1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2 % 可调参数
3 %{
4 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';
5 neighbour_pixels_affect=3;
6 target_digit=2;
7 % forestTrain()参数设置
8 % .M - [1] number of trees to train
9 % .H - [max(hs)] number of classes
10 % .N1 - [5*N/M] number of data points for training each tree
11 % .F1 - [sqrt(F)] number features to sample for each node split
12 % .split - ['gini'] options include 'gini', 'entropy' and 'twoing'
13 % .minCount - [1] minimum number of data points to allow split
14 % .minChild - [1] minimum number of data points allowed at child nodes
15 % .maxDepth - [64] maximum depth of tree
16 % .dWts - [] weights used for sampling and weighing each data point
17 % .fWts - [] weights used for sampling features
18 % .discretize - [] optional function mapping structured to class labels
19 % format: [hsClass,hBest] = discretize(hsStructured,H);
20 varargin.M=1000;
21 %varargin.H=10;
22
23 % forestApply()的输入设置
24 % data - [NxF] N length F feature vectors
25 % forest - learned forest classification model
26 % maxDepth - [] maximum depth of tree
27 % minCount - [] minimum number of data points to allow split
28 % best - [0] if true use single best prediction per tree
29
30 % forestApply()输出结果及对比的阀值
31 % hs - [Nx1] predicted output labels
32 % ps - [NxH] predicted output label probabilities
33 ps_val_more_than0_3=0.2;
34
35 %滑窗检测,窗口尺度，步长
36 win_h=20;
37 win_w=20;
38 step=1;
39 disp('参数配置成功...');
40 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
41 disp('正在读入图片及特征提取...');
42 %读入图片及特征提取
43 data=[];
44 label=[];
45 temp_r1=0;
46 temp_c1=0;
47
48 for i_digit=0:10
49 % if(i_digit==target_digit) %%%%%%%%%%%%%%%%%%%%%%
50 % this_image_label=1;
51 % end
52 %数字转字符
53 str=num2str(i_digit); %%数据是不是不平衡
54 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,'\');
55 file=dir(path_temp);
56 for i=3:length(file)
57 path= strcat(path_temp,file(i).name);
58
59 %%%%%%%%%%%%%%%%%%%%%%%%%%
60 % 加载图片
61 %%%%%%%%%%%%%%%%%%%%%%%%%%
62 I=imread(path);
63 %I=imread('E:/WeChat.jpg');
64 %%%%%%%%%%%%%%%%%%%%%%%%%%
65 % 提取channel features
66 %%%%%%%%%%%%%%%%%%%%%%%%%%
67 [all_channel_difference_features,temp_r1,temp_c1]=extract_features(I,neighbour_pixels_affect,1);
68 data=[data,all_channel_difference_features];
69 label=[label;i_digit+1];
70 if(rem(i,200)==0)
71 disp('...');
72 end
73 end % for i=3:length(file)
74 disp('数字')
75 i_digit
76 disp('的特征提取完毕...');
77 end % for i_digit=0:9
78 disp('读入图片及特征提取完毕...');
79 %%%%%%%%%%%%%%%%%%%%%%%%%%
80 % 扔进分类器中，训练
81 %%%%%%%%%%%%%%%%%%%%%%%%%%
82 data=data';
83 disp('正在训练，请稍等...');
84 forest = forestTrain( data, label, varargin );
85 disp('训练完毕...');
86 %}
87 %%%%%%%%%%%%%%%%%%%%%%%%%%
88 % 检测，测试
89 test_label=[];
90 test_label_p=[];
91 win_h=45;
92 win_w=30;
93 windSize = [30,40];
94 step=1;
95 ps_val_more_than0_3=0.07;
96 count=0;
97
98 disp('正在检测...');
99 test_image=imread(test_path);
100 %滑窗检测,窗口尺度，步长
101 [test_r,test_c,test_z]=size(test_image);
102 figure;
103 imshow(test_image);
104 hold on
105
106 for j_test=1:step:(test_c-win_w+1)
107 for i_test=1:step:(test_r-win_h+1)
108 %model
109
110 model=test_image(i_test:i_test+win_h-1,j_test:j_test+win_w-1,:);
111 %resize
112 test_image_rs=imresize(model,[temp_r1 temp_c1]);
113 test_data=extract_features(test_image_rs,neighbour_pixels_affect,0);
114 test_data=test_data';
115 test_data=single(test_data);
116
117 [hs,ps] = forestApply( test_data, forest,0,0,1);%尺度问题
118 test_label=[test_label,hs];
119 test_label_p=[test_label_p,ps(hs)];
120 %if(ps>ps_val_more_than0_3)
121 if (hs~=11) && (ps(hs)>0.194)
122 %画框
123 %%%%i_test
124 %j_test
125 count=count+1;
126 %hs
127 switch hs
128 case 1
129 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','r');
130 case 2
131 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','g');
132 case 3
133 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','b');
134 case 4
135 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','c');
136 case 5
137 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','m');
138 case 6
139 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','y');
140 case 7
141 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','b');
142 case 8
143 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','m');
144 case 9
145 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','r');
146 case 10
147 rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','g');
148 end
149
150 %rectangle('Position',[j_test,i_test,30,40],'LineWidth',2,'EdgeColor','r');
151 %pointAll = [i_test,j_test];
152 %[state,results]=draw_rect(test_image,pointAll,windSize);
153 hold on
154 end
155
156 end
157 end
158 disp(' the value of count is:')
159 count
160 disp('检测完毕！恭喜恭喜！')
161 %%%%%%%%%%%%%%%%%%%%%%%%%%
