时间数字识别项目之大体完成
大框已经接近收尾。接下来剩下：
1）噪声添加
2）类标返回
3）汉字负样本添加
4）困难样本回收训练
5）......
这些细节，更加提高准确率。
1 %{ 2 clear all; 3 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 4 % 可调参数 5 6 %test_path='C:\Users\cong\Desktop\研一实战\项目\图像中时间数字识别\OCR\one\22.jpg';%#### 7 %test_path='C:\Users\cong\Desktop\研一实战\项目\图像中时间数字识别\big.jpg'; 8 %target_digit=2; 9 %local_step=3;%#### 10 11 %滑窗检测,窗口尺度，步长 12 % win_h=20; 13 % win_w=20; 14 15 % step=1; 16 17 disp('参数配置成功...'); 18 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 19 disp('正在读入图片及特征提取...'); 20 %读入图片及特征提取 21 %data=[]; 22 %label=[]; 23 temp_r1=0; 24 temp_c1=0; 25 k_data=1; 26 %k_label=1; 27 for i_digit=0:1:10 28 % if(i_digit==target_digit) %%%%%%%%%%%%%%%%%%%%%% 29 % this_image_label=1; 30 % end 31 %数字转字符 32 str=num2str(i_digit); %%数据是不是不平衡 33 path_temp=strcat('C:\Users\cong\Desktop\研一实战\项目\图像中时间数字识别\trainingSample\',str,'\'); 34 %path_temp=strcat('C:\Users\cong\Desktop\研一实战\项目\图像中时间数字识别\trainingSample\',str,'\'); 35 %path_temp=strcat('C:\Users\cong\Desktop\研一实战\项目\图像中时间数字识别\trainingSample\num',str,'\'); 36 file=dir(path_temp); 37 for i=3:length(file) 38 path= strcat(path_temp,file(i).name); 39 40 %%%%%%%%%%%%%%%%%%%%%%%%%% 41 % 加载图片 42 %%%%%%%%%%%%%%%%%%%%%%%%%% 43 I=imread(path); 44 % if(i_digit==10) 45 % I=imresize(I,[80 70]); 46 % end 47 %I=imread('E:/WeChat.jpg'); 48 %%%%%%%%%%%%%%%%%%%%%%%%%% 49 % 提取channel features 50 %%%%%%%%%%%%%%%%%%%%%%%%%% 51 %[all_channel_difference_features,temp_r1,temp_c1]=channel_feature_2_vector(I,1); 52 %data=[data,all_channel_difference_features]; 53 %[d3_data,~,~]=channel_feature_2_vector(I,1); 54 pChns=chnsCompute(); 55 chns = chnsCompute( I, pChns ); 56 %all_channel_difference_features=cat(3,chns.data{:}); 57 %data(:,:,:,k_data)=single(d3_data); 58 data(:,:,:,k_data)=cat(3,chns.data{:}); 59 label_t(1,k_data)=i_digit; 60 k_data=k_data+1; 61 %d3_data=channel_feature_2_vector(I,1); 62 %data=[data,d3_data]; 63 %label=[label;i_digit];%类标从0开始 64 if(rem(i,500)==0) 65 disp('...'); 66 end 67 end % for i=3:length(file) 68 disp('数字') 69 i_digit 70 disp('的特征提取完毕...'); 71 end % for i_digit=0:9 72 disp('读入图片及特征提取完毕...'); 73 %%%%%%%%%%%%%%%%%%%%%%%%%% 74 % 扔进分类器中，训练 75 %%%%%%%%%%%%%%%%%%%%%%%%%% 76 %参数设置 77 %data=data'; 78 param.nclass = 11; % number of classes 79 param.ntree = 100; % number of tree 80 param.depth = 16; % the maximum depth of tree 81 param.sigma = 5; % random parameter 82 83 %% 84 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 85 %训练 86 disp('正在训练，请稍等...'); 87 handle = crndforest(); % call constructor 88 89 %data=single(data); 90 label=int32(label_t); 91 crndforest(handle, 'train', data, label, param); % 92 crndforest(handle, 'save', 'forest_model2'); % optionally save into disk 93 crndforest(handle, 'delete'); % release object 94 disp('训练完毕...'); 95 %} 96 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 97 %测试 98 %{ 99 clear all; 100 data_num=1; 101 disp('正在测试...'); 102 handle = crndforest(); 103 crndforest(handle, 'load', 'forest_model2'); 104 %载入图像 105 %输入data，提取特征 106 test_digit=2;%%%%%%%%%%%%%%%%%%%%% 107 all_test_result=[]; 108 %all_test_backgroud=[]; 109 %all_channel_difference_features=[]; 110 for test_digit=0:10 111 %test_backgroud=[]; 112 test_result=0; 113 str=num2str(test_digit); 114 path_temp=strcat('C:\Users\cong\Desktop\研一实战\项目\图像中时间数字识别\trainingSample\no',str,'\'); 115 file=dir(path_temp); 116 117 for i=3:length(file) 118 path= strcat(path_temp,file(i).name); 119 I=imread(path); 120 % I=imresize(I,[80 70]); 121 %[all_channel_difference_features,temp_r1,temp_c1]=channel_feature_2_vector(I,1);%all_channel_difference_features就是data 122 %随机森林预测 123 124 pChns=chnsCompute(); 125 chns = chnsCompute( I, pChns ); 126 all_channel_difference_features(:,:,:,data_num)=cat(3,chns.data{:}); 127 label_test_t(data_num)=test_digit; 128 %all_channel_difference_features=single(all_channel_difference_features); 129 data_num=data_num+1; 130 end 131 data_num=1; 132 [pred_label, prob] =crndforest(handle, 'predict', all_channel_difference_features); 133 label_test=int32(label_test_t); 134 label_test=label_test'; 135 test_result=sum(pred_label==label_test); 136 test_result 137 test_result=test_result/(length(file)-2); 138 % test_backgroud 139 %test_result=test_result/198; 140 all_test_result=[ all_test_result,test_result]; 141 %all_test_backgroud=[ all_test_backgroud,test_backgroud]; 142 disp('检测数字：') 143 test_digit+1 144 end 145 %输出结果 146 disp('恭喜恭喜，结果出来啦！') 147 all_test_result 148 crndforest(handle, 'delete'); 149 disp('测试完毕...'); 150 %} 151 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 152 %} 153 % 检测，测试 154 %参数设置 155 clear all; 156 scale_num=5; 157 scale_factor=1.2; 158 all_channel_difference_features=single(zeros(11,8,10,2));%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%先这么滴 159 %输入图，并提取特征 160 test_path='E:\研一实战\项目\图像中时间数字识别\OCR\one\3.jpg'; 161 I=imread(test_path); 162 pChns=chnsCompute(); 163 chns = chnsCompute( I, pChns ); 164 channel_feature=cat(3,chns.data{:}); 165 [ch_r,ch_c,ch_num]=size(channel_feature); 166 %原图和通道特征图的比例 167 [I_r,I_c,~]=size(I); 168 radio_r=I_r/ch_r; 169 radio_c=I_c/ch_c; 170 %多个尺度对原图处理 171 %数据结构 172 scale_factor_t(1)=scale_factor; 173 for i=2:scale_num 174 scale_factor_t(i)=scale_factor_t(i-1)*scale_factor; 175 end 176 177 178 for i=1:scale_num 179 [chf_r,chf_c]=size(imresize(channel_feature(:,:,1),scale_factor_t(i))); 180 temp=zeros(chf_r,chf_c,ch_num);%预先求矩阵的尺度 181 for ii=1:ch_num 182 %元胞的个数 183 temp(:,:,ii)=imresize(channel_feature(:,:,ii),scale_factor_t(i));%用元胞来装吧，数据大小不一样了,temp是global吗 184 end 185 channel_feature_scale{i}= temp; 186 end 187 188 %获得训练图的尺度大小 189 patch_row=11; 190 patch_column=8; 191 %all_channel_difference_features=single(zeros(11,8,10,2)); 192 data_num=1; 193 coordinate=zeros(4,2); 194 %all_boundingbox=zeros(5,2); 195 all_boundingbox=[]; 196 for j=1:scale_num 197 I_scale=channel_feature_scale{j};%第j个尺度的图 198 [row,column,~]=size(I_scale); 199 200 for l=1:ch_num %第l个通道的图 201 for m=1:2:row-patch_row+1 %某个patch 202 for n=1:2:column-patch_column+1 203 all_channel_difference_features(:,:,:,data_num)=I_scale(m:m+patch_row-1,n:n+patch_column-1,:);%把滑窗的数据依次记录下来 204 %坐标记录下来 205 %coordinate[data_num}=[m,l,patch_row,patch_column];%存坐标 206 coordinate(:,data_num)=[m*radio_r,n*radio_c,patch_row*radio_r,patch_column*radio_c]; 207 data_num=data_num+1; 208 end 209 end 210 end 211 data_num=data_num-1; 212 %在多个尺度图中缩放图中滑窗并检测，坐标和预测类标，置信度存好 213 handle = crndforest(); 214 crndforest(handle, 'load', 'forest_model2'); 215 [hs, ps] =crndforest(handle, 'predict', all_channel_difference_features);%尺度问题 216 %对存好的坐标进行变换，映射到原图 217 % for i=1:data_num 218 % coordinate{i}=coordinate{i}/scale_factor_t(j); 219 % end 220 coordinate=coordinate/scale_factor_t(j); 221 %所有的boundingbox组合作为输入 222 %prob=ps(hs+1,:); 223 [rr,~]=size(hs); 224 prob=zeros(rr,1); 225 for t=1:rr 226 prob(t)=ps(hs(t,1)+1,t); 227 end 228 hs=double(hs); 229 bb_partial=[coordinate',prob,hs]; 230 %all_boundingbox(:,j)=[coordinate,hs,ps(hs)]; 231 all_boundingbox=[all_boundingbox;bb_partial]; 232 disp('第') 233 j 234 disp('个尺度'); 235 data_num=1; 236 end 237 %nms函数 238 % bbs1 = bbNms(all_boundingbox, 'type','max' ); 239 % %显示 240 % im(I); 241 % bbApply('draw',bbs1(1:4),'g');% all_boundingbox(:,[1,2])=all_boundingbox(:,[2,1]); 242 % all_boundingbox(:,[3,4])=all_boundingbox(:,[4,3]); 243 disp('正在显示，请稍等...'); 244 [all_bb_r,all_bb_c]=size(all_boundingbox); 245 bbs=[]; 246 for x=1:all_bb_r 247 % if all_boundingbox(x,6)~=10 248 if all_boundingbox(x,6)==5 249 bbs=[bbs;all_boundingbox(x,:)]; 250 end 251 end 252 253 %bbs1 = bbNms(bbs, 'type','max' ); 254 bbs1 = bbNms(bbs, 'thr',.35, 'type','ms'); 255 256 im(I); 257 bbApply('draw',bbs1,'g'); 258 disp('显示完成！恭喜恭喜!'); 259 %}
注：
要附上其它代码：
1）随机森林c++版本（mex编译）
2）channel features
