Prj-linux and Prj-win add double-deck support

new Prj-linux with end-end model

Prj-Linux/CMakeLists.txt

@@ -1,3 +1,3 @@
 cmake_minimum_required(VERSION 3.6)
 project(HyperLPR)
-add_subdirectory(hyperlpr)
+add_subdirectory(lpr)

Prj-Linux/lpr/CMakeLists.txt

@@ -3,49 +3,15 @@ project(SwiftPR)
 
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
-find_package(OpenCV 3.3.0 REQUIRED)
+find_package(OpenCV 3.4.3 REQUIRED)
 include_directories( ${OpenCV_INCLUDE_DIRS})
 include_directories(include)
 
-set(SRC_DETECTION src/PlateDetection.cpp  src/util.h include/PlateDetection.h)
-set(SRC_FINEMAPPING src/FineMapping.cpp )
-set(SRC_FASTDESKEW src/FastDeskew.cpp )
-set(SRC_SEGMENTATION src/PlateSegmentation.cpp )
-set(SRC_RECOGNIZE src/Recognizer.cpp src/CNNRecognizer.cpp)
+set(SRC_DETECTION src/PlateDetection.cpp)
+set(SRC_FINETUNE src/FineTune.cpp)
+set(SRC_RECOGNIZE src/PlateRecognation.cpp)
 set(SRC_PIPLINE src/Pipeline.cpp)
-set(SRC_SEGMENTATIONFREE src/SegmentationFreeRecognizer.cpp )
-
-#set(SOURCE_FILES main.cpp)
-#add_executable(HyperLPR_cpp ${SOURCE_FILES})
-
-#TEST_DETECTION
-add_executable(TEST_Detection ${SRC_DETECTION} tests/test_detection.cpp)
-target_link_libraries(TEST_Detection ${OpenCV_LIBS})
-
-#TEST_FINEMAPPING
-add_executable(TEST_FINEMAPPING ${SRC_FINEMAPPING} tests/test_finemapping.cpp)
-target_link_libraries(TEST_FINEMAPPING ${OpenCV_LIBS})
-
-#TEST_DESKEW
-
-add_executable(TEST_FASTDESKEW ${SRC_FASTDESKEW} tests/test_fastdeskew.cpp)
-target_link_libraries(TEST_FASTDESKEW ${OpenCV_LIBS})
-
-#TEST_SEGMENTATION
-
-add_executable(TEST_SEGMENTATION ${SRC_SEGMENTATION} ${SRC_RECOGNIZE} tests/test_segmentation.cpp)
-target_link_libraries(TEST_SEGMENTATION ${OpenCV_LIBS})
-
-#TEST_RECOGNIZATION
-
-add_executable(TEST_RECOGNIZATION  ${SRC_RECOGNIZE} tests/test_recognization.cpp)
-target_link_libraries(TEST_RECOGNIZATION ${OpenCV_LIBS})
-
-#TEST_SEGMENTATIONFREE
-add_executable(TEST_SEGMENTATIONFREE ${SRC_SEGMENTATIONFREE} tests/test_segmentationFree.cpp)
-target_link_libraries(TEST_SEGMENTATIONFREE ${OpenCV_LIBS})
 
 #TEST_PIPELINE
-
-add_executable(TEST_PIPLINE ${SRC_DETECTION} ${SRC_FINEMAPPING} ${SRC_FASTDESKEW} ${SRC_SEGMENTATION} ${SRC_RECOGNIZE} ${SRC_PIPLINE} ${SRC_SEGMENTATIONFREE} tests/test_pipeline.cpp)
+add_executable(TEST_PIPLINE ${SRC_DETECTION} ${SRC_FINETUNE} ${SRC_RECOGNIZE} ${SRC_PIPLINE} tests/testPipeLine.cpp)
 target_link_libraries(TEST_PIPLINE ${OpenCV_LIBS})

Prj-Linux/lpr/include/CNNRecognizer.h

@@ -1,24 +0,0 @@
-//
-// Created by Jack Yu on 21/10/2017.
-//
-
-#ifndef SWIFTPR_CNNRECOGNIZER_H
-#define SWIFTPR_CNNRECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr{
-    class CNNRecognizer: public GeneralRecognizer{
-    public:
-        const int CHAR_INPUT_W = 14;
-        const int CHAR_INPUT_H = 30;
-
-        CNNRecognizer(std::string prototxt,std::string caffemodel);
-        label recognizeCharacter(cv::Mat character);
-    private:
-        cv::dnn::Net net;
-
-    };
-
-}
-
-#endif //SWIFTPR_CNNRECOGNIZER_H

Prj-Linux/lpr/include/FastDeskew.h

@@ -1,18 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FASTDESKEW_H
-#define SWIFTPR_FASTDESKEW_H
-
-#include <math.h>
-#include <opencv2/opencv.hpp>
-namespace pr{
-
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize);
-//    cv::Mat spatialTransformer(cv::Mat skewImage);
-
-}//namepace pr
-
-
-#endif //SWIFTPR_FASTDESKEW_H

Prj-Linux/lpr/include/FineMapping.h

@@ -1,32 +0,0 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
-#ifndef SWIFTPR_FINEMAPPING_H
-#define SWIFTPR_FINEMAPPING_H
-
-#include <opencv2/opencv.hpp>
-#include <opencv2/dnn.hpp>
-
-#include <string>
-namespace pr{
-    class FineMapping{
-    public:
-        FineMapping();
-
-
-        FineMapping(std::string prototxt,std::string caffemodel);
-        static cv::Mat FineMappingVertical(cv::Mat InputProposal,int sliceNum=15,int upper=0,int lower=-50,int windows_size=17);
-        cv::Mat FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding);
-
-
-    private:
-        cv::dnn::Net net;
-
-    };
-
-
-
-
-}
-#endif //SWIFTPR_FINEMAPPING_H

Prj-Linux/lpr/include/Finetune.h

@@ -0,0 +1,18 @@
+#ifndef _FINETUNE_H_
+#define _FINETUNE_H_
+#include<vector>
+#include<opencv2/dnn.hpp>
+#include<opencv2/opencv.hpp>
+namespace pr {
+	class FineTune {
+	public:
+
+		FineTune(std::string finetune_prototxt, std::string finetune_caffemodel);
+		void Finetune(cv::Mat img, cv::Mat &resImg);
+		void to_refine(cv::Mat img, std::vector<cv::Point> pts, cv::Mat &out);
+		void affine_crop(cv::Mat img, std::vector<cv::Point> pts, cv::Mat &out);
+	private:
+		cv::dnn::Net FTNet;
+	};
+}//namespace pr
+#endif // !_FINETUNE_H_

Prj-Linux/lpr/include/Pipeline.h

@@ -1,60 +1,40 @@
-//
-// Created by 庾金科 on 22/10/2017.
-//
-
-#ifndef SWIFTPR_PIPLINE_H
-#define SWIFTPR_PIPLINE_H
-
-#include "PlateDetection.h"
-#include "PlateSegmentation.h"
-#include "CNNRecognizer.h"
-#include "PlateInfo.h"
-#include "FastDeskew.h"
-#include "FineMapping.h"
-#include "Recognizer.h"
-#include "SegmentationFreeRecognizer.h"
-
-namespace pr{
-
-    const std::vector<std::string> CH_PLATE_CODE{"京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
-                                        "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
-                                        "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
-                                        "Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空"};
-
-
-
-    const int SEGMENTATION_FREE_METHOD = 0;
-    const int SEGMENTATION_BASED_METHOD = 1;
-
-    class PipelinePR{
-        public:
-            GeneralRecognizer *generalRecognizer;
-            PlateDetection *plateDetection;
-            PlateSegmentation *plateSegmentation;
-            FineMapping *fineMapping;
-            SegmentationFreeRecognizer *segmentationFreeRecognizer;
-
-            PipelinePR(std::string detector_filename,
-                       std::string finemapping_prototxt,std::string finemapping_caffemodel,
-                       std::string segmentation_prototxt,std::string segmentation_caffemodel,
-                       std::string charRecognization_proto,std::string charRecognization_caffemodel,
-                       std::string segmentationfree_proto,std::string segmentationfree_caffemodel
-                       );
-            ~PipelinePR();
-
-
-
-            std::vector<std::string> plateRes;
-            std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage,int method);
-
-
-
-
-
-
-
-    };
-
-
+#pragma warning(disable:4430)
+#ifndef _PIPLINE_H
+#define _PIPLINE_H
+#include <vector>
+#include "Finetune.h"
+#include "Platedetect.h"
+#include "PlateRecognation.h"
+//#include "PlateColor.h"
+using namespace std;
+using namespace cv;
+namespace pr
+{
+	const std::vector<std::string> CH_PLATE_CODE{ "京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
+		"琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
+		"B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
+		"Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空" };
+
+
+	class PipelinePR {
+
+		public:
+			PlateDetection *platedetection;
+			FineTune *finetune;
+			PlateRecognation *platerecognation;
+			DBDetection *dbdetection;
+
+			
+
+			PipelinePR(std::string detect_prototxt, std::string detect_caffemodel,
+				std::string finetune_prototxt, std::string finetune_caffemodel,
+				std::string platerec_prototxt, std::string platerec_caffemodel,
+				std::string dbstring);
+			~PipelinePR();
+
+			std::vector<std::string> plateRes;
+			std::vector<PlateInfo> RunPiplineAsImage(cv::Mat srcImage,int IsDB);
+
+	};
 }
-#endif //SWIFTPR_PIPLINE_H
+#endif // !SWIFTPR_PIPLINE_H

Prj-Linux/lpr/include/PlateDetection.h

@@ -1,25 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#ifndef SWIFTPR_PLATEDETECTION_H
-#define SWIFTPR_PLATEDETECTION_H
-
-#include <opencv2/opencv.hpp>
-#include <PlateInfo.h>
-#include <vector>
-namespace pr{
-    class PlateDetection{
-    public:
-        PlateDetection(std::string filename_cascade);
-        PlateDetection();
-        void LoadModel(std::string filename_cascade);
-        void plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w=36,int max_w=800);
-    private:
-        cv::CascadeClassifier cascade;
-    };
-
-}// namespace pr
-
-#endif //SWIFTPR_PLATEDETECTION_H
-

Prj-Linux/lpr/include/PlateInfo.h

@@ -1,14 +1,14 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
 #ifndef SWIFTPR_PLATEINFO_H
 #define SWIFTPR_PLATEINFO_H
 #include <opencv2/opencv.hpp>
 namespace pr {
+
     typedef std::vector<cv::Mat> Character;
+
     enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN};
     enum CharType {CHINESE,LETTER,LETTER_NUMS,INVALID};
+
+
     class PlateInfo {
         public:
             std::vector<std::pair<CharType,cv::Mat>> plateChars;
@@ -58,7 +58,11 @@ namespace pr {
             int getPlateType() {
                 return Type;
             }
-
+			int setPlateType(PlateColor platetype)
+			{
+				Type = platetype;
+				return 0;
+			}
             void appendPlateChar(const std::pair<CharType,cv::Mat> &plateChar)
             {
                 plateChars.push_back(plateChar);
@@ -68,6 +72,10 @@ namespace pr {
                 plateCoding.push_back(charProb);
             }
 
+    //        cv::Mat getPlateChars(int id) {
+    //            if(id<PlateChars.size())
+    //                return PlateChars[id];
+    //        }
             std::string decodePlateNormal(std::vector<std::string> mappingTable) {
                 std::string decode;
                 for(auto plate:plateCoding) {
@@ -76,6 +84,10 @@ namespace pr {
 
                         decode += mappingTable[std::max_element(prob,prob+31) - prob];
                         confidence+=*std::max_element(prob,prob+31);
+
+
+//                        std::cout<<*std::max_element(prob,prob+31)<<std::endl;
+
                     }
 
                     else if(plate.first == LETTER) {
@@ -86,16 +98,22 @@ namespace pr {
                     else if(plate.first == LETTER_NUMS) {
                         decode += mappingTable[std::max_element(prob+31,prob+65)- prob];
                         confidence+=*std::max_element(prob+31,prob+65);
+//                        std::cout<<*std::max_element(prob+31,prob+65)<<std::endl;
+
                     }
                     else if(plate.first == INVALID)
                     {
                         decode+='*';
                     }
+
                 }
                 name = decode;
+
                 confidence/=7;
+
                 return decode;
             }
+
     private:
         cv::Mat licensePlate;
         cv::Rect ROI;

Prj-Linux/lpr/include/PlateRecognation.h

@@ -0,0 +1,15 @@
+#ifndef _PLATERECOGNATION_H_
+#define _PLATERECOGNATION_H_
+#include <opencv2/dnn.hpp>
+#include "PlateInfo.h"
+namespace pr {
+	class PlateRecognation {
+	public:
+		PlateRecognation(std::string Rec_prototxt, std::string Rec_cafffemodel);
+		void segmentation_free_recognation(cv::Mat src, pr::PlateInfo &plateinfo);
+	private:
+		cv::dnn::Net RecNet;
+	};
+}//namespace pr
+#endif // !_PLATERECOGNATION_H_
+

Prj-Linux/lpr/include/PlateSegmentation.h

@@ -1,35 +0,0 @@
-#ifndef SWIFTPR_PLATESEGMENTATION_H
-#define SWIFTPR_PLATESEGMENTATION_H
-
-#include "opencv2/opencv.hpp"
-#include <opencv2/dnn.hpp>
-#include "PlateInfo.h"
-
-namespace pr{
-
-
-    class PlateSegmentation{
-    public:
-        const int PLATE_NORMAL = 6;
-        const int PLATE_NORMAL_GREEN = 7;
-        const int DEFAULT_WIDTH = 20;
-        PlateSegmentation(std::string phototxt,std::string caffemodel);
-        PlateSegmentation(){}
-        void segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects);
-
-        void segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones);
-        void templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int>> &candidatePts);
-        void refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects);
-        void ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects);
-        cv::Mat classifyResponse(const cv::Mat &cropped);
-    private:
-        cv::dnn::Net net;
-
-
-//        RefineRegion()
-
-    };
-
-}//namespace pr
-
-#endif //SWIFTPR_PLATESEGMENTATION_H

Prj-Linux/lpr/include/Platedetect.h

@@ -0,0 +1,23 @@
+#ifndef _PLATEDETECT_H_
+#define _PLATEDETECT_H_
+#include <opencv2/opencv.hpp>
+#include <vector>
+#include "PlateInfo.h"
+namespace pr
+{
+	class PlateDetection {
+	public:
+		PlateDetection(std::string ssd_prototxt, std::string ssd_caffe_model);
+		void Detectssd(cv::Mat inputImg, std::vector<pr::PlateInfo>  &plateInfos);
+	private:
+		cv::dnn::Net ssdNet;
+	};
+	class DBDetection{
+		public:
+		DBDetection(std::string cascadestring);
+		void DBDetect(cv::Mat inputImg,std::vector<pr::PlateInfo> &plateInfos,int min_w,int max_w);
+	private:
+		cv::CascadeClassifier dbcascade;
+	};
+}//namespace pr
+#endif // !_PLATEDETECT_H_

Prj-Linux/lpr/include/Recognizer.h

@@ -1,23 +0,0 @@
-//
-// Created by 庾金科 on 20/10/2017.
-//
-
-
-#ifndef SWIFTPR_RECOGNIZER_H
-#define SWIFTPR_RECOGNIZER_H
-
-#include "PlateInfo.h"
-#include "opencv2/dnn.hpp"
-namespace pr{
-    typedef cv::Mat label;
-    class GeneralRecognizer{
-        public:
-            virtual label recognizeCharacter(cv::Mat character) = 0;
-//            virtual cv::Mat SegmentationFreeForSinglePlate(cv::Mat plate) = 0;
-            void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
-            void SegmentationFreeSequenceRecognition(PlateInfo &plateInfo);
-
-    };
-
-}
-#endif //SWIFTPR_RECOGNIZER_H

Prj-Linux/lpr/include/SegmentationFreeRecognizer.h

@@ -1,28 +0,0 @@
-//
-// Created by 庾金科 on 28/11/2017.
-//
-
-#ifndef SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
-#define SWIFTPR_SEGMENTATIONFREERECOGNIZER_H
-
-#include "Recognizer.h"
-namespace pr{
-
-
-    class SegmentationFreeRecognizer{
-    public:
-        const int CHAR_INPUT_W = 14;
-        const int CHAR_INPUT_H = 30;
-        const int CHAR_LEN = 84;
-
-        SegmentationFreeRecognizer(std::string prototxt,std::string caffemodel);
-        std::pair<std::string,float> SegmentationFreeForSinglePlate(cv::Mat plate,std::vector<std::string> mapping_table);
-
-
-    private:
-        cv::dnn::Net net;
-
-    };
-
-}
-#endif //SWIFTPR_SEGMENTATIONFREERECOGNIZER_H

Prj-Linux/lpr/include/niBlackThreshold.h

@@ -1,103 +0,0 @@
-//
-// Created by 庾金科 on 26/10/2017.
-//
-
-#ifndef SWIFTPR_NIBLACKTHRESHOLD_H
-#define SWIFTPR_NIBLACKTHRESHOLD_H
-
-
-#include <opencv2/opencv.hpp>
-using namespace cv;
-
-enum LocalBinarizationMethods{
-    BINARIZATION_NIBLACK = 0, //!< Classic Niblack binarization. See @cite Niblack1985 .
-    BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
-    BINARIZATION_WOLF = 2,    //!< Wolf's technique. See @cite Wolf2004 .
-    BINARIZATION_NICK = 3     //!< NICK technique. See @cite Khurshid2009 .
-};
-
-
-void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
-                       int type, int blockSize, double k, int binarizationMethod )
-{
-    // Input grayscale image
-    Mat src = _src.getMat();
-    CV_Assert(src.channels() == 1);
-    CV_Assert(blockSize % 2 == 1 && blockSize > 1);
-    if (binarizationMethod == BINARIZATION_SAUVOLA) {
-        CV_Assert(src.depth() == CV_8U);
-    }
-    type &= THRESH_MASK;
-    // Compute local threshold (T = mean + k * stddev)
-    // using mean and standard deviation in the neighborhood of each pixel
-    // (intermediate calculations are done with floating-point precision)
-    Mat test;
-    Mat thresh;
-    {
-        // note that: Var[X] = E[X^2] - E[X]^2
-        Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
-        double srcMin, stddevMax;
-        boxFilter(src, mean, CV_32F, Size(blockSize, blockSize),
-                  Point(-1,-1), true, BORDER_REPLICATE);
-        sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize),
-                     Point(-1,-1), true, BORDER_REPLICATE);
-        variance = sqmean - mean.mul(mean);
-        sqrt(variance, stddev);
-        switch (binarizationMethod)
-        {
-            case BINARIZATION_NIBLACK:
-                thresh = mean + stddev * static_cast<float>(k);
-
-                break;
-            case BINARIZATION_SAUVOLA:
-                thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
-                break;
-            case BINARIZATION_WOLF:
-                minMaxIdx(src, &srcMin,NULL);
-                minMaxIdx(stddev, NULL, &stddevMax);
-                thresh = mean - static_cast<float>(k) * (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
-                break;
-            case BINARIZATION_NICK:
-                sqrt(variance + sqmean, sqrtVarianceMeanSum);
-                thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
-                break;
-            default:
-                CV_Error( CV_StsBadArg, "Unknown binarization method" );
-                break;
-        }
-        thresh.convertTo(thresh, src.depth());
-
-        thresh.convertTo(test, src.depth());
-    }
-    // Prepare output image
-    _dst.create(src.size(), src.type());
-    Mat dst = _dst.getMat();
-    CV_Assert(src.data != dst.data);  // no inplace processing
-    // Apply thresholding: ( pixel > threshold ) ? foreground : background
-    Mat mask;
-    switch (type)
-    {
-        case THRESH_BINARY:      // dst = (src > thresh) ? maxval : 0
-        case THRESH_BINARY_INV:  // dst = (src > thresh) ? 0 : maxval
-            compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            dst.setTo(maxValue, mask);
-            break;
-        case THRESH_TRUNC:       // dst = (src > thresh) ? thresh : src
-            compare(src, thresh, mask, CMP_GT);
-            src.copyTo(dst);
-            thresh.copyTo(dst, mask);
-            break;
-        case THRESH_TOZERO:      // dst = (src > thresh) ? src : 0
-        case THRESH_TOZERO_INV:  // dst = (src > thresh) ? 0 : src
-            compare(src, thresh, mask, (type == THRESH_TOZERO ? CMP_GT : CMP_LE));
-            dst.setTo(0);
-            src.copyTo(dst, mask);
-            break;
-        default:
-            CV_Error( CV_StsBadArg, "Unknown threshold type" );
-            break;
-    }
-}
-
-#endif //SWIFTPR_NIBLACKTHRESHOLD_H

Prj-Linux/lpr/model/CharacterRecognization.prototxt

@@ -1,123 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 30
-input_dim: 14
-layer {
-  name: "conv2d_1"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv2d_1"
-  convolution_param {
-    num_output: 32
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_1"
-  type: "ReLU"
-  bottom: "conv2d_1"
-  top: "activation_1"
-}
-layer {
-  name: "max_pooling2d_1"
-  type: "Pooling"
-  bottom: "activation_1"
-  top: "max_pooling2d_1"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_2"
-  type: "Convolution"
-  bottom: "max_pooling2d_1"
-  top: "conv2d_2"
-  convolution_param {
-    num_output: 64
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_2"
-  type: "ReLU"
-  bottom: "conv2d_2"
-  top: "activation_2"
-}
-layer {
-  name: "max_pooling2d_2"
-  type: "Pooling"
-  bottom: "activation_2"
-  top: "max_pooling2d_2"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_3"
-  type: "Convolution"
-  bottom: "max_pooling2d_2"
-  top: "conv2d_3"
-  convolution_param {
-    num_output: 128
-    bias_term: true
-    pad: 0
-    kernel_size: 2
-    stride: 1
-  }
-}
-layer {
-  name: "activation_3"
-  type: "ReLU"
-  bottom: "conv2d_3"
-  top: "activation_3"
-}
-layer {
-  name: "flatten_1"
-  type: "Flatten"
-  bottom: "activation_3"
-  top: "flatten_1"
-}
-layer {
-  name: "dense_1"
-  type: "InnerProduct"
-  bottom: "flatten_1"
-  top: "dense_1"
-  inner_product_param {
-    num_output: 256
-  }
-}
-layer {
-  name: "relu2"
-  type: "ReLU"
-  bottom: "dense_1"
-  top: "relu2"
-}
-layer {
-  name: "dense2"
-  type: "InnerProduct"
-  bottom: "relu2"
-  top: "dense2"
-  inner_product_param {
-    num_output: 65
-  }
-}
-
-layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "dense2"
-  top: "prob"
-}

Prj-Linux/lpr/model/README.md

@@ -0,0 +1,11 @@
+将/hyperlpr_pip_pkg/hyperlpr/models/dnn/目录下的
+
+mininet_ssd_v1.caffemodel
+mininet_ssd_v1.prototxt
+refinenet.caffemodel
+refinenet.prototxt
+SegmenationFree-Inception.caffemodel
+SegmenationFree-Inception.prototxt
+
+
+放置在该目录

Prj-Linux/lpr/model/Segmentation.prototxt

@@ -1,114 +0,0 @@
-input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 22
-input_dim: 22
-layer {
-  name: "conv2d_12"
-  type: "Convolution"
-  bottom: "data"
-  top: "conv2d_12"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_18"
-  type: "ReLU"
-  bottom: "conv2d_12"
-  top: "activation_18"
-}
-layer {
-  name: "max_pooling2d_10"
-  type: "Pooling"
-  bottom: "activation_18"
-  top: "max_pooling2d_10"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "conv2d_13"
-  type: "Convolution"
-  bottom: "max_pooling2d_10"
-  top: "conv2d_13"
-  convolution_param {
-    num_output: 16
-    bias_term: true
-    pad: 0
-    kernel_size: 3
-    stride: 1
-  }
-}
-layer {
-  name: "activation_19"
-  type: "ReLU"
-  bottom: "conv2d_13"
-  top: "activation_19"
-}
-layer {
-  name: "max_pooling2d_11"
-  type: "Pooling"
-  bottom: "activation_19"
-  top: "max_pooling2d_11"
-  pooling_param {
-    pool: MAX
-    kernel_size: 2
-    stride: 2
-    pad: 0
-  }
-}
-layer {
-  name: "flatten_6"
-  type: "Flatten"
-  bottom: "max_pooling2d_11"
-  top: "flatten_6"
-}
-layer {
-  name: "dense_9"
-  type: "InnerProduct"
-  bottom: "flatten_6"
-  top: "dense_9"
-  inner_product_param {
-    num_output: 256
-  }
-}
-layer {
-  name: "dropout_9"
-  type: "Dropout"
-  bottom: "dense_9"
-  top: "dropout_9"
-  dropout_param {
-    dropout_ratio: 0.5
-  }
-}
-layer {
-  name: "activation_20"
-  type: "ReLU"
-  bottom: "dropout_9"
-  top: "activation_20"
-}
-layer {
-  name: "dense_10"
-  type: "InnerProduct"
-  bottom: "activation_20"
-  top: "dense_10"
-  inner_product_param {
-    num_output: 3
-  }
-}
-
-
-  layer {
-  name: "prob"
-  type: "Softmax"
-  bottom: "dense_10"
-  top: "prob"
-}

Prj-Linux/lpr/model/cascade_double.xml

@@ -0,0 +1,504 @@
+<?xml version="1.0"?>
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+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>LBP</featureType>
+  <height>24</height>
+  <width>44</width>
+  <stageParams>
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+    <minHitRate>9.9599999189376831e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>28</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>256</maxCatCount>
+    <featSize>1</featSize></featureParams>
+  <stageNum>11</stageNum>
+  <stages>
+    <!-- stage 0 -->
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+      <stageThreshold>-1.1049392223358154e+00</stageThreshold>
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+        2 1 5 2</rect></_>
+    <_>
+      <rect>
+        2 4 9 2</rect></_>
+    <_>
+      <rect>
+        3 2 5 2</rect></_>
+    <_>
+      <rect>
+        4 0 11 3</rect></_>
+    <_>
+      <rect>
+        4 21 9 1</rect></_>
+    <_>
+      <rect>
+        7 1 5 5</rect></_>
+    <_>
+      <rect>
+        7 7 6 2</rect></_>
+    <_>
+      <rect>
+        10 0 3 5</rect></_>
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+        11 1 9 3</rect></_>
+    <_>
+      <rect>
+        11 2 11 4</rect></_>
+    <_>
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+        12 0 4 3</rect></_>
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+        17 3 4 5</rect></_>
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+        17 6 4 3</rect></_>
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+        18 12 3 2</rect></_>
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+        19 0 3 3</rect></_>
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+        19 3 2 6</rect></_>
+    <_>
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+        19 4 2 4</rect></_>
+    <_>
+      <rect>
+        19 12 2 4</rect></_>
+    <_>
+      <rect>
+        21 2 2 5</rect></_>
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+      <rect>
+        22 5 3 4</rect></_>
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+        22 14 3 3</rect></_>
+    <_>
+      <rect>
+        24 1 6 5</rect></_>
+    <_>
+      <rect>
+        25 3 4 5</rect></_>
+    <_>
+      <rect>
+        25 6 6 1</rect></_>
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+        26 1 2 5</rect></_>
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+        26 3 5 4</rect></_>
+    <_>
+      <rect>
+        26 21 6 1</rect></_>
+    <_>
+      <rect>
+        27 1 5 1</rect></_>
+    <_>
+      <rect>
+        29 3 4 6</rect></_>
+    <_>
+      <rect>
+        30 0 4 5</rect></_>
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+      <rect>
+        30 2 3 7</rect></_>
+    <_>
+      <rect>
+        34 1 3 7</rect></_>
+    <_>
+      <rect>
+        34 8 3 2</rect></_>
+    <_>
+      <rect>
+        34 15 3 3</rect></_>
+    <_>
+      <rect>
+        35 3 2 5</rect></_>
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+      <rect>
+        35 5 2 5</rect></_>
+    <_>
+      <rect>
+        39 12 1 3</rect></_>
+    <_>
+      <rect>
+        41 0 1 7</rect></_>
+    <_>
+      <rect>
+        41 10 1 4</rect></_>
+    <_>
+      <rect>
+        41 11 1 3</rect></_></features></cascade>
+</opencv_storage>

Prj-Linux/lpr/model/refinenet.prototxt

@@ -0,0 +1,300 @@
+name: "ONet"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 48
+input_dim: 120
+
+##################################
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 32
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu1"
+  type: "PReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "pool1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+    num_output: 64
+    kernel_size: 3
+    stride: 1
+     weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "prelu2"
+  type: "PReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+	num_output: 64
+	kernel_size: 3
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu3"
+  type: "PReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3"
+  top: "pool3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4"
+  type: "Convolution"
+  bottom: "pool3"
+  top: "conv4"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  convolution_param {
+	num_output: 128
+	kernel_size: 2
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+layer {
+  name: "prelu4"
+  type: "PReLU"
+  bottom: "conv4"
+  top: "conv4"
+}
+
+
+layer {
+  name: "conv5i"
+  type: "InnerProduct"
+  bottom: "conv4"
+  top: "conv5"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+	#kernel_size: 3
+	num_output: 256
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "drop5i"
+  type: "Dropout"
+  bottom: "conv5"
+  top: "conv5"
+  dropout_param {
+    dropout_ratio: 0.25
+  }
+}
+layer {
+  name: "prelu5"
+  type: "PReLU"
+  bottom: "conv5"
+  top: "conv5"
+}
+
+
+layer {
+  name: "conv6i-1"
+  type: "InnerProduct"
+  bottom: "conv5"
+  top: "conv6-1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+    #kernel_size: 1
+	num_output: 2
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+
+layer {
+  name: "conv6i-2"
+  type: "InnerProduct"
+  bottom: "conv5"
+  top: "conv6-2"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+  	#kernel_size: 1
+	num_output: 4
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+layer {
+  name: "conv6-3"
+  type: "InnerProduct"
+  bottom: "conv5"
+  top: "conv6-3"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 1
+  }
+  inner_product_param {
+  	#kernel_size: 1
+	num_output: 8
+    weight_filler {
+      type: "xavier"
+	}
+    bias_filler {
+      type: "constant"
+      value: 0
+    }
+  }
+}
+
+
+layer {
+  name: "prob1"
+  type: "Softmax"
+  bottom: "conv6-1"
+  top: "prob1"
+}

Prj-Linux/lpr/src/CNNRecognizer.cpp

@@ -1,19 +0,0 @@
-//
-// Created by Jack Yu on 21/10/2017.
-//
-
-#include "../include/CNNRecognizer.h"
-
-namespace pr{
-    CNNRecognizer::CNNRecognizer(std::string prototxt,std::string caffemodel){
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-
-    label CNNRecognizer::recognizeCharacter(cv::Mat charImage){
-        if(charImage.channels()== 3)
-            cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(charImage, 1/255.0, cv::Size(CHAR_INPUT_W,CHAR_INPUT_H), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-}

Prj-Linux/lpr/src/FastDeskew.cpp

@@ -1,108 +0,0 @@
-//
-// Created by Jack Yu on 02/10/2017.
-//
-
-
-
-#include <../include/FastDeskew.h>
-
-namespace pr{
-    const int ANGLE_MIN = 30 ;
-    const int ANGLE_MAX = 150 ;
-    const int PLATE_H = 36;
-    const int PLATE_W = 136;
-    
-    int angle(float x,float y)
-    {
-        return atan2(x,y)*180/3.1415;
-    }
-
-    std::vector<float> avgfilter(std::vector<float> angle_list,int windowsSize) {
-        std::vector<float> angle_list_filtered(angle_list.size() - windowsSize + 1);
-        for (int i = 0; i < angle_list.size() - windowsSize + 1; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                avg += angle_list[i + j];
-            }
-            avg = avg / windowsSize;
-            angle_list_filtered[i] = avg;
-        }
-
-        return angle_list_filtered;
-    }
-
-
-    void drawHist(std::vector<float> seq){
-        cv::Mat image(300,seq.size(),CV_8U);
-        image.setTo(0);
-
-        for(int i = 0;i<seq.size();i++)
-        {
-            float l = *std::max_element(seq.begin(),seq.end());
-
-            int p = int(float(seq[i])/l*300);
-
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        cv::imshow("vis",image);
-    }
-
-    cv::Mat  correctPlateImage(cv::Mat skewPlate,float angle,float maxAngle)
-    {
-        cv::Mat dst;
-        cv::Size size_o(skewPlate.cols,skewPlate.rows);
-        int extend_padding = 0;
-        extend_padding = static_cast<int>(skewPlate.rows*tan(cv::abs(angle)/180* 3.14) );
-        cv::Size size(skewPlate.cols + extend_padding ,skewPlate.rows);
-        float interval = abs(sin((angle /180) * 3.14)* skewPlate.rows);
-        cv::Point2f pts1[4] = {cv::Point2f(0,0),cv::Point2f(0,size_o.height),cv::Point2f(size_o.width,0),cv::Point2f(size_o.width,size_o.height)};
-        if(angle>0) {
-            cv::Point2f pts2[4] = {cv::Point2f(interval, 0), cv::Point2f(0, size_o.height),
-                                   cv::Point2f(size_o.width, 0), cv::Point2f(size_o.width - interval, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size);
-        }
-        else {
-            cv::Point2f pts2[4] = {cv::Point2f(0, 0), cv::Point2f(interval, size_o.height), cv::Point2f(size_o.width-interval, 0),
-                                   cv::Point2f(size_o.width, size_o.height)};
-            cv::Mat M  = cv::getPerspectiveTransform(pts1,pts2);
-            cv::warpPerspective(skewPlate,dst,M,size,cv::INTER_CUBIC);
-        }
-        return  dst;
-    }
-    cv::Mat fastdeskew(cv::Mat skewImage,int blockSize){
-        const int FILTER_WINDOWS_SIZE = 5;
-        std::vector<float> angle_list(180);
-        memset(angle_list.data(),0,angle_list.size()*sizeof(int));
-        cv::Mat bak;
-        skewImage.copyTo(bak);
-        if(skewImage.channels() == 3)
-            cv::cvtColor(skewImage,skewImage,cv::COLOR_RGB2GRAY);
-        if(skewImage.channels() == 1)
-        {
-            cv::Mat eigen;
-            cv::cornerEigenValsAndVecs(skewImage,eigen,blockSize,5);
-            for( int j = 0; j < skewImage.rows; j+=blockSize )
-            { for( int i = 0; i < skewImage.cols; i+=blockSize )
-                {
-                    float x2 = eigen.at<cv::Vec6f>(j, i)[4];
-                    float y2 = eigen.at<cv::Vec6f>(j, i)[5];
-                    int angle_cell = angle(x2,y2);
-                    angle_list[(angle_cell + 180)%180]+=1.0;
-                }
-            }
-        }
-        std::vector<float> filtered = avgfilter(angle_list,5);
-        int maxPos = std::max_element(filtered.begin(),filtered.end()) - filtered.begin() + FILTER_WINDOWS_SIZE/2;
-        if(maxPos>ANGLE_MAX)
-            maxPos = (-maxPos+90+180)%180;
-        if(maxPos<ANGLE_MIN)
-            maxPos-=90;
-        maxPos=90-maxPos;
-      cv::Mat deskewed = correctPlateImage(bak, static_cast<float>(maxPos),60.0f);
-        return deskewed;
-    }
-
-
-
-}//namespace pr

Prj-Linux/lpr/src/FineMapping.cpp

@@ -1,163 +0,0 @@
-#include "FineMapping.h"
-namespace pr{
-    const int FINEMAPPING_H = 60 ;
-    const int FINEMAPPING_W = 140;
-    const int PADDING_UP_DOWN = 30;
-    void drawRect(cv::Mat image,cv::Rect rect)
-    {
-        cv::Point p1(rect.x,rect.y);
-        cv::Point p2(rect.x+rect.width,rect.y+rect.height);
-        cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
-    }
-
-
-    FineMapping::FineMapping(std::string prototxt,std::string caffemodel) {
-         net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-
-    cv::Mat FineMapping::FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding)
-    {
-        cv::Mat inputBlob = cv::dnn::blobFromImage(FinedVertical, 1/255.0, cv::Size(66,16),
-                                      cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        cv::Mat prob = net.forward();
-        int front = static_cast<int>(prob.at<float>(0,0)*FinedVertical.cols);
-        int back = static_cast<int>(prob.at<float>(0,1)*FinedVertical.cols);
-        front -= leftPadding ;
-        if(front<0) front = 0;
-        back +=rightPadding;
-        if(back>FinedVertical.cols-1) back=FinedVertical.cols - 1;
-        cv::Mat cropped  = FinedVertical.colRange(front,back).clone();
-        return  cropped;
-    }
-
-    std::pair<int,int> FitLineRansac(std::vector<cv::Point> pts,int zeroadd = 0 )
-    {
-        std::pair<int,int> res;
-        if(pts.size()>2)
-        {
-            cv::Vec4f line;
-            cv::fitLine(pts,line,cv::DIST_HUBER,0,0.01,0.01);
-            float vx = line[0];
-            float vy = line[1];
-            float x = line[2];
-            float y = line[3];
-            int lefty = static_cast<int>((-x * vy / vx) + y);
-            int righty = static_cast<int>(((136- x) * vy / vx) + y);
-            res.first = lefty+PADDING_UP_DOWN+zeroadd;
-            res.second = righty+PADDING_UP_DOWN+zeroadd;
-            return res;
-        }
-        res.first = zeroadd;
-        res.second = zeroadd;
-        return res;
-    }
-
-    cv::Mat FineMapping::FineMappingVertical(cv::Mat InputProposal,int sliceNum,int upper,int lower,int windows_size){
-        cv::Mat PreInputProposal;
-        cv::Mat proposal;
-        cv::resize(InputProposal,PreInputProposal,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-        if(InputProposal.channels() == 3)
-            cv::cvtColor(PreInputProposal,proposal,cv::COLOR_BGR2GRAY);
-        else
-            PreInputProposal.copyTo(proposal);
-        // this will improve some sen
-        cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,3));
-        float diff = static_cast<float>(upper-lower);
-        diff/=static_cast<float>(sliceNum-1);
-        cv::Mat binary_adaptive;
-        std::vector<cv::Point> line_upper;
-        std::vector<cv::Point> line_lower;
-        int contours_nums=0;
-        for(int i = 0 ; i < sliceNum ; i++)
-        {
-            std::vector<std::vector<cv::Point> > contours;
-            float k =lower + i*diff;
-            cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-            cv::Mat draw;
-            binary_adaptive.copyTo(draw);
-            cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-            for(auto contour: contours)
-            {
-                cv::Rect bdbox =cv::boundingRect(contour);
-                float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                int  bdboxAera = bdbox.width*bdbox.height;
-                if ((   lwRatio>0.7&&bdbox.width*bdbox.height>100 && bdboxAera<300)
-                    || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                {
-                    cv::Point p1(bdbox.x, bdbox.y);
-                    cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                    line_upper.push_back(p1);
-                    line_lower.push_back(p2);
-                    contours_nums+=1;
-                }
-            }
-        }
-        if(contours_nums<41)
-        {
-            cv::bitwise_not(InputProposal,InputProposal);
-            cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,5));
-            cv::Mat bak;
-            cv::resize(InputProposal,bak,cv::Size(FINEMAPPING_W,FINEMAPPING_H));
-            cv::erode(bak,bak,kernal);
-            if(InputProposal.channels() == 3)
-                cv::cvtColor(bak,proposal,cv::COLOR_BGR2GRAY);
-            else
-                proposal = bak;
-            int contours_nums=0;
-            for(int i = 0 ; i < sliceNum ; i++)
-            {
-                std::vector<std::vector<cv::Point> > contours;
-                float k =lower + i*diff;
-                cv::adaptiveThreshold(proposal,binary_adaptive,255,cv::ADAPTIVE_THRESH_MEAN_C,cv::THRESH_BINARY,windows_size,k);
-                cv::Mat draw;
-                binary_adaptive.copyTo(draw);
-                cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
-                for(auto contour: contours)
-                {
-                    cv::Rect bdbox =cv::boundingRect(contour);
-                    float lwRatio = bdbox.height/static_cast<float>(bdbox.width);
-                    int  bdboxAera = bdbox.width*bdbox.height;
-                    if ((   lwRatio>0.7&&bdbox.width*bdbox.height>120 && bdboxAera<300)
-                        || (lwRatio>3.0 && bdboxAera<100 && bdboxAera>10))
-                    {
-
-                        cv::Point p1(bdbox.x, bdbox.y);
-                        cv::Point p2(bdbox.x + bdbox.width, bdbox.y + bdbox.height);
-                        line_upper.push_back(p1);
-                        line_lower.push_back(p2);
-                        contours_nums+=1;
-                    }
-                }
-            }
-        }
-            cv::Mat rgb;
-            cv::copyMakeBorder(PreInputProposal, rgb, PADDING_UP_DOWN, PADDING_UP_DOWN, 0, 0, cv::BORDER_REPLICATE);
-            std::pair<int, int> A;
-            std::pair<int, int> B;
-            A = FitLineRansac(line_upper, -1);
-            B = FitLineRansac(line_lower, 1);
-            int leftyB = A.first;
-            int rightyB = A.second;
-            int leftyA = B.first;
-            int rightyA = B.second;
-            int cols = rgb.cols;
-            int rows = rgb.rows;
-            std::vector<cv::Point2f> corners(4);
-            corners[0] = cv::Point2f(cols - 1, rightyA);
-            corners[1] = cv::Point2f(0, leftyA);
-            corners[2] = cv::Point2f(cols - 1, rightyB);
-            corners[3] = cv::Point2f(0, leftyB);
-            std::vector<cv::Point2f> corners_trans(4);
-            corners_trans[0] = cv::Point2f(136, 36);
-            corners_trans[1] = cv::Point2f(0, 36);
-            corners_trans[2] = cv::Point2f(136, 0);
-            corners_trans[3] = cv::Point2f(0, 0);
-            cv::Mat transform = cv::getPerspectiveTransform(corners, corners_trans);
-            cv::Mat quad = cv::Mat::zeros(36, 136, CV_8UC3);
-            cv::warpPerspective(rgb, quad, transform, quad.size());
-        return quad;
-    }
-}
-
-

Prj-Linux/lpr/src/FineTune.cpp

@@ -0,0 +1,67 @@
+#include "../include/Finetune.h"
+using namespace std;
+using namespace cv;
+
+namespace pr {
+	FineTune::FineTune(string finetune_prototxt, string finetune_caffemodel) {
+		FTNet = dnn::readNetFromCaffe(finetune_prototxt, finetune_caffemodel);
+	}
+	void FineTune::affine_crop(Mat src, vector<Point> pts, Mat &crop)
+	{
+		Point2f dst[4] = { Point2f(0,0),Point2f(160,0),Point2f(160,40),Point2f(0,40) };
+		Point2f srcpt[4] = { Point2f(pts[0]),Point2f(pts[1]) ,Point2f(pts[2]) ,Point2f(pts[3]) };
+		Mat _mat = getPerspectiveTransform(srcpt, dst);
+		warpPerspective(src, crop, _mat, Size(160, 40));
+	}
+	void FineTune::to_refine(Mat src, vector<Point>pts, Mat& crop)
+	{
+		float scale = 3.f;
+		int cx = 64; int cy = 24;
+		int cw = 64; int ch = 24;
+		int tx1 = cx - cw / 2;
+		int ty1 = cy - ch / 2;
+		int tx2 = cx + cw / 2;
+		int ty2 = cy - ch / 2;
+		int tx3 = cx + cw / 2;
+		int ty3 = cy + ch / 2;
+		int tx4 = cx - cw / 2;
+		int ty4 = cy + ch / 2;
+		vector<Point2f> dstp(4);
+		Point2f dst[4] = { (Point2f(tx1*scale, ty1*scale)) ,(Point2f(tx2*scale, ty2*scale)) ,(Point2f(tx3*scale, ty3*scale)) ,(Point2f(tx4*scale, ty4*scale)) };
+		Point2f pt[4] = { Point2f(pts[0]),Point2f(pts[1]) ,Point2f(pts[2]) ,Point2f(pts[3]) };
+		//estimater
+		Mat _mat = getPerspectiveTransform(pt, dst);
+		warpPerspective(src, crop, _mat, Size(120 * scale, 48 * scale));
+	}
+	void FineTune::Finetune(Mat src, Mat& dst)
+	{
+		Mat tof;// = src.clone();
+		resize(src, tof, Size(120, 48));
+		Mat blob = dnn::blobFromImage(tof, 0.0078125, Size(120, 48), Scalar(127.5, 127.5, 127.5), false, false);
+		FTNet.setInput(blob);
+		Mat outblob = FTNet.forward("conv6-3");
+
+		float *data = outblob.ptr<float>();
+		vector<Point> pts(4);
+		Mat fineMat(Size(2, 4), CV_32F, data);
+		for (int i = 0; i < fineMat.rows; i++)
+		{
+			pts[i].x = fineMat.at<float>(i, 0)*src.cols;
+			pts[i].y = fineMat.at<float>(i, 1)*src.rows;
+		}
+		Mat crop;
+		to_refine(src, pts, crop);
+		blob = dnn::blobFromImage(crop, 0.0078128, Size(120, 48), Scalar(127.5, 127.5, 127.5), false, false);
+		FTNet.setInput(blob);
+		outblob = FTNet.forward("conv6-3");
+		data = outblob.ptr<float>();
+		Mat fineMat2(Size(2, 4), CV_32F, data);
+		for (int i = 0; i < fineMat.rows; i++)
+		{
+			pts[i].x = fineMat2.at<float>(i, 0)*crop.cols;
+			pts[i].y = fineMat2.at<float>(i, 1)*crop.rows;
+		}
+		affine_crop(crop, pts, crop);
+		dst = crop.clone();
+	}
+}

Prj-Linux/lpr/src/Pipeline.cpp

@@ -1,74 +1,70 @@
-//
-// Created by Jack Yu on 23/10/2017.
-//
-
 #include "../include/Pipeline.h"
 
-
 namespace pr {
+	PipelinePR::PipelinePR(std::string detect_prototxt, std::string detect_caffemodel,
+		std::string finetune_prototxt, std::string finetune_caffemodel,
+		std::string platerec_prototxt, std::string platerec_caffemodel,
+		std::string dbstring)
+	{
+		platedetection = new PlateDetection(detect_prototxt, detect_caffemodel);
+		finetune = new FineTune(finetune_prototxt, finetune_caffemodel);
+		platerecognation = new PlateRecognation(platerec_prototxt, platerec_caffemodel);
+		dbdetection = new DBDetection(dbstring);
+	}
+	PipelinePR::~PipelinePR()
+	{
+		delete platedetection;
+		delete finetune;
+		delete platerecognation;
+		delete dbdetection;
+	}
+    cv::Mat DBcropFromImage(const cv::Mat &image){
+		cv::Mat cropped;
+        image.copyTo(cropped);
+		int cropped_w = cropped.cols;
+		int cropped_h = cropped.rows;
+		cv::Rect up,down;
+		up.y = cropped_h*0.05;up.x = cropped_w*0.2;up.height = cropped_h*0.35;up.width = cropped_w*0.55;
+		down.y = cropped_h*0.4;down.x = cropped_w*0.05;down.height = cropped_h*0.6;down.width = cropped_w*0.95;
+		cv::Mat cropUp,cropDown;
+		cropped(up).copyTo(cropUp);
+		cropped(down).copyTo(cropDown);
+		cv::resize(cropUp,cropUp,cv::Size(64,40));
+		cv::resize(cropDown,cropDown,cv::Size(96,40));
+		cv::Mat crop = cv::Mat(40,160,CV_8UC3);
+		cropUp.copyTo(crop(cv::Rect(0,0,64,40)));
+		cropDown.copyTo(crop(cv::Rect(64,0,96,40)));
+        return crop;
 
-    const int HorizontalPadding = 4;
-    PipelinePR::PipelinePR(std::string detector_filename,
-                           std::string finemapping_prototxt, std::string finemapping_caffemodel,
-                           std::string segmentation_prototxt, std::string segmentation_caffemodel,
-                           std::string charRecognization_proto, std::string charRecognization_caffemodel,
-                           std::string segmentationfree_proto,std::string segmentationfree_caffemodel) {
-        plateDetection = new PlateDetection(detector_filename);
-        fineMapping = new FineMapping(finemapping_prototxt, finemapping_caffemodel);
-        plateSegmentation = new PlateSegmentation(segmentation_prototxt, segmentation_caffemodel);
-        generalRecognizer = new CNNRecognizer(charRecognization_proto, charRecognization_caffemodel);
-        segmentationFreeRecognizer =  new SegmentationFreeRecognizer(segmentationfree_proto,segmentationfree_caffemodel);
-    }
-
-    PipelinePR::~PipelinePR() {
-        delete plateDetection;
-        delete fineMapping;
-        delete plateSegmentation;
-        delete generalRecognizer;
-        delete segmentationFreeRecognizer;
     }
-
-    std::vector<PlateInfo> PipelinePR:: RunPiplineAsImage(cv::Mat plateImage,int method) {
-        std::vector<PlateInfo> results;
-        std::vector<pr::PlateInfo> plates;
-        plateDetection->plateDetectionRough(plateImage,plates,36,700);
-        for (pr::PlateInfo plateinfo:plates) {
-
-            cv::Mat image_finemapping = plateinfo.getPlateImage();
-            image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
-            image_finemapping = pr::fastdeskew(image_finemapping, 5);
-            //Segmentation-based
-            if(method==SEGMENTATION_BASED_METHOD)
-            {
-                image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2, HorizontalPadding);
-                cv::resize(image_finemapping, image_finemapping, cv::Size(136+HorizontalPadding, 36));
-                plateinfo.setPlateImage(image_finemapping);
-                std::vector<cv::Rect> rects;
-                plateSegmentation->segmentPlatePipline(plateinfo, 1, rects);
-                plateSegmentation->ExtractRegions(plateinfo, rects);
-                cv::copyMakeBorder(image_finemapping, image_finemapping, 0, 0, 0, 20, cv::BORDER_REPLICATE);
-                plateinfo.setPlateImage(image_finemapping);
-                generalRecognizer->SegmentBasedSequenceRecognition(plateinfo);
-                plateinfo.decodePlateNormal(pr::CH_PLATE_CODE);
-
-            }
-                //Segmentation-free
-            else if(method==SEGMENTATION_FREE_METHOD)
-            {
-                image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 4, HorizontalPadding+3);
-                cv::resize(image_finemapping, image_finemapping, cv::Size(136+HorizontalPadding, 36));
-                plateinfo.setPlateImage(image_finemapping);
-                std::pair<std::string,float> res = segmentationFreeRecognizer->SegmentationFreeForSinglePlate(plateinfo.getPlateImage(),pr::CH_PLATE_CODE);
-                plateinfo.confidence = res.second;
-                plateinfo.setPlateName(res.first);
-            }
-            results.push_back(plateinfo);
-        }
-
-        return results;
-
-    }//namespace pr
-
-
-
+	std::vector<PlateInfo> PipelinePR::RunPiplineAsImage(cv::Mat plateimg,int IsDB)
+	{
+		std::vector<pr::PlateInfo> plates;
+		std::vector<PlateInfo> plateres;
+		if(IsDB==1)
+		{
+			dbdetection->DBDetect(plateimg,plates,30,1280);
+		}
+		else
+		{
+			platedetection->Detectssd(plateimg, plates);
+		}
+		for (pr::PlateInfo plateinfo : plates) {
+			cv::Mat image = plateinfo.getPlateImage();
+			cv::Mat CropImg;
+			
+			if(IsDB==1)
+			{
+				CropImg = DBcropFromImage(image);
+				platerecognation->segmentation_free_recognation(CropImg, plateinfo);
+			}
+			else
+			{
+				finetune->Finetune(image, CropImg);
+				platerecognation->segmentation_free_recognation(CropImg, plateinfo);
+			}
+			plateres.push_back(plateinfo);
+		}
+		return plateres;
+	}
 }

Prj-Linux/lpr/src/PlateDetection.cpp

@@ -1,34 +1,103 @@
-#include "../include/PlateDetection.h"
-#include "util.h"
-namespace pr{
-    PlateDetection::PlateDetection(std::string filename_cascade){
-        cascade.load(filename_cascade);
+#include "../include/Platedetect.h"
 
-    };
-    void PlateDetection::plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w,int max_w){
-        cv::Mat processImage;
+using namespace cv;
+using namespace std;
+namespace pr {
+
+	PlateDetection::PlateDetection(std::string ssd_prototxt, std::string ssd_caffemodel)
+	{
+		ssdNet = cv::dnn::readNetFromCaffe(ssd_prototxt, ssd_caffemodel);
+	}
+	DBDetection::DBDetection(std::string cascadestring)
+	{
+		dbcascade.load(cascadestring);
+	}
+	void PlateDetection::Detectssd(cv::Mat img, std::vector<pr::PlateInfo>  &plateInfos)
+	{
+		int cols = img.cols;
+		int rows = img.rows;
+		Mat in;
+		img.convertTo(in, CV_32F);
+		Mat input(img.size(), CV_32FC3);
+		Mat inputblob1 = input.reshape(1, { 1, 3,rows,cols });
+		Mat input_blob = dnn::blobFromImages(in, 0.225, Size(), Scalar(103.53, 116.28, 123.675), false);
+		float *blobdata = input_blob.ptr<float>();
+		float *blobdata2 = inputblob1.ptr<float>();
+		{
+			for (int i = 0; i < rows; i++)
+			{
+				memcpy(blobdata2 + i * cols, blobdata + 3 * i * cols, cols * sizeof(float));
+				memcpy(blobdata2 + i * cols + rows * cols, blobdata + (1 + 3 * i) * cols, cols * sizeof(float));
+				memcpy(blobdata2 + i * cols + rows * cols * 2, blobdata + (2 + 3 * i) * cols, cols * sizeof(float));
+			}
+		}
+		ssdNet.setInput(inputblob1);
+
+		Mat outputBlob = ssdNet.forward("detection_out");
+
+		Mat detectmat(outputBlob.size[2], outputBlob.size[3], CV_32F, outputBlob.ptr<float>());
+		vector<Rect> recs;
+		vector<float>scs;
+		for (int i = 0; i < detectmat.rows; i++)
+		{
+			float confidence = detectmat.at<float>(i, 2);
+			if (confidence > 0.5)
+			{
+				int x1, x2, y1, y2;
+				Rect rec;
+				Mat cimg;
+				x1 = int(detectmat.at<float>(i, 3) * cols);
+				y1 = int(detectmat.at<float>(i, 4) * rows);
+				x2 = int(detectmat.at<float>(i, 5) * cols);
+				y2 = int(detectmat.at<float>(i, 6) * rows);
+				x1 = max(x1, 0);
+				y1 = max(y1, 0);
+				x2 = min(x2, cols - 1);
+				y2 = min(y2, rows - 1);
+				rec.x = x1; rec.y = y1; rec.width = (x2 - x1 + 1); rec.height = (y2 - y1 + 1);
+				img(rec).copyTo(cimg);
+				PlateInfo plateInfo(cimg, rec);
+				plateInfos.push_back(plateInfo);
+			}
+		}
+	}
+    cv::Mat cropFromImage(const cv::Mat &image,cv::Rect rect){
+        int w = image.cols-1;
+        int h = image.rows-1;
+        rect.x = std::max(rect.x,0);
+        rect.y = std::max(rect.y,0);
+        rect.height = std::min(rect.height,h-rect.y);
+        rect.width = std::min(rect.width,w-rect.x);
+        cv::Mat temp(rect.size(), image.type());
+        cv::Mat cropped;
+        temp = image(rect);
+        temp.copyTo(cropped);
+        return cropped;
+    }
+	void DBDetection::DBDetect(cv::Mat InputImage,std::vector<pr::PlateInfo> &plateInfos,int min_w,int max_w)
+	{
+		cv::Mat processImage;
         cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY);
         std::vector<cv::Rect> platesRegions;
         cv::Size minSize(min_w,min_w/4);
         cv::Size maxSize(max_w,max_w/4);
         if (&processImage == NULL)
             return;
-        cascade.detectMultiScale( processImage, platesRegions,
+        dbcascade.detectMultiScale( processImage, platesRegions,
                                   1.1, 3, cv::CASCADE_SCALE_IMAGE,minSize,maxSize);
         for(auto plate:platesRegions)
         {
-            int zeroadd_w  = static_cast<int>(plate.width*0.30);
-            int zeroadd_h = static_cast<int>(plate.height*2);
-            int zeroadd_x = static_cast<int>(plate.width*0.15);
-            int zeroadd_y = static_cast<int>(plate.height*1);
+            int zeroadd_w  = static_cast<int>(plate.width*0.28);
+            int zeroadd_h = static_cast<int>(plate.height*0.35);
+            int zeroadd_x = static_cast<int>(plate.width*0.14);
+            int zeroadd_y = static_cast<int>(plate.height*0.15);
             plate.x-=zeroadd_x;
             plate.y-=zeroadd_y;
             plate.height += zeroadd_h;
             plate.width += zeroadd_w;
-                cv::Mat plateImage = util::cropFromImage(InputImage,plate);
+			cv::Mat plateImage = cropFromImage(InputImage,plate);
             PlateInfo plateInfo(plateImage,plate);
             plateInfos.push_back(plateInfo);
-
         }
     }
-}//namespace pr
+}

Prj-Linux/lpr/src/PlateRecognation.cpp

@@ -0,0 +1,61 @@
+#include "../include/PlateRecognation.h"
+#include "../include/Pipeline.h"
+using namespace std;
+using namespace cv;
+namespace pr {
+
+	PlateRecognation::PlateRecognation(std::string rec_prototxt, std::string rec_caffemodel)
+	{
+		RecNet = cv::dnn::readNetFromCaffe(rec_prototxt, rec_caffemodel);
+	}
+	void PlateRecognation::segmentation_free_recognation(cv::Mat src, pr::PlateInfo &plateinfo)
+	{
+		float score = 0;
+		string text = "";
+		Mat src1 = src.clone();
+		Mat inputMat(Size(40, 160), CV_8UC3);
+
+		for (int j = 0; j < src.rows; j++)
+		{
+			for (int i = 0; i < src.cols; i++)
+			{
+				inputMat.at<Vec3b>(i, j) = src1.at<Vec3b>(j, i);
+			}
+		}
+		Mat blob = dnn::blobFromImage(inputMat, 1 / 255.f, Size(40, 160), Scalar(0, 0, 0), false, false);
+		RecNet.setInput(blob);
+		Mat outblob = RecNet.forward();
+		int x = outblob.size[2];
+		int y = outblob.size[0];
+		float *data = outblob.ptr<float>();
+		vector<float> scores(84);
+		vector<int>maxidxs;
+		vector<float> maxscore;
+		for (int i = 2; i < 20; i++)
+		{
+			for (int j = 0; j < 84; j++)
+			{
+				scores[j] = data[j * 20 + i];
+			}
+			int idx = max_element(scores.begin(), scores.end()) - scores.begin();
+			maxidxs.push_back(idx);
+			maxscore.push_back(scores[idx]);
+		}
+		int charnum = 0;
+		for (int i = 0; i < maxidxs.size(); i++)
+		{
+			if (maxidxs[i] < pr::CH_PLATE_CODE.size() && (i == 0 || (maxidxs[i - 1] != maxidxs[i])))
+			{
+				text += pr::CH_PLATE_CODE[maxidxs[i]];
+				score += maxscore[i];
+				charnum++;
+			}
+		}
+		if (charnum > 0)
+		{
+			score /= charnum;
+		}
+		plateinfo.setPlateName(text);
+		plateinfo.confidence = score;
+	}
+}

Prj-Linux/lpr/src/PlateSegmentation.cpp

@@ -1,404 +0,0 @@
-//
-// Created by Jack  Yu on 16/10/2017.
-//
-
-#include "../include/PlateSegmentation.h"
-#include "../include/niBlackThreshold.h"
-
-
-//#define DEBUG
-namespace pr{
-
-    PlateSegmentation::PlateSegmentation(std::string prototxt,std::string caffemodel) {
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-    cv::Mat PlateSegmentation::classifyResponse(const cv::Mat &cropped){
-        cv::Mat inputBlob = cv::dnn::blobFromImage(cropped, 1/255.0, cv::Size(22,22), cv::Scalar(0,0,0),false);
-        net.setInput(inputBlob,"data");
-        return net.forward();
-    }
-
-    void drawHist(float* seq,int size,const char* name){
-        cv::Mat image(300,size,CV_8U);
-        image.setTo(0);
-        float* start =seq;
-        float* end = seq+size;
-        float l = *std::max_element(start,end);
-        for(int i = 0;i<size;i++)
-        {
-            int p = int(float(seq[i])/l*300);
-            cv::line(image,cv::Point(i,300),cv::Point(i,300-p),cv::Scalar(255,255,255));
-        }
-        cv::resize(image,image,cv::Size(600,100));
-        cv::imshow(name,image);
-    }
-
-    inline void computeSafeMargin(int &val,const int &rows){
-        val = std::min(val,rows);
-        val = std::max(val,0);
-    }
-
-    cv::Rect boxFromCenter(const cv::Point center,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-        cv::Point p1(center.x -  left ,center.y - top);
-        cv::Point p2( center.x + right, center.y + bottom);
-        p1.x = std::max(0,p1.x);
-        p1.y = std::max(0,p1.y);
-        p2.x = std::min(p2.x,bdSize.width-1);
-        p2.y = std::min(p2.y,bdSize.height-1);
-        cv::Rect rect(p1,p2);
-        return rect;
-    }
-
-    cv::Rect boxPadding(cv::Rect rect,int left,int right,int top,int bottom,cv::Size bdSize)
-    {
-
-        cv::Point center(rect.x+(rect.width>>1),rect.y + (rect.height>>1));
-        int rebuildLeft  = (rect.width>>1 )+ left;
-        int rebuildRight = (rect.width>>1 )+ right;
-        int rebuildTop = (rect.height>>1 )+ top;
-        int rebuildBottom = (rect.height>>1 )+ bottom;
-        return boxFromCenter(center,rebuildLeft,rebuildRight,rebuildTop,rebuildBottom,bdSize);
-
-    }
-
-
-
-    void PlateSegmentation:: refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects){
-        int w = candidatePts[5] - candidatePts[4];
-        int cols = plateImage.cols;
-        int rows = plateImage.rows;
-        for(int i = 0 ; i < candidatePts.size()  ; i++)
-        {
-            int left = 0;
-            int right = 0 ;
-
-            if(i == 0 ){
-                left= candidatePts[i];
-                right = left+w+padding;
-                }
-            else {
-                left = candidatePts[i] - padding;
-                right = left + w + padding * 2;
-            }
-
-            computeSafeMargin(right,cols);
-            computeSafeMargin(left,cols);
-            cv::Rect roi(left,0,right - left,rows-1);
-            cv::Mat roiImage;
-            plateImage(roi).copyTo(roiImage);
-
-            if (i>=1)
-            {
-
-                cv::Mat roi_thres;
-//                cv::threshold(roiImage,roi_thres,0,255,cv::THRESH_OTSU|cv::THRESH_BINARY);
-
-                niBlackThreshold(roiImage,roi_thres,255,cv::THRESH_BINARY,15,0.27,BINARIZATION_NIBLACK);
-
-                std::vector<std::vector<cv::Point>> contours;
-                cv::findContours(roi_thres,contours,cv::RETR_LIST,cv::CHAIN_APPROX_SIMPLE);
-                cv::Point boxCenter(roiImage.cols>>1,roiImage.rows>>1);
-
-                cv::Rect final_bdbox;
-                cv::Point final_center;
-                int final_dist = INT_MAX;
-
-
-                for(auto contour:contours)
-                {
-                    cv::Rect bdbox = cv::boundingRect(contour);
-                    cv::Point center(bdbox.x+(bdbox.width>>1),bdbox.y + (bdbox.height>>1));
-                    int dist = (center.x - boxCenter.x)*(center.x - boxCenter.x);
-                    if(dist<final_dist and  bdbox.height > rows>>1)
-                    {   final_dist =dist;
-                        final_center = center;
-                        final_bdbox = bdbox;
-                    }
-                }
-
-                //rebuild box
-                if(final_bdbox.height/ static_cast<float>(final_bdbox.width) > 3.5 && final_bdbox.width*final_bdbox.height<10)
-                    final_bdbox = boxFromCenter(final_center,8,8,(rows>>1)-3 , (rows>>1) - 2,roiImage.size());
-                else {
-                    if(i == candidatePts.size()-1)
-                        final_bdbox = boxPadding(final_bdbox, padding/2, padding, padding/2, padding/2, roiImage.size());
-                    else
-                        final_bdbox = boxPadding(final_bdbox, padding, padding, padding, padding, roiImage.size());
-
-
-//                    std::cout<<final_bdbox<<std::endl;
-//                    std::cout<<roiImage.size()<<std::endl;
-#ifdef DEBUG
-                    cv::imshow("char_thres",roi_thres);
-
-                    cv::imshow("char",roiImage(final_bdbox));
-                    cv::waitKey(0);
-#endif
-
-
-                }
-
-
-                final_bdbox.x += left;
-
-                rects.push_back(final_bdbox);
-//
-
-            }
-            else
-            {
-                rects.push_back(roi);
-            }
-
-//            else
-//            {
-//
-//            }
-
-//            cv::GaussianBlur(roiImage,roiImage,cv::Size(7,7),3);
-//
-//            cv::imshow("image",roiImage);
-//            cv::waitKey(0);
-
-
-        }
-
-
-
-    }
-    void avgfilter(float *angle_list,int size,int windowsSize) {
-        float *filterd = new float[size];
-        for(int i = 0 ; i < size ; i++) filterd [i] = angle_list[i];
-//        memcpy(filterd,angle_list,size);
-
-        cv::Mat kernal_gaussian = cv::getGaussianKernel(windowsSize,3,CV_32F);
-        float *kernal = (float*)kernal_gaussian.data;
-//        kernal+=windowsSize;
-        int r = windowsSize/2;
-
-
-
-
-        for (int i = 0; i < size; i++) {
-            float avg = 0.00f;
-            for (int j = 0; j < windowsSize; j++) {
-                if(i+j-r>0&&i+j+r<size-1)
-                    avg += filterd[i + j-r]*kernal[j];
-            }
-//            avg = avg / windowsSize;
-            angle_list[i] = avg;
-
-        }
-
-        delete filterd;
-    }
-
-    void PlateSegmentation::templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int>> &candidatePts){
-        int rows = respones.rows;
-        int cols = respones.cols;
-
-
-
-        float *data = (float*)respones.data;
-        float *engNum_prob = data;
-        float *false_prob = data+cols;
-        float *ch_prob = data+cols*2;
-
-        avgfilter(engNum_prob,cols,5);
-        avgfilter(false_prob,cols,5);
-//        avgfilter(ch_prob,cols,5);
-        std::vector<int> candidate_pts(7);
-#ifdef DEBUG
-        drawHist(engNum_prob,cols,"engNum_prob");
-        drawHist(false_prob,cols,"false_prob");
-        drawHist(ch_prob,cols,"ch_prob");
-                cv::waitKey(0);
-#endif
-
-
-
-
-        int cp_list[7];
-        float loss_selected = -10;
-
-        for(int start = 0 ; start < 20 ; start+=2)
-            for(int  width = windowsWidth-5; width < windowsWidth+5 ; width++ ){
-                for(int interval = windowsWidth/2; interval < windowsWidth; interval++)
-                {
-                    int cp1_ch  = start;
-                    int cp2_p0 = cp1_ch+ width;
-                    int cp3_p1 = cp2_p0+ width + interval;
-                    int cp4_p2 = cp3_p1 + width;
-                    int cp5_p3 = cp4_p2 + width+1;
-                    int cp6_p4 = cp5_p3 + width+2;
-                    int cp7_p5= cp6_p4+ width+2;
-
-                    int md1 = (cp1_ch+cp2_p0)>>1;
-                    int md2 = (cp2_p0+cp3_p1)>>1;
-                    int md3 = (cp3_p1+cp4_p2)>>1;
-                    int md4 = (cp4_p2+cp5_p3)>>1;
-                    int md5 = (cp5_p3+cp6_p4)>>1;
-                    int md6 = (cp6_p4+cp7_p5)>>1;
-
-
-
-
-                    if(cp7_p5>=cols)
-                        continue;
-//                    float loss = ch_prob[cp1_ch]+
-//                       engNum_prob[cp2_p0] +engNum_prob[cp3_p1]+engNum_prob[cp4_p2]+engNum_prob[cp5_p3]+engNum_prob[cp6_p4] +engNum_prob[cp7_p5]
-//                    + (false_prob[md2]+false_prob[md3]+false_prob[md4]+false_prob[md5]+false_prob[md5] + false_prob[md6]);
-                    float loss = ch_prob[cp1_ch]*3 -(false_prob[cp3_p1]+false_prob[cp4_p2]+false_prob[cp5_p3]+false_prob[cp6_p4]+false_prob[cp7_p5]);
-
-                    if(loss>loss_selected)
-                    {
-                        loss_selected = loss;
-                        cp_list[0]= cp1_ch;
-                        cp_list[1]= cp2_p0;
-                        cp_list[2]= cp3_p1;
-                        cp_list[3]= cp4_p2;
-                        cp_list[4]= cp5_p3;
-                        cp_list[5]= cp6_p4;
-                        cp_list[6]= cp7_p5;
-                    }
-                }
-            }
-        candidate_pts[0] = cp_list[0];
-        candidate_pts[1] = cp_list[1];
-        candidate_pts[2] = cp_list[2];
-        candidate_pts[3] = cp_list[3];
-        candidate_pts[4] = cp_list[4];
-        candidate_pts[5] = cp_list[5];
-        candidate_pts[6] = cp_list[6];
-
-        candidatePts.first = loss_selected;
-        candidatePts.second = candidate_pts;
-
-    };
-
-
-    void PlateSegmentation::segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones){
-
-
-//        cv::resize(plateImage,plateImage,cv::Size(136,36));
-
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-        int padding  =  plateImage.cols-136 ;
-//        int padding  =  0 ;
-        int height = plateImage.rows - 1;
-        int width = plateImage.cols - 1 - padding;
-        for(int i = 0 ; i < width - windowsWidth +1 ; i +=stride)
-        {
-            cv::Rect roi(i,0,windowsWidth,height);
-            cv::Mat roiImage = plateImageGray(roi);
-            cv::Mat response = classifyResponse(roiImage);
-            respones.push_back(response);
-        }
-
-
-
-
-        respones =  respones.t();
-//        std::pair<float,std::vector<int>> images ;
-//
-//
-//        std::cout<<images.first<<" ";
-//        for(int i = 0 ; i < images.second.size() ; i++)
-//        {
-//            std::cout<<images.second[i]<<" ";
-////            cv::line(plateImageGray,cv::Point(images.second[i],0),cv::Point(images.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-//        }
-
-//        int w = images.second[5] - images.second[4];
-
-//        cv::line(plateImageGray,cv::Point(images.second[5]+w,0),cv::Point(images.second[5]+w,36),cv::Scalar(255,255,255),1); //DEBUG
-//        cv::line(plateImageGray,cv::Point(images.second[5]+2*w,0),cv::Point(images.second[5]+2*w,36),cv::Scalar(255,255,255),1); //DEBUG
-
-
-//        RefineRegion(plateImageGray,images.second,5);
-
-//        std::cout<<w<<std::endl;
-
-//        std::cout<<<<std::endl;
-
-//        cv::resize(plateImageGray,plateImageGray,cv::Size(600,100));
-
-
-
-    }
-
-//    void filterGaussian(cv::Mat &respones,float sigma){
-//
-//    }
-
-
-    void PlateSegmentation::segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects){
-        cv::Mat plateImage = plateInfo.getPlateImage(); // get src image .
-        cv::Mat plateImageGray;
-        cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY);
-        //do binarzation
-        //
-        std::pair<float,std::vector<int>> sections ; // segment points variables .
-
-        cv::Mat respones; //three response of every sub region from origin image .
-        segmentPlateBySlidingWindows(plateImage,DEFAULT_WIDTH,1,respones);
-        templateMatchFinding(respones,DEFAULT_WIDTH/stride,sections);
-        for(int i = 0; i < sections.second.size() ; i++)
-        {
-            sections.second[i]*=stride;
-
-        }
-
-//        std::cout<<sections<<std::endl;
-
-        refineRegion(plateImageGray,sections.second,5,Char_rects);
-#ifdef DEBUG
-        for(int i = 0 ; i < sections.second.size() ; i++)
-        {
-            std::cout<<sections.second[i]<<" ";
-            cv::line(plateImageGray,cv::Point(sections.second[i],0),cv::Point(sections.second[i],36),cv::Scalar(255,255,255),1); //DEBUG
-        }
-        cv::imshow("plate",plateImageGray);
-        cv::waitKey(0);
-#endif
-//        cv::waitKey(0);
-
-    }
-
-    void PlateSegmentation::ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects){
-        cv::Mat plateImage = plateInfo.getPlateImage();
-        for(int i = 0 ; i < rects.size() ; i++){
-            cv::Mat charImage;
-            plateImage(rects[i]).copyTo(charImage);
-            if(charImage.channels())
-                cv::cvtColor(charImage,charImage,cv::COLOR_BGR2GRAY);
-//            cv::imshow("image",charImage);
-//            cv::waitKey(0);
-            cv::equalizeHist(charImage,charImage);
-//
-
-//
-
-
-            std::pair<CharType,cv::Mat> char_instance;
-            if(i == 0 ){
-
-                char_instance.first = CHINESE;
-
-
-            } else if(i == 1){
-                char_instance.first = LETTER;
-            }
-            else{
-                char_instance.first = LETTER_NUMS;
-            }
-            char_instance.second = charImage;
-            plateInfo.appendPlateChar(char_instance);
-
-        }
-
-    }
-
-}//namespace pr

Prj-Linux/lpr/src/Recognizer.cpp

@@ -1,23 +0,0 @@
-//
-// Created by Jack Yu on 22/10/2017.
-//
-
-#include "../include/Recognizer.h"
-
-namespace pr{
-    void GeneralRecognizer::SegmentBasedSequenceRecognition(PlateInfo &plateinfo){
-        for(auto char_instance:plateinfo.plateChars)
-        {
-            std::pair<CharType,cv::Mat> res;
-            if(char_instance.second.rows*char_instance.second.cols>40) {
-                label code_table = recognizeCharacter(char_instance.second);
-                res.first = char_instance.first;
-                code_table.copyTo(res.second);
-                plateinfo.appendPlateCoding(res);
-            } else{
-                res.first = INVALID;
-                plateinfo.appendPlateCoding(res);
-            }
-        }
-    }
-}

Prj-Linux/lpr/src/SegmentationFreeRecognizer.cpp

@@ -1,89 +0,0 @@
-//
-// Created by Jack Yu on 28/11/2017.
-//
-#include "../include/SegmentationFreeRecognizer.h"
-
-namespace pr {
-    SegmentationFreeRecognizer::SegmentationFreeRecognizer(std::string prototxt, std::string caffemodel) {
-        net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);
-    }
-    inline int judgeCharRange(int id)
-    {return id<31 || id>63;
-    }
-    std::pair<std::string,float> decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table,float thres)
-    {
-        cv::MatSize mtsize = code_table.size;
-        int sequencelength = mtsize[2];
-        int labellength = mtsize[1];
-        cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-        std::string name = "";
-        std::vector<int> seq(sequencelength);
-        std::vector<std::pair<int,float>> seq_decode_res;
-        for(int i = 0 ; i < sequencelength;  i++) {
-            float *fstart = ((float *) (code_table.data) + i * labellength );
-            int id = std::max_element(fstart,fstart+labellength) - fstart;
-            seq[i] =id;
-        }
-
-        float sum_confidence = 0;
-        int plate_lenghth  = 0 ;
-        for(int i = 0 ; i< sequencelength ; i++)
-        {
-            if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-            {
-                float *fstart = ((float *) (code_table.data) + i * labellength );
-                float confidence = *(fstart+seq[i]);
-                std::pair<int,float> pair_(seq[i],confidence);
-                seq_decode_res.push_back(pair_);
-            }
-        }
-        int  i = 0;
-        if (seq_decode_res.size()>1 && judgeCharRange(seq_decode_res[0].first) && judgeCharRange(seq_decode_res[1].first))
-        {
-            i=2;
-            int c = seq_decode_res[0].second<seq_decode_res[1].second;
-            name+=mapping_table[seq_decode_res[c].first];
-            sum_confidence+=seq_decode_res[c].second;
-            plate_lenghth++;
-        }
-
-        for(; i < seq_decode_res.size();i++)
-        {
-            name+=mapping_table[seq_decode_res[i].first];
-            sum_confidence +=seq_decode_res[i].second;
-            plate_lenghth++;
-        }
-        std::pair<std::string,float> res;
-        res.second = sum_confidence/plate_lenghth;
-        res.first = name;
-        return res;
-
-    }
-    std::string decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table)
-    {
-        cv::MatSize mtsize = code_table.size;
-        int sequencelength = mtsize[2];
-        int labellength = mtsize[1];
-        cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-        std::string name = "";
-        std::vector<int> seq(sequencelength);
-        for(int i = 0 ; i < sequencelength;  i++) {
-            float *fstart = ((float *) (code_table.data) + i * labellength );
-            int id = std::max_element(fstart,fstart+labellength) - fstart;
-            seq[i] =id;
-        }
-        for(int i = 0 ; i< sequencelength ; i++)
-        {
-            if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-                name+=mapping_table[seq[i]];
-        }
-        return name;
-    }
-    std::pair<std::string,float> SegmentationFreeRecognizer::SegmentationFreeForSinglePlate(cv::Mat Image,std::vector<std::string> mapping_table) {
-        cv::transpose(Image,Image);
-        cv::Mat inputBlob = cv::dnn::blobFromImage(Image, 1 / 255.0, cv::Size(40,160));
-        net.setInput(inputBlob, "data");
-        cv::Mat char_prob_mat = net.forward();
-        return decodeResults(char_prob_mat,mapping_table,0.00);
-    }
-}

Prj-Linux/lpr/src/util.h

@@ -1,67 +0,0 @@
-//
-// Created by Jack Yu on 04/04/2017.
-//
-
-#include <opencv2/opencv.hpp>
-namespace util{
-    template <class T> void swap ( T& a, T& b )
-    {
-        T c(a); a=b; b=c;
-    }
-    template <class T> T min(T& a,T& b )
-    {
-        return a>b?b:a;
-    }
-
-    cv::Mat cropFromImage(const cv::Mat &image,cv::Rect rect){
-        int w = image.cols-1;
-        int h = image.rows-1;
-        rect.x = std::max(rect.x,0);
-        rect.y = std::max(rect.y,0);
-        rect.height = std::min(rect.height,h-rect.y);
-        rect.width = std::min(rect.width,w-rect.x);
-        cv::Mat temp(rect.size(), image.type());
-        cv::Mat cropped;
-        temp = image(rect);
-        temp.copyTo(cropped);
-        return cropped;
-
-    }
-
-    cv::Mat cropBox2dFromImage(const cv::Mat &image,cv::RotatedRect rect)
-    {
-        cv::Mat M, rotated, cropped;
-        float angle = rect.angle;
-        cv::Size rect_size(rect.size.width,rect.size.height);
-        if (rect.angle < -45.) {
-            angle += 90.0;
-            swap(rect_size.width, rect_size.height);
-        }
-        M = cv::getRotationMatrix2D(rect.center, angle, 1.0);
-        cv::warpAffine(image, rotated, M, image.size(), cv::INTER_CUBIC);
-        cv::getRectSubPix(rotated, rect_size, rect.center, cropped);
-        return cropped;
-    }
-
-    cv::Mat calcHist(const cv::Mat &image)
-    {
-        cv::Mat hsv;
-        std::vector<cv::Mat> hsv_planes;
-        cv::cvtColor(image,hsv,cv::COLOR_BGR2HSV);
-        cv::split(hsv,hsv_planes);
-        cv::Mat hist;
-        int histSize = 256;
-        float range[] = {0,255};
-        const float* histRange = {range};
-        cv::calcHist( &hsv_planes[0], 1, 0, cv::Mat(), hist, 1, &histSize, &histRange,true, true);
-        return hist;
-    }
-    
-    float computeSimilir(const cv::Mat &A,const cv::Mat &B)
-    {
-        cv::Mat histA,histB;
-        histA = calcHist(A);
-        histB = calcHist(B);
-        return cv::compareHist(histA,histB,CV_COMP_CORREL);
-    }
-}//namespace util

Prj-Linux/lpr/tests/testPipeLine.cpp

@@ -0,0 +1,28 @@
+#include"../include/Pipeline.h"
+
+void TEST_PIPELINE()
+{
+	pr::PipelinePR prc("../lpr/model/mininet_ssd_v1.prototxt", "../lpr/model/mininet_ssd_v1.caffemodel",
+		"../lpr/model/refinenet.prototxt", "../lpr/model/refinenet.caffemodel",
+		"../lpr/model/SegmenationFree-Inception.prototxt", "../lpr/model/SegmenationFree-Inception.caffemodel",
+		"../lpr/model/cascade_double.xml");
+	cv::Mat img = cv::imread("../lpr//res//test.jpg");
+	std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(img,0);
+	for (auto st : res) {
+		if (st.confidence > 0.75) {
+			std::cout << st.getPlateName() << " " << st.confidence << std::endl;
+			cv::Rect region = st.getPlateRect();
+
+			cv::rectangle(img, cv::Point(region.x, region.y), cv::Point(region.x + region.width, region.y + region.height), cv::Scalar(255, 255, 0), 2);
+		}
+	}
+
+	//cv::imshow("image", img);
+	//cv::waitKey(0);
+
+}
+int main()
+{
+	TEST_PIPELINE();
+	return 0;
+}

Prj-Linux/lpr/tests/test_detection.cpp

@@ -1,32 +0,0 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
-
-#include <../include/PlateDetection.h>
-
-
-void drawRect(cv::Mat image,cv::Rect rect)
-{
-    cv::Point p1(rect.x,rect.y);
-    cv::Point p2(rect.x+rect.width,rect.y+rect.height);
-    cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
-}
-
-
-int main()
-{
-    cv::Mat image = cv::imread("res/test1.jpg");
-    pr::PlateDetection plateDetection("model/cascade.xml");
-    std::vector<pr::PlateInfo> plates;
-    plateDetection.plateDetectionRough(image,plates);
-    for(pr::PlateInfo platex:plates)
-    {
-        drawRect(image,platex.getPlateRect());
-        cv::imwrite("res/cache/test.png",platex.getPlateImage());
-        cv::imshow("image",platex.getPlateImage());
-        cv::waitKey(0);
-    }
-    cv::imshow("image",image);
-    cv::waitKey(0);
-    return 0 ;
-}

Prj-Linux/lpr/tests/test_fastdeskew.cpp

@@ -1,34 +0,0 @@
-//
-// Created by Jack Yu on 02/10/2017.
-//
-
-
-#include <../include/FastDeskew.h>
-
-
-void drawRect(cv::Mat image,cv::Rect rect)
-{
-    cv::Point p1(rect.x,rect.y);
-    cv::Point p2(rect.x+rect.width,rect.y+rect.height);
-    cv::rectangle(image,p1,p2,cv::Scalar(0,255,0),1);
-}
-void TEST_DESKEW(){
-
-    cv::Mat image = cv::imread("res/3.png",cv::IMREAD_GRAYSCALE);
-//    cv::resize(image,image,cv::Size(136*2,36*2));
-    cv::Mat deskewed = pr::fastdeskew(image,12);
-//    cv::imwrite("./res/4.png",deskewed);
-//     cv::Mat deskewed2 = pr::fastdeskew(deskewed,12);
-//
-    cv::imshow("image",deskewed);
-    cv::waitKey(0);
-
-}
-int main()
-{
-
-    TEST_DESKEW();
-    return 0 ;
-
-
-}

Prj-Linux/lpr/tests/test_finemapping.cpp

@@ -1,25 +0,0 @@
-//
-// Created by Jack Yu on 24/09/2017.
-//
-
-#include "FineMapping.h"
-
-
-
-
-
-
-int main()
-{
-    cv::Mat image = cv::imread("res/cache/test.png");
-    cv::Mat image_finemapping = pr::FineMapping::FineMappingVertical(image);
-    pr::FineMapping finemapper =  pr::FineMapping("model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel");
-     image_finemapping = finemapper.FineMappingHorizon(image_finemapping,0,-3);
-    cv::imwrite("res/cache/finemappingres.png",image_finemapping);
-    cv::imshow("image",image_finemapping);
-    cv::waitKey(0);
-
-
-    return 0 ;
-
-}

Prj-Linux/lpr/tests/test_pipeline.cpp

@@ -1,179 +0,0 @@
-//
-// Created by Jack Yu on 23/10/2017.
-//
-
-#include "../include/Pipeline.h"
-
-
-
-
-using namespace std;
-
-template<class T>
-static unsigned int levenshtein_distance(const T &s1, const T &s2) {
-    const size_t len1 = s1.size(), len2 = s2.size();
-    std::vector<unsigned int> col(len2 + 1), prevCol(len2 + 1);
-
-    for (unsigned int i = 0; i < prevCol.size(); i++) prevCol[i] = i;
-    for (unsigned int i = 0; i < len1; i++) {
-        col[0] = i + 1;
-        for (unsigned int j = 0; j < len2; j++)
-            col[j + 1] = min(
-                    min(prevCol[1 + j] + 1, col[j] + 1),
-                    prevCol[j] + (s1[i] == s2[j] ? 0 : 1));
-        col.swap(prevCol);
-    }
-    return prevCol[len2];
-}
-
-
-
-
-void TEST_ACC(){
-
-    pr::PipelinePR prc("model/cascade.xml",
-                       "model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel",
-                       "model/Segmentation.prototxt","model/Segmentation.caffemodel",
-                       "model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel",
-                       "model/SegmenationFree-Inception.prototxt","model/SegmenationFree-Inception.caffemodel"
-    );
-
-    ifstream file;
-    string imagename;
-    int n = 0,correct = 0,j = 0,sum = 0;
-    char filename[] = "/Users/yujinke/Downloads/general_test/1.txt";
-    string pathh = "/Users/yujinke/Downloads/general_test/";
-    file.open(filename, ios::in);
-    while (!file.eof())
-    {
-        file >> imagename;
-        string imgpath = pathh + imagename;
-        std::cout << "------------------------------------------------" << endl;
-        cout << "图片名：" << imagename << endl;
-        cv::Mat image = cv::imread(imgpath);
-//		cv::imshow("image", image);
-//		cv::waitKey(0);
-
-        std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(image,pr::SEGMENTATION_FREE_METHOD);
-
-        float conf = 0;
-        vector<float> con ;
-        vector<string> name;
-        for (auto st : res) {
-            if (st.confidence > 0.1) {
-                //std::cout << st.getPlateName() << " " << st.confidence << std::endl;
-                con.push_back(st.confidence);
-                name.push_back(st.getPlateName());
-                //conf += st.confidence;
-            }
-            else
-                cout << "no string" << endl;
-        }
-        //	std::cout << conf << std::endl;
-        int num = con.size();
-        float max = 0;
-        string platestr, chpr, ch;
-        int diff = 0,dif = 0;
-        for (int i = 0; i < num; i++) {
-
-            if (con.at(i) > max)
-            {
-                max = con.at(i);
-                platestr = name.at(i);
-            }
-
-        }
-        //	cout << "max:"<<max << endl;
-        cout << "string:" << platestr << endl;
-        chpr = platestr.substr(0, 2);
-        ch = imagename.substr(0, 2);
-        diff = levenshtein_distance(imagename, platestr);
-        dif = diff - 4;
-        cout << "差距:" <<dif << endl;
-        sum += dif;
-        if (ch != chpr) n++;
-        if (diff == 0)	correct++;
-        j++;
-    }
-    float cha = 1 - float(n) / float(j);
-    std::cout << "------------------------------------------------" << endl;
-    cout << "车牌总数：" << j << endl;
-    cout << "汉字识别准确率："<<cha << endl;
-    float chaccuracy = 1 - float(sum - n * 2) /float(j * 8);
-    cout << "字符识别准确率：" << chaccuracy << endl;
-
-}
-
-
-void TEST_PIPELINE(){
-
-    pr::PipelinePR prc("model/cascade.xml",
-                      "model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel",
-                      "model/Segmentation.prototxt","model/Segmentation.caffemodel",
-                      "model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel",
-                       "model/SegmenationFree-Inception.prototxt","model/SegmenationFree-Inception.caffemodel"
-                    );
-
-    cv::Mat image = cv::imread("/Users/yujinke/ClionProjects/cpp_ocr_demo/test.png");
-
-
-    std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(image,pr::SEGMENTATION_FREE_METHOD);
-
-    for(auto st:res) {
-        if(st.confidence>0.75) {
-            std::cout << st.getPlateName() << " " << st.confidence << std::endl;
-            cv::Rect region = st.getPlateRect();
-
-            cv::rectangle(image,cv::Point(region.x,region.y),cv::Point(region.x+region.width,region.y+region.height),cv::Scalar(255,255,0),2);
-        }
-    }
-
-    cv::imshow("image",image);
-    cv::waitKey(0);
-
-}
-
-
-void TEST_CAM()
-{
-    cv::VideoCapture capture("test1.mp4");
-    cv::Mat frame;
-    pr::PipelinePR prc("model/cascade.xml",
-                       "model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel",
-                       "model/Segmentation.prototxt","model/Segmentation.caffemodel",
-                       "model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel",
-                       "model/SegmenationFree-Inception.prototxt","model/SegmenationFree-Inception.caffemodel"
-    );
-    while(1) {
-        //读取下一帧
-        if (!capture.read(frame)) {
-            std::cout << "读取视频失败" << std::endl;
-            exit(1);
-        }
-        std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(frame,pr::SEGMENTATION_FREE_METHOD);
-
-        for(auto st:res) {
-            if(st.confidence>0.75) {
-                std::cout << st.getPlateName() << " " << st.confidence << std::endl;
-                cv::Rect region = st.getPlateRect();
-                cv::rectangle(frame,cv::Point(region.x,region.y),cv::Point(region.x+region.width,region.y+region.height),cv::Scalar(255,255,0),2);
-            }
-        }
-        cv::imshow("image",frame);
-        cv::waitKey(1);
-    }
-
-}
-
-
-int main()
-{
-    TEST_ACC();
-
-//    TEST_CAM();
-//    TEST_PIPELINE();
-
-    return 0 ;
-
-
-}

Prj-Linux/lpr/tests/test_recognization.cpp

@@ -1,54 +0,0 @@
-//
-// Created by Jack Yu on 23/10/2017.
-//
-
-#include "../include/CNNRecognizer.h"
-
-std::vector<std::string> chars{"京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","鲁","豫","鄂","湘","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z"};
-
-#include <opencv2/dnn.hpp>
-using namespace cv::dnn;
-
-
-void getMaxClass(cv::Mat &probBlob, int *classId, double *classProb)
-{
-//    cv::Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix
-    cv::Point classNumber;
-
-    cv::minMaxLoc(probBlob, NULL, classProb, NULL, &classNumber);
-
-    *classId = classNumber.x;
-}
-
-void TEST_RECOGNIZATION(){
-//        pr::CNNRecognizer instance("model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel");
-         Net net  = cv::dnn::readNetFromCaffe("model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel");
-    cv::Mat image = cv::imread("res/char1.png",cv::IMREAD_GRAYSCALE);
-    cv::resize(image,image,cv::Size(14,30));
-    cv::equalizeHist(image,image);
-    cv::Mat inputBlob = cv::dnn::blobFromImage(image, 1/255.0, cv::Size(14,30), false);
-
-    net.setInput(inputBlob,"data");
-
-    cv::Mat res = net.forward();
-    std::cout<<res<<std::endl;
-    float *p = (float*)res.data;
-    int maxid= 0;
-    double prob = 0;
-
-    getMaxClass(res,&maxid,&prob);
-
-
-
-        std::cout<<chars[maxid]<<std::endl;
-
-
-
-
-
-};
-int main()
-{TEST_RECOGNIZATION();
-
-
-}

Prj-Linux/lpr/tests/test_segmentation.cpp

@@ -1,43 +0,0 @@
-//
-// Created by Jack Yu on 16/10/2017.
-//
-
-
-#include "../include/PlateSegmentation.h"
-#include "../include/CNNRecognizer.h"
-#include "../include/Recognizer.h"
-
-
-std::vector<std::string> chars{"京","沪","津","渝","冀","晋","蒙","辽","吉","黑","苏","浙","皖","闽","赣","鲁","豫","鄂","湘","粤","桂","琼","川","贵","云","藏","陕","甘","青","宁","新","0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z"};
-
-
-void TEST_SLIDINGWINDOWS_EVAL(){
-    cv::Mat demo = cv::imread("res/cache/finemappingres.png");
-    cv::resize(demo,demo,cv::Size(136,36));
-
-    cv::Mat respones;
-    pr::PlateSegmentation plateSegmentation("model/Segmentation.prototxt","model/Segmentation.caffemodel");
-    pr::PlateInfo plate;
-    plate.setPlateImage(demo);
-    std::vector<cv::Rect> rects;
-    plateSegmentation.segmentPlatePipline(plate,1,rects);
-    plateSegmentation.ExtractRegions(plate,rects);
-
-    pr::GeneralRecognizer *recognizer = new pr::CNNRecognizer("model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel");
-    recognizer->SegmentBasedSequenceRecognition(plate);
-    std::cout<<plate.decodePlateNormal(chars)<<std::endl;
-
-
-    delete(recognizer);
-
-
-
-
-
-}
-int main(){
-
-    TEST_SLIDINGWINDOWS_EVAL();
-
-    return  0;
-}

Prj-Linux/lpr/tests/test_segmentationFree.cpp

@@ -1,54 +0,0 @@
-//
-// Created by Jack Yu on 29/11/2017.
-//
-#include "../include/SegmentationFreeRecognizer.h"
-#include "../include/Pipeline.h"
-
-#include "../include/PlateInfo.h"
-
-
-
-std::string decodeResults(cv::Mat code_table,std::vector<std::string> mapping_table)
-{
-    cv::MatSize mtsize = code_table.size;
-    int sequencelength = mtsize[2];
-    int labellength = mtsize[1];
-    cv::transpose(code_table.reshape(1,1).reshape(1,labellength),code_table);
-    std::string name = "";
-    std::vector<int> seq(sequencelength);
-    for(int i = 0 ; i < sequencelength;  i++) {
-        float *fstart = ((float *) (code_table.data) + i * labellength );
-        int id = std::max_element(fstart,fstart+labellength) - fstart;
-        seq[i] =id;
-    }
-    for(int i = 0 ; i< sequencelength ; i++)
-    {
-        if(seq[i]!=labellength-1 && (i==0 || seq[i]!=seq[i-1]))
-            name+=mapping_table[seq[i]];
-    }
-    std::cout<<name;
-    return name;
-}
-
-
-int main()
-{
-    cv::Mat image = cv::imread("res/cache/chars_segment.jpg");
-//    cv::transpose(image,image);
-
-//    cv::resize(image,image,cv::Size(160,40));
-    cv::imshow("xxx",image);
-    cv::waitKey(0);
-    pr::SegmentationFreeRecognizer recognizr("model/SegmenationFree-Inception.prototxt","model/ISegmenationFree-Inception.caffemodel");
-    std::pair<std::string,float> res = recognizr.SegmentationFreeForSinglePlate(image,pr::CH_PLATE_CODE);
-    std::cout<<res.first<<" "
-                  <<res.second<<std::endl;
-
-
-//    decodeResults(plate,pr::CH_PLATE_CODE);
-    cv::imshow("image",image);
-    cv::waitKey(0);
-
-    return  0;
-
-}

Prj-Win/lpr/include/Pipeline.h

@@ -22,18 +22,18 @@ namespace pr
 			PlateDetection *platedetection;
 			FineTune *finetune;
 			PlateRecognation *platerecognation;
-			//PlateColorClass *platecolorclass;
+			DBDetection *dbdetection;
 
 			
 
 			PipelinePR(std::string detect_prototxt, std::string detect_caffemodel,
 				std::string finetune_prototxt, std::string finetune_caffemodel,
-				std::string platerec_prototxt, std::string platerec_caffemodel/*,
-				std::string platecolor_mnn*/);
+				std::string platerec_prototxt, std::string platerec_caffemodel,
+				std::string dbstring);
 			~PipelinePR();
 
 			std::vector<std::string> plateRes;
-			std::vector<PlateInfo> RunPiplineAsImage(cv::Mat srcImage);
+			std::vector<PlateInfo> RunPiplineAsImage(cv::Mat srcImage,int IsDB);
 
 	};
 }

Prj-Win/lpr/include/Platedetect.h

@@ -12,5 +12,12 @@ namespace pr
 	private:
 		cv::dnn::Net ssdNet;
 	};
+	class DBDetection{
+		public:
+		DBDetection(std::string cascadestring);
+		void DBDetect(cv::Mat inputImg,std::vector<pr::PlateInfo> &plateInfos,int min_w,int max_w);
+	private:
+		cv::CascadeClassifier dbcascade;
+	};
 }//namespace pr
 #endif // !_PLATEDETECT_H_

Prj-Win/lpr/model/cascade_double.xml

@@ -0,0 +1,504 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>LBP</featureType>
+  <height>24</height>
+  <width>44</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9599999189376831e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>28</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>256</maxCatCount>
+    <featSize>1</featSize></featureParams>
+  <stageNum>11</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.1049392223358154e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 17 -3334 -513 -573452048 -1057 -1 -267777 -1 -1025</internalNodes>
+          <leafValues>
+            -9.4297146797180176e-01 7.8370976448059082e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 36 -262198 -12062208 -169869377 -674954752 -7602177
+            -16781941 -67109937 -1073</internalNodes>
+          <leafValues>
+            -8.8537323474884033e-01 7.2940820455551147e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 9 1431697887 1431687647 -545270049 -262145 1413609965
+            1364250775 -1065985 -67108865</internalNodes>
+          <leafValues>
+            -8.3008646965026855e-01 7.2340542078018188e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.0404651165008545e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 19 -524289 -136314913 1360393719 -2097153 -134219777
+            -4194817 274068735 -1</internalNodes>
+          <leafValues>
+            -8.5476654767990112e-01 8.4671354293823242e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 29 -16515073 -150929664 -1 -8650881 -8388609 -11403265
+            -1 -2097153</internalNodes>
+          <leafValues>
+            -7.7620923519134521e-01 7.8929436206817627e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 1 2013233135 2147450879 -45760593 2147474943 2146959359
+            2104754047 2080374783 2147417087</internalNodes>
+          <leafValues>
+            -8.9144438505172729e-01 5.9051072597503662e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.1397969722747803e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 15 -1041 -8225 -546308113 -1 -33554433 -394753 -2359297
+            -1</internalNodes>
+          <leafValues>
+            -8.5447394847869873e-01 7.4388968944549561e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 27 -1053970 -17 -35651601 -1048625 -345331574 -5247030
+            -74453009 -1</internalNodes>
+          <leafValues>
+            -8.6756819486618042e-01 6.3549250364303589e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 43 -1050897 -1025 -71304209 -134219793 -1033 -559948801
+            -67110929 1996976642</internalNodes>
+          <leafValues>
+            -8.6068797111511230e-01 5.8224511146545410e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-9.8026764392852783e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 12 -1077953601 -1 -1073758273 -16387 -147457 -1
+            -1073758209 -1</internalNodes>
+          <leafValues>
+            -8.2477015256881714e-01 7.1671992540359497e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 47 -4345 -989855745 -271062524 -319815697 -134742265
+            1423962843 -134218173 -1</internalNodes>
+          <leafValues>
+            -7.4230498075485229e-01 7.3275351524353027e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 37 -22347778 -3477554 -33554433 -2066 -9438209 -65537
+            -1 -1048577</internalNodes>
+          <leafValues>
+            -8.3125960826873779e-01 5.8680748939514160e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.2986627817153931e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 4 -8388737 -262273 -10485761 -137 -8388745 -129 -262273
+            -1</internalNodes>
+          <leafValues>
+            -8.7727063894271851e-01 6.1173391342163086e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 20 -1 -10241 1431688703 -1 -1 -1042 1163263999
+            2002780159</internalNodes>
+          <leafValues>
+            -8.2954949140548706e-01 5.9151750802993774e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 3 -44786212 -33726467 -268509185 -17409 -657175332
+            -2270500 -1198986500 -1148450934</internalNodes>
+          <leafValues>
+            -7.6026451587677002e-01 5.8319890499114990e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 28 -15794258 -78643282 -821817846 -552742962 -653344834
+            -91750417 -1622147105 -7340065</internalNodes>
+          <leafValues>
+            -7.6077878475189209e-01 5.5891805887222290e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 30 1427232247 357954767 1971310559 2012602108
+            -202375953 -44049 -4456789 -18</internalNodes>
+          <leafValues>
+            -6.7643576860427856e-01 6.0950380563735962e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.3029408454895020e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 39 -134218753 -1 -1 -202899593 -1 -4194305 -67108865
+            -527497</internalNodes>
+          <leafValues>
+            -7.5626724958419800e-01 7.5137990713119507e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 6 -8388737 -8388737 -1 -1 -8421505 -129 -8388737 -1</internalNodes>
+          <leafValues>
+            -7.7118909358978271e-01 6.3570922613143921e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 45 -825233914 -654313761 -589830738 -35651585 -16778427
+            -83886281 -151000316 -1056964737</internalNodes>
+          <leafValues>
+            -7.2490030527114868e-01 5.7298541069030762e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 18 2002780159 2136866815 -67109377 -19969 2147344383
+            2101472763 2108680957 2147450607</internalNodes>
+          <leafValues>
+            -8.5162043571472168e-01 4.5608481764793396e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 16 -7472470 -3505654 -29841 -65536 -1166086177
+            -67109121 -288690177 -32085</internalNodes>
+          <leafValues>
+            -7.9073858261108398e-01 5.0315058231353760e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.3164747953414917e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 41 -1 -1025 -1 -2228225 -8432299 -571189899 -139265 -1</internalNodes>
+          <leafValues>
+            -7.8127676248550415e-01 6.5899217128753662e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 2 -34275329 -198665 -2113 -12289 -573243396 -590292744
+            -1049857 -277</internalNodes>
+          <leafValues>
+            -7.3563832044601440e-01 6.3897633552551270e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 14 -1565523969 -1347420193 -277086209 -1342177313
+            -134217729 -263169 -285212673 -1459618305</internalNodes>
+          <leafValues>
+            -8.1234931945800781e-01 4.9628043174743652e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 33 -1079312417 -83826176 -33686017 -570426508
+            -1627464961 -5377 -277761 -17</internalNodes>
+          <leafValues>
+            -7.0463657379150391e-01 5.2093976736068726e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 25 -66 -4116 1607291240 -5298489 -9847160 2011036101
+            357852669 1476259327</internalNodes>
+          <leafValues>
+            -8.2066470384597778e-01 4.9184983968734741e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 7 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.1098697185516357e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 44 -134217949 -167773185 -404750589 -1 -134744525
+            -1846018321 -2097357 -1</internalNodes>
+          <leafValues>
+            -7.6240319013595581e-01 6.8946647644042969e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 7 -12633797 -524321 1058880319 -129 -50790401 -262405
+            -1075052545 -5</internalNodes>
+          <leafValues>
+            -7.1431988477706909e-01 6.2125796079635620e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 24 -144703501 -10486797 -134217729 -136317057
+            -271646721 -174069583 -168952849 -1072726014</internalNodes>
+          <leafValues>
+            -6.5710061788558960e-01 6.1531358957290649e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 38 -134218774 -149487872 -33554433 -537927872 -69209089
+            -145228029 -2360849 -524449</internalNodes>
+          <leafValues>
+            -7.3570650815963745e-01 4.9681660532951355e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 5 -136380419 -8390657 -2228225 -196707 1565810157
+            2147048386 -268702725 2080373485</internalNodes>
+          <leafValues>
+            -7.2735637426376343e-01 5.2713739871978760e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-2.0547957420349121e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 13 -11141121 -44695553 -1 -11144193 -9217 -321
+            -335811841 -4216577</internalNodes>
+          <leafValues>
+            -7.3916637897491455e-01 6.7595410346984863e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 26 -369104657 -4194321 -1061429013 -67114529 -251662085
+            -138412033 3334395 -234882305</internalNodes>
+          <leafValues>
+            -6.9217604398727417e-01 5.4744583368301392e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 11 1373590751 1373632511 -262169 -33859589 -572533249
+            -572524625 -135266305 -32833</internalNodes>
+          <leafValues>
+            -7.1100026369094849e-01 5.3469187021255493e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 23 -3148053 -1054802 -1 -5 -7340125 -3689942 -74448917
+            -687087094</internalNodes>
+          <leafValues>
+            -5.6383520364761353e-01 6.3540917634963989e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 8 245501180 -1615197700 -46219265 -1075925028
+            -307580929 -373826 -1076187139 -1343746644</internalNodes>
+          <leafValues>
+            -5.8823972940444946e-01 6.1824375391006470e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 9 -->
+    <_>
+      <maxWeakCount>6</maxWeakCount>
+      <stageThreshold>-1.6300759315490723e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 42 -1 -4129 -8193 -135795737 -1 -6417 -1 -137887866</internalNodes>
+          <leafValues>
+            -7.7990013360977173e-01 6.1912822723388672e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 31 -12845643 -1934361103 -581837313 -1644167171
+            -1175537153 -1392516625 -1681655299 -1358963807</internalNodes>
+          <leafValues>
+            -7.1357387304306030e-01 5.6909996271133423e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 34 288428543 -34262659 1976906747 -42117703 858797567
+            -4965441 2008290292 -1146080848</internalNodes>
+          <leafValues>
+            -6.7784935235977173e-01 5.4983222484588623e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 22 -25297611 -100663553 -9830515 -570556417 -53741251
+            -36570627 -67437302 -12583252</internalNodes>
+          <leafValues>
+            -6.3567954301834106e-01 5.9044981002807617e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 46 -403706354 82769839 -446830048 -989858098 -8921600
+            1087893408 -100663520 -134217729</internalNodes>
+          <leafValues>
+            -7.0569902658462524e-01 5.4874616861343384e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 0 -965744853 -420544541 -8392718 -1569784218 -192940313
+            1744830335 -9 -1003227661</internalNodes>
+          <leafValues>
+            -5.7118493318557739e-01 6.4167028665542603e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 10 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-2.1370947360992432e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 40 -673196033 -2144789 287251423 -538968593 -1399068449
+            -73535505 -13631489 -526857</internalNodes>
+          <leafValues>
+            -7.2344118356704712e-01 6.8316829204559326e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 32 -214216429 -2112561 -445819937 1964790555 -17185861
+            -303183720 -357832229 -3148837</internalNodes>
+          <leafValues>
+            -7.1031409502029419e-01 5.4083776473999023e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 10 -9043969 -36118539 2147450623 -4194305 -2246657
+            -102721404 1073685759 -1366622208</internalNodes>
+          <leafValues>
+            -5.8772116899490356e-01 6.4753490686416626e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 35 1167938037 16237049 -1120535169 1029034397 9567155
+            -2001626128 -1146622977 -1142248004</internalNodes>
+          <leafValues>
+            -6.4004391431808472e-01 5.6415843963623047e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 21 -70126622 -134227061 -536243648 188013035
+            -1234960385 1416625375 -486868997 62913535</internalNodes>
+          <leafValues>
+            -8.8218396902084351e-01 4.1129270195960999e-01</leafValues></_></weakClassifiers></_></stages>
+  <features>
+    <_>
+      <rect>
+        0 1 1 6</rect></_>
+    <_>
+      <rect>
+        1 0 8 3</rect></_>
+    <_>
+      <rect>
+        1 4 8 3</rect></_>
+    <_>
+      <rect>
+        1 6 13 2</rect></_>
+    <_>
+      <rect>
+        2 1 5 2</rect></_>
+    <_>
+      <rect>
+        2 4 9 2</rect></_>
+    <_>
+      <rect>
+        3 2 5 2</rect></_>
+    <_>
+      <rect>
+        4 0 11 3</rect></_>
+    <_>
+      <rect>
+        4 21 9 1</rect></_>
+    <_>
+      <rect>
+        7 1 5 5</rect></_>
+    <_>
+      <rect>
+        7 7 6 2</rect></_>
+    <_>
+      <rect>
+        10 0 3 5</rect></_>
+    <_>
+      <rect>
+        11 1 9 3</rect></_>
+    <_>
+      <rect>
+        11 2 11 4</rect></_>
+    <_>
+      <rect>
+        12 0 4 3</rect></_>
+    <_>
+      <rect>
+        14 2 5 2</rect></_>
+    <_>
+      <rect>
+        14 6 3 3</rect></_>
+    <_>
+      <rect>
+        15 1 5 4</rect></_>
+    <_>
+      <rect>
+        16 0 9 4</rect></_>
+    <_>
+      <rect>
+        17 3 4 5</rect></_>
+    <_>
+      <rect>
+        17 6 4 3</rect></_>
+    <_>
+      <rect>
+        18 12 3 2</rect></_>
+    <_>
+      <rect>
+        19 0 3 3</rect></_>
+    <_>
+      <rect>
+        19 3 2 6</rect></_>
+    <_>
+      <rect>
+        19 4 2 4</rect></_>
+    <_>
+      <rect>
+        19 12 2 4</rect></_>
+    <_>
+      <rect>
+        21 2 2 5</rect></_>
+    <_>
+      <rect>
+        22 5 3 4</rect></_>
+    <_>
+      <rect>
+        22 14 3 3</rect></_>
+    <_>
+      <rect>
+        24 1 6 5</rect></_>
+    <_>
+      <rect>
+        25 3 4 5</rect></_>
+    <_>
+      <rect>
+        25 6 6 1</rect></_>
+    <_>
+      <rect>
+        26 1 2 5</rect></_>
+    <_>
+      <rect>
+        26 3 5 4</rect></_>
+    <_>
+      <rect>
+        26 21 6 1</rect></_>
+    <_>
+      <rect>
+        27 1 5 1</rect></_>
+    <_>
+      <rect>
+        29 3 4 6</rect></_>
+    <_>
+      <rect>
+        30 0 4 5</rect></_>
+    <_>
+      <rect>
+        30 2 3 7</rect></_>
+    <_>
+      <rect>
+        34 1 3 7</rect></_>
+    <_>
+      <rect>
+        34 8 3 2</rect></_>
+    <_>
+      <rect>
+        34 15 3 3</rect></_>
+    <_>
+      <rect>
+        35 3 2 5</rect></_>
+    <_>
+      <rect>
+        35 5 2 5</rect></_>
+    <_>
+      <rect>
+        39 12 1 3</rect></_>
+    <_>
+      <rect>
+        41 0 1 7</rect></_>
+    <_>
+      <rect>
+        41 10 1 4</rect></_>
+    <_>
+      <rect>
+        41 11 1 3</rect></_></features></cascade>
+</opencv_storage>

Prj-Win/lpr/src/Pipeline.cpp

@@ -3,35 +3,68 @@
 namespace pr {
 	PipelinePR::PipelinePR(std::string detect_prototxt, std::string detect_caffemodel,
 		std::string finetune_prototxt, std::string finetune_caffemodel,
-		std::string platerec_prototxt, std::string platerec_caffemodel/*,
-		std::string platecolor_color*/)
+		std::string platerec_prototxt, std::string platerec_caffemodel,
+		std::string dbstring)
 	{
 		platedetection = new PlateDetection(detect_prototxt, detect_caffemodel);
 		finetune = new FineTune(finetune_prototxt, finetune_caffemodel);
 		platerecognation = new PlateRecognation(platerec_prototxt, platerec_caffemodel);
-		//platecolorclass = new PlateColorClass(platecolor_color);
+		dbdetection = new DBDetection(dbstring);
 	}
 	PipelinePR::~PipelinePR()
 	{
 		delete platedetection;
 		delete finetune;
 		delete platerecognation;
-		//delete platecolorclass;
+		delete dbdetection;
 	}
-	std::vector<PlateInfo> PipelinePR::RunPiplineAsImage(cv::Mat plateimg)
+    cv::Mat DBcropFromImage(const cv::Mat &image){
+		cv::Mat cropped;
+        image.copyTo(cropped);
+		int cropped_w = cropped.cols;
+		int cropped_h = cropped.rows;
+		cv::Rect up,down;
+		up.y = cropped_h*0.05;up.x = cropped_w*0.2;up.height = cropped_h*0.35;up.width = cropped_w*0.55;
+		down.y = cropped_h*0.4;down.x = cropped_w*0.05;down.height = cropped_h*0.6;down.width = cropped_w*0.95;
+		cv::Mat cropUp,cropDown;
+		cropped(up).copyTo(cropUp);
+		cropped(down).copyTo(cropDown);
+		cv::resize(cropUp,cropUp,cv::Size(64,40));
+		cv::resize(cropDown,cropDown,cv::Size(96,40));
+		cv::Mat crop = cv::Mat(40,160,CV_8UC3);
+		cropUp.copyTo(crop(cv::Rect(0,0,64,40)));
+		cropDown.copyTo(crop(cv::Rect(64,0,96,40)));
+        return crop;
+
+    }
+	std::vector<PlateInfo> PipelinePR::RunPiplineAsImage(cv::Mat plateimg,int IsDB)
 	{
 		std::vector<pr::PlateInfo> plates;
 		std::vector<PlateInfo> plateres;
-
-		platedetection->Detectssd(plateimg, plates);
+		if(IsDB==1)
+		{
+			dbdetection->DBDetect(plateimg,plates,30,1280);
+		}
+		else
+		{
+			platedetection->Detectssd(plateimg, plates);
+		}
 		for (pr::PlateInfo plateinfo : plates) {
 			cv::Mat image = plateinfo.getPlateImage();
 			cv::Mat CropImg;
-			finetune->Finetune(image, CropImg);
-			platerecognation->segmentation_free_recognation(CropImg, plateinfo);
-			//platecolorclass->plateColor(CropImg, plateinfo);
+			
+			if(IsDB==1)
+			{
+				CropImg = DBcropFromImage(image);
+				platerecognation->segmentation_free_recognation(CropImg, plateinfo);
+			}
+			else
+			{
+				finetune->Finetune(image, CropImg);
+				platerecognation->segmentation_free_recognation(CropImg, plateinfo);
+			}
 			plateres.push_back(plateinfo);
 		}
 		return plateres;
 	}
-}
+}

Prj-Win/lpr/src/PlateDetection.cpp

@@ -8,6 +8,10 @@ namespace pr {
 	{
 		ssdNet = cv::dnn::readNetFromCaffe(ssd_prototxt, ssd_caffemodel);
 	}
+	DBDetection::DBDetection(std::string cascadestring)
+	{
+		dbcascade.load(cascadestring);
+	}
 	void PlateDetection::Detectssd(cv::Mat img, std::vector<pr::PlateInfo>  &plateInfos)
 	{
 		int cols = img.cols;
@@ -57,4 +61,43 @@ namespace pr {
 			}
 		}
 	}
+    cv::Mat cropFromImage(const cv::Mat &image,cv::Rect rect){
+        int w = image.cols-1;
+        int h = image.rows-1;
+        rect.x = std::max(rect.x,0);
+        rect.y = std::max(rect.y,0);
+        rect.height = std::min(rect.height,h-rect.y);
+        rect.width = std::min(rect.width,w-rect.x);
+        cv::Mat temp(rect.size(), image.type());
+        cv::Mat cropped;
+        temp = image(rect);
+        temp.copyTo(cropped);
+        return cropped;
+    }
+	void DBDetection::DBDetect(cv::Mat InputImage,std::vector<pr::PlateInfo> &plateInfos,int min_w,int max_w)
+	{
+		cv::Mat processImage;
+        cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY);
+        std::vector<cv::Rect> platesRegions;
+        cv::Size minSize(min_w,min_w/4);
+        cv::Size maxSize(max_w,max_w/4);
+        if (&processImage == NULL)
+            return;
+        dbcascade.detectMultiScale( processImage, platesRegions,
+                                  1.1, 3, cv::CASCADE_SCALE_IMAGE,minSize,maxSize);
+        for(auto plate:platesRegions)
+        {
+            int zeroadd_w  = static_cast<int>(plate.width*0.28);
+            int zeroadd_h = static_cast<int>(plate.height*0.35);
+            int zeroadd_x = static_cast<int>(plate.width*0.14);
+            int zeroadd_y = static_cast<int>(plate.height*0.15);
+            plate.x-=zeroadd_x;
+            plate.y-=zeroadd_y;
+            plate.height += zeroadd_h;
+            plate.width += zeroadd_w;
+			cv::Mat plateImage = cropFromImage(InputImage,plate);
+            PlateInfo plateInfo(plateImage,plate);
+            plateInfos.push_back(plateInfo);
+        }
+    }
 }

Prj-Win/lpr/tests/testPipeLine.cpp

@@ -4,9 +4,10 @@ void TEST_PIPELINE()
 {
 	pr::PipelinePR prc("../lpr\\model\\mininet_ssd_v1.prototxt", "../lpr\\model\\mininet_ssd_v1.caffemodel",
 		"../lpr\\model\\refinenet.prototxt", "../lpr\\model\\refinenet.caffemodel",
-		"../lpr\\model\\SegmenationFree-Inception.prototxt", "../lpr\\model\\SegmenationFree-Inception.caffemodel");
+		"../lpr\\model\\SegmenationFree-Inception.prototxt", "../lpr\\model\\SegmenationFree-Inception.caffemodel",
+	"../lpr\\model\\cascade_double.xml");
 	cv::Mat img = cv::imread("../lpr\\res\\test.jpg");
-	std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(img);
+	std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(img,0);
 	for (auto st : res) {
 		if (st.confidence > 0.75) {
 			std::cout << st.getPlateName() << " " << st.confidence << std::endl;
@@ -16,12 +17,12 @@ void TEST_PIPELINE()
 		}
 	}
 
-	cv::imshow("image", img);
-	cv::waitKey(0);
+	//cv::imshow("image", img);
+	//cv::waitKey(0);
 
 }
 int main()
 {
 	TEST_PIPELINE();
 	return 0;
-}
+}

WebAPI.py

@@ -1,55 +0,0 @@
-#coding=utf-8
-from flask import Flask, render_template, request
-from werkzeug import secure_filename
-
-import cv2
-import numpy as np
-
-#导入opencv
-
-from hyperlpr import pipline
-#导入车牌识别库
-
-
-app = Flask(__name__)
-#设置App name
-
-
-def recognize(filename):
-    image = cv2.imread(filename)
-    return HyperLPR_plate_recognition(image)
-    #return pipline.RecognizePlateJson(image)
-
-import base64
-
-def recognizeBase64(base64_code):
-    file_bytes = np.asarray(bytearray(base64.b64decode(base64_code)),dtype=np.uint8)
-    image_data_ndarray = cv2.imdecode(file_bytes,1)
-    return pipline.RecognizePlateJson(image_data_ndarray)
-
-
-import time
-
-@app.route('/uploader', methods=['GET', 'POST'])#设置请求路由
-def upload_file():
-    if request.method == 'POST':
-        #如果请求方法是POST
-        f = request.files['file']
-        f.save("./images_rec/"+secure_filename(f.filename))
-        #保存请求上来的文件
-        t0 = time.time()
-        res = recognize("./images_rec/"+secure_filename(f.filename))
-        print "识别时间",time.time() - t0
-        return res
-        #返回识别结果
-        # return 'file uploaded successfully'
-    return render_template('upload.html')
-
-
-
-if __name__ == '__main__':
-    #入口函数
-
-    app.run("0.0.0.0",port=8000)
-    #运行app 指定IP 指定端口
-

hyperlpr_pip_pkg/demo.py

@@ -1,5 +1,5 @@
 import cv2
 from hyperlpr import *
 
-image = cv2.imread("./test_images/test_db2.jpg")
-print(HyperLPR_plate_recognition(image, 16, charSelectionDeskew=False, DB=True))
+image = cv2.imread("./test_images/test.jpg")
+print(HyperLPR_plate_recognition(image, 16, charSelectionDeskew=False, DB=False))

hyperlpr_pip_pkg/hyperlpr/table_chs.py

@@ -1,3 +1,4 @@
+#coding=utf-8
 chars = [
     u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁",
     u"豫", u"鄂", u"湘", u"粤", u"桂", u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0",

model/cascade_lbp.xml

@@ -1,842 +0,0 @@
-<?xml version="1.0"?>
-<opencv_storage>
-<cascade>
-  <stageType>BOOST</stageType>
-  <featureType>LBP</featureType>
-  <height>16</height>
-  <width>64</width>
-  <stageParams>
-    <boostType>GAB</boostType>
-    <minHitRate>9.9500000476837158e-01</minHitRate>
-    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
-    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
-    <maxDepth>1</maxDepth>
-    <maxWeakCount>25</maxWeakCount></stageParams>
-  <featureParams>
-    <maxCatCount>256</maxCatCount>
-    <featSize>1</featSize></featureParams>
-  <stageNum>13</stageNum>
-  <stages>
-    <!-- stage 0 -->
-    <_>
-      <maxWeakCount>4</maxWeakCount>
-      <stageThreshold>-1.6219481229782104e+00</stageThreshold>
-      <weakClassifiers>
-        <_>
-          <internalNodes>
-            0 -1 43 -2561 -234882069 -578037494 -3147793 -203032597
-            1902231761 -3407926 -2049</internalNodes>
-          <leafValues>
-            -7.8116589784622192e-01 6.8186044692993164e-01</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 57 352326928 1058083280 1502920181 -6613668 -1735873027
-            -1141159777 -1142412372 -1157101560</internalNodes>
-          <leafValues>
-            -7.9259228706359863e-01 6.2141162157058716e-01</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 28 -50 -586226738 -552468344 -110118166 -717113442
-            21449 1474142699 1442839519</internalNodes>
-          <leafValues>
-            -7.1683514118194580e-01 6.0926103591918945e-01</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 3 -1054002 -460394782 -286332350 -487653842 -134742175
-            74798917 -545795120 -176179201</internalNodes>
-          <leafValues>
-            -6.4934897422790527e-01 6.6864526271820068e-01</leafValues></_></weakClassifiers></_>
-    <!-- stage 1 -->
-    <_>
-      <maxWeakCount>5</maxWeakCount>
-      <stageThreshold>-1.2461665868759155e+00</stageThreshold>
-      <weakClassifiers>
-        <_>
-          <internalNodes>
-            0 -1 40 -134217745 -235407873 -767164534 -9442626 -134744129
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-          <leafValues>
-            -8.2479608058929443e-01 4.7822877764701843e-01</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 7 -1845456648 -585870960 319829493 -45086460 -922740488
-            -776006 -1433921089 1000341512</internalNodes>
-          <leafValues>
-            -7.0899802446365356e-01 5.1915192604064941e-01</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 81 -67371134 -487394324 -649661312 -1031543834
-            -1149046011 34820455 2002998528 -764152457</internalNodes>
-          <leafValues>
-            -7.0243948698043823e-01 5.5623692274093628e-01</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 52 605031448 -1084468744 -1655103491 2147294680
-            -1316447491 -637769064 -1074216962 -1144512372</internalNodes>
-          <leafValues>
-            -7.3613041639328003e-01 5.2777582406997681e-01</leafValues></_>
-        <_>
-          <internalNodes>
-            0 -1 74 -17825814 -209070806 1224757760 1301260154
-            -243867776 537980928 822665600 1453848575</internalNodes>
-          <leafValues>
-            -7.6585876941680908e-01 5.4007095098495483e-01</leafValues></_></weakClassifiers></_>
-    <!-- stage 2 -->
-    <_>
-      <maxWeakCount>5</maxWeakCount>
-      <stageThreshold>-1.0288889408111572e+00</stageThreshold>
-      <weakClassifiers>
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wxpy_uploader.py

@@ -1,28 +0,0 @@
-#coding=utf-8
-
-from wxpy import *
-import numpy
-import cv2
-import time
-import os
-from hyperlpr import pipline
-
-def recognize(filename):
-    image = cv2.imread(filename)
-    return pipline.RecognizePlateJson(image)
-
-bot = Bot(console_qr=True, cache_path=True)
-
-@bot.register(Friend,PICTURE)
-def pr_msg(msg):
-    image_name = msg.file_name
-    friend = msg.chat
-    print(msg.chat)
-    print('接收图片')
-    # face(image_name)
-    msg.get_file('' + msg.file_name)
-    json_text = recognize(image_name)
-    msg.reply(json_text)
-    msg.reply_image("0.jpg")
-    os.remove(image_name)
-embed()