+(pi/2))*180)./pi; elseif angle==360grad_angle= ((atan2(grad_y,grad_x)+pi).*180)./pi; end% orient bin bin_angle=angle/bin_num; grad_orient=ceil(grad_angle./bin_angle);% 計算block的個數(shù) block_size=cell_size*nblock; skip_step=block_size*overlap; x_step_num=floor((width-block_size)/skip_step+1); y_step_num=floor((height-block_size)/skip_step+1);% 初始化hog特征描述子 feat_dim=bin_num*nblock^2; HOGFeature=zeros(feat_dim,x_step_num*y_step_num);for k=1:y_step_numfor j=1:x_step_num% block的左上角坐標(biāo)x_off = (j-1)*skip_step+1;y_off = (k-1)*skip_step+1;% 取得block的梯度大小和方向b_mag=grad_mag(y_off:y_off+block_size-1,x_off:x_off+block_size-1);b_orient=grad_orient(y_off:y_off+block_size-1,x_off:x_off+block_size-1);% 當(dāng)前block的hog直方圖currFeat = BinHOGFeature(b_mag, b_orient, cell_size,nblock, bin_num, false);HOGFeature(:, (k-1)*x_step_num+j) = currFeat;end endend function blockfeat = BinHOGFeature( b_mag,b_orient,cell_size,nblock,...bin_num, weight_vote) % 計算1個block的hog % weight_vote: 是否進行高斯加權(quán)投票% block的HOG直方圖 blockfeat=zeros(bin_num*nblock^2,1);% 高斯權(quán)重 gaussian_weight=fspecial('gaussian',cell_size*nblock,0.5*cell_size*nblock);% 分割block for n=1:nblockfor m=1:nblock% cell的左上角坐標(biāo)x_off = (m-1)*cell_size+1;y_off = (n-1)*cell_size+1;% cell的梯度大小和方向c_mag=b_mag(y_off:y_off+cell_size-1,x_off:x_off+cell_size-1);c_orient=b_orient(y_off:y_off+cell_size-1,x_off:x_off+cell_size-1);% cell的hog直方圖c_feat=zeros(bin_num,1);for i=1:bin_num% 是否進行高斯加權(quán) 投票if weight_vote==falsec_feat(i)=sum(c_mag(c_orient==i));elsec_feat(i)=sum(c_mag(c_orient==i).*gaussian_weight(c_orient==i));endend% 合并到block的HOG直方圖中count=(n-1)*nblock+m;blockfeat((count-1)*bin_num+1:count*bin_num,1)=c_feat;end end% 歸一化 L2-norm sump=sum(blockfeat.^2); blockfeat = blockfeat./sqrt(sump+eps^2);

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