static std::vector<std::string> plate_recognize(const char* image,
const char* model_svm,
const char* model_ann,
const bool life_mode = true) {
cv::Mat img = cv::imread(image);
_ASSERT(!img.empty());
CPlateRecognize pr;
pr.setLifemode(life_mode);
pr.setDebug(false);
std::vector<std::string> results;
pr.plateRecognize(img, results);
return std::move(results);
}
int test_plate_recognize()
{
cout << "test_plate_recognize" << endl;
Mat src = imread(img_plate_recognize);
CPlateRecognize pr;
pr.LoadANN("model/ann.xml");
pr.LoadSVM("model/svm.xml");
pr.setLifemode(true);
pr.setDebug(true);
vector<string> plateVec;
int result = pr.plateRecognize(src, plateVec);
if (result == 0)
{
int num = plateVec.size();
for (int j = 0; j < num; j++)
{
cout << "plateRecognize: " << plateVec[j] << endl;
}
}
if (result != 0)
cout << "result:" << result << endl;
return result;
}
JNIEXPORT jbyteArray JNICALL Java_com_example_carplate_CarPlateDetection_ImageProc(
JNIEnv *env, jclass obj, jstring imgpath, jstring svmpath,
jstring annpath) {
CPlateRecognize pr;
// const string *img = (*env)->GetStringUTFChars(env, imgpath, 0);
// const string *svm = (*env)->GetStringUTFChars(env, svmpath, 0);
// const string *ann = (*env)->GetStringUTFChars(env, annpath, 0);
char* img = jstring2str(env,imgpath);
char* svm = jstring2str(env,svmpath);
char* ann = jstring2str(env,annpath);
Mat src = imread(img);
pr.LoadSVM(svm);
pr.LoadANN(ann);
pr.setGaussianBlurSize(5);
pr.setMorphSizeWidth(17);
pr.setVerifyMin(3);
pr.setVerifyMax(20);
pr.setLiuDingSize(7);
pr.setColorThreshold(150);
vector < string > plateVec;
int count = pr.plateRecognize(src, plateVec);
string str = "0";
if (count == 0) {
str = plateVec[0];
}
char *result = new char[str.length() + 1];
strcpy(result, str.c_str());
jbyte *by = (jbyte*) result;
jbyteArray jarray = env->NewByteArray(strlen(result));
env->SetByteArrayRegion(jarray, 0, strlen(result), by);
return jarray;
}
int test_plate_recognize()
{
cout << "test_plate_recognize" << endl;
Mat src = imread("image/plate_locate.jpg");
//Mat src = imread("image/baidu_image/test6.jpg");
CPlateRecognize pr;
pr.LoadANN("model/ann.xml");
pr.LoadSVM("model/svm.xml");
pr.setGaussianBlurSize(5);
pr.setMorphSizeWidth(17);
pr.setVerifyMin(3);
pr.setVerifyMax(20);
pr.setLiuDingSize(7);
pr.setColorThreshold(150);
vector<string> plateVec;
int result = pr.plateRecognize(src, plateVec);
if (result == 0)
{
int num = plateVec.size();
for (int j = 0; j < num; j++)
{
cout << "plateRecognize: " << plateVec[j] << endl;
}
}
cout << "Enter 1 for coninue:";
int a = 0;
cin >> a;
return result;
}
static std::vector<CPlate> tcl_plate_recognize1(cv::Mat img,
const char* model_svm,
const char* model_ann,
const bool life_mode1 = true) {
// img = cv::imread(image);
assert(!img.empty());
CPlateRecognize pr;
pr.LoadSVM(model_svm);
pr.LoadANN(model_ann);
pr.setLifemode(life_mode1);
pr.setDebug(false);
std::vector<std::string> results;
std::vector<CPlate> allPlates;
pr.tcl_plateRecognize(img, results, 0, allPlates);
return allPlates;
// return std::move(results);
}
static std::vector<std::string> plate_recognize(const char* image,
const char* model_svm,
const char* model_ann,
const bool life_mode = true) {
cv::Mat img = cv::imread(image);
assert(!img.empty());
CPlateRecognize pr;
pr.LoadSVM(model_svm);
pr.LoadANN(model_ann);
pr.setLifemode(life_mode);
pr.setDebug(false);
std::vector<std::string> results;
std::vector<easypr::CPlate> plates;
pr.plateRecognize(img, results, plates);
return std::move(results);
}
int acurayTest(const string& test_path) {
////获取该路径下的所有文件
auto files = Utils::getFiles(test_path);
// CPlateLocate lo;
// CPlateJudge ju;
CPlateRecognize pr;
pr.LoadANN("model/ann.xml");
pr.LoadSVM("model/svm.xml");
pr.setLifemode(true);
pr.setDebug(false);
// 设置要处理的一张图片中最多有多少车牌
pr.setMaxPlates(4);
// CPlateDetect pd;
// pd.LoadSVM("model/svm.xml");
// pd.setPDLifemode(true);
int size = files.size();
// int size = 200;
if (0 == size) {
cout << "No File Found in general_test/native_test!" << endl;
return 0;
}
cout << "Begin to test the easypr accuracy!" << endl;
// 总的测试图片数量
int count_all = 0;
// 错误的图片数量
int count_err = 0;
// 未识别的图片数量
int count_norecogin = 0;
// 总的字符差距
float diff_all = 0;
// 平均字符差距
float diff_avg = 0;
// 完全匹配的识别次数
float match_count = 0;
// 完全匹配的识别次数所占识别图片中的比例
float match_rate = 0;
// 开始和结束时间
time_t begin, end;
time(&begin);
for (int i = 0; i < size; i++) {
string filepath = files[i].c_str();
cout << "------------------" << endl;
// 获取真实的车牌
string plateLicense = Utils::getFileName(filepath);
cout << "原牌:" << plateLicense << endl;
// EasyPR开始判断车牌
Mat src = imread(filepath);
vector<string> plateVec;
int result = pr.plateRecognize(src, plateVec, i);
if (result == 0) {
int num = plateVec.size();
if (num == 0) {
cout << "无车牌" << endl;
if (plateLicense != "无车牌") count_norecogin++;
} else if (num > 1) {
// 多车牌使用diff最小的那个记录
int mindiff = 10000;
for (int j = 0; j < num; j++) {
cout << plateVec[j] << " (" << j + 1 << ")" << endl;
string colorplate = plateVec[j];
// 计算"蓝牌:苏E7KU22"中冒号后面的车牌大小"
vector<string> spilt_plate = Utils::splitString(colorplate, ':');
int size = spilt_plate.size();
if (size == 2 && spilt_plate[1] != "") {
int diff =
levenshtein_distance(plateLicense, spilt_plate[size - 1]);
if (diff < mindiff) mindiff = diff;
}
}
cout << "差距:" << mindiff << "个字符" << endl;
if (mindiff == 0) {
// 完全匹配
match_count++;
}
diff_all = diff_all + mindiff;
} else {
// 单车牌只计算一次diff
for (int j = 0; j < num; j++) {
cout << plateVec[j] << endl;
string colorplate = plateVec[j];
// 计算"蓝牌:苏E7KU22"中冒号后面的车牌大小"
vector<string> spilt_plate = Utils::splitString(colorplate, ':');
int size = spilt_plate.size();
if (size == 2 && spilt_plate[1] != "") {
int diff =
levenshtein_distance(plateLicense, spilt_plate[size - 1]);
cout << "差距:" << diff << "个字符" << endl;
if (diff == 0) {
// 完全匹配
match_count++;
}
diff_all = diff_all + diff;
}
}
}
} else {
cout << "错误码:" << result << endl;
count_err++;
}
count_all++;
}
time(&end);
cout << "------------------" << endl;
cout << "Easypr accuracy test end!" << endl;
cout << "------------------" << endl;
cout << endl;
cout << "统计参数:" << endl;
cout << "总图片数:" << count_all << "张, ";
cout << "未识出图片:" << count_norecogin << "张, ";
float count_recogin = count_all - (count_err + count_norecogin);
float count_rate = count_recogin / count_all;
//float count_norate = 1 - count_rate;
cout << "定位率:" << count_rate * 100 << "% " << endl;
diff_avg = diff_all / count_recogin;
match_rate = match_count / count_recogin * 100;
cout << "平均字符差距:" << diff_avg << "个, ";
cout << "完全匹配数:" << match_count << "张, ";
cout << "完全匹配率:" << match_rate << "% " << endl;
double seconds = difftime(end, begin);
double avgsec = seconds / double(count_all);
cout << "总时间:" << seconds << "秒, ";
cout << "平均执行时间:" << avgsec << "秒 " << endl;
cout << endl;
cout << "------------------" << endl;
ofstream myfile("run_accuracy.txt", ios::app);
if (myfile.is_open()) {
time_t t = time(0); // get time now
struct tm* now = localtime(&t);
char buf[80];
strftime(buf, sizeof(buf), "%Y-%m-%d %X", now);
myfile << string(buf) << endl;
myfile << "总图片数:" << count_all << "张, ";
myfile << "未识出图片:" << count_norecogin << "张, ";
myfile << "定位率:" << count_rate * 100 << "% " << endl;
myfile << "平均字符差距:" << diff_avg << "个, ";
myfile << "完全匹配数:" << match_count << "张, ";
myfile << "完全匹配率:" << match_rate << "% " << endl;
myfile << "总时间:" << seconds << "秒, ";
myfile << "平均执行时间:" << avgsec << "秒 " << endl;
myfile.close();
} else {
cout << "Unable to open file";
}
return 0;
}
int general_test()
{
////获取该路径下的所有文件
vector<string> files;
getFiles(your_data_path, files);
CPlateLocate lo;
CPlateJudge ju;
CPlateRecognize pr;
pr.LoadANN("model/ann.xml");
pr.LoadSVM("model/svm.xml");
pr.setLifemode(true);
int size = files.size();
//int size = 200;
if (0 == size)
{
cout << "No File Found!" << endl;
return 0;
}
cout << "Begin to prepare general_test!" << endl;
for (int i = 0; i < size; i++)
{
string filepath = files[i].c_str();
cout << "------------------" << endl;
// EasyPR开始判断车牌
Mat src = imread(filepath);
vector<string> plateVec;
int result = pr.plateRecognize(src, plateVec);
if (result == 0)
{
int num = plateVec.size();
if (num == 0)
{
cout << ""<< "无车牌" <<endl;
}
else
{
cout << plateVec[0] <<endl;
string colorplate = plateVec[0];
// 输出"蓝牌:苏E7KU22"中冒号后面的车牌
vector<string> spilt_plate;
SplitString(colorplate, spilt_plate, ":");
int size = spilt_plate.size();
if (size == 2)
{
stringstream ss(stringstream::in | stringstream::out);
ss << "F:/data/easypr-data/tmp-4" << "/" << spilt_plate[size-1] << ".jpg";
imwrite(ss.str(), src);
}
}
}
else
{
cout << "错误码:" << result << endl;
}
}
return 0;
}
int plate_recognize_main(){
cout << "process plate_detect_main..." << endl;
// Mat src = imread(NATIVE_RECG_PATH+"/test.yuv");
Mat src; //= imread(NATIVE_RECG_PATH+"/test.yuv");
src.create(480,640, CV_8UC3);
FILE *in;
in=fopen("RECG/data.input","r");
int i,j,y=0,u=0,v=0;
char t,handle=1;
double R,G,B;
char temp;
for (j=0;j<480;j++){
for (i=0;i<640;i++){
if (OPTION_HEX)
fscanf(in,"%c%c%X",&temp,&temp,&y);
else
fscanf(in,"%X",&y);
if (handle){
if (OPTION_HEX)
fscanf(in,"%c%c%X",&temp,&temp,&u);
else
fscanf(in,"%X",&u);
handle=0;
}else{
if (OPTION_HEX)
fscanf(in,"%c%c%X",&temp,&temp,&v);
else
fscanf(in,"%X",&v);
handle=1;
}
R=(y+1.4075*(v-128));
G=(y-0.455*(u-128)-0.7169*(v-128));
B=(y+1.779*(u-128));
src.at<Vec3b>(j,i)[0] = (unsigned char)(B<0)?0:(B>255)?255:(unsigned char)B;
src.at<Vec3b>(j,i)[1] = (unsigned char)(G<0)?0:(G>255)?255:(unsigned char)G;
src.at<Vec3b>(j,i)[2] = (unsigned char)(R<0)?0:(R>255)?255:(unsigned char)R;
}
}
fclose(in);
// show_dialog(src);
// exit;
CPlateRecognize plate;
plate.LoadANN("model/ann.xml");
plate.LoadSVM("model/svm.xml");
plate.setDebug(OPTION_DEBUG);
plate.setLifemode(OPTION_LIFEMODE);
vector<string> plateVec;
int result = plate.plateRecognize(src, plateVec);
if (result == 0)
{
int num = plateVec.size();
for (int j = 0; j < num; j++)
{
cout << "plateRecognize[" << j << "]： "<< plateVec[j] << endl;
}
}
if (result != 0)
cout << "result:" << result << endl;
return result;
}
void CBATCHDlg::OnBnClickedButtonPlaterecognize2()
{
int i;
CPlateRecognize pr;
pr.LoadANN("model/ann.xml");
pr.LoadSVM("model/svm.xml");
//pr.setGaussianBlurSize(5);
//pr.setMorphSizeWidth(17);
//pr.setVerifyMin(3);
//pr.setVerifyMax(20);
//pr.setLiuDingSize(7);
pr.setColorThreshold(150);
stringstream ss(stringstream::in | stringstream::out);
int num_char = 0;
int num_plate = 0;
for(vector<CString>::size_type v_i = 0; v_i < m_images.size(); ++v_i)
{
vector<string> plateVec;
ss << m_images[v_i] << " Result: ";
vector<Mat> resultVec;
string str = m_images[v_i].GetBuffer(0);
int index1 = str.find_last_of("\\");
int index2 = str.find_last_of(".");
string name = str.substr(index1 + 1,index2 - index1 - 1);
Mat src = imread(str, 1);
string str_cr;
int result = pr.plateRecognize2(src, plateVec);
int max_tmp;
int num_tmp;
if (result == 0)
{
num_tmp = 0;
max_tmp = 0;
for(i = 0; i < plateVec.size(); ++i)
{
num_tmp = 0;
ss << " " << plateVec[i];
if(2 <= plateVec[i].size() && plateVec[i][0] == name[0]) ++num_tmp;
for(int j = 2; j < plateVec[i].size() && j <= 8; ++j)
{
if(plateVec[i][j] == name[j]) ++num_tmp;
}
if(num_tmp > max_tmp) max_tmp = num_tmp;
}
if(max_tmp == 7) ++num_plate;
num_char += max_tmp;
ss << "\r\n";
}
else
{
ss << "No Answer\r\n";
}
}
ss << "Char accuracy rate:" << (1.0 * num_char / (m_images.size() * 7)) << "\r\n";
ss << "Plate accuracy rate:" << (1.0 * num_plate / m_images.size()) << "\r\n";
m_res = ss.str().c_str();
UpdateData(FALSE);
CString filePath = this->m_savepath + "\\plate_recognize.txt";
std::ofstream resfile(filePath);
resfile << ss.str().c_str();
resfile.close();
}
void CBATCHDlg::OnBnClickedButtonPlaterecognize()
{
CPlateRecognize pr;
pr.LoadANN("model/ann.xml");
pr.LoadSVM("model/svm.xml");
pr.setGaussianBlurSize(5);
pr.setMorphSizeWidth(17);
pr.setVerifyMin(3);
pr.setVerifyMax(20);
pr.setLiuDingSize(7);
pr.setColorThreshold(150);
string res_str = "";
int num_char = 0;
for(vector<CString>::size_type v_i = 0; v_i < m_images.size(); ++v_i)
{
vector<string> plateVec;
res_str += m_images[v_i];
res_str += " Result: ";
vector<Mat> resultVec;
string str = m_images[v_i].GetBuffer(0);
int index1 = str.find_last_of("\\");
int index2 = str.find_last_of(".");
string name = str.substr(index1 + 1,index2 - index1 - 1);
Mat src = imread(str, 1);
string str_cr;
int result = pr.plateRecognize(src, plateVec);
if (result == 0)
{
for(int i = 0; i < plateVec.size(); ++i)
{
res_str += " ";
res_str += plateVec[i];
for(int j = 2; j < plateVec[i].size() && j < 8; ++j)
{
if(plateVec[i][j] == name[j]) ++num_char;
}
}
res_str += "\r\n";
}
else
{
res_str += "No Answer\r\n";
}
}
res_str += "Char accuracy rate:";
res_str += (1.0 * num_char / (m_images.size() * 6));
res_str += "\r\n";
m_res = res_str.c_str();
UpdateData(FALSE);
CString filePath = this->m_savepath + "\\plate_recognize.txt";
std::ofstream resfile(filePath);
resfile << res_str.c_str();
resfile.close();
}
int accuracyTest(const char* test_path) {
std::shared_ptr<easypr::Kv> kv(new easypr::Kv);
kv->load("etc/chinese_mapping");
map<string, vector<CPlate>> xmlMap;
string path(test_path);
path = path + "/GroundTruth.xml";
getGroundTruth(xmlMap, path.c_str());
//cout << xmlMap.size() << endl;
//cout << xmlMap.begin()->first << endl;
//vector<CPlate> plateVec = xmlMap.begin()->second;
//cout << plateVec[0].getPlateStr() << endl;
XMLNode xMainNode = XMLNode::createXMLTopNode("tagset");
#ifdef OS_WINDOWS
XMLNode::setGlobalOptions(XMLNode::char_encoding_GBK);
#endif
auto files = Utils::getFiles(test_path);
std::string path_result = "result/Result.xml";
CPlateRecognize pr;
// 设置Debug模式
pr.setDebug(false);
pr.setLifemode(true);
// 设置要处理的一张图片中最多有多少车牌
pr.setMaxPlates(4);
pr.setDetectType(PR_DETECT_COLOR | PR_DETECT_SOBEL);
//pr.setDetectType(PR_DETECT_CMSER);
int size = files.size();
if (0 == size) {
cout << "No File Found in general_test/native_test!" << endl;
return 0;
}
cout << "Begin to test the easypr accuracy!" << endl;
// 总的测试图片数量
int count_all = 0;
// 错误的图片数量
int count_err = 0;
// 未识别的图片数量
int count_norecogin = 0;
std::list<std::string> not_recognized_files;
// 总的字符差距
float diff_all = 0;
// 平均字符差距
float diff_avg = 0;
// 完全匹配的识别次数
float match_count = 0;
// 完全匹配的识别次数所占识别图片中的比例
float match_rate = 0;
// 开始和结束时间
time_t begin, end;
time(&begin);
for (int i = 0; i < size; i++) {
string filepath = files[i].c_str();
// EasyPR开始判断车牌
Mat src = imread(filepath);
// 如果是非图像文件，直接过去
if (!src.data) continue;
cout << "------------------" << endl;
// 获取真实的车牌
string plateLicense = Utils::getFileName(filepath);
cout << kv->get("original_plate") << ":" << plateLicense << endl;
XMLNode xNode = xMainNode.addChild("image");
xNode.addChild("imageName").addText(plateLicense.c_str());
map<string, vector<CPlate>>::iterator it;
it = xmlMap.find(plateLicense);
if (it != xmlMap.end()) {
cout << it->first << endl;
vector<CPlate> plateVec = it->second;
for (auto plate : plateVec) {
cout << plate.getPlateStr() << " (g)" << endl;
}
}
XMLNode rectangleNodes = xNode.addChild("taggedRectangles");
vector<CPlate> plateVec;
int result = pr.plateRecognize(src, plateVec);
//int result = pr.plateRecognizeAsText(src, plateVec);
if (result == 0) {
int num = plateVec.size();
if (num == 0) {
cout << kv->get("empty_plate") << endl;
if (plateLicense != kv->get("empty_plate")) {
not_recognized_files.push_back(plateLicense);
count_norecogin++;
}
}
else if (num > 1) {
// 多车牌使用diff最小的那个记录
int mindiff = 10000;
for (int j = 0; j < num; j++) {
cout << plateVec[j].getPlateStr() << " (" << j + 1 << ")" << endl;
XMLNode rectangleNode = rectangleNodes.addChild("taggedRectangle");
RotatedRect rr = plateVec[j].getPlatePos();
LocateType locateType = plateVec[j].getPlateLocateType();
rectangleNode.addAttribute("x", to_string((int)rr.center.x).c_str());
rectangleNode.addAttribute("y", to_string((int)rr.center.y).c_str());
rectangleNode.addAttribute("width", to_string((int)rr.size.width).c_str());
rectangleNode.addAttribute("height", to_string((int)rr.size.height).c_str());
rectangleNode.addAttribute("rotation", to_string((int)rr.angle).c_str());
rectangleNode.addAttribute("locateType", to_string(locateType).c_str());
rectangleNode.addText(plateVec[j].getPlateStr().c_str());
string colorplate = plateVec[j].getPlateStr();
// 计算"蓝牌:苏E7KU22"中冒号后面的车牌大小"
vector<string> spilt_plate = Utils::splitString(colorplate, ':');
int size = spilt_plate.size();
if (size == 2 && spilt_plate[1] != "") {
int diff = utils::levenshtein_distance(plateLicense,
spilt_plate[size - 1]);
if (diff < mindiff) mindiff = diff;
}
}
cout << kv->get("diff") << ":" << mindiff << kv->get("char") << endl;
if (mindiff == 0) {
// 完全匹配
match_count++;
}
diff_all = diff_all + mindiff;
}
else {
// 单车牌只计算一次diff
for (int j = 0; j < num; j++) {
cout << plateVec[j].getPlateStr() << endl;
XMLNode rectangleNode = rectangleNodes.addChild("taggedRectangle");
RotatedRect rr = plateVec[j].getPlatePos();
LocateType locateType = plateVec[j].getPlateLocateType();
rectangleNode.addAttribute("x", to_string((int)rr.center.x).c_str());
rectangleNode.addAttribute("y", to_string((int)rr.center.y).c_str());
rectangleNode.addAttribute("width", to_string((int)rr.size.width).c_str());
rectangleNode.addAttribute("height", to_string((int)rr.size.height).c_str());
rectangleNode.addAttribute("rotation", to_string((int)rr.angle).c_str());
rectangleNode.addAttribute("locateType", to_string(locateType).c_str());
rectangleNode.addText(plateVec[j].getPlateStr().c_str());
string colorplate = plateVec[j].getPlateStr();
// 计算"蓝牌:苏E7KU22"中冒号后面的车牌大小"
vector<string> spilt_plate = Utils::splitString(colorplate, ':');
int size = spilt_plate.size();
if (size == 2 && spilt_plate[1] != "") {
int diff = utils::levenshtein_distance(plateLicense,
spilt_plate[size - 1]);
cout << kv->get("diff") << ":" << diff << kv->get("char") << endl;
if (diff == 0) {
// 完全匹配
match_count++;
}
diff_all = diff_all + diff;
}
}
}
}
else {
cout << kv->get("error_code") << ":" << result << endl;
count_err++;
}
count_all++;
}
time(&end);
cout << "------------------" << endl;
cout << "Easypr accuracy test end!" << endl;
cout << "------------------" << endl;
cout << endl;
cout << kv->get("summaries") << ":" << endl;
cout << kv->get("sum_pictures") << ":" << count_all << ", ";
cout << kv->get("unrecognized") << ":" << count_norecogin << ", ";
xMainNode.writeToFile(path_result.c_str());
float count_recogin = float(count_all - (count_err + count_norecogin));
float count_rate = count_recogin / count_all;
cout << kv->get("locate_rate") << ":" << count_rate * 100 << "% " << endl;
if (count_recogin > 0) {
diff_avg = diff_all / count_recogin;
}
if (count_recogin > 0) {
match_rate = match_count / count_recogin * 100;
}
cout << kv->get("diff_average") << ":" << diff_avg << ", ";
cout << kv->get("full_match") << ":" << match_count << ", ";
cout << kv->get("full_rate") << ":" << match_rate << "% " << endl;
double seconds = difftime(end, begin);
double avgsec = seconds / double(count_all);
cout << kv->get("seconds") << ":" << seconds << kv->get("sec") << ", ";
cout << kv->get("seconds_average") << ":" << avgsec << kv->get("sec") << endl;
cout << kv->get("unrecognized") << ":" << endl;
for (auto it = not_recognized_files.begin(); it != not_recognized_files.end();
++it) {
cout << *it << endl;
}
cout << endl;
cout << "------------------" << endl;
ofstream myfile("accuracy.txt", ios::app);
if (myfile.is_open()) {
time_t t = time(0); // get time now
struct tm* now = localtime(&t);
char buf[80];
strftime(buf, sizeof(buf), "%Y-%m-%d %X", now);
myfile << string(buf) << endl;
myfile << kv->get("sum_pictures") << ":" << count_all << ", ";
myfile << kv->get("unrecognized") << ":" << count_norecogin << ", ";
myfile << kv->get("locate_rate") << ":" << count_rate * 100 << "% "
<< endl;
myfile << kv->get("diff_average") << ":" << diff_avg << ", ";
myfile << kv->get("full_match") << ":" << match_count << ", ";
myfile << kv->get("full_rate") << ":" << match_rate << "% " << endl;
myfile << kv->get("seconds") << ":" << seconds << kv->get("sec") << ", ";
myfile << kv->get("seconds_average") << ":" << avgsec << kv->get("sec")
<< endl;
myfile.close();
}
else {
cout << "Unable to open file";
}
return 0;
}
