项目代码matlab

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 可调参数

test_path='';
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
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

data=[];
label=[];
temp_r1=0;
temp_c1=0;

for i_digit=0:9
%     if(i_digit==target_digit)                                %%%%%%%%%%%%%%%%%%%%%%
%         this_image_label=1;
%     end
%数字转字符
str=num2str(i);                                          %%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);

%%%%%%%%%%%%%%%%%%%%%%%%%%
% 加载图片
%%%%%%%%%%%%%%%%%%%%%%%%%%
I=imread(path);
%I=imread('E:/WeChat.jpg');
%%%%%%%%%%%%%%%%%%%%%%%%%%
% 提取channel features
%%%%%%%%%%%%%%%%%%%%%%%%%%
[all_channel_difference_features,temp_r1,temp_c1]=extract_features(I,1);
data=[data,all_channel_difference_features];
label=[label;i_digit+1];

%         if(i>100 && this_image_label~=1)               %%这里只取了前100帧，实际上可以随意抽取一百张
%             break;
%         end
end % for i=3:length(file)

end  % for i_digit=0:9

%%%%%%%%%%%%%%%%%%%%%%%%%%
% 扔进分类器中，训练
%%%%%%%%%%%%%%%%%%%%%%%%%%

forest = forestTrain( data, label, varargin );

%%%%%%%%%%%%%%%%%%%%%%%%%%
% 检测，测试
test_image=imread(test_path);
%滑窗检测,窗口尺度，步长
[test_r,test_c,test_z]=size(test_image);
for i_test=1:test_r
%model

%resize
test_image=imresize(model,temp_r1,temp_c1);
test_data=extract_features(test_image,1);
[hs,ps] = forestApply( test_data, forest, [], [], [] );%尺度问题
if(ps>ps_val_more_than0_3)
%画框

end
end

%%%%%%%%%%%%%%%%%%%%%%%%%%

function [ all_channel_difference_features,,r1,c1 ] = extract_features( I,shrink_or_not )
%EXTRACT_FEATURES 此处显示有关此函数的摘要
%   此处显示详细说明
%%%%%%%%%%%%%%%%%%%%%%%%%%
% 提取channel features
%%%%%%%%%%%%%%%%%%%%%%%%%%
% 参数设置
if(shrink_or_not==1)
pChns.shrink=4;
end

pChns.pColor.enabled=1;
pChns.pColor.smooth=1;
pChns.pColor.colorSpace='luv';

pChns.pGradMag.enabled=1;
pChns.pGradMag.colorChn=0;
pChns.pGradMag.normRad=5;
pChns.pGradMag.normConst=.005;
pChns.pGradMag.full=0;

pChns.pGradHist.enabled=1;
%pChns.pGradHist.binSize=
pChns.pGradHist.nOrients=6;
pChns.pGradHist.softBin=0;
pChns.pGradHist.useHog=0;
pChns.pGradHist.clipHog=.2;

%pChns.pCustom.**

%pChns.complete=

% 提取channel features
chns = chnsCompute( I, pChns );
% 将各个通道放在矩阵中
[r1,c1,ch1]=size(chns.data{1});
[r2,c2,ch2]=size(chns.data{2});
[r3,c3,ch3]=size(chns.data{3});
ch=ch1+ch2+ch3;
all_channel=zeros(r1,c1,ch);
all_channel(:,:,1:ch1)=chns.data{1};
all_channel(:,:,ch1+1:ch1+ch2)=chns.data{2};
all_channel(:,:,ch1+ch2+1:ch)=chns.data{3};
%%%%%%%%%%%%%%%%%%%%%%%%%%
% pooling
%%%%%%%%%%%%%%%%%%%%%%%%%%
for ii=1:ch
%向下采样
all_pooling(:,:,ii)=imresize(all_channel(:,:,ii),0.2);
end

%%%%%%%%%%%%%%%%%%%%%%%%%%
% 再次做相减特征
%%%%%%%%%%%%%%%%%%%%%%%%%%
all_channel_difference_features=[];
for ij=1:ch
temp=difference_features( all_pooling(:,:,ij),neighbour_pixels_affect );
all_channel_difference_features = [all_channel_difference_features;temp];
end

end

function [ one_channel_difference_features ] = difference_features( one_channel_features,neighbour_pixels_affect )
%DIFFERENCE_FEATURES 计算邻域内个特征之间两两相减
%input:
% one_channel_features
%neighbour_pixels_affect
%output:
%one_channel_difference_features

[r,c]=size(one_channel_features);

one_channel_difference_features=[];
for i=1:r-neighbour_pixels_affect+1
for j=1:c-neighbour_pixels_affect+1
local_features=one_channel_features(i:i+neighbour_pixels_affect-1,j:j+neighbour_pixels_affect-1);
temp=local_feature_compute(local_features);
one_channel_difference_features=[one_channel_difference_features;temp];%特征拼接
end
end
end

function [ local_differece_feature ]=local_feature_compute( local_features )
[r,c]=size(local_features);
result_mat=local_features-local_features(1,1).*ones(r,c);
result_vector=reshape(result_mat,r*c,1);
local_differece_feature=result_vector(2:r*c,1);%把第一个特征去掉，自己减自己没有任何特征信息可言
end

%{
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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...');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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=0:9
%     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);

%%%%%%%%%%%%%%%%%%%%%%%%%%
% 加载图片
%%%%%%%%%%%%%%%%%%%%%%%%%%
I=imread(path);
%I=imread('E:/WeChat.jpg');
%%%%%%%%%%%%%%%%%%%%%%%%%%
% 提取channel features
%%%%%%%%%%%%%%%%%%%%%%%%%%
[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,100)==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...');
%%%%%%%%%%%%%%%%%%%%%%%%%%
% 扔进分类器中，训练
%%%%%%%%%%%%%%%%%%%%%%%%%%
data=data';
disp('正在训练，请稍等...');
forest = forestTrain( data, label, varargin );
disp('训练完毕...');
%}
%%%%%%%%%%%%%%%%%%%%%%%%%%
% 检测，测试
test_label=[];
test_label_p=[];
win_h=40;
win_w=30;
windSize = [30,40];
step=2;
ps_val_more_than0_3=0.07;

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 i_test=1:step:test_r-win_h+1
for j_test=1:step:test_c-win_w+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)
%画框
%draw_rect(test_image,);
i_test
j_test
rectangle('Position',[i_test,j_test,20,20],'LineWidth',4,'EdgeColor','r');
%pointAll = [i_test,j_test];
%[state,results]=draw_rect(test_image,pointAll,windSize);
hold on
end

end
hold on
end
disp('检测完毕！恭喜恭喜！')
%%%%%%%%%%%%%%%%%%%%%%%%%%