target

res/image/GDSL.txt

@@ -0,0 +1,94 @@
+                            General Data Share License
+                                 通用数据共享协议
+
+                             Version 0.1, 2015-01-25
+
+当您开始使用EasyPR中的GDTS(General Data Test Set，通用数据测试集)也就是image/general_test文件夹里的任一数据时，您必须遵守以下协议的条款。
+EasyPR只允许对GDTS里的数据进行非商业性目的的使用，任何商业行为(售卖或者随产品附赠等)都属于违反本协议约定的行为。
+EasyPR保留任何权利。
+
+本协议的起草参考了以下两个协议：GPL v2.0与ODL(Open Database License)。其核心思想与这两个协议基本相同，但也有许多相异的地方。
+
+本协议与GPL协议相同的地方在于：本协议与GPL协议都是“传染性”协议。
+当您使用，拷贝，转移了GDTS中数据(或其中一部分时)，您务必要保证这个协议随数据同行。
+同时假若您把GDTS中数据(或其中一部分时)进行修改或与其他数据进行合并时，那您需要确保新的数据集也必须遵循此GDSL协议的约定条款。
+与GPL协议不同的地方在于，GPL协议保护的是代码(code)以及基于代码的工作(work)，而本协议保护的是数据(data)。
+注意，本协议中所保护的数据仅保护这些原始的图片数据，并不针对于您通过这些图片数据训练出的模型，
+以及您通过这些图片数据截取出的仅包含车牌的图片，您可以将这些训练模型或车牌图片按照非本协议约定的条款进行操作。
+
+本协议与ODL协议相同的地方在于：本协议与ODL协议都是保护数据(data)的协议，并且也提倡数据的开放，共享。
+但ODL协议针对的主要是结构化数据，而本协议针对的主要是图片数据。
+另外，ODL协议强调的开放，不限制对数据的商业性使用。但本协议规定了数据仅仅只能用于进行非商业性的目的行为，
+包括学习与研究，但不包括商业性行为，例如销售与随产品赠送等等。
+
+目前本协议的版本为0.1修正稿，任何对本协议的修改与建议都可以跟本协议的组织方联系(easypr_dev@163.com) 。
+
+
+
+本协议主要分为三个部分：
+1.版权声明:约定了对GDTS数据使用的规范。
+2.捐赠说明:说明了如何对GDTS数据进行捐赠的方法。
+3.免责声明:声明免责条款。
+
+如果您仅仅是使用EasyPR进行开发与研究，那您仅需要读完第一部分。如果您愿意对EasyPR进行捐赠，那您需要读完第二部分。
+第三部分声明了EasyPR在各个部分的免责条款。
+
+
+一.版权声明
+
+EasyPR中GDTS(通用数据测试集)的数据仅用作学习与研究之用。尽管EasyPR遵循的是商业友好的开源协议Acache2.0，但那个协议仅适用于您对代码的修改权。
+这些测试数据集并不在您可以修改并且出卖的范围之内，请确保这些数据集仅仅作为您进行EasyPR图像测试效率的验证与参考。
+这些图片是用来测试EasyPR的效果与指标的，仅仅用于开源学习目的。任何商业化的使用这些数据（例如出卖数据）或者将这些数据作为产品的附赠都是不允许的。
+
+为了保证EasyPR中使用的测试数据不有任何侵犯权利的可能性，每个在GDTS(通用数据测试集)上上传的数据都具有以下几个特征：
+1.年代久远，不具有时效性的数据（例如，至少半年以前的数据）或者已经处理过相关版权事宜的数据。
+2.在上传前，使用EasyPR提供的函数对图片进行模糊化，裁剪性处理，确保图片不透露任何可能关于地点，时间，位置等相关信息。
+3.在上传前，使用EasyPR提供的反人脸识别工具进行处理，确保图片不侵犯到任何人的隐私权和肖像权。
+
+如果您发现EasyPR中存在任何侵犯您可能权利的图片时，请跟我们联系(easypr_dev@163.com) 。我们的工作人员会跟您协商，将这些图片修改或做删除处理。
+
+
+二.捐赠说明
+
+EasyPR里的数据来源广泛，部分来自于网络公开途径，也有好心的网友以及匿名人士对EasyPR的捐赠。通过这些数据
+有效地改善了EasyPR的识别效果与准确率，为推广车牌识别技术在中国的开源发展做出了贡献。如果您愿意，您也可以向EasyPR捐赠。
+在一般的开源与众包软件中，捐赠的方式一般是金钱等物质性财产。但EasyPR的捐赠略有不同，我们不接受财物的捐赠，相反，我们接受的是数据的捐赠。
+如果您有车牌的图片，符合以下三个条件，并且您愿意捐赠给EasyPR作为研究与开发的帮助，那么您可以跟我们联系(easypr_dev@163.com) ，
+一般选择捐赠的图片在5-30张之间，最好每张图片有足够不同的特点，我们非常高兴能够接受您对我们开发与研究的支持。
+
+捐赠方式可以选择公开或匿名，并且您可以选择捐赠后的数据是纳入GDTS作为通用测试集或者保持私密性仅仅作为EasyPR核心团队训练与测试的图片。
+捐赠的图片数据不需要太多，我们建议您不要捐赠超过30张以上的图片。感谢您为中国开源软件的发展与数据开源运动所做出的贡献！
+
+为了保证EasyPR中使用的测试数据不有任何侵犯权利的可能性，每个在GDTS(通用数据测试集)上上传的数据都具有以下几个特征：
+1.年代久远，不具有时效性的数据（例如，至少半年以前的数据）或者已经处理过相关版权事宜的数据
+2.在上传前，使用EasyPR提供的函数对图片进行模糊化，裁剪性处理，确保图片不透露任何可能关于地点，时间，位置等相关信息
+3.在上传前，使用EasyPR提供的反人脸识别工具进行处理，确保图片不侵犯到任何人的隐私权和肖像权
+
+只有经过以上处理，满足以上三个条件的数据，EasyPR才会纳入到GDTS里作为车牌识别的准确率衡判依据。
+任何不满足以上三个条件的数据EasyPR都会保持这些数据的隐秘，不公开等特性，并且确保这些数据处于保密状态。
+
+EasyPR团队保证，这些研究仅仅作为开源社区学习机器学习、深度学习、图像识别以及计算机视觉的相关资料与参考，不会用作任何商用或者恶意行为。
+
+
+三.免责声明
+
+EasyPR中GDTS(通用数据测试集)的使用仅仅是为了研究与学习目的。
+
+任何使用这些数据进行商用或者恶意窥探目的的行为都违反EasyPR所遵循的开源法则以及研究目的。EasyPR不对这些恶意后果负有任何责任。
+EasyPR谴责这些行为，但EasyPR不为这些违反开源原则行为的后果负有责任。
+当恶意使用者以及用数据牟利者违反了EasyPR约定的这些条款时，也就意味着EasyPR不会对他们所造成的任何行为负有责任。
+当您使用这些数据时，就意味着您已经同意EasyPR的这些约定，您对EasyPR通用测试数据集的滥用以及恶意窥探目行为的后果需要您自己承担，
+EasyPR及其开源团队与贡献者不承担任何相关的责任。
+
+EasyPR团队保留所有权利。
+
+
+
+联系方式
+
+EasyPR开发团队的官方邮箱：
+(easypr_dev@163.com) 
+
+
+
+###当您复制了EasyPR声明权利的这些数据(或者其中一部分)的同时，您也必须将这份协议复制一份，并保持协议随数据时刻同行。

res/image/使用说明.txt

@@ -0,0 +1,14 @@
+1.general_test：存放通用数据测试集（GDTS）的文件夹，EasyPR的开发者会依照这里的图片来判断EasyPR新算法的改进性。
+其他开发者也可以通过这个测试集测试自己修改的程序在通用图片集上的表现。
+一般来说，这个数据集的里的效果表现是低于下面的native_test的。
+你可以通过启动EasyPR->2.批量测试->1.general_test来测试EasyPR在通用数据集上的效果表现。
+
+2.native_test：存放使用者特定图片数据集的地方。把你特定环境下的图片放到这里进行测试，跟GDTS分开。
+由于你希望EasyPR只在你的数据集下表现良好，那么你可以只把你的数据放到native_test文件夹下（把里面原有的文件删除），然后运行测试。
+你可以试着改变图像处理算法，也可以通过EasyPR的训练功能对你的数据集进行训练，然后用训练好的模型替代EasyPR
+的模型（这样的模型比EasyPR的原模型对你的数据适应性更好）。
+你可以通过启动EasyPR->2.批量测试->2.native_test来测试你的效果表现。
+
+3.tmp文件夹：存放EasyPR处理过程的中间图片，用于调试使用。
+
+注意:通用数据测试集(GDTS)里的数据遵循GDSL协议，请阅读同目录下的"GDSL.txt"获得更多信息。

target/classes/application-dev.yml

@@ -0,0 +1,19 @@
+#server:
+#  port: 8888
+  
+spring: 
+  druid: 
+  #datasource: 
+    #eclipse启动的时候，默认加载的是target目录下的文件
+    url: jdbc:sqlite::resource:yx_image_recognition.db?date_string_format=yyyy-MM-dd HH:mm:ss
+    username: sqlite
+    password: sqlite
+    driver-class-name: org.sqlite.JDBC
+    max-active: 10 #最大连接数
+    min-idle: 5 #最小连接数
+    max-wait: 10000 #获取连接的最大等待时间
+    time-between-eviction-runs-millis: 60000 #空闲连接的检查时间间隔
+    min-evictable-idle-time-millis: 300000 #空闲连接最小空闲时间
+    
+
+ 

target/classes/application-sqlite.yml

@@ -0,0 +1,15 @@
+spring: 
+  druid: 
+  #datasource: 
+    #eclipse启动的时候，默认加载的是target目录下的文件
+    # :config  当前jar包所在目录下的config
+    url: jdbc:sqlite:config/yx_image_recognition.db?date_string_format=yyyy-MM-dd HH:mm:ss
+    username: sqlite
+    password: sqlite
+    driver-class-name: org.sqlite.JDBC
+    max-active: 10 #最大连接数
+    min-idle: 5 #最小连接数
+    max-wait: 10000 #获取连接的最大等待时间
+    time-between-eviction-runs-millis: 60000 #空闲连接的检查时间间隔
+    min-evictable-idle-time-millis: 300000 #空闲连接最小空闲时间
+ 

target/classes/application.yml

@@ -0,0 +1,52 @@
+server:
+  port: 16666
+  servlet:
+    context-path: /
+
+spring:
+  application:
+    name : demo
+  mvc: 
+    favicon: 
+      enabled: true
+  messages:
+    basename: i18n.login
+    
+  jackson:
+    time-zone: GMT+8
+    date-format: yyyy-MM-dd HH:mm:ss
+    
+## 环境配置文件 dev sqlite
+  profiles:
+    active: dev
+   
+## 静态页面配置
+  thymeleaf: 
+    #热部署文件，页面不产生缓存，及时更新
+    cache: false
+    prefix: classpath:static/templates/
+    suffix: .html
+    encoding: UTF-8
+
+## Mybatis config
+mybatis:
+  mapperLocations: classpath:mapper/**/*.xml
+  configLocation: classpath:mybatis.xml
+
+## pagehelper
+pagehelper: 
+  helperDialect: sqlite #postgresql
+  reasonable: true
+  supportMethodsArguments: true
+  params: countSql
+  count: countSql
+  returnPageInfo: check
+
+## 记录日志
+logging: 
+  config: classpath:logback-spring.xml
+## Start logging
+  level: 
+    root: INFO
+
+

target/classes/banner.txt

@@ -0,0 +1,7 @@
+██╗   ██╗██╗   ██╗██╗  ██╗██╗   ██╗███████╗
+╚██╗ ██╔╝██║   ██║╚██╗██╔╝██║   ██║██╔════╝
+ ╚████╔╝ ██║   ██║ ╚███╔╝ ██║   ██║█████╗  
+  ╚██╔╝  ██║   ██║ ██╔██╗ ██║   ██║██╔══╝  
+   ██║   ╚██████╔╝██╔╝ ██╗╚██████╔╝███████╗
+   ╚═╝    ╚═════╝ ╚═╝  ╚═╝ ╚═════╝ ╚══════╝                                          
+ :: YX Boot ::  Power By SpringBoot      (v2.1.0.RELEASE)

target/classes/easypr-c++/ann_train.cpp

@@ -0,0 +1,324 @@
+#include <numeric>
+#include <ctime>
+
+#include "easypr/train/ann_train.h"
+#include "easypr/config.h"
+#include "easypr/core/chars_identify.h"
+#include "easypr/core/feature.h"
+#include "easypr/core/core_func.h"
+#include "easypr/train/create_data.h"
+#include "easypr/util/util.h"
+
+
+
+// 原版C++语言  训练代码
+namespace easypr {
+
+AnnTrain::AnnTrain(const char* chars_folder, const char* xml): chars_folder_(chars_folder), ann_xml_(xml) {
+  ann_ = cv::ml::ANN_MLP::create();
+  type = 0;   // type=0, 所有字符；type=1, 只有中文字符
+  kv_ = std::shared_ptr<Kv>(new Kv);
+  kv_->load("resources/text/province_mapping");   // zh_cuan    川   zh_gan1    甘
+}
+
+void AnnTrain::train() {
+  
+  int classNumber = 0;
+
+  cv::Mat layers;
+
+  int input_number = 0;
+  int hidden_number = 0;
+  int output_number = 0;
+
+  if (type == 0) {
+    classNumber = kCharsTotalNumber;
+    input_number = kAnnInput;
+    hidden_number = kNeurons;
+    output_number = classNumber;
+  } else if (type == 1) {
+    classNumber = kChineseNumber;
+
+    input_number = kAnnInput;
+    hidden_number = kNeurons;
+    output_number = classNumber;
+  }
+
+  int N = input_number;
+  int m = output_number;
+  int first_hidden_neurons = int(std::sqrt((m + 2) * N) + 2 * std::sqrt(N / (m + 2)));
+  int second_hidden_neurons = int(m * std::sqrt(N / (m + 2)));
+
+  bool useTLFN = false;
+  if (!useTLFN) {
+    layers.create(1, 3, CV_32SC1);
+    layers.at<int>(0) = input_number;
+    layers.at<int>(1) = hidden_number;
+    layers.at<int>(2) = output_number;
+  } else {
+    // 两层神经网络很难训练，所以不要尝试
+    fprintf(stdout, ">> Use two-layers neural networks,\n");
+    layers.create(1, 4, CV_32SC1);
+    layers.at<int>(0) = input_number;
+    layers.at<int>(1) = first_hidden_neurons;
+    layers.at<int>(2) = second_hidden_neurons;
+    layers.at<int>(3) = output_number;
+  }
+
+  ann_->setLayerSizes(layers);
+  ann_->setActivationFunction(cv::ml::ANN_MLP::SIGMOID_SYM, 1, 1);
+  ann_->setTrainMethod(cv::ml::ANN_MLP::TrainingMethods::BACKPROP);
+  ann_->setTermCriteria(cvTermCriteria(CV_TERMCRIT_ITER, 30000, 0.0001));
+  ann_->setBackpropWeightScale(0.1);
+  ann_->setBackpropMomentumScale(0.1);
+
+  auto files = Utils::getFiles(chars_folder_);
+  if (files.size() == 0) {
+    fprintf(stdout, "No file found in the train folder!\n");
+    return;
+  }
+
+  // 使用原始数据  或者原始数据+合成数据
+  auto traindata = sdata(350);
+
+  ann_->train(traindata);
+
+  ann_->save(ann_xml_);
+  
+  test();
+}
+
+// 识别 中文
+std::pair<std::string, std::string> AnnTrain::identifyChinese(cv::Mat input) {
+  cv::Mat feature = charFeatures2(input, kPredictSize);
+  float maxVal = -2;
+  int result = 0;
+
+  cv::Mat output(1, kChineseNumber, CV_32FC1);
+  ann_->predict(feature, output);
+
+  for (int j = 0; j < kChineseNumber; j++) {
+    float val = output.at<float>(j);
+    if (val > maxVal) {
+      maxVal = val;
+      result = j;
+    }
+  }
+
+  auto index = result + kCharsTotalNumber - kChineseNumber;
+  const char* key = kChars[index];
+  std::string s = key;
+  std::string province = kv_->get(s);
+
+  return std::make_pair(s, province);
+}
+
+// 识别 字符
+std::pair<std::string, std::string> AnnTrain::identify(cv::Mat input) {
+  cv::Mat feature = charFeatures2(input, kPredictSize);
+  float maxVal = -2;
+  int result = 0;
+
+  cv::Mat output(1, kCharsTotalNumber, CV_32FC1);
+  ann_->predict(feature, output);
+  for (int j = 0; j < kCharsTotalNumber; j++) {
+    float val = output.at<float>(j);
+    if (val > maxVal) {
+      maxVal = val;
+      result = j;
+    }
+  }
+
+  auto index = result;
+  if (index < kCharactersNumber) {
+    return std::make_pair(kChars[index], kChars[index]);
+  }
+  else {
+    const char* key = kChars[index];
+    std::string s = key;
+    std::string province = kv_->get(s);
+    return std::make_pair(s, province);
+  }
+}
+
+// 测试并计算准确率
+void AnnTrain::test() {
+  assert(chars_folder_);
+
+  int classNumber = 0;
+  if (type == 0) classNumber = kCharsTotalNumber;
+  if (type == 1) classNumber = kChineseNumber;
+
+  int corrects_all = 0, sum_all = 0;
+  std::vector<float> rate_list;
+  for (int i = 0; i < classNumber; ++i) {
+    auto char_key = kChars[i + kCharsTotalNumber - classNumber];
+    char sub_folder[512] = { 0 };
+
+    sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
+    fprintf(stdout, ">> Testing characters %s in %s \n", char_key, sub_folder);
+
+    auto chars_files = utils::getFiles(sub_folder);
+    int corrects = 0, sum = 0;
+    std::vector<std::pair<std::string, std::string>> error_files;
+
+    for (auto file : chars_files) {
+      auto img = cv::imread(file, 0);  // 读取灰度图像
+      if (!img.data) {
+        continue;
+      }
+      std::pair<std::string, std::string> ch;
+
+      if (type == 0) ch = identify(img);
+      if (type == 1) ch = identifyChinese(img);
+
+      if (ch.first == char_key) {
+        ++corrects;
+        ++corrects_all;
+      } else {
+        error_files.push_back(std::make_pair(utils::getFileName(file), ch.second));
+      }
+      ++sum;
+      ++sum_all;
+    }
+    float rate = (float)corrects / (sum == 0 ? 1 : sum);
+    rate_list.push_back(rate);
+
+    std::string error_string;
+    auto end = error_files.end();
+    if (error_files.size() >= 10) {
+      end -= static_cast<size_t>(error_files.size() * (1 - 0.1));
+    }
+    for (auto k = error_files.begin(); k != end; ++k) {
+      auto kv = *k;
+      error_string.append("       ").append(kv.first).append(": ").append(kv.second);
+      if (k != end - 1) {
+        error_string.append(",\n");
+      } else {
+        error_string.append("\n       ...");
+      }
+    }
+    fprintf(stdout, ">>   [\n%s\n     ]\n", error_string.c_str());
+  }
+  fprintf(stdout, ">>   [sum_all: %d, correct_all: %d, rate: %.4f]\n", sum_all, corrects_all, (float)corrects_all / (sum_all == 0 ? 1 : sum_all));
+
+  double rate_sum = std::accumulate(rate_list.begin(), rate_list.end(), 0.0);
+  double rate_mean = rate_sum / (rate_list.size() == 0 ? 1 : rate_list.size());
+
+  fprintf(stdout, ">>   [classNumber: %d, avg_rate: %.4f]\n", classNumber, rate_mean);
+}
+
+// 获取合成图像
+cv::Mat getSyntheticImage(const Mat& image) {
+  int rand_type = rand();
+  Mat result = image.clone();
+
+  if (rand_type % 2 == 0) {
+    int ran_x = rand() % 5 - 2;
+    int ran_y = rand() % 5 - 2;
+
+    result = translateImg(result, ran_x, ran_y);
+  } else if (rand_type % 2 != 0) {
+    float angle = float(rand() % 15 - 7);
+    result = rotateImg(result, angle);
+  }
+  
+  return result;
+}
+
+// 处理训练文件
+cv::Ptr<cv::ml::TrainData> AnnTrain::sdata(size_t number_for_count) {
+  assert(chars_folder_);
+
+  cv::Mat samples;
+  std::vector<int> labels;
+
+  int classNumber = 0;
+  if (type == 0) classNumber = kCharsTotalNumber;
+  if (type == 1) classNumber = kChineseNumber;
+  
+  srand((unsigned)time(0));
+  for (int i = 0; i < classNumber; ++i) {
+   
+    auto char_key = kChars[i + kCharsTotalNumber - classNumber];
+    char sub_folder[512] = { 0 };
+
+    sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
+    fprintf(stdout, ">> Testing characters %s in %s \n", char_key, sub_folder);
+
+    auto chars_files = utils::getFiles(sub_folder);
+    size_t char_size = chars_files.size();
+    fprintf(stdout, ">> Characters count: %d \n", int(char_size));
+
+    std::vector<cv::Mat> matVec;
+    matVec.reserve(number_for_count);
+    for (auto file : chars_files) {
+      auto img = cv::imread(file, 0);  // a grayscale image
+      matVec.push_back(img);
+    }
+
+    for (int t = 0; t < (int)number_for_count - (int)char_size; t++) {
+      int rand_range = char_size + t;
+      int ran_num = rand() % rand_range;
+      auto img = matVec.at(ran_num);
+      auto simg = getSyntheticImage(img);
+      matVec.push_back(simg);
+    }
+
+    for (auto img : matVec) {
+      auto fps = charFeatures2(img, kPredictSize);
+      samples.push_back(fps);
+      labels.push_back(i);
+    }
+  } 
+
+  cv::Mat samples_;
+  samples.convertTo(samples_, CV_32F);
+  cv::Mat train_classes =  cv::Mat::zeros((int)labels.size(), classNumber, CV_32F);
+
+  for (int i = 0; i < train_classes.rows; ++i) {
+    train_classes.at<float>(i, labels[i]) = 1.f;
+  }
+
+  return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE, train_classes);
+}
+
+cv::Ptr<cv::ml::TrainData> AnnTrain::tdata() {
+  assert(chars_folder_);
+
+  cv::Mat samples;
+  std::vector<int> labels;
+
+  std::cout << "Collecting chars in " << chars_folder_ << std::endl;
+
+  int classNumber = 0;
+  if (type == 0) classNumber = kCharsTotalNumber;
+  if (type == 1) classNumber = kChineseNumber;
+
+  for (int i = 0; i < classNumber; ++i) {
+    auto char_key = kChars[i + kCharsTotalNumber - classNumber];
+    char sub_folder[512] = {0};
+
+    sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
+    std::cout << "  >> Featuring characters " << char_key << " in " << sub_folder << std::endl;
+
+    auto chars_files = utils::getFiles(sub_folder);
+    for (auto file : chars_files) {
+      auto img = cv::imread(file, 0);  // 读取灰度图像
+      auto fps = charFeatures2(img, kPredictSize);
+      samples.push_back(fps);
+      labels.push_back(i);
+    }
+  }
+
+  cv::Mat samples_;
+  samples.convertTo(samples_, CV_32F);
+  cv::Mat train_classes = cv::Mat::zeros((int)labels.size(), classNumber, CV_32F);
+
+  for (int i = 0; i < train_classes.rows; ++i) {
+    train_classes.at<float>(i, labels[i]) = 1.f;
+  }
+
+  return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE, train_classes);
+}
+}

target/classes/easypr-c++/chars_identify.cpp

@@ -0,0 +1,454 @@
+#include "easypr/core/chars_identify.h"
+#include "easypr/core/character.hpp"
+#include "easypr/core/core_func.h"
+#include "easypr/core/feature.h"
+#include "easypr/core/params.h"
+#include "easypr/config.h"
+
+using namespace cv;
+
+namespace easypr {
+
+CharsIdentify* CharsIdentify::instance_ = nullptr;
+
+CharsIdentify* CharsIdentify::instance() {
+  if (!instance_) {
+    instance_ = new CharsIdentify;
+  }
+  return instance_;
+}
+
+CharsIdentify::CharsIdentify() {
+  LOAD_ANN_MODEL(ann_, kDefaultAnnPath);
+  LOAD_ANN_MODEL(annChinese_, kChineseAnnPath);
+  LOAD_ANN_MODEL(annGray_, kGrayAnnPath);
+
+  kv_ = std::shared_ptr<Kv>(new Kv);
+  kv_->load(kChineseMappingPath);
+
+  extractFeature = getGrayPlusProject;
+}
+
+void CharsIdentify::LoadModel(std::string path) {
+  if (path != std::string(kDefaultAnnPath)) {
+    if (!ann_->empty())
+      ann_->clear();
+    LOAD_ANN_MODEL(ann_, path);
+  }
+}
+
+void CharsIdentify::LoadChineseModel(std::string path) {
+  if (path != std::string(kChineseAnnPath)) {
+    if (!annChinese_->empty())
+      annChinese_->clear();
+    LOAD_ANN_MODEL(annChinese_, path);
+  }
+}
+
+void CharsIdentify::LoadGrayChANN(std::string path) {
+  if (path != std::string(kGrayAnnPath)) {
+    if (!annGray_->empty())
+      annGray_->clear();
+    LOAD_ANN_MODEL(annGray_, path);
+  }
+}
+
+void CharsIdentify::LoadChineseMapping(std::string path) {
+  kv_->clear();
+  kv_->load(path);
+}
+
+void CharsIdentify::classify(cv::Mat featureRows, std::vector<int>& out_maxIndexs,
+                             std::vector<float>& out_maxVals, std::vector<bool> isChineseVec){
+  int rowNum = featureRows.rows;
+
+  cv::Mat output(rowNum, kCharsTotalNumber, CV_32FC1);
+  ann_->predict(featureRows, output);
+
+  for (int output_index = 0; output_index < rowNum; output_index++) {
+    Mat output_row = output.row(output_index);
+    int result = 0;
+    float maxVal = -2.f;
+    bool isChinses = isChineseVec[output_index];
+    if (!isChinses) {
+      result = 0;
+      for (int j = 0; j < kCharactersNumber; j++) {
+        float val = output_row.at<float>(j);
+        // std::cout << "j:" << j << "val:" << val << std::endl;
+        if (val > maxVal) {
+          maxVal = val;
+          result = j;
+        }
+      }
+    }
+    else {
+      result = kCharactersNumber;
+      for (int j = kCharactersNumber; j < kCharsTotalNumber; j++) {
+        float val = output_row.at<float>(j);
+        //std::cout << "j:" << j << "val:" << val << std::endl;
+        if (val > maxVal) {
+          maxVal = val;
+          result = j;
+        }
+      }
+    }
+    out_maxIndexs[output_index] = result;
+    out_maxVals[output_index] = maxVal;
+  }
+}
+
+
+void CharsIdentify::classify(std::vector<CCharacter>& charVec){
+  size_t charVecSize = charVec.size();
+
+  if (charVecSize == 0)
+    return;
+
+  Mat featureRows;
+  for (size_t index = 0; index < charVecSize; index++) {
+    Mat charInput = charVec[index].getCharacterMat();
+    Mat feature = charFeatures(charInput, kPredictSize);
+    featureRows.push_back(feature);
+  }
+
+  cv::Mat output(charVecSize, kCharsTotalNumber, CV_32FC1);
+  ann_->predict(featureRows, output);
+
+  for (size_t output_index = 0; output_index < charVecSize; output_index++) {
+    CCharacter& character = charVec[output_index];
+    Mat output_row = output.row(output_index);
+
+    int result = 0;
+    float maxVal = -2.f;
+    std::string label = "";
+
+    bool isChinses = character.getIsChinese();
+    if (!isChinses) {
+      result = 0;
+      for (int j = 0; j < kCharactersNumber; j++) {
+        float val = output_row.at<float>(j);
+        //std::cout << "j:" << j << "val:" << val << std::endl;
+        if (val > maxVal) {
+          maxVal = val;
+          result = j;
+        }
+      }
+      label = std::make_pair(kChars[result], kChars[result]).second;
+    }
+    else {
+      result = kCharactersNumber;
+      for (int j = kCharactersNumber; j < kCharsTotalNumber; j++) {
+        float val = output_row.at<float>(j);
+        //std::cout << "j:" << j << "val:" << val << std::endl;
+        if (val > maxVal) {
+          maxVal = val;
+          result = j;
+        }
+      }
+      const char* key = kChars[result];
+      std::string s = key;
+      std::string province = kv_->get(s);
+      label = std::make_pair(s, province).second;
+    }
+    /*std::cout << "result:" << result << std::endl;
+    std::cout << "maxVal:" << maxVal << std::endl;*/
+    character.setCharacterScore(maxVal);
+    character.setCharacterStr(label);
+  }
+}
+
+
+void CharsIdentify::classifyChineseGray(std::vector<CCharacter>& charVec){
+  size_t charVecSize = charVec.size();
+  if (charVecSize == 0)
+    return;
+
+  Mat featureRows;
+  for (size_t index = 0; index < charVecSize; index++) {
+    Mat charInput = charVec[index].getCharacterMat();
+    cv::Mat feature;
+    extractFeature(charInput, feature);
+    featureRows.push_back(feature);
+  }
+
+  cv::Mat output(charVecSize, kChineseNumber, CV_32FC1);
+  annGray_->predict(featureRows, output);
+
+  for (size_t output_index = 0; output_index < charVecSize; output_index++) {
+    CCharacter& character = charVec[output_index];
+    Mat output_row = output.row(output_index);
+    bool isChinese = true;
+
+    float maxVal = -2;
+    int result = 0;
+
+    for (int j = 0; j < kChineseNumber; j++) {
+      float val = output_row.at<float>(j);
+      //std::cout << "j:" << j << "val:" << val << std::endl;
+      if (val > maxVal) {
+        maxVal = val;
+        result = j;
+      }
+    }
+
+    // no match
+    if (-1 == result) {
+      result = 0;
+      maxVal = 0;
+      isChinese = false;
+    }
+
+    auto index = result + kCharsTotalNumber - kChineseNumber;
+    const char* key = kChars[index];
+    std::string s = key;
+    std::string province = kv_->get(s);
+
+    /*std::cout << "result:" << result << std::endl;
+    std::cout << "maxVal:" << maxVal << std::endl;*/
+
+    character.setCharacterScore(maxVal);
+    character.setCharacterStr(province);
+    character.setIsChinese(isChinese);
+  }
+}
+
+void CharsIdentify::classifyChinese(std::vector<CCharacter>& charVec){
+  size_t charVecSize = charVec.size();
+
+  if (charVecSize == 0)
+    return;
+
+  Mat featureRows;
+  for (size_t index = 0; index < charVecSize; index++) {
+    Mat charInput = charVec[index].getCharacterMat();
+    Mat feature = charFeatures(charInput, kChineseSize);
+    featureRows.push_back(feature);
+  }
+
+  cv::Mat output(charVecSize, kChineseNumber, CV_32FC1);
+  annChinese_->predict(featureRows, output);
+
+  for (size_t output_index = 0; output_index < charVecSize; output_index++) {
+    CCharacter& character = charVec[output_index];
+    Mat output_row = output.row(output_index);
+    bool isChinese = true;
+
+    float maxVal = -2;
+    int result = 0;
+
+    for (int j = 0; j < kChineseNumber; j++) {
+      float val = output_row.at<float>(j);
+      //std::cout << "j:" << j << "val:" << val << std::endl;
+      if (val > maxVal) {
+        maxVal = val;
+        result = j;
+      }
+    }
+
+    // no match
+    if (-1 == result) {
+      result = 0;
+      maxVal = 0;
+      isChinese = false;
+    }
+
+    auto index = result + kCharsTotalNumber - kChineseNumber;
+    const char* key = kChars[index];
+    std::string s = key;
+    std::string province = kv_->get(s);
+
+    /*std::cout << "result:" << result << std::endl;
+    std::cout << "maxVal:" << maxVal << std::endl;*/
+
+    character.setCharacterScore(maxVal);
+    character.setCharacterStr(province);
+    character.setIsChinese(isChinese);
+  }
+}
+
+int CharsIdentify::classify(cv::Mat f, float& maxVal, bool isChinses, bool isAlphabet){
+  int result = 0;
+
+  cv::Mat output(1, kCharsTotalNumber, CV_32FC1);
+  ann_->predict(f, output);
+
+  maxVal = -2.f;
+  if (!isChinses) {
+    if (!isAlphabet) {
+      result = 0;
+      for (int j = 0; j < kCharactersNumber; j++) {
+        float val = output.at<float>(j);
+        // std::cout << "j:" << j << "val:" << val << std::endl;
+        if (val > maxVal) {
+          maxVal = val;
+          result = j;
+        }
+      }
+    }
+    else {
+      result = 0;
+      // begin with 11th char, which is 'A'
+      for (int j = 10; j < kCharactersNumber; j++) {
+        float val = output.at<float>(j);
+        // std::cout << "j:" << j << "val:" << val << std::endl;
+        if (val > maxVal) {
+          maxVal = val;
+          result = j;
+        }
+      }
+    }
+  }
+  else {
+    result = kCharactersNumber;
+    for (int j = kCharactersNumber; j < kCharsTotalNumber; j++) {
+      float val = output.at<float>(j);
+      //std::cout << "j:" << j << "val:" << val << std::endl;
+      if (val > maxVal) {
+        maxVal = val;
+        result = j;
+      }
+    }
+  }
+  //std::cout << "maxVal:" << maxVal << std::endl;
+  return result;
+}
+
+bool CharsIdentify::isCharacter(cv::Mat input, std::string& label, float& maxVal, bool isChinese) {
+  cv::Mat feature = charFeatures(input, kPredictSize);
+  auto index = static_cast<int>(classify(feature, maxVal, isChinese));
+
+  if (isChinese) {
+    //std::cout << "maxVal:" << maxVal << std::endl;
+  }
+
+  float chineseMaxThresh = 0.2f;
+
+  if (maxVal >= 0.9 || (isChinese && maxVal >= chineseMaxThresh)) {
+    if (index < kCharactersNumber) {
+      label = std::make_pair(kChars[index], kChars[index]).second;
+    }
+    else {
+      const char* key = kChars[index];
+      std::string s = key;
+      std::string province = kv_->get(s);
+      label = std::make_pair(s, province).second;
+    }
+    return true;
+  }
+  else
+    return false;
+}
+
+std::pair<std::string, std::string> CharsIdentify::identifyChinese(cv::Mat input, float& out, bool& isChinese) {
+  cv::Mat feature = charFeatures(input, kChineseSize);
+  float maxVal = -2;
+  int result = 0;
+
+  cv::Mat output(1, kChineseNumber, CV_32FC1);
+  annChinese_->predict(feature, output);
+
+  for (int j = 0; j < kChineseNumber; j++) {
+    float val = output.at<float>(j);
+    //std::cout << "j:" << j << "val:" << val << std::endl;
+    if (val > maxVal) {
+      maxVal = val;
+      result = j;
+    }
+  }
+
+  // no match
+  if (-1 == result) {
+    result = 0;
+    maxVal = 0;
+    isChinese = false;
+  }
+  else if (maxVal > 0.9){
+    isChinese = true;
+  }
+
+  auto index = result + kCharsTotalNumber - kChineseNumber;
+  const char* key = kChars[index];
+  std::string s = key;
+  std::string province = kv_->get(s);
+  out = maxVal;
+
+  return std::make_pair(s, province);
+}
+
+std::pair<std::string, std::string> CharsIdentify::identifyChineseGray(cv::Mat input, float& out, bool& isChinese) {
+  cv::Mat feature;
+  extractFeature(input, feature);
+  float maxVal = -2;
+  int result = 0;
+  cv::Mat output(1, kChineseNumber, CV_32FC1);
+  annGray_->predict(feature, output);
+
+  for (int j = 0; j < kChineseNumber; j++) {
+    float val = output.at<float>(j);
+    //std::cout << "j:" << j << "val:" << val << std::endl;
+    if (val > maxVal) {
+      maxVal = val;
+      result = j;
+    }
+  }
+  // no match
+  if (-1 == result) {
+    result = 0;
+    maxVal = 0;
+    isChinese = false;
+  } else if (maxVal > 0.9){
+    isChinese = true;
+  }
+  auto index = result + kCharsTotalNumber - kChineseNumber;
+  const char* key = kChars[index];
+  std::string s = key;
+  std::string province = kv_->get(s);
+  out = maxVal;
+  return std::make_pair(s, province);
+}
+
+
+std::pair<std::string, std::string> CharsIdentify::identify(cv::Mat input, bool isChinese, bool isAlphabet) {
+  cv::Mat feature = charFeatures(input, kPredictSize);
+  float maxVal = -2;
+  auto index = static_cast<int>(classify(feature, maxVal, isChinese, isAlphabet));
+  if (index < kCharactersNumber) {
+    return std::make_pair(kChars[index], kChars[index]);
+  }
+  else {
+    const char* key = kChars[index];
+    std::string s = key;
+    std::string province = kv_->get(s);
+    return std::make_pair(s, province);
+  }
+}
+
+int CharsIdentify::identify(std::vector<cv::Mat> inputs, std::vector<std::pair<std::string, std::string>>& outputs,
+                            std::vector<bool> isChineseVec) {
+  Mat featureRows;
+  size_t input_size = inputs.size();
+  for (size_t i = 0; i < input_size; i++) {
+    Mat input = inputs[i];
+    cv::Mat feature = charFeatures(input, kPredictSize);
+    featureRows.push_back(feature);
+  }
+
+  std::vector<int> maxIndexs;
+  std::vector<float> maxVals;
+  classify(featureRows, maxIndexs, maxVals, isChineseVec);
+
+  for (size_t row_index = 0; row_index < input_size; row_index++) {
+    int index = maxIndexs[row_index];
+    if (index < kCharactersNumber) {
+      outputs[row_index] = std::make_pair(kChars[index], kChars[index]);
+    }
+    else {
+      const char* key = kChars[index];
+      std::string s = key;
+      std::string province = kv_->get(s);
+      outputs[row_index] = std::make_pair(s, province);
+    }
+  }
+  return 0;
+}
+}

target/classes/easypr-c++/chars_recognise.cpp

@@ -0,0 +1,117 @@
+#include "easypr/core/chars_recognise.h"
+#include "easypr/core/character.hpp"
+#include "easypr/util/util.h"
+#include <ctime>
+
+namespace easypr {
+
+CCharsRecognise::CCharsRecognise() { m_charsSegment = new CCharsSegment(); }
+
+CCharsRecognise::~CCharsRecognise() { SAFE_RELEASE(m_charsSegment); }
+
+int CCharsRecognise::charsRecognise(Mat plate, std::string& plateLicense) {
+  std::vector<Mat> matChars;
+  int result = m_charsSegment->charsSegment(plate, matChars);
+  if (result == 0) {
+    int num = matChars.size();
+    for (int j = 0; j < num; j++)
+    {
+      Mat charMat = matChars.at(j);
+      bool isChinses = false;
+      float maxVal = 0;
+      if (j == 0) {
+        bool judge = true;
+        isChinses = true;
+        auto character = CharsIdentify::instance()->identifyChinese(charMat, maxVal, judge);
+        plateLicense.append(character.second);
+      }
+      else {
+        isChinses = false;
+        auto character = CharsIdentify::instance()->identify(charMat, isChinses);
+        plateLicense.append(character.second);
+      }
+    }
+
+  }
+  if (plateLicense.size() < 7) {
+    return -1;
+  }
+
+  return result;
+}
+
+
+int CCharsRecognise::charsRecognise(CPlate& plate, std::string& plateLicense) {
+  std::vector<Mat> matChars;
+  std::vector<Mat> grayChars;
+  Mat plateMat = plate.getPlateMat();
+  if (0) writeTempImage(plateMat, "plateMat/plate");
+  Color color;
+  if (plate.getPlateLocateType() == CMSER) {
+    color = plate.getPlateColor();
+  }
+  else {
+    int w = plateMat.cols;
+    int h = plateMat.rows;
+    Mat tmpMat = plateMat(Rect_<double>(w * 0.1, h * 0.1, w * 0.8, h * 0.8));
+    color = getPlateType(tmpMat, true);
+  }
+
+  int result = m_charsSegment->charsSegmentUsingOSTU(plateMat, matChars, grayChars, color);
+
+  if (result == 0) {
+    int num = matChars.size();
+    for (int j = 0; j < num; j++)
+    {
+      Mat charMat = matChars.at(j);
+      Mat grayChar = grayChars.at(j);
+      if (color != Color::BLUE)
+        grayChar = 255 - grayChar;
+
+      bool isChinses = false;
+      std::pair<std::string, std::string> character;
+      float maxVal;
+      if (0 == j) {
+        isChinses = true;
+        bool judge = true;
+        character = CharsIdentify::instance()->identifyChineseGray(grayChar, maxVal, judge);
+        plateLicense.append(character.second);
+
+        // set plate chinese mat and str
+        plate.setChineseMat(grayChar);
+        plate.setChineseKey(character.first);
+        if (0) writeTempImage(grayChar, "char_data/" + character.first + "/chars_");
+      }
+      else if (1 == j) {
+        isChinses = false;
+        bool isAbc = true;
+        character = CharsIdentify::instance()->identify(charMat, isChinses, isAbc);
+        plateLicense.append(character.second);
+      }
+      else {
+        isChinses = false;
+        SHOW_IMAGE(charMat, 0);
+        character = CharsIdentify::instance()->identify(charMat, isChinses);
+        plateLicense.append(character.second);
+      }
+
+      CCharacter charResult;
+      charResult.setCharacterMat(charMat);
+      charResult.setCharacterGrayMat(grayChar);
+      if (isChinses)
+        charResult.setCharacterStr(character.first);
+      else
+        charResult.setCharacterStr(character.second);
+
+      plate.addReutCharacter(charResult);
+    }
+    if (plateLicense.size() < 7) {
+      return -1;
+    }
+  }
+
+  return result;
+}
+
+
+}

target/classes/easypr-c++/feature.cpp

@@ -0,0 +1,466 @@
+#include "easypr/core/feature.h"
+#include "easypr/core/core_func.h"
+#include "thirdparty/LBP/lbp.hpp"
+
+namespace easypr {
+
+
+Mat getHistogram(Mat in) {
+  const int VERTICAL = 0;
+  const int HORIZONTAL = 1;
+
+  // Histogram features
+  Mat vhist = ProjectedHistogram(in, VERTICAL);
+  Mat hhist = ProjectedHistogram(in, HORIZONTAL);
+
+  // Last 10 is the number of moments components
+  int numCols = vhist.cols + hhist.cols;
+
+  Mat out = Mat::zeros(1, numCols, CV_32F);
+
+  int j = 0;
+  for (int i = 0; i < vhist.cols; i++) {
+    out.at<float>(j) = vhist.at<float>(i);
+    j++;
+  }
+  for (int i = 0; i < hhist.cols; i++) {
+    out.at<float>(j) = hhist.at<float>(i);
+    j++;
+  }
+
+  return out;
+}
+
+void getHistogramFeatures(const Mat& image, Mat& features) {
+  Mat grayImage;
+  cvtColor(image, grayImage, CV_RGB2GRAY);
+
+  //grayImage = histeq(grayImage);
+
+  Mat img_threshold;
+  threshold(grayImage, img_threshold, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
+  //Mat img_threshold = grayImage.clone();
+  //spatial_ostu(img_threshold, 8, 2, getPlateType(image, false));
+
+  features = getHistogram(img_threshold);
+}
+
+// compute color histom
+void getColorFeatures(const Mat& src, Mat& features) {
+  Mat src_hsv;
+
+  //grayImage = histeq(grayImage);
+  cvtColor(src, src_hsv, CV_BGR2HSV);
+  int channels = src_hsv.channels();
+  int nRows = src_hsv.rows;
+
+  // consider multi channel image
+  int nCols = src_hsv.cols * channels;
+  if (src_hsv.isContinuous()) {
+    nCols *= nRows;
+    nRows = 1;
+  }
+
+  const int sz = 180;
+  int h[sz] = { 0 };
+
+  uchar* p;
+  for (int i = 0; i < nRows; ++i) {
+    p = src_hsv.ptr<uchar>(i);
+    for (int j = 0; j < nCols; j += 3) {
+      int H = int(p[j]);      // 0-180
+      if (H > sz - 1) H = sz - 1;
+      if (H < 0) H = 0;
+      h[H]++;
+    }
+  }
+
+  Mat mhist = Mat::zeros(1, sz, CV_32F);
+  for (int j = 0; j < sz; j++) {
+    mhist.at<float>(j) = (float)h[j];
+  }
+
+  // Normalize histogram
+  double min, max;
+  minMaxLoc(mhist, &min, &max);
+
+  if (max > 0)
+    mhist.convertTo(mhist, -1, 1.0f / max, 0);
+
+  features = mhist;
+}
+
+
+void getHistomPlusColoFeatures(const Mat& image, Mat& features) {
+  // TODO
+  Mat feature1, feature2;
+  getHistogramFeatures(image, feature1);
+  getColorFeatures(image, feature2);
+  hconcat(feature1.reshape(1, 1), feature2.reshape(1, 1), features);
+}
+
+
+void getSIFTFeatures(const Mat& image, Mat& features) {
+  // TODO
+}
+
+
+//HOG Features
+void getHOGFeatures(const Mat& image, Mat& features) {
+  //HOG descripter
+  HOGDescriptor hog(cvSize(128, 64), cvSize(16, 16), cvSize(8, 8), cvSize(8, 8), 3); //these parameters work well
+	std::vector<float> descriptor;
+
+  // resize input image to (128,64) for compute
+	Size dsize = Size(128,64);
+	Mat trainImg = Mat(dsize, CV_32S);
+	resize(image, trainImg, dsize);
+
+  // compute descripter
+	hog.compute(trainImg, descriptor, Size(8, 8));
+
+  // copy the result
+	Mat mat_featrue(descriptor);
+	mat_featrue.copyTo(features);
+}
+
+
+void getHSVHistFeatures(const Mat& image, Mat& features) {
+  // TODO
+}
+
+//! LBP feature
+void getLBPFeatures(const Mat& image, Mat& features) {
+
+  Mat grayImage;
+  cvtColor(image, grayImage, CV_RGB2GRAY);
+
+  Mat lbpimage;
+  lbpimage = libfacerec::olbp(grayImage);
+  Mat lbp_hist = libfacerec::spatial_histogram(lbpimage, 32, 4, 4);
+
+  features = lbp_hist;
+}
+
+Mat charFeatures(Mat in, int sizeData) {
+  const int VERTICAL = 0;
+  const int HORIZONTAL = 1;
+
+  // cut the cetner, will afect 5% perices.
+  Rect _rect = GetCenterRect(in);
+  Mat tmpIn = CutTheRect(in, _rect);
+  //Mat tmpIn = in.clone();
+
+  // Low data feature
+  Mat lowData;
+  resize(tmpIn, lowData, Size(sizeData, sizeData));
+
+  // Histogram features
+  Mat vhist = ProjectedHistogram(lowData, VERTICAL);
+  Mat hhist = ProjectedHistogram(lowData, HORIZONTAL);
+
+  // Last 10 is the number of moments components
+  int numCols = vhist.cols + hhist.cols + lowData.cols * lowData.cols;
+
+  Mat out = Mat::zeros(1, numCols, CV_32F);
+  // Asign values to
+
+  int j = 0;
+  for (int i = 0; i < vhist.cols; i++) {
+    out.at<float>(j) = vhist.at<float>(i);
+    j++;
+  }
+  for (int i = 0; i < hhist.cols; i++) {
+    out.at<float>(j) = hhist.at<float>(i);
+    j++;
+  }
+  for (int x = 0; x < lowData.cols; x++) {
+    for (int y = 0; y < lowData.rows; y++) {
+      out.at<float>(j) += (float)lowData.at <unsigned char>(x, y);
+      j++;
+    }
+  }
+
+  //std::cout << out << std::endl;
+
+  return out;
+}
+
+
+Mat charFeatures2(Mat in, int sizeData) {
+  const int VERTICAL = 0;
+  const int HORIZONTAL = 1;
+
+  // cut the cetner, will afect 5% perices.
+  Rect _rect = GetCenterRect(in);
+  Mat tmpIn = CutTheRect(in, _rect);
+  //Mat tmpIn = in.clone();
+
+  // Low data feature
+  Mat lowData;
+  resize(tmpIn, lowData, Size(sizeData, sizeData));
+
+  // Histogram features
+  Mat vhist = ProjectedHistogram(lowData, VERTICAL);
+  Mat hhist = ProjectedHistogram(lowData, HORIZONTAL);
+
+  // Last 10 is the number of moments components
+  int numCols = vhist.cols + hhist.cols + lowData.cols * lowData.cols;
+
+  Mat out = Mat::zeros(1, numCols, CV_32F);
+
+  int j = 0;
+  for (int i = 0; i < vhist.cols; i++) {
+    out.at<float>(j) = vhist.at<float>(i);
+    j++;
+  }
+  for (int i = 0; i < hhist.cols; i++) {
+    out.at<float>(j) = hhist.at<float>(i);
+    j++;
+  }
+  for (int x = 0; x < lowData.cols; x++) {
+    for (int y = 0; y < lowData.rows; y++) {
+      out.at<float>(j) += (float)lowData.at <unsigned char>(x, y);
+      j++;
+    }
+  }
+
+  //std::cout << out << std::endl;
+
+  return out;
+}
+
+Mat charProjectFeatures(const Mat& in, int sizeData) {
+  const int VERTICAL = 0;
+  const int HORIZONTAL = 1;
+
+  SHOW_IMAGE(in, 0);
+  // cut the cetner, will afect 5% perices.
+
+  Mat lowData;
+  resize(in, lowData, Size(sizeData, sizeData));
+
+  SHOW_IMAGE(lowData, 0);
+  // Histogram features
+  Mat vhist = ProjectedHistogram(lowData, VERTICAL);
+  Mat hhist = ProjectedHistogram(lowData, HORIZONTAL);
+
+  // Last 10 is the number of moments components
+  int numCols = vhist.cols + hhist.cols;
+
+  Mat out = Mat::zeros(1, numCols, CV_32F);
+
+  int j = 0;
+  for (int i = 0; i < vhist.cols; i++) {
+    out.at<float>(j) = vhist.at<float>(i);
+    j++;
+  }
+  for (int i = 0; i < hhist.cols; i++) {
+    out.at<float>(j) = hhist.at<float>(i);
+    j++;
+  }
+  //std::cout << out << std::endl;
+
+  return out;
+}
+
+void getGrayCharFeatures(const Mat& grayChar, Mat& features) {
+  // TODO: check channnels == 1
+  SHOW_IMAGE(grayChar, 0);
+  SHOW_IMAGE(255 - grayChar, 0);
+
+  // resize to uniform size, like 20x32
+  bool useResize = false;
+  bool useConvert = true;
+  bool useMean = true;
+  bool useLBP = false;
+
+  Mat char_mat;
+  if (useResize) {
+    char_mat.create(kGrayCharHeight, kGrayCharWidth, CV_8UC1);
+    resize(grayChar, char_mat, char_mat.size(), 0, 0, INTER_LINEAR);
+  } else {
+    char_mat = grayChar;
+  }
+  SHOW_IMAGE(char_mat, 0);
+
+  // convert to float
+  Mat float_img;
+  if (useConvert) {
+    float scale = 1.f / 255;
+    char_mat.convertTo(float_img, CV_32FC1, scale, 0);
+  } else {
+    float_img = char_mat;
+  }
+  SHOW_IMAGE(float_img, 0);
+
+  // cut from mean, it can be optional
+
+  Mat mean_img;
+  if (useMean) {
+    float_img -= mean(float_img);
+    mean_img = float_img;
+  } else {
+    mean_img = float_img;
+  }
+  SHOW_IMAGE(mean_img, 0);
+
+  // use lbp to get features, it can be changed to other
+  Mat feautreImg;
+  if (useLBP) {
+    Mat lbpimage = libfacerec::olbp(char_mat);
+    SHOW_IMAGE(lbpimage, 0);
+    feautreImg = libfacerec::spatial_histogram(lbpimage, kCharLBPPatterns, kCharLBPGridX, kCharLBPGridY);
+  } else {
+    feautreImg = mean_img.reshape(1, 1);
+  }
+
+  // return back
+  features = feautreImg;
+}
+
+
+void getGrayPlusProject(const Mat& grayChar, Mat& features)
+{
+  // TODO: check channnels == 1
+  SHOW_IMAGE(grayChar, 0);
+  SHOW_IMAGE(255 - grayChar, 0);
+
+  // resize to uniform size, like 20x32
+  bool useResize = false;
+  bool useConvert = true;
+  bool useMean = true;
+  bool useLBP = false;
+
+  Mat char_mat;
+  if (useResize) {
+    char_mat.create(kGrayCharHeight, kGrayCharWidth, CV_8UC1);
+    resize(grayChar, char_mat, char_mat.size(), 0, 0, INTER_LINEAR);
+  }
+  else {
+    char_mat = grayChar;
+  }
+  SHOW_IMAGE(char_mat, 0);
+
+  // convert to float
+  Mat float_img;
+  if (useConvert) {
+    float scale = 1.f / 255;
+    char_mat.convertTo(float_img, CV_32FC1, scale, 0);
+  }
+  else {
+    float_img = char_mat;
+  }
+  SHOW_IMAGE(float_img, 0);
+
+  // cut from mean, it can be optional
+
+  Mat mean_img;
+  if (useMean) {
+    float_img -= mean(float_img);
+    mean_img = float_img;
+  }
+  else {
+    mean_img = float_img;
+  }
+  SHOW_IMAGE(mean_img, 0);
+
+  // use lbp to get features, it can be changed to other
+  Mat feautreImg;
+  if (useLBP) {
+    Mat lbpimage = libfacerec::olbp(char_mat);
+    SHOW_IMAGE(lbpimage, 0);
+    feautreImg = libfacerec::spatial_histogram(lbpimage, kCharLBPPatterns, kCharLBPGridX, kCharLBPGridY);
+  }
+  else {
+    feautreImg = mean_img.reshape(1, 1);
+  }
+  SHOW_IMAGE(grayChar, 0);
+  Mat binaryChar;
+  threshold(grayChar, binaryChar, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
+  SHOW_IMAGE(binaryChar, 0);
+  Mat projectFeature = charProjectFeatures(binaryChar, 32);
+
+  hconcat(feautreImg.reshape(1, 1), projectFeature.reshape(1, 1), features);
+}
+
+
+void getGrayPlusLBP(const Mat& grayChar, Mat& features)
+{
+  // TODO: check channnels == 1
+  SHOW_IMAGE(grayChar, 0);
+  SHOW_IMAGE(255 - grayChar, 0);
+
+  // resize to uniform size, like 20x32
+  bool useResize = false;
+  bool useConvert = true;
+  bool useMean = true;
+  bool useLBP = true;
+
+  Mat char_mat;
+  if (useResize) {
+    char_mat.create(kGrayCharHeight, kGrayCharWidth, CV_8UC1);
+    resize(grayChar, char_mat, char_mat.size(), 0, 0, INTER_LINEAR);
+  }
+  else {
+    char_mat = grayChar;
+  }
+  SHOW_IMAGE(char_mat, 0);
+
+  // convert to float
+  Mat float_img;
+  if (useConvert) {
+    float scale = 1.f / 255;
+    char_mat.convertTo(float_img, CV_32FC1, scale, 0);
+  }
+  else {
+    float_img = char_mat;
+  }
+  SHOW_IMAGE(float_img, 0);
+
+  // cut from mean, it can be optional
+
+  Mat mean_img;
+  if (useMean) {
+    float_img -= mean(float_img);
+    mean_img = float_img;
+  }
+  else {
+    mean_img = float_img;
+  }
+  SHOW_IMAGE(mean_img, 0);
+
+  // use lbp to get features, it can be changed to other
+  Mat originImage = mean_img.clone();
+  Mat lbpimage = libfacerec::olbp(mean_img);
+  SHOW_IMAGE(lbpimage, 0);
+  lbpimage = libfacerec::spatial_histogram(lbpimage, kCharLBPPatterns, kCharLBPGridX, kCharLBPGridY);
+
+  // 32x20 + 16x16
+  hconcat(mean_img.reshape(1, 1), lbpimage.reshape(1, 1), features);
+}
+
+void getLBPplusHistFeatures(const Mat& image, Mat& features) {
+  Mat grayImage;
+  cvtColor(image, grayImage, CV_RGB2GRAY);
+
+  Mat lbpimage;
+  lbpimage = libfacerec::olbp(grayImage);
+  Mat lbp_hist = libfacerec::spatial_histogram(lbpimage, 64, 8, 4);
+  //features = lbp_hist.reshape(1, 1);
+
+  Mat greyImage;
+  cvtColor(image, greyImage, CV_RGB2GRAY);
+
+  //grayImage = histeq(grayImage);
+  Mat img_threshold;
+  threshold(greyImage, img_threshold, 0, 255,
+    CV_THRESH_OTSU + CV_THRESH_BINARY);
+  Mat histomFeatures = getHistogram(img_threshold);
+
+  hconcat(lbp_hist.reshape(1, 1), histomFeatures.reshape(1, 1), features);
+  //std::cout << features << std::endl;
+  //features = histomFeatures;
+}
+
+}

target/classes/easypr-c++/params.cpp

@@ -0,0 +1,12 @@
+#include "easypr/core/params.h"
+
+namespace easypr {
+  CParams* CParams::instance_ = nullptr;
+
+  CParams* CParams::instance() {
+    if (!instance_) {
+      instance_ = new CParams;
+    }
+    return instance_;
+  }
+}/*! \namespace easypr*/

target/classes/easypr-c++/plate_detect.cpp

@@ -0,0 +1,77 @@
+#include "easypr/core/plate_detect.h"
+#include "easypr/util/util.h"
+#include "easypr/core/core_func.h"
+#include "easypr/config.h"
+
+namespace easypr {
+
+  CPlateDetect::CPlateDetect() {
+    m_plateLocate = new CPlateLocate();
+    m_maxPlates = 3;
+    m_type = 0;
+    m_showDetect = false;
+  }
+
+  CPlateDetect::~CPlateDetect() { SAFE_RELEASE(m_plateLocate); }
+
+  int CPlateDetect::plateDetect(Mat src, std::vector<CPlate> &resultVec, int type,
+    bool showDetectArea, int img_index) {
+    std::vector<CPlate> sobel_Plates;
+    sobel_Plates.reserve(16);
+    std::vector<CPlate> color_Plates;
+    color_Plates.reserve(16);
+    std::vector<CPlate> mser_Plates;
+    mser_Plates.reserve(16);
+    std::vector<CPlate> all_result_Plates;
+    all_result_Plates.reserve(64);
+#pragma omp parallel sections
+    {
+#pragma omp section
+      {
+        if (!type || type & PR_DETECT_SOBEL) {
+          m_plateLocate->plateSobelLocate(src, sobel_Plates, img_index);
+        }
+      }
+#pragma omp section
+      {
+        if (!type || type & PR_DETECT_COLOR) {
+          m_plateLocate->plateColorLocate(src, color_Plates, img_index);
+        }
+      }
+#pragma omp section
+      {
+        if (!type || type & PR_DETECT_CMSER) {
+          m_plateLocate->plateMserLocate(src, mser_Plates, img_index);
+        }
+      }
+    }
+    for (auto plate : sobel_Plates) {
+      plate.setPlateLocateType(SOBEL);
+      all_result_Plates.push_back(plate);
+    }
+    for (auto plate : color_Plates) {
+      plate.setPlateLocateType(COLOR);
+      all_result_Plates.push_back(plate);
+    }
+    for (auto plate : mser_Plates) {
+      plate.setPlateLocateType(CMSER);
+      all_result_Plates.push_back(plate);
+    }
+    // use nms to judge plate
+    PlateJudge::instance()->plateJudgeUsingNMS(all_result_Plates, resultVec, m_maxPlates);
+
+    if (0)
+      showDectectResults(src, resultVec, m_maxPlates);
+    return 0;
+  }
+
+  int CPlateDetect::plateDetect(Mat src, std::vector<CPlate> &resultVec, int img_index) {
+    int result = plateDetect(src, resultVec, m_type, false, img_index);
+    return result;
+  }
+
+  void CPlateDetect::LoadSVM(std::string path) {
+    PlateJudge::instance()->LoadModel(path);
+  }
+
+}

target/classes/easypr-c++/plate_judge.cpp

@@ -0,0 +1,193 @@
+#include "easypr/core/plate_judge.h"
+#include "easypr/config.h"
+#include "easypr/core/core_func.h"
+#include "easypr/core/params.h"
+
+namespace easypr {
+
+  PlateJudge* PlateJudge::instance_ = nullptr;
+
+  PlateJudge* PlateJudge::instance() {
+    if (!instance_) {
+      instance_ = new PlateJudge;
+    }
+    return instance_;
+  }
+
+  PlateJudge::PlateJudge() { 
+    bool useLBP = false;
+    if (useLBP) {
+      LOAD_SVM_MODEL(svm_, kLBPSvmPath);
+      extractFeature = getLBPFeatures;
+    }
+    else {
+      LOAD_SVM_MODEL(svm_, kHistSvmPath);
+      extractFeature = getHistomPlusColoFeatures;
+    }
+  }
+
+  void PlateJudge::LoadModel(std::string path) {
+    if (path != std::string(kDefaultSvmPath)) {
+      if (!svm_->empty())
+        svm_->clear();
+      LOAD_SVM_MODEL(svm_, path);
+    }
+  }
+
+  // set the score of plate
+  // 0 is plate, -1 is not.
+  int PlateJudge::plateSetScore(CPlate& plate) {
+    Mat features;
+    extractFeature(plate.getPlateMat(), features);
+    float score = svm_->predict(features, noArray(), cv::ml::StatModel::Flags::RAW_OUTPUT);
+    //std::cout << "score:" << score << std::endl;
+    if (0) {
+      imshow("plate", plate.getPlateMat());
+      waitKey(0);
+      destroyWindow("plate");
+    }
+    // score is the distance of margin，below zero is plate, up is not
+    // when score is below zero, the samll the value, the more possibliy to be a plate.
+    plate.setPlateScore(score);
+    if (score < 0.5) return 0;
+    else return -1;      
+  }
+
+  int PlateJudge::plateJudge(const Mat& plateMat) {
+    CPlate plate;
+    plate.setPlateMat(plateMat);
+    return plateSetScore(plate);
+  }
+
+  int PlateJudge::plateJudge(const std::vector<Mat> &inVec,
+    std::vector<Mat> &resultVec) {
+    int num = inVec.size();
+    for (int j = 0; j < num; j++) {
+      Mat inMat = inVec[j];
+
+      int response = -1;
+      response = plateJudge(inMat);
+
+      if (response == 0) resultVec.push_back(inMat);
+    }
+    return 0;
+  }
+
+  int PlateJudge::plateJudge(const std::vector<CPlate> &inVec,
+    std::vector<CPlate> &resultVec) {
+    int num = inVec.size();
+    for (int j = 0; j < num; j++) {
+      CPlate inPlate = inVec[j];
+      Mat inMat = inPlate.getPlateMat();
+      int response = -1;
+      response = plateJudge(inMat);
+
+      if (response == 0)
+        resultVec.push_back(inPlate);
+      else {
+        int w = inMat.cols;
+        int h = inMat.rows;
+        Mat tmpmat = inMat(Rect_<double>(w * 0.05, h * 0.1, w * 0.9, h * 0.8));
+        Mat tmpDes = inMat.clone();
+        resize(tmpmat, tmpDes, Size(inMat.size()));
+
+        response = plateJudge(tmpDes);
+        if (response == 0) resultVec.push_back(inPlate);
+      }
+    }
+    return 0;
+  }
+
+  // non-maximum suppression
+  void NMS(std::vector<CPlate> &inVec, std::vector<CPlate> &resultVec, double overlap) {
+    std::sort(inVec.begin(), inVec.end());
+    std::vector<CPlate>::iterator it = inVec.begin();
+    for (; it != inVec.end(); ++it) {
+      CPlate plateSrc = *it;
+      //std::cout << "plateScore:" << plateSrc.getPlateScore() << std::endl;
+      Rect rectSrc = plateSrc.getPlatePos().boundingRect();
+      std::vector<CPlate>::iterator itc = it + 1;
+      for (; itc != inVec.end();) {
+        CPlate plateComp = *itc;
+        Rect rectComp = plateComp.getPlatePos().boundingRect();
+        float iou = computeIOU(rectSrc, rectComp);
+        if (iou > overlap) {
+          itc = inVec.erase(itc);
+        }
+        else {
+          ++itc;
+        }
+      }
+    }
+    resultVec = inVec;
+  }
+
+  // judge plate using nms
+  int PlateJudge::plateJudgeUsingNMS(const std::vector<CPlate> &inVec, std::vector<CPlate> &resultVec, int maxPlates) {
+    std::vector<CPlate> plateVec;
+    int num = inVec.size();
+    bool useCascadeJudge = true;
+
+    for (int j = 0; j < num; j++) {
+      CPlate plate = inVec[j];
+      Mat inMat = plate.getPlateMat();
+      int result = plateSetScore(plate);
+      if (0 == result) {
+        if (0) {
+          imshow("inMat", inMat);
+          waitKey(0);
+          destroyWindow("inMat");
+        }
+
+        if (plate.getPlateLocateType() == CMSER) {
+          int w = inMat.cols;
+          int h = inMat.rows;
+          Mat tmpmat = inMat(Rect_<double>(w * 0.05, h * 0.1, w * 0.9, h * 0.8));
+          Mat tmpDes = inMat.clone();
+          resize(tmpmat, tmpDes, Size(inMat.size()));
+          plate.setPlateMat(tmpDes);
+          if (useCascadeJudge) {
+            int resultCascade = plateSetScore(plate);
+            if (plate.getPlateLocateType() != CMSER)
+              plate.setPlateMat(inMat);
+            if (resultCascade == 0) {
+              if (0) {
+                imshow("tmpDes", tmpDes);
+                waitKey(0);
+                destroyWindow("tmpDes");
+              }
+              plateVec.push_back(plate);
+            }
+          }
+          else 
+            plateVec.push_back(plate);
+        }
+        else 
+          plateVec.push_back(plate);                  
+      }
+    }
+
+    std::vector<CPlate> reDupPlateVec;
+    double overlap = 0.5;
+    // double overlap = CParams::instance()->getParam1f();
+    // use NMS to get the result plates
+    NMS(plateVec, reDupPlateVec, overlap);
+    // sort the plates due to their scores
+    std::sort(reDupPlateVec.begin(), reDupPlateVec.end());
+    // output the plate judge plates
+    std::vector<CPlate>::iterator it = reDupPlateVec.begin();
+    int count = 0;
+    for (; it != reDupPlateVec.end(); ++it) {
+      resultVec.push_back(*it);
+      if (0) {
+        imshow("plateMat", it->getPlateMat());
+        waitKey(0);
+        destroyWindow("plateMat");
+      }
+      count++;
+      if (count >= maxPlates)
+        break;
+    }
+    return 0;
+  }
+}

target/classes/easypr-c++/plate_locate.cpp

@@ -0,0 +1,999 @@
+#include "easypr/core/plate_locate.h"
+#include "easypr/core/core_func.h"
+#include "easypr/util/util.h"
+#include "easypr/core/params.h"
+
+using namespace std;
+
+namespace easypr {
+
+const float DEFAULT_ERROR = 0.9f;    // 0.6
+const float DEFAULT_ASPECT = 3.75f;  // 3.75
+
+CPlateLocate::CPlateLocate() {
+  m_GaussianBlurSize = DEFAULT_GAUSSIANBLUR_SIZE;
+  m_MorphSizeWidth = DEFAULT_MORPH_SIZE_WIDTH;
+  m_MorphSizeHeight = DEFAULT_MORPH_SIZE_HEIGHT;
+
+  m_error = DEFAULT_ERROR;
+  m_aspect = DEFAULT_ASPECT;
+  m_verifyMin = DEFAULT_VERIFY_MIN;
+  m_verifyMax = DEFAULT_VERIFY_MAX;
+
+  m_angle = DEFAULT_ANGLE;
+
+  m_debug = DEFAULT_DEBUG;
+}
+
+void CPlateLocate::setLifemode(bool param) {
+  if (param) {
+    setGaussianBlurSize(5);
+    setMorphSizeWidth(10);
+    setMorphSizeHeight(3);
+    setVerifyError(0.75);
+    setVerifyAspect(4.0);
+    setVerifyMin(1);
+    setVerifyMax(200);
+  } else {
+    setGaussianBlurSize(DEFAULT_GAUSSIANBLUR_SIZE);
+    setMorphSizeWidth(DEFAULT_MORPH_SIZE_WIDTH);
+    setMorphSizeHeight(DEFAULT_MORPH_SIZE_HEIGHT);
+    setVerifyError(DEFAULT_ERROR);
+    setVerifyAspect(DEFAULT_ASPECT);
+    setVerifyMin(DEFAULT_VERIFY_MIN);
+    setVerifyMax(DEFAULT_VERIFY_MAX);
+  }
+}
+
+bool CPlateLocate::verifySizes(RotatedRect mr) {
+  float error = m_error;
+  // Spain car plate size: 52x11 aspect 4,7272
+  // China car plate size: 440mm*140mm，aspect 3.142857
+
+  // Real car plate size: 136 * 32, aspect 4
+  float aspect = m_aspect;
+
+  // Set a min and max area. All other patchs are discarded
+  // int min= 1*aspect*1; // minimum area
+  // int max= 2000*aspect*2000; // maximum area
+  int min = 34 * 8 * m_verifyMin;  // minimum area
+  int max = 34 * 8 * m_verifyMax;  // maximum area
+
+  // Get only patchs that match to a respect ratio.
+  float rmin = aspect - aspect * error;
+  float rmax = aspect + aspect * error;
+
+  float 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))
+    return false;
+  else
+    return true;
+}
+
+//! mser search method
+int CPlateLocate::mserSearch(const Mat &src,  vector<Mat> &out,
+  vector<vector<CPlate>>& out_plateVec, bool usePlateMser, vector<vector<RotatedRect>>& out_plateRRect,
+  int img_index, bool showDebug) {
+  vector<Mat> match_grey;
+
+  vector<CPlate> plateVec_blue;
+  plateVec_blue.reserve(16);
+  vector<RotatedRect> plateRRect_blue;
+  plateRRect_blue.reserve(16);
+
+  vector<CPlate> plateVec_yellow;
+  plateVec_yellow.reserve(16);
+
+  vector<RotatedRect> plateRRect_yellow;
+  plateRRect_yellow.reserve(16);
+
+  mserCharMatch(src, match_grey, plateVec_blue, plateVec_yellow, usePlateMser, plateRRect_blue, plateRRect_yellow, img_index, showDebug);
+
+  out_plateVec.push_back(plateVec_blue);
+  out_plateVec.push_back(plateVec_yellow);
+
+  out_plateRRect.push_back(plateRRect_blue);
+  out_plateRRect.push_back(plateRRect_yellow);
+
+  out = match_grey;
+
+  return 0;
+}
+
+
+int CPlateLocate::colorSearch(const Mat &src, const Color r, Mat &out,
+                              vector<RotatedRect> &outRects) {
+  Mat match_grey;
+
+  // width is important to the final results;
+  const int color_morph_width = 10;
+  const int color_morph_height = 2;
+
+  colorMatch(src, match_grey, r, false);
+  SHOW_IMAGE(match_grey, 0);
+
+  Mat src_threshold;
+  threshold(match_grey, src_threshold, 0, 255,
+            CV_THRESH_OTSU + CV_THRESH_BINARY);
+
+  Mat element = getStructuringElement(
+      MORPH_RECT, Size(color_morph_width, color_morph_height));
+  morphologyEx(src_threshold, src_threshold, MORPH_CLOSE, element);
+
+  //if (m_debug) {
+  //  utils::imwrite("resources/image/tmp/color.jpg", src_threshold);
+  //}
+
+  src_threshold.copyTo(out);
+
+
+  vector<vector<Point>> contours;
+
+  findContours(src_threshold,
+               contours,               // a vector of contours
+               CV_RETR_EXTERNAL,
+               CV_CHAIN_APPROX_NONE);  // all pixels of each contours
+
+  vector<vector<Point>>::iterator itc = contours.begin();
+  while (itc != contours.end()) {
+    RotatedRect mr = minAreaRect(Mat(*itc));
+
+    if (!verifySizes(mr))
+      itc = contours.erase(itc);
+    else {
+      ++itc;
+      outRects.push_back(mr);
+    }
+  }
+
+  return 0;
+}
+
+
+int CPlateLocate::sobelFrtSearch(const Mat &src,
+                                 vector<Rect_<float>> &outRects) {
+  Mat src_threshold;
+
+  sobelOper(src, src_threshold, m_GaussianBlurSize, m_MorphSizeWidth,
+            m_MorphSizeHeight);
+
+  vector<vector<Point>> contours;
+  findContours(src_threshold,
+               contours,               // a vector of contours
+               CV_RETR_EXTERNAL,
+               CV_CHAIN_APPROX_NONE);  // all pixels of each contours
+
+  vector<vector<Point>>::iterator itc = contours.begin();
+
+  vector<RotatedRect> first_rects;
+
+  while (itc != contours.end()) {
+    RotatedRect mr = minAreaRect(Mat(*itc));
+
+
+    if (verifySizes(mr)) {
+      first_rects.push_back(mr);
+
+      float 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;
+    }
+
+    ++itc;
+  }
+
+  for (size_t i = 0; i < first_rects.size(); i++) {
+    RotatedRect roi_rect = first_rects[i];
+
+    Rect_<float> safeBoundRect;
+    if (!calcSafeRect(roi_rect, src, safeBoundRect)) continue;
+
+    outRects.push_back(safeBoundRect);
+  }
+  return 0;
+}
+
+
+int CPlateLocate::sobelSecSearchPart(Mat &bound, Point2f refpoint,
+                                     vector<RotatedRect> &outRects) {
+  Mat bound_threshold;
+
+  sobelOperT(bound, bound_threshold, 3, 6, 2);
+
+  Mat tempBoundThread = bound_threshold.clone();
+
+  clearLiuDingOnly(tempBoundThread);
+
+  int posLeft = 0, posRight = 0;
+  if (bFindLeftRightBound(tempBoundThread, posLeft, posRight)) {
+
+    // find left and right bounds to repair
+
+    if (posRight != 0 && posLeft != 0 && posLeft < posRight) {
+      int posY = int(bound_threshold.rows * 0.5);
+      for (int i = posLeft + (int) (bound_threshold.rows * 0.1);
+           i < posRight - 4; i++) {
+        bound_threshold.data[posY * bound_threshold.cols + i] = 255;
+      }
+    }
+
+    utils::imwrite("resources/image/tmp/repaireimg1.jpg", bound_threshold);
+
+    // remove the left and right boundaries
+
+    for (int i = 0; i < bound_threshold.rows; i++) {
+      bound_threshold.data[i * bound_threshold.cols + posLeft] = 0;
+      bound_threshold.data[i * bound_threshold.cols + posRight] = 0;
+    }
+    utils::imwrite("resources/image/tmp/repaireimg2.jpg", bound_threshold);
+  }
+
+  vector<vector<Point>> contours;
+  findContours(bound_threshold,
+               contours,               // a vector of contours
+               CV_RETR_EXTERNAL,
+               CV_CHAIN_APPROX_NONE);  // all pixels of each contours
+
+  vector<vector<Point>>::iterator itc = contours.begin();
+
+  vector<RotatedRect> second_rects;
+  while (itc != contours.end()) {
+    RotatedRect mr = minAreaRect(Mat(*itc));
+    second_rects.push_back(mr);
+    ++itc;
+  }
+
+  for (size_t i = 0; i < second_rects.size(); i++) {
+    RotatedRect roi = second_rects[i];
+    if (verifySizes(roi)) {
+      Point2f refcenter = roi.center + refpoint;
+      Size2f size = roi.size;
+      float angle = roi.angle;
+
+      RotatedRect refroi(refcenter, size, angle);
+      outRects.push_back(refroi);
+    }
+  }
+
+  return 0;
+}
+
+
+int CPlateLocate::sobelSecSearch(Mat &bound, Point2f refpoint,
+                                 vector<RotatedRect> &outRects) {
+  Mat bound_threshold;
+
+
+  sobelOper(bound, bound_threshold, 3, 10, 3);
+
+  utils::imwrite("resources/image/tmp/sobelSecSearch.jpg", bound_threshold);
+
+  vector<vector<Point>> contours;
+  findContours(bound_threshold,
+               contours,               // a vector of contours
+               CV_RETR_EXTERNAL,
+               CV_CHAIN_APPROX_NONE);  // all pixels of each contours
+
+  vector<vector<Point>>::iterator itc = contours.begin();
+
+  vector<RotatedRect> second_rects;
+  while (itc != contours.end()) {
+    RotatedRect mr = minAreaRect(Mat(*itc));
+    second_rects.push_back(mr);
+    ++itc;
+  }
+
+  for (size_t i = 0; i < second_rects.size(); i++) {
+    RotatedRect roi = second_rects[i];
+    if (verifySizes(roi)) {
+      Point2f refcenter = roi.center + refpoint;
+      Size2f size = roi.size;
+      float angle = roi.angle;
+
+      RotatedRect refroi(refcenter, size, angle);
+      outRects.push_back(refroi);
+    }
+  }
+
+  return 0;
+}
+
+
+int CPlateLocate::sobelOper(const Mat &in, Mat &out, int blurSize, int morphW,
+                            int morphH) {
+  Mat mat_blur;
+  mat_blur = in.clone();
+  GaussianBlur(in, mat_blur, Size(blurSize, blurSize), 0, 0, BORDER_DEFAULT);
+
+  Mat mat_gray;
+  if (mat_blur.channels() == 3)
+    cvtColor(mat_blur, mat_gray, CV_RGB2GRAY);
+  else
+    mat_gray = mat_blur;
+
+  int scale = SOBEL_SCALE;
+  int delta = SOBEL_DELTA;
+  int ddepth = SOBEL_DDEPTH;
+
+  Mat grad_x, grad_y;
+  Mat abs_grad_x, abs_grad_y;
+
+
+  Sobel(mat_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT);
+  convertScaleAbs(grad_x, abs_grad_x);
+
+  Mat grad;
+  addWeighted(abs_grad_x, SOBEL_X_WEIGHT, 0, 0, 0, grad);
+
+  Mat mat_threshold;
+  double otsu_thresh_val =
+      threshold(grad, mat_threshold, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
+
+
+  Mat element = getStructuringElement(MORPH_RECT, Size(morphW, morphH));
+  morphologyEx(mat_threshold, mat_threshold, MORPH_CLOSE, element);
+
+  out = mat_threshold;
+
+  return 0;
+}
+
+void deleteNotArea(Mat &inmat, Color color = UNKNOWN) {
+  Mat input_grey;
+  cvtColor(inmat, input_grey, CV_BGR2GRAY);
+
+  int w = inmat.cols;
+  int h = inmat.rows;
+
+  Mat tmpMat = inmat(Rect_<double>(w * 0.15, h * 0.1, w * 0.7, h * 0.7));
+
+  Color plateType;
+  if (UNKNOWN == color) {
+    plateType = getPlateType(tmpMat, true);
+  }
+  else {
+    plateType = color;
+  }
+
+  Mat img_threshold;
+
+  if (BLUE == plateType) {
+    img_threshold = input_grey.clone();
+    Mat tmp = input_grey(Rect_<double>(w * 0.15, h * 0.15, w * 0.7, h * 0.7));
+    int threadHoldV = ThresholdOtsu(tmp);
+
+    threshold(input_grey, img_threshold, threadHoldV, 255, CV_THRESH_BINARY);
+    // threshold(input_grey, img_threshold, 5, 255, CV_THRESH_OTSU +
+    // CV_THRESH_BINARY);
+
+    utils::imwrite("resources/image/tmp/inputgray2.jpg", img_threshold);
+
+  } else if (YELLOW == plateType) {
+    img_threshold = input_grey.clone();
+    Mat tmp = input_grey(Rect_<double>(w * 0.1, h * 0.1, w * 0.8, h * 0.8));
+    int threadHoldV = ThresholdOtsu(tmp);
+
+    threshold(input_grey, img_threshold, threadHoldV, 255,
+              CV_THRESH_BINARY_INV);
+
+    utils::imwrite("resources/image/tmp/inputgray2.jpg", img_threshold);
+
+    // threshold(input_grey, img_threshold, 10, 255, CV_THRESH_OTSU +
+    // CV_THRESH_BINARY_INV);
+  } else
+    threshold(input_grey, img_threshold, 10, 255,
+              CV_THRESH_OTSU + CV_THRESH_BINARY);
+
+  //img_threshold = input_grey.clone();
+  //spatial_ostu(img_threshold, 8, 2, plateType);
+
+  int posLeft = 0;
+  int posRight = 0;
+
+  int top = 0;
+  int bottom = img_threshold.rows - 1;
+  clearLiuDing(img_threshold, top, bottom);
+
+  if (0) {
+    imshow("inmat", inmat);
+    waitKey(0);
+    destroyWindow("inmat");
+  }
+
+  if (bFindLeftRightBound1(img_threshold, posLeft, posRight)) {
+    inmat = inmat(Rect(posLeft, top, w - posLeft, bottom - top));
+    if (0) {
+      imshow("inmat", inmat);
+      waitKey(0);
+      destroyWindow("inmat");
+    }
+  }
+}
+
+
+int CPlateLocate::deskew(const Mat &src, const Mat &src_b,
+                         vector<RotatedRect> &inRects,
+                         vector<CPlate> &outPlates, bool useDeteleArea, Color color) {
+  Mat mat_debug;
+  src.copyTo(mat_debug);
+
+  for (size_t i = 0; i < inRects.size(); i++) {
+    RotatedRect roi_rect = inRects[i];
+
+    float r = (float) roi_rect.size.width / (float) roi_rect.size.height;
+    float roi_angle = roi_rect.angle;
+
+    Size roi_rect_size = roi_rect.size;
+    if (r < 1) {
+      roi_angle = 90 + roi_angle;
+      swap(roi_rect_size.width, roi_rect_size.height);
+    }
+
+    if (m_debug) {
+      Point2f rect_points[4];
+      roi_rect.points(rect_points);
+      for (int j = 0; j < 4; j++)
+        line(mat_debug, rect_points[j], rect_points[(j + 1) % 4],
+             Scalar(0, 255, 255), 1, 8);
+    }
+
+    // changed
+    // rotation = 90 - abs(roi_angle);
+    // rotation < m_angel;
+
+    // m_angle=60
+    if (roi_angle - m_angle < 0 && roi_angle + m_angle > 0) {
+      Rect_<float> safeBoundRect;
+      bool isFormRect = calcSafeRect(roi_rect, src, safeBoundRect);
+      if (!isFormRect) continue;
+
+      Mat bound_mat = src(safeBoundRect);
+      Mat bound_mat_b = src_b(safeBoundRect);
+
+      if (0) {
+        imshow("bound_mat_b", bound_mat_b);
+        waitKey(0);
+        destroyWindow("bound_mat_b");
+      }
+
+      Point2f roi_ref_center = roi_rect.center - safeBoundRect.tl();
+
+      Mat deskew_mat;
+      if ((roi_angle - 5 < 0 && roi_angle + 5 > 0) || 90.0 == roi_angle ||
+          -90.0 == roi_angle) {
+        deskew_mat = bound_mat;
+      } else {
+        Mat rotated_mat;
+        Mat rotated_mat_b;
+
+        if (!rotation(bound_mat, rotated_mat, roi_rect_size, roi_ref_center, roi_angle))
+          continue;
+
+        if (!rotation(bound_mat_b, rotated_mat_b, roi_rect_size, roi_ref_center, roi_angle))
+          continue;
+
+        // we need affine for rotatioed image
+        double roi_slope = 0;
+        // imshow("1roated_mat",rotated_mat);
+        // imshow("rotated_mat_b",rotated_mat_b);
+        if (isdeflection(rotated_mat_b, roi_angle, roi_slope)) {
+          affine(rotated_mat, deskew_mat, roi_slope);
+        } else
+          deskew_mat = rotated_mat;
+      }
+
+      Mat plate_mat;
+      plate_mat.create(HEIGHT, WIDTH, TYPE);
+
+      // haitungaga add，affect 25% to full recognition.
+      if (useDeteleArea)
+        deleteNotArea(deskew_mat, color);
+
+      if (deskew_mat.cols * 1.0 / deskew_mat.rows > 2.3 && deskew_mat.cols * 1.0 / deskew_mat.rows < 6) {
+        if (deskew_mat.cols >= WIDTH || deskew_mat.rows >= HEIGHT)
+          resize(deskew_mat, plate_mat, plate_mat.size(), 0, 0, INTER_AREA);
+        else
+          resize(deskew_mat, plate_mat, plate_mat.size(), 0, 0, INTER_CUBIC);
+
+        CPlate plate;
+        plate.setPlatePos(roi_rect);
+        plate.setPlateMat(plate_mat);
+        if (color != UNKNOWN) plate.setPlateColor(color);
+        outPlates.push_back(plate);
+      }
+    }
+  }
+  return 0;
+}
+
+
+bool CPlateLocate::rotation(Mat &in, Mat &out, const Size rect_size,
+                            const Point2f center, const double angle) {
+  if (0) {
+    imshow("in", in);
+    waitKey(0);
+    destroyWindow("in");
+  }
+
+  Mat in_large;
+  in_large.create(int(in.rows * 1.5), int(in.cols * 1.5), in.type());
+
+  float x = in_large.cols / 2 - center.x > 0 ? in_large.cols / 2 - center.x : 0;
+  float y = in_large.rows / 2 - center.y > 0 ? in_large.rows / 2 - center.y : 0;
+
+  float width = x + in.cols < in_large.cols ? in.cols : in_large.cols - x;
+  float height = y + in.rows < in_large.rows ? in.rows : in_large.rows - y;
+
+  /*assert(width == in.cols);
+  assert(height == in.rows);*/
+
+  if (width != in.cols || height != in.rows) return false;
+
+  Mat imageRoi = in_large(Rect_<float>(x, y, width, height));
+  addWeighted(imageRoi, 0, in, 1, 0, imageRoi);
+
+  Point2f center_diff(in.cols / 2.f, in.rows / 2.f);
+  Point2f new_center(in_large.cols / 2.f, in_large.rows / 2.f);
+
+  Mat rot_mat = getRotationMatrix2D(new_center, angle, 1);
+
+  /*imshow("in_copy", in_large);
+  waitKey(0);*/
+
+  Mat mat_rotated;
+  warpAffine(in_large, mat_rotated, rot_mat, Size(in_large.cols, in_large.rows),
+             CV_INTER_CUBIC);
+
+  /*imshow("mat_rotated", mat_rotated);
+  waitKey(0);*/
+
+  Mat img_crop;
+  getRectSubPix(mat_rotated, Size(rect_size.width, rect_size.height),
+                new_center, img_crop);
+
+  out = img_crop;
+
+  if (0) {
+    imshow("out", out);
+    waitKey(0);
+    destroyWindow("out");
+  }
+
+  /*imshow("img_crop", img_crop);
+  waitKey(0);*/
+
+  return true;
+}
+
+bool CPlateLocate::isdeflection(const Mat &in, const double angle,
+                                double &slope) { /*imshow("in",in);
+                                                waitKey(0);*/
+  if (0) {
+    imshow("in", in);
+    waitKey(0);
+    destroyWindow("in");
+  }
+  
+  int nRows = in.rows;
+  int nCols = in.cols;
+
+  assert(in.channels() == 1);
+
+  int comp_index[3];
+  int len[3];
+
+  comp_index[0] = nRows / 4;
+  comp_index[1] = nRows / 4 * 2;
+  comp_index[2] = nRows / 4 * 3;
+
+  const uchar* p;
+
+  for (int i = 0; i < 3; i++) {
+    int index = comp_index[i];
+    p = in.ptr<uchar>(index);
+
+    int j = 0;
+    int value = 0;
+    while (0 == value && j < nCols) value = int(p[j++]);
+
+    len[i] = j;
+  }
+
+  // cout << "len[0]:" << len[0] << endl;
+  // cout << "len[1]:" << len[1] << endl;
+  // cout << "len[2]:" << len[2] << endl;
+
+  // len[0]/len[1]/len[2] are used to calc the slope
+
+  double maxlen = max(len[2], len[0]);
+  double minlen = min(len[2], len[0]);
+  double difflen = abs(len[2] - len[0]);
+
+  double PI = 3.14159265;
+
+  double g = tan(angle * PI / 180.0);
+
+  if (maxlen - len[1] > nCols / 32 || len[1] - minlen > nCols / 32) {
+
+    double slope_can_1 =
+        double(len[2] - len[0]) / double(comp_index[1]);
+    double slope_can_2 = double(len[1] - len[0]) / double(comp_index[0]);
+    double slope_can_3 = double(len[2] - len[1]) / double(comp_index[0]);
+    // cout<<"angle:"<<angle<<endl;
+    // cout<<"g:"<<g<<endl;
+    // cout << "slope_can_1:" << slope_can_1 << endl;
+    // cout << "slope_can_2:" << slope_can_2 << endl;
+    // cout << "slope_can_3:" << slope_can_3 << endl;
+    // if(g>=0)
+    slope = abs(slope_can_1 - g) <= abs(slope_can_2 - g) ? slope_can_1
+                                                         : slope_can_2;
+    // cout << "slope:" << slope << endl;
+    return true;
+  } else {
+    slope = 0;
+  }
+
+  return false;
+}
+
+
+void CPlateLocate::affine(const Mat &in, Mat &out, const double slope) {
+  // imshow("in", in);
+  // waitKey(0);
+
+  Point2f dstTri[3];
+  Point2f plTri[3];
+
+  float height = (float) in.rows;
+  float width = (float) in.cols;
+  float xiff = (float) abs(slope) * height;
+
+  if (slope > 0) {
+
+    // right, new position is xiff/2
+
+    plTri[0] = Point2f(0, 0);
+    plTri[1] = Point2f(width - xiff - 1, 0);
+    plTri[2] = Point2f(0 + xiff, height - 1);
+
+    dstTri[0] = Point2f(xiff / 2, 0);
+    dstTri[1] = Point2f(width - 1 - xiff / 2, 0);
+    dstTri[2] = Point2f(xiff / 2, height - 1);
+  } else {
+
+    // left, new position is -xiff/2
+
+    plTri[0] = Point2f(0 + xiff, 0);
+    plTri[1] = Point2f(width - 1, 0);
+    plTri[2] = Point2f(0, height - 1);
+
+    dstTri[0] = Point2f(xiff / 2, 0);
+    dstTri[1] = Point2f(width - 1 - xiff + xiff / 2, 0);
+    dstTri[2] = Point2f(xiff / 2, height - 1);
+  }
+
+  Mat warp_mat = getAffineTransform(plTri, dstTri);
+
+  Mat affine_mat;
+  affine_mat.create((int) height, (int) width, TYPE);
+
+  if (in.rows > HEIGHT || in.cols > WIDTH)
+
+    warpAffine(in, affine_mat, warp_mat, affine_mat.size(),
+               CV_INTER_AREA);
+  else
+    warpAffine(in, affine_mat, warp_mat, affine_mat.size(), CV_INTER_CUBIC);
+
+  out = affine_mat;
+}
+
+int CPlateLocate::plateColorLocate(Mat src, vector<CPlate> &candPlates,
+                                   int index) {
+  vector<RotatedRect> rects_color_blue;
+  rects_color_blue.reserve(64);
+  vector<RotatedRect> rects_color_yellow;
+  rects_color_yellow.reserve(64);
+
+  vector<CPlate> plates_blue;
+  plates_blue.reserve(64);
+  vector<CPlate> plates_yellow;
+  plates_yellow.reserve(64);
+
+  Mat src_clone = src.clone();
+
+  Mat src_b_blue;
+  Mat src_b_yellow;
+#pragma omp parallel sections
+  {
+#pragma omp section
+    {
+      colorSearch(src, BLUE, src_b_blue, rects_color_blue);
+      deskew(src, src_b_blue, rects_color_blue, plates_blue, true, BLUE);
+    }
+#pragma omp section
+    {
+      colorSearch(src_clone, YELLOW, src_b_yellow, rects_color_yellow);
+      deskew(src_clone, src_b_yellow, rects_color_yellow, plates_yellow, true, YELLOW);
+    }
+  }
+
+  candPlates.insert(candPlates.end(), plates_blue.begin(), plates_blue.end());
+  candPlates.insert(candPlates.end(), plates_yellow.begin(), plates_yellow.end());
+
+  return 0;
+}
+
+
+//! MSER plate locate
+int CPlateLocate::plateMserLocate(Mat src, vector<CPlate> &candPlates, int img_index) {
+  std::vector<Mat> channelImages;
+  std::vector<Color> flags;
+  flags.push_back(BLUE);
+  flags.push_back(YELLOW);
+
+  bool usePlateMser = false;
+  int scale_size = 1000;
+  //int scale_size = CParams::instance()->getParam1i();
+  double scale_ratio = 1;
+
+  // only conside blue plate
+  if (1) {
+    Mat grayImage;
+    cvtColor(src, grayImage, COLOR_BGR2GRAY);
+    channelImages.push_back(grayImage);
+  }
+
+  for (size_t i = 0; i < channelImages.size(); ++i) {
+    vector<vector<RotatedRect>> plateRRectsVec;
+    vector<vector<CPlate>> platesVec;
+    vector<Mat> src_b_vec;
+
+    Mat channelImage = channelImages.at(i);   
+    Mat image = scaleImage(channelImage, Size(scale_size, scale_size), scale_ratio);
+
+    // vector<RotatedRect> rects;
+    mserSearch(image, src_b_vec, platesVec, usePlateMser, plateRRectsVec, img_index, false);
+
+    for (size_t j = 0; j < flags.size(); j++) {
+      vector<CPlate>& plates = platesVec.at(j);
+      Mat& src_b = src_b_vec.at(j);
+      Color color = flags.at(j);
+
+      vector<RotatedRect> rects_mser;
+      rects_mser.reserve(64);
+      std::vector<CPlate> deskewPlate;
+      deskewPlate.reserve(64);
+      std::vector<CPlate> mserPlate;
+      mserPlate.reserve(64);
+
+      // deskew for rotation and slope image
+      for (auto plate : plates) {
+        RotatedRect rrect = plate.getPlatePos();
+        RotatedRect scaleRect = scaleBackRRect(rrect, (float)scale_ratio);
+        plate.setPlatePos(scaleRect);
+        plate.setPlateColor(color);
+
+        rects_mser.push_back(scaleRect);
+        mserPlate.push_back(plate);
+      }
+
+      Mat resize_src_b;
+      resize(src_b, resize_src_b, Size(channelImage.cols, channelImage.rows));
+
+      deskew(src, resize_src_b, rects_mser, deskewPlate, false, color);
+
+      for (auto dplate : deskewPlate) {
+        RotatedRect drect = dplate.getPlatePos();
+        Mat dmat = dplate.getPlateMat();
+
+        for (auto splate : mserPlate) {
+          RotatedRect srect = splate.getPlatePos();
+          float iou = 0.f;
+          bool isSimilar = computeIOU(drect, srect, src.cols, src.rows, 0.95f, iou);
+          if (isSimilar) {
+            splate.setPlateMat(dmat);
+            candPlates.push_back(splate);
+            break;
+          }
+        }
+      }
+    }
+  }
+
+  if (0) {
+    imshow("src", src);
+    waitKey(0);
+    destroyWindow("src");
+  }
+
+  return 0;
+}
+
+int CPlateLocate::sobelOperT(const Mat &in, Mat &out, int blurSize, int morphW,
+                             int morphH) {
+  Mat mat_blur;
+  mat_blur = in.clone();
+  GaussianBlur(in, mat_blur, Size(blurSize, blurSize), 0, 0, BORDER_DEFAULT);
+
+  Mat mat_gray;
+  if (mat_blur.channels() == 3)
+    cvtColor(mat_blur, mat_gray, CV_BGR2GRAY);
+  else
+    mat_gray = mat_blur;
+
+  utils::imwrite("resources/image/tmp/grayblure.jpg", mat_gray);
+
+  // equalizeHist(mat_gray, mat_gray);
+
+  int scale = SOBEL_SCALE;
+  int delta = SOBEL_DELTA;
+  int ddepth = SOBEL_DDEPTH;
+
+  Mat grad_x, grad_y;
+  Mat abs_grad_x, abs_grad_y;
+
+  Sobel(mat_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT);
+  convertScaleAbs(grad_x, abs_grad_x);
+
+  Mat grad;
+  addWeighted(abs_grad_x, 1, 0, 0, 0, grad);
+
+  utils::imwrite("resources/image/tmp/graygrad.jpg", grad);
+
+  Mat mat_threshold;
+  double otsu_thresh_val =
+      threshold(grad, mat_threshold, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
+
+  utils::imwrite("resources/image/tmp/grayBINARY.jpg", mat_threshold);
+
+  Mat element = getStructuringElement(MORPH_RECT, Size(morphW, morphH));
+  morphologyEx(mat_threshold, mat_threshold, MORPH_CLOSE, element);
+
+  utils::imwrite("resources/image/tmp/phologyEx.jpg", mat_threshold);
+
+  out = mat_threshold;
+
+  return 0;
+}
+
+int CPlateLocate::plateSobelLocate(Mat src, vector<CPlate> &candPlates,
+                                   int index) {
+  vector<RotatedRect> rects_sobel_all;
+  rects_sobel_all.reserve(256);
+
+  vector<CPlate> plates;
+  plates.reserve(32);
+
+  vector<Rect_<float>> bound_rects;
+  bound_rects.reserve(256);
+
+  sobelFrtSearch(src, bound_rects);
+
+  vector<Rect_<float>> bound_rects_part;
+  bound_rects_part.reserve(256);
+
+  // enlarge area 
+  for (size_t i = 0; i < bound_rects.size(); i++) {
+    float fRatio = bound_rects[i].width * 1.0f / bound_rects[i].height;
+    if (fRatio < 3.0 && fRatio > 1.0 && bound_rects[i].height < 120) {
+      Rect_<float> itemRect = bound_rects[i];
+
+      itemRect.x = itemRect.x - itemRect.height * (4 - fRatio);
+      if (itemRect.x < 0) {
+        itemRect.x = 0;
+      }
+      itemRect.width = itemRect.width + itemRect.height * 2 * (4 - fRatio);
+      if (itemRect.width + itemRect.x >= src.cols) {
+        itemRect.width = src.cols - itemRect.x;
+      }
+
+      itemRect.y = itemRect.y - itemRect.height * 0.08f;
+      itemRect.height = itemRect.height * 1.16f;
+
+      bound_rects_part.push_back(itemRect);
+    }
+  }
+
+  // second processing to split one
+#pragma omp parallel for
+  for (int i = 0; i < (int)bound_rects_part.size(); i++) {
+    Rect_<float> bound_rect = bound_rects_part[i];
+    Point2f refpoint(bound_rect.x, bound_rect.y);
+
+    float x = bound_rect.x > 0 ? bound_rect.x : 0;
+    float y = bound_rect.y > 0 ? bound_rect.y : 0;
+
+    float width =
+        x + bound_rect.width < src.cols ? bound_rect.width : src.cols - x;
+    float height =
+        y + bound_rect.height < src.rows ? bound_rect.height : src.rows - y;
+
+    Rect_<float> safe_bound_rect(x, y, width, height);
+    Mat bound_mat = src(safe_bound_rect);
+
+    vector<RotatedRect> rects_sobel;
+    rects_sobel.reserve(128);
+    sobelSecSearchPart(bound_mat, refpoint, rects_sobel);
+
+#pragma omp critical
+    {
+      rects_sobel_all.insert(rects_sobel_all.end(), rects_sobel.begin(), rects_sobel.end());
+    }
+  }
+
+#pragma omp parallel for
+  for (int i = 0; i < (int)bound_rects.size(); i++) {
+    Rect_<float> bound_rect = bound_rects[i];
+    Point2f refpoint(bound_rect.x, bound_rect.y);
+
+    float x = bound_rect.x > 0 ? bound_rect.x : 0;
+    float y = bound_rect.y > 0 ? bound_rect.y : 0;
+
+    float width =
+        x + bound_rect.width < src.cols ? bound_rect.width : src.cols - x;
+    float height =
+        y + bound_rect.height < src.rows ? bound_rect.height : src.rows - y;
+
+    Rect_<float> safe_bound_rect(x, y, width, height);
+    Mat bound_mat = src(safe_bound_rect);
+
+    vector<RotatedRect> rects_sobel;
+    rects_sobel.reserve(128);
+    sobelSecSearch(bound_mat, refpoint, rects_sobel);
+
+#pragma omp critical
+    {
+      rects_sobel_all.insert(rects_sobel_all.end(), rects_sobel.begin(), rects_sobel.end());
+    }
+  }
+
+  Mat src_b;
+  sobelOper(src, src_b, 3, 10, 3);
+
+  deskew(src, src_b, rects_sobel_all, plates);
+
+  //for (size_t i = 0; i < plates.size(); i++) 
+  //  candPlates.push_back(plates[i]);
+
+  candPlates.insert(candPlates.end(), plates.begin(), plates.end());
+
+  return 0;
+}
+
+
+int CPlateLocate::plateLocate(Mat src, vector<Mat> &resultVec, int index) {
+  vector<CPlate> all_result_Plates;
+
+  plateColorLocate(src, all_result_Plates, index);
+  plateSobelLocate(src, all_result_Plates, index);
+  plateMserLocate(src, all_result_Plates, index);
+
+  for (size_t i = 0; i < all_result_Plates.size(); i++) {
+    CPlate plate = all_result_Plates[i];
+    resultVec.push_back(plate.getPlateMat());
+  }
+
+  return 0;
+}
+
+int CPlateLocate::plateLocate(Mat src, vector<CPlate> &resultVec, int index) {
+  vector<CPlate> all_result_Plates;
+
+  plateColorLocate(src, all_result_Plates, index);
+  plateSobelLocate(src, all_result_Plates, index);
+  plateMserLocate(src, all_result_Plates, index);
+
+  for (size_t i = 0; i < all_result_Plates.size(); i++) {
+    resultVec.push_back(all_result_Plates[i]);
+  }
+
+  return 0;
+}
+
+}

target/classes/easypr-c++/plate_recognize.cpp

@@ -0,0 +1,105 @@
+#include "easypr/core/plate_recognize.h"
+#include "easypr/config.h"
+#include "thirdparty/textDetect/erfilter.hpp"
+
+namespace easypr {
+
+CPlateRecognize::CPlateRecognize() { 
+  m_showResult = false;
+}
+
+
+// main method, plate recognize, contain two parts
+// 1. plate detect
+// 2. chars recognize
+int CPlateRecognize::plateRecognize(const Mat& src, std::vector<CPlate> &plateVecOut, int img_index) {
+  // resize to uniform sizes
+  float scale = 1.f;
+  Mat img = uniformResize(src, scale);
+
+  // 1. plate detect
+  std::vector<CPlate> plateVec;
+  int resultPD = plateDetect(img, plateVec, img_index);
+  if (resultPD == 0) {
+    size_t num = plateVec.size();
+    for (size_t j = 0; j < num; j++) {
+      CPlate& item = plateVec.at(j);
+      Mat plateMat = item.getPlateMat();
+      SHOW_IMAGE(plateMat, 0);
+
+      // scale the rect to src;
+      item.setPlateScale(scale);
+      RotatedRect rect = item.getPlatePos();
+      item.setPlatePos(scaleBackRRect(rect, 1.f / scale));
+
+      // get plate color
+      Color color = item.getPlateColor();
+      if (color == UNKNOWN) {
+        color = getPlateType(plateMat, true);
+        item.setPlateColor(color);
+      }
+      std::string plateColor = getPlateColor(color);
+      if (0) {
+        std::cout << "plateColor:" << plateColor << std::endl;
+      }
+
+      // 2. chars recognize
+      std::string plateIdentify = "";
+      int resultCR = charsRecognise(item, plateIdentify);
+      if (resultCR == 0) {
+        std::string license = plateColor + ":" + plateIdentify;
+        item.setPlateStr(license);
+        plateVecOut.push_back(item);
+        if (0) std::cout << "resultCR:" << resultCR << std::endl;
+      }
+      else {
+        std::string license = plateColor;
+        item.setPlateStr(license);
+        plateVecOut.push_back(item);
+        if (0) std::cout << "resultCR:" << resultCR << std::endl;
+      }
+    }
+    if (getResultShow()) {
+      // param type: 0 detect, 1 recognize;
+      int showType = 1;
+      if (0 == showType)
+        showDectectResults(img, plateVec, num);
+      else
+        showDectectResults(img, plateVecOut, num);
+    }
+  }
+  return resultPD;
+}
+
+void CPlateRecognize::LoadSVM(std::string path) {
+  PlateJudge::instance()->LoadModel(path);
+}
+
+void CPlateRecognize::LoadANN(std::string path) {
+  CharsIdentify::instance()->LoadModel(path);
+}
+
+void CPlateRecognize::LoadChineseANN(std::string path) {
+  CharsIdentify::instance()->LoadChineseModel(path);
+}
+
+void CPlateRecognize::LoadGrayChANN(std::string path) {
+  CharsIdentify::instance()->LoadGrayChANN(path);
+}
+
+void CPlateRecognize::LoadChineseMapping(std::string path) {
+  CharsIdentify::instance()->LoadChineseMapping(path);
+}
+
+// deprected
+int CPlateRecognize::plateRecognize(const Mat& src, std::vector<std::string> &licenseVec) {
+  vector<CPlate> plates;
+  int resultPR = plateRecognize(src, plates, 0);
+
+  for (auto plate : plates) {
+    licenseVec.push_back(plate.getPlateStr());
+  }
+  return resultPR;
+}
+
+}

target/classes/easypr-c++/svm_train.cpp

@@ -0,0 +1,196 @@
+#include "easypr/train/svm_train.h"
+#include "easypr/util/util.h"
+#include "easypr/config.h"
+
+#ifdef OS_WINDOWS
+#include <ctime>
+#endif
+
+using namespace cv;
+using namespace cv::ml;
+
+
+// 原版C++语言  训练代码
+namespace easypr {
+
+SvmTrain::SvmTrain(const char* plates_folder, const char* xml): plates_folder_(plates_folder), svm_xml_(xml) {
+  assert(plates_folder);
+  assert(xml);
+  extractFeature = getHistomPlusColoFeatures;
+}
+
+void SvmTrain::train() {
+  svm_ = cv::ml::SVM::create();
+  svm_->setType(cv::ml::SVM::C_SVC);
+  svm_->setKernel(cv::ml::SVM::RBF);
+  svm_->setDegree(0.1);
+  // 1.4 bug fix: old 1.4 ver gamma is 1
+  svm_->setGamma(0.1); 
+  svm_->setCoef0(0.1);
+  svm_->setC(1);
+  svm_->setNu(0.1);
+  svm_->setP(0.1);
+  svm_->setTermCriteria(cvTermCriteria(CV_TERMCRIT_ITER, 20000, 0.0001));
+
+  this->prepare();
+
+  if (train_file_list_.size() == 0) {
+    fprintf(stdout, "No file found in the train folder!\n");
+    fprintf(stdout, "You should create a folder named \"tmp\" in EasyPR main folder.\n");
+    fprintf(stdout, "Copy train data folder(like \"SVM\") under \"tmp\". \n");
+    return;
+  }
+  auto train_data = tdata();
+
+  fprintf(stdout, ">> Training SVM model, please wait...\n");
+  long start = utils::getTimestamp();
+  svm_->trainAuto(train_data, 10, SVM::getDefaultGrid(SVM::C),
+                  SVM::getDefaultGrid(SVM::GAMMA), SVM::getDefaultGrid(SVM::P),
+                  SVM::getDefaultGrid(SVM::NU), SVM::getDefaultGrid(SVM::COEF),
+                  SVM::getDefaultGrid(SVM::DEGREE), true);
+  //svm_->train(train_data);
+
+  long end = utils::getTimestamp();
+  fprintf(stdout, ">> Training done. Time elapse: %ldms\n", end - start);
+  fprintf(stdout, ">> Saving model file...\n");
+  svm_->save(svm_xml_);
+
+  fprintf(stdout, ">> Your SVM Model was saved to %s\n", svm_xml_);
+  fprintf(stdout, ">> Testing...\n");
+
+  this->test();
+  
+}
+
+void SvmTrain::test() {
+  // 1.4 bug fix: old 1.4 ver there is no null judge
+  // if (NULL == svm_)
+  LOAD_SVM_MODEL(svm_, svm_xml_);
+
+  if (test_file_list_.empty()) {
+    this->prepare();
+  }
+ 
+  double count_all = test_file_list_.size();
+  double ptrue_rtrue = 0;
+  double ptrue_rfalse = 0;
+  double pfalse_rtrue = 0;
+  double pfalse_rfalse = 0;
+
+  for (auto item : test_file_list_) {
+    auto image = cv::imread(item.file);
+    if (!image.data) {
+      std::cout << "no" << std::endl;
+      continue;
+    }
+    cv::Mat feature;
+    extractFeature(image, feature);
+
+    auto predict = int(svm_->predict(feature));
+    //std::cout << "predict: " << predict << std::endl;
+
+    auto real = item.label;
+    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++;
+  }
+
+  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 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;
+  }
+}
+
+void SvmTrain::prepare() {
+  srand(unsigned(time(NULL)));
+
+  char buffer[260] = {0};
+
+  sprintf(buffer, "%s/has/train", plates_folder_);
+  auto has_file_train_list = utils::getFiles(buffer);
+  std::random_shuffle(has_file_train_list.begin(), has_file_train_list.end());
+
+  sprintf(buffer, "%s/has/test", plates_folder_);
+  auto has_file_test_list = utils::getFiles(buffer);
+  std::random_shuffle(has_file_test_list.begin(), has_file_test_list.end());
+
+  sprintf(buffer, "%s/no/train", plates_folder_);
+  auto no_file_train_list = utils::getFiles(buffer);
+  std::random_shuffle(no_file_train_list.begin(), no_file_train_list.end());
+
+  sprintf(buffer, "%s/no/test", plates_folder_);
+  auto no_file_test_list = utils::getFiles(buffer);
+  std::random_shuffle(no_file_test_list.begin(), no_file_test_list.end());
+
+  fprintf(stdout, ">> Collecting train data...\n");
+
+  for (auto file : has_file_train_list)
+    train_file_list_.push_back({ file, kForward });
+
+  for (auto file : no_file_train_list)
+    train_file_list_.push_back({ file, kInverse });
+
+  fprintf(stdout, ">> Collecting test data...\n");
+
+  for (auto file : has_file_test_list)
+    test_file_list_.push_back({ file, kForward });
+
+  for (auto file : no_file_test_list)
+    test_file_list_.push_back({ file, kInverse });
+}
+
+cv::Ptr<cv::ml::TrainData> SvmTrain::tdata() {
+  cv::Mat samples;
+  std::vector<int> responses;
+
+  for (auto f : train_file_list_) {
+    auto image = cv::imread(f.file);
+    if (!image.data) {
+      fprintf(stdout, ">> Invalid image: %s  ignore.\n", f.file.c_str());
+      continue;
+    }
+    cv::Mat feature;
+    extractFeature(image, feature);
+    feature = feature.reshape(1, 1);
+
+    samples.push_back(feature);
+    responses.push_back(int(f.label));
+  }
+
+  cv::Mat samples_, responses_;
+  samples.convertTo(samples_, CV_32FC1);
+  cv::Mat(responses).copyTo(responses_);
+
+  return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE, responses_);
+}
+
+}  // namespace easypr

target/classes/easypr-java/.classpath

@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<classpath>
+	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
+	<classpathentry kind="output" path="bin"/>
+</classpath>

target/classes/easypr-java/.project

@@ -0,0 +1,17 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+	<name>easypr-java</name>
+	<comment></comment>
+	<projects>
+	</projects>
+	<buildSpec>
+		<buildCommand>
+			<name>org.eclipse.jdt.core.javabuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+	</buildSpec>
+	<natures>
+		<nature>org.eclipse.jdt.core.javanature</nature>
+	</natures>
+</projectDescription>

target/classes/easypr-java/.settings/org.eclipse.core.resources.prefs

@@ -0,0 +1,2 @@
+eclipse.preferences.version=1
+encoding/SVMTrain1.java=UTF-8

target/classes/easypr-java/ANNTrain1.java

@@ -0,0 +1,159 @@
+package com.yuxue.train;
+
+import java.util.Vector;
+
+import static org.bytedeco.javacpp.opencv_core.*;
+import static org.bytedeco.javacpp.opencv_ml.*;
+
+import org.bytedeco.javacpp.opencv_imgcodecs;
+import org.bytedeco.javacpp.opencv_core.Mat;
+
+import com.yuxue.constant.Constant;
+import com.yuxue.easypr.core.CoreFunc;
+import com.yuxue.util.FileUtil;
+
+/**
+ * 基于org.bytedeco.javacpp包实现的训练
+ * 
+ * 图片文字识别训练
+ * 训练出来的库文件，用于识别图片中的文字
+ * 
+ * 训练的ann.xml应用：
+ * 1、替换res/model/ann.xml文件
+ * 2、修改com.yuxue.easypr.core.CharsIdentify.charsIdentify(Mat, Boolean, Boolean)方法
+ * 
+ * @author yuxue
+ * @date 2020-05-14 22:16
+ */
+public class ANNTrain1 {
+
+    private ANN_MLP ann = ANN_MLP.create();
+
+    // 默认的训练操作的根目录
+    private static final String DEFAULT_PATH = "D:/PlateDetect/train/chars_recognise_ann/";
+
+    // 训练模型文件保存位置
+    private static final String MODEL_PATH = "res/model/ann.xml";
+    
+    
+    public void train(int _predictsize, int _neurons) {
+        Mat samples = new Mat(); // 使用push_back，行数列数不能赋初始值
+        Vector<Integer> trainingLabels = new Vector<Integer>();
+        // 加载数字及字母字符
+        for (int i = 0; i < Constant.numCharacter; i++) {
+            String str = DEFAULT_PATH + "learn/" + Constant.strCharacters[i];
+            Vector<String> files = new Vector<String>();
+            FileUtil.getFiles(str, files);
+
+            int size = (int) files.size();
+            for (int j = 0; j < size; j++) {
+                Mat img = opencv_imgcodecs.imread(files.get(j), 0);
+                // System.err.println(files.get(j)); // 文件名不能包含中文
+                Mat f = CoreFunc.features(img, _predictsize);
+                samples.push_back(f);
+                trainingLabels.add(i); // 每一幅字符图片所对应的字符类别索引下标
+            }
+        }
+
+        // 加载汉字字符
+        for (int i = 0; i < Constant.strChinese.length; i++) {
+            String str = DEFAULT_PATH + "learn/" + Constant.strChinese[i];
+            Vector<String> files = new Vector<String>();
+            FileUtil.getFiles(str, files);
+
+            int size = (int) files.size();
+            for (int j = 0; j < size; j++) {
+                Mat img = opencv_imgcodecs.imread(files.get(j), 0);
+                // System.err.println(files.get(j));   // 文件名不能包含中文
+                Mat f = CoreFunc.features(img, _predictsize);
+                samples.push_back(f);
+                trainingLabels.add(i + Constant.numCharacter);
+            }
+        }
+
+
+        //440   vhist.length + hhist.length + lowData.cols() * lowData.rows();
+        // CV_32FC1 CV_32SC1 CV_32F
+        Mat classes = new Mat(trainingLabels.size(), Constant.numAll, CV_32F);
+        
+        float[] labels = new float[trainingLabels.size()];
+        for (int i = 0; i < labels.length; ++i) {
+            classes.ptr(i, trainingLabels.get(i)).putFloat(1.f);
+            
+        }
+
+        // samples.type() == CV_32F || samples.type() == CV_32S 
+        TrainData train_data = TrainData.create(samples, ROW_SAMPLE, classes);
+
+        ann.clear();
+        Mat layers = new Mat(1, 3, CV_32SC1);
+        layers.ptr(0, 0).putInt(samples.cols());
+        layers.ptr(0, 1).putInt(_neurons);
+        layers.ptr(0, 2).putInt(classes.cols());
+
+        System.out.println(layers);
+        
+        ann.setLayerSizes(layers);
+        ann.setActivationFunction(ANN_MLP.SIGMOID_SYM, 1, 1);
+        ann.setTrainMethod(ANN_MLP.BACKPROP);
+        TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 30000, 0.0001);
+        ann.setTermCriteria(criteria);
+        ann.setBackpropWeightScale(0.1);
+        ann.setBackpropMomentumScale(0.1);
+        ann.train(train_data);
+
+        //FileStorage fsto = new FileStorage(MODEL_PATH, FileStorage.WRITE);
+        //ann.write(fsto, "ann");
+        ann.save(MODEL_PATH);
+    }
+    
+    
+    public void predict() {
+        ann.clear();
+        ann = ANN_MLP.load(MODEL_PATH);
+        //ann = ANN_MLP.loadANN_MLP(MODEL_PATH, "ann");
+        Vector<String> files = new Vector<String>();
+        FileUtil.getFiles(DEFAULT_PATH + "test/", files);
+        
+        for (String string : files) {
+            Mat img = opencv_imgcodecs.imread(string);
+            Mat f = CoreFunc.features(img, Constant.predictSize);
+            
+            // 140 predictSize = 10; vhist.length + hhist.length + lowData.cols() * lowData.rows();
+            // 440 predictSize = 20;
+            Mat output = new Mat(1, 140, CV_32F);
+            //ann.predict(f, output, 0);  // 预测结果
+            // System.err.println(string + "===>" + (int) ann.predict(f, output, 0));
+
+            int index = (int) ann.predict(f, output, 0);
+            
+            String result = "";
+            if (index < Constant.numCharacter) {
+                result = String.valueOf(Constant.strCharacters[index]);
+            } else {
+                String s = Constant.strChinese[index - Constant.numCharacter];
+                result = Constant.KEY_CHINESE_MAP.get(s);   // 编码转中文
+            }
+            System.err.println(string + "===>" + result);
+            
+            // ann.predict(f, output, 0);
+            // System.err.println(string + "===>" + output.get(0, 0)[0]);
+            
+        }
+    }
+
+    public static void main(String[] args) {
+        
+        ANNTrain1 annT = new ANNTrain1();
+        // 这里演示只训练model文件夹下的ann.xml，此模型是一个predictSize=10,neurons=40的ANN模型
+        // 可根据需要训练不同的predictSize或者neurons的ANN模型
+        // 根据机器的不同，训练时间不一样，但一般需要10分钟左右，所以慢慢等一会吧。
+        annT.train(Constant.predictSize, Constant.neurons);
+
+        annT.predict();
+        
+        System.out.println("The end.");
+    }
+
+
+}

target/classes/easypr-java/CharsIdentify.java

@@ -0,0 +1,83 @@
+package com.yuxue.easypr.core;
+
+import org.bytedeco.javacpp.opencv_core;
+import org.bytedeco.javacpp.opencv_core.Mat;
+import org.bytedeco.javacpp.opencv_ml.ANN_MLP;
+
+import com.yuxue.constant.Constant;
+import com.yuxue.util.Convert;
+
+
+/**
+ * 字符检测
+ * @author yuxue
+ * @date 2020-04-24 15:31
+ */
+public class CharsIdentify {
+
+    private ANN_MLP ann=ANN_MLP.create();
+
+    public CharsIdentify() {
+        loadModel(Constant.DEFAULT_ANN_PATH);
+    }
+
+    public void loadModel(String path) {
+        this.ann.clear();
+        // 加载ann配置文件  图像转文字的训练库文件
+        //ann=ANN_MLP.loadANN_MLP(path, "ann"); 
+        ann = ANN_MLP.load(path);
+    }
+    
+
+    /**
+     * @param input
+     * @param isChinese
+     * @return
+     */
+    public String charsIdentify(final Mat input, final Boolean isChinese, final Boolean isSpeci) {
+        String result = "";
+        
+        /*String name = "D:/PlateDetect/train/chars_recognise_ann/" + System.currentTimeMillis() + ".jpg";
+        opencv_imgcodecs.imwrite(name, input);
+        Mat img = opencv_imgcodecs.imread(name);
+        Mat f = CoreFunc.features(img, Constant.predictSize);*/
+        
+        Mat f = CoreFunc.features(input, Constant.predictSize);
+
+        int index = this.classify(f, isChinese, isSpeci);
+        
+        System.err.print(index);
+        if (index < Constant.numCharacter) {
+            result = String.valueOf(Constant.strCharacters[index]);
+        } else {
+            String s = Constant.strChinese[index - Constant.numCharacter];
+            result = Constant.KEY_CHINESE_MAP.get(s);   // 编码转中文
+        }
+        System.err.println(result);
+        return result;
+    }
+    
+    private int classify(final Mat f, final Boolean isChinses, final Boolean isSpeci) {
+        int result = -1;
+        
+        Mat output = new Mat(1, 140, opencv_core.CV_32F);
+
+        ann.predict(f, output, 0);  // 预测结果
+
+        int ann_min = (!isChinses) ? ((isSpeci) ? 10 : 0) : Constant.numCharacter;
+        int ann_max = (!isChinses) ? Constant.numCharacter : Constant.numAll;
+
+        float maxVal = -2;
+
+        for (int j = ann_min; j < ann_max; j++) {
+            float val = Convert.toFloat(output.ptr(0, j));
+            if (val > maxVal) {
+                maxVal = val;
+                result = j;
+            }
+        }
+        return result;
+    }
+
+
+}

target/classes/easypr-java/CharsRecognise.java

@@ -0,0 +1,136 @@
+package com.yuxue.easypr.core;
+
+import java.util.Vector;
+
+import org.bytedeco.javacpp.opencv_core.Mat;
+
+import com.yuxue.enumtype.PlateColor;
+
+/**
+ * 字符识别
+ * 
+ * @author yuxue
+ * @date 2020-04-24 15:31
+ */
+public class CharsRecognise {
+    
+    private CharsSegment charsSegment = new CharsSegment();
+
+    private CharsIdentify charsIdentify = new CharsIdentify();
+    
+
+    public void loadANN(final String s) {
+        charsIdentify.loadModel(s);
+    }
+
+    /**
+     * Chars segment and identify 字符分割与识别
+     * 
+     * @param plate： the input plate
+     * @return the result of plate recognition
+     */
+    public String charsRecognise(final Mat plate, String tempPath) {
+
+        // 车牌字符方块集合
+        Vector<Mat> matVec = new Vector<Mat>();
+        // 车牌识别结果
+        String plateIdentify = "";
+
+        int result = charsSegment.charsSegment(plate, matVec, tempPath);
+        if (0 == result) {
+            for (int j = 0; j < matVec.size(); j++) {
+                Mat charMat = matVec.get(j);
+                // 默认首个字符块是中文字符   第二个字符块是字母
+                String charcater = charsIdentify.charsIdentify(charMat, (0 == j), (1 == j));
+                plateIdentify = plateIdentify + charcater;
+            }
+        }
+
+        return plateIdentify;
+    }
+
+    /**
+     * 是否开启调试模式
+     * 
+     * @param isDebug
+     */
+    public void setCRDebug(final boolean isDebug) {
+        charsSegment.setDebug(isDebug);
+    }
+
+    /**
+     * 获取调试模式状态
+     * 
+     * @return
+     */
+    public boolean getCRDebug() {
+        return charsSegment.getDebug();
+    }
+
+    /**
+     * 获得车牌颜色
+     * 
+     * @param input
+     * @return
+     */
+    public final String getPlateType(final Mat input) {
+        PlateColor result = CoreFunc.getPlateType(input, true);
+        return result.desc;
+    }
+
+    /**
+     * 设置柳丁大小变量
+     * 
+     * @param param
+     */
+    public void setLiuDingSize(final int param) {
+        charsSegment.setLiuDingSize(param);
+    }
+
+    /**
+     * 设置颜色阈值
+     * 
+     * @param param
+     */
+    public void setColorThreshold(final int param) {
+        charsSegment.setColorThreshold(param);
+    }
+
+    /**
+     * 设置蓝色百分比
+     * 
+     * @param param
+     */
+    public void setBluePercent(final float param) {
+        charsSegment.setBluePercent(param);
+    }
+
+    /**
+     * 得到蓝色百分比
+     * 
+     * @param param
+     */
+    public final float getBluePercent() {
+        return charsSegment.getBluePercent();
+    }
+
+    /**
+     * 设置白色百分比
+     * 
+     * @param param
+     */
+    public void setWhitePercent(final float param) {
+        charsSegment.setWhitePercent(param);
+    }
+
+    /**
+     * 得到白色百分比
+     * 
+     * @param param
+     */
+    public final float getWhitePercent() {
+        return charsSegment.getWhitePercent();
+    }
+
+   
+}

target/classes/easypr-java/CharsSegment.java

@@ -0,0 +1,453 @@
+package com.yuxue.easypr.core;
+
+import static com.yuxue.easypr.core.CoreFunc.getPlateType;
+import static org.bytedeco.javacpp.opencv_core.CV_32F;
+import static org.bytedeco.javacpp.opencv_core.countNonZero;
+import static org.bytedeco.javacpp.opencv_imgproc.CV_CHAIN_APPROX_NONE;
+import static org.bytedeco.javacpp.opencv_imgproc.CV_RETR_EXTERNAL;
+import static org.bytedeco.javacpp.opencv_imgproc.CV_RGB2GRAY;
+import static org.bytedeco.javacpp.opencv_imgproc.CV_THRESH_BINARY;
+import static org.bytedeco.javacpp.opencv_imgproc.CV_THRESH_BINARY_INV;
+import static org.bytedeco.javacpp.opencv_imgproc.CV_THRESH_OTSU;
+import static org.bytedeco.javacpp.opencv_imgproc.INTER_LINEAR;
+import static org.bytedeco.javacpp.opencv_imgproc.boundingRect;
+import static org.bytedeco.javacpp.opencv_imgproc.cvtColor;
+import static org.bytedeco.javacpp.opencv_imgproc.findContours;
+import static org.bytedeco.javacpp.opencv_imgproc.resize;
+import static org.bytedeco.javacpp.opencv_imgproc.threshold;
+import static org.bytedeco.javacpp.opencv_imgproc.warpAffine;
+
+import java.util.Vector;
+
+import org.bytedeco.javacpp.opencv_core;
+import org.bytedeco.javacpp.opencv_core.Mat;
+import org.bytedeco.javacpp.opencv_core.MatVector;
+import org.bytedeco.javacpp.opencv_core.Rect;
+import org.bytedeco.javacpp.opencv_core.Scalar;
+import org.bytedeco.javacpp.opencv_core.Size;
+import org.bytedeco.javacpp.opencv_imgcodecs;
+
+import com.yuxue.enumtype.PlateColor;
+import com.yuxue.util.Convert;
+
+/**
+ * 字符分割
+ * @author yuxue
+ * @date 2020-04-28 09:45
+ */
+public class CharsSegment {
+
+    // preprocessChar所用常量
+    final static int CHAR_SIZE = 20;
+    final static int HORIZONTAL = 1;
+    final static int VERTICAL = 0;
+
+    final static int DEFAULT_LIUDING_SIZE = 7;
+    final static int DEFAULT_MAT_WIDTH = 136;
+
+    final static int DEFAULT_COLORTHRESHOLD = 150;
+    final static float DEFAULT_BLUEPERCEMT = 0.3f;
+    final static float DEFAULT_WHITEPERCEMT = 0.1f;
+
+    private int liuDingSize = DEFAULT_LIUDING_SIZE;
+    private int theMatWidth = DEFAULT_MAT_WIDTH;
+    
+    private int colorThreshold = DEFAULT_COLORTHRESHOLD;
+
+    private float bluePercent = DEFAULT_BLUEPERCEMT;
+    private float whitePercent = DEFAULT_WHITEPERCEMT;
+
+    private boolean isDebug = true;
+    
+
+    /**
+     * 字符分割
+     * 
+     * @param input
+     * @param resultVec
+     * @return <ul>
+     *         <li>more than zero: the number of chars;
+     *         <li>-3: null;
+     *         </ul>
+     */
+    public int charsSegment(final Mat input, Vector<Mat> resultVec, String tempPath) {
+        if (input.data().isNull()) {
+            return -3;
+        }
+
+        // 判断车牌颜色以此确认threshold方法
+        Mat img_threshold = new Mat();
+
+        Mat input_grey = new Mat();
+        cvtColor(input, input_grey, CV_RGB2GRAY);
+
+        int w = input.cols();
+        int h = input.rows();
+        Mat tmpMat = new Mat(input, new Rect((int) (w * 0.1), (int) (h * 0.1), (int) (w * 0.8), (int) (h * 0.8)));
+        
+        PlateColor color= getPlateType(tmpMat, true);
+        switch (color) {
+        case BLUE:
+            threshold(input_grey, img_threshold, 10, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
+            break;
+
+        case YELLOW:
+            threshold(input_grey, img_threshold, 10, 255, CV_THRESH_OTSU + CV_THRESH_BINARY_INV);
+            break;
+            
+        case GREEN:
+            threshold(input_grey, img_threshold, 10, 255, CV_THRESH_OTSU + CV_THRESH_BINARY_INV);
+            break;
+
+        default:
+            return -3;
+        }
+
+        if (this.isDebug) {
+            opencv_imgcodecs.imwrite(tempPath + "debug_char_threshold.jpg", img_threshold);
+        }
+
+        // 去除车牌上方的柳钉以及下方的横线等干扰  //会导致虚拟机崩溃
+        // clearLiuDing(img_threshold);
+        /*if (this.isDebug) {
+            String str = tempPath + "debug_char_clearLiuDing.jpg";
+            opencv_imgcodecs.imwrite(str, img_threshold);
+        }*/
+
+        // 找轮廓
+        Mat img_contours = new Mat();
+        img_threshold.copyTo(img_contours);
+
+        MatVector contours = new MatVector();
+
+        findContours(img_contours, contours, // a vector of contours
+                CV_RETR_EXTERNAL, // retrieve the external contours
+                CV_CHAIN_APPROX_NONE); // all pixels of each contours
+
+        // Remove patch that are no inside limits of aspect ratio and area.
+        // 将不符合特定尺寸的图块排除出去
+        Vector<Rect> vecRect = new Vector<Rect>();
+        for (int i = 0; i < contours.size(); ++i) {
+            Rect mr = boundingRect(contours.get(i));
+            Mat contour = new Mat(img_threshold, mr);
+            
+            if (this.isDebug) {
+                String str = tempPath + "debug_char_contour"+i+".jpg";
+                opencv_imgcodecs.imwrite(str, contour);
+            }
+            
+            if (verifySizes(contour)) { // 将不符合特定尺寸的图块排除出去
+                vecRect.add(mr);
+            }
+        }
+        if (vecRect.size() == 0) {
+            return -3;
+        }
+
+        Vector<Rect> sortedRect = new Vector<Rect>();
+        // 对符合尺寸的图块按照从左到右进行排序
+        SortRect(vecRect, sortedRect);
+
+        // 获得指示城市的特定Rect,如苏A的"A"
+        int specIndex = GetSpecificRect(sortedRect, color);
+
+        if (this.isDebug) {
+            if (specIndex < sortedRect.size()) {
+                Mat specMat = new Mat(img_threshold, sortedRect.get(specIndex));
+                String str = tempPath + "debug_specMat.jpg";
+                opencv_imgcodecs.imwrite(str, specMat);
+            }
+        }
+
+        // 根据特定Rect向左反推出中文字符
+        // 这样做的主要原因是根据findContours方法很难捕捉到中文字符的准确Rect，因此仅能
+        // 通过特定算法来指定
+        Rect chineseRect = new Rect();
+        if (specIndex < sortedRect.size()) {
+            chineseRect = GetChineseRect(sortedRect.get(specIndex));
+        } else {
+            return -3;
+        }
+
+        if (this.isDebug) {
+            Mat chineseMat = new Mat(img_threshold, chineseRect);
+            String str = tempPath + "debug_chineseMat.jpg";
+            opencv_imgcodecs.imwrite(str, chineseMat);
+        }
+
+        // 新建一个全新的排序Rect
+        // 将中文字符Rect第一个加进来，因为它肯定是最左边的
+        // 其余的Rect只按照顺序去6个，车牌只可能是7个字符！这样可以避免阴影导致的“1”字符
+        Vector<Rect> newSortedRect = new Vector<Rect>();
+        newSortedRect.add(chineseRect);
+        RebuildRect(sortedRect, newSortedRect, specIndex, color);
+
+        if (newSortedRect.size() == 0) {
+            return -3;
+        }
+
+        for (int i = 0; i < newSortedRect.size(); i++) {
+            Rect mr = newSortedRect.get(i);
+            Mat auxRoi = new Mat(img_threshold, mr);
+
+            auxRoi = preprocessChar(auxRoi);
+            if (this.isDebug) {
+                String str = tempPath + "debug_char_auxRoi_" + Integer.valueOf(i).toString() + ".jpg";
+                opencv_imgcodecs.imwrite(str, auxRoi);
+            }
+            resultVec.add(auxRoi);
+        }
+        return 0;
+    }
+
+    /**
+     * 字符尺寸验证；去掉尺寸不符合的图块
+     * @param r
+     * @return
+     */
+    public static Boolean verifySizes(Mat r) {
+        float aspect = 45.0f / 90.0f;
+        float charAspect = (float) r.cols() / (float) r.rows();
+        float error = 0.7f;
+        float minHeight = 10f;
+        float maxHeight = 35f;
+        // We have a different aspect ratio for number 1, and it can be ~0.2
+        float minAspect = 0.05f;
+        float maxAspect = aspect + aspect * error;
+        // area of pixels
+        float area = countNonZero(r);
+        // bb area
+        float bbArea = r.cols() * r.rows();
+        // % of pixel in area
+        float percPixels = area / bbArea;
+
+        return percPixels <= 1 && charAspect > minAspect && charAspect < maxAspect && r.rows() >= minHeight && r.rows() < maxHeight;
+    }
+
+    /**
+     * 字符预处理: 统一每个字符的大小
+     * 
+     * @param in
+     * @return
+     */
+    private Mat preprocessChar(Mat in) {
+        int h = in.rows();
+        int w = in.cols();
+        int charSize = CHAR_SIZE;
+        Mat transformMat = Mat.eye(2, 3, CV_32F).asMat();
+        int m = Math.max(w, h);
+        transformMat.ptr(0, 2).put(Convert.getBytes(((m - w) / 2f)));
+        transformMat.ptr(1, 2).put(Convert.getBytes((m - h) / 2f));
+
+        Mat warpImage = new Mat(m, m, in.type());
+        warpAffine(in, warpImage, transformMat, warpImage.size(), INTER_LINEAR, opencv_core.BORDER_CONSTANT, new Scalar(0));
+
+        Mat out = new Mat();
+        resize(warpImage, out, new Size(charSize, charSize));
+
+        return out;
+    }
+
+    /**
+     * 去除车牌上方的钮钉
+     * <p>
+     * 计算每行元素的阶跃数，如果小于X认为是柳丁，将此行全部填0（涂黑）， X可根据实际调整
+     * 
+     * @param img
+     * @return
+     */
+    private Mat clearLiuDing(Mat img) {
+        final int x = this.liuDingSize;
+
+        Mat jump = Mat.zeros(1, img.rows(), CV_32F).asMat();
+        for (int i = 0; i < img.rows(); i++) {
+            int jumpCount = 0;
+            for (int j = 0; j < img.cols() - 1; j++) {
+                if (img.ptr(i, j).get() != img.ptr(i, j + 1).get())
+                    jumpCount++;
+            }
+            jump.ptr(i).put(Convert.getBytes((float) jumpCount));
+        }
+        for (int i = 0; i < img.rows(); i++) {
+            if (Convert.toFloat(jump.ptr(i)) <= x) {
+                for (int j = 0; j < img.cols(); j++) {
+                    img.ptr(i, j).put((byte) 0);
+                }
+            }
+        }
+        return img;
+    }
+
+    /**
+     * 根据特殊车牌来构造猜测中文字符的位置和大小
+     * 
+     * @param rectSpe
+     * @return
+     */
+    private Rect GetChineseRect(final Rect rectSpe) {
+        int height = rectSpe.height();
+        float newwidth = rectSpe.width() * 1.15f;
+        int x = rectSpe.x();
+        int y = rectSpe.y();
+
+        int newx = x - (int) (newwidth * 1.15);
+        newx = Math.max(newx, 0);
+        Rect a = new Rect(newx, y, (int) newwidth, height);
+        return a;
+    }
+
+    /**
+     * 找出指示城市的字符的Rect，例如苏A7003X，就是A的位置
+     * 之所以选择城市的字符位置，是因为该位置不管什么字母，占用的宽度跟高度的差不多，能大大提高位置的准确性
+     * @param vecRect
+     * @return
+     */
+    private int GetSpecificRect(final Vector<Rect> vecRect, PlateColor color) {
+        Vector<Integer> xpositions = new Vector<Integer>();
+        int maxHeight = 0;
+        int maxWidth = 0;
+        for (int i = 0; i < vecRect.size(); i++) {
+            xpositions.add(vecRect.get(i).x());
+
+            if (vecRect.get(i).height() > maxHeight) {
+                maxHeight = vecRect.get(i).height();
+            }
+            if (vecRect.get(i).width() > maxWidth) {
+                maxWidth = vecRect.get(i).width();
+            }
+        }
+
+        int specIndex = 0;
+        for (int i = 0; i < vecRect.size(); i++) {
+            Rect mr = vecRect.get(i);
+            int midx = mr.x() + mr.width() / 2;
+
+            if(PlateColor.GREEN.equals(color)) {
+                if ((mr.width() > maxWidth * 0.8 || mr.height() > maxHeight * 0.8)
+                        && (midx < this.theMatWidth * 2 / 8 && midx > this.theMatWidth / 8)) {
+                    specIndex = i;
+                }
+            } else {
+                // 如果一个字符有一定的大小，并且在整个车牌的1/7到2/7之间，则是我们要找的特殊车牌
+                if ((mr.width() > maxWidth * 0.8 || mr.height() > maxHeight * 0.8)
+                        && (midx < this.theMatWidth * 2 / 7 && midx > this.theMatWidth / 7)) {
+                    specIndex = i;
+                } 
+            }
+        }
+
+        return specIndex;
+    }
+
+    /**
+     * 这个函数做两个事情
+     * <ul>
+     * <li>把特殊字符Rect左边的全部Rect去掉，后面再重建中文字符的位置;
+     * <li>从特殊字符Rect开始，依次选择6个Rect，多余的舍去。
+     * <ul>
+     * 
+     * @param vecRect
+     * @param outRect
+     * @param specIndex
+     * @return
+     */
+    private int RebuildRect(final Vector<Rect> vecRect, Vector<Rect> outRect, int specIndex, PlateColor color) {
+        // 最大只能有7个Rect,减去中文的就只有6个Rect
+        int count = 6;
+        if(PlateColor.GREEN.equals(color)) {
+            count = 7; // 绿牌要多一个
+        }
+        for (int i = 0; i < vecRect.size(); i++) {
+            // 将特殊字符左边的Rect去掉，这个可能会去掉中文Rect，不过没关系，我们后面会重建。
+            if (i < specIndex)
+                continue;
+
+            outRect.add(vecRect.get(i));
+            if (--count == 0)
+                break;
+        }
+
+        return 0;
+    }
+
+    /**
+     * 将Rect按位置从左到右进行排序
+     * 
+     * @param vecRect
+     * @param out
+     * @return
+     */
+    public static void SortRect(final Vector<Rect> vecRect, Vector<Rect> out) {
+        Vector<Integer> orderIndex = new Vector<Integer>();
+        Vector<Integer> xpositions = new Vector<Integer>();
+        for (int i = 0; i < vecRect.size(); ++i) {
+            orderIndex.add(i);
+            xpositions.add(vecRect.get(i).x());
+        }
+
+        float min = xpositions.get(0);
+        int minIdx;
+        for (int i = 0; i < xpositions.size(); ++i) {
+            min = xpositions.get(i);
+            minIdx = i;
+            for (int j = i; j < xpositions.size(); ++j) {
+                if (xpositions.get(j) < min) {
+                    min = xpositions.get(j);
+                    minIdx = j;
+                }
+            }
+            int aux_i = orderIndex.get(i);
+            int aux_min = orderIndex.get(minIdx);
+            orderIndex.remove(i);
+            orderIndex.insertElementAt(aux_min, i);
+            orderIndex.remove(minIdx);
+            orderIndex.insertElementAt(aux_i, minIdx);
+
+            float aux_xi = xpositions.get(i);
+            float aux_xmin = xpositions.get(minIdx);
+            xpositions.remove(i);
+            xpositions.insertElementAt((int) aux_xmin, i);
+            xpositions.remove(minIdx);
+            xpositions.insertElementAt((int) aux_xi, minIdx);
+        }
+
+        for (int i = 0; i < orderIndex.size(); i++)
+            out.add(vecRect.get(orderIndex.get(i)));
+
+        return;
+    }
+
+    public void setLiuDingSize(int param) {
+        this.liuDingSize = param;
+    }
+
+    public void setColorThreshold(int param) {
+        this.colorThreshold = param;
+    }
+
+    public void setBluePercent(float param) {
+        this.bluePercent = param;
+    }
+
+    public final float getBluePercent() {
+        return this.bluePercent;
+    }
+
+    public void setWhitePercent(float param) {
+        this.whitePercent = param;
+    }
+
+    public final float getWhitePercent() {
+        return this.whitePercent;
+    }
+
+    public boolean getDebug() {
+        return this.isDebug;
+    }
+
+    public void setDebug(boolean isDebug) {
+        this.isDebug = isDebug;
+    }
+
+
+}

target/classes/easypr-java/CoreFunc.java

@@ -0,0 +1,269 @@
+package com.yuxue.easypr.core;
+
+import org.bytedeco.javacpp.BytePointer;
+import org.bytedeco.javacpp.opencv_core;
+import org.bytedeco.javacpp.opencv_core.Mat;
+import org.bytedeco.javacpp.opencv_core.MatVector;
+import org.bytedeco.javacpp.opencv_core.Size;
+import org.bytedeco.javacpp.opencv_highgui;
+import org.bytedeco.javacpp.opencv_imgproc;
+import org.bytedeco.javacpp.indexer.FloatIndexer;
+
+import com.yuxue.enumtype.Direction;
+import com.yuxue.enumtype.PlateColor;
+
+
+/**
+ * 
+ * @author yuxue
+ * @date 2020-05-16 21:09
+ */
+public class CoreFunc {
+
+    /**
+     * 根据一幅图像与颜色模板获取对应的二值图
+     * 
+     * @param src
+     *            输入RGB图像
+     * @param r
+     *            颜色模板（蓝色、黄色）
+     * @param adaptive_minsv
+     *            S和V的最小值由adaptive_minsv这个bool值判断
+     *            <ul>
+     *            <li>如果为true，则最小值取决于H值，按比例衰减
+     *            <li>如果为false，则不再自适应，使用固定的最小值minabs_sv
+     *            </ul>
+     * @return 输出灰度图（只有0和255两个值，255代表匹配，0代表不匹配）
+     */
+    public static Mat colorMatch(final Mat src, final PlateColor r, final boolean adaptive_minsv) {
+        final float max_sv = 255;
+        final float minref_sv = 64;
+        final float minabs_sv = 95;
+
+        // 转到HSV空间进行处理，颜色搜索主要使用的是H分量进行蓝色与黄色的匹配工作
+        Mat src_hsv = new Mat();
+        opencv_imgproc.cvtColor(src, src_hsv, opencv_imgproc.CV_BGR2HSV);
+        MatVector hsvSplit = new MatVector();
+        opencv_core.split(src_hsv, hsvSplit);
+        opencv_imgproc.equalizeHist(hsvSplit.get(2), hsvSplit.get(2));
+        opencv_core.merge(hsvSplit, src_hsv);
+
+        // 匹配模板基色,切换以查找想要的基色
+        int min_h = r.minH;
+        int max_h = r.maxH;
+
+        float diff_h = (float) ((max_h - min_h) / 2);
+        int avg_h = (int) (min_h + diff_h);
+
+        int channels = src_hsv.channels();
+        int nRows = src_hsv.rows();
+        // 图像数据列需要考虑通道数的影响；
+        int nCols = src_hsv.cols() * channels;
+
+        // 连续存储的数据，按一行处理
+        if (src_hsv.isContinuous()) {
+            nCols *= nRows;
+            nRows = 1;
+        }
+
+        for (int i = 0; i < nRows; ++i) {
+            BytePointer p = src_hsv.ptr(i);
+            for (int j = 0; j < nCols; j += 3) {
+                int H = p.get(j) & 0xFF;
+                int S = p.get(j + 1) & 0xFF;
+                int V = p.get(j + 2) & 0xFF;
+
+                boolean colorMatched = false;
+
+                if (H > min_h && H < max_h) {
+                    int Hdiff = 0;
+                    if (H > avg_h)
+                        Hdiff = H - avg_h;
+                    else
+                        Hdiff = avg_h - H;
+
+                    float Hdiff_p = Hdiff / diff_h;
+
+                    float min_sv = 0;
+                    if (true == adaptive_minsv)
+                        min_sv = minref_sv - minref_sv / 2 * (1 - Hdiff_p);
+                    else
+                        min_sv = minabs_sv;
+
+                    if ((S > min_sv && S <= max_sv) && (V > min_sv && V <= max_sv))
+                        colorMatched = true;
+                }
+
+                if (colorMatched == true) {
+                    p.put(j, (byte) 0);
+                    p.put(j + 1, (byte) 0);
+                    p.put(j + 2, (byte) 255);
+                } else {
+                    p.put(j, (byte) 0);
+                    p.put(j + 1, (byte) 0);
+                    p.put(j + 2, (byte) 0);
+                }
+            }
+        }
+
+        // 获取颜色匹配后的二值灰度图
+        MatVector hsvSplit_done = new MatVector();
+        opencv_core.split(src_hsv, hsvSplit_done);
+        Mat src_grey = hsvSplit_done.get(2);
+
+        return src_grey;
+    }
+
+    /**
+     * 判断一个车牌的颜色
+     * 
+     * @param src
+     *            车牌mat
+     * @param r
+     *            颜色模板
+     * @param adaptive_minsv
+     *            S和V的最小值由adaptive_minsv这个bool值判断
+     *            <ul>
+     *            <li>如果为true，则最小值取决于H值，按比例衰减
+     *            <li>如果为false，则不再自适应，使用固定的最小值minabs_sv
+     *            </ul>
+     * @return
+     */
+    public static boolean plateColorJudge(final Mat src, final PlateColor color, final boolean adaptive_minsv) {
+        // 判断阈值
+        final float thresh = 0.49f;
+
+        Mat gray = colorMatch(src, color, adaptive_minsv);
+
+        float percent = (float) opencv_core.countNonZero(gray) / (gray.rows() * gray.cols());
+
+        return (percent > thresh) ? true : false;
+    }
+
+    /**
+     * getPlateType 判断车牌的类型
+     * 
+     * @param src
+     * @param adaptive_minsv
+     *            S和V的最小值由adaptive_minsv这个bool值判断
+     *            <ul>
+     *            <li>如果为true，则最小值取决于H值，按比例衰减
+     *            <li>如果为false，则不再自适应，使用固定的最小值minabs_sv
+     *            </ul>
+     * @return
+     */
+    public static PlateColor getPlateType(final Mat src, final boolean adaptive_minsv) {
+        if (plateColorJudge(src, PlateColor.BLUE, adaptive_minsv) == true) {
+            return PlateColor.BLUE;
+        } else if (plateColorJudge(src, PlateColor.YELLOW, adaptive_minsv) == true) {
+            return PlateColor.YELLOW;
+        } else if (plateColorJudge(src, PlateColor.GREEN, adaptive_minsv) == true) {
+            return PlateColor.GREEN;
+        } else {
+            return PlateColor.UNKNOWN;
+        }
+    }
+
+    /**
+     * 获取垂直或水平方向直方图
+     * 
+     * @param img
+     * @param direction
+     * @return
+     */
+    public static float[] projectedHistogram(final Mat img, Direction direction) {
+        int sz = 0;
+        switch (direction) {
+        case HORIZONTAL:
+            sz = img.rows();
+            break;
+
+        case VERTICAL:
+            sz = img.cols();
+            break;
+
+        default:
+            break;
+        }
+
+        // 统计这一行或一列中，非零元素的个数，并保存到nonZeroMat中
+        float[] nonZeroMat = new float[sz];
+        opencv_core.extractChannel(img, img, 0);
+        for (int j = 0; j < sz; j++) {
+            Mat data = (direction == Direction.HORIZONTAL) ? img.row(j) : img.col(j);
+            int count = opencv_core.countNonZero(data);
+            nonZeroMat[j] = count;
+        }
+
+        // Normalize histogram
+        float max = 0;
+        for (int j = 0; j < nonZeroMat.length; ++j) {
+            max = Math.max(max, nonZeroMat[j]);
+        }
+
+        if (max > 0) {
+            for (int j = 0; j < nonZeroMat.length; ++j) {
+                nonZeroMat[j] /= max;
+            }
+        }
+
+        return nonZeroMat;
+    }
+
+    /**
+     * Assign values to feature
+     * <p>
+     * 样本特征为水平、垂直直方图和低分辨率图像所组成的矢量
+     * 
+     * @param in
+     * @param sizeData
+     *            低分辨率图像size = sizeData*sizeData, 可以为0
+     * @return
+     */
+    public static Mat features(final Mat in, final int sizeData) {
+
+        float[] vhist = projectedHistogram(in, Direction.VERTICAL);
+        float[] hhist = projectedHistogram(in, Direction.HORIZONTAL);
+
+        Mat lowData = new Mat();
+        if (sizeData > 0) {
+            // resize.cpp:3784: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'
+            opencv_imgproc.resize(in, lowData, new Size(sizeData, sizeData));
+        }
+
+        int numCols = vhist.length + hhist.length + lowData.cols() * lowData.rows();
+        Mat out = Mat.zeros(1, numCols, opencv_core.CV_32F).asMat();
+        FloatIndexer idx = out.createIndexer();
+
+        int j = 0;
+        for (int i = 0; i < vhist.length; ++i, ++j) {
+            idx.put(0, j, vhist[i]);
+        }
+        for (int i = 0; i < hhist.length; ++i, ++j) {
+            idx.put(0, j, hhist[i]);
+        }
+        for (int x = 0; x < lowData.cols(); x++) {
+            for (int y = 0; y < lowData.rows(); y++, ++j) {
+                float val = lowData.ptr(x, y).get(0) & 0xFF;
+                idx.put(0, j, val);
+            }
+        }
+
+        return out;
+    }
+
+    
+    
+    /**
+     * 显示图像
+     * @param title
+     * @param src
+     */
+    public static void showImage(final String title, final Mat src) {
+        if (src != null) {
+            opencv_highgui.imshow(title, src);
+            opencv_highgui.cvWaitKey(0);
+        }
+    }
+
+}

target/classes/easypr-java/Features.java

@@ -0,0 +1,90 @@
+package com.yuxue.easypr.core;
+
+import static com.yuxue.easypr.core.CoreFunc.features;
+import static org.bytedeco.javacpp.opencv_core.merge;
+import static org.bytedeco.javacpp.opencv_core.split;
+
+import org.bytedeco.javacpp.opencv_core.Mat;
+import org.bytedeco.javacpp.opencv_core.MatVector;
+import org.bytedeco.javacpp.opencv_imgproc;
+
+/**
+ * 
+ * @author yuxue
+ * @date 2020-05-05 08:26
+ */
+public class Features implements SVMCallback {
+
+    /***
+     * EasyPR的getFeatures回调函数
+     * 本函数是生成直方图均衡特征的回调函数
+     * @param image
+     * @return
+     */
+    @Override
+    public Mat getHisteqFeatures(final Mat image) {
+        return histeq(image);
+    }
+    
+    private Mat histeq(Mat in) {
+        Mat out = new Mat(in.size(), in.type());
+        if (in.channels() == 3) {
+            Mat hsv = new Mat();
+            MatVector hsvSplit = new MatVector();
+            opencv_imgproc.cvtColor(in, hsv, opencv_imgproc.CV_BGR2HSV);
+            split(hsv, hsvSplit);
+            opencv_imgproc.equalizeHist(hsvSplit.get(2), hsvSplit.get(2));
+            merge(hsvSplit, hsv);
+            opencv_imgproc.cvtColor(hsv, out, opencv_imgproc.CV_HSV2BGR);
+            hsv = null;
+            hsvSplit = null;
+            System.gc();
+        } else if (in.channels() == 1) {
+            opencv_imgproc.equalizeHist(in, out);
+        }
+        return out;
+    }
+    
+    /**
+     * EasyPR的getFeatures回调函数
+     * 本函数是获取垂直和水平的直方图图值
+     * @param image
+     * @return
+     */
+    @Override
+    public Mat getHistogramFeatures(Mat image) {
+        Mat grayImage = new Mat();
+        opencv_imgproc.cvtColor(image, grayImage, opencv_imgproc.CV_RGB2GRAY);
+
+        Mat img_threshold = new Mat();
+        opencv_imgproc.threshold(grayImage, img_threshold, 0, 255, opencv_imgproc.CV_THRESH_OTSU + opencv_imgproc.CV_THRESH_BINARY);
+
+        return features(img_threshold, 0);
+    }
+
+    /**
+     * 本函数是获取SITF特征子的回调函数
+     * 
+     * @param image  
+     * @return
+     */
+    @Override
+    public Mat getSIFTFeatures(final Mat image) {
+        // TODO: 待完善
+        return null;
+    }
+
+    /**
+     * 本函数是获取HOG特征子的回调函数
+     * 
+     * @param image
+     * @return
+     */
+    @Override
+    public Mat getHOGFeatures(final Mat image) {
+        // TODO: 待完善
+        return null;
+    }
+
+    
+}

target/classes/easypr-java/PlateDetect.java

@@ -0,0 +1,111 @@
+package com.yuxue.easypr.core;
+
+import java.util.Vector;
+
+import org.bytedeco.javacpp.opencv_core.Mat;
+
+
+/**
+ * 车牌检测识别
+ * 分两个步骤： 1、车牌定位 2、车牌判断
+ * @author yuxue
+ * @date 2020-04-24 15:33
+ */
+public class PlateDetect {
+
+    // 车牌定位， 图片处理对象
+    private PlateLocate plateLocate = new PlateLocate();
+
+    // 切图判断对象
+    private PlateJudge plateJudge = new PlateJudge();
+
+    /**
+     * @param src 图片路径，不能包含中文及特殊字符
+     * @param resultVec 车牌的图块集合
+     * @return the error number
+     *         <ul>
+     *         <li>0: plate detected successfully;
+     *         <li>-1: source Mat is empty;
+     *         <li>-2: plate not detected.
+     *         </ul>
+     */
+    public int plateDetect(final Mat src, Vector<Mat> resultVec) {
+        Vector<Mat> matVec = plateLocate.plateLocate(src);  // 定位
+
+        if (0 == matVec.size()) {
+            return -1;
+        }
+
+        if (0 != plateJudge.plateJudge(matVec, resultVec)) { //对多幅图像进行SVM判断
+            return -2;
+        }
+        return 0;
+    }
+
+
+    /**
+     * 生活模式与工业模式切换
+     * @param pdLifemode
+     */
+    public void setPDLifemode(boolean pdLifemode) {
+        plateLocate.setLifemode(pdLifemode);
+    }
+
+    public void setGaussianBlurSize(int gaussianBlurSize) {
+        plateLocate.setGaussianBlurSize(gaussianBlurSize);
+    }
+
+    public final int getGaussianBlurSize() {
+        return plateLocate.getGaussianBlurSize();
+    }
+
+    public void setMorphSizeWidth(int morphSizeWidth) {
+        plateLocate.setMorphSizeWidth(morphSizeWidth);
+    }
+
+    public final int getMorphSizeWidth() {
+        return plateLocate.getMorphSizeWidth();
+    }
+
+    public void setMorphSizeHeight(int morphSizeHeight) {
+        plateLocate.setMorphSizeHeight(morphSizeHeight);
+    }
+
+    public final int getMorphSizeHeight() {
+        return plateLocate.getMorphSizeHeight();
+    }
+
+    public void setVerifyError(float verifyError) {
+        plateLocate.setVerifyError(verifyError);
+    }
+
+    public final float getVerifyError() {
+        return plateLocate.getVerifyError();
+    }
+
+    public void setVerifyAspect(float verifyAspect) {
+        plateLocate.setVerifyAspect(verifyAspect);
+    }
+
+    public final float getVerifyAspect() {
+        return plateLocate.getVerifyAspect();
+    }
+
+    public void setVerifyMin(int verifyMin) {
+        plateLocate.setVerifyMin(verifyMin);
+    }
+
+    public void setVerifyMax(int verifyMax) {
+        plateLocate.setVerifyMax(verifyMax);
+    }
+
+    public void setJudgeAngle(int judgeAngle) {
+        plateLocate.setJudgeAngle(judgeAngle);
+    }
+    
+    public void setDebug(boolean debug, String tempPath) {
+        plateLocate.setDebug(debug);
+        plateLocate.setTempPath(tempPath);
+    }
+
+}

target/classes/easypr-java/PlateJudge.java

@@ -0,0 +1,107 @@
+package com.yuxue.easypr.core;
+
+import org.bytedeco.javacpp.opencv_core;
+import org.bytedeco.javacpp.opencv_imgproc;
+
+import java.util.Vector;
+
+import org.bytedeco.javacpp.opencv_core.Mat;
+import org.bytedeco.javacpp.opencv_core.Rect;
+import org.bytedeco.javacpp.opencv_core.Size;
+import org.bytedeco.javacpp.opencv_ml.SVM;
+
+import com.yuxue.constant.Constant;
+
+
+/**
+ * 车牌判断
+ * @author yuxue
+ * @date 2020-04-26 15:21
+ */
+public class PlateJudge {
+
+    private SVM svm = SVM.create();
+
+    public PlateJudge() {
+        loadSVM(Constant.DEFAULT_SVM_PATH);
+    }
+    
+    public void loadSVM(String path) {
+        svm.clear();
+        // svm=SVM.loadSVM(path, "svm");
+        svm=SVM.load(path);
+    }
+    
+    /**
+     * EasyPR的getFeatures回调函数, 用于从车牌的image生成svm的训练特征features
+     */
+    private SVMCallback features = new Features();
+    
+
+    /**
+     * 对单幅图像进行SVM判断
+     * @param inMat
+     * @return
+     */
+    public int plateJudge(final Mat inMat) {
+        int ret = 1;
+        // 使用com.yuxue.train.SVMTrain 生成的训练库文件
+        Mat features = this.features.getHistogramFeatures(inMat);
+        /*Mat samples = features.reshape(1, 1);
+        samples.convertTo(samples, opencv_core.CV_32F);*/
+        
+        Mat p = features.reshape(1, 1);
+        p.convertTo(p, opencv_core.CV_32FC1);
+        ret = (int) svm.predict(features);
+        return ret;
+        
+        // 使用com.yuxue.train.PlateRecoTrain 生成的训练库文件
+        // 在使用的过程中，传入的样本切图要跟训练的时候处理切图的方法一致
+        /*Mat grayImage = new Mat();
+        opencv_imgproc.cvtColor(inMat, grayImage, opencv_imgproc.CV_RGB2GRAY);
+        Mat dst = new Mat();
+        opencv_imgproc.Canny(grayImage, dst, 130, 250);
+        Mat samples = dst.reshape(1, 1);
+        samples.convertTo(samples, opencv_core.CV_32F);*/
+        
+        // 正样本为0 负样本为1
+        /*if(svm.predict(samples) <= 0) {
+            ret = 1;
+        }*/
+        /*ret = (int)svm.predict(samples);
+        System.err.println(ret);
+        return ret ;*/
+        
+    }
+
+    /**
+     * 对多幅图像进行SVM判断
+     * @param inVec
+     * @param resultVec
+     * @return
+     */
+    public int plateJudge(Vector<Mat> inVec, Vector<Mat> resultVec) {
+
+        for (int j = 0; j < inVec.size(); j++) {
+            Mat inMat = inVec.get(j);
+
+            if (1 == plateJudge(inMat)) {
+                resultVec.add(inMat);
+            } else { // 再取中间部分判断一次
+                int w = inMat.cols();
+                int h = inMat.rows();
+
+                Mat tmpDes = inMat.clone();
+                Mat tmpMat = new Mat(inMat, new Rect((int) (w * 0.05), (int) (h * 0.1), (int) (w * 0.9), (int) (h * 0.8)));
+                opencv_imgproc.resize(tmpMat, tmpDes, new Size(inMat.size()));
+
+                if (plateJudge(tmpDes) == 1) {
+                    resultVec.add(inMat);
+                }
+            }
+        }
+        return 0;
+    }
+
+
+}

target/classes/easypr-java/PlateLocate.java

@@ -0,0 +1,354 @@
+package com.yuxue.easypr.core;
+
+import java.util.Vector;
+
+import static org.bytedeco.javacpp.opencv_core.*;
+import static org.bytedeco.javacpp.opencv_imgproc.*;
+
+import com.yuxue.constant.Constant;
+import org.bytedeco.javacpp.opencv_imgcodecs;
+
+import org.bytedeco.javacpp.opencv_core.CvPoint2D32f;
+import org.bytedeco.javacpp.opencv_core.Mat;
+import org.bytedeco.javacpp.opencv_core.MatVector;
+import org.bytedeco.javacpp.opencv_core.Point;
+import org.bytedeco.javacpp.opencv_core.Point2f;
+import org.bytedeco.javacpp.opencv_core.RotatedRect;
+import org.bytedeco.javacpp.opencv_core.Scalar;
+import org.bytedeco.javacpp.opencv_core.Size;
+
+
+/**
+ * 车牌定位
+ * @author yuxue
+ * @date 2020-04-24 15:33
+ */
+public class PlateLocate {
+
+    // PlateLocate所用常量
+    public static final int DEFAULT_GAUSSIANBLUR_SIZE = 5;
+    public static final int SOBEL_SCALE = 1;
+    public static final int SOBEL_DELTA = 0;
+    public static final int SOBEL_DDEPTH = CV_16S;
+    public static final int SOBEL_X_WEIGHT = 1;
+    public static final int SOBEL_Y_WEIGHT = 0;
+    public static final int DEFAULT_MORPH_SIZE_WIDTH = 17;
+    public static final int DEFAULT_MORPH_SIZE_HEIGHT = 3;
+
+    // showResultMat所用常量
+    public static final int WIDTH = 136;
+    public static final int HEIGHT = 36;
+    public static final int TYPE = CV_8UC3;
+
+    // verifySize所用常量
+    public static final int DEFAULT_VERIFY_MIN = 3;
+    public static final int DEFAULT_VERIFY_MAX = 20;
+
+    final float DEFAULT_ERROR = 0.6f;
+    final float DEFAULT_ASPECT = 3.75f;
+
+    // 角度判断所用常量
+    public static final int DEFAULT_ANGLE = 30;
+
+    // 高斯模糊所用变量
+    protected int gaussianBlurSize = DEFAULT_GAUSSIANBLUR_SIZE;
+
+    // 连接操作所用变量
+    protected int morphSizeWidth = DEFAULT_MORPH_SIZE_WIDTH;
+    protected int morphSizeHeight = DEFAULT_MORPH_SIZE_HEIGHT;
+
+    // verifySize所用变量
+    protected float error = DEFAULT_ERROR;
+    protected float aspect = DEFAULT_ASPECT;
+    protected int verifyMin = DEFAULT_VERIFY_MIN;
+    protected int verifyMax = DEFAULT_VERIFY_MAX;
+
+    // 角度判断所用变量
+    protected int angle = DEFAULT_ANGLE;
+
+    // 是否开启调试模式，0关闭，非0开启 
+    protected boolean debug = true;
+
+    // 开启调试模式之后，切图文件保存路径
+    protected String tempPath = Constant.DEFAULT_TEMP_DIR + System.currentTimeMillis() + "/";
+
+    /**
+     * 生活模式与工业模式切换
+     * @param islifemode
+     * 如果为真，则设置各项参数为定位生活场景照片（如百度图片）的参数，否则恢复默认值。
+     * 
+     */
+    public void setLifemode(boolean islifemode) {
+        if (islifemode) {
+            setGaussianBlurSize(5);
+            setMorphSizeWidth(9);
+            setMorphSizeHeight(3);
+            setVerifyError(0.9f);
+            setVerifyAspect(4);
+            setVerifyMin(1);
+            setVerifyMax(30);
+        } else {
+            setGaussianBlurSize(DEFAULT_GAUSSIANBLUR_SIZE);
+            setMorphSizeWidth(DEFAULT_MORPH_SIZE_WIDTH);
+            setMorphSizeHeight(DEFAULT_MORPH_SIZE_HEIGHT);
+            setVerifyError(DEFAULT_ERROR);
+            setVerifyAspect(DEFAULT_ASPECT);
+            setVerifyMin(DEFAULT_VERIFY_MIN);
+            setVerifyMax(DEFAULT_VERIFY_MAX);
+        }
+    }
+
+    /**
+     * 定位车牌图像
+     * @param src 原始图像
+     * @return 一个Mat的向量，存储所有抓取到的图像
+     */
+    public Vector<Mat> plateLocate(Mat src) {
+        Vector<Mat> resultVec = new Vector<Mat>();
+
+        Mat src_blur = new Mat();
+        Mat src_gray = new Mat();
+        Mat grad = new Mat();
+
+        int scale = SOBEL_SCALE;
+        int delta = SOBEL_DELTA;
+        int ddepth = SOBEL_DDEPTH;
+
+        // 高斯模糊。Size中的数字影响车牌定位的效果。
+        GaussianBlur(src, src_blur, new Size(gaussianBlurSize, gaussianBlurSize), 0, 0, BORDER_DEFAULT);
+        if (debug) {
+            opencv_imgcodecs.imwrite(tempPath + "debug_GaussianBlur.jpg", src_blur);
+        }
+
+        // Convert it to gray 将图像进行灰度化
+        cvtColor(src_blur, src_gray, CV_RGB2GRAY);
+        if (debug) {
+            opencv_imgcodecs.imwrite(tempPath + "debug_gray.jpg", src_gray);
+        }
+
+        // 对图像进行Sobel 运算，得到的是图像的一阶水平方向导数。
+
+        // Generate grad_x and grad_y
+        Mat grad_x = new Mat();
+        Mat grad_y = new Mat();
+        Mat abs_grad_x = new Mat();
+        Mat abs_grad_y = new Mat();
+
+        Sobel(src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT);
+        convertScaleAbs(grad_x, abs_grad_x);
+
+        Sobel(src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT);
+        convertScaleAbs(grad_y, abs_grad_y);
+
+        // Total Gradient (approximate)
+        addWeighted(abs_grad_x, SOBEL_X_WEIGHT, abs_grad_y, SOBEL_Y_WEIGHT, 0, grad);
+
+        if (debug) {
+            opencv_imgcodecs.imwrite(tempPath + "debug_Sobel.jpg", grad);
+        }
+
+        // 对图像进行二值化。将灰度图像（每个像素点有256 个取值可能）转化为二值图像（每个像素点仅有1 和0 两个取值可能）。
+
+        Mat img_threshold = new Mat();
+        threshold(grad, img_threshold, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
+
+        if (debug) {
+            opencv_imgcodecs.imwrite(tempPath + "debug_threshold.jpg", img_threshold);
+        }
+
+        // 使用闭操作。对图像进行闭操作以后，可以看到车牌区域被连接成一个矩形装的区域。
+
+        Mat element = getStructuringElement(MORPH_RECT, new Size(morphSizeWidth, morphSizeHeight));
+        morphologyEx(img_threshold, img_threshold, MORPH_CLOSE, element);
+
+        if (debug) {
+            opencv_imgcodecs.imwrite(tempPath + "debug_morphology.jpg", img_threshold);
+        }
+
+        // Find 轮廓 of possibles plates 求轮廓。求出图中所有的轮廓。这个算法会把全图的轮廓都计算出来，因此要进行筛选。
+
+        MatVector contours = new MatVector();
+        findContours(img_threshold, contours, // a vector of contours
+                CV_RETR_EXTERNAL, // 提取外部轮廓
+                CV_CHAIN_APPROX_NONE); // all pixels of each contours
+
+        Mat result = new Mat();
+        if (debug) {
+            src.copyTo(result);
+            // 将轮廓描绘到图上输出
+            drawContours(result, contours, -1, new Scalar(0, 0, 255, 255));
+            opencv_imgcodecs.imwrite(tempPath + "debug_Contours.jpg", result);
+        }
+
+        // Start to iterate to each contour founded
+        // 筛选。对轮廓求最小外接矩形，然后验证，不满足条件的淘汰。
+        Vector<RotatedRect> rects = new Vector<RotatedRect>();
+
+        for (int i = 0; i < contours.size(); ++i) {
+            RotatedRect mr = minAreaRect(contours.get(i));
+            if (verifySizes(mr))
+                rects.add(mr);
+        }
+
+        int k = 1;
+        for (int i = 0; i < rects.size(); i++) {
+            RotatedRect minRect = rects.get(i);
+            /*if (debug) {
+                Point2f rect_points = new Point2f(4);
+                minRect.points(rect_points);
+
+                for (int j = 0; j < 4; j++) {
+                    Point pt1 = new Point(new CvPoint2D32f(rect_points.position(j)));
+                    Point pt2 = new Point(new CvPoint2D32f(rect_points.position((j + 1) % 4)));
+
+                    line(result, pt1, pt2, new Scalar(0, 255, 255, 255), 1, 8, 0);
+                }
+            }*/
+
+            // rotated rectangle drawing
+            // 旋转这部分代码确实可以将某些倾斜的车牌调整正，但是它也会误将更多正的车牌搞成倾斜！所以综合考虑，还是不使用这段代码。
+            // 2014-08-14,由于新到的一批图片中发现有很多车牌是倾斜的，因此决定再次尝试这段代码。
+
+            float r = minRect.size().width() / minRect.size().height();
+            float angle = minRect.angle();
+            Size rect_size = new Size((int) minRect.size().width(), (int) minRect.size().height());
+            if (r < 1) {
+                angle = 90 + angle;
+                rect_size = new Size(rect_size.height(), rect_size.width());
+            }
+            // 如果抓取的方块旋转超过m_angle角度，则不是车牌，放弃处理
+            if (angle - this.angle < 0 && angle + this.angle > 0) {
+                Mat img_rotated = new Mat();
+                Mat rotmat = getRotationMatrix2D(minRect.center(), angle, 1);
+                warpAffine(src, img_rotated, rotmat, src.size()); // CV_INTER_CUBIC
+
+                Mat resultMat = showResultMat(img_rotated, rect_size, minRect.center(), k++);
+                resultVec.add(resultMat);
+            }
+        }
+
+        return resultVec;
+    }
+
+
+    /**
+     * 对minAreaRect获得的最小外接矩形，用纵横比进行判断
+     * 
+     * @param mr
+     * @return
+     */
+    private boolean verifySizes(RotatedRect mr) {
+        float error = this.error;
+
+        // China car plate size: 440mm*140mm，aspect 3.142857      
+        float aspect = this.aspect;
+        int min = 44 * 14 * verifyMin; // minimum area
+        int max = 44 * 14 * verifyMax; // maximum area
+
+        // Get only patchs that match to a respect ratio.
+        float rmin = aspect - aspect * error;
+        float rmax = aspect + aspect * error;
+
+        int area = (int) (mr.size().height() * mr.size().width());
+        float r = mr.size().width() / mr.size().height();
+        if (r < 1)
+            r = mr.size().height() / mr.size().width();
+
+        return area >= min && area <= max && r >= rmin && r <= rmax;
+    }
+
+    /**
+     * 显示最终生成的车牌图像，便于判断是否成功进行了旋转。
+     * @param src
+     * @param rect_size
+     * @param center
+     * @param index
+     * @return
+     */
+    private Mat showResultMat(Mat src, Size rect_size, Point2f center, int index) {
+        Mat img_crop = new Mat();
+        getRectSubPix(src, rect_size, center, img_crop);
+
+        if (debug) {
+            opencv_imgcodecs.imwrite(tempPath + "debug_crop_" + index + ".jpg", img_crop);
+        }
+
+        Mat resultResized = new Mat();
+        resultResized.create(HEIGHT, WIDTH, TYPE);
+        resize(img_crop, resultResized, resultResized.size(), 0, 0, INTER_CUBIC);
+        if (debug) {
+            opencv_imgcodecs.imwrite(tempPath + "debug_resize_" + index + ".jpg", resultResized);
+        }
+        return resultResized;
+    }
+
+
+
+    public String getTempPath() {
+        return tempPath;
+    }
+
+    public void setTempPath(String tempPath) {
+        this.tempPath = tempPath;
+    }
+
+    public void setGaussianBlurSize(int gaussianBlurSize) {
+        this.gaussianBlurSize = gaussianBlurSize;
+    }
+
+    public final int getGaussianBlurSize() {
+        return this.gaussianBlurSize;
+    }
+
+    public void setMorphSizeWidth(int morphSizeWidth) {
+        this.morphSizeWidth = morphSizeWidth;
+    }
+
+    public final int getMorphSizeWidth() {
+        return this.morphSizeWidth;
+    }
+
+    public void setMorphSizeHeight(int morphSizeHeight) {
+        this.morphSizeHeight = morphSizeHeight;
+    }
+
+    public final int getMorphSizeHeight() {
+        return this.morphSizeHeight;
+    }
+
+    public void setVerifyError(float error) {
+        this.error = error;
+    }
+
+    public final float getVerifyError() {
+        return this.error;
+    }
+
+    public void setVerifyAspect(float aspect) {
+        this.aspect = aspect;
+    }
+
+    public final float getVerifyAspect() {
+        return this.aspect;
+    }
+
+    public void setVerifyMin(int verifyMin) {
+        this.verifyMin = verifyMin;
+    }
+
+    public void setVerifyMax(int verifyMax) {
+        this.verifyMax = verifyMax;
+    }
+
+    public void setJudgeAngle(int angle) {
+        this.angle = angle;
+    }
+
+    public void setDebug(boolean debug) {
+        this.debug = debug;
+    }
+    public boolean getDebug() {
+        return debug;
+    }
+
+}

target/classes/easypr-java/SVMCallback.java

@@ -0,0 +1,44 @@
+package com.yuxue.easypr.core;
+
+import org.bytedeco.javacpp.opencv_core.Mat;
+
+
+/**
+ * @author Created by fanwenjie
+ * @author lin.yao
+ *
+ */
+public interface SVMCallback {
+
+    /***
+     * EasyPR的getFeatures回调函数,本函数是生成直方图均衡特征的回调函数
+     * 
+     * @param image
+     * @return
+     */
+    public abstract Mat getHisteqFeatures(final Mat image);
+
+    /**
+     * EasyPR的getFeatures回调函数, 本函数是获取垂直和水平的直方图图值
+     * 
+     * @param image
+     * @return
+     */
+    public abstract Mat getHistogramFeatures(final Mat image);
+
+    /**
+     * 本函数是获取SITF特征子的回调函数
+     * 
+     * @param image
+     * @return
+     */
+    public abstract Mat getSIFTFeatures(final Mat image);
+
+    /**
+     * 本函数是获取HOG特征子的回调函数
+     * 
+     * @param image
+     * @return
+     */
+    public abstract Mat getHOGFeatures(final Mat image);
+}

target/classes/easypr-java/SVMTrain1.java

@@ -0,0 +1,382 @@
+package com.yuxue.train;
+
+import java.util.*;
+
+import static org.bytedeco.javacpp.opencv_core.*;
+import static org.bytedeco.javacpp.opencv_ml.*;
+
+import org.bytedeco.javacpp.opencv_core;
+import org.bytedeco.javacpp.opencv_imgcodecs;
+
+import com.yuxue.easypr.core.Features;
+import com.yuxue.easypr.core.SVMCallback;
+import com.yuxue.util.Convert;
+import com.yuxue.util.FileUtil;
+
+/**
+ * 鍩轰簬org.bytedeco.javacpp鍖呭疄鐜扮殑璁粌
+ * JavaCPP 鏄竴涓紑婧愬簱锛屽畠鎻愪緵浜嗗湪 Java 涓珮鏁堣闂湰鍦<EFBFBD> C++鐨勬柟娉<EFBFBD>
+ * 
+ * 鍥剧墖璇嗗埆杞︾墝璁粌
+ * 璁粌鍑烘潵鐨勫簱鏂囦欢锛岀敤浜庡垽鏂垏鍥炬槸鍚﹀寘鍚溅鐗<EFBFBD>
+ * 
+ * 璁粌鐨剆vm.xml搴旂敤锛<EFBFBD>
+ * 1銆佹浛鎹es/model/svm.xml鏂囦欢
+ * 2銆佷慨鏀筩om.yuxue.easypr.core.PlateJudge.plateJudge(Mat) 鏂规硶
+ *      灏嗘牱鏈鐞嗘柟娉曞垏鎹竴涓嬶紝鍗冲皢瀵瑰簲琚敞閲婃帀鐨勬ā鍧椾唬鐮佸彇娑堟敞閲<EFBFBD>
+ * @author yuxue
+ * @date 2020-05-14 22:16
+ */
+public class SVMTrain1 {
+
+    private SVMCallback callback = new Features();
+
+    // 榛樿鐨勮缁冩搷浣滅殑鏍圭洰褰<E6B4B0>
+    private static final String DEFAULT_PATH = "D:/PlateDetect/train/plate_detect_svm/";
+
+    // 璁粌妯″瀷鏂囦欢淇濆瓨浣嶇疆
+    private static final String MODEL_PATH = DEFAULT_PATH + "svm4.xml";
+
+    private static final String hasPlate = "HasPlate";
+    private static final String noPlate = "NoPlate";
+
+    public SVMTrain1() {
+    }
+
+    public SVMTrain1(SVMCallback callback) {
+        this.callback = callback;
+    }
+
+    /**
+     * 灏唋earn鏂囦欢澶逛笅鐨勫浘鐗囷紝杞瓨鍒皌ain test鏂囦欢澶逛笅锛屽尯鍒唄asPalte noPlate
+     * 闅忔満閫夊彇bound%浣滀负璁粌鏁版嵁锛<EFBFBD>30%浣滀负娴嬭瘯鏁版嵁
+     * @param bound
+     * @param name
+     */
+    private void learn2Plate(float bound, final String name) {
+        final String filePath = DEFAULT_PATH + "learn/" + name;
+        Vector<String> files = new Vector<String>();
+
+        //// 鑾峰彇璇ヨ矾寰勪笅鐨勬墍鏈夋枃浠<E69E83>
+        FileUtil.getFiles(filePath, files);
+        int size = files.size();
+        if (0 == size) {
+            System.err.println("褰撳墠鐩綍涓嬫病鏈夋枃浠<E69E83>: " + filePath);
+            return;
+        }
+        Collections.shuffle(files, new Random(new Date().getTime()));
+
+        //// 闅忔満閫夊彇70%浣滀负璁粌鏁版嵁锛<E5B581>30%浣滀负娴嬭瘯鏁版嵁
+        int boundry = (int) (bound * size);
+
+        // 閲嶆柊鍒涘缓鐩綍
+        FileUtil.recreateDir(DEFAULT_PATH + "train/" + name);
+        FileUtil.recreateDir(DEFAULT_PATH + "test/" + name);
+
+        for (int i = 0; i < boundry; i++) {
+            Mat img = opencv_imgcodecs.imread(files.get(i));
+            String str = DEFAULT_PATH + "train/" + name + "/" + name + "_" + Integer.valueOf(i).toString() + ".jpg";
+            opencv_imgcodecs.imwrite(str, img);
+        }
+
+        for (int i = boundry; i < size; i++) {
+            Mat img = opencv_imgcodecs.imread(files.get(i));
+            String str = DEFAULT_PATH + "test/" + name + "/" + name + "_" + Integer.valueOf(i).toString() + ".jpg";
+            opencv_imgcodecs.imwrite(str, img);
+        }
+    }
+
+    /**
+     * 鑾峰彇璁粌鍥剧墖
+     * @param trainingImages
+     * @param trainingLabels
+     * @param name
+     */
+    private void getPlateTrain(Mat trainingImages, Vector<Integer> trainingLabels, final String name, int label) {
+        // int label = 1;
+        final String filePath = DEFAULT_PATH + "train/" + name;
+        Vector<String> files = new Vector<String>();
+
+        // 鑾峰彇璇ヨ矾寰勪笅鐨勬墍鏈夋枃浠<E69E83>
+        FileUtil.getFiles(filePath, files);
+
+        int size = files.size();
+        if (null == files || size <= 0) {
+            System.out.println("File not found in " + filePath);
+            return;
+        }
+        for (int i = 0; i < size; i++) {
+            // System.out.println(files.get(i));
+            Mat inMat = opencv_imgcodecs.imread(files.get(i));
+            // 璋冪敤鍥炶皟鍑芥暟鍐冲畾鐗瑰緛
+            // Mat features = this.callback.getHisteqFeatures(inMat);
+            Mat features = this.callback.getHistogramFeatures(inMat);
+            // 閫氳繃鐩存柟鍥惧潎琛″寲鍚庣殑褰╄壊鍥捐繘琛岄娴<EE95A9>
+            Mat p = features.reshape(1, 1);
+            p.convertTo(p, opencv_core.CV_32F);
+
+            // 136  36  14688   1   鍙樻崲灏哄
+            // System.err.println(inMat.cols() + "\t" + inMat.rows() + "\t" + p.cols() + "\t" + p.rows());
+
+            trainingImages.push_back(p); // 鍚堝苟鎴愪竴寮犲浘鐗<E6B598>
+            trainingLabels.add(label);
+        }
+    }
+
+    private void getPlateTest(MatVector testingImages, Vector<Integer> testingLabels, final String name, int label) {
+        // int label = 1;
+        final String filePath = DEFAULT_PATH + "test/" + name;
+        Vector<String> files = new Vector<String>();
+        FileUtil.getFiles(filePath, files);
+
+        int size = files.size();
+        if (0 == size) {
+            System.out.println("File not found in " + filePath);
+            return;
+        }
+        System.out.println("get " + name + " test!");
+        for (int i = 0; i < size; i++) {
+            Mat inMat = opencv_imgcodecs.imread(files.get(i));
+            testingImages.push_back(inMat);
+            testingLabels.add(label);
+        }
+    }
+
+    // ! 娴嬭瘯SVM鐨勫噯纭巼锛屽洖褰掔巼浠ュ強FScore
+    public void getAccuracy(Mat testingclasses_preditc, Mat testingclasses_real) {
+        int channels = testingclasses_preditc.channels();
+        System.out.println("channels: " + Integer.valueOf(channels).toString());
+        int nRows = testingclasses_preditc.rows();
+        System.out.println("nRows: " + Integer.valueOf(nRows).toString());
+        int nCols = testingclasses_preditc.cols() * channels;
+        System.out.println("nCols: " + Integer.valueOf(nCols).toString());
+        int channels_real = testingclasses_real.channels();
+        System.out.println("channels_real: " + Integer.valueOf(channels_real).toString());
+        int nRows_real = testingclasses_real.rows();
+        System.out.println("nRows_real: " + Integer.valueOf(nRows_real).toString());
+        int nCols_real = testingclasses_real.cols() * channels;
+        System.out.println("nCols_real: " + Integer.valueOf(nCols_real).toString());
+
+        double count_all = 0;
+        double ptrue_rtrue = 0;
+        double ptrue_rfalse = 0;
+        double pfalse_rtrue = 0;
+        double pfalse_rfalse = 0;
+
+        for (int i = 0; i < nRows; i++) {
+
+            final float predict = Convert.toFloat(testingclasses_preditc.ptr(i));
+            final float real = Convert.toFloat(testingclasses_real.ptr(i));
+
+            count_all++;
+
+            // System.out.println("predict:" << predict).toString());
+            // System.out.println("real:" << real).toString());
+
+            if (predict == 1.0 && real == 1.0)
+                ptrue_rtrue++;
+            if (predict == 1.0 && real == 0)
+                ptrue_rfalse++;
+            if (predict == 0 && real == 1.0)
+                pfalse_rtrue++;
+            if (predict == 0 && real == 0)
+                pfalse_rfalse++;
+        }
+
+        System.out.println("count_all: " + Double.valueOf(count_all).toString());
+        System.out.println("ptrue_rtrue: " + Double.valueOf(ptrue_rtrue).toString());
+        System.out.println("ptrue_rfalse: " + Double.valueOf(ptrue_rfalse).toString());
+        System.out.println("pfalse_rtrue: " + Double.valueOf(pfalse_rtrue).toString());
+        System.out.println("pfalse_rfalse: " + Double.valueOf(pfalse_rfalse).toString());
+
+        double precise = 0;
+        if (ptrue_rtrue + ptrue_rfalse != 0) {
+            precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse);
+            System.out.println("precise: " + Double.valueOf(precise).toString());
+        } else {
+            System.out.println("precise: NA");
+        }
+
+        double recall = 0;
+        if (ptrue_rtrue + pfalse_rtrue != 0) {
+            recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue);
+            System.out.println("recall: " + Double.valueOf(recall).toString());
+        } else {
+            System.out.println("recall: NA");
+        }
+
+        if (precise + recall != 0) {
+            double F = (precise * recall) / (precise + recall);
+            System.out.println("F: " + Double.valueOf(F).toString());
+        } else {
+            System.out.println("F: NA");
+        }
+    }
+
+    /**
+     * 璁粌
+     * @param dividePrepared
+     * @return
+     */
+    public int svmTrain(boolean dividePrepared) {
+
+        Mat classes = new Mat();
+        Mat trainingData = new Mat();
+        Mat trainingImages = new Mat();
+        Vector<Integer> trainingLabels = new Vector<Integer>();
+
+        // 鍒嗗壊learn閲岀殑鏁版嵁鍒皌rain鍜宼est閲<74> // 浠庡簱閲岄潰閫夊彇璁粌鏍锋湰
+        if (!dividePrepared) {
+            learn2Plate(0.1f, hasPlate);  // 鎬ц兘涓嶅ソ鐨勬満鍣紝鏈<E7B49D>濂戒笉瑕佹寫閫夊お澶氱殑鏍锋湰锛岃繖涓柟妗堝お娑堣<E5A891>楄祫婧愪簡銆<E7B0A1>
+            learn2Plate(0.1f, noPlate);
+        }
+
+        // System.err.println("Begin to get train data to memory");
+
+        getPlateTrain(trainingImages, trainingLabels, hasPlate, 0);
+        getPlateTrain(trainingImages, trainingLabels, noPlate, 1);
+
+        // System.err.println(trainingImages.cols());
+
+        trainingImages.copyTo(trainingData);
+        trainingData.convertTo(trainingData, CV_32F);
+
+        int[] labels = new int[trainingLabels.size()];
+        for (int i = 0; i < trainingLabels.size(); ++i) {
+            labels[i] = trainingLabels.get(i).intValue();
+        }
+        new Mat(labels).copyTo(classes);
+
+        TrainData train_data = TrainData.create(trainingData, ROW_SAMPLE, classes);
+
+        SVM svm = SVM.create();
+
+        try {
+            TermCriteria criteria = new TermCriteria(TermCriteria.EPS + TermCriteria.MAX_ITER, 20000, 0.0001);
+            svm.setTermCriteria(criteria); // 鎸囧畾
+            svm.setKernel(SVM.RBF); // 浣跨敤棰勫厛瀹氫箟鐨勫唴鏍稿垵濮嬪寲
+            svm.setType(SVM.C_SVC); // SVM鐨勭被鍨<E8A2AB>,榛樿鏄細SVM.C_SVC
+            svm.setGamma(0.1); // 鏍稿嚱鏁扮殑鍙傛暟
+            svm.setNu(0.1); // SVM浼樺寲闂鍙傛暟
+            svm.setC(1); // SVM浼樺寲闂鐨勫弬鏁癈
+            svm.setP(0.1);
+            svm.setDegree(0.1);
+            svm.setCoef0(0.1);
+
+            svm.trainAuto(train_data, 10,
+                    SVM.getDefaultGrid(SVM.C), 
+                    SVM.getDefaultGrid(SVM.GAMMA),
+                    SVM.getDefaultGrid(SVM.P),
+                    SVM.getDefaultGrid(SVM.NU),
+                    SVM.getDefaultGrid(SVM.COEF),
+                    SVM.getDefaultGrid(SVM.DEGREE),
+                    true);
+
+        } catch (Exception err) {
+            System.out.println(err.getMessage());
+        }
+
+        System.out.println("Svm generate done!");
+
+        /*FileStorage fsTo = new FileStorage(MODEL_PATH, FileStorage.WRITE);
+        svm.write(fsTo, "svm");*/
+        svm.save(MODEL_PATH);
+        return 0;
+    }
+
+    // 娴嬭瘯
+    public int svmPredict() {
+        SVM svm = SVM.create();
+        try {
+            svm.clear();
+            // svm = SVM.loadSVM(MODEL_PATH, "svm");
+            svm = SVM.load(MODEL_PATH);
+        } catch (Exception err) {
+            System.err.println(err.getMessage());
+            return 0; // next predict requires svm
+        }
+
+        System.out.println("Begin to predict");
+        // Test SVM
+        MatVector testingImages = new MatVector();
+        Vector<Integer> testingLabels_real = new Vector<Integer>();
+
+        // 灏嗘祴璇曟暟鎹姞杞藉叆鍐呭瓨
+        getPlateTest(testingImages, testingLabels_real, hasPlate, 0);
+        getPlateTest(testingImages, testingLabels_real, noPlate, 1);
+
+        double count_all = 0;
+        double ptrue_rtrue = 0;
+        double ptrue_rfalse = 0;
+        double pfalse_rtrue = 0;
+        double pfalse_rfalse = 0;
+
+        long size =  testingImages.size();
+        System.err.println(size);
+
+        for (int i = 0; i < size; i++) {
+            Mat inMat = testingImages.get(i);
+
+            // Mat features = callback.getHisteqFeatures(inMat);
+            Mat features = callback.getHistogramFeatures(inMat);
+            Mat p = features.reshape(1, 1);
+            p.convertTo(p, opencv_core.CV_32F);
+
+            // System.out.println(p.cols() + "\t" + p.rows() + "\t" + p.type());
+
+            // samples.cols == var_count && samples.type() == CV_32F
+            // var_count 鐨勫<E990A8>间細鍦╯vm.xml搴撴枃浠朵腑鏈変綋鐜<E7B68B>
+            float predoct = svm.predict(features);
+
+            int predict = (int) predoct; // 棰勬湡鍊<E6B9A1>
+            int real = testingLabels_real.get(i); // 瀹為檯鍊<E6AAAF>
+
+            if (predict == 1 && real == 1)
+                ptrue_rtrue++;
+            if (predict == 1 && real == 0)
+                ptrue_rfalse++;
+            if (predict == 0 && real == 1)
+                pfalse_rtrue++;
+            if (predict == 0 && real == 0)
+                pfalse_rfalse++;
+        }
+
+        count_all = size;
+        System.out.println("Get the Accuracy!");
+
+        System.out.println("count_all: " + Double.valueOf(count_all).toString());
+        System.out.println("ptrue_rtrue: " + Double.valueOf(ptrue_rtrue).toString());
+        System.out.println("ptrue_rfalse: " + Double.valueOf(ptrue_rfalse).toString());
+        System.out.println("pfalse_rtrue: " + Double.valueOf(pfalse_rtrue).toString());
+        System.out.println("pfalse_rfalse: " + Double.valueOf(pfalse_rfalse).toString());
+
+        double precise = 0;
+        if (ptrue_rtrue + ptrue_rfalse != 0) {
+            precise = ptrue_rtrue / (ptrue_rtrue + ptrue_rfalse);
+            System.out.println("precise: " + Double.valueOf(precise).toString());
+        } else
+            System.out.println("precise: NA");
+
+        double recall = 0;
+        if (ptrue_rtrue + pfalse_rtrue != 0) {
+            recall = ptrue_rtrue / (ptrue_rtrue + pfalse_rtrue);
+            System.out.println("recall: " + Double.valueOf(recall).toString());
+        } else
+            System.out.println("recall: NA");
+
+        double Fsocre = 0;
+        if (precise + recall != 0) {
+            Fsocre = 2 * (precise * recall) / (precise + recall);
+            System.out.println("Fsocre: " + Double.valueOf(Fsocre).toString());
+        } else
+            System.out.println("Fsocre: NA");
+        return 0;
+    }
+
+    public static void main(String[] args) {
+        SVMTrain1 s = new SVMTrain1();
+        s.svmTrain(true);
+        s.svmPredict();
+    }
+
+}

target/classes/logback-spring.xml

@@ -0,0 +1,86 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<configuration>
+    <include resource="org/springframework/boot/logging/logback/base.xml"/>
+
+    <!--定义日志文件的存储地址-->
+    <property name="LOG_HOME" value="logs"/>
+
+
+    <!-- %m输出的信息,%p日志级别,%t线程名,%d日期,%c类的全名,,,, -->
+    <appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
+        <encoder>
+            <!--<pattern>%d %p (%file:%line\)- %m%n</pattern>-->
+            <!--格式化输出：%d:表示日期    %thread:表示线程名     %-5level:级别从左显示5个字符宽度  %msg:日志消息    %n:是换行符-->
+            <pattern>1-%d{yyyy-MM-dd HH:mm:ss} [%thread] %-5level %logger - %msg%n</pattern>
+            <charset>UTF-8</charset>
+        </encoder>
+        <filter class="ch.qos.logback.classic.filter.LevelFilter">
+            <level>ERROR</level>
+            <onMatch>ACCEPT</onMatch>
+            <onMismatch>DENY</onMismatch>
+        </filter>
+    </appender>
+
+
+    <!-- 按照每天生成日志文件 -->
+    <appender name="ACCESS" class="ch.qos.logback.core.rolling.RollingFileAppender">
+        <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
+            <!--日志文件输出的文件名-->
+            <FileNamePattern>${LOG_HOME}/access_%d{yyyy-MM-dd}.%i.log</FileNamePattern>
+            <!--日志文件保留天数-->
+            <MaxHistory>30</MaxHistory>
+            <!--日志文件最大的大小-->
+            <timeBasedFileNamingAndTriggeringPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedFNATP">
+                <maxFileSize>10MB</maxFileSize>
+            </timeBasedFileNamingAndTriggeringPolicy>
+        </rollingPolicy>
+        <encoder class="ch.qos.logback.classic.encoder.PatternLayoutEncoder">
+            <!--格式化输出：%d表示日期，%thread表示线程名，%-5level：级别从左显示5个字符宽度%msg：日志消息，%n是换行符-->
+            <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{50} - %msg%n</pattern>
+            <charset>UTF-8</charset>
+        </encoder>
+        <filter class="ch.qos.logback.classic.filter.LevelFilter">
+            <level>INFO</level>
+            <onMatch>ACCEPT</onMatch>
+            <onMismatch>DENY</onMismatch>
+        </filter>
+    </appender>
+    
+    <appender name="ERROR" class="ch.qos.logback.core.rolling.RollingFileAppender">
+        <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
+            <FileNamePattern>${LOG_HOME}/errors_%d{yyyy-MM-dd}.%i.log</FileNamePattern>
+            <MaxHistory>30</MaxHistory>
+            <timeBasedFileNamingAndTriggeringPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedFNATP">
+                <maxFileSize>10MB</maxFileSize>
+            </timeBasedFileNamingAndTriggeringPolicy>
+        </rollingPolicy>
+        <encoder class="ch.qos.logback.classic.encoder.PatternLayoutEncoder">
+            <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{50} - %msg%n</pattern>
+            <charset>UTF-8</charset>
+        </encoder>
+        <filter class="ch.qos.logback.classic.filter.LevelFilter">
+            <level>ERROR</level>
+            <onMatch>ACCEPT</onMatch>
+            <onMismatch>DENY</onMismatch>
+        </filter>
+    </appender>
+    
+
+    <logger name="org.springframework.web" level="ERROR"/>
+    <logger name="org.springboot.sample" level="ERROR"/>
+    <logger name="com.yuxue" level="DEBUG"/>
+
+    <!-- 开发、测试环境 -->
+    <springProfile name="dev,test">
+        <root level="DEBUG">
+			<appender-ref ref="ACCESS" />
+		</root>
+    </springProfile>
+    
+	<!-- 所有环境都要记录错误日志 -->
+    <root level="ERROR">
+        <appender-ref ref="ERROR"/>
+    </root>
+
+
+</configuration>

target/classes/mapper/PlateFileMapper.xml

@@ -0,0 +1,244 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE mapper PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN" "http://mybatis.org/dtd/mybatis-3-mapper.dtd">
+<mapper namespace="com.yuxue.mapper.PlateFileMapper">
+    
+    <resultMap id="BaseResultMap" type="com.yuxue.entity.PlateFileEntity">
+        <id column="id" jdbcType="INTEGER" property="id" />
+        <result column="file_name" jdbcType="VARCHAR" property="fileName" />
+        <result column="file_path" jdbcType="VARCHAR" property="filePath" />
+        <result column="file_type" jdbcType="VARCHAR" property="fileType" />
+        <result column="file_length" jdbcType="INTEGER" property="fileLength" />
+        <result column="plate" jdbcType="VARCHAR" property="plate" />
+        <result column="plate_color" jdbcType="VARCHAR" property="plateColor" />
+        <result column="last_reco_time" jdbcType="VARCHAR" property="lastRecoTime" />
+        <result column="temp_path" jdbcType="VARCHAR" property="tempPath" />
+        <result column="reco_plate" jdbcType="VARCHAR" property="recoPlate" />
+        <result column="reco_color" jdbcType="VARCHAR" property="recoColor" />
+        <result column="reco_correct" jdbcType="INTEGER" property="recoCorrect" />
+    </resultMap>
+    
+    <sql id="Base_Column_List">
+        id, file_name, file_path, file_type, file_length, plate, plate_color, last_reco_time, 
+        temp_path, reco_plate, reco_color, reco_correct
+    </sql>
+    
+    <sql id="Base_Where_Clause">
+        <where>
+            <if test="fileName != null">
+                 and file_name = #{fileName,jdbcType=VARCHAR}
+            </if>
+            <if test="filePath != null">
+                 and file_path = #{filePath,jdbcType=VARCHAR}
+            </if>
+            <if test="fileType != null">
+                 and file_type = #{fileType,jdbcType=VARCHAR}
+            </if>
+            <if test="fileLength != null">
+                 and file_length = #{fileLength,jdbcType=INTEGER}
+            </if>
+            <if test="plate != null">
+                 and plate = #{plate,jdbcType=VARCHAR}
+            </if>
+            <if test="plateColor != null">
+                 and plate_color = #{plateColor,jdbcType=VARCHAR}
+            </if>
+            <if test="lastRecoTime != null">
+                 and last_reco_time = #{lastRecoTime,jdbcType=VARCHAR}
+            </if>
+            <if test="tempPath != null">
+                 and temp_path = #{tempPath,jdbcType=VARCHAR}
+            </if>
+            <if test="recoPlate != null">
+                 and reco_plate = #{recoPlate,jdbcType=VARCHAR}
+            </if>
+            <if test="recoColor != null">
+                 and reco_color = #{recoColor,jdbcType=VARCHAR}
+            </if>
+            <if test="recoCorrect != null">
+                 and reco_correct = #{recoCorrect,jdbcType=INTEGER}
+            </if>
+        </where>
+    </sql>
+    
+    <select id="selectByPrimaryKey" parameterType="java.lang.Integer" resultMap="BaseResultMap">
+        select 
+        <include refid="Base_Column_List" />
+        from t_plate_file
+        where id = #{id,jdbcType=INTEGER}
+    </select>
+    
+    <select id="selectByCondition" resultMap="BaseResultMap">
+        select id, file_name, file_path, file_type, file_length, plate, plate_color, last_reco_time, 
+        temp_path, reco_plate, reco_color, reco_correct
+        from t_plate_file
+        <include refid="Base_Where_Clause" />
+        order by id desc
+    </select>
+    
+    <insert id="insert" parameterType="com.yuxue.entity.PlateFileEntity">
+        <selectKey keyProperty="id" order="AFTER" resultType="java.lang.Integer">
+            select seq from sqlite_sequence WHERE name = 't_plate_file'
+        </selectKey>
+        insert into t_plate_file (file_name, file_path, file_type, 
+            file_length, plate, plate_color, 
+            last_reco_time, temp_path, reco_plate, 
+            reco_color, reco_correct)
+        values (#{fileName,jdbcType=VARCHAR}, #{filePath,jdbcType=VARCHAR}, #{fileType,jdbcType=VARCHAR}, 
+            #{fileLength,jdbcType=INTEGER}, #{plate,jdbcType=VARCHAR}, #{plateColor,jdbcType=VARCHAR}, 
+            #{lastRecoTime,jdbcType=VARCHAR}, #{tempPath,jdbcType=VARCHAR}, #{recoPlate,jdbcType=VARCHAR}, 
+            #{recoColor,jdbcType=VARCHAR}, #{recoCorrect,jdbcType=INTEGER})
+    </insert>
+    
+    <insert id="insertSelective" parameterType="com.yuxue.entity.PlateFileEntity">
+        <selectKey keyProperty="id" order="AFTER" resultType="java.lang.Integer">
+            select seq from sqlite_sequence WHERE name = 't_plate_file'
+        </selectKey>
+        insert into t_plate_file
+        <trim prefix="(" suffix=")" suffixOverrides=",">
+            <if test="fileName != null">
+                file_name,
+            </if>
+            <if test="filePath != null">
+                file_path,
+            </if>
+            <if test="fileType != null">
+                file_type,
+            </if>
+            <if test="fileLength != null">
+                file_length,
+            </if>
+            <if test="plate != null">
+                plate,
+            </if>
+            <if test="plateColor != null">
+                plate_color,
+            </if>
+            <if test="lastRecoTime != null">
+                last_reco_time,
+            </if>
+            <if test="tempPath != null">
+                temp_path,
+            </if>
+            <if test="recoPlate != null">
+                reco_plate,
+            </if>
+            <if test="recoColor != null">
+                reco_color,
+            </if>
+            <if test="recoCorrect != null">
+                reco_correct,
+            </if>
+        </trim>
+        <trim prefix="values (" suffix=")" suffixOverrides=",">
+            <if test="fileName != null">
+                #{fileName,jdbcType=VARCHAR},
+            </if>
+            <if test="filePath != null">
+                #{filePath,jdbcType=VARCHAR},
+            </if>
+            <if test="fileType != null">
+                #{fileType,jdbcType=VARCHAR},
+            </if>
+            <if test="fileLength != null">
+                #{fileLength,jdbcType=INTEGER},
+            </if>
+            <if test="plate != null">
+                #{plate,jdbcType=VARCHAR},
+            </if>
+            <if test="plateColor != null">
+                #{plateColor,jdbcType=VARCHAR},
+            </if>
+            <if test="lastRecoTime != null">
+                #{lastRecoTime,jdbcType=VARCHAR},
+            </if>
+            <if test="tempPath != null">
+                #{tempPath,jdbcType=VARCHAR},
+            </if>
+            <if test="recoPlate != null">
+                #{recoPlate,jdbcType=VARCHAR},
+            </if>
+            <if test="recoColor != null">
+                #{recoColor,jdbcType=VARCHAR},
+            </if>
+            <if test="recoCorrect != null">
+                #{recoCorrect,jdbcType=INTEGER},
+            </if>
+        </trim>
+    </insert>
+    
+    <update id="updateByPrimaryKeySelective" parameterType="com.yuxue.entity.PlateFileEntity">
+        update t_plate_file
+        <set>
+            <if test="fileName != null">
+                file_name = #{fileName,jdbcType=VARCHAR},
+            </if>
+            <if test="filePath != null">
+                file_path = #{filePath,jdbcType=VARCHAR},
+            </if>
+            <if test="fileType != null">
+                file_type = #{fileType,jdbcType=VARCHAR},
+            </if>
+            <if test="fileLength != null">
+                file_length = #{fileLength,jdbcType=INTEGER},
+            </if>
+            <if test="plate != null">
+                plate = #{plate,jdbcType=VARCHAR},
+            </if>
+            <if test="plateColor != null">
+                plate_color = #{plateColor,jdbcType=VARCHAR},
+            </if>
+            <if test="lastRecoTime != null">
+                last_reco_time = #{lastRecoTime,jdbcType=VARCHAR},
+            </if>
+            <if test="tempPath != null">
+                temp_path = #{tempPath,jdbcType=VARCHAR},
+            </if>
+            <if test="recoPlate != null">
+                reco_plate = #{recoPlate,jdbcType=VARCHAR},
+            </if>
+            <if test="recoColor != null">
+                reco_color = #{recoColor,jdbcType=VARCHAR},
+            </if>
+            <if test="recoCorrect != null">
+                reco_correct = #{recoCorrect,jdbcType=INTEGER},
+            </if>
+        </set>
+        where id = #{id,jdbcType=INTEGER}
+    </update>
+    
+    <update id="updateByPrimaryKey" parameterType="com.yuxue.entity.PlateFileEntity">
+        update t_plate_file
+        set file_name = #{fileName,jdbcType=VARCHAR},
+            file_path = #{filePath,jdbcType=VARCHAR},
+            file_type = #{fileType,jdbcType=VARCHAR},
+            file_length = #{fileLength,jdbcType=INTEGER},
+            plate = #{plate,jdbcType=VARCHAR},
+            plate_color = #{plateColor,jdbcType=VARCHAR},
+            last_reco_time = #{lastRecoTime,jdbcType=VARCHAR},
+            temp_path = #{tempPath,jdbcType=VARCHAR},
+            reco_plate = #{recoPlate,jdbcType=VARCHAR},
+            reco_color = #{recoColor,jdbcType=VARCHAR},
+            reco_correct = #{recoCorrect,jdbcType=INTEGER}
+        where id = #{id,jdbcType=INTEGER}
+    </update>
+    
+    
+    <delete id="deleteByPrimaryKey" parameterType="java.lang.Integer">
+        delete from t_plate_file
+        where id = #{id,jdbcType=INTEGER}
+    </delete>
+    
+    
+    <select id="getUnRecogniseList" resultMap="BaseResultMap">
+        select id, file_name, file_path, file_type, file_length, plate, plate_color, last_reco_time, 
+        temp_path, reco_plate, reco_color, reco_correct
+        from t_plate_file
+        <where>
+            <!-- 0未识别 1正确 2错误 -->
+            reco_correct = 0
+        </where>
+        order by id desc
+    </select>    
+    
+    
+</mapper>

target/classes/mapper/PlateRecoDebugMapper.xml

@@ -0,0 +1,220 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE mapper PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN" "http://mybatis.org/dtd/mybatis-3-mapper.dtd">
+<mapper namespace="com.yuxue.mapper.PlateRecoDebugMapper">
+    
+    <resultMap id="BaseResultMap" type="com.yuxue.entity.PlateRecoDebugEntity">
+        <id column="id" jdbcType="INTEGER" property="id" />
+        <result column="parent_id" jdbcType="INTEGER" property="parentId" />
+        <result column="file_name" jdbcType="VARCHAR" property="fileName" />
+        <result column="file_path" jdbcType="VARCHAR" property="filePath" />
+        <result column="debug_type" jdbcType="VARCHAR" property="debugType" />
+        <result column="file_length" jdbcType="INTEGER" property="fileLength" />
+        <result column="last_reco_time" jdbcType="VARCHAR" property="lastRecoTime" />
+        <result column="reco_plate" jdbcType="VARCHAR" property="recoPlate" />
+        <result column="plate_color" jdbcType="VARCHAR" property="plateColor" />
+        <result column="sort" jdbcType="INTEGER" property="sort" />
+    </resultMap>
+    
+    <sql id="Base_Column_List">
+        id, parent_id, file_name, file_path, debug_type, file_length, last_reco_time, reco_plate, 
+        plate_color, sort
+    </sql>
+    
+    <sql id="Base_Where_Clause">
+        <where>
+            <if test="parentId != null">
+                 and parent_id = #{parentId,jdbcType=INTEGER}
+            </if>
+            <if test="fileName != null">
+                 and file_name = #{fileName,jdbcType=VARCHAR}
+            </if>
+            <if test="filePath != null">
+                 and file_path = #{filePath,jdbcType=VARCHAR}
+            </if>
+            <if test="debugType != null">
+                 and debug_type = #{debugType,jdbcType=VARCHAR}
+            </if>
+            <if test="fileLength != null">
+                 and file_length = #{fileLength,jdbcType=INTEGER}
+            </if>
+            <if test="lastRecoTime != null">
+                 and last_reco_time = #{lastRecoTime,jdbcType=VARCHAR}
+            </if>
+            <if test="recoPlate != null">
+                 and reco_plate = #{recoPlate,jdbcType=VARCHAR}
+            </if>
+            <if test="plateColor != null">
+                 and plate_color = #{plateColor,jdbcType=VARCHAR}
+            </if>
+            <if test="sort != null">
+                 and sort = #{sort,jdbcType=INTEGER}
+            </if>
+        </where>
+    </sql>
+    
+    <select id="selectByPrimaryKey" parameterType="java.lang.Integer" resultMap="BaseResultMap">
+        select 
+        <include refid="Base_Column_List" />
+        from t_plate_reco_debug
+        where id = #{id,jdbcType=INTEGER}
+    </select>
+    
+    <select id="selectByCondition" resultMap="BaseResultMap">
+        select id, parent_id, file_name, file_path, debug_type, file_length, last_reco_time, 
+        reco_plate, plate_color, sort
+        from t_plate_reco_debug
+        <include refid="Base_Where_Clause" />
+        order by sort, file_name
+    </select>
+    
+    <insert id="insert" parameterType="com.yuxue.entity.PlateRecoDebugEntity">
+        <selectKey keyProperty="id" order="AFTER" resultType="java.lang.Integer">
+            Sqlite
+        </selectKey>
+        insert into t_plate_reco_debug (parent_id, file_name, file_path, 
+            debug_type, file_length, last_reco_time, 
+            reco_plate, plate_color, sort
+            )
+        values (#{parentId,jdbcType=INTEGER}, #{fileName,jdbcType=VARCHAR}, #{filePath,jdbcType=VARCHAR}, 
+            #{debugType,jdbcType=VARCHAR}, #{fileLength,jdbcType=INTEGER}, #{lastRecoTime,jdbcType=VARCHAR}, 
+            #{recoPlate,jdbcType=VARCHAR}, #{plateColor,jdbcType=VARCHAR}, #{sort,jdbcType=INTEGER}
+            )
+    </insert>
+    
+    <insert id="insertSelective" parameterType="com.yuxue.entity.PlateRecoDebugEntity">
+        <selectKey keyProperty="id" order="AFTER" resultType="java.lang.Integer">
+            Sqlite
+        </selectKey>
+        insert into t_plate_reco_debug
+        <trim prefix="(" suffix=")" suffixOverrides=",">
+            <if test="parentId != null">
+                parent_id,
+            </if>
+            <if test="fileName != null">
+                file_name,
+            </if>
+            <if test="filePath != null">
+                file_path,
+            </if>
+            <if test="debugType != null">
+                debug_type,
+            </if>
+            <if test="fileLength != null">
+                file_length,
+            </if>
+            <if test="lastRecoTime != null">
+                last_reco_time,
+            </if>
+            <if test="recoPlate != null">
+                reco_plate,
+            </if>
+            <if test="plateColor != null">
+                plate_color,
+            </if>
+            <if test="sort != null">
+                sort,
+            </if>
+        </trim>
+        <trim prefix="values (" suffix=")" suffixOverrides=",">
+            <if test="parentId != null">
+                #{parentId,jdbcType=INTEGER},
+            </if>
+            <if test="fileName != null">
+                #{fileName,jdbcType=VARCHAR},
+            </if>
+            <if test="filePath != null">
+                #{filePath,jdbcType=VARCHAR},
+            </if>
+            <if test="debugType != null">
+                #{debugType,jdbcType=VARCHAR},
+            </if>
+            <if test="fileLength != null">
+                #{fileLength,jdbcType=INTEGER},
+            </if>
+            <if test="lastRecoTime != null">
+                #{lastRecoTime,jdbcType=VARCHAR},
+            </if>
+            <if test="recoPlate != null">
+                #{recoPlate,jdbcType=VARCHAR},
+            </if>
+            <if test="plateColor != null">
+                #{plateColor,jdbcType=VARCHAR},
+            </if>
+            <if test="sort != null">
+                #{sort,jdbcType=INTEGER},
+            </if>
+        </trim>
+    </insert>
+    
+    <update id="updateByPrimaryKeySelective" parameterType="com.yuxue.entity.PlateRecoDebugEntity">
+        update t_plate_reco_debug
+        <set>
+            <if test="parentId != null">
+                parent_id = #{parentId,jdbcType=INTEGER},
+            </if>
+            <if test="fileName != null">
+                file_name = #{fileName,jdbcType=VARCHAR},
+            </if>
+            <if test="filePath != null">
+                file_path = #{filePath,jdbcType=VARCHAR},
+            </if>
+            <if test="debugType != null">
+                debug_type = #{debugType,jdbcType=VARCHAR},
+            </if>
+            <if test="fileLength != null">
+                file_length = #{fileLength,jdbcType=INTEGER},
+            </if>
+            <if test="lastRecoTime != null">
+                last_reco_time = #{lastRecoTime,jdbcType=VARCHAR},
+            </if>
+            <if test="recoPlate != null">
+                reco_plate = #{recoPlate,jdbcType=VARCHAR},
+            </if>
+            <if test="plateColor != null">
+                plate_color = #{plateColor,jdbcType=VARCHAR},
+            </if>
+            <if test="sort != null">
+                sort = #{sort,jdbcType=INTEGER},
+            </if>
+        </set>
+        where id = #{id,jdbcType=INTEGER}
+    </update>
+    
+    <update id="updateByPrimaryKey" parameterType="com.yuxue.entity.PlateRecoDebugEntity">
+        update t_plate_reco_debug
+        set parent_id = #{parentId,jdbcType=INTEGER},
+            file_name = #{fileName,jdbcType=VARCHAR},
+            file_path = #{filePath,jdbcType=VARCHAR},
+            debug_type = #{debugType,jdbcType=VARCHAR},
+            file_length = #{fileLength,jdbcType=INTEGER},
+            last_reco_time = #{lastRecoTime,jdbcType=VARCHAR},
+            reco_plate = #{recoPlate,jdbcType=VARCHAR},
+            plate_color = #{plateColor,jdbcType=VARCHAR},
+            sort = #{sort,jdbcType=INTEGER}
+        where id = #{id,jdbcType=INTEGER}
+    </update>
+    
+    <delete id="deleteByPrimaryKey" parameterType="java.lang.Integer">
+        delete from t_plate_reco_debug
+        where id = #{id,jdbcType=INTEGER}
+    </delete>
+    
+    
+    <delete id="deleteByParentId" parameterType="java.lang.Integer">
+        delete from t_plate_reco_debug
+        where parent_id = #{parentId,jdbcType=INTEGER}
+    </delete>
+    
+    
+    <insert id="batchInsert">
+        insert into t_plate_reco_debug (parent_id, file_name, file_path, 
+            debug_type, reco_plate, plate_color, sort )
+        values
+        <foreach collection="list" index="index" item="item" open="(" close=")" separator="),(">
+            ifnull(#{item.parentId,jdbcType=INTEGER}, 0), ifnull(#{item.fileName,jdbcType=VARCHAR}, ''), ifnull(#{item.filePath,jdbcType=VARCHAR}, ''), 
+            ifnull(#{item.debugType,jdbcType=VARCHAR}, ''), ifnull(#{item.recoPlate,jdbcType=VARCHAR}, ''), 
+            ifnull(#{item.plateColor,jdbcType=VARCHAR}, ''), ifnull(#{item.sort,jdbcType=INTEGER}, 0)
+        </foreach>
+    </insert>
+    
+</mapper>

target/classes/mapper/SystemMenuMapper.xml

@@ -0,0 +1,272 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE mapper PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN" "http://mybatis.org/dtd/mybatis-3-mapper.dtd">
+<mapper namespace="com.yuxue.mapper.SystemMenuMapper">
+    
+    <resultMap id="BaseResultMap" type="com.yuxue.entity.SystemMenuEntity">
+        <id column="id" jdbcType="INTEGER" property="id" />
+        <result column="menu_name" jdbcType="VARCHAR" property="menuName" />
+        <result column="menu_url" jdbcType="VARCHAR" property="menuUrl" />
+        <result column="parent_id" jdbcType="INTEGER" property="parentId" />
+        <result column="sort" jdbcType="INTEGER" property="sort" />
+        <result column="menu_level" jdbcType="INTEGER" property="menuLevel" />
+        <result column="menu_icon" jdbcType="VARCHAR" property="menuIcon" />
+        <result column="show_flag" jdbcType="INTEGER" property="showFlag" />
+        <result column="platform" jdbcType="INTEGER" property="platform" />
+        <result column="menu_type" jdbcType="INTEGER" property="menuType" />
+        <result column="permission" jdbcType="VARCHAR" property="permission" />
+        <result column="update_time" jdbcType="TIMESTAMP" property="updateTime" />
+        <result column="editor_id" jdbcType="INTEGER" property="editorId" />
+        <result column="create_time" jdbcType="TIMESTAMP" property="createTime" />
+        <result column="creator_id" jdbcType="INTEGER" property="creatorId" />
+        <result column="version" jdbcType="INTEGER" property="version" />
+        <result column="del_flag" jdbcType="INTEGER" property="delFlag" />
+    </resultMap>
+    
+    <sql id="Base_Column_List">
+        id, menu_name, menu_url, parent_id, sort, menu_level, menu_icon, show_flag, platform, 
+        menu_type, permission, update_time, editor_id, create_time, creator_id, version, 
+        del_flag
+    </sql>
+    
+    <sql id="Base_Where_Clause">
+        <where>
+            <if test="menuName != null">
+                 and menu_name = #{menuName,jdbcType=VARCHAR}
+            </if>
+            <if test="menuUrl != null">
+                 and menu_url = #{menuUrl,jdbcType=VARCHAR}
+            </if>
+            <if test="parentId != null">
+                 and parent_id = #{parentId,jdbcType=INTEGER}
+            </if>
+            <if test="sort != null">
+                 and sort = #{sort,jdbcType=INTEGER}
+            </if>
+            <if test="menuLevel != null">
+                 and menu_level = #{menuLevel,jdbcType=INTEGER}
+            </if>
+            <if test="menuIcon != null">
+                 and menu_icon = #{menuIcon,jdbcType=VARCHAR}
+            </if>
+            <if test="showFlag != null">
+                 and show_flag = #{showFlag,jdbcType=INTEGER}
+            </if>
+            <if test="platform != null">
+                 and platform = #{platform,jdbcType=INTEGER}
+            </if>
+            <if test="menuType != null">
+                 and menu_type = #{menuType,jdbcType=INTEGER}
+            </if>
+            <if test="permission != null">
+                 and permission = #{permission,jdbcType=VARCHAR}
+            </if>
+            <if test="updateTime != null">
+                 and update_time = #{updateTime,jdbcType=TIMESTAMP}
+            </if>
+            <if test="editorId != null">
+                 and editor_id = #{editorId,jdbcType=INTEGER}
+            </if>
+            <if test="createTime != null">
+                 and create_time = #{createTime,jdbcType=TIMESTAMP}
+            </if>
+            <if test="creatorId != null">
+                 and creator_id = #{creatorId,jdbcType=INTEGER}
+            </if>
+            and del_flag = 0
+        </where>
+    </sql>
+    
+    <select id="selectByPrimaryKey" parameterType="java.lang.Integer" resultMap="BaseResultMap">
+        select 
+        <include refid="Base_Column_List" />
+        from t_system_menu
+        where id = #{id,jdbcType=INTEGER}
+    </select>
+    
+    <select id="selectByCondition" resultMap="BaseResultMap">
+        select id, menu_name, menu_url, parent_id, sort, menu_level, menu_icon, show_flag, 
+        platform, menu_type, permission, update_time, editor_id, create_time, creator_id, 
+        version, del_flag
+        from t_system_menu
+        <include refid="Base_Where_Clause" />
+        order by menu_level, sort, id
+    </select>
+    
+    <insert id="insert" parameterType="com.yuxue.entity.SystemMenuEntity">
+        <selectKey keyProperty="id" order="AFTER" resultType="java.lang.Integer">
+            select seq from sqlite_sequence where name = 't_system_menu'
+        </selectKey>
+        insert into t_system_menu (menu_name, menu_url, parent_id, 
+            sort, menu_level, menu_icon, 
+            show_flag, platform, menu_type, 
+            permission, editor_id, create_time, 
+            creator_id)
+        values (#{menuName,jdbcType=VARCHAR}, #{menuUrl,jdbcType=VARCHAR}, #{parentId,jdbcType=INTEGER}, 
+            #{sort,jdbcType=INTEGER}, #{menuLevel,jdbcType=INTEGER}, #{menuIcon,jdbcType=VARCHAR}, 
+            #{showFlag,jdbcType=INTEGER}, #{platform,jdbcType=INTEGER}, #{menuType,jdbcType=INTEGER}, 
+            #{permission,jdbcType=VARCHAR}, #{editorId,jdbcType=INTEGER}, #{createTime,jdbcType=TIMESTAMP}, 
+            #{creatorId,jdbcType=INTEGER})
+    </insert>
+    
+    <insert id="insertSelective" parameterType="com.yuxue.entity.SystemMenuEntity">
+        <selectKey keyProperty="id" order="AFTER" resultType="java.lang.Integer">
+            select seq from sqlite_sequence where name = 't_system_menu'
+        </selectKey>
+        insert into t_system_menu
+        <trim prefix="(" suffix=")" suffixOverrides=",">
+            <if test="menuName != null">
+                menu_name,
+            </if>
+            <if test="menuUrl != null">
+                menu_url,
+            </if>
+            <if test="parentId != null">
+                parent_id,
+            </if>
+            <if test="sort != null">
+                sort,
+            </if>
+            <if test="menuLevel != null">
+                menu_level,
+            </if>
+            <if test="menuIcon != null">
+                menu_icon,
+            </if>
+            <if test="showFlag != null">
+                show_flag,
+            </if>
+            <if test="platform != null">
+                platform,
+            </if>
+            <if test="menuType != null">
+                menu_type,
+            </if>
+            <if test="permission != null">
+                permission,
+            </if>
+            <if test="editorId != null">
+                editor_id,
+            </if>
+            <if test="createTime != null">
+                create_time,
+            </if>
+            <if test="creatorId != null">
+                creator_id,
+            </if>
+        </trim>
+        <trim prefix="values (" suffix=")" suffixOverrides=",">
+            <if test="menuName != null">
+                #{menuName,jdbcType=VARCHAR},
+            </if>
+            <if test="menuUrl != null">
+                #{menuUrl,jdbcType=VARCHAR},
+            </if>
+            <if test="parentId != null">
+                #{parentId,jdbcType=INTEGER},
+            </if>
+            <if test="sort != null">
+                #{sort,jdbcType=INTEGER},
+            </if>
+            <if test="menuLevel != null">
+                #{menuLevel,jdbcType=INTEGER},
+            </if>
+            <if test="menuIcon != null">
+                #{menuIcon,jdbcType=VARCHAR},
+            </if>
+            <if test="showFlag != null">
+                #{showFlag,jdbcType=INTEGER},
+            </if>
+            <if test="platform != null">
+                #{platform,jdbcType=INTEGER},
+            </if>
+            <if test="menuType != null">
+                #{menuType,jdbcType=INTEGER},
+            </if>
+            <if test="permission != null">
+                #{permission,jdbcType=VARCHAR},
+            </if>
+            <if test="editorId != null">
+                #{editorId,jdbcType=INTEGER},
+            </if>
+            <if test="createTime != null">
+                #{createTime,jdbcType=TIMESTAMP},
+            </if>
+            <if test="creatorId != null">
+                #{creatorId,jdbcType=INTEGER},
+            </if>
+        </trim>
+    </insert>
+    
+    <update id="updateByPrimaryKeySelective" parameterType="com.yuxue.entity.SystemMenuEntity">
+        update t_system_menu
+        <set>
+            <if test="menuName != null">
+                menu_name = #{menuName,jdbcType=VARCHAR},
+            </if>
+            <if test="menuUrl != null">
+                menu_url = #{menuUrl,jdbcType=VARCHAR},
+            </if>
+            <if test="parentId != null">
+                parent_id = #{parentId,jdbcType=INTEGER},
+            </if>
+            <if test="sort != null">
+                sort = #{sort,jdbcType=INTEGER},
+            </if>
+            <if test="menuLevel != null">
+                menu_level = #{menuLevel,jdbcType=INTEGER},
+            </if>
+            <if test="menuIcon != null">
+                menu_icon = #{menuIcon,jdbcType=VARCHAR},
+            </if>
+            <if test="showFlag != null">
+                show_flag = #{showFlag,jdbcType=INTEGER},
+            </if>
+            <if test="platform != null">
+                platform = #{platform,jdbcType=INTEGER},
+            </if>
+            <if test="menuType != null">
+                menu_type = #{menuType,jdbcType=INTEGER},
+            </if>
+            <if test="permission != null">
+                permission = #{permission,jdbcType=VARCHAR},
+            </if>
+            <if test="updateTime != null">
+                update_time = #{updateTime,jdbcType=TIMESTAMP},
+            </if>
+            <if test="editorId != null">
+                editor_id = #{editorId,jdbcType=INTEGER},
+            </if>
+            <if test="creatorId != null">
+                creator_id = #{creatorId,jdbcType=INTEGER},
+            </if>
+             version = version + 1, 
+        </set>
+        where id = #{id,jdbcType=INTEGER}
+          and version = #{version, jdbcType=INTEGER} 
+    </update>
+    
+    <update id="updateByPrimaryKey" parameterType="com.yuxue.entity.SystemMenuEntity">
+        update t_system_menu
+        set menu_name = #{menuName,jdbcType=VARCHAR},
+            menu_url = #{menuUrl,jdbcType=VARCHAR},
+            parent_id = #{parentId,jdbcType=INTEGER},
+            sort = #{sort,jdbcType=INTEGER},
+            menu_level = #{menuLevel,jdbcType=INTEGER},
+            menu_icon = #{menuIcon,jdbcType=VARCHAR},
+            show_flag = #{showFlag,jdbcType=INTEGER},
+            platform = #{platform,jdbcType=INTEGER},
+            menu_type = #{menuType,jdbcType=INTEGER},
+            permission = #{permission,jdbcType=VARCHAR},
+            update_time = #{updateTime,jdbcType=TIMESTAMP},
+            editor_id = #{editorId,jdbcType=INTEGER},
+            creator_id = #{creatorId,jdbcType=INTEGER},
+            version =  version + 1 
+        where id = #{id,jdbcType=INTEGER}
+          and version = #{version, jdbcType=INTEGER} 
+    </update>
+    
+    <update id="deleteByPrimaryKey" parameterType="java.lang.Integer">
+        update t_system_menu set del_flag = 1 
+        where id = #{id,jdbcType=INTEGER}
+    </update>
+</mapper>

target/classes/mapper/TempPlateFileMapper.xml

@@ -0,0 +1,183 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE mapper PUBLIC "-//mybatis.org//DTD Mapper 3.0//EN" "http://mybatis.org/dtd/mybatis-3-mapper.dtd">
+<mapper namespace="com.yuxue.mapper.TempPlateFileMapper">
+    
+    <resultMap id="BaseResultMap" type="com.yuxue.entity.TempPlateFileEntity">
+        <id column="id" jdbcType="INTEGER" property="id" />
+        <result column="file_name" jdbcType="VARCHAR" property="fileName" />
+        <result column="file_path" jdbcType="VARCHAR" property="filePath" />
+        <result column="file_type" jdbcType="VARCHAR" property="fileType" />
+        <result column="file_length" jdbcType="INTEGER" property="fileLength" />
+        <result column="parent_id" jdbcType="INTEGER" property="parentId" />
+        <result column="level" jdbcType="INTEGER" property="level" />
+    </resultMap>
+    
+    <sql id="Base_Column_List">
+        id, file_name, file_path, file_type, file_length, parent_id,  level 
+    </sql>
+    
+    <sql id="Base_Where_Clause">
+        <where>
+            <if test="fileName != null">
+                 and file_name = #{fileName,jdbcType=VARCHAR}
+            </if>
+            <if test="filePath != null">
+                 and file_path = #{filePath,jdbcType=VARCHAR}
+            </if>
+            <if test="fileType != null">
+                 and file_type = #{fileType,jdbcType=VARCHAR}
+            </if>
+            <if test="fileLength != null">
+                 and file_length = #{fileLength,jdbcType=INTEGER}
+            </if>
+            <if test="parentId != null">
+                 and parent_id = #{parentId,jdbcType=INTEGER}
+            </if>
+            <if test="level != null">
+                 and  level  = #{level,jdbcType=INTEGER}
+            </if>
+        </where>
+    </sql>
+    
+    <select id="selectByPrimaryKey" parameterType="java.lang.Integer" resultMap="BaseResultMap">
+        select 
+        <include refid="Base_Column_List" />
+        from temp_plate_file
+        where id = #{id,jdbcType=INTEGER}
+    </select>
+    
+    <select id="selectByCondition" resultMap="BaseResultMap">
+        select id, file_name, file_path, file_type, file_length, parent_id,  level 
+        from temp_plate_file
+        <include refid="Base_Where_Clause" />
+        order by id desc
+    </select>
+    
+    <insert id="insert" parameterType="com.yuxue.entity.TempPlateFileEntity">
+        <selectKey keyProperty="id" order="AFTER" resultType="java.lang.Integer">
+            select seq from sqlite_sequence WHERE name = 'temp_plate_file'
+        </selectKey>
+        insert into temp_plate_file (file_name, file_path, file_type, 
+            file_length, parent_id,  level 
+            )
+        values (#{fileName,jdbcType=VARCHAR}, #{filePath,jdbcType=VARCHAR}, #{fileType,jdbcType=VARCHAR}, 
+            #{fileLength,jdbcType=INTEGER}, #{parentId,jdbcType=INTEGER}, #{level,jdbcType=INTEGER}
+            )
+    </insert>
+    
+    <insert id="insertSelective" parameterType="com.yuxue.entity.TempPlateFileEntity">
+        <selectKey keyProperty="id" order="AFTER" resultType="java.lang.Integer">
+            select seq from sqlite_sequence WHERE name = 'temp_plate_file'
+        </selectKey>
+        insert into temp_plate_file
+        <trim prefix="(" suffix=")" suffixOverrides=",">
+            <if test="fileName != null">
+                file_name,
+            </if>
+            <if test="filePath != null">
+                file_path,
+            </if>
+            <if test="fileType != null">
+                file_type,
+            </if>
+            <if test="fileLength != null">
+                file_length,
+            </if>
+            <if test="parentId != null">
+                parent_id,
+            </if>
+            <if test="level != null">
+                 level ,
+            </if>
+        </trim>
+        <trim prefix="values (" suffix=")" suffixOverrides=",">
+            <if test="fileName != null">
+                #{fileName,jdbcType=VARCHAR},
+            </if>
+            <if test="filePath != null">
+                #{filePath,jdbcType=VARCHAR},
+            </if>
+            <if test="fileType != null">
+                #{fileType,jdbcType=VARCHAR},
+            </if>
+            <if test="fileLength != null">
+                #{fileLength,jdbcType=INTEGER},
+            </if>
+            <if test="parentId != null">
+                #{parentId,jdbcType=INTEGER},
+            </if>
+            <if test="level != null">
+                #{level,jdbcType=INTEGER},
+            </if>
+        </trim>
+    </insert>
+    
+    <update id="updateByPrimaryKeySelective" parameterType="com.yuxue.entity.TempPlateFileEntity">
+        update temp_plate_file
+        <set>
+            <if test="fileName != null">
+                file_name = #{fileName,jdbcType=VARCHAR},
+            </if>
+            <if test="filePath != null">
+                file_path = #{filePath,jdbcType=VARCHAR},
+            </if>
+            <if test="fileType != null">
+                file_type = #{fileType,jdbcType=VARCHAR},
+            </if>
+            <if test="fileLength != null">
+                file_length = #{fileLength,jdbcType=INTEGER},
+            </if>
+            <if test="parentId != null">
+                parent_id = #{parentId,jdbcType=INTEGER},
+            </if>
+            <if test="level != null">
+                 level  = #{level,jdbcType=INTEGER},
+            </if>
+        </set>
+        where id = #{id,jdbcType=INTEGER}
+    </update>
+    
+    <update id="updateByPrimaryKey" parameterType="com.yuxue.entity.TempPlateFileEntity">
+        update temp_plate_file
+        set file_name = #{fileName,jdbcType=VARCHAR},
+            file_path = #{filePath,jdbcType=VARCHAR},
+            file_type = #{fileType,jdbcType=VARCHAR},
+            file_length = #{fileLength,jdbcType=INTEGER},
+            parent_id = #{parentId,jdbcType=INTEGER},
+             level  = #{level,jdbcType=INTEGER}
+        where id = #{id,jdbcType=INTEGER}
+    </update>
+    
+    <delete id="deleteByPrimaryKey" parameterType="java.lang.Integer">
+        delete from temp_plate_file
+        where id = #{id,jdbcType=INTEGER}
+    </delete>
+    
+    
+    <delete id="turncateTable">
+        delete from temp_plate_file;
+        delete from sqlite_sequence WHERE name = 'temp_plate_file'
+    </delete>
+    
+    
+    <insert id="batchInsert" parameterType = "com.yuxue.entity.TempPlateFileEntity">
+        insert into temp_plate_file (file_name, file_path, file_type)
+        values 
+        <foreach collection="list" index="index" item="item" open="(" close=")" separator="),(">
+            ifnull(#{item.fileName, jdbcType=VARCHAR}, ''),
+            ifnull(#{item.filePath, jdbcType=VARCHAR}, ''),
+            ifnull(#{item.fileType, jdbcType=VARCHAR}, '')
+        </foreach>
+    </insert>
+    
+    
+    <insert id="updateFileInfo">
+        insert into t_plate_file (file_name, file_path, file_type)
+        select file_name, file_path, file_type from temp_plate_file temp
+        where not exists (select 1 from t_plate_file t where t.file_path = temp.file_path )
+    </insert>
+    
+    
+    
+    
+</mapper>

target/classes/mybatis.xml

@@ -0,0 +1,18 @@
+<?xml version="1.0" encoding="UTF-8" ?>
+<!DOCTYPE configuration PUBLIC "-//mybatis.org//DTD Config 3.2//EN"
+        "http://mybatis.org/dtd/mybatis-3-config.dtd">
+<configuration>
+	<settings>
+		<setting name="cacheEnabled" value="false" />
+		<setting name="lazyLoadingEnabled" value="false" />
+		<setting name="callSettersOnNulls" value="true" />
+		<!-- 仅当select返回的对象是实体类的时候生效，如果返回的时候map则不生效 -->
+		<setting name="mapUnderscoreToCamelCase" value="true" />
+	</settings>
+	<typeAliases>
+
+	</typeAliases>
+	<mappers>
+
+	</mappers>
+</configuration>

target/classes/static/css/common/common.css

@@ -0,0 +1,64 @@
+label.error {
+    font-weight: normal;
+    margin: 0
+}
+.fa-cb-container {
+    display: inline-block;
+}
+.fa-cb-box {
+    overflow: hidden;
+    display: flex;
+    display: -webkit-flex;
+    display: -ms-flexbox;
+    align-items: center;
+}
+.fa-cb-label {
+    display: inline-block;
+    text-align: right;
+    padding-right: 5px;
+}
+.ck-box input {
+    display: none;
+}
+.ck-box .ck-bg {
+    display: inline-block;
+    width: 100%;
+    height: 100%;
+}
+.ck-16 {
+    width: 16px;
+    height: 16px;
+}
+.ck-22 {
+    width: 22px;
+    height: 22px;
+}
+.ck-16 .ck-bg {
+    background: url('../../img/common/ck_bg_16.png') no-repeat 0 0;
+}
+.ck-16 .ck-bg:hover {
+    background: url('../../img/common/ck_bg_16.png') no-repeat -17px 0;
+}
+.ck-16 input:checked+.ck-bg {
+    background-position: -35px 0;
+}
+.ck-22 .ck-bg {
+    width: 22px;
+    height: 22px;
+    background: url('../img/ck_bg_22.png') no-repeat 0 0;
+}
+.ck-22 .ck-bg:hover {
+    background: url('../img/ck_bg_22.png') no-repeat -24px 0;
+}
+.ck-22 input:checked+.ck-bg {
+    background-position: -48px 0;
+}
+.w100{
+    width:100%;
+}
+.h100{
+    height:100%;
+}
+.dis-none{
+    display: none
+}

target/classes/static/css/common/css.css

@@ -0,0 +1,336 @@
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Source Sans Pro Italic'), local('SourceSansPro-Italic'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7qsDJT9g.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Source Sans Pro Italic'), local('SourceSansPro-Italic'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7jsDJT9g.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Source Sans Pro Italic'), local('SourceSansPro-Italic'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7rsDJT9g.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Source Sans Pro Italic'), local('SourceSansPro-Italic'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7ksDJT9g.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Source Sans Pro Italic'), local('SourceSansPro-Italic'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7osDJT9g.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Source Sans Pro Italic'), local('SourceSansPro-Italic'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7psDJT9g.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: italic;
+  font-weight: 400;
+  src: local('Source Sans Pro Italic'), local('SourceSansPro-Italic'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK1dSBYKcSV-LCoeQqfX1RYOo3qPZ7nsDI.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Source Sans Pro Light'), local('SourceSansPro-Light'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmhduz8A.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Source Sans Pro Light'), local('SourceSansPro-Light'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwkxduz8A.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Source Sans Pro Light'), local('SourceSansPro-Light'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmxduz8A.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Source Sans Pro Light'), local('SourceSansPro-Light'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlBduz8A.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Source Sans Pro Light'), local('SourceSansPro-Light'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmBduz8A.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Source Sans Pro Light'), local('SourceSansPro-Light'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwmRduz8A.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 300;
+  src: local('Source Sans Pro Light'), local('SourceSansPro-Light'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ik4zwlxdu.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Source Sans Pro Regular'), local('SourceSansPro-Regular'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNa7lqDY.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Source Sans Pro Regular'), local('SourceSansPro-Regular'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK3dSBYKcSV-LCoeQqfX1RYOo3qPK7lqDY.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Source Sans Pro Regular'), local('SourceSansPro-Regular'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNK7lqDY.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Source Sans Pro Regular'), local('SourceSansPro-Regular'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK3dSBYKcSV-LCoeQqfX1RYOo3qO67lqDY.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Source Sans Pro Regular'), local('SourceSansPro-Regular'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK3dSBYKcSV-LCoeQqfX1RYOo3qN67lqDY.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Source Sans Pro Regular'), local('SourceSansPro-Regular'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK3dSBYKcSV-LCoeQqfX1RYOo3qNq7lqDY.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 400;
+  src: local('Source Sans Pro Regular'), local('SourceSansPro-Regular'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xK3dSBYKcSV-LCoeQqfX1RYOo3qOK7l.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 600;
+  src: local('Source Sans Pro SemiBold'), local('SourceSansPro-SemiBold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmhduz8A.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 600;
+  src: local('Source Sans Pro SemiBold'), local('SourceSansPro-SemiBold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwkxduz8A.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 600;
+  src: local('Source Sans Pro SemiBold'), local('SourceSansPro-SemiBold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmxduz8A.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 600;
+  src: local('Source Sans Pro SemiBold'), local('SourceSansPro-SemiBold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlBduz8A.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 600;
+  src: local('Source Sans Pro SemiBold'), local('SourceSansPro-SemiBold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmBduz8A.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 600;
+  src: local('Source Sans Pro SemiBold'), local('SourceSansPro-SemiBold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwmRduz8A.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 600;
+  src: local('Source Sans Pro SemiBold'), local('SourceSansPro-SemiBold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3i54rwlxdu.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Source Sans Pro Bold'), local('SourceSansPro-Bold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmhduz8A.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Source Sans Pro Bold'), local('SourceSansPro-Bold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwkxduz8A.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Source Sans Pro Bold'), local('SourceSansPro-Bold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmxduz8A.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Source Sans Pro Bold'), local('SourceSansPro-Bold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlBduz8A.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Source Sans Pro Bold'), local('SourceSansPro-Bold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmBduz8A.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Source Sans Pro Bold'), local('SourceSansPro-Bold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwmRduz8A.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 700;
+  src: local('Source Sans Pro Bold'), local('SourceSansPro-Bold'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3ig4vwlxdu.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 900;
+  src: local('Source Sans Pro Black'), local('SourceSansPro-Black'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3iu4nwmhduz8A.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C88, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 900;
+  src: local('Source Sans Pro Black'), local('SourceSansPro-Black'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3iu4nwkxduz8A.woff2) format('woff2');
+  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 900;
+  src: local('Source Sans Pro Black'), local('SourceSansPro-Black'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3iu4nwmxduz8A.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 900;
+  src: local('Source Sans Pro Black'), local('SourceSansPro-Black'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3iu4nwlBduz8A.woff2) format('woff2');
+  unicode-range: U+0370-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 900;
+  src: local('Source Sans Pro Black'), local('SourceSansPro-Black'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3iu4nwmBduz8A.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 900;
+  src: local('Source Sans Pro Black'), local('SourceSansPro-Black'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3iu4nwmRduz8A.woff2) format('woff2');
+  unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Source Sans Pro';
+  font-style: normal;
+  font-weight: 900;
+  src: local('Source Sans Pro Black'), local('SourceSansPro-Black'), url(http://fonts.gstatic.com/s/sourcesanspro/v11/6xKydSBYKcSV-LCoeQqfX1RYOo3iu4nwlxdu.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}

target/classes/static/css/common/tab.css

@@ -0,0 +1,98 @@
+ifr-section {
+  display: -webkit-flex;
+  display: flex;
+  -webkit-flex-wrap: wrap;
+  flex-wrap: wrap;
+  height:100%;
+}
+.ifr{
+	
+    
+}
+.ifr>label {
+  background: #fff; 
+  border-right: 1px solid #ddd;
+  border-bottom: 1px solid #ddd; 
+  padding-top: 12px;
+  padding-bottom: 12px;
+  padding-left: 20px;
+  padding-right: 0px;
+  cursor: pointer;
+  z-index: 1;
+  margin-left: 0px;
+  margin-bottom: 0px;
+  font-weight: 500; 
+  height:45px;
+}
+.ifr>.tab-item {
+  width: 100%;
+  margin-top: -1px;
+  padding: 1em;
+  border-top: 1px solid #ddd;
+  -webkit-order: 1;
+  order: 1;
+}
+.ifr>input[type=radio], .tab-item {
+  display: none;
+}
+.ifr>input[type=radio]:checked + label {
+  background: #eee;
+  border-bottom: 1px solid #eee;
+}
+.ifr>input[type=radio]:checked + label + .tab-item{
+  display: block;
+}
+.ifr-tab-title{
+	float:left;
+	font-family:"΢<><CEA2><EFBFBD>ź<EFBFBD>";
+	font-size: 14px;
+	line-height:20px;
+}
+
+
+.icon-close>div{
+	line-height:18px;
+	
+}
+.icon-close {
+	float:right;
+
+	
+}
+.icon-close i{
+	width:10px;
+	
+}
+
+
+.fw-admin-iframe {
+    position: absolute;
+    width: 100%;
+    height: 100%;
+    left: 0;
+    top: 0;
+    right: 0;
+    bottom: 0;
+}
+iframe[Attributes Style] {
+    border-top-width: 0px;
+    border-right-width: 0px;
+    border-bottom-width: 0px;
+    border-left-width: 0px;
+}
+iframe {
+    border-width: 0px;
+    border-style: inset;
+    border-color: initial;
+    border-image: initial;
+}
+
+.tab-item{
+	position: relative;
+    bottom: 0px;
+    left: 0px;
+    top: 0px;
+    right: 0px;
+    overflow: hidden;
+	height:100%;
+}

target/classes/static/css/fonts/glyphicons-halflings-regular.svg

@@ -0,0 +1,288 @@
+<?xml version="1.0" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
+<svg xmlns="http://www.w3.org/2000/svg">
+<metadata></metadata>
+<defs>
+<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
+<font-face units-per-em="1200" ascent="960" descent="-240" />
+<missing-glyph horiz-adv-x="500" />
+<glyph horiz-adv-x="0" />
+<glyph horiz-adv-x="400" />
+<glyph unicode=" " />
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+<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
+<glyph unicode="&#xa0;" />
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+<glyph unicode="&#x2000;" horiz-adv-x="650" />
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+<glyph unicode="&#x2006;" horiz-adv-x="216" />
+<glyph unicode="&#x2007;" horiz-adv-x="216" />
+<glyph unicode="&#x2008;" horiz-adv-x="162" />
+<glyph unicode="&#x2009;" horiz-adv-x="260" />
+<glyph unicode="&#x200a;" horiz-adv-x="72" />
+<glyph unicode="&#x202f;" horiz-adv-x="260" />
+<glyph unicode="&#x205f;" horiz-adv-x="325" />
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+<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
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+</defs></svg> 

target/classes/static/css/vel/bootstrap-reset.css

@@ -0,0 +1,711 @@
+
+.row {
+  margin-right: -10px;
+  margin-left: -10px;
+}
+.col-lg-1,
+.col-lg-10,
+.col-lg-11,
+.col-lg-12,
+.col-lg-2,
+.col-lg-3,
+.col-lg-4,
+.col-lg-5,
+.col-lg-6,
+.col-lg-7,
+.col-lg-8,
+.col-lg-9,
+.col-md-1,
+.col-md-10,
+.col-md-11,
+.col-md-12,
+.col-md-2,
+.col-md-3,
+.col-md-4,
+.col-md-5,
+.col-md-6,
+.col-md-7,
+.col-md-8,
+.col-md-9,
+.col-sm-1,
+.col-sm-10,
+.col-sm-11,
+.col-sm-12,
+.col-sm-2,
+.col-sm-3,
+.col-sm-4,
+.col-sm-5,
+.col-sm-6,
+.col-sm-7,
+.col-sm-8,
+.col-sm-9,
+.col-xs-1,
+.col-xs-10,
+.col-xs-11,
+.col-xs-12,
+.col-xs-2,
+.col-xs-3,
+.col-xs-4,
+.col-xs-5,
+.col-xs-6,
+.col-xs-7,
+.col-xs-8,
+.col-xs-9 {
+  padding-left: 10px;
+  padding-right: 10px;
+}
+a{
+    color: #333b4d;
+    cursor: pointer;
+    outline: none !important;
+}
+a:hover,a:focus{
+    color: #2A3542;
+    text-decoration: none;
+}
+
+ul, ol {
+    margin-top: 0;
+    margin-bottom: 0px;
+}
+
+
+.list-group{
+    margin-bottom: 0px;
+}
+.popover {
+    border-radius: 3px;
+}
+/*panel*/
+.panel{
+    padding: 20px 30px;
+    border: none;
+    border-top: 1px solid #ddd;
+    margin-bottom: 20px;
+    box-shadow: none;
+}
+.panel .panel-body{
+    padding: 0px;
+    padding-top: 20px;
+}
+.panel .panel-body p{
+    margin: 0px;
+}
+.panel .panel-body p+p {
+    margin-top: 15px;
+}
+.panel-default > .panel-heading {
+    background-color: #FFFFFF;
+    border-color: #DDDDDD;
+    color: #797979;
+}
+.panel-heading {
+    border-color:#eff2f7 ;
+    font-size: 16px;
+    padding: 0;
+    padding-bottom: 15px;
+}
+.panel-title {
+    font-size: 18px;
+    font-weight: 600;
+    margin-bottom: 0;
+    margin-top: 0;
+}
+.panel-footer {
+    margin: 0px -30px -30px;
+    background: #eee;
+    border-top: 0px;
+}
+.panel-group .panel .panel-heading {
+padding-bottom: 0;
+border-bottom: 0;
+}
+.panel-group .panel {
+margin-bottom: 0;
+border-radius: 0;
+}
+
+/*label*/
+
+.label {
+padding: 0.4em .8em;
+}
+
+.label-default {
+    background-color: #a1a1a1;
+}
+
+.label-primary {
+    background-color: #3bc0c3;
+}
+
+.label-success {
+    background-color: #2eb398;
+}
+
+.label-info {
+    background-color: #5bc0de;
+}
+
+.label-warning {
+    background-color: #f4984e;
+}
+
+.label-danger {
+    background-color: #FF6C60;
+}
+
+.label-inverse {
+    background-color: #344860;
+}
+.label-purple{
+    background-color: #7266ba;
+}
+.label-pink{
+    background-color: #f13c6e;
+}
+
+
+
+.badge {
+display: inline-block;
+min-width: 10px;
+padding: 3px 6px;
+font-size: 11px !important;
+font-weight: normal;
+color: #ffffff;
+line-height: 1;
+vertical-align: baseline;
+white-space: nowrap;
+text-align: center;
+background-color: #777777;
+border-color: #777777;
+border-radius: 12px;
+}
+.badge-danger{
+    background-color: #cb2a2a;
+}
+.badge-info{
+    background-color: #1ca8dd;
+}
+.btn{
+    border-radius: 2px;
+    opacity: 0.9;
+}
+.btn:hover{
+    opacity: 1;
+}
+.btn-primary,.btn-success,.btn-info,.btn-warning,.btn-danger,.btn-inverse,.btn-purple,.btn-pink{
+    color: #FFFFFF !important;
+}
+.btn-default:hover,.btn-default:focus,.btn-default:active{
+    background-color: #e6eaed;
+}
+
+.btn-primary,.btn-primary:hover,.btn-primary:focus,.btn-primary:active,
+.btn-primary.active, .btn-primary.focus, .btn-primary:active, .btn-primary:focus, .btn-primary:hover, .open>.dropdown-toggle.btn-primary{
+    background-color: #3bc0c3;
+    border: 1px solid #3bc0c3;
+}
+.btn-success,.btn-success:hover,.btn-success:focus,.btn-success:active,
+.btn-success.active, .btn-success.focus, .btn-success:active, .btn-success:focus, .btn-success:hover, .open>.dropdown-toggle.btn-success{
+    background-color: #33b86c;
+    border: 1px solid #33b86c;
+}
+.btn-info,.btn-info:hover,.btn-info:focus,.btn-info:active,
+.btn-info.active, .btn-info.focus, .btn-info:active, .btn-info:focus, .btn-info:hover, .open>.dropdown-toggle.btn-info{
+    background-color: #1ca8dd;
+    border: 1px solid #1ca8dd;
+}
+.btn-warning,.btn-warning:hover,.btn-warning:focus,.btn-warning:active,
+.btn-warning.active, .btn-warning.focus, .btn-warning:active, .btn-warning:focus, .btn-warning:hover, .open>.dropdown-toggle.btn-warning{
+    background-color: #ebc142;
+    border: 1px solid #ebc142;
+}
+.btn-danger,.btn-danger:active,.btn-danger:focus,.btn-danger:hover,
+.btn-danger.active, .btn-danger.focus, .btn-danger:active, .btn-danger:focus, .btn-danger:hover, .open>.dropdown-toggle.btn-danger{
+    background-color: #cb2a2a;
+    border: 1px solid #cb2a2a;
+}
+.btn-inverse,.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,
+.btn-inverse.active, .btn-inverse.focus, .btn-inverse:active, .btn-inverse:focus, .btn-inverse:hover, .open>.dropdown-toggle.btn-inverse{
+    background-color: #14082d;
+    border: 1px solid #14082d;
+    color: #FFFFFF;
+}
+.btn-purple,.btn-purple:hover,.btn-purple:focus,.btn-purple:active{
+    background-color: #615ca8;
+    border: 1px solid #615ca8;
+    color: #FFFFFF;
+}
+.btn-pink,.btn-pink:hover,.btn-pink:focus,.btn-pink:active{
+    background-color: #f13c6e;
+    border: 1px solid #f13c6e;
+    color: #FFFFFF;
+}
+/*text color*/
+.text-white {
+    color: #ffffff;
+}
+
+.text-danger {
+    color: #cb2a2a;
+}
+
+.text-muted {
+    color: #98a6ad;
+}
+
+.text-primary {
+    color: #3bc0c3;
+}
+
+.text-warning {
+    color: #ebc142;
+}
+
+.text-success {
+    color: #33b86c;
+}
+
+.text-info {
+    color: #1ca8dd;
+}
+
+.text-inverse {
+    color: #14082d;
+}
+
+.text-pink {
+    color: #F13C6E;
+}
+.text-purple {
+    color: #615ca8;
+}
+/* text-color */
+.text-dark {
+      color: #797979;
+}
+/*modal*/
+.modal .modal-dialog .modal-content {
+-webkit-box-shadow: none;
+-moz-box-shadow: none;
+box-shadow: none;
+border-color: #DDDDDD;
+padding: 30px;
+border-radius: 2px;
+}
+.modal .modal-dialog .modal-content .modal-header {
+margin: 0;
+padding: 0;
+border-bottom-width: 2px;
+padding-bottom: 15px;
+}
+.modal .modal-dialog .modal-content .modal-body {
+padding: 20px 0;
+}
+.modal .modal-dialog .modal-content .modal-footer {
+padding: 0;
+padding-top: 15px;
+}
+.modal-full {
+    width: 98%;
+}
+
+/*text input*/
+
+.form-control {
+    background-color: #fafafa;
+  color: rgba(0,0,0,0.6);
+  font-size: 14px;
+  -webkit-border-radius: 2px;
+  -moz-border-radius: 2px;
+  border-radius: 2px;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0,0,0,0.1);
+  -moz-box-shadow: inset 0 1px 2px rgba(0,0,0,0.1);
+  box-shadow: inset 0 1px 2px rgba(0,0,0,0.1);
+  border: 1px solid #eee;
+  box-shadow: none;
+}
+.form-control:focus {
+  border: 1px solid #e0e0e0;
+  background: #FFF;
+  box-shadow: none;
+}
+
+
+
+input, textarea, select, button {
+    outline: none !important;
+}
+textarea.form-control {
+  height: auto;
+  min-height: 100px;
+}
+.input-group-addon {
+    border: 1px solid #eee;
+    border-radius: 2px;
+}
+/*list*/
+
+
+
+
+/*dropdown select bg*/
+.dropdown-menu {
+    border-radius: 2px;
+-webkit-box-shadow: 0 2px 6px rgba(0,0,0,0.1);
+box-shadow: 0 2px 6px rgba(0,0,0,0.1);
+margin-top: 0px;
+}
+.dropdown-menu > li > a:hover, .dropdown-menu > li > a:focus {
+    background-color: #edf1f2!important;
+    color: #141719;
+    text-decoration: none;
+    outline: none;
+}
+.dropdown-menu .divider {
+    margin: 6px 0;
+}
+
+/*split dropdown btn*/
+
+.btn-white {
+    background-clip: padding-box;
+    background-color: #FFFFFF;
+    border-color: rgba(150, 160, 180, 0.3);
+    box-shadow: 0 -1px 1px rgba(0, 0, 0, 0.05) inset;
+}
+
+/*breadcrumbs*/
+
+.breadcrumb {
+    background-color: transparent;
+}
+
+
+/*tab*/
+
+.nav-tabs > li > a {
+    margin-right: 1px;
+}
+
+
+
+/*nav inverse*/
+
+.navbar-inverse {
+    background-color: #7087A3;
+    border-color: #7087A3;
+}
+
+.navbar-inverse .navbar-nav > .active > a, .navbar-inverse .navbar-nav > .active > a:hover, .navbar-inverse .navbar-nav > .active > a:focus,
+.navbar-inverse .navbar-nav > .open > a, .navbar-inverse .navbar-nav > .open > a:focus{
+    background-color: #61748d;
+}
+
+.navbar-inverse .navbar-nav > li a:hover {
+    color: #2A3542;
+}
+
+.navbar-inverse .navbar-nav > li > ul > li a:hover {
+    color: #fff;
+}
+
+.navbar-inverse .navbar-brand {
+    color: #FFFFFF;
+}
+
+.navbar-inverse .navbar-nav > li > a {
+    color: #fff;
+}
+
+.navbar-inverse .navbar-nav > .dropdown > a .caret {
+    border-bottom-color: #fff;
+    border-top-color: #fff;
+}
+
+.navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
+    color: #000;
+}
+.navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover {
+    color: #fff;
+}
+
+/*nav justified*/
+
+.nav-justified li:last-child > a:hover, .nav-justified li.active:last-child > a {
+    border-radius: 0 4px 0 0 !important;
+    -webkit-border-radius: 0 4px 0 0 !important;
+}
+
+/*list group*/
+
+.list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus {
+   background-color: #ddd;
+  border-color: #ddd;
+  color: #444;
+    z-index: 2;
+}
+
+.list-group-item,.list-group-item:first-child ,.list-group-item:last-child  {
+    border-radius: 0px;
+    padding: 12px 20px;
+}
+
+.list-group-item-heading {
+    font-weight: 300;
+}
+.list-group-item.active>.badge, .nav-pills>.active>a>.badge {
+    color: #3bc0c3;
+}
+.list-group-item.active .list-group-item-text, .list-group-item.active:focus .list-group-item-text, .list-group-item.active:hover .list-group-item-text {
+    color: #3bc0c3;
+}
+/*progress*/
+
+.progress {
+    box-shadow: none;
+    background: #f0f2f7;
+}
+
+/*alert*/
+
+.alert-success, .alert-danger, .alert-info, .alert-warning {
+    border: none;
+}
+
+/*table*/
+
+.table thead > tr > th, .table tbody > tr > th, .table tfoot > tr > th, .table thead > tr > td, .table tbody > tr > td, .table tfoot > tr > td {
+    padding: 10px;
+}
+
+
+
+.bg-primary{
+    background-color: rgba(59, 192, 195, 0.8);
+}
+.bg-success{
+    background-color: rgba(51, 184, 108, 0.8);
+}
+.bg-info{
+    background-color: rgba(28, 168, 221, 0.8);
+}
+.bg-warning{
+    background-color: rgba(235, 193, 66, 0.8);
+}
+.bg-danger{
+    background-color: rgba(203, 42, 42, 0.8);
+}
+.bg-muted {
+    background-color: #d0d0d0;
+}
+.bg-inverse {
+    background-color: rgba(20, 8, 45, 0.8);
+}
+.bg-purple {
+    background-color: rgba(97, 92, 168, 0.8) !important;
+}
+.bg-pink {
+    background-color: rgba(241, 60, 110, 0.8);
+}
+.white-bg{
+    background-color: #ffffff;
+}
+.bg-fb{
+    background-color: #3b5998;
+}
+.bg-gp{
+    background-color: #dd4b39;
+}
+.bg-tw{
+    background-color: #00aced;
+}
+.bg-dribbble{
+    background-color: #ea4c89;
+}
+
+
+
+
+/* ------ btn-custom -----*/
+.btn-custom{
+    background: transparent;
+    -moz-border-radius: 2px;
+    -webkit-border-radius: 2px;
+    border-radius: 2px;
+    border-width: 1px;
+    -webkit-transition: all 400ms ease-in-out;
+    -moz-transition: all 400ms ease-in-out;
+    -o-transition: all 400ms ease-in-out;
+    transition: all 400ms ease-in-out;
+}
+.btn-custom.btn-default:hover,.btn-custom.btn-default:active,.btn-custom.btn-default:focus{
+    color: #333 !important;
+}
+.btn-custom.btn-primary{
+    color: #3bc0c3 !important;
+}
+.btn-custom.btn-success{
+    color: #33b86c !important;
+}
+.btn-custom.btn-info{
+    color: #1ca8dd !important;
+}
+.btn-custom.btn-warning{
+    color: #ebc142 !important;
+}
+.btn-custom.btn-danger{
+    color: #cb2a2a !important;
+}
+.btn-custom.btn-inverse{
+    color: #14082d !important;
+}
+.btn-custom.btn-purple{
+    color: #615ca8 !important;
+}
+.btn-custom.btn-pink{
+    color: #f13c6e !important;
+}
+.btn-custom:hover,.btn-custom:focus{
+    color: #FFFFFF !important;
+}
+
+.btn-rounded {
+    border-radius: 2em;
+}
+
+
+
+.panel.panel-color .panel-heading {
+margin-top: -20px;
+margin-left: -30px;
+margin-right: -30px;
+padding: 20px 30px;
+border-bottom: 0;
+}
+.panel-group {
+margin-bottom: 30px;
+}
+.panel-group >.panel.panel-color .panel-heading {
+margin-top: -20px;
+margin-left: -30px;
+margin-right: -30px;
+padding: 20px 30px;
+border-bottom: 0;
+margin-bottom: -20px !important;
+border-radius: 0px !important;
+}
+
+.panel.panel-primary .panel-heading {
+    background-color: #3bc0c3;
+    color: #fff;
+}
+
+.panel.panel-success .panel-heading {
+background-color: #33b86c;
+color: #fff;
+}
+
+.panel.panel-info .panel-heading {
+background-color: #1ca8dd;
+color: #fff;
+}
+
+.panel.panel-warning .panel-heading {
+background-color: #ebc142;
+color: #fff;
+}
+
+.panel.panel-danger .panel-heading {
+background-color: #cb2a2a;
+color: #fff;
+}
+
+.panel.panel-inverse .panel-heading {
+background-color: #14082d;
+color: #fff;
+}
+
+.panel.panel-purple .panel-heading {
+background-color: #615ca8;
+color: #fff;
+}
+
+.panel.panel-pink .panel-heading {
+background-color: #f13c6e;
+color: #fff;
+}
+
+
+
+.progress {
+    overflow: hidden;
+    margin-bottom: 18px;
+    background-color: #f5f5f5;
+    border-radius: 0px;
+    -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1) !important;
+    box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1) !important;
+    height: 10px;
+}
+.progress-bar{
+    font-size: 8px;
+    line-height: 12px;
+    font-weight: 600;
+}
+.progress.progress-md {
+height: 15px !important;
+}
+.progress.progress-lg {
+height: 20px !important;
+}
+.progress.progress-md .progress-bar {
+font-size: 10.8px;
+line-height: 14.4px;
+}
+.progress.progress-lg .progress-bar {
+font-size: 12px;
+line-height: 20px;
+}
+.progress-bar-primary {
+    background-color: #3bc0c3;
+}
+
+.progress-bar-success {
+    background-color: #33b86c;
+}
+
+.progress-bar-info {
+    background-color: #1ca8dd;
+}
+
+.progress-bar-warning {
+    background-color: #ebc142;
+}
+
+.progress-bar-danger {
+    background-color: #cb2a2a;
+}
+
+.progress-bar-inverse {
+    background-color: #14082d;
+}
+
+.progress-bar-purple {
+    background-color: #615ca8;
+}
+
+.progress-bar-pink {
+    background-color: #f13c6e;
+}
+
+.pagination>li>a, .pagination>li>span {
+color: #373e4a;
+background-color: #fff;
+border: 1px solid #ddd;
+}
+.pagination>.active>a, .pagination>.active>span, .pagination>.active>a:hover, .pagination>.active>span:hover, .pagination>.active>a:focus, .pagination>.active>span:focus {
+background-color: #1c2b36;
+border-color: #1c2b36;
+}

target/classes/static/css/vel/bootstrap-theme.css

@@ -0,0 +1,587 @@
+/*!
+ * Bootstrap v3.3.6 (http://getbootstrap.com)
+ * Copyright 2011-2015 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+.btn-default,
+.btn-primary,
+.btn-success,
+.btn-info,
+.btn-warning,
+.btn-danger {
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, .2);
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);
+          box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 1px rgba(0, 0, 0, .075);
+}
+.btn-default:active,
+.btn-primary:active,
+.btn-success:active,
+.btn-info:active,
+.btn-warning:active,
+.btn-danger:active,
+.btn-default.active,
+.btn-primary.active,
+.btn-success.active,
+.btn-info.active,
+.btn-warning.active,
+.btn-danger.active {
+  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);
+          box-shadow: inset 0 3px 5px rgba(0, 0, 0, .125);
+}
+.btn-default.disabled,
+.btn-primary.disabled,
+.btn-success.disabled,
+.btn-info.disabled,
+.btn-warning.disabled,
+.btn-danger.disabled,
+.btn-default[disabled],
+.btn-primary[disabled],
+.btn-success[disabled],
+.btn-info[disabled],
+.btn-warning[disabled],
+.btn-danger[disabled],
+fieldset[disabled] .btn-default,
+fieldset[disabled] .btn-primary,
+fieldset[disabled] .btn-success,
+fieldset[disabled] .btn-info,
+fieldset[disabled] .btn-warning,
+fieldset[disabled] .btn-danger {
+  -webkit-box-shadow: none;
+          box-shadow: none;
+}
+.btn-default .badge,
+.btn-primary .badge,
+.btn-success .badge,
+.btn-info .badge,
+.btn-warning .badge,
+.btn-danger .badge {
+  text-shadow: none;
+}
+.btn:active,
+.btn.active {
+  background-image: none;
+}
+.btn-default {
+  text-shadow: 0 1px 0 #fff;
+  background-image: -webkit-linear-gradient(top, #fff 0%, #e0e0e0 100%);
+  background-image:      -o-linear-gradient(top, #fff 0%, #e0e0e0 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#e0e0e0));
+  background-image:         linear-gradient(to bottom, #fff 0%, #e0e0e0 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe0e0e0', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #dbdbdb;
+  border-color: #ccc;
+}
+.btn-default:hover,
+.btn-default:focus {
+  background-color: #e0e0e0;
+  background-position: 0 -15px;
+}
+.btn-default:active,
+.btn-default.active {
+  background-color: #e0e0e0;
+  border-color: #dbdbdb;
+}
+.btn-default.disabled,
+.btn-default[disabled],
+fieldset[disabled] .btn-default,
+.btn-default.disabled:hover,
+.btn-default[disabled]:hover,
+fieldset[disabled] .btn-default:hover,
+.btn-default.disabled:focus,
+.btn-default[disabled]:focus,
+fieldset[disabled] .btn-default:focus,
+.btn-default.disabled.focus,
+.btn-default[disabled].focus,
+fieldset[disabled] .btn-default.focus,
+.btn-default.disabled:active,
+.btn-default[disabled]:active,
+fieldset[disabled] .btn-default:active,
+.btn-default.disabled.active,
+.btn-default[disabled].active,
+fieldset[disabled] .btn-default.active {
+  background-color: #e0e0e0;
+  background-image: none;
+}
+.btn-primary {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #265a88 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #265a88 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#265a88));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #265a88 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff265a88', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #245580;
+}
+.btn-primary:hover,
+.btn-primary:focus {
+  background-color: #265a88;
+  background-position: 0 -15px;
+}
+.btn-primary:active,
+.btn-primary.active {
+  background-color: #265a88;
+  border-color: #245580;
+}
+.btn-primary.disabled,
+.btn-primary[disabled],
+fieldset[disabled] .btn-primary,
+.btn-primary.disabled:hover,
+.btn-primary[disabled]:hover,
+fieldset[disabled] .btn-primary:hover,
+.btn-primary.disabled:focus,
+.btn-primary[disabled]:focus,
+fieldset[disabled] .btn-primary:focus,
+.btn-primary.disabled.focus,
+.btn-primary[disabled].focus,
+fieldset[disabled] .btn-primary.focus,
+.btn-primary.disabled:active,
+.btn-primary[disabled]:active,
+fieldset[disabled] .btn-primary:active,
+.btn-primary.disabled.active,
+.btn-primary[disabled].active,
+fieldset[disabled] .btn-primary.active {
+  background-color: #265a88;
+  background-image: none;
+}
+.btn-success {
+  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #419641 100%);
+  background-image:      -o-linear-gradient(top, #5cb85c 0%, #419641 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#419641));
+  background-image:         linear-gradient(to bottom, #5cb85c 0%, #419641 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff419641', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #3e8f3e;
+}
+.btn-success:hover,
+.btn-success:focus {
+  background-color: #419641;
+  background-position: 0 -15px;
+}
+.btn-success:active,
+.btn-success.active {
+  background-color: #419641;
+  border-color: #3e8f3e;
+}
+.btn-success.disabled,
+.btn-success[disabled],
+fieldset[disabled] .btn-success,
+.btn-success.disabled:hover,
+.btn-success[disabled]:hover,
+fieldset[disabled] .btn-success:hover,
+.btn-success.disabled:focus,
+.btn-success[disabled]:focus,
+fieldset[disabled] .btn-success:focus,
+.btn-success.disabled.focus,
+.btn-success[disabled].focus,
+fieldset[disabled] .btn-success.focus,
+.btn-success.disabled:active,
+.btn-success[disabled]:active,
+fieldset[disabled] .btn-success:active,
+.btn-success.disabled.active,
+.btn-success[disabled].active,
+fieldset[disabled] .btn-success.active {
+  background-color: #419641;
+  background-image: none;
+}
+.btn-info {
+  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);
+  background-image:      -o-linear-gradient(top, #5bc0de 0%, #2aabd2 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#2aabd2));
+  background-image:         linear-gradient(to bottom, #5bc0de 0%, #2aabd2 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2aabd2', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #28a4c9;
+}
+.btn-info:hover,
+.btn-info:focus {
+  background-color: #2aabd2;
+  background-position: 0 -15px;
+}
+.btn-info:active,
+.btn-info.active {
+  background-color: #2aabd2;
+  border-color: #28a4c9;
+}
+.btn-info.disabled,
+.btn-info[disabled],
+fieldset[disabled] .btn-info,
+.btn-info.disabled:hover,
+.btn-info[disabled]:hover,
+fieldset[disabled] .btn-info:hover,
+.btn-info.disabled:focus,
+.btn-info[disabled]:focus,
+fieldset[disabled] .btn-info:focus,
+.btn-info.disabled.focus,
+.btn-info[disabled].focus,
+fieldset[disabled] .btn-info.focus,
+.btn-info.disabled:active,
+.btn-info[disabled]:active,
+fieldset[disabled] .btn-info:active,
+.btn-info.disabled.active,
+.btn-info[disabled].active,
+fieldset[disabled] .btn-info.active {
+  background-color: #2aabd2;
+  background-image: none;
+}
+.btn-warning {
+  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);
+  background-image:      -o-linear-gradient(top, #f0ad4e 0%, #eb9316 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#eb9316));
+  background-image:         linear-gradient(to bottom, #f0ad4e 0%, #eb9316 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffeb9316', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #e38d13;
+}
+.btn-warning:hover,
+.btn-warning:focus {
+  background-color: #eb9316;
+  background-position: 0 -15px;
+}
+.btn-warning:active,
+.btn-warning.active {
+  background-color: #eb9316;
+  border-color: #e38d13;
+}
+.btn-warning.disabled,
+.btn-warning[disabled],
+fieldset[disabled] .btn-warning,
+.btn-warning.disabled:hover,
+.btn-warning[disabled]:hover,
+fieldset[disabled] .btn-warning:hover,
+.btn-warning.disabled:focus,
+.btn-warning[disabled]:focus,
+fieldset[disabled] .btn-warning:focus,
+.btn-warning.disabled.focus,
+.btn-warning[disabled].focus,
+fieldset[disabled] .btn-warning.focus,
+.btn-warning.disabled:active,
+.btn-warning[disabled]:active,
+fieldset[disabled] .btn-warning:active,
+.btn-warning.disabled.active,
+.btn-warning[disabled].active,
+fieldset[disabled] .btn-warning.active {
+  background-color: #eb9316;
+  background-image: none;
+}
+.btn-danger {
+  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c12e2a 100%);
+  background-image:      -o-linear-gradient(top, #d9534f 0%, #c12e2a 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c12e2a));
+  background-image:         linear-gradient(to bottom, #d9534f 0%, #c12e2a 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc12e2a', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-color: #b92c28;
+}
+.btn-danger:hover,
+.btn-danger:focus {
+  background-color: #c12e2a;
+  background-position: 0 -15px;
+}
+.btn-danger:active,
+.btn-danger.active {
+  background-color: #c12e2a;
+  border-color: #b92c28;
+}
+.btn-danger.disabled,
+.btn-danger[disabled],
+fieldset[disabled] .btn-danger,
+.btn-danger.disabled:hover,
+.btn-danger[disabled]:hover,
+fieldset[disabled] .btn-danger:hover,
+.btn-danger.disabled:focus,
+.btn-danger[disabled]:focus,
+fieldset[disabled] .btn-danger:focus,
+.btn-danger.disabled.focus,
+.btn-danger[disabled].focus,
+fieldset[disabled] .btn-danger.focus,
+.btn-danger.disabled:active,
+.btn-danger[disabled]:active,
+fieldset[disabled] .btn-danger:active,
+.btn-danger.disabled.active,
+.btn-danger[disabled].active,
+fieldset[disabled] .btn-danger.active {
+  background-color: #c12e2a;
+  background-image: none;
+}
+.thumbnail,
+.img-thumbnail {
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
+          box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
+}
+.dropdown-menu > li > a:hover,
+.dropdown-menu > li > a:focus {
+  background-color: #e8e8e8;
+  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image:      -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));
+  background-image:         linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);
+  background-repeat: repeat-x;
+}
+.dropdown-menu > .active > a,
+.dropdown-menu > .active > a:hover,
+.dropdown-menu > .active > a:focus {
+  background-color: #2e6da4;
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
+  background-repeat: repeat-x;
+}
+.navbar-default {
+  background-image: -webkit-linear-gradient(top, #fff 0%, #f8f8f8 100%);
+  background-image:      -o-linear-gradient(top, #fff 0%, #f8f8f8 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fff), to(#f8f8f8));
+  background-image:         linear-gradient(to bottom, #fff 0%, #f8f8f8 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff8f8f8', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-radius: 4px;
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);
+          box-shadow: inset 0 1px 0 rgba(255, 255, 255, .15), 0 1px 5px rgba(0, 0, 0, .075);
+}
+.navbar-default .navbar-nav > .open > a,
+.navbar-default .navbar-nav > .active > a {
+  background-image: -webkit-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);
+  background-image:      -o-linear-gradient(top, #dbdbdb 0%, #e2e2e2 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#dbdbdb), to(#e2e2e2));
+  background-image:         linear-gradient(to bottom, #dbdbdb 0%, #e2e2e2 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdbdbdb', endColorstr='#ffe2e2e2', GradientType=0);
+  background-repeat: repeat-x;
+  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, .075);
+          box-shadow: inset 0 3px 9px rgba(0, 0, 0, .075);
+}
+.navbar-brand,
+.navbar-nav > li > a {
+  text-shadow: 0 1px 0 rgba(255, 255, 255, .25);
+}
+.navbar-inverse {
+  background-image: -webkit-linear-gradient(top, #3c3c3c 0%, #222 100%);
+  background-image:      -o-linear-gradient(top, #3c3c3c 0%, #222 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#3c3c3c), to(#222));
+  background-image:         linear-gradient(to bottom, #3c3c3c 0%, #222 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff3c3c3c', endColorstr='#ff222222', GradientType=0);
+  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
+  background-repeat: repeat-x;
+  border-radius: 4px;
+}
+.navbar-inverse .navbar-nav > .open > a,
+.navbar-inverse .navbar-nav > .active > a {
+  background-image: -webkit-linear-gradient(top, #080808 0%, #0f0f0f 100%);
+  background-image:      -o-linear-gradient(top, #080808 0%, #0f0f0f 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#080808), to(#0f0f0f));
+  background-image:         linear-gradient(to bottom, #080808 0%, #0f0f0f 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff080808', endColorstr='#ff0f0f0f', GradientType=0);
+  background-repeat: repeat-x;
+  -webkit-box-shadow: inset 0 3px 9px rgba(0, 0, 0, .25);
+          box-shadow: inset 0 3px 9px rgba(0, 0, 0, .25);
+}
+.navbar-inverse .navbar-brand,
+.navbar-inverse .navbar-nav > li > a {
+  text-shadow: 0 -1px 0 rgba(0, 0, 0, .25);
+}
+.navbar-static-top,
+.navbar-fixed-top,
+.navbar-fixed-bottom {
+  border-radius: 0;
+}
+@media (max-width: 767px) {
+  .navbar .navbar-nav .open .dropdown-menu > .active > a,
+  .navbar .navbar-nav .open .dropdown-menu > .active > a:hover,
+  .navbar .navbar-nav .open .dropdown-menu > .active > a:focus {
+    color: #fff;
+    background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+    background-image:      -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+    background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
+    background-image:         linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
+    filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
+    background-repeat: repeat-x;
+  }
+}
+.alert {
+  text-shadow: 0 1px 0 rgba(255, 255, 255, .2);
+  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);
+          box-shadow: inset 0 1px 0 rgba(255, 255, 255, .25), 0 1px 2px rgba(0, 0, 0, .05);
+}
+.alert-success {
+  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);
+  background-image:      -o-linear-gradient(top, #dff0d8 0%, #c8e5bc 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#c8e5bc));
+  background-image:         linear-gradient(to bottom, #dff0d8 0%, #c8e5bc 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffc8e5bc', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #b2dba1;
+}
+.alert-info {
+  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #b9def0 100%);
+  background-image:      -o-linear-gradient(top, #d9edf7 0%, #b9def0 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#b9def0));
+  background-image:         linear-gradient(to bottom, #d9edf7 0%, #b9def0 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffb9def0', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #9acfea;
+}
+.alert-warning {
+  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);
+  background-image:      -o-linear-gradient(top, #fcf8e3 0%, #f8efc0 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#f8efc0));
+  background-image:         linear-gradient(to bottom, #fcf8e3 0%, #f8efc0 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fff8efc0', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #f5e79e;
+}
+.alert-danger {
+  background-image: -webkit-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);
+  background-image:      -o-linear-gradient(top, #f2dede 0%, #e7c3c3 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#e7c3c3));
+  background-image:         linear-gradient(to bottom, #f2dede 0%, #e7c3c3 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffe7c3c3', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #dca7a7;
+}
+.progress {
+  background-image: -webkit-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);
+  background-image:      -o-linear-gradient(top, #ebebeb 0%, #f5f5f5 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#ebebeb), to(#f5f5f5));
+  background-image:         linear-gradient(to bottom, #ebebeb 0%, #f5f5f5 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffebebeb', endColorstr='#fff5f5f5', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #286090 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #286090 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#286090));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #286090 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff286090', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-success {
+  background-image: -webkit-linear-gradient(top, #5cb85c 0%, #449d44 100%);
+  background-image:      -o-linear-gradient(top, #5cb85c 0%, #449d44 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5cb85c), to(#449d44));
+  background-image:         linear-gradient(to bottom, #5cb85c 0%, #449d44 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5cb85c', endColorstr='#ff449d44', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-info {
+  background-image: -webkit-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);
+  background-image:      -o-linear-gradient(top, #5bc0de 0%, #31b0d5 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#5bc0de), to(#31b0d5));
+  background-image:         linear-gradient(to bottom, #5bc0de 0%, #31b0d5 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff31b0d5', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-warning {
+  background-image: -webkit-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);
+  background-image:      -o-linear-gradient(top, #f0ad4e 0%, #ec971f 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f0ad4e), to(#ec971f));
+  background-image:         linear-gradient(to bottom, #f0ad4e 0%, #ec971f 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff0ad4e', endColorstr='#ffec971f', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-danger {
+  background-image: -webkit-linear-gradient(top, #d9534f 0%, #c9302c 100%);
+  background-image:      -o-linear-gradient(top, #d9534f 0%, #c9302c 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9534f), to(#c9302c));
+  background-image:         linear-gradient(to bottom, #d9534f 0%, #c9302c 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9534f', endColorstr='#ffc9302c', GradientType=0);
+  background-repeat: repeat-x;
+}
+.progress-bar-striped {
+  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);
+  background-image:      -o-linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);
+  background-image:         linear-gradient(45deg, rgba(255, 255, 255, .15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, .15) 50%, rgba(255, 255, 255, .15) 75%, transparent 75%, transparent);
+}
+.list-group {
+  border-radius: 4px;
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
+          box-shadow: 0 1px 2px rgba(0, 0, 0, .075);
+}
+.list-group-item.active,
+.list-group-item.active:hover,
+.list-group-item.active:focus {
+  text-shadow: 0 -1px 0 #286090;
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2b669a 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #2b669a 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2b669a));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #2b669a 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2b669a', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #2b669a;
+}
+.list-group-item.active .badge,
+.list-group-item.active:hover .badge,
+.list-group-item.active:focus .badge {
+  text-shadow: none;
+}
+.panel {
+  -webkit-box-shadow: 0 1px 2px rgba(0, 0, 0, .05);
+          box-shadow: 0 1px 2px rgba(0, 0, 0, .05);
+}
+.panel-default > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image:      -o-linear-gradient(top, #f5f5f5 0%, #e8e8e8 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f5f5f5), to(#e8e8e8));
+  background-image:         linear-gradient(to bottom, #f5f5f5 0%, #e8e8e8 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#ffe8e8e8', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-primary > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image:      -o-linear-gradient(top, #337ab7 0%, #2e6da4 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#337ab7), to(#2e6da4));
+  background-image:         linear-gradient(to bottom, #337ab7 0%, #2e6da4 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff337ab7', endColorstr='#ff2e6da4', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-success > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);
+  background-image:      -o-linear-gradient(top, #dff0d8 0%, #d0e9c6 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#dff0d8), to(#d0e9c6));
+  background-image:         linear-gradient(to bottom, #dff0d8 0%, #d0e9c6 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffdff0d8', endColorstr='#ffd0e9c6', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-info > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);
+  background-image:      -o-linear-gradient(top, #d9edf7 0%, #c4e3f3 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#d9edf7), to(#c4e3f3));
+  background-image:         linear-gradient(to bottom, #d9edf7 0%, #c4e3f3 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffd9edf7', endColorstr='#ffc4e3f3', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-warning > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);
+  background-image:      -o-linear-gradient(top, #fcf8e3 0%, #faf2cc 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#fcf8e3), to(#faf2cc));
+  background-image:         linear-gradient(to bottom, #fcf8e3 0%, #faf2cc 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffcf8e3', endColorstr='#fffaf2cc', GradientType=0);
+  background-repeat: repeat-x;
+}
+.panel-danger > .panel-heading {
+  background-image: -webkit-linear-gradient(top, #f2dede 0%, #ebcccc 100%);
+  background-image:      -o-linear-gradient(top, #f2dede 0%, #ebcccc 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#f2dede), to(#ebcccc));
+  background-image:         linear-gradient(to bottom, #f2dede 0%, #ebcccc 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2dede', endColorstr='#ffebcccc', GradientType=0);
+  background-repeat: repeat-x;
+}
+.well {
+  background-image: -webkit-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);
+  background-image:      -o-linear-gradient(top, #e8e8e8 0%, #f5f5f5 100%);
+  background-image: -webkit-gradient(linear, left top, left bottom, from(#e8e8e8), to(#f5f5f5));
+  background-image:         linear-gradient(to bottom, #e8e8e8 0%, #f5f5f5 100%);
+  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffe8e8e8', endColorstr='#fff5f5f5', GradientType=0);
+  background-repeat: repeat-x;
+  border-color: #dcdcdc;
+  -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);
+          box-shadow: inset 0 1px 3px rgba(0, 0, 0, .05), 0 1px 0 rgba(255, 255, 255, .1);
+}
+/*# sourceMappingURL=bootstrap-theme.css.map */

target/classes/static/css/vel/helper.css

@@ -0,0 +1,299 @@
+/* Animation / Shadow / Radius */
+
+.log-in-detail a,
+.icon-list div:hover,
+.widget-style-1 i,
+.profile-widget,
+.portlet,
+.ionicon-list i {
+    -webkit-transition: all 220ms ease-in-out;
+    -moz-transition: all 220ms ease-in-out;
+    -o-transition: all 220ms ease-in-out;
+    transition: all 220ms ease-in-out;
+}
+.bx-s,
+.panel,
+.portlet,
+.nav.nav-tabs+.tab-content,
+.tabs-vertical-env .tab-content,
+.widget-panel,
+.profile-widget,
+.profile-widget img,
+.tiles,
+.tile-stats,
+.mini-stat,
+header {
+    -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+    -moz-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+    box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
+}
+.br-radius,
+.tiles,
+.tile-stats,
+.portlet {
+    -webkit-border-radius: 5px;
+    -moz-border-radius: 5px;
+    border-radius: 5px;
+}
+/* Padding - Margin */
+
+.p-0 {
+    padding: 0px !important;
+}
+.p-t-0 {
+    padding-top: 0px !important;
+}
+.p-t-10 {
+    padding-top: 10px !important;
+}
+.p-b-10 {
+    padding-bottom: 10px !important;
+}
+.m-0 {
+    margin: 0px !important;
+}
+.m-r-5 {
+    margin-right: 5px;
+}
+.m-r-10 {
+    margin-right: 10px;
+}
+.m-r-15 {
+    margin-right: 15px;
+}
+.m-l-10 {
+    margin-left: 10px;
+}
+.m-l-15 {
+    margin-left: 15px;
+}
+.m-t-5 {
+    margin-top: 5px !important;
+}
+.m-t-0 {
+    margin-top: 0px;
+}
+.m-t-10 {
+    margin-top: 10px !important;
+}
+.m-t-15 {
+    margin-top: 15px;
+}
+.m-t-20 {
+    margin-top: 20px;
+}
+.m-t-30 {
+    margin-top: 30px !important;
+}
+.m-t-40 {
+    margin-top: 40px !important;
+}
+.m-b-0 {
+    margin-bottom: 0px;
+}
+.m-b-5 {
+    margin-bottom: 5px;
+}
+.m-b-10 {
+    margin-bottom: 10px;
+}
+.m-b-15 {
+    margin-bottom: 15px;
+}
+.m-b-30 {
+    margin-bottom: 30px;
+}
+/* ---- Width-Sizes ---*/
+
+.w-xs {
+    min-width: 80px;
+}
+.w-sm {
+    min-width: 95px;
+}
+.w-md {
+    min-width: 110px;
+}
+.w-lg {
+    min-width: 140px;
+}
+/* ---- Images-Sizes ---*/
+
+.thumb-sm {
+    height: 32px;
+    width: 32px;
+}
+.thumb-sm img {
+    height: auto;
+    max-width: 100%;
+    vertical-align: middle;
+}
+.thumb-xs {
+    height: 24px;
+    width: 24px;
+}
+.thumb-md {
+    width: 64px;
+    height: 64px;
+}
+.thumb-lg {
+    height: 84px;
+    width: 84px;
+}
+/* Text-sizes */
+
+.text-xs {
+    font-size: 12px;
+}
+.text-sm {
+    font-size: 16px;
+}
+.text-md {
+    font-size: 20px;
+}
+.text-lg {
+    font-size: 24px !important;
+}
+/* Extra */
+
+.m-h-50 {
+    min-height: 50px;
+}
+.l-h-34 {
+    line-height: 34px;
+}
+.font-light {
+    font-weight: 300;
+}
+.wrapper-md {
+    padding: 20px;
+}
+.pull-in {
+    margin-left: -15px;
+    margin-right: -15px;
+}
+.b-0 {
+    border: none !important;
+}
+/* Dropcap */
+
+.dropcap {
+    font-size: 3.1em;
+}
+.dropcap,
+.dropcap-circle,
+.dropcap-square {
+    display: block;
+    float: left;
+    font-weight: 400;
+    line-height: 36px;
+    margin-right: 6px;
+    text-shadow: none;
+}
+
+/* Custom Radio/Checkbox */
+.cr-styled {
+    display: inline-block;
+    margin: 0px 2px;
+    line-height: 20px;
+    font-weight: normal;
+    cursor: pointer;
+}
+.cr-styled i {
+    display: inline-block;
+    height: 18px;
+    width: 18px;
+    cursor: pointer;
+    vertical-align: middle;
+    border: 2px solid #CCC;
+    border-radius: 3px;
+    text-align: center;
+    padding-top: 1px;
+    font-family: 'FontAwesome';
+    margin-top: -4px;
+    margin-right: 3px;
+    font-size: 12px;
+}
+.cr-styled input {
+    visibility: hidden;
+    display: none;
+}
+.cr-styled input[type=checkbox]:checked + i:before {
+    content: "\f00c";
+}
+.cr-styled input[type=radio] + i {
+    border-radius: 18px;
+    font-size: 11px;
+    line-height: 13px;
+}
+
+.cr-styled input[type=radio]:checked + i:before {
+    content: "\f111";
+}
+.cr-styled input:checked + i {
+    border-color: #3bc0c3;
+    color: #3bc0c3;
+}
+/* Icon-list (Used Icon-page only) */
+
+.icon-list div {
+    line-height: 40px;
+    white-space: nowrap;
+    cursor: pointer;
+}
+.icon-list i {
+    display: inline-block;
+    width: 40px;
+    margin: 0;
+    font-size: 14px;
+    text-align: center;
+    vertical-align: middle;
+    -webkit-transition: font-size .2s;
+    transition: font-size .2s;
+}
+.ionicon-list i {
+    font-size: 16px;
+}
+.ionicon-list .col-md-3:hover i,
+.icon-list .col-md-3:hover i {
+    moz-transform: scale(2);
+    -webkit-transform: scale(2);
+    -o-transform: scale(2);
+    transform: scale(2);
+}
+/* Grid-structure (Used Grid-page only) */
+
+.grid-structure .grid-container {
+    background-color: #e6eaed;
+    padding: 10px 20px;
+    margin-bottom: 10px;
+}
+/* Custom Choose-button */
+
+.fileUpload {
+    position: relative;
+    overflow: hidden;
+}
+.fileUpload input.upload {
+    position: absolute;
+    top: 0;
+    right: 0;
+    margin: 0;
+    padding: 0;
+    font-size: 20px;
+    cursor: pointer;
+    opacity: 0;
+    filter: alpha(opacity=0);
+}
+
+
+/* Only Mozila */
+@-moz-document url-prefix() { 
+    
+    .cr-styled i {
+        padding-top: 0px;
+    }
+    label {
+        font-weight: 600;
+    }
+}

target/classes/static/css/vel/style-responsive.css

@@ -0,0 +1,110 @@
+/*** Aside Collapsed ***/
+@media (min-width: 769px) {
+  aside.left-panel.collapsed {
+    width: 75px;
+    text-align: center;
+  }
+
+  aside.left-panel.collapsed + .content {
+    margin-left: 75px;
+  }
+
+  aside.left-panel.collapsed .user .user-login,
+aside.left-panel.collapsed span.nav-label {
+    display: none;
+  }
+
+  aside.left-panel.collapsed .navigation > ul > li > a {
+    padding: 20px;
+  }
+
+  aside.left-panel.collapsed i.fa {
+    font-size: 22px;
+  }
+
+  aside.left-panel.collapsed .navigation > ul > li.has-submenu:after {
+    display: none;
+  }
+}
+
+/*** Aside Collapsed Sub Menu ***/
+@media (min-width: 769px) {
+  aside.left-panel.collapsed .navigation > ul > li > ul {
+    position: absolute;
+    z-index: 3;
+    left: 100%;
+    top: 0px;
+    background-color: #162338;
+    box-shadow: none;
+    padding: 10px 0px;
+    min-width: 200px;
+  }
+
+  aside.left-panel.collapsed .navigation > ul > li:hover  > ul {
+    display: block !important;
+  }
+
+  aside.left-panel.collapsed .navigation ul li ul li a {
+    border: 0px;
+    color: #b4b6bd;
+    padding: 8px 25px 8px 40px;
+  }
+
+  aside.left-panel.collapsed .navigation ul li ul li a:hover {
+    color: #fff;
+  }
+
+  aside.left-panel.collapsed .navigation ul li ul li ul li a:hover {
+    color: #dedede;
+  }
+}
+@media (max-width: 768px) {
+    
+    
+    
+  aside.left-panel.collapsed {
+    width: 250px;
+    left: 0px;
+    overflow: hidden !important;
+  }
+
+  aside.left-panel.collapsed + .content {
+    margin-left: 0px;
+    transform: translate3d(250px, 0px, 0px);
+    -ms-transform: translate3d(250px, 0px, 0px);
+    -webkit-transform: translate3d(250px, 0px, 0px);
+    -moz-transition: translate3d(250px, 0px, 0px);
+    -o-transition: translate3d(250px, 0px, 0px);
+  }
+
+  aside.left-panel {
+    left: 100%;
+  }
+
+  .footer {
+    left: 0;
+  }
+
+  section.content {
+    margin-left: 0px;
+  }
+
+  .content > .container-fluid {
+    padding-left: 15px;
+    padding-right: 15px;
+  }
+
+  .page-header h1 {
+    margin-top: 0px;
+  }
+}
+
+@media (max-width: 450px) {
+
+  .username {
+		display: none;
+	}
+	.dropdown .extended i {
+		display: none;
+	}
+}

target/classes/static/iconfont/iconfont.css

@@ -0,0 +1,27 @@
+
+@font-face {font-family: "iconfont";
+  src: url('iconfont.eot?t=1527468348729'); /* IE9*/
+  src: url('iconfont.eot?t=1527468348729#iefix') format('embedded-opentype'), /* IE6-IE8 */
+  url('data:application/x-font-woff;charset=utf-8;base64,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') format('woff'),
+  url('iconfont.ttf?t=1527468348729') format('truetype'), /* chrome, firefox, opera, Safari, Android, iOS 4.2+*/
+  url('iconfont.svg?t=1527468348729#iconfont') format('svg'); /* iOS 4.1- */
+}
+
+.iconfont {
+  font-family:"iconfont" !important;
+  font-size:16px;
+  font-style:normal;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+.icon-user:before { content: "\e6a3"; }
+
+.icon-chahao:before { content: "\e604"; }
+
+.icon-mima:before { content: "\e603"; }
+
+.icon-jiantou:before { content: "\e64a"; }
+
+.icon-loading:before { content: "\e6cd"; }
+