项目调试3

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% 可调参数
%{
test_path='C:\Users\cong\Desktop\研一实战\项目\图像中时间数字识别\OCR\one\3.jpg';
neighbour_pixels_affect=3;
target_digit=2;
% forestTrain()参数设置
%   .M          - [1] number of trees to train
%   .H          - [max(hs)] number of classes
%   .N1         - [5*N/M] number of data points for training each tree
%   .F1         - [sqrt(F)] number features to sample for each node split
%   .split      - ['gini'] options include 'gini', 'entropy' and 'twoing'
%   .minCount   - [1] minimum number of data points to allow split
%   .minChild   - [1] minimum number of data points allowed at child nodes
%   .maxDepth   - [64] maximum depth of tree
%   .dWts       - [] weights used for sampling and weighing each data point
%   .fWts       - [] weights used for sampling features
%   .discretize - [] optional function mapping structured to class labels
%                    format: [hsClass,hBest] = discretize(hsStructured,H);
varargin.M=1000;
%varargin.H=10;

% forestApply()的输入设置
%  data     - [NxF] N length F feature vectors
%  forest   - learned forest classification model
%  maxDepth - [] maximum depth of tree
%  minCount - [] minimum number of data points to allow split
%  best     - [0] if true use single best prediction per tree

%  forestApply()输出结果及对比的阀值
%  hs       - [Nx1] predicted output labels
%  ps       - [NxH] predicted output label probabilities
ps_val_more_than0_3=0.2;

%滑窗检测,窗口尺度，步长
win_h=20;
win_w=20;
step=1;
disp('参数配置成功...');
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disp('正在读入图片及特征提取...');
%读入图片及特征提取
data=[];
label=[];
temp_r1=0;
temp_c1=0;

for i_digit=0:10
%     if(i_digit==target_digit)                                %%%%%%%%%%%%%%%%%%%%%%
%         this_image_label=1;
%     end
%数字转字符
str=num2str(i_digit);                                          %%数据是不是不平衡
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,'\');
file=dir(path_temp);
for i=3:length(file)
path= strcat(path_temp,file(i).name);

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% 加载图片
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I=imread(path);
%I=imread('E:/WeChat.jpg');
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% 提取channel features
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[all_channel_difference_features,temp_r1,temp_c1]=extract_features(I,neighbour_pixels_affect,1);
data=[data,all_channel_difference_features];
label=[label;i_digit+1];
if(rem(i,200)==0)
disp('...');
end
end % for i=3:length(file)
disp('数字')
i_digit
disp('的特征提取完毕...');
end  % for i_digit=0:9
disp('读入图片及特征提取完毕...');
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% 扔进分类器中，训练
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data=data';
disp('正在训练，请稍等...');
forest = forestTrain( data, label, varargin );
disp('训练完毕...');
%}
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% 检测，测试
test_label=[];
test_label_p=[];
win_h=45;
win_w=30;
windSize = [30,40];
step=1;
ps_val_more_than0_3=0.07;
count=0;

disp('正在检测...');
test_image=imread(test_path);
%滑窗检测,窗口尺度，步长
[test_r,test_c,test_z]=size(test_image);
figure;
imshow(test_image);
hold on

for j_test=1:step:(test_c-win_w+1)
for i_test=1:step:(test_r-win_h+1)
%model

model=test_image(i_test:i_test+win_h-1,j_test:j_test+win_w-1,:);
%resize
test_image_rs=imresize(model,[temp_r1 temp_c1]);
test_data=extract_features(test_image_rs,neighbour_pixels_affect,0);
test_data=test_data';
test_data=single(test_data);

[hs,ps] = forestApply( test_data, forest,0,0,1);%尺度问题
test_label=[test_label,hs];
test_label_p=[test_label_p,ps(hs)];
%if(ps>ps_val_more_than0_3)
if (hs~=11) && (ps(hs)>0.194)
%画框
%%%%i_test
%j_test
count=count+1;
%hs

rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','r');
%pointAll = [i_test,j_test];
%[state,results]=draw_rect(test_image,pointAll,windSize);
hold on
end

end
end
disp(' the value of count is:')
count
disp('检测完毕！恭喜恭喜！')
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