# 自适应高斯滤波.zip

• wanzhe119
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• 2020-07-05 14:19
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• 自适应高斯滤波.txt
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%function:通过高斯核产生相应的密度矩阵 %parameter： im：输入图像，灰度图； points：标注的点[X Y]，n*2的矩阵 function im_density = get_density_map_autogaussian(im,points) im_density = zeros(size(im)); [h,w] = size(im_density); if(isempty(points)) return; end %points为1行 if(length(points(:,1))==1) x1 = max(1,min(w,round(points(1,1)))); %round：四舍五入，x1变成points(1,1)处的整数 y1 = max(1,min(h,round(points(1,2)))); im_density(y1,x1) = 255; return; end for j = 1:length(points) max_kernel = 65; %最大高斯核尺寸 normal_kernel = 35; %默认高斯核尺寸 beta = 0.3; %MCNN中给定的参数 k = 6; %近邻数 maxpixel = 80; %近邻的最大距离，像素 % f_sz = normal_kernel; % sigma = beta * f_sz; x = min(w,max(1,abs(double(floor(points(j,1)))))); y = min(h,max(1,abs(double(floor(points(j,2)))))); if(x > w || y > h) continue; end %--------%auto adaptive 自适应高斯核-------- atemp = []; for i = 1:length(points) if i == j continue; end xk = min(w,max(1,abs(double(floor(points(i,1)))))); yk = min(w,max(1,abs(double(floor(points(i,2)))))); if(xk > w || yk > h) continue; end dis = sqrt( ((xk-x)^2 + (yk-y)^2) ); atemp = [atemp dis]; end btemp = sort(atemp); sum = 0; count = 0; if k >= length(points) k = length(points) - 1; end for m = 1:k if btemp(m) > maxpixel break; end sum = sum + btemp(m); count = count + 1; end if count > 0 temp = sum/count; else temp = normal_kernel; end f_sz = double(floor(temp)); if mod(f_sz, 2) == 0 f_sz = double(f_sz + 1); end if f_sz > (max_kernel + 1) f_sz = max_kernel; end sigma = beta * f_sz; % fprintf("x:%d,y:%d,f_sz:%d,sigma：%d\n",x,y,f_sz, sigma); %--------auto adaptive 自适应高斯核定义结束-------- H = fspecial('Gaussian',[f_sz, f_sz],sigma); %高斯核边界限定，x方向：→，y方向：↓ x1 = x - double(floor(f_sz/2)); y1 = y - double(floor(f_sz/2)); %x1左边界，y1上边界 x2 = x + double(floor(f_sz/2)); y2 = y + double(floor(f_sz/2)); %x2右边界，y2下边界 dfx1 = 0; dfy1 = 0; dfx2 = 0; dfy2 = 0; change_H = false; if(x1 < 1) dfx1 = abs(x1)+1; x1 = 1; change_H = true; end if(y1 < 1) dfy1 = abs(y1)+1; y1 = 1; change_H = true; end if(x2 > w) dfx2 = x2 - w; x2 = w; change_H = true; end if(y2 > h) dfy2 = y2 - h; y2 = h; change_H = true; end x1h = 1+dfx1; y1h = 1+dfy1; x2h = f_sz - dfx2; y2h = f_sz - dfy2; if (change_H == true) H = fspecial('Gaussian',[double(y2h-y1h+1), double(x2h-x1h+1)],sigma); end im_density(y1:y2,x1:x2) = im_density(y1:y2,x1:x2) + H; end end i = 45; dataset = 'A'; path = ['../data/original/shanghaitech/part_' dataset '_final/train_data/images/']; gt_path = ['../data/original/shanghaitech/part_' dataset '_final/train_data/ground_truth/']; load(strcat(gt_path, 'GT_IMG_',num2str(i),'.mat')) ; input_img_name = strcat(path,'IMG_',num2str(i),'.jpg'); im = imread(input_img_name); [h, w, c] = size(im); if (c == 3) im = rgb2gray(im); %转化成[h w 1]的矩阵灰度图 end figure(1) imshow(im) hold on annPoints = image_info{1}.location; im_density = get_density_map_autogaussian(im,annPoints); %sum(sum(im_density)) %im_density = get_density_map_gaussian(im,annPoints); figure(2) cmap = colormap(jet(210)); imagesc(im_density,[0 max(max(im_density))]) %imagesc(im_density,[0 0.038]) colorbar

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