242 lines
6.4 KiB
C++
242 lines
6.4 KiB
C++
/*
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主函数文件：segmentation.cpp 主函数的实现文件
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*/
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#include "segmentation.h"
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#pragma comment( linker, "/subsystem:windows /entry:mainCRTStartup" )//无界面运行
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/*主函数*/
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int main(int argc,char* argv[])
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{
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if(argc<2) return -1;
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//for(int iii=0;iii<argc;iii++)
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// cout<<"argv"<<iii<<" "<<argv[iii]<<endl;
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char *fpname1 = argv[1];
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// cout<<"开始鉴定"<<fpname1<<endl;
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//char *fpname1 = "010209400748";
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/*变量定义*/
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int i, ii, jj, feature[50][50][30] = { 0 }, featureall = 0;
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double featurep[50][50][30] = { 0 };
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double bzcu[50][50] = { 0 };
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double bzckesa[50][50] = { 0 };
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double wcd[30] = { 0 };
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int featx[50][50] = { 0 };
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int featdif[30] = { 0 };
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float maxx = 0; //最大特征值的标号与值
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int xyimgnum = 0; //嫌疑图片的数目
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char str[80]; //存储地址
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vector<string> suspict; //记录嫌疑图片地址
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vector<float> suspict_wcd; //嫌疑图片的wcd值
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vector<string> files; //存储该生所有考试文件路径
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vector<string> dateVec, subjectVec, stuNum2;
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vector<string> flagVec;//记录查到的学生的所有考试信息
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/*读取配置文件，并配置各项参数*/
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if (!ReadConfig("D:/HWCV/config/configure.cfg"))
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{
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SaveLog("\t配置文件读取失败\n", g_log_adr, "a");
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return 0;
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}
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// cout<<"ReadConfig success"<<endl;
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//
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// string temp=g_db_hostName;
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// temp+=g_db_dBName;temp+=g_db_userName;temp+=g_db_password;
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// SaveLog((char*)temp.c_str(), g_log_adr, "a");
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//
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//查询数据库
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string stuNum = fpname1;
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if (!DbImg(stuNum, dateVec, subjectVec, stuNum2))
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{
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memset(g_log_rec, 0, sizeof(g_log_rec));
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strcat(g_log_rec, GetTime());
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strcat(g_log_rec, "\t数据库查询不到考号为：");
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strcat(g_log_rec,fpname1);
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strcat(g_log_rec, "的学生\n");
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SaveLog(g_log_rec, g_log_adr, "a");
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return 0;
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}
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//SaveLog("成功DB！！！\n", g_log_adr, "a");
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// cout << "成功DB\n数量为：" << subjectVec.size() << endl;
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//生成路径
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int nn = 0;
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for (int cp_i = 0; cp_i < subjectVec.size(); cp_i++)
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{
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if (!strcmp(subjectVec[cp_i].substr(0, 1).c_str(), "4") || !strcmp(subjectVec[cp_i].substr(0, 1).c_str(), "9"))
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{
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// cout << "无图像：" <<subjectVec[cp_i].c_str()<<" "<<dateVec[cp_i].c_str()<<" "<<stuNum2[cp_i].c_str()<< endl;
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continue;
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}
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files.push_back(CrPath(dateVec[cp_i], subjectVec[cp_i], stuNum2[cp_i]));
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// cout<<"PATH:"<<files[nn++].c_str()<<endl;
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// SaveLog((char*)files[cp_i].c_str(), g_log_adr, "a");
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// SaveLog("\n", g_log_adr, "a");
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}
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// cout << "生成路径结束" << endl;
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int size = files.size();/*找到的路径的数量*/
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/*对每一张图片进行处理*/
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int realSize = 0;
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for (i = 0; i < size; i++)
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{
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memset(str, 0, sizeof(str));
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memset(featx, 0, sizeof(featx));
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memset(bzcu, 0, sizeof(bzcu));
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strcpy(str, files[i].c_str());
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try{
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singlefeature(str, featx);//featx[][50]
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//正常处理了图像，说明图像可以照常处理
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realSize += 1;
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}
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catch (_com_error e)
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{
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//没有正常处理图像，说明此地址下的图像不可用
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memset(g_log_rec, 0, sizeof(g_log_rec));
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strcat(g_log_rec, "\n读取路径：");
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strcat(g_log_rec, str);
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strcat(g_log_rec, " 的图像失败:");
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strcat(g_log_rec, (char*)e.Description());
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strcat(g_log_rec, " 的图像失败！\n");
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SaveLog(g_log_rec, g_log_adr, "a");
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continue;
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}
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// cout << "可以opencv\n" << endl;
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featureall = 0; //图像特征值和的初始化
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for (ii = 0; ii < 48; ii++) //将featx存起来,回头看能不能用函数换掉
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for (jj = ii + 1; jj < 47; jj++)
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{
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feature[ii][jj][i] = featx[ii][jj];
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featureall = featureall + featx[ii][jj];
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}
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/*求轮廓方向特征featurep，式(5) 与标准差中的u的和*/
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for (ii = 0; ii < 48; ii++)
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for (jj = ii + 1; jj < 47; jj++)
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{
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featurep[ii][jj][i] = (double)featx[ii][jj] / featureall;
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bzcu[ii][jj] += (double)featx[ii][jj] / featureall * 1000; //标准差的值过小,进行放大（1）
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}
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}/*处理完全部图片*/
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if (realSize == 0)
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{
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SaveLog("\t此考生没有一张数据图像可用！\n", g_log_adr, "a");
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return 0;
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}
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/*求标准差中u*/
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for (ii = 0; ii < 48; ii++)
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for (jj = ii + 1; jj < 47; jj++)
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bzcu[ii][jj] = bzcu[ii][jj] / realSize;
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/*步骤：读取标准差文件*/
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int bzccolumns = 47;//txt文件中有47列
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vector<double *> output_bzc;
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if (!ReadScanf("D:\\HWCV\\config\\stdfile.db", bzccolumns, output_bzc))
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{
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memset(g_log_rec, 0, sizeof(g_log_rec));
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SaveLog("\n读取路径：D:\\HWCV\\config\\stdfile.db 的标准差文件失败！\n", g_log_adr, "a");
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return 0;
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}
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for (ii = 0; ii < 48; ii++)//output_vector可视为二维数组;输出数组元素：
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for (jj = ii + 1; jj < 47; jj++)
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bzckesa[ii][jj] = output_bzc[ii][jj];
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/*求相似性，即带权卡方wcd*/
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for (i = 0; i < realSize; i++)
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for (ii = 0; ii < 48; ii++)
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for (jj = ii + 1; jj < 47; jj++)
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if (featurep[ii][jj][i] * featurep[ii][jj][g_conti] != 0 && bzckesa[ii][jj] != -1)
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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]);
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//标出所有有嫌疑的图像1，无嫌疑的图像0
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for (i = 0; i < dateVec.size(); i++)
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{
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if (wcd[i] > 0.12)
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{
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xyimgnum++;
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suspict.push_back(files[i].c_str());
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suspict_wcd.push_back(wcd[i]);
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flagVec.push_back("1");//嫌疑标记1
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}
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else
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{
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flagVec.push_back("0");
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}
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}
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//结果更新数据库
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DbUpdate(stuNum, dateVec, subjectVec, stuNum2, flagVec);
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// cout << "数据库更新完毕" << endl;
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/*将结果存入log文件*/
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strcat(g_log_rec, "\n");
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memset(g_log_rec, 0, sizeof(g_log_rec));
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strcat(g_log_rec, GetTime());
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strcat(g_log_rec, "\t考生考号：");
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strcat(g_log_rec, fpname1);
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strcat(g_log_rec, " 图片总数为：");
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char pic_num[20];
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_itoa(realSize, pic_num, 10);
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strcat(g_log_rec, pic_num);
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if (xyimgnum > 0)
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{
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/* strcat(g_log_rec, "\t");
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strcat(g_log_rec, suspict[0].c_str());
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strcat(g_log_rec, "\t");
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char a[20];
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sprintf(a, "%g", suspict_wcd[0]);
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strcat(g_log_rec, a);
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*/ strcat(g_log_rec, "\n");
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for (i = 0; i < xyimgnum; i++)
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{
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strcat(g_log_rec, "\t\t\t\t嫌疑图像：");
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strcat(g_log_rec, suspict[i].c_str());
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strcat(g_log_rec, "\t相似度：");
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float sim=(1.0-suspict_wcd[i])*100;
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char a[20];
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sprintf(a, "%g", sim);
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strcat(g_log_rec, a);
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strcat(g_log_rec, "%%\n");
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}
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}
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else
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strcat(g_log_rec, "\t该考生没有嫌疑图像！\n");
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SaveLog(g_log_rec, g_log_adr, "a");
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/*善后*/
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suspict.clear();
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suspict_wcd.clear();
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output_bzc.clear();
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memset(g_log_rec, 0, sizeof(g_log_rec));
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memset(feature, 0, sizeof(feature));
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memset(featurep, 0, sizeof(featurep));
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memset(bzckesa, 0, sizeof(bzckesa));
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memset(wcd, 0, sizeof(wcd));
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memset(featdif, 0, sizeof(featdif));
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files.clear();
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dateVec.clear();
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subjectVec.clear();
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stuNum2.clear();
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flagVec.clear();
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/*返回值*/
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return 0;
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}
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