笔迹鉴别程序

考试的笔迹鉴别程序，分辨出不同人写的笔迹
2017-05-17 16:50:37 +08:00
parent abe00d2e02
commit 962de04ffb
205 changed files with 17672 additions and 0 deletions
--- a/测试/本机测试/1-我-算法/HWCV-exe.sln
+++ b/测试/本机测试/1-我-算法/HWCV-exe.sln
@@ -0,0 +1,22 @@
+
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio 2013
+VisualStudioVersion = 12.0.30501.0
+MinimumVisualStudioVersion = 10.0.40219.1
+Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "HWCV-exe", "HWCV-exe\HWCV-exe.vcxproj", "{FB6A1EE7-710E-424B-A80E-A931EDB7DDBF}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+		Debug|Win32 = Debug|Win32
+		Release|Win32 = Release|Win32
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{FB6A1EE7-710E-424B-A80E-A931EDB7DDBF}.Debug|Win32.ActiveCfg = Debug|Win32
+		{FB6A1EE7-710E-424B-A80E-A931EDB7DDBF}.Debug|Win32.Build.0 = Debug|Win32
+		{FB6A1EE7-710E-424B-A80E-A931EDB7DDBF}.Release|Win32.ActiveCfg = Release|Win32
+		{FB6A1EE7-710E-424B-A80E-A931EDB7DDBF}.Release|Win32.Build.0 = Release|Win32
+	EndGlobalSection
+	GlobalSection(SolutionProperties) = preSolution
+		HideSolutionNode = FALSE
+	EndGlobalSection
+EndGlobal
--- a/测试/本机测试/1-我-算法/HWCV-exe/DBop.cpp
+++ b/测试/本机测试/1-我-算法/HWCV-exe/DBop.cpp
@@ -0,0 +1,209 @@
+/*
+实现文件：DBop.cpp 数据库操作实现文件
+*/
+#include "DBop.h"
+#include "path.h"
+
+/*本地的全局变量*/
+
+_ConnectionPtr	p_conn;			/*全局变量 连接对象*/
+_RecordsetPtr	p_recordset;	/*全局变量 记录集对象*/
+_CommandPtr		p_cmd;			/*全局变量 操作集对象*/
+string			str_conn;		/*全局变量 连接字符串设置*/
+
+
+/**
+程序功能：	根据string类的stu【存储学号】，搜索出所有此人的考试信息,并分别存储在不同的变量中
+
+@变量 stuNum 学号
+@变量 date<vector>	考试时间
+@变量 subject<vector> 考试科目
+@变量 stuNum<vector> 考号
+@返回值 成功1 失败0
+*/
+int DbImg(string stuNum, vector<string>& dateVec, vector<string>& subjectVec, vector<string>& stuNum2)//搜寻图片
+{
+	/*字符转换，方便使用*/
+	string  userName(g_db_userName.c_str());
+	string  password(g_db_password.c_str());
+	string  hostName(g_db_hostName.c_str());
+	string  dBName(g_db_dBName.c_str());
+
+	//cout<<g_db_userName.c_str()<<endl;
+	//cout<<g_db_password.c_str()<<endl;
+	//cout<<g_db_hostName.c_str()<<endl;
+	//cout<<g_db_dBName.c_str()<<endl;
+
+	/*创建链接描述符*/
+	//Provider=OraOLEDB.Oracle.1;Persist Security Info = true;User ID = BJSH;Password=bjshadmin;Data Source = (DESCRIPTION = (ADDRESS = (PROTOCOL = TCP)(HOST=192.168.200.97)(PORT=1521))
+	//(CONNECT_DATA=(SERVICE_NAME=purple)))",
+	str_conn = "Provider=OraOLEDB.Oracle.1;Persist Security Info = true;User ID = ";	
+	str_conn += userName;		//===仅初始化一次===//
+	str_conn += ";Password=";	//==================//
+	str_conn += password;
+	str_conn += ";Data Source = (DESCRIPTION = (ADDRESS = (PROTOCOL = TCP)(HOST=";
+	str_conn += hostName;
+	str_conn += ")(PORT=1521))(CONNECT_DATA=(SERVICE_NAME=";
+	str_conn += dBName;
+	str_conn += ")))";
+
+	//构造查询语句				下句注释是sql语句，需要构造成这种类型
+	/*select KS_ZKZ, KSSJ, TJ_KC_DM from ZK.T_BYSQ_KS_KC WHERE KSSJ between '200907' and '201510' and KS_ZKZ = 010207203867*/
+	/*string  sql = "select KS_ZKZ2, KSSJ, TJ_KC_DM from ZK.T_BYSQ_KS_KC WHERE KSSJ between '200907' and '201504' AND KS_ZKZ = ";*/
+	// select KS_ZKZ, KSSJ, TJ_KC_DM from ZK.T_BYSQ_KS_KC WHERE KSSJ between '200907' and '201510' and KS_ZKZ = 010214100225
+	//"select KS_ZKZ, KSSJ, TJ_KC_DM from ZK.T_BYSQ_KS_KC WHERE KSSJ between '200907' and '201510' and KS_ZKZ = 010214100225",
+	string sql = "select KS_ZKZ, KSSJ, TJ_KC_DM from ";
+	sql += g_db_hoster_zk;
+	sql += ".T_BYSQ_KS_KC WHERE KSSJ between ";
+	sql += g_db_qurry_start;
+	sql += " and ";
+	sql += g_db_qurry_end;
+	sql += "AND KS_ZKZ =";
+	sql += stuNum.c_str();
+
+	_bstr_t _vstr_sql(sql.c_str());/* 转换string为_variant_t */
+	::CoInitialize(NULL);//初始化com组件
+
+	/*创建、打开连接*/
+	try{
+		p_conn.CreateInstance("ADODB.Connection");//创建连接
+		p_recordset.CreateInstance("ADODB.Recordset");//创建结果集,也就是实例化
+
+
+		p_conn->CursorLocation = adUseClient; //存储过程同时返回记录集和返回值
+		p_conn->Open(_bstr_t(str_conn.c_str()),
+		//p_conn->Open("Provider=OraOLEDB.Oracle.1;Persist Security Info = true;User ID = BJSH;Password=bjshadmin;Data Source = (DESCRIPTION = (ADDRESS = (PROTOCOL = TCP)(HOST=192.168.200.97)(PORT=1521))(CONNECT_DATA=(SERVICE_NAME=purple)))",
+			_bstr_t(userName.c_str()),
+			_bstr_t(password.c_str()),
+			adConnectUnspecified);
+		
+
+
+
+	//	SaveLog((char*)str_conn.c_str(), g_log_adr, "a");
+		HRESULT hr = p_recordset->Open(_bstr_t(_vstr_sql),//执行sq语句，查询一个学生的所有考试信息
+		//HRESULT hr = p_recordset->Open("select KS_ZKZ, KSSJ, TJ_KC_DM from ZK.T_BYSQ_KS_KC WHERE KSSJ between '200907' and '201510' and KS_ZKZ = 010214100225",
+			p_conn.GetInterfacePtr(),
+			adOpenStatic,
+			adLockOptimistic,
+			adCmdText);
+		cout<<"count:"<<p_recordset->RecordCount <<endl;
+		//将结果集输出到三个vector变量中
+		if (p_recordset->RecordCount < 1)//结果集为空
+		{
+			return 0;//没有信息，直接跳过这个人
+		}
+		do{ //将结果集输出到三个vector变量中
+			dateVec.push_back((string)(_bstr_t)(p_recordset->Fields->GetItem(_variant_t("KSSJ"))->GetValue()));
+			subjectVec.push_back((string)(_bstr_t)(p_recordset->Fields->GetItem(_variant_t("TJ_KC_DM"))->GetValue()));
+			stuNum2.push_back((string)(_bstr_t)(p_recordset->Fields->GetItem(_variant_t("KS_ZKZ"))->GetValue()));
+			p_recordset->MoveNext();
+		} while (!p_recordset->EndOfFile);
+	}
+	catch (_com_error e)
+	{
+		/*printf("%x", e.Error());
+		cout<<"Failed"<<endl;*/
+		memset(g_log_rec, 0, sizeof(g_log_rec));
+
+		strcat(g_log_rec,  "DBImg-Err:");
+		strcat(g_log_rec, (char*)e.Description());
+		strcat(g_log_rec,  " ");
+		strcat(g_log_rec, (char*)e.Error());
+		strcat(g_log_rec,  " ");
+		SaveLog(g_log_rec, g_log_adr, "a");
+
+		::CoUninitialize(); 
+		return 0;
+	}
+
+	/*关闭查询*/
+
+	return 1;
+}
+
+
+
+/**
+程序功能：	更新学生数据库信息,根据学生的鉴定结果，将结果输出到数据库中
+
+@变量 stuNum：学号
+@变量 subject：考试科目
+@变量 flagCheat：作弊标记
+@返回值 成功1失败0
+*/
+int DbUpdate(string stuNum,	vector<string> dateVec,	vector<string> subjectVec,	vector<string> stuNum2,	vector<string> flagVec)
+{
+	/*字符转换，方便使用*/
+	string  userName(g_db_userName.c_str());
+	string  password(g_db_password.c_str());
+	string  hostName(g_db_hostName.c_str());
+	string  dBName(g_db_dBName.c_str());
+
+	/*更新字串设置*/
+	int count = subjectVec.size();
+	vector<string> sqlKC; //课程表：作弊第一字段
+	int ci = 0;	//循环
+
+
+	/*构造更新语句*/
+	for (ci = 0; ci < count; ++ci)
+	{
+		/*作弊的*/
+		string  sqlKC1 = "UPDATE ";
+		sqlKC1 += g_db_hoster_zk;
+		sqlKC1 += ".T_BYSQ_KS_KC SET BJSH_JG_JQ =";//天津_课程_代码	
+		sqlKC1 += flagVec[ci].c_str();
+		sqlKC1 += " WHERE KS_ZKZ2=";
+		sqlKC1 += stuNum2[ci].c_str();
+		sqlKC1 += " AND TJ_KC_DM=";
+		sqlKC1 += subjectVec[ci].c_str();
+		sqlKC1 += " AND KSSJ=";
+		sqlKC1 += dateVec[ci].c_str();
+		sqlKC1 += " AND  KS_ZKZ=";
+		sqlKC1 += stuNum.c_str();
+
+		sqlKC.push_back(sqlKC1);
+	}
+
+	::CoInitialize(NULL);//初始化com组件
+
+	/*更新数据库表*/
+	try{
+		p_conn.CreateInstance("ADODB.Connection");//创建连接
+		p_recordset.CreateInstance("ADODB.Recordset");//创建结果集,也就是实例化
+		p_cmd.CreateInstance("ADODB.Command");
+
+		p_conn->CursorLocation = adUseClient; //存储过程同时返回记录集和返回值
+		p_conn->Open(_bstr_t(str_conn.c_str()), _bstr_t(userName.c_str()), _bstr_t(password.c_str()), adConnectUnspecified);
+		p_cmd->ActiveConnection = p_conn;
+
+		for (ci = 0; ci < count; ++ci)
+		{
+			p_recordset->Open(_bstr_t(sqlKC[ci].c_str()),//更新第二条
+				p_conn.GetInterfacePtr(),
+				adOpenStatic,
+				adLockOptimistic,
+				adCmdText);
+		}
+	}
+	catch (_com_error e)
+	{
+		memset(g_log_rec, 0, sizeof(g_log_rec));
+
+		strcat(g_log_rec, "DBUpdate-Err:");
+		strcat(g_log_rec, (char*)e.Description());
+		strcat(g_log_rec, " ");
+		strcat(g_log_rec, (char*)e.Error());
+		strcat(g_log_rec, " ");
+		SaveLog(g_log_rec, g_log_adr, "a");
+
+		::CoUninitialize();
+		return 0;
+	}
+
+	/*关闭查询*/
+	::CoUninitialize();
+	
+	return 1;
+}
--- a/测试/本机测试/1-我-算法/HWCV-exe/DBop.h
+++ b/测试/本机测试/1-我-算法/HWCV-exe/DBop.h
@@ -0,0 +1,55 @@
+/*
+头文件：DBop.h  数据库操作头文件
+*/
+#pragma once
+#import "C:\\Program Files\\Common Files\\System\\ado\\msado15.dll" no_namespace rename("EOF","EndOfFile")
+#include <vector> 
+#include <string> 
+#include <iostream> 
+#include <windows.h> 
+#include <iomanip> 
+#include <stdio.h> 
+
+using namespace std;
+
+/*全局变量*/
+
+extern char g_log_adr[50];				/*全局变量 程序日志存储地址*/
+extern char g_log_rec[500];				/*全局变量 程序日志专用变量*/
+extern FILE *g_log_fpzz;				/*全局变量 程序日志专用文件句柄*/
+
+extern string g_db_hostName;		/*全局变量 服务器ip或名称*/
+extern string g_db_dBName;			/*全局变量 服务器ODBC数据源*/
+extern string g_db_userName;		/*全局变量 服务器用户名*/
+extern string g_db_password;		/*全局变量 服务器密码*/
+
+extern string g_db_qurry_start;		/*全局变量 数据库查询_开始日期*/
+extern string g_db_qurry_end;		/*全局变量 数据库查询_结束日期*/
+extern string g_db_qurry_zone;		/*全局变量 数据库查询_特定区域*/
+extern string g_db_qurry_stu_num;	/*全局变量 数据库查询_特定考号*/
+extern bool   g_db_qurry_all;		/*全局变量 数据库查询_查询全部标记*/
+extern string g_db_hoster_zk;		/*全局变量 数据库用户，zk（考试院的zk，本地的yannsy）*/
+/*****************************************函数原型*************************************/
+
+
+/**
+程序功能：	根据string类的stu【存储学号】，搜索出所有此人的考试信息,并分别存储在不同的变量中
+
+@变量 stuNum 学号
+@变量 date<vector>	考试时间
+@变量 subject<vector> 考试科目
+@变量 stuNum<vector> 考号
+@返回值 成功1 失败0
+*/
+int DbImg(string stuNum, vector<string>& dateVec, vector<string>& subjectVec, vector<string>& stuNum2);
+
+
+/**
+程序功能：	更新学生数据库信息,根据学生的鉴定结果，将结果输出到数据库中
+
+@变量 stuNum：学号
+@变量 subject：考试科目
+@变量 flagCheat：作弊标记
+@返回值 成功1失败0
+*/
+int DbUpdate(string stuNum, vector<string> dateVec, vector<string> subjectVec, vector<string> stuNum2, vector<string> flagVec);
--- a/测试/本机测试/1-我-算法/HWCV-exe/HWCV-exe.vcxproj
+++ b/测试/本机测试/1-我-算法/HWCV-exe/HWCV-exe.vcxproj
@@ -0,0 +1,106 @@
+<?xml version="1.0" encoding="utf-8"?>
+<Project DefaultTargets="Build" ToolsVersion="12.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
+  <ItemGroup Label="ProjectConfigurations">
+    <ProjectConfiguration Include="Debug|Win32">
+      <Configuration>Debug</Configuration>
+      <Platform>Win32</Platform>
+    </ProjectConfiguration>
+    <ProjectConfiguration Include="Release|Win32">
+      <Configuration>Release</Configuration>
+      <Platform>Win32</Platform>
+    </ProjectConfiguration>
+  </ItemGroup>
+  <PropertyGroup Label="Globals">
+    <ProjectGuid>{FB6A1EE7-710E-424B-A80E-A931EDB7DDBF}</ProjectGuid>
+    <Keyword>Win32Proj</Keyword>
+    <RootNamespace>HWCVexe</RootNamespace>
+    <ProjectName>HWCV</ProjectName>
+  </PropertyGroup>
+  <Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" Label="Configuration">
+    <ConfigurationType>Application</ConfigurationType>
+    <UseDebugLibraries>true</UseDebugLibraries>
+    <PlatformToolset>v120</PlatformToolset>
+    <CharacterSet>Unicode</CharacterSet>
+    <UseOfMfc>Static</UseOfMfc>
+  </PropertyGroup>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" Label="Configuration">
+    <ConfigurationType>Application</ConfigurationType>
+    <UseDebugLibraries>false</UseDebugLibraries>
+    <PlatformToolset>v120_xp</PlatformToolset>
+    <WholeProgramOptimization>true</WholeProgramOptimization>
+    <CharacterSet>Unicode</CharacterSet>
+    <UseOfMfc>Static</UseOfMfc>
+  </PropertyGroup>
+  <Import Project="$(VCTargetsPath)\Microsoft.Cpp.props" />
+  <ImportGroup Label="ExtensionSettings">
+  </ImportGroup>
+  <ImportGroup Label="PropertySheets" Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
+    <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
+  </ImportGroup>
+  <ImportGroup Label="PropertySheets" Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
+    <Import Project="$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props" Condition="exists('$(UserRootDir)\Microsoft.Cpp.$(Platform).user.props')" Label="LocalAppDataPlatform" />
+  </ImportGroup>
+  <PropertyGroup Label="UserMacros" />
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
+    <LinkIncremental>true</LinkIncremental>
+  </PropertyGroup>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
+    <LinkIncremental>false</LinkIncremental>
+    <LibraryPath>C:\Program Files %28x86%29\OpenCV\lib;$(VC_LibraryPath_x86);$(WindowsSdk_71A_LibraryPath_x86)</LibraryPath>
+    <ExecutablePath>C:\Program Files %28x86%29\OpenCV\otherlibs;C:\Program Files %28x86%29\OpenCV\bin;C:\Program Files %28x86%29\OpenCV\cvaux\include;$(VC_ExecutablePath_x86);$(WindowsSdk_71A_ExecutablePath);$(WindowsSDK_ExecutablePath);$(VS_ExecutablePath);$(MSBuild_ExecutablePath);$(SystemRoot)\SysWow64;$(FxCopDir);$(PATH)</ExecutablePath>
+    <IncludePath>C:\Program Files %28x86%29\OpenCV\cv\include;C:\Program Files %28x86%29\OpenCV\cvaux\include;C:\Program Files %28x86%29\OpenCV\cxcore\include;C:\Program Files %28x86%29\OpenCV\ml\include;C:\Program Files %28x86%29\OpenCV\otherlibs\highgui;C:\Program Files (x86)\OpenCV\;$(VC_IncludePath);$(WindowsSDK_IncludePath)</IncludePath>
+  </PropertyGroup>
+  <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
+    <ClCompile>
+      <PrecompiledHeader>
+      </PrecompiledHeader>
+      <WarningLevel>Level3</WarningLevel>
+      <Optimization>Disabled</Optimization>
+      <PreprocessorDefinitions>WIN32;_DEBUG;_CONSOLE;_LIB;_CRT_SECURE_NO_WARNINGS;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <SDLCheck>true</SDLCheck>
+    </ClCompile>
+    <Link>
+      <SubSystem>Console</SubSystem>
+      <GenerateDebugInformation>true</GenerateDebugInformation>
+      <AdditionalDependencies>libjasperd.lib;libjpegd.lib;libpngd.lib;libtiffd.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;comctl32.lib;%(AdditionalDependencies)</AdditionalDependencies>
+    </Link>
+  </ItemDefinitionGroup>
+  <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
+    <ClCompile>
+      <WarningLevel>Level3</WarningLevel>
+      <PrecompiledHeader>
+      </PrecompiledHeader>
+      <Optimization>MaxSpeed</Optimization>
+      <FunctionLevelLinking>true</FunctionLevelLinking>
+      <IntrinsicFunctions>true</IntrinsicFunctions>
+      <PreprocessorDefinitions>WIN32;NDEBUG;_CONSOLE;_LIB;_CRT_SECURE_NO_WARNINGS;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <SDLCheck>true</SDLCheck>
+      <RuntimeLibrary>MultiThreaded</RuntimeLibrary>
+    </ClCompile>
+    <Link>
+      <SubSystem>Console</SubSystem>
+      <GenerateDebugInformation>true</GenerateDebugInformation>
+      <EnableCOMDATFolding>true</EnableCOMDATFolding>
+      <OptimizeReferences>true</OptimizeReferences>
+      <AdditionalDependencies>cv.lib;cvaux.lib;cxcore.lib;highgui.lib;kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib</AdditionalDependencies>
+    </Link>
+  </ItemDefinitionGroup>
+  <ItemGroup>
+    <ClInclude Include="path.h" />
+    <ClInclude Include="Point.h" />
+    <ClInclude Include="process.h" />
+    <ClInclude Include="segmentation.h" />
+    <ClInclude Include="StdAfx.h" />
+  </ItemGroup>
+  <ItemGroup>
+    <ClCompile Include="path.cpp" />
+    <ClCompile Include="process.cpp" />
+    <ClCompile Include="segmentation.cpp" />
+    <ClCompile Include="seg_bck.cpp" />
+    <ClCompile Include="StdAfx.cpp" />
+  </ItemGroup>
+  <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
+  <ImportGroup Label="ExtensionTargets">
+  </ImportGroup>
+</Project>
--- a/测试/本机测试/1-我-算法/HWCV-exe/HWCV-exe.vcxproj.filters
+++ b/测试/本机测试/1-我-算法/HWCV-exe/HWCV-exe.vcxproj.filters
@@ -0,0 +1,51 @@
+<?xml version="1.0" encoding="utf-8"?>
+<Project ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
+  <ItemGroup>
+    <Filter Include="源文件">
+      <UniqueIdentifier>{4FC737F1-C7A5-4376-A066-2A32D752A2FF}</UniqueIdentifier>
+      <Extensions>cpp;c;cc;cxx;def;odl;idl;hpj;bat;asm;asmx</Extensions>
+    </Filter>
+    <Filter Include="头文件">
+      <UniqueIdentifier>{93995380-89BD-4b04-88EB-625FBE52EBFB}</UniqueIdentifier>
+      <Extensions>h;hh;hpp;hxx;hm;inl;inc;xsd</Extensions>
+    </Filter>
+    <Filter Include="资源文件">
+      <UniqueIdentifier>{67DA6AB6-F800-4c08-8B7A-83BB121AAD01}</UniqueIdentifier>
+      <Extensions>rc;ico;cur;bmp;dlg;rc2;rct;bin;rgs;gif;jpg;jpeg;jpe;resx;tiff;tif;png;wav;mfcribbon-ms</Extensions>
+    </Filter>
+  </ItemGroup>
+  <ItemGroup>
+    <ClInclude Include="path.h">
+      <Filter>头文件</Filter>
+    </ClInclude>
+    <ClInclude Include="Point.h">
+      <Filter>头文件</Filter>
+    </ClInclude>
+    <ClInclude Include="process.h">
+      <Filter>头文件</Filter>
+    </ClInclude>
+    <ClInclude Include="segmentation.h">
+      <Filter>头文件</Filter>
+    </ClInclude>
+    <ClInclude Include="StdAfx.h">
+      <Filter>头文件</Filter>
+    </ClInclude>
+  </ItemGroup>
+  <ItemGroup>
+    <ClCompile Include="path.cpp">
+      <Filter>源文件</Filter>
+    </ClCompile>
+    <ClCompile Include="process.cpp">
+      <Filter>源文件</Filter>
+    </ClCompile>
+    <ClCompile Include="segmentation.cpp">
+      <Filter>源文件</Filter>
+    </ClCompile>
+    <ClCompile Include="StdAfx.cpp">
+      <Filter>源文件</Filter>
+    </ClCompile>
+    <ClCompile Include="seg_bck.cpp">
+      <Filter>源文件</Filter>
+    </ClCompile>
+  </ItemGroup>
+</Project>
--- a/测试/本机测试/1-我-算法/HWCV-exe/Point.h
+++ b/测试/本机测试/1-我-算法/HWCV-exe/Point.h
@@ -0,0 +1,13 @@
+/*
+头文件：Point.h 图像中的像素点定义
+*/
+#pragma once
+class Point{
+private:
+
+public:
+	int x;
+	int y;
+
+    void setpoint(int a,int b){x=a;y=b;}
+};
--- a/测试/本机测试/1-我-算法/HWCV-exe/StdAfx.cpp
+++ b/测试/本机测试/1-我-算法/HWCV-exe/StdAfx.cpp
@@ -0,0 +1,8 @@
+// stdafx.cpp : source file that includes just the standard includes
+//	HWCV.pch will be the pre-compiled header
+//	stdafx.obj will contain the pre-compiled type information
+
+#include "stdafx.h"
+
+// TODO: reference any additional headers you need in STDAFX.H
+// and not in this file
--- a/测试/本机测试/1-我-算法/HWCV-exe/StdAfx.h
+++ b/测试/本机测试/1-我-算法/HWCV-exe/StdAfx.h
@@ -0,0 +1,24 @@
+// stdafx.h : include file for standard system include files,
+//  or project specific include files that are used frequently, but
+//      are changed infrequently
+//
+
+#if !defined(AFX_STDAFX_H__0B833134_257D_4F21_9707_24C780138809__INCLUDED_)
+#define AFX_STDAFX_H__0B833134_257D_4F21_9707_24C780138809__INCLUDED_
+
+#if _MSC_VER > 1000
+#pragma once
+#endif // _MSC_VER > 1000
+
+
+// Insert your headers here
+#define WIN32_LEAN_AND_MEAN		// Exclude rarely-used stuff from Windows headers
+
+#include <windows.h>
+
+// TODO: reference additional headers your program requires here
+
+//{{AFX_INSERT_LOCATION}}
+// Microsoft Visual C++ will insert additional declarations immediately before the previous line.
+
+#endif // !defined(AFX_STDAFX_H__0B833134_257D_4F21_9707_24C780138809__INCLUDED_)
--- a/测试/本机测试/1-我-算法/HWCV-exe/path.cpp
+++ b/测试/本机测试/1-我-算法/HWCV-exe/path.cpp
@@ -0,0 +1,264 @@
+/*
+实现文件：path.cpp 路径操作实现文件
+*/
+#include "path.h"
+
+
+/**
+获取并返回当前时间
+ */
+char* GetTime()
+{
+	time( &ltime );
+	srcTime = ctime( &ltime );
+	strncpy(timeNow, srcTime, strlen(srcTime)-1);  //不拷贝换行
+	timeNow[strlen(srcTime)-1] = '\0';   //加结束符'\0'
+
+	return timeNow;
+}
+
+
+/**
+根据学生信息创建文件路径，用于文件读取
+
+@变量 date 考试日期
+@变量 subject 考试科目
+@变量 stuNum 考号
+@返回值 返回生成的文件路径
+*/
+string CrPath(string date, string subject, string stuNum)
+{
+	string temp = g_dir; temp += date; temp += "/";
+	temp += subject.substr(0, 4); temp += "/";
+	temp += stuNum; temp += ".jpg";
+
+	return temp;
+}
+
+
+/*
+功能：读取标准差文件
+@变量 filesname 文件名
+@变量 col 行数
+@变量 _vector 读取到的标准差存到vector中
+@返回值 成功1失败0
+*/
+int ReadScanf(const string &filename, const int &cols, vector<double *> &_vector)
+{
+	// 功能：将filename 中的数据（共cols列）读取到_vector中，_vector可视为二维数组
+	FILE *fp = fopen(filename.c_str(), "r");//打开并读取文件
+	bool flag = true;
+	int i = 0;
+
+	//	printf("--read_scanf--");
+	if (!fp){ return 0; }
+	while (flag){
+		double *point = new double[cols];
+		for (i = 0; i<cols; i++){ //读取数据，存在_vector[cols]中
+			if (EOF == fscanf(fp, "%lf", &point[i])) {
+				flag = false; break;
+			}
+			if (EOF == fgetc(fp)) {
+				flag = false; i++; break;
+			}
+		}
+		if (cols == i)
+			_vector.push_back(point);
+	}
+	fclose(fp);
+
+	return 1;
+}
+
+
+/**
+读取配置文件，并配置各项参数
+
+@变量 filename 配置文件的路径
+@返回值 成功1  失败0
+*/
+int ReadConfig(char *filename)
+{
+	ifstream file(filename);
+
+	if (!file)/*"配置文件不存在！"*/
+	{
+		/*写入时间*/
+		memset(g_log_rec, 0, sizeof(g_log_rec));
+		cout<<"read"<<endl;
+
+		strcat(g_log_rec, "--ERR:配置文件不存在!");
+		SaveLog(g_log_rec, g_err_adr, "a");
+
+		return 0;
+	}
+
+	/*步骤：开始读取信息*/
+
+	string temp;/*仅用作过滤字符*/
+	file >> temp >> temp;
+	/*---此行6个，参考配置信息：图片对比参数*/
+	file >> temp >> temp >> temp >> temp >> temp >>temp;
+	file  >> g_dir >> temp >> temp >> temp;
+	string g_log_adr_t;
+	file  >> g_log_adr_t >> temp;
+
+	memset(g_log_adr, 0, sizeof(g_log_adr));
+	strcpy(g_log_adr, (char*)g_log_adr_t.c_str());
+
+
+	/*---此行6个，参考配置信息：网络配置参数*/
+	file >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp;
+
+
+	/*---此行5个，参考配置信息：控制参数*/
+	file >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp;
+
+	/*---此行5个，参考配置信息：数据库查询参数*/
+	file >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp >> temp;
+
+	file.close();/*关闭文件句柄*/
+	return 1;
+}
+
+
+/**
+函数功能：存储过程数据到txt文件
+
+@变量： record 存储的语句
+@变量 g_txt_file_path 存储的位置
+@返回值 1成功 0失败
+*/
+int SaveLog(char *txt, string txt_file_path, char *type)
+{
+	FILE* fpzz = fopen(txt_file_path.c_str(), type);  //创建文件	
+	if (NULL == fpzz) 
+	{
+		return 0; 
+	}//要返回错误代码
+	fprintf(fpzz, txt); //从控制台中读入并在文本输出
+	fclose(fpzz);
+	fpzz = NULL;//需要指向空，否则会指向原打开文件地址  
+
+	return 1;
+}
+
+
+/*
+功能：保存中间鉴定图像(不实现，没有必要)
+@变量
+@变量
+@返回值
+*/
+int SaveImg(IplImage *img, char *g_process_img_adr){
+	return 1;
+}
+
+
+/*
+功能：获取指定目录下的文件
+@变量 path 路径
+@变量 files 输出vector格式的文件
+*/
+void getFiles(string path, vector<string>& files)
+{
+	using namespace std;//引入整个名空间
+	//文件句柄  
+	long   hFile = 0;
+	//文件信息  
+	struct _finddata_t fileinfo;
+	string p;
+	if ((hFile = _findfirst(p.assign(path).append("/*").c_str(), &fileinfo)) != -1)
+	{
+		do
+		{
+			//如果是目录,迭代之  
+
+			if ((fileinfo.attrib &  _A_SUBDIR))
+			{
+				if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)
+					getFiles(p.assign(path).append("/").append(fileinfo.name), files);
+			} //如果不是,加入列表  
+			else
+			{
+				files.push_back(p.assign(path).append("/").append(fileinfo.name));
+			}
+		} while (_findnext(hFile, &fileinfo) == 0);
+
+		_findclose(hFile);
+	}
+}
+
+
+/*
+功能：获取指定目录下的目录
+@变量 path 目录
+@变量 files 返回的目录vector
+@返回值 成功1失败0
+*/
+int getFolders(string path, vector<string>& files)
+{
+	using namespace std;//引入整个名空间
+	//文件句柄  
+	long   hFile = 0;
+	//文件信息  
+	struct _finddata_t fileinfo;
+	string p;
+
+	int i = 0;
+	if ((hFile = _findfirst(p.assign(path).append("/*").c_str(), &fileinfo)) != -1)
+	{
+		do
+		{
+			if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0)
+			{
+				files.push_back(p.assign(path).append("/").append(fileinfo.name));
+				printf("文件夹：%s\n", files[i].c_str());
+				i++;
+			}
+
+		} while (_findnext(hFile, &fileinfo) == 0);
+
+		_findclose(hFile);
+	}
+	return 0;
+
+}
+
+
+/*
+功能：搜索目录
+*/
+int searchDir(char *path, vector<string> &dir)
+{
+	using namespace std;
+	struct _finddata_t fa;//创建找到的结构体
+	long handle;
+	int flag = 0;
+	char temp[100] = { 0 };
+	string path_temp = path;
+	//	path_temp=path_temp.substr(0,path_temp.length()-1);
+
+	if ((handle = _findfirst(strcat(path, "*"), &fa)) == -1L)//如果不是目录的话
+		return 0;
+
+	do//是目录，先执行循环
+	{
+		if (fa.attrib == _A_SUBDIR && ~strcmp(fa.name, ".") && ~strcmp(fa.name, ".."))
+		{
+			strcat(temp, path_temp.c_str());
+			strcat(temp, fa.name);
+
+			if (flag++)
+				dir.push_back(temp);
+			else;
+
+			memset(temp, 0, 100);
+		}
+	} while (_findnext(handle, &fa) == 0); /* 成功找到时返回0*/
+
+	_findclose(handle);
+
+	return 0;
+
+}
--- a/测试/本机测试/1-我-算法/HWCV-exe/path.h
+++ b/测试/本机测试/1-我-算法/HWCV-exe/path.h
@@ -0,0 +1,122 @@
+/*
+头文件：path.h 与路径相关操作的函数头文件以及函数原型 
+*/
+#pragma once
+#include <iostream>
+#include <fstream>
+#include <string>
+#include <vector>
+#include <ctime>
+#include <cv.h> 
+#include <io.h>
+
+using namespace std;
+
+
+extern int    g_bi_threshold;		/*全局变量 二值化阈值*/
+extern double g_std_kesa[50][50];	/*全局变量 标准差数组*/
+extern float  g_doubt_threshold;	/*全局变量 作弊嫌疑阈值*/
+extern string g_dir;				/*全局变量 总路径的目录*/
+extern int    g_conti;				/*全局变量 比较标准*/
+
+extern string g_db_hostName;		/*全局变量 服务器ip或名称*/	
+extern string g_db_dBName;			/*全局变量 服务器ODBC数据源*/
+extern string g_db_userName;		/*全局变量 服务器用户名*/	
+extern string g_db_password;		/*全局变量 服务器密码*/	
+
+extern char g_log_adr[50];				/*全局变量 程序日志存储地址*/
+extern char g_err_adr[50];			/*全局变量 错误日志存储地址*/
+extern char g_log_rec[500];			/*全局变量 程序日志专用变量*/
+		/*全局变量 待定*/
+		/*全局变量 待定*/
+
+extern string g_db_qurry_start;		/*全局变量 数据库查询_开始日期*/
+extern string g_db_qurry_end;		/*全局变量 数据库查询_结束日期*/
+extern string g_db_qurry_zone;		/*全局变量 数据库查询_特定区域*/
+extern string g_db_qurry_stu_num;	/*全局变量 数据库查询_特定考号*/
+extern bool g_db_qurry_all;			/*全局变量 数据库查询_查询全部标记*/
+extern string g_db_hoster_zk;
+
+
+
+extern	time_t ltime;
+extern	char *srcTime;
+extern	char timeNow[32];
+extern  char msg[100];
+
+/**
+获取并返回当前时间
+ */
+char* GetTime();
+
+
+/**
+根据学生信息创建文件路径，用于文件读取
+
+@变量 date 考试日期
+@变量 subject 考试科目
+@变量 stuNum 考号
+@返回值 返回生成的文件路径
+*/
+string CrPath(string date, string subject, string stuNum);
+
+
+/*
+功能：读取标准差文件
+@变量 filesname 文件名
+@变量 col 行数
+@变量 _vector 读取到的标准差存到vector中
+@返回值 成功1失败0
+*/
+int ReadScanf(const string &filename, const int &cols, vector<double *> &_vector);
+
+
+/**
+读取配置文件，并配置各项参数
+
+@变量 filename 配置文件的路径
+@返回值 成功1  失败0
+*/
+int ReadConfig(char *filename);
+
+
+/**
+函数功能：存储过程数据到txt文件
+
+@变量： record 存储的语句
+@变量 g_txt_file_path 存储的位置
+@返回值 1成功 0失败
+*/
+int SaveLog(char *record, string txt_file_path, char *type);
+
+
+/*
+功能：保存中间鉴定图像
+@变量 
+@变量 
+@返回值 
+*/
+int SaveImg(IplImage *img, char *g_process_img_adr);
+
+
+/*
+功能：获取指定目录下的文件
+@变量 path 路径
+@变量 files 输出vector格式的文件
+*/
+void getFiles(string path, vector<string>& files);
+
+
+/*
+功能：获取指定目录下的目录
+@变量 path 目录
+@变量 files 返回的目录vector
+@返回值 成功1失败0
+*/
+int getFolders(string path, vector<string>& files);
+
+
+/*
+功能：搜索目录
+*/
+int searchDir(char *path, vector<string> &dir);
--- a/测试/本机测试/1-我-算法/HWCV-exe/process.cpp
+++ b/测试/本机测试/1-我-算法/HWCV-exe/process.cpp
@@ -0,0 +1,665 @@
+/*
+实现文件：process.cpp 图像处理过程的实现文件
+*/
+#include "process.h"
+
+/*
+功能：读入图像文件，进行二值化
+
+@变量 img iplimage图像文件
+@变量 bithro 二值化阈值
+@返回值 黑像素的数目（待用）
+*/
+int*  binary(IplImage*  img, int g_bi_threshold)
+{
+	int height, width, step, channels;
+	uchar *data;
+	int i, j;
+	static int black[1000];   //C语言不提倡返回一个局部变量的地址以外的功能，所以你必须定义的局部变量，如静态变量。
+	/* 获取图像信息*/
+	height = img->height;
+	width = img->width;
+	step = img->widthStep;
+	channels = img->nChannels;
+	data = (uchar *)img->imageData;
+
+	/*二值化，并统计黑像素的个数*/
+	for (i = 0; i<height; i++)
+	{
+		for (j = 0; j<width; j++)//对图像每个点进行二值化,原值为128
+			data[i*step + j*channels] = (data[i*step + j*channels]>g_bi_threshold) ? 255 : 0;
+	}
+
+	/*计算每一行的黑像素个数*/
+	int tempBlackPixel = 0;
+
+	memset(black, 0, 1000);            //##初始化内存，这里用做清零black数组
+	for (i = height - 1; i>0; i--)
+	{
+		for (int j = 0; j<width; j++)
+		{
+			if (data[i*step + j*channels] == 0)        //计算黑色的像素数
+				tempBlackPixel += 1;
+		}
+		black[height - i] = tempBlackPixel;   //black记录黑色像素数
+		tempBlackPixel = 0;
+	}
+	//二值化，并统计黑像素的个数**********
+	return black;
+}
+
+
+
+/* 	
+功能：读入图像文件，对图像进行裁剪
+
+@变量 img iplimage图像文件
+@变量 img 裁剪后的iplimage图像文件
+@jbwhite
+@jbblack
+@返回值 返回裁剪后的图像
+*/
+IplImage* Cjbsb(IplImage* img, IplImage* imgjbsb, int jbwhite, int jbblack)
+{
+	/*定义变量*/
+	int i, j, jbi = 0, jbj = 0;
+	int height, width, step, channels;
+	uchar *data;
+	int brklab = 0;
+	/* 获取图像信息*/
+	height = img->height;
+	width = img->width;
+	step = img->widthStep;
+	channels = img->nChannels;
+	data = (uchar *)img->imageData;
+
+	//    IplImage* imgjbsb = cvCreateImage(cvGetSize(img),img->depth,img->nChannels);
+	cvCopy(img, imgjbsb, NULL);
+	uchar *imgjbsbdata = (uchar *)imgjbsb->imageData;
+
+	//以角标为起点进行裁剪与画框
+	CvSize jbcjsize = cvSize(835, 165);           //角标裁剪框的大小，宽为835象素，高为165象素
+	IplImage* imgjbcj = cvCreateImage(jbcjsize, img->depth, img->nChannels);
+	uchar *imgjbcjdata = (uchar *)imgjbcj->imageData;
+	int jbcjstep = imgjbcj->widthStep;
+	int jbcjchannels = imgjbcj->nChannels;
+	for (i = 0; i<165; i++)
+		for (j = 0; j<835; j++)
+			imgjbcjdata[i*jbcjstep + j*jbcjchannels] = data[(i + jbi)*step + (j + jbj)*channels];
+	for (i = 0; i<165; i = i + 2)
+	{
+		imgjbsbdata[(i + jbi)*step + jbj*channels] = 0;
+		imgjbsbdata[(i + jbi)*step + (jbj + 835)*channels] = 0;
+	}
+	for (j = 0; j<835; j = j + 2)
+	{
+		imgjbsbdata[jbi*step + (j + jbj)*channels] = 0;
+		imgjbsbdata[(jbi + 165)*step + (j + jbj)*channels] = 0;
+	}
+
+	return imgjbcj;
+}
+
+
+/*
+功能：计算图像的特征
+
+@变量 imgbj 笔迹部分的图像
+@返回值 计算得到的特征图像
+*/
+IplImage* outline(IplImage* imgbj)
+{
+	/*定义变量*/
+	int i, j;
+	int height, width, step, channels;
+	uchar *data;
+
+	/*定义新的图像*/
+	IplImage* imglk = cvCreateImage(cvGetSize(imgbj), imgbj->depth, imgbj->nChannels);
+
+	/* 获取图像信息*/
+	height = imgbj->height;
+	width = imgbj->width;
+	step = imgbj->widthStep;
+	channels = imgbj->nChannels;
+	data = (uchar *)imgbj->imageData;
+
+	//	printf("--outline--");
+	for (j = 0; j<height; j++){
+		for (i = 0; i<width; i++){
+			imglk->imageData[j*step + i*channels] = 255;
+		}
+		for (i = 0; i<width - 1; i++){
+			if (data[j*step + (i + 1)*channels] - data[j*step + i*channels] == 255)             //竖线右侧框
+				imglk->imageData[j*step + i*channels] = 0;
+			else if (data[j*step + i*channels] - data[j*step + (i + 1)*channels] == 255)        //竖线左侧框
+				imglk->imageData[j*step + (i + 1)*channels] = 0;
+		}
+	}
+
+	for (i = 0; i<width; i++){
+		for (j = 0; j<height - 1; j++){
+			if (data[j*step + i*channels] - data[(j + 1)*step + i*channels] == 255)             //横线下侧框
+				imglk->imageData[(j + 1)*step + i*channels] = 0;
+			else if (data[(j + 1)*step + i*channels] - data[j*step + i*channels] == 255)        //横线上侧框
+				imglk->imageData[j*step + i*channels] = 0;
+		}
+	}
+	return imglk;
+}
+
+
+/*
+功能：输入图像的特征轮廓图，返回图像的特征值
+@变量 imglk 输入的图像轮廓图
+@变量 feature 得到的图像特征
+@返回值 成功1失败0
+*/
+int outlinefeature(IplImage* imglk, int feature[][50])
+{
+	//定义变量
+	int i, j;
+	int height, width, step, channels;
+	uchar *data;
+
+	int feat[50][50] = { 0 };             //特征值初始化  
+	Point featblk[32];                //标记相同H的黑点坐标
+	int featk;                        //标记相同H的黑点数目
+	int m;                            //for 里面的变量
+	//	printf("--outlinefeature--");
+	// 获取图像信息
+	height = imglk->height;
+	width = imglk->width;
+	step = imglk->widthStep;
+	channels = imglk->nChannels;
+	data = (uchar *)imglk->imageData;
+
+	//初始化特征矩阵    最大值为47    ，非空的特征字有1081个
+	int outllab[9][9] = { \
+	{3, 37, 10, 36, 2, 35, 9, 34, 1}, { 38, 3, 21, 20, 2, 19, 18, 1, 33 }, \
+	{11, 22, 3, 10, 2, 9, 1, 17, 8}, { 39, 23, 11, 3, 2, 1, 8, 16, 32 }, \
+	{4, 4, 4, 4, 0, 0, 0, 0, 0}, { 40, 24, 12, 5, 6, 7, 15, 31, 47 }, \
+	{12, 25, 5, 13, 6, 14, 7, 30, 15}, { 41, 5, 26, 27, 6, 28, 29, 7, 46 }, \
+	{5, 42, 13, 43, 6, 44, 14, 45, 7} };
+
+
+	for (i = 4; i <= width - 5; i++){
+		for (j = 4; j <= height - 5; j++){
+			if (data[j*step + i*channels] == 0){
+				//**************H=1
+
+				memset(featblk, 0, sizeof(Point) * 32);              //归零                
+				featk = 0;
+				if (data[j*step + (i + 1)*channels] == 0){                 //右侧点
+					featblk[featk].x = i + 1;
+					featblk[featk].y = j;
+					featk++;
+				}
+				for (m = i + 1; m >= i - 1; m--){                              //上排点
+					if (data[(j - 1)*step + m*channels] == 0) {
+						featblk[featk].x = m;
+						featblk[featk].y = j - 1;
+						featk++;
+					}
+				}
+				if (data[j*step + (i - 1)*channels] == 0){                 //左侧点
+					featblk[featk].x = i - 1;
+					featblk[featk].y = j;
+					featk++;
+				}
+				for (m = i - 1; m <= i + 1; m++) {                             //下排点
+					if (data[(j + 1)*step + m*channels] == 0){
+						featblk[featk].x = m;
+						featblk[featk].y = j + 1;
+						featk++;
+					}
+				}
+				//统计特征点
+				if (featk >= 2){
+					for (m = 1; m <= featk - 1; m++){
+						feat[outllab[featblk[m - 1].x - i + 4][featblk[m - 1].y - j + 4]][outllab[featblk[m].x - i + 4][featblk[m].y - j + 4]]++;
+					}
+				}
+				//H=1*******************
+
+
+				//*********************H=2
+				memset(featblk, 0, sizeof(Point) * 32);                 //归零                
+				featk = 0;
+
+				for (m = j + 1; m >= j - 2; m--){
+					if (data[m*step + (i + 2)*channels] == 0){                //右排点
+						featblk[featk].x = i + 2;
+						featblk[featk].y = m;
+						featk++;
+					}
+				}
+				for (m = i + 1; m >= i - 2; m--){                                  //上排点
+					if (data[(j - 2)*step + m*channels] == 0){
+						featblk[featk].x = m;
+						featblk[featk].y = j - 2;
+						featk++;
+					}
+				}
+				for (m = j - 1; m <= j + 2; m++){                                  //左侧点
+					if (data[m*step + (i - 2)*channels] == 0){
+						featblk[featk].x = i - 2;
+						featblk[featk].y = m;
+						featk++;
+					}
+				}
+				for (m = i - 1; m <= i + 2; m++){                                 //下排点
+					if (data[(j + 2)*step + m*channels] == 0){
+						featblk[featk].x = m;
+						featblk[featk].y = j + 2;
+						featk++;
+					}
+				}
+				//统计特征点
+				if (featk >= 2){
+					for (m = 1; m <= featk - 1; m++){
+						feat[outllab[featblk[m - 1].x - i + 4][featblk[m - 1].y - j + 4]][outllab[featblk[m].x - i + 4][featblk[m].y - j + 4]]++;
+					}
+				}
+				//H=2********************
+
+				//*********************H=3
+				memset(featblk, 0, sizeof(Point) * 32);                 //归零                
+				featk = 0;
+
+				for (m = j + 2; m >= j - 3; m--){
+					if (data[m*step + (i + 3)*channels] == 0){                //右排点
+						featblk[featk].x = i + 3;
+						featblk[featk].y = m;
+						featk++;
+					}
+				}
+				for (m = i + 2; m >= i - 3; m--){                                  //上排点
+					if (data[(j - 3)*step + m*channels] == 0){
+						featblk[featk].x = m;
+						featblk[featk].y = j - 3;
+						featk++;
+					}
+				}
+				for (m = j - 2; m <= j + 3; m++){                                  //左侧点
+					if (data[m*step + (i - 3)*channels] == 0){
+						featblk[featk].x = i - 3;
+						featblk[featk].y = m;
+						featk++;
+					}
+				}
+				for (m = i - 2; m <= i + 3; m++){                                  //下排点
+					if (data[(j + 3)*step + m*channels] == 0){
+						featblk[featk].x = m;
+						featblk[featk].y = j + 3;
+						featk++;
+					}
+				}
+				//统计特征点
+				if (featk >= 2){
+					for (m = 1; m <= featk - 1; m++){
+						feat[outllab[featblk[m - 1].x - i + 4][featblk[m - 1].y - j + 4]][outllab[featblk[m].x - i + 4][featblk[m].y - j + 4]]++;
+					}
+				}
+				//H=3********************
+
+				//*********************H=4  
+				memset(featblk, 0, sizeof(Point) * 32);                 //归零                
+				featk = 0;
+
+				for (m = j + 3; m >= j - 4; m--){
+					if (data[m*step + (i + 4)*channels] == 0){                 //右排点
+						featblk[featk].x = i + 4;
+						featblk[featk].y = m;
+						featk++;
+					}
+				}
+				for (m = i + 3; m >= i - 4; m--) {                                 //上排点
+					if (data[(j - 4)*step + m*channels] == 0){
+						featblk[featk].x = m;
+						featblk[featk].y = j - 4;
+						featk++;
+					}
+				}
+				for (m = j - 3; m <= j + 4; m++){                                  //左侧点
+					if (data[m*step + (i - 4)*channels] == 0){
+						featblk[featk].x = i - 4;
+						featblk[featk].y = m;
+						featk++;
+					}
+				}
+				for (m = i - 3; m <= i + 4; m++){                                 //下排点
+					if (data[(j + 4)*step + m*channels] == 0){
+						featblk[featk].x = m;
+						featblk[featk].y = j + 4;
+						featk++;
+					}
+				}
+				//统计特征点
+				if (featk >= 2){
+					for (m = 1; m <= featk - 1; m++){
+						feat[outllab[featblk[m - 1].x - i + 4][featblk[m - 1].y - j + 4]][outllab[featblk[m].x - i + 4][featblk[m].y - j + 4]]++;
+					}
+				}
+				//H=4***********************
+			}// if    		
+		}  //for j
+	}  //for i
+
+	//****注：最终特征值为feat(x,y)+feat(y,x)，放入feat(x,y)中，x<y
+	for (i = 1; i<50; i++)
+		for (j = 0; j<i; j++){
+		feat[j][i] = feat[i][j] + feat[j][i];
+		feat[i][j] = 0;
+		}
+	memcpy(feature, feat, 2500 * 4);        //int有四个字节
+	//	printf("轮廓特征值计算完成\n");
+	return 0;
+}
+
+
+/*
+功能：对单张图像的处理，最终得到一个特征值，用来计算各个图像之间的卡方距离
+
+@变量 path 图像的物理地址
+@变量 feature 图像的特征值
+@返回值 处理后的图像
+*/
+IplImage* singlefeature(char* path, int feature[][50])
+{
+	//定义变量
+	        //原图
+	IplImage* imglk = 0;        //轮廓图
+	IplImage* imggj = 0;        //骨架图
+	IplImage* imgjbsb = 0;      //角标识别图
+	IplImage* imgbj = 0;        //只提取笔记部分的图像
+	IplImage* imgbjhf = 0;      //为文字区域画上方格
+	IplImage* imgwzbj = 0;      //为文字区域标出是否为文字（文字标记）
+
+	int height, width, step, channels;
+	uchar *data;
+
+	int i, j;                //用于返回图像每行黑像素的个数
+	//int feature[50][50]={0};    //特征值初始化
+
+	IplImage* img  = cvLoadImage(path, 0);
+
+	/* 获取图像信息*/	
+	
+	height = img->height;
+	width = img->width;
+	step = img->widthStep;
+	channels = img->nChannels;
+	data = (uchar *)img->imageData;
+
+	/*开始处理*/
+
+	/*图像放大*/
+	IplImage* imgbig = 0;          //原图的放大图
+	CvSize dst_cvsize;             //目标图像的大小
+	float scale = 1;
+	if (width<840){
+		scale = (float)840 / width;
+		dst_cvsize.width = 840;
+		dst_cvsize.height = (int)(height*scale);
+	}
+	else
+	{
+		dst_cvsize.width = width;
+		dst_cvsize.height = height;
+	}
+
+	imgbig = cvCreateImage(dst_cvsize, img->depth, img->nChannels);
+	cvResize(img, imgbig, CV_INTER_LINEAR);	// CV_INTER_NN - 最近邻插值,
+											//CV_INTER_LINEAR - 双线性插值 (缺省使用),
+											//CV_INTER_AREA - 使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现。
+											//CV_INTER_CUBIC - 立方插值.
+
+	/*二值化*/ 
+	binary(imgbig, g_bi_threshold);
+	//SaveLog("singlefeature_binary\n", "D:\\HWCV\\numtxt.txt", "a");
+
+
+
+	/*裁剪识别*/
+	int jbwhite = 5, jbblack = 4;
+	imgjbsb = cvCreateImage(cvGetSize(imgbig), imgbig->depth, imgbig->nChannels);
+	imgbj = Cjbsb(imgbig, imgjbsb, jbwhite, jbblack);                              //返回文字的笔迹部分
+
+
+	/*计算骨架图*/
+	imggj = cvCreateImage(cvGetSize(imgbj), imgbj->depth, imgbj->nChannels);   //复制
+	cvCopy(imgbj, imggj, NULL);
+	uchar *gjdata = (uchar *)imggj->imageData;
+
+	beforethin(gjdata, gjdata, imggj->width, imggj->height);
+
+	/*笔迹图像颜色范围转换，以进行细化*/
+	for (j = 0; j<imggj->height; j++)//取值范围转到0--1  
+	{ 
+		for (i = 0; i<imggj->width; i++)
+		{
+			if (gjdata[j*imggj->widthStep + i] == 255)
+				gjdata[j*imggj->widthStep + i] = 1;
+		}
+	}
+
+	/*细化*/
+	ThinnerRosenfeld(imggj->imageData, imggj->height, imggj->width);
+
+	/*笔记图像颜色范围转化回正常水平*/
+	for (j = 0; j<imggj->height; j++)//取值范围转到0--255,反转过来
+	{
+		for (i = 0; i<imggj->width; i++)
+		{
+			if (gjdata[j*imggj->widthStep + i] == 1)
+				gjdata[j*imggj->widthStep + i] = 0;
+			else
+				gjdata[j*imggj->widthStep + i] = 255;
+		}
+	}
+
+	/*计算骨架特征徝*/
+	outlinefeature(imggj, feature);   //特征值占48*48的右上三角形，feature调用返回
+
+	/*释放内存*/
+	cvReleaseImage(&imgbig);
+	cvReleaseImage(&img);
+	cvReleaseImage(&imgbj);
+	cvReleaseImage(&imglk);
+	cvReleaseImage(&imgjbsb);
+	cvReleaseImage(&imgbjhf);
+	cvReleaseImage(&imgwzbj);
+	cvDestroyAllWindows();
+
+	return imggj;
+}
+
+
+
+/*
+功能：细化之前的图像颜色处理，将颜色范围转换到0-1
+
+@变量 ip 图像的句柄
+@变量 jp
+@变量 lx 图象宽度
+@变量 ly 图象高度
+@返回值 空
+*/
+void beforethin(unsigned char *ip, unsigned char *jp, unsigned long lx, unsigned long ly)
+{
+	unsigned long i, j;
+
+	for (i = 0; i<ly; i++){
+		for (j = 0; j<lx; j++){
+			//这里要视前景是白点还是黑点而定，可以改动
+			//如果前景是白点，就是这样；反之反过来
+			//jp[i*lx+j]=ip[i*lx+j];
+			/*  jp[i*lx+j]=255;*/
+			if (ip[i*lx + j]>0)
+				jp[i*lx + j] = 0;
+			else
+				jp[i*lx + j] = 255;
+		}
+	}
+}
+
+
+/*功能：细化算法 ，Rosenfeld细化算法，用于完成对笔迹图像的股价提取
+
+@变量 image 代表图象的一维数组
+@变量 lx 图象宽度
+@变量 ly 图象高度
+@返回值 无返回值
+*/
+void ThinnerRosenfeld(void *image, unsigned long lx, unsigned long ly)
+{
+	char *f, *g;
+	char n[10];
+	char a[5] = { 0, -1, 1, 0, 0 };
+	char b[5] = { 0, 0, 0, 1, -1 };
+	char nrnd, cond, n48, n26, n24, n46, n68, n82, n123, n345, n567, n781;
+	short k, shori;
+	unsigned long i, j;
+	long ii, jj, kk, kk1, kk2, kk3, size;
+
+	//	printf("--Thinner_Rosenfeld--");
+	size = (long)lx * (long)ly;
+
+	g = (char *)malloc(size);
+	if (g == NULL){
+		printf("error in alocating mmeory!\n");
+		return;
+	}
+
+	f = (char *)image;
+	for (kk = 0l; kk<size; kk++){
+		g[kk] = f[kk];
+	}
+
+	do{
+		shori = 0;
+		for (k = 1; k <= 4; k++){
+			for (i = 1; i<lx - 1; i++){
+				ii = i + a[k];
+
+				for (j = 1; j<ly - 1; j++){
+					kk = i*ly + j;
+
+					if (!f[kk])
+						continue;
+
+					jj = j + b[k];
+					kk1 = ii*ly + jj;
+
+					if (f[kk1])
+						continue;
+
+					kk1 = kk - ly - 1;
+					kk2 = kk1 + 1;
+					kk3 = kk2 + 1;
+					n[3] = f[kk1];
+					n[2] = f[kk2];
+					n[1] = f[kk3];
+					kk1 = kk - 1;
+					kk3 = kk + 1;
+					n[4] = f[kk1];
+					n[8] = f[kk3];
+					kk1 = kk + ly - 1;
+					kk2 = kk1 + 1;
+					kk3 = kk2 + 1;
+					n[5] = f[kk1];
+					n[6] = f[kk2];
+					n[7] = f[kk3];
+
+					nrnd = n[1] + n[2] + n[3] + n[4]
+						+ n[5] + n[6] + n[7] + n[8];
+					if (nrnd <= 1)
+						continue;
+
+					cond = 0;
+					n48 = n[4] + n[8];
+					n26 = n[2] + n[6];
+					n24 = n[2] + n[4];
+					n46 = n[4] + n[6];
+					n68 = n[6] + n[8];
+					n82 = n[8] + n[2];
+					n123 = n[1] + n[2] + n[3];
+					n345 = n[3] + n[4] + n[5];
+					n567 = n[5] + n[6] + n[7];
+					n781 = n[7] + n[8] + n[1];
+
+					if (n[2] == 1 && n48 == 0 && n567>0){
+						if (!cond)
+							continue;
+						g[kk] = 0;
+						shori = 1;
+						continue;
+					}
+					if (n[6] == 1 && n48 == 0 && n123>0)	{
+						if (!cond)
+							continue;
+						g[kk] = 0;
+						shori = 1;
+						continue;
+					}
+					if (n[8] == 1 && n26 == 0 && n345>0){
+						if (!cond)
+							continue;
+						g[kk] = 0;
+						shori = 1;
+						continue;
+					}
+					if (n[4] == 1 && n26 == 0 && n781>0)	{
+						if (!cond)
+							continue;
+						g[kk] = 0;
+						shori = 1;
+						continue;
+					}
+					if (n[5] == 1 && n46 == 0){
+						if (!cond)
+							continue;
+						g[kk] = 0;
+						shori = 1;
+						continue;
+					}
+					if (n[7] == 1 && n68 == 0){
+						if (!cond)
+							continue;
+						g[kk] = 0;
+						shori = 1;
+						continue;
+					}
+					if (n[1] == 1 && n82 == 0){
+						if (!cond)
+							continue;
+						g[kk] = 0;
+						shori = 1;
+						continue;
+					}
+					if (n[3] == 1 && n24 == 0){
+						if (!cond)
+							continue;
+						g[kk] = 0;
+						shori = 1;
+						continue;
+					}
+					cond = 1;
+					if (!cond)
+						continue;
+					g[kk] = 0;
+					shori = 1;
+				}
+			}
+
+			for (i = 0; i<lx; i++){
+				for (j = 0; j<ly; j++){
+					kk = i*ly + j;
+					f[kk] = g[kk];
+				}
+			}
+		}
+	} while (shori);
+	free(g);
+}
--- a/测试/本机测试/1-我-算法/HWCV-exe/process.h
+++ b/测试/本机测试/1-我-算法/HWCV-exe/process.h
@@ -0,0 +1,97 @@
+/*
+头文件：process.h 图像处理函数头文件
+*/
+#pragma once
+#include "Point.h"
+#include "path.h"
+#include "process.h"
+#include <cv.h> 
+#include <direct.h>
+#include <io.h> 
+#include <iostream>
+#include <math.h>
+#include <malloc.h>
+#include <highgui.h> 
+#include <stdlib.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <vector>
+
+using namespace std;
+
+/*全局变量*/
+extern IplImage* src;
+
+/***************************************函数原型****************************************/
+
+/*
+功能：读入图像文件，进行二值化
+
+@变量 img iplimage图像文件
+@变量 bithro 二值化阈值
+@返回值 黑像素的数目（待用）
+*/
+int*  binary(IplImage*  img, int bithro);
+
+
+/*
+功能：读入图像文件，对图像进行裁剪
+
+@变量 img iplimage图像文件
+@变量 img 裁剪后的iplimage图像文件
+@jbwhite
+@jbblack
+@返回值 返回裁剪后的图像
+*/
+IplImage* Cjbsb(IplImage* img, IplImage* imgjbsb, int jbwhite, int jbblack);
+
+
+/*
+功能：计算图像的特征
+
+@变量 imgbj 笔迹部分的图像
+@返回值 计算得到的特征图像
+*/
+IplImage* outline(IplImage* imgbj);
+
+
+/*
+功能：输入图像的特征轮廓图，返回图像的特征值
+@变量 imglk 输入的图像轮廓图
+@变量 feature 得到的图像特征
+@返回值 成功1失败0
+*/
+int outlinefeature(IplImage* imglk, int feature[][50]);
+
+
+/*
+功能：对单张图像的处理，最终得到一个特征值，用来计算各个图像之间的卡方距离
+
+@变量 path 图像的物理地址
+@变量 feature 图像的特征值
+@返回值 处理后的图像
+*/
+IplImage* singlefeature(char* path, int feature[][50]);
+
+
+/*
+功能：细化之前的图像颜色处理，将颜色范围转换到0-1
+
+@变量 ip 图像的句柄
+@变量 jp
+@变量 lx 图象宽度
+@变量 ly 图象高度
+@返回值 空
+*/
+void beforethin(unsigned char *ip, unsigned char *jp, unsigned long lx, unsigned long ly);
+
+
+/*功能：细化算法 ，Rosenfeld细化算法，用于完成对笔迹图像的股价提取
+
+@变量 image 代表图象的一维数组
+@变量 lx 图象宽度
+@变量 ly 图象高度
+@返回值 无返回值
+*/
+void ThinnerRosenfeld(void *image, unsigned long lx, unsigned long ly);
--- a/测试/本机测试/1-我-算法/HWCV-exe/seg_bck.cpp
+++ b/测试/本机测试/1-我-算法/HWCV-exe/seg_bck.cpp
@@ -0,0 +1,219 @@
+///*
+//主函数文件：segmentation.cpp 主函数的实现文件
+//*/
+//#include "segmentation.h"
+////#pragma comment( linker, "/subsystem:windows /entry:mainCRTStartup" )//无界面运行
+///*主函数*/
+//int ComputeImage(vector<string> files, double bzckesa[50][50], double *wcd, int conti);
+//int main(int argc, char* argv[])
+//{
+//	/*变量定义*/
+//	string dir = "E:\\xiangmu\\Img\\imgjiaobiao\\010211100518"; //存储目录
+//	//string dir;
+//	//if (argc < 2)
+//	//	return -1;
+//	//else
+//	//	dir = argv[1];
+//	cout << (char*)dir.c_str() << endl;
+//	char	record[2400] = { 0 };
+//	FILE*	fpzz = NULL;//需要注意
+//	int		i, ii, jj, feature[50][50][30] = { 0 }, featureall = 0;
+//	double	featurep[50][50][30] = { 0 };
+//	double  bzcu[50][50] = { 0 };
+//	double  bzckesa[50][50] = { 0 };
+//	double  wcd[30] = { 0 };
+//
+//	int		featx[50][50] = { 0 };
+//	int		featdif[30] = { 0 };
+//	float	maxx = 0;			//最大特征值的标号与值
+//	int		xyimgnum = 0;				//嫌疑图片的数目
+//	char	str[80];					//存储地址
+//
+//	vector<string> suspict;		//记录嫌疑图片地址
+//	vector<float> suspict_wcd;	//嫌疑图片的wcd值
+//	vector<string> files;		//存储该生所有考试文件路径
+//
+//	/*读取配置文件，并配置各项参数*/
+//	if (!ReadConfig("D:/HWCV/config/configure.cfg"))
+//	{
+//		//		SaveLog("\t配置文件读取失败\n", g_log_adr, "a");
+//		SaveLog("\n0\n", g_log_adr, "a");
+//		return 0;
+//	}
+//	char path[100] = "E:/xiangmu/Img/imgjiaobiao/";
+//
+//
+//
+//	/*步骤：读取标准差文件*/
+//	int bzccolumns = 47;//txt文件中有47列
+//	vector<double *> output_bzc;
+//	if (!ReadScanf("D:\\HWCV\\config\\stdfile.db", bzccolumns, output_bzc))
+//	{
+//		memset(g_log_rec, 0, sizeof(g_log_rec));
+//
+//		//		SaveLog("\n读取路径：D:\\HWCV\\config\\stdfile.db 的标准差文件失败！\n", g_log_adr, "a");
+//		SaveLog("\n0\n", g_log_adr, "a");
+//		return 0;
+//	}
+//
+//	//开始检测
+//	//-------------------------------------------------------------//
+//	getFiles(dir.c_str(), files);	//遍历当前文件夹下的所有文件
+//	int size = files.size();
+//
+//	for (i = 0; i < size; i++)
+//	{
+//		//		cout << ".";
+//		memset(str, 0, sizeof(str));
+//		memset(featx, 0, sizeof(featx));
+//		memset(bzcu, 0, sizeof(bzcu));
+//
+//		strcpy(str, files[i].c_str());
+//		singlefeature(str, featx);//featx[][50]
+//
+//		featureall = 0;                       //图像特征值和的初始化
+//		for (ii = 0; ii < 48; ii++)           //将featx存起来,回头看能不能用函数换掉
+//			for (jj = ii + 1; jj < 47; jj++)
+//			{
+//			feature[ii][jj][i] = featx[ii][jj];
+//			featureall = featureall + featx[ii][jj];
+//			}
+//		/*求轮廓方向特征featurep，式(5)  与标准差中的u的和*/
+//		for (ii = 0; ii < 48; ii++)
+//			for (jj = ii + 1; jj < 47; jj++)
+//			{
+//			featurep[ii][jj][i] = (double)featx[ii][jj] / featureall;
+//			bzcu[ii][jj] += (double)featx[ii][jj] / featureall * 1000;     //标准差的值过小,进行放大（1）
+//			}
+//	}/*处理完全部图片*/
+//
+//	/*求标准差中u*/
+//	for (ii = 0; ii < 48; ii++)
+//		for (jj = ii + 1; jj < 47; jj++)
+//			bzcu[ii][jj] = bzcu[ii][jj] / size;
+//
+//
+//	for (ii = 0; ii < 48; ii++)//output_vector可视为二维数组;输出数组元素：
+//		for (jj = ii + 1; jj < 47; jj++)
+//			bzckesa[ii][jj] = output_bzc[ii][jj];
+//
+//	/*求相似性，即带权卡方wcd*/
+//	for (i = 0; i < size; i++)
+//		for (ii = 0; ii < 48; ii++)
+//			for (jj = ii + 1; jj < 47; jj++)
+//				if (featurep[ii][jj][i] * featurep[ii][jj][g_conti] != 0 && bzckesa[ii][jj] != -1)
+//					wcd[i] += pow((featurep[ii][jj][i] - featurep[ii][jj][g_conti]), 2) / ((featurep[ii][jj][i] + featurep[ii][jj][g_conti])*bzckesa[ii][jj]);
+//
+//	//标出所有有嫌疑的图像1，无嫌疑的图像0
+//	for (i = 0; i < size; i++)
+//	{
+//		if (wcd[i] > 0.12)
+//		{
+//			xyimgnum++;
+//			suspict.push_back(files[i].c_str());
+//			suspict_wcd.push_back(wcd[i]);
+//		}
+//	}
+//
+//	/*将结果存入log文件*/
+//	strcat(g_log_rec, "\n");
+//	memset(g_log_rec, 0, sizeof(g_log_rec));
+//	//	strcat(g_log_rec, GetTime());
+//	//	strcat(g_log_rec, "\t考生考号：");
+//	strcat(g_log_rec, dir.substr(27, 22).c_str());
+//	strcat(g_log_rec, "\t");//图片总数为：
+//	char pic_num[20];
+//	_itoa(size, pic_num, 10);
+//	strcat(g_log_rec, pic_num);
+//	if (xyimgnum > 0)
+//	{
+//		char b[20];
+//		sprintf(b, "\t%d", xyimgnum);
+//		strcat(g_log_rec, b);
+//		strcat(g_log_rec, "\n");
+//		for (i = 0; i < xyimgnum; i++)
+//		{
+//			//			cout << "嫌疑图像：" << files[i].c_str() << endl;
+//			strcat(g_log_rec, "\t\t\t");//\t嫌疑图像：
+//			strcat(g_log_rec, suspict[i].c_str());
+//			strcat(g_log_rec, "\t");//相似度：
+//			float sim = (1.0 - suspict_wcd[i]) * 100;
+//			char a[20];
+//			sprintf(a, "%g", sim);
+//			strcat(g_log_rec, a);
+//			strcat(g_log_rec, "%%\n");
+//		}
+//	}
+//	else
+//	{
+//		//		strcat(g_log_rec, "\t该考生没有嫌疑图像！\n");
+//		strcat(g_log_rec, "\t0\n");
+//		//		cout << "该考生无嫌疑图像！" << endl;
+//	}
+//	SaveLog(g_log_rec, g_log_adr, "a");
+//
+//
+//	/*善后*/
+//	suspict.clear();
+//	suspict_wcd.clear();
+//	memset(g_log_rec, 0, sizeof(g_log_rec));
+//	memset(feature, 0, sizeof(feature));
+//	memset(featurep, 0, sizeof(featurep));
+//	memset(bzckesa, 0, sizeof(bzckesa));
+//	memset(wcd, 0, sizeof(wcd));
+//	memset(featdif, 0, sizeof(featdif));
+//	files.clear();
+//	memset(g_log_rec, 0, sizeof(g_log_rec));
+//
+//	/*返回值*/
+//	return 0;
+//}
+//
+//
+//int ComputeImage(vector<string> files, double bzckesa[50][50], double *wcd, int conti)
+//{
+//	int i, ii, jj, k, size;
+//	double	bzcu[50][50] = { 0 };			//标准差中的u
+//	double	featurep[50][50][30] = { 0 };	//所有图像的轮廓方向特征初始化//干什么	//30
+//	int		feature[50][50][30] = { 0 };	//所有图像的特征值初始化	//所有图像指的什么意思	//30找出30的位置或者50的位置限制。。。。带入num_dir==49的情况进行类比
+//	int		featx[50][50] = { 0 };			//循环赋值的feature
+//	int		featureall;					//图像特征值和				//做什么用
+//	IplImage* imglk[30];				//轮廓图变量				//30
+//
+//	size = files.size();
+//	for (i = 0; i < size; i++)
+//	{
+//		memset(featx, 0, sizeof(featx));
+//		//		strcpy(str,files[i].c_str());
+//		imglk[i] = singlefeature((char*)files[i].c_str(), featx);				//featx[][50]
+//		featureall = 0;                                    //图像特征值和的初始化
+//		for (ii = 0; ii<48; ii++)                             //将featx存起来,回头看能不能用函数换掉
+//			for (jj = ii + 1; jj<47; jj++)
+//			{
+//			feature[ii][jj][i] = featx[ii][jj];
+//			featureall = featureall + featx[ii][jj];
+//			}
+//		//求轮廓方向特征featurep，式(5)  与标准差中的u的和
+//		for (ii = 0; ii<48; ii++)
+//			for (jj = ii + 1; jj<47; jj++)
+//			{
+//			featurep[ii][jj][i] = (double)featx[ii][jj] / featureall;
+//			bzcu[ii][jj] += (double)featx[ii][jj] / featureall * 1000;     //标准差的值过小,进行放大（1）
+//			}
+//	}
+//	//处理完一个人的每一张图片后
+//	for (ii = 0; ii<48; ii++)//求标准差中的u
+//		for (jj = ii + 1; jj<47; jj++)
+//			bzcu[ii][jj] = bzcu[ii][jj] / size;
+//	//求相似性就是带权卡方wcd
+//	for (i = 0; i < size; i++)
+//		for (ii = 0; ii<48; ii++)
+//			for (jj = ii + 1; jj<47; jj++)
+//				if (featurep[ii][jj][i] * featurep[ii][jj][conti] != 0 && bzckesa[ii][jj] != -1)
+//					wcd[i] += pow((featurep[ii][jj][i] - featurep[ii][jj][conti]), 2) / ((featurep[ii][jj][i] + featurep[ii][jj][conti])*bzckesa[ii][jj]);
+//	memset(imglk, 0, sizeof(imglk));
+//	memset(feature, 0, sizeof(feature));
+//	memset(featurep, 0, sizeof(featurep));
+//
+//	return 1;
+//}
--- a/测试/本机测试/1-我-算法/HWCV-exe/segmentation.cpp
+++ b/测试/本机测试/1-我-算法/HWCV-exe/segmentation.cpp
@@ -0,0 +1,225 @@
+/*
+主函数文件：segmentation.cpp 主函数的实现文件
+*/
+#include "segmentation.h"
+//#pragma comment( linker, "/subsystem:windows /entry:mainCRTStartup" )//无界面运行
+/*主函数*/
+
+
+int ComputeImage(vector<string> files, double bzckesa[50][50], double *wcd, int conti)
+{
+	int i, ii, jj, k, size;
+	double	bzcu[50][50] = { 0 };			//标准差中的u
+	double	featurep[50][50][30] = { 0 };	//所有图像的轮廓方向特征初始化//干什么	//30
+	int		feature[50][50][30] = { 0 };	//所有图像的特征值初始化	//所有图像指的什么意思	//30找出30的位置或者50的位置限制。。。。带入num_dir==49的情况进行类比
+	int		featx[50][50] = { 0 };			//循环赋值的feature
+	int		featureall;					//图像特征值和				//做什么用
+
+	size = files.size();
+	for (i = 0; i < size; i++)
+	{
+		memset(featx, 0, sizeof(featx));
+		//		strcpy(str,files[i].c_str());
+		singlefeature((char*)files[i].c_str(), featx);				//featx[][50]
+		featureall = 0;                                    //图像特征值和的初始化
+		for (ii = 0; ii < 48; ii++)                             //将featx存起来,回头看能不能用函数换掉
+			for (jj = ii + 1; jj < 47; jj++)
+			{
+			feature[ii][jj][i] = featx[ii][jj];
+			featureall = featureall + featx[ii][jj];
+			}
+		//求轮廓方向特征featurep，式(5)  与标准差中的u的和
+		for (ii = 0; ii < 48; ii++)
+			for (jj = ii + 1; jj < 47; jj++)
+			{
+			featurep[ii][jj][i] = (double)featx[ii][jj] / featureall;
+			bzcu[ii][jj] += (double)featx[ii][jj] / featureall * 1000;     //标准差的值过小,进行放大（1）
+			}
+	}
+	//处理完一个人的每一张图片后
+	for (ii = 0; ii < 48; ii++)//求标准差中的u
+		for (jj = ii + 1; jj < 47; jj++)
+			bzcu[ii][jj] = bzcu[ii][jj] / size;
+	//求相似性就是带权卡方wcd
+	for (i = 0; i < size; i++)
+		for (ii = 0; ii < 48; ii++)
+			for (jj = ii + 1; jj < 47; jj++)
+				if (featurep[ii][jj][i] * featurep[ii][jj][conti] != 0 && bzckesa[ii][jj] != -1)
+					wcd[i] += pow((featurep[ii][jj][i] - featurep[ii][jj][conti]), 2) / ((featurep[ii][jj][i] + featurep[ii][jj][conti])*bzckesa[ii][jj]);
+	memset(feature, 0, sizeof(feature));
+	memset(featurep, 0, sizeof(featurep));
+
+	return 1;
+}
+
+
+
+int main(int argc, char* argv[])
+{
+	/*变量定义*/
+	//string dir;
+	//if (argc < 2)
+	//	return -1;
+	//else
+	//	dir = argv[1];
+
+
+	// 调试
+	//	string dir = "E:/xiangmu/Img/imgjiaobiao/010211100518"; //存储目录
+
+	// 定义变量    
+	vector<string> dir;				//存储目录
+	int conti = 1;     //对比图像的标号
+	int size_dir, num_dir;
+	char	record[2400] = { 0 };
+
+	// 获取待检测文件夹到size
+	char path[100] = "E:/xiangmu/Img/imgjiaobiao/";//D:/xiangmu/Img/imgjiaobiao/
+	searchDir(path, dir);//获取filePath下的所有一级目录并存储到dir中
+	//	dir.push_back("E:/xiangmu/Img/imgjiaobiao/010211100518"); //存储目录
+	size_dir = dir.size(); //dir的大小就是学生的数量
+	stuAll = size_dir;
+	cout << "学生总数为" << stuAll << endl;
+
+	// 开始检测每个文件夹下的
+	for (num_dir = 0; num_dir < size_dir; num_dir++)//对每一个学生目录进行循环
+	{
+		cout <<(char*)dir[num_dir].c_str();
+		char	record[2400] = { 0 };
+		FILE*	fpzz = NULL;//需要注意
+		int		i, ii, jj, feature[50][50][30] = { 0 }, featureall = 0;
+		double	featurep[50][50][30] = { 0 };
+		double  bzcu[50][50] = { 0 };
+		double  bzckesa[50][50] = { 0 };
+		double  wcd[30] = { 0 };
+
+		int		featx[50][50] = { 0 };
+		int		featdif[30] = { 0 };
+		float	maxx = 0;			//最大特征值的标号与值
+		int		xyimgnum = 0;				//嫌疑图片的数目
+		char	str[80];					//存储地址
+
+		vector<string> suspict;		//记录嫌疑图片地址
+		vector<float> suspict_wcd;	//嫌疑图片的wcd值
+		vector<string> files;		//存储该生所有考试文件路径
+
+		/*读取配置文件，并配置各项参数*/
+		if (!ReadConfig("D:/HWCV/config/configure.cfg"))
+		{
+			//		SaveLog("\t配置文件读取失败\n", g_log_adr, "a");
+			SaveLog("\n0\n", g_log_adr, "a");
+			return 0;
+		}
+		char path[100] = "E:/xiangmu/Img/imgjiaobiao/";
+
+
+
+		/*步骤：读取标准差文件*/
+		int bzccolumns = 47;//txt文件中有47列
+		vector<double *> output_bzc;
+		if (!ReadScanf("D:\\HWCV\\config\\stdfile.db", bzccolumns, output_bzc))
+		{
+			memset(g_log_rec, 0, sizeof(g_log_rec));
+
+			//		SaveLog("\n读取路径：D:\\HWCV\\config\\stdfile.db 的标准差文件失败！\n", g_log_adr, "a");
+			SaveLog("\n0\n", g_log_adr, "a");
+			return 0;
+		}
+		for (ii = 0; ii < 48; ii++)//output_vector可视为二维数组;输出数组元素：
+			for (jj = ii + 1; jj < 47; jj++)
+				bzckesa[ii][jj] = output_bzc[ii][jj];
+
+		//开始检测
+		//-------------------------------------------------------------//
+		getFiles(dir[num_dir].c_str(), files);	//遍历当前文件夹下的所有文件
+		int size = files.size();
+		cout << " 文件数：" << size;
+
+		//开始对每一张图片进行处理
+		for (int r = 0; r < size; r++)
+		{
+			memset(wcd, 0, sizeof(wcd));
+			ComputeImage(files, bzckesa, wcd, r);
+			xyimgnum = 0;
+			//求卡方距离的最大值 
+			for (i = 0; i < size; i++)
+			{
+//				cout << files[i].c_str() << "  " << wcd[i] << endl;
+				if (wcd[i]>0.12)
+				{
+					xyimgnum++;
+					suspict.push_back(files[i].c_str());
+					suspict_wcd.push_back(wcd[i]);
+				}
+			}
+			if (xyimgnum < 3) break;
+		}
+
+		/*将结果存入log文件*/
+		memset(g_log_rec, 0, sizeof(g_log_rec));
+		//	strcat(g_log_rec, GetTime());
+		//	strcat(g_log_rec, "\t考生考号：");
+		strcat(g_log_rec, dir[num_dir].substr(27, 22).c_str());//学号
+		strcat(g_log_rec, ",");//图片总数为：
+		char pic_num[20];
+		_itoa(size, pic_num, 10);
+		strcat(g_log_rec, pic_num);
+		if (xyimgnum > 0)
+		{
+			stuSus++;
+			char b[20];
+			sprintf(b, ",%d", xyimgnum);
+			strcat(g_log_rec, b);
+			strcat(g_log_rec, "\n");
+			for (i = 0; i < xyimgnum; i++)
+			{
+	//			cout << "嫌疑图像：" << files[i].c_str() << endl;
+				strcat(g_log_rec, ",,,");//\t嫌疑图像：
+				strcat(g_log_rec, suspict[i].c_str());
+				strcat(g_log_rec, ",");//相似度：
+				float sim = (1.0 - suspict_wcd[i]) * 100;
+				char a[20];
+				sprintf(a, "%g", sim);
+				strcat(g_log_rec, a);
+				strcat(g_log_rec, "%%\n");
+			}
+		}
+		else
+		{
+			//		strcat(g_log_rec, "\t该考生没有嫌疑图像！\n");
+			strcat(g_log_rec, ",0,");
+//			cout << "该考生无嫌疑图像！" << endl;
+		}
+		SaveLog(g_log_rec, g_log_adr, "a");
+
+		printf("嫌疑数量：%d, ", xyimgnum);
+		picAll += size;
+		picSus += xyimgnum;
+		printf("全部:%d嫌疑：%d比例为：%g\n", picAll, picSus, ((float)picSus) / ((float)picAll));
+		xyimgnum = 0;
+
+		/*善后*/
+		suspict.clear();
+		suspict_wcd.clear();
+		memset(g_log_rec, 0, sizeof(g_log_rec));
+		memset(feature, 0, sizeof(feature));
+		memset(featurep, 0, sizeof(featurep));
+		memset(bzckesa, 0, sizeof(bzckesa));
+		memset(wcd, 0, sizeof(wcd));
+		memset(featdif, 0, sizeof(featdif));
+		files.clear();
+		memset(g_log_rec, 0, sizeof(g_log_rec));
+
+		/*返回值*/
+	}
+
+	dir.clear();
+
+	cout << "学生总数:" << stuAll << " 作弊人数：" << stuSus << endl;
+	printf("已经打印到txt中！");
+	string open = "start " + (string)g_log_adr;
+	system(open.c_str());
+	system("pause");
+
+	return 0; //(1-wcd[maxi])*100
+}
--- a/测试/本机测试/1-我-算法/HWCV-exe/segmentation.h
+++ b/测试/本机测试/1-我-算法/HWCV-exe/segmentation.h
@@ -0,0 +1,45 @@
+/*
+头文件：segmentation.h 主函数头文件
+*/
+#pragma once
+#ifdef WIN32                            //屏蔽VC6对STL的一些不完全支持造成
+#pragma warning (disable: 4514 4786)
+#endif
+#include "StdAfx.h"
+#include "path.h"
+#include "Point.h"
+#include "process.h"
+
+using namespace std;
+
+/*定义全局变量*/
+
+
+int		g_bi_threshold = 230;				/* 全局变量 二值化阈值*/
+double  g_std_kesa[50][50];			/* 全局变量 标准差数组*/
+float   g_doubt_threshold = 0.12;	/* 全局变量 作弊嫌疑阈值*/
+string  g_dir = "Y:/";						/* 全局变量 总路径的目录*/
+int		g_cheat_num_threshold = 0;	/* 全局变量 默认作弊阈值*/
+int		g_conti = 1;					/* 全局变量 默认作弊比较的图片*/
+
+int g_all_img_num = 0;				/* 全局变量 已鉴定的全部图片数量*/
+int g_doubt_img_num = 0;			/* 全局变量 已鉴定怀疑的图片数量*/
+int g_all_stu_num = 0;				/* 全局变量 已鉴定的全部学生数量*/
+int g_doubt_stu_num = 0;			/* 全局变量 已鉴定怀疑的学生数量*/
+
+bool g_output_cmd_config = false;	/*全局变量 输出参数控制*/
+bool g_output_txt_config = false;	/*全局变量 输出中间文件选项*/
+char g_log_adr[50] = "D:/HWCV/log_ori.txt";	/*全局变量 程序日志存储地址*/
+char g_log_rec[500] = { 0 };		/*全局变量 程序日志专用变量*/
+char g_err_adr[50]= "D:/HWCV/err_ori.txt";					/*错误日志路径*/
+/*全局变量 输出txt结果文件*/
+/*全局变量 输出txt结果文件地址*/
+int picAll =0, picSus=0;
+int stuAll = 0, stuSus = 0;
+
+
+
+time_t ltime;
+char *srcTime=NULL;
+char timeNow[32]={0};
+char msg[100]={0};