diff --git a/Demo.vcxproj b/Demo.vcxproj index 75ab5ba7..ff7fa5cc 100644 --- a/Demo.vcxproj +++ b/Demo.vcxproj @@ -61,6 +61,7 @@ Disabled WIN32;_DEBUG;_CONSOLE;_LIB;%(PreprocessorDefinitions) $(SolutionDir)\include;%(AdditionalIncludeDirectories) + MultiThreadedDebugDLL Console diff --git a/README.md b/README.md index 69625c19..63b670f6 100644 --- a/README.md +++ b/README.md @@ -10,23 +10,13 @@ EasyPR是一个中文的开源车牌识别系统，其目标是成为一个简 ### 更新 -本次更新是1.3 正式版，主要改进在于几个方面： +本次更新是1.3 修正版，主要改进在于几个方面： -1.相对原先的1.2版本，在完整识别率上有所提升： +1.修正了SVM训练的问题： -![效果比较](resources/doc/res/13ver.jpg) +2.增加ANN训练功能。 -2.增加了30张左右的新图片，目前的测试图片数达到了204张。 - -3.对“字符分割”类的代码做了改善。 - -在1.4版本中计划做以下几点改善： - -1.开放ANN训练功能。 - -2.新的评价框架，更加合理的评估数据。 - -3.新的车牌定位算法。 +训练前请先记得解压训练的数据包。 ### 跨平台 diff --git a/accuracy.txt b/accuracy.txt index e4358fb8..2ed21c89 100644 --- a/accuracy.txt +++ b/accuracy.txt @@ -1185,3 +1185,35 @@ ͼƬ:204, δʶͼƬ:6, λ:97.0588% ƽַ:0.520202, ȫƥ:145, ȫƥ:73.2323% ʱ:176, ƽִʱ:0.862745 +2016-01-12 11:52:36 +ͼƬ:204, δʶͼƬ:6, λ:97.0588% +ƽַ:0.520202, ȫƥ:145, ȫƥ:73.2323% +ʱ:175, ƽִʱ:0.857843 +2016-01-12 12:03:37 +ͼƬ:204, δʶͼƬ:12, λ:94.1176% +ƽַ:0.479167, ȫƥ:141, ȫƥ:73.4375% +ʱ:192, ƽִʱ:0.941176 +2016-01-12 12:11:18 +ͼƬ:204, δʶͼƬ:12, λ:94.1176% +ƽַ:0.953125, ȫƥ:109, ȫƥ:56.7708% +ʱ:189, ƽִʱ:0.926471 +2016-01-12 12:23:55 +ͼƬ:204, δʶͼƬ:12, λ:94.1176% +ƽַ:0.479167, ȫƥ:140, ȫƥ:72.9167% +ʱ:196, ƽִʱ:0.960784 +2016-01-12 12:33:46 +ͼƬ:204, δʶͼƬ:12, λ:94.1176% +ƽַ:0.484375, ȫƥ:139, ȫƥ:72.3958% +ʱ:191, ƽִʱ:0.936275 +2016-01-12 12:37:05 +ͼƬ:204, δʶͼƬ:6, λ:97.0588% +ƽַ:0.540404, ȫƥ:142, ȫƥ:71.7172% +ʱ:184, ƽִʱ:0.901961 +2016-01-12 16:18:50 +ͼƬ:204, δʶͼƬ:6, λ:97.0588% +ƽַ:0.540404, ȫƥ:142, ȫƥ:71.7172% +ʱ:179, ƽִʱ:0.877451 +2016-01-12 17:52:47 +ͼƬ:204, δʶͼƬ:6, λ:97.0588% +ƽַ:0.540404, ȫƥ:142, ȫƥ:71.7172% +ʱ:197, ƽִʱ:0.965686 diff --git a/resources/model/ann.xml b/resources/model/ann.xml index bbf5a9ed..03ecc0af 100644 --- a/resources/model/ann.xml +++ b/resources/model/ann.xml @@ -21,126 +21,126 @@ 9.9999997764825821e-003 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/dev/null differ diff --git a/resources/train/data/plate_detect_svm/train/NoPlate/S22_KG2187_4.jpg b/resources/train/data/plate_detect_svm/train/NoPlate/S22_KG2187_4.jpg deleted file mode 100644 index 475dd793..00000000 Binary files a/resources/train/data/plate_detect_svm/train/NoPlate/S22_KG2187_4.jpg and /dev/null differ diff --git a/src/core/plate_locate.cpp b/src/core/plate_locate.cpp index bd733267..b4981428 100644 --- a/src/core/plate_locate.cpp +++ b/src/core/plate_locate.cpp @@ -69,17 +69,26 @@ bool CPlateLocate::verifySizes(RotatedRect mr) { float rmin = aspect - aspect * error; float rmax = aspect + aspect * error; - float area = mr.size.height * mr.size.width; + int area = mr.size.height * mr.size.width; float r = (float)mr.size.width / (float)mr.size.height; if (r < 1) r = (float)mr.size.height / (float)mr.size.width; // cout << "area:" << area << endl; // cout << "r:" << r << endl; - if ((area < min || area > max) || (r < rmin || r > rmax)) + if (area < min) return false; - else - return true; + + if (area > max) + return false; + + if (r < rmin) + return false; + + if (r > rmax) + return false; + + return true; } // !HSVռɫ @@ -123,19 +132,28 @@ int CPlateLocate::colorSearch(const Mat& src, const Color r, Mat& out, CV_RETR_EXTERNAL, // ȡⲿ CV_CHAIN_APPROX_NONE); // all pixels of each contours - vector>::iterator itc = contours.begin(); - while (itc != contours.end()) { - RotatedRect mr = minAreaRect(Mat(*itc)); + //vector>::iterator itc = contours.begin(); + //while (itc != contours.end()) { + // RotatedRect mr = minAreaRect(Mat(*itc)); + + // // ҪдСߴж + // if (!verifySizes(mr)) + // itc = contours.erase(itc); + // else { + // ++itc; + // outRects.push_back(mr); + // } + //} - // ҪдСߴж - if (!verifySizes(mr)) - itc = contours.erase(itc); - else { - ++itc; + size_t size = contours.size(); + for (int i = 0; i < size; i++) + { + RotatedRect mr = minAreaRect(Mat(contours[i])); + if (verifySizes(mr)) outRects.push_back(mr); - } } + return 0; } diff --git a/src/train/ann_train.cpp b/src/train/ann_train.cpp index 75cc0b78..5176cac8 100644 --- a/src/train/ann_train.cpp +++ b/src/train/ann_train.cpp @@ -240,7 +240,7 @@ int annMain() { saveModel(10, 40); cout << "To be end." << endl; - int end; - cin >> end; + //int end; + //cin >> end; return 0; } \ No newline at end of file diff --git a/src/train/svm_train.cpp b/src/train/svm_train.cpp index a756969b..778c9018 100644 --- a/src/train/svm_train.cpp +++ b/src/train/svm_train.cpp @@ -1,266 +1,288 @@ #include "easypr/svm_train.h" #include #include "easypr/core_func.h" +#include "easypr/feature.h" #include "easypr/util.h" namespace easypr { -Svm::Svm(const char* forward_data_folder, const char* inverse_data_folder) + Svm::Svm(const char* forward_data_folder, const char* inverse_data_folder) : forward_(forward_data_folder), inverse_(inverse_data_folder) { - assert(forward_); - assert(inverse_); -} - -void Svm::divide(const char* images_folder, float percentage /* = 0.7 */) { - auto files = utils::getFiles(images_folder); - if (files.empty()) { - std::cout << "No file found in " << images_folder << std::endl; - return; + assert(forward_); + assert(inverse_); } - srand(unsigned(time(NULL))); - random_shuffle(files.begin(), files.end()); - - size_t split_index = size_t(files.size() * percentage); - for (size_t i = 0; i < files.size(); ++i) { - // TODO: Move files directly to improve efficiency. - auto f = files[i]; - auto file_name = utils::getFileName(f, true); - auto image = cv::imread(f); - char save_to[255] = {0}; - assert(!image.empty()); - if (i < split_index) { - sprintf(save_to, "%s/train/%s", images_folder, file_name.c_str()); - } else { - sprintf(save_to, "%s/test/%s", images_folder, file_name.c_str()); + void Svm::divide(const char* images_folder, float percentage /* = 0.7 */) { + auto files = utils::getFiles(images_folder); + if (files.empty()) { + std::cout << "No file found in " << images_folder << std::endl; + return; } - utils::imwrite(save_to, image); - std::cout << f << " -> " << save_to << std::endl; - } -} -void Svm::get_train() { - std::cout << "Collecting train data..." << std::endl; + srand(unsigned(time(NULL))); + random_shuffle(files.begin(), files.end()); + + size_t split_index = size_t(files.size() * percentage); + for (size_t i = 0; i < files.size(); ++i) { + // TODO: Move files directly to improve efficiency. + auto f = files[i]; + auto file_name = utils::getFileName(f, true); + auto image = cv::imread(f); + char save_to[255] = { 0 }; + assert(!image.empty()); + if (i < split_index) { + sprintf(save_to, "%s/train/%s", images_folder, file_name.c_str()); + } + else { + sprintf(save_to, "%s/test/%s", images_folder, file_name.c_str()); + } + utils::imwrite(save_to, image); + std::cout << f << " -> " << save_to << std::endl; + } + } - std::vector labels; - auto folder = std::string(forward_).append("/train"); + void Svm::get_train() { + std::cout << "Collecting train data..." << std::endl; - // iterate forward data + std::vector labels; + auto folder = std::string(forward_).append("/train"); - auto files = Utils::getFiles(folder); + // iterate forward data - if (files.empty()) { - std::cout << "No file found in " << folder << std::endl; - return; - } + auto files = Utils::getFiles(folder); - std::cout << "Collecting train data in " << folder << std::endl; - for (auto f : files) { - auto image = cv::imread(f); + if (files.empty()) { + std::cout << "No file found in " << folder << std::endl; + return; + } - auto features = easypr::histeq(image); - features = features.reshape(1, 1); + std::cout << "Collecting train data in " << folder << std::endl; + for (auto f : files) { + auto image = cv::imread(f); - this->trainingData_.push_back(features); - labels.push_back(Label::kForward); // Note here - } + //auto features = easypr::histeq(image); + //features = features.reshape(1, 1); - // iterate inverse data + Mat features; + getHistogramFeatures(image, features); + features = features.reshape(1, 1); - files.clear(); - folder = std::string(inverse_).append("/train"); - files = Utils::getFiles(folder); + this->trainingData_.push_back(features); + labels.push_back(Label::kForward); // Note here + } - if (files.empty()) { - std::cout << "No file found in " << folder << std::endl; - return; - } + // iterate inverse data - std::cout << "Collecting train data in " << folder << std::endl; - for (auto f : files) { - auto image = cv::imread(f); + files.clear(); + folder = std::string(inverse_).append("/train"); + files = Utils::getFiles(folder); - auto features = easypr::histeq(image); - features = features.reshape(1, 1); + if (files.empty()) { + std::cout << "No file found in " << folder << std::endl; + return; + } - this->trainingData_.push_back(features); - labels.push_back(Label::kInverse); // Note here - } + std::cout << "Collecting train data in " << folder << std::endl; + for (auto f : files) { + auto image = cv::imread(f); - // - cv::Mat out; - this->trainingData_.convertTo(out, CV_32FC1); - out.copyTo(this->trainingData_); - cv::Mat(labels).copyTo(this->classes_); -} + /*auto features = easypr::histeq(image); + features = features.reshape(1, 1);*/ -void Svm::get_test() { - std::cout << "Tesing preparation..." << std::endl; + Mat features; + getHistogramFeatures(image, features); + features = features.reshape(1, 1); - auto folder = std::string(forward_).append("/test"); - auto files = Utils::getFiles(folder); + this->trainingData_.push_back(features); + labels.push_back(Label::kInverse); // Note here + } - size_t size = files.size(); - if (0 == size) { - std::cout << "No file found in " << folder << std::endl; - return; - } - std::cout << "Prepare testing data in " << folder << std::endl; - for (auto f : files) { - auto img = cv::imread(f); - test_imgaes_.push_back(img); - test_labels_.push_back(kForward); + // + cv::Mat out; + this->trainingData_.convertTo(out, CV_32FC1); + out.copyTo(this->trainingData_); + cv::Mat(labels).copyTo(this->classes_); } - // + void Svm::get_test() { + std::cout << "Tesing preparation..." << std::endl; - folder.clear(); - folder = std::string(inverse_).append("/test"); - files = Utils::getFiles(folder); + auto folder = std::string(forward_).append("/test"); + auto files = Utils::getFiles(folder); - size = files.size(); - if (0 == size) { - std::cout << "No file found in " << folder << std::endl; - return; - } - std::cout << "Prepare testing data in " << folder << std::endl; - for (auto f : files) { - auto img = cv::imread(f); - test_imgaes_.push_back(img); - test_labels_.push_back(kInverse); - } -} + size_t size = files.size(); + if (0 == size) { + std::cout << "No file found in " << folder << std::endl; + return; + } + std::cout << "Prepare testing data in " << folder << std::endl; + for (auto f : files) { + auto img = cv::imread(f); + test_imgaes_.push_back(img); + test_labels_.push_back(kForward); + } -void Svm::train(bool divide /* = true */, float divide_percentage /* = 0.7 */, - bool train /* = true */, - const char* out_svm_path /* = NULL */) { - if (out_svm_path == NULL) { - out_svm_path = "resources/model/svm.xml"; - } + // - if (divide) { - std::cout << "Dividing data to be trained and tested..." << std::endl; - this->divide(forward_, divide_percentage); - this->divide(inverse_, divide_percentage); + folder.clear(); + folder = std::string(inverse_).append("/test"); + files = Utils::getFiles(folder); + + size = files.size(); + if (0 == size) { + std::cout << "No file found in " << folder << std::endl; + return; + } + std::cout << "Prepare testing data in " << folder << std::endl; + for (auto f : files) { + auto img = cv::imread(f); + test_imgaes_.push_back(img); + test_labels_.push_back(kInverse); + } } - CvSVM svm; + void Svm::train(bool divide /* = true */, float divide_percentage /* = 0.7 */, + bool train /* = true */, + const char* out_svm_path /* = NULL */) + { + + if (out_svm_path == NULL) { + out_svm_path = "resources/train/svm.xml"; + } + + if (divide) { + std::cout << "Dividing data to be trained and tested..." << std::endl; + this->divide(forward_, divide_percentage); + this->divide(inverse_, divide_percentage); + } + + CvSVM svm; - // 70% training procedure - if (train) { - this->get_train(); + // 70% training procedure + if (train) { + this->get_train(); - if (!this->classes_.empty() && !this->trainingData_.empty()) { - // need to be trained first - CvSVMParams SVM_params; - SVM_params.svm_type = CvSVM::C_SVC; - // SVM_params.kernel_type = CvSVM::LINEAR; //CvSVM::LINEAR; - // ͣҲ޺ - SVM_params.kernel_type = + if (!this->classes_.empty() && !this->trainingData_.empty()) { + // need to be trained first + CvSVMParams SVM_params; + SVM_params.svm_type = CvSVM::C_SVC; + // SVM_params.kernel_type = CvSVM::LINEAR; //CvSVM::LINEAR; + // ͣҲ޺ + SVM_params.kernel_type = CvSVM::RBF; // CvSVM::RBF ҲǸ˹ - SVM_params.degree = 0.1; - SVM_params.gamma = 1; - SVM_params.coef0 = 0.1; - SVM_params.C = 1; - SVM_params.nu = 0.1; - SVM_params.p = 0.1; - SVM_params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100000, 0.0001); - - std::cout << "Generating svm model file, please wait..." << std::endl; - - try { - // CvSVM svm(trainingData, classes, cv::Mat(), cv::Mat(), SVM_params); - svm.train_auto(this->trainingData_, this->classes_, cv::Mat(), - cv::Mat(), SVM_params, 10, - CvSVM::get_default_grid(CvSVM::C), - CvSVM::get_default_grid(CvSVM::GAMMA), - CvSVM::get_default_grid(CvSVM::P), - CvSVM::get_default_grid(CvSVM::NU), - CvSVM::get_default_grid(CvSVM::COEF), - CvSVM::get_default_grid(CvSVM::DEGREE), true); - } catch (const cv::Exception& err) { - std::cout << err.what() << std::endl; + SVM_params.degree = 0.1; + SVM_params.gamma = 0.1; + SVM_params.coef0 = 0.1; + SVM_params.C = 1; + SVM_params.nu = 0.1; + SVM_params.p = 0.1; + SVM_params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100000, 0.0001); + + std::cout << "Generating svm model file, please wait..." << std::endl; + + try { + // CvSVM svm(trainingData, classes, cv::Mat(), cv::Mat(), SVM_params); + /*svm.train_auto(this->trainingData_, this->classes_, cv::Mat(), + cv::Mat(), SVM_params, 10, + CvSVM::get_default_grid(CvSVM::C), + CvSVM::get_default_grid(CvSVM::GAMMA), + CvSVM::get_default_grid(CvSVM::P), + CvSVM::get_default_grid(CvSVM::NU), + CvSVM::get_default_grid(CvSVM::COEF), + CvSVM::get_default_grid(CvSVM::DEGREE), true);*/ + svm.train(this->trainingData_, this->classes_, cv::Mat(), + cv::Mat(), SVM_params); + } + catch (const cv::Exception& err) { + std::cout << err.what() << std::endl; + } + + //utils::mkdir(out_svm_path); + cv::FileStorage fsTo(out_svm_path, cv::FileStorage::WRITE); + svm.write(*fsTo, "svm"); + + std::cout << "Generate done! The model file is located at " + << out_svm_path << std::endl; } - - utils::mkdir(out_svm_path); - cv::FileStorage fsTo(out_svm_path, cv::FileStorage::WRITE); - svm.write(*fsTo, "svm"); - - std::cout << "Generate done! The model file is located at " - << out_svm_path << std::endl; - } else { - // don't train, use ready-made model file - try { - svm.load("resources/train/svm.xml", "svm"); - } catch (const cv::Exception& err) { - std::cout << err.what() << std::endl; + else { + // don't train, use ready-made model file + try { + svm.load("resources/train/svm.xml", "svm"); + } + catch (const cv::Exception& err) { + std::cout << err.what() << std::endl; + } } - } - } // if train + } // if train - // TODO Check whether the model file exists or not. - svm.load(out_svm_path, "svm"); // make sure svm model was loaded + // TODO Check whether the model file exists or not. + //svm.load(out_svm_path, "svm"); // make sure svm model was loaded - // 30% testing procedure - this->get_test(); + // 30% testing procedure + this->get_test(); - std::cout << "Testing..." << std::endl; + std::cout << "Testing..." << std::endl; - double count_all = test_imgaes_.size(); - double ptrue_rtrue = 0; - double ptrue_rfalse = 0; - double pfalse_rtrue = 0; - double pfalse_rfalse = 0; + double count_all = test_imgaes_.size(); + double ptrue_rtrue = 0; + double ptrue_rfalse = 0; + double pfalse_rtrue = 0; + double pfalse_rfalse = 0; - size_t label_index = 0; - for (auto image : test_imgaes_) { - //ûص - auto features = easypr::histeq(image); - features = features.reshape(1, 1); - cv::Mat out; - features.convertTo(out, CV_32FC1); + size_t label_index = 0; + for (auto image : test_imgaes_) { + //ûص + //auto features = easypr::histeq(image); - Label predict = ((int)svm.predict(out)) == 1 ? kForward : kInverse; - Label real = test_labels_[label_index++]; + Mat features; + getHistogramFeatures(image, features); + features = features.reshape(1, 1); + features.convertTo(features, CV_32FC1); - if (predict == kForward && real == kForward) ptrue_rtrue++; - if (predict == kForward && real == kInverse) ptrue_rfalse++; - if (predict == kInverse && real == kForward) pfalse_rtrue++; - if (predict == kInverse && real == kInverse) pfalse_rfalse++; - } + Label predict = ((int)svm.predict(features)) == 1 ? kForward : kInverse; + Label real = test_labels_[label_index++]; - std::cout << "count_all: " << count_all << std::endl; - std::cout << "ptrue_rtrue: " << ptrue_rtrue << std::endl; - std::cout << "ptrue_rfalse: " << ptrue_rfalse << std::endl; - std::cout << "pfalse_rtrue: " << pfalse_rtrue << std::endl; - std::cout << "pfalse_rfalse: " << pfalse_rfalse << std::endl; - - double precise = 0; - if (ptrue_rtrue + ptrue_rfalse != 0) { - precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse); - std::cout << "precise: " << precise << std::endl; - } else { - std::cout << "precise: " - << "NA" << std::endl; - } + if (predict == kForward && real == kForward) ptrue_rtrue++; + if (predict == kForward && real == kInverse) ptrue_rfalse++; + if (predict == kInverse && real == kForward) pfalse_rtrue++; + if (predict == kInverse && real == kInverse) pfalse_rfalse++; + } - double recall = 0; - if (ptrue_rtrue + pfalse_rtrue != 0) { - recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue); - std::cout << "recall: " << recall << std::endl; - } else { - std::cout << "recall: " - << "NA" << std::endl; - } + std::cout << "count_all: " << count_all << std::endl; + std::cout << "ptrue_rtrue: " << ptrue_rtrue << std::endl; + std::cout << "ptrue_rfalse: " << ptrue_rfalse << std::endl; + std::cout << "pfalse_rtrue: " << pfalse_rtrue << std::endl; + std::cout << "pfalse_rfalse: " << pfalse_rfalse << std::endl; + + double precise = 0; + if (ptrue_rtrue + ptrue_rfalse != 0) { + precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse); + std::cout << "precise: " << precise << std::endl; + } + else { + std::cout << "precise: " + << "NA" << std::endl; + } - double Fsocre = 0; - if (precise + recall != 0) { - Fsocre = 2 * (precise * recall) / (precise + recall); - std::cout << "Fsocre: " << Fsocre << std::endl; - } else { - std::cout << "Fsocre: " - << "NA" << std::endl; + double recall = 0; + if (ptrue_rtrue + pfalse_rtrue != 0) { + recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue); + std::cout << "recall: " << recall << std::endl; + } + else { + std::cout << "recall: " + << "NA" << std::endl; + } + + double Fsocre = 0; + if (precise + recall != 0) { + Fsocre = 2 * (precise * recall) / (precise + recall); + std::cout << "Fsocre: " << Fsocre << std::endl; + } + else { + std::cout << "Fsocre: " + << "NA" << std::endl; + } } -} } // namespace easypr diff --git a/test/main.cpp b/test/main.cpp index 940c6b44..fd420cbd 100644 --- a/test/main.cpp +++ b/test/main.cpp @@ -5,6 +5,8 @@ #include "chars.hpp" #include "plate.hpp" +int annMain(); + namespace easypr { namespace demo { @@ -397,7 +399,7 @@ int main(int argc, const char* argv[]) { while (!isExit) { std::cout << "////////////////////////////////////" << std::endl; const char* options[] = {"EasyPR Option:", "1. ;", "2. ;", - "3. SVMѵ;", "4. ANNѵ(δ);", + "3. SVMѵ;", "4. ANNѵ;", "5. GDTS;", "6. Ŷ;", "7. л;", "8. ˳;", NULL}; easypr::Utils::print_str_lines(options); @@ -419,9 +421,18 @@ int main(int argc, const char* argv[]) { case 3: // easypr::demo::svmMain(); std::cout << "Run \"easypr_test svm\" for more usage." << std::endl; + { + easypr::Svm svm("resources/train/data/plate_detect_svm/learn/HasPlate", + "resources/train/data/plate_detect_svm/learn/NoPlate"); + svm.train(); + } + break; case 4: // TODO + { + annMain(); + } break; case 5: easypr::preprocess::generate_gdts();