基于支持向量机SVM的新鲜度等级预测，基于自适应粒子群优化长短期神经网络的新鲜度等级预测（代码完整，数据齐全）

clc clear close all data=imread('2.jpg'); [mx,my,mz]= size(data); data0=data; figure imshow(data) a1 = reshape(data(:,:,1),mx,my); a2 = reshape(data(:,:,2),mx,my); a3 = reshape(data(:,:,3),mx,my); n=1; [x,y] = ginput(n); x = round(x); y = round(y); cc=5; tu = data(y-cc:y+cc,(x-cc:x+cc),:); [mx,my,mz]=size(tu); numu = []; for ii = 1:mx for jj = 1:my numu = [numu;reshape(tu(ii,jj,:),1,3)]; end end zx = mean(numu); pan =15;%黄色聚类参数 t=0; [mx1,my1,mz1] = size(data); for ii = 1:mx1 for jj = 1:my1 t = t+1; if sqrt(sum((data(ii,jj,1)-zx(1,1)).^2+(data(ii,jj,2)-zx(1,2)).^2+(data(ii,jj,3)-zx(1,3)).^2))>pan data(ii,jj,:) = [0 0 0]; end end end I=data; hsv = rgb2hsv( I ); g = rgb2gray( I ); % rgb转hsv % 分别取出RGB的三个通道数据 R = I(:,:,1); G = I(:,:,2); B = I(:,:,3); % 设置R、G、B三个通道的阈值 R_th = mean(mean(R(:,1:10))); G_th = mean(mean(G(:,1:10))); B_th = mean(mean(B(:,1:10))); f = 1 * ones( size(g) ); II = double(I); for ii = 1:size( g,1 ) for jj = 1:size(g,2) % 结合灰度值、hsv颜色分布、rgb颜色分布，找到趋向白色区域 if sqrt(sum((II(ii,jj,1)-R_th).^2 + (II(ii,jj,2)-G_th).^2 + (II(ii,jj,3)-B_th).^2 ))>10 f( ii, jj ) = 0; end end end % 设置R、G、B三个通道的阈值 % f = ~round((f1+~f)/2); % 显示 figure, subplot(3,3,1), imshow( I ), title('原图'); subplot(3,3,2), imshow( f ), title('颜色分割'); % 取反 f = ~f; subplot(3,3,3),imshow(f), title('颜色分割'); out=bwareaopen(f,5000); subplot(3,3,5),imshow(out), title('去除小面积干扰区域'); out = ~out; out=bwareaopen(out,5000); out =~out; % 设置滑动窗口大小 se = strel('disk',2); % 膨胀计算 out = imdilate(out,se); subplot(3,3,6),imshow(out), title('图像膨胀') % 腐蚀计算 out = imerode(out,se); subplot(3,3,4),imshow(out), title('图像腐蚀'); % 去img1除面积小于50个像素点的区域 ; % 填充孔洞 pic = imfill(out,'holes'); out = ~out; out=bwareaopen(out,5000); pic =~out; subplot(3,3,7),imshow(pic), title('填充孔洞'); % 去除面积小于2000个像素点的区域 pic=bwareaopen(pic,2000); subplot(3,3,8), imshow( pic ), title('去除小面积干扰区域'); % 图像联通区域标记 [ L, num ] = bwlabel( pic ); bw = pic; res = []; % figure, for i = 1:num [r,c] = find(L == i); % 各个联通区域质心 coreX = mean( c ); coreY = mean( r ); % 各个联通区域坐标最大、最小值 x_min = min( c ); x_max = max( c ); y_min = min( r ); y_max = max( r ); % 联通区域中心位置 imgMiddleX = ( x_min + x_max )/2; imgMiddleY = ( y_min + y_max )/2; % 坐标的矩形面积 s = (y_max-y_min)*(x_max-x_min); res = [ res; [ coreX coreY imgMiddleX imgMiddleY; length(r) s length(r)/s s-length(r) ] ]; if length(r)/s<0.5 % for k = 1:length(r) bw( r(k), c(k) ) = 0; end end end subplot(3,3,9), imshow( bw ), title('二值图'); figure, subplot( 1,2,1 ), imshow( I ), title('原图'); % figure,imshow( cat(3, I(:,:,1) & bw,I(:,:,2) & bw,I(:,:,3) & bw) ); bw = double( bw ); I = double( I ); % data保存荷花区域图，其他区域为0 data = cat(3,I(:,:,1).*bw, I(:,:,2).*bw, I(:,:,3).*bw); [mx,my,mz]= size(data); for ii = 1:mx for jj = 1:my if bw(ii,jj)==1 data0(ii,jj,:) = [0 255 0]'; end end end subplot( 1,2,2 ), imshow( uint8(data0) ), title('人像区域图'); data=imread('2.jpg'); [mx,my,mz]= size(data); figure imshow(data) a1 = reshape(data(:,:,1),mx,my); a2 = reshape(data(:,:,2),mx,my); a3 = reshape(data(:,:,3),mx,my); n=1; [x,y] = ginput(n); x = round(x); y = round(y); cc=5; tu = data(y-cc:y+cc,(x-cc:x+cc),:); [mx,my,mz]=size(tu); numu = []; for ii = 1:mx for jj = 1:my numu = [numu;reshape(tu(ii,jj,:),1,3)]; end end zx = mean(numu); pan =10;%黄色聚类参数 t=0; [mx1,my1,mz1] = size(data); for ii = 1:mx1 for jj = 1:my1 t = t+1; if sqrt(sum((data(ii,jj,1)-zx(1,1)).^2+(data(ii,jj,2)-zx(1,2)).^2+(data(ii,jj,3)-zx(1,3)).^2))>pan data(ii,jj,:) = [0 0 0]; end end end I=data; hsv = rgb2hsv( I ); g = rgb2gray( I ); % rgb转hsv % 分别取出RGB的三个通道数据 R = I(:,:,1); G = I(:,:,2); B = I(:,:,3); % 设置R、G、B三个通道的阈值 R_th = mean(mean(R(:,1:10))); G_th = mean(mean(G(:,1:10))); B_th = mean(mean(B(:,1:10))); f = 1 * ones( size(g) ); II = double(I); for ii = 1:size( g,1 ) for jj = 1:size(g,2) % 结合灰度值、hsv颜色分布、rgb颜色分布，找到趋向白色区域 if sqrt(sum((II(ii,jj,1)-R_th).^2 + (II(ii,jj,2)-G_th).^2 + (II(ii,jj,3)-B_th).^2 ))>10 f( ii, jj ) = 0; end end end % 设置R、G、B三个通道的阈值 % f = ~round((f1+~f)/2); % 显示 figure, subplot(3,3,1), imshow( I ), title('原图'); subplot(3,3,2), imshow( f ), title('颜色分割'); % 取反 f = ~f; subplot(3,3,3),imshow(f), title('颜色分割'); out=bwareaopen(f,5000); subplot(3,3,5),imshow(out), title('去除小面积干扰区域'); out = ~out; out=bwareaopen(out,5000); out =~out; % 设置滑动窗口大小 se = strel('disk',3); % 膨胀计算 out = imdilate(out,se); subplot(3,3,6),imshow(out), title('图像膨胀') % 腐蚀计算 se = strel('disk',2); out = imerode(out,se); subplot(3,3,4),imshow(out), title('图像腐蚀'); % 去img1除面积小于50个像素点的区域 ; % 填充孔洞 pic = imfill(out,'holes'); out = ~out; out=bwareaopen(out,5000); pic =~out; subplot(3,3,7),imshow(pic), title('填充孔洞'); % 去除面积小于2000个像素点的区域 pic=bwareaopen(pic,2000); subplot(3,3,8), imshow( pic ), title('去除小面积干扰区域'); % 图像联通区域标记 [ L, num ] = bwlabel( pic ); bw = pic; % res = []; % % figure, % for i = 1:num % [r,c] = find(L == i); % % 各个联通区域质心 % coreX = mean( c ); % coreY = mean( r ); % % 各个联通区域坐标最大、最小值 % x_min = min( c ); % x_max = max( c ); % y_min = min( r ); % y_max = max( r ); % % 联通区域中心位置 % imgMiddleX = ( x_min + x_max )/2; % imgMiddleY = ( y_min + y_max )/2; % % % 坐标的矩形面积 % s = (y_max-y_min)*(x_max-x_min); % % % res = [ res; [ coreX coreY imgMiddleX imgMiddleY; length(r) s length(r)/s s-length(r) ] ]; % % % if length(r)/s<0.5 % % for k = 1:length(r) % bw( r(k), c(k) ) = 0; % end % end % % end subplot(3,3,9), imshow( bw ), title('二值图'); figure, subplot( 1,2,1 ), imshow( I ), title('原图'); % figure,imshow( cat(3, I(:,:,1) & bw,I(:,:,2) & bw,I(:,:,3) & bw) ); bw = double( bw ); I = double( I ); % data保存荷花区域图，其他区域为0 data = cat(3,I(:,:,1).*bw, I(:,:,2).*bw, I(:,:,3).*bw); [mx,my,mz]= size(data); for ii = 1:mx for jj = 1:my if bw(ii,jj)==1 data0(ii,jj,:) = [0 0 255 ]'; end end end subplot( 1,2,2 ), imshow( uint8(data0) ), title('人像区域图'); data=imread('2.jpg'); [mx,my,mz]= size(data); figure imshow(data) a1 = reshape(data(:,:,1),mx,my); a2 = reshape(data(:,:,2),mx,my); a3 = reshape(data(:,:,3),mx,my); n=1; [x,y] = ginput(n); x = round(x); y = round(y); cc=5; tu = data(y-cc:y+cc,(x-cc:x+cc),:); [mx,my,mz]=size(tu); numu = []; for ii = 1:mx for jj = 1:my numu = [numu;reshape(tu(ii,jj,:),1,3)]; end end zx = mean(numu); pan =15;%黄色聚类参数 t=0; [mx1,my1,mz1] = size(data); for ii = 1:mx1 for jj = 1:my1 t = t+1; if sqrt(sum((data(ii,jj,1)-zx(1,1)).^2+(data(ii,jj,2)-zx(1,2)).^2+(data(ii,jj,3)-zx(1,3)).^2))>pan data(ii,jj,:) = [0 0 0]; end end end I=data; hsv = rgb2hsv( I ); g = rgb2gray( I ); % rgb转hsv % 分别取出RGB的三个通道数据 R = I(:,:,1); G = I(:,:,2); B = I(:,:,3); % 设置R、G、B三个通道的阈值 R_