add LoadModel, maxPlates

accuracy.txt

@@ -1561,3 +1561,23 @@
 总图片数:200,  未识出图片:7,  定位率:96.5%  
 平均字符差距:0.689119,  完全匹配数:132,  完全匹配率:68.3938%  
 总时间:330秒,  平均执行时间:1.65秒
+2016-06-15 19:58:37
+总图片数:200,  未识出图片:7,  定位率:96.5%  
+平均字符差距:0.689119,  完全匹配数:132,  完全匹配率:68.3938%  
+总时间:181秒,  平均执行时间:0.905秒
+2016-06-15 20:13:59
+总图片数:200,  未识出图片:7,  定位率:96.5%  
+平均字符差距:0.689119,  完全匹配数:132,  完全匹配率:68.3938%  
+总时间:181秒,  平均执行时间:0.905秒
+2016-06-15 21:05:45
+总图片数:50,  未识出图片:26,  定位率:48%  
+平均字符差距:2.45833,  完全匹配数:6,  完全匹配率:25%  
+总时间:456秒,  平均执行时间:9.12秒
+2016-06-15 21:25:41
+总图片数:200,  未识出图片:7,  定位率:96.5%  
+平均字符差距:0.689119,  完全匹配数:132,  完全匹配率:68.3938%  
+总时间:186秒,  平均执行时间:0.93秒
+2016-06-15 21:51:53
+总图片数:50,  未识出图片:18,  定位率:64%  
+平均字符差距:0,  完全匹配数:0,  完全匹配率:0%  
+总时间:273秒,  平均执行时间:5.46秒

include/easypr/core/chars_identify.h

@@ -16,6 +16,8 @@ class CharsIdentify {
   std::pair<std::string, std::string> identify(cv::Mat input, bool isChinese = false);
 
   bool isCharacter(cv::Mat input, std::string& label, float& maxVal, bool isChinese = false);
+
+  void LoadModel(std::string path);
  private:
   CharsIdentify();
 

include/easypr/core/core_func.h

@@ -14,6 +14,13 @@ enum Color { BLUE, YELLOW, WHITE, UNKNOWN };
 
 enum LocateType { SOBEL, COLOR, CMSER, OTHER };
 
+enum
+{
+  PR_DETECT_SOBEL = 0x01,  /**Sobel detect type，using twice Sobel  */
+  PR_DETECT_COLOR = 0x02,  /**Color detect type   */
+  PR_DETECT_CMSER = 0x04,  /**Character detect type，using mser  */
+};
+
 //! 根据一幅图像与颜色模板获取对应的二值图
 //! 输入RGB图像, 颜色模板（蓝色、黄色）
 //! 输出灰度图（只有0和255两个值，255代表匹配，0代表不匹配）

include/easypr/core/plate_detect.h

@@ -12,13 +12,6 @@ class CPlateDetect {
 
   ~CPlateDetect();
 
-  enum 
-  {
-    PR_DETECT_SOBEL = 0x01,  /**Sobel detect type，using twice Sobel  */
-    PR_DETECT_COLOR = 0x02,  /**Color detect type   */
-    PR_DETECT_CMSER = 0x04,  /**Character detect type，using mser  */
-  };
-
   /** @brief Plate detect in an image.
 
   The function detects plate in an image. It can use sobel, color, and character method or the combinations of them.
@@ -29,9 +22,24 @@ class CPlateDetect {
   @param showDetectArea 
   @param index
   */
-  int plateDetect(Mat src, std::vector<CPlate> &resultVec, int type = 0,
+  int plateDetect(Mat src, std::vector<CPlate> &resultVec, int type,
                   bool showDetectArea = true, int index = 0);
 
+
+  /** @brief Plate detect in an image.
+
+  The function detects plate in an image. It can use sobel, color, and character method or the combinations of them. 
+  Use default m_type, it can use setDetectType() to set it;
+
+  @param src Source image.
+  @param resultVec Destination vector of CPlate.
+  @param showDetectArea
+  @param index
+  */
+  int plateDetect(Mat src, std::vector<CPlate> &resultVec,
+    bool showDetectArea = true, int index = 0);
+
+
   //! 展示中间的结果
 
   int showResult(const Mat &result);
@@ -52,6 +60,9 @@ class CPlateDetect {
 
   inline bool getPDDebug() { return m_plateLocate->getDebug(); }
 
+
+  inline void setDetectType(int param) { m_type = param; }
+
   //! 设置与读取变量
 
   inline void setGaussianBlurSize(int param) {
@@ -113,6 +124,11 @@ class CPlateDetect {
   //! 车牌定位
 
   CPlateLocate* m_plateLocate;
+
+  int m_type;
+
+  static std::string m_pathSvm;
+
 };
 
 }

include/easypr/core/plate_judge.h

@@ -13,8 +13,10 @@ class PlateJudge {
 
   //! 对多幅车牌进行SVM判断
 
+  void LoadModel(std::string path);
+
   int plateJudge(const std::vector<CPlate> &, std::vector<CPlate> &);
-  int plateJudgeUsingNMS(const std::vector<CPlate> &, std::vector<CPlate> &);
+  int plateJudgeUsingNMS(const std::vector<CPlate> &, std::vector<CPlate> &, int maxPlates = 5);
 
   //! 车牌判断
 

include/easypr/core/plate_recognize.h

@@ -38,6 +38,9 @@ namespace easypr {
     //! 是否开启调试模式
 
     inline void setDebug(bool param) { CPlateDetect::setPDDebug(param); }
+
+
+    inline void setDetectType(int param) { CPlateDetect::setDetectType(param); }
   };
 
 } /* \namespace easypr  */

src/core/chars_identify.cpp

@@ -19,6 +19,11 @@ namespace easypr {
     kv_->load("etc/province_mapping");
   }
 
+  void CharsIdentify::LoadModel(std::string path) {
+    ann_->clear();
+    ann_->ml::ANN_MLP::load<ml::ANN_MLP>(path);
+  }
+
   int CharsIdentify::classify(cv::Mat f, float& maxVal, bool isChinses){
     int result = -1;
 

src/core/core_func.cpp

@@ -803,11 +803,11 @@ Mat preprocessChar(Mat in, int char_size) {
 
 Rect GetChineseRect(const Rect rectSpe) {
   int height = rectSpe.height;
-  float newwidth = rectSpe.width * 1.2f;
+  float newwidth = rectSpe.width * 1.10f;
   int x = rectSpe.x;
   int y = rectSpe.y;
 
-  int newx = x - int(newwidth * 1.2);
+  int newx = x - int(newwidth * 1.10f);
   newx = newx > 0 ? newx : 0;
 
   Rect a(newx, y, int(newwidth), height);
@@ -1007,14 +1007,14 @@ Mat mserMatch(const Mat &src, Mat &match, const Color r,
       if (verifyCharSizes(rect)) {
         float aspect = float(rect.width) / float(rect.height);
 
-        Mat region = image(rect);
+        //Mat region = image(rect);
-        Mat binary_region;
+        //Mat binary_region;
-        threshold(region, binary_region, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
+        //threshold(region, binary_region, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
 
-        Mat charInput = preprocessChar(binary_region, 20);
+        //Mat charInput = preprocessChar(binary_region, 20);
-        std::string label = "";
+        //std::string label = "";
-        float maxVal = -2.f;
+        //float maxVal = -2.f;
-        bool isCharacter = CharsIdentify::instance()->isCharacter(charInput, label, maxVal);
+        //bool isCharacter = CharsIdentify::instance()->isCharacter(charInput, label, maxVal);
 
         if (1) {
           //match(rect) = min(max(0, int(maxVal * 255)),255);

src/core/plate_detect.cpp

@@ -6,9 +6,8 @@ namespace easypr {
   CPlateDetect::CPlateDetect() {
     m_plateLocate = new CPlateLocate();
 
-    // 默认EasyPR在一幅图中定位最多3个车
-
     m_maxPlates = 3;
+    m_type = 0;
   }
 
   CPlateDetect::~CPlateDetect() { SAFE_RELEASE(m_plateLocate); }
@@ -54,7 +53,7 @@ namespace easypr {
       }
     }
 
-    if (!type || type & PR_DETECT_CMSER)
+    if ( !type || type & PR_DETECT_CMSER)
     {
 
       m_plateLocate->plateMserLocate(src, mser_Plates, index);
@@ -69,13 +68,18 @@ namespace easypr {
       }
     }
 
-
     // 使用非极大值抑制来判断车牌
-    PlateJudge::instance()->plateJudgeUsingNMS(all_result_Plates, resultVec);
+    PlateJudge::instance()->plateJudgeUsingNMS(all_result_Plates, resultVec, m_maxPlates);
 
     return 0;
   }
 
+  int CPlateDetect::plateDetect(Mat src, std::vector<CPlate> &resultVec,
+    bool showDetectArea, int index) {
+    int result =  plateDetect(src, resultVec, m_type, showDetectArea, index);
+    return result;
+  }
+
   int CPlateDetect::showResult(const Mat &result) {
     namedWindow("EasyPR", CV_WINDOW_AUTOSIZE);
 

src/core/plate_judge.cpp

@@ -14,6 +14,11 @@ namespace easypr {
 
   PlateJudge::PlateJudge() { svm_ = ml::SVM::load<ml::SVM>(kDefaultSvmPath); }
 
+  void PlateJudge::LoadModel(std::string path) {
+    svm_->clear();
+    svm_->ml::SVM::load<ml::SVM>(path);
+  }
+
   //! 对单幅图像进行SVM判断
 
   int PlateJudge::plateJudge(const Mat &inMat, int &result) {
@@ -98,7 +103,7 @@ namespace easypr {
   }
 
   //! 使用非极大值抑制的车牌判断
-  int PlateJudge::plateJudgeUsingNMS(const std::vector<CPlate> &inVec, std::vector<CPlate> &resultVec) {
+  int PlateJudge::plateJudgeUsingNMS(const std::vector<CPlate> &inVec, std::vector<CPlate> &resultVec, int maxPlates) {
     std::vector<CPlate> plateVec;
     int num = inVec.size();
 
@@ -139,10 +144,22 @@ namespace easypr {
       }
     }
 
+    std::vector<CPlate> reDupPlateVec;
+
     // 使用非极大值抑制来去除那些重叠的车牌
     // overlap阈值设置为0.5
     double overlap = 0.5;
-    NMS(plateVec, resultVec, overlap);
+    NMS(plateVec, reDupPlateVec, overlap);
+  
+    std::vector<CPlate>::iterator it = reDupPlateVec.begin();
+    int count = 0;
+    for (; it != reDupPlateVec.end(); ++it) {
+      resultVec.push_back(*it);
+      count++;
+      if (count >= maxPlates)
+        break;
+    }
+
 
     return 0;
   }

src/core/plate_locate.cpp

@@ -87,7 +87,7 @@ int CPlateLocate::mserSearch(const Mat &src, const Color r, Mat &out,
   // width值对最终结果影响很大，可以考虑进行多次colorSerch，每次不同的值
   // 另一种解决方案就是在结果输出到SVM之前，进行线与角的再纠正
 
-  const int color_morph_width = 20;
+  const int color_morph_width = 30;
   const int color_morph_height = 5;
 
   std::vector<RotatedRect> plateRects;
@@ -550,6 +550,10 @@ int CPlateLocate::deskew(const Mat &src, const Mat &src_b,
              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;
@@ -595,7 +599,6 @@ int CPlateLocate::deskew(const Mat &src, const Mat &src_b,
       plate_mat.create(HEIGHT, WIDTH, TYPE);
 
       // haitungaga添加，删除非区域，这个函数影响了25%的完整定位率
-
       DeleteNotArea(deskew_mat);
 
       // 这里对deskew_mat进行了一个筛选
@@ -615,6 +618,12 @@ int CPlateLocate::deskew(const Mat &src, const Mat &src_b,
         CPlate plate;
         plate.setPlatePos(roi_rect);
         plate.setPlateMat(plate_mat);
+
+        if (0) {
+          imshow("plate_mat", plate_mat);
+          waitKey(0);
+        }
+
         outPlates.push_back(plate);
       }
     }

src/core/plate_recognize.cpp

@@ -248,19 +248,20 @@ namespace easypr {
 
       // 对图像进行统一缩放，确保图像不要过大
       // 对速度提升帮助不大
-      int scale_size = 1600;
+      int scale_size = 1200;
       double scale_ratio = 1;
       Mat image = scaleImage(channelImage, Size(scale_size, scale_size), scale_ratio);
+
       Mat result = image;
       cvtColor(result, result, COLOR_GRAY2BGR);
 
       int imageArea = image.rows * image.cols;
 
       if (3 == flags[i]) {
-        mser = MSER::create(3, 30, int(0.25 * imageArea));
+        mser = MSER::create(3, 30, int(0.05 * imageArea));
       }
       else {
-        mser = MSER::create(1, 30, int(0.25 * imageArea));
+        mser = MSER::create(1, 30, int(0.05 * imageArea));
       }
       mser->detectRegions(image, all_contours[i], all_boxes[i]);
 
@@ -399,7 +400,7 @@ namespace easypr {
     //src = ret;
 
     // 进行深度定位，使用颜色信息与二次Sobel
-    int resultPD = plateDetect(src, plateVec, CPlateDetect::PR_DETECT_SOBEL | CPlateDetect::PR_DETECT_COLOR);
+    int resultPD = plateDetect(src, plateVec);
 
     if (resultPD == 0) {
       size_t num = plateVec.size();

test/accuracy.hpp

@@ -91,6 +91,9 @@ namespace easypr {
 
       pr.setMaxPlates(4);
 
+      //pr.setDetectType(PR_DETECT_COLOR | PR_DETECT_SOBEL);
+      pr.setDetectType(PR_DETECT_CMSER);
+
       int size = files.size();
 
       if (0 == size) {

test/plate.hpp

@@ -110,12 +110,13 @@ int test_plate_recognize() {
   CPlateRecognize pr;
   pr.setLifemode(true);
   pr.setDebug(true);
+  pr.setDetectType(easypr::PR_DETECT_CMSER);
 
   //vector<string> plateVec;
   vector<CPlate> plateVec;
 
-  int result = pr.plateRecognize(src, plateVec);
+  //int result = pr.plateRecognize(src, plateVec);
-  //int result = pr.plateRecognizeAsText(src, plateVec);
+  int result = pr.plateRecognizeAsText(src, plateVec);
   if (result == 0) {
     size_t num = plateVec.size();
     for (size_t j = 0; j < num; j++) {