更新vs工程到端到端模型

Prj-Win/Prj-Win/Prj-Win.vcxproj

@@ -1,5 +1,5 @@
 <?xml version="1.0" encoding="utf-8"?>
-<Project DefaultTargets="Build" ToolsVersion="14.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
+<Project DefaultTargets="Build" ToolsVersion="15.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">
   <ItemGroup Label="ProjectConfigurations">
     <ProjectConfiguration Include="Debug|Win32">
       <Configuration>Debug</Configuration>
@@ -22,32 +22,32 @@
     <ProjectGuid>{69FAD143-D7C9-4804-A186-90254BD80549}</ProjectGuid>
     <Keyword>Win32Proj</Keyword>
     <RootNamespace>PrjWin</RootNamespace>
-    <WindowsTargetPlatformVersion>8.1</WindowsTargetPlatformVersion>
+    <WindowsTargetPlatformVersion>10.0.17763.0</WindowsTargetPlatformVersion>
   </PropertyGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" Label="Configuration">
     <ConfigurationType>Application</ConfigurationType>
     <UseDebugLibraries>true</UseDebugLibraries>
-    <PlatformToolset>v140</PlatformToolset>
+    <PlatformToolset>v141</PlatformToolset>
     <CharacterSet>Unicode</CharacterSet>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'" Label="Configuration">
     <ConfigurationType>Application</ConfigurationType>
     <UseDebugLibraries>false</UseDebugLibraries>
-    <PlatformToolset>v140</PlatformToolset>
+    <PlatformToolset>v141</PlatformToolset>
     <WholeProgramOptimization>true</WholeProgramOptimization>
     <CharacterSet>Unicode</CharacterSet>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'" Label="Configuration">
     <ConfigurationType>Application</ConfigurationType>
     <UseDebugLibraries>true</UseDebugLibraries>
-    <PlatformToolset>v140</PlatformToolset>
+    <PlatformToolset>v141</PlatformToolset>
     <CharacterSet>Unicode</CharacterSet>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'" Label="Configuration">
     <ConfigurationType>Application</ConfigurationType>
     <UseDebugLibraries>false</UseDebugLibraries>
-    <PlatformToolset>v140</PlatformToolset>
+    <PlatformToolset>v141</PlatformToolset>
     <WholeProgramOptimization>true</WholeProgramOptimization>
     <CharacterSet>Unicode</CharacterSet>
   </PropertyGroup>
@@ -76,9 +76,9 @@
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
     <LinkIncremental>true</LinkIncremental>
-    <IncludePath>D:\Prj-Win\lpr\include;D:\opencv\build\include\opencv2;D:\opencv\build\include\opencv;D:\opencv\build\include;$(IncludePath)</IncludePath>
-    <LibraryPath>D:\opencv\build\x64\vc14\lib;$(LibraryPath)</LibraryPath>
-    <OutDir>$(SolutionDir)Build</OutDir>
+    <IncludePath>D:\Prj-Win\lpr\include;D:\opencv\build\include;D:\opencv\build\include\opencv2;$(IncludePath)</IncludePath>
+    <LibraryPath>D:\opencv\build\x64\vc15\lib;$(LibraryPath)</LibraryPath>
+    <OutDir>$(SolutionDir)Build\</OutDir>
   </PropertyGroup>
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
     <LinkIncremental>false</LinkIncremental>
@@ -112,7 +112,7 @@
     <Link>
       <SubSystem>Console</SubSystem>
       <GenerateDebugInformation>true</GenerateDebugInformation>
-      <AdditionalDependencies>opencv_world330d.lib;%(AdditionalDependencies)</AdditionalDependencies>
+      <AdditionalDependencies>opencv_world400d.lib;opencv_world400.lib;%(AdditionalDependencies)</AdditionalDependencies>
     </Link>
   </ItemDefinitionGroup>
   <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
@@ -161,15 +161,18 @@
     <ClInclude Include="..\lpr\include\PlateInfo.h" />
     <ClInclude Include="..\lpr\include\PlateSegmentation.h" />
     <ClInclude Include="..\lpr\include\Recognizer.h" />
+    <ClInclude Include="..\lpr\include\SegmentationFreeRecognizer.h" />
     <ClInclude Include="..\lpr\src\util.h" />
   </ItemGroup>
   <ItemGroup>
     <ClCompile Include="..\lpr\src\CNNRecognizer.cpp" />
     <ClCompile Include="..\lpr\src\FastDeskew.cpp" />
     <ClCompile Include="..\lpr\src\FineMapping.cpp" />
+    <ClCompile Include="..\lpr\src\Pipeline.cpp" />
     <ClCompile Include="..\lpr\src\PlateDetection.cpp" />
     <ClCompile Include="..\lpr\src\PlateSegmentation.cpp" />
     <ClCompile Include="..\lpr\src\Recognizer.cpp" />
+    <ClCompile Include="..\lpr\src\SegmentationFreeRecognizer.cpp" />
     <ClCompile Include="..\lpr\tests\test_pipeline.cpp" />
   </ItemGroup>
   <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />

Prj-Win/Prj-Win/Prj-Win.vcxproj.filters

@@ -48,6 +48,9 @@
     <ClInclude Include="..\lpr\src\util.h">
       <Filter>源文件</Filter>
     </ClInclude>
+    <ClInclude Include="..\lpr\include\SegmentationFreeRecognizer.h">
+      <Filter>头文件</Filter>
+    </ClInclude>
   </ItemGroup>
   <ItemGroup>
     <ClCompile Include="..\lpr\src\CNNRecognizer.cpp">
@@ -71,5 +74,11 @@
     <ClCompile Include="..\lpr\tests\test_pipeline.cpp">
       <Filter>源文件\test</Filter>
     </ClCompile>
+    <ClCompile Include="..\lpr\src\Pipeline.cpp">
+      <Filter>源文件</Filter>
+    </ClCompile>
+    <ClCompile Include="..\lpr\src\SegmentationFreeRecognizer.cpp">
+      <Filter>源文件</Filter>
+    </ClCompile>
   </ItemGroup>
 </Project>

Prj-Win/lpr/include/CNNRecognizer.h

@@ -1,5 +1,5 @@
 //
-// Created by 庾金科 on 21/10/2017.
+// Created by Jack Yu on 21/10/2017.
 //
 
 #ifndef SWIFTPR_CNNRECOGNIZER_H

Prj-Win/lpr/include/Pipeline.h

@@ -1,85 +1,60 @@
-//
-// Created by �׽�� 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"
-
-namespace pr{
-    class PipelinePR{
-	    public:
-            GeneralRecognizer *generalRecognizer;
-            PlateDetection *plateDetection;
-            PlateSegmentation *plateSegmentation;
-            FineMapping *fineMapping;
-			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
-			) {
-				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); 
-			}
-
-			~PipelinePR() {
-
-				delete plateDetection;
-				delete fineMapping;
-				delete plateSegmentation;
-				delete generalRecognizer;
-			}
-
-			std::vector<std::string> chars_code{ "京","沪","津","�","冀","晋","蒙","辽","�","黑","�","浙","皖","闽","赣","�","豫","鄂","湘","粤","桂","�","�","贵","云","�","陕","甘","�","�","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" };
-
-			std::vector<std::string> plateRes;
-			std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage) {
-				std::vector<PlateInfo> results;
-				std::vector<pr::PlateInfo> plates;
-				plateDetection->plateDetectionRough(plateImage, plates);
-
-				for (pr::PlateInfo plateinfo : plates) {
-
-					cv::Mat image_finemapping = plateinfo.getPlateImage();
-					image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
-					image_finemapping = pr::fastdeskew(image_finemapping, 5);
-					image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2, 5);
-					cv::resize(image_finemapping, image_finemapping, cv::Size(136, 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(chars_code);
-					results.push_back(plateinfo);
-					std::cout << plateinfo.getPlateName() << std::endl;
-
-
-				}
-
-				//        for (auto str:results) {
-				//            std::cout << str << std::endl;
-				//        }
-				return results;
-			}
-
-
-
-
-    };
-
-
-}
-#endif //SWIFTPR_PIPLINE_H
+//
+// Created by â×½ð¿Æ 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{"¾©", "»¦", "½ò", "Óå", "¼½", "½ú", "ÃÉ", "ÁÉ", "¼ª", "ºÚ", "ËÕ", "Õã", "Íî", "Ãö", "¸Ó", "³", "Ô¥", "¶õ", "Ïæ", "ÔÁ", "¹ð",
+                                        "Çí", "´¨", "¹ó", "ÔÆ", "²Ø", "ÉÂ", "¸Ê", "Çà", "Äþ", "ÐÂ", "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","¸Û","ѧ","ʹ","¾¯","°Ä","¹Ò","¾ü","±±","ÄÏ","¹ã","Éò","À¼","³É","¼Ã","º£","Ãñ","º½","¿Õ"};
+
+
+
+    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);
+
+
+
+
+
+
+
+    };
+
+
+}
+#endif //SWIFTPR_PIPLINE_H

Prj-Win/lpr/include/PlateDetection.h

@@ -6,7 +6,7 @@
 #define SWIFTPR_PLATEDETECTION_H
 
 #include <opencv2/opencv.hpp>
-#include "PlateInfo.h"
+#include <PlateInfo.h>
 #include <vector>
 namespace pr{
     class PlateDetection{

Prj-Win/lpr/include/PlateInfo.h

@@ -10,17 +10,14 @@ namespace pr {
     typedef std::vector<cv::Mat> Character;
 
     enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN};
-    enum CharType {CHINESE,LETTER,LETTER_NUMS};
+    enum CharType {CHINESE,LETTER,LETTER_NUMS,INVALID};
 
 
     class PlateInfo {
         public:
-        std::vector<std::pair<CharType,cv::Mat>> plateChars;
+            std::vector<std::pair<CharType,cv::Mat>> plateChars;
             std::vector<std::pair<CharType,cv::Mat>> plateCoding;
-
             float confidence = 0;
-
-
             PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect, PlateColor plateType) {
                     licensePlate = plateData;
                     name = plateName;
@@ -93,17 +90,21 @@ namespace pr {
 
                     }
 
-                    if(plate.first == LETTER) {
+                    else if(plate.first == LETTER) {
                         decode += mappingTable[std::max_element(prob+41,prob+65)- prob];
                         confidence+=*std::max_element(prob+41,prob+65);
                     }
 
-                    if(plate.first == LETTER_NUMS) {
+                    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;
@@ -113,12 +114,10 @@ namespace pr {
                 return decode;
             }
 
-
-
     private:
         cv::Mat licensePlate;
         cv::Rect ROI;
-        std::string name;
+        std::string name ;
         PlateColor Type;
     };
 }

Prj-Win/lpr/include/PlateSegmentation.h

@@ -1,7 +1,3 @@
-//
-// Created by 庾金科 on 16/10/2017.
-//
-
 #ifndef SWIFTPR_PLATESEGMENTATION_H
 #define SWIFTPR_PLATESEGMENTATION_H
 

Prj-Win/lpr/include/Recognizer.h

@@ -13,7 +13,9 @@ namespace pr{
     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);
 
     };
 

Prj-Win/lpr/include/SegmentationFreeRecognizer.h

@@ -0,0 +1,28 @@
+//
+// 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-Win/lpr/include/niBlackThreshold.h

@@ -62,8 +62,9 @@ void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
                 thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
                 break;
             default:
-                CV_Error( CV_StsBadArg, "Unknown binarization method" );
-                break;
+//              CV_Error( CV_StsBadArg, "Unknown binarization method" );
+				CV_Error(-5, "Unknown binarization method");
+				break;
         }
         thresh.convertTo(thresh, src.depth());
 
@@ -99,7 +100,8 @@ void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
             src.copyTo(dst, mask);
             break;
         default:
-            CV_Error( CV_StsBadArg, "Unknown threshold type" );
+//          CV_Error( CV_StsBadArg, "Unknown threshold type" );
+			CV_Error(-5, "Unknown threshold type");
             break;
     }
 }

Prj-Win/lpr/model/README.md

@@ -10,8 +10,8 @@ HorizonalFinemapping.caffemodel
 
 HorizonalFinemapping.prototxt
 
-Segmentation.caffemodel
+SegmentationFree.caffemodel
 
-Segmentation.prototxt
+SegmentationFree.prototxt
 
 放置在该目录

Prj-Win/lpr/src/CNNRecognizer.cpp

@@ -1,5 +1,5 @@
 //
-// Created by 庾金科 on 21/10/2017.
+// Created by Jack Yu on 21/10/2017.
 //
 
 #include "../include/CNNRecognizer.h"
@@ -16,4 +16,4 @@ namespace pr{
         net.setInput(inputBlob,"data");
         return net.forward();
     }
-}
+}

Prj-Win/lpr/src/FastDeskew.cpp

@@ -1,19 +1,17 @@
 //
-// Created by 庾金科 on 02/10/2017.
+// Created by Jack Yu on 02/10/2017.
 //
 
 
 
-#include "FastDeskew.h"
+#include <../include/FastDeskew.h>
 
 namespace pr{
-
-
-    const int  ANGLE_MIN = 30 ;
+    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;
@@ -51,59 +49,38 @@ namespace pr{
 
     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;
-//        if(angle<0)
-            extend_padding = static_cast<int>(skewPlate.rows*tan(cv::abs(angle)/180* 3.14) );
-//        else
-//            extend_padding = static_cast<int>(skewPlate.rows/tan(cv::abs(angle)/180* 3.14) );
-
-//        std::cout<<"extend:"<<extend_padding<<std::endl;
-
+        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);
-//        std::cout<<interval<<std::endl;
-
         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 )
@@ -112,12 +89,10 @@ namespace pr{
                     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;

Prj-Win/lpr/src/FineMapping.cpp

@@ -1,12 +1,8 @@
-//
-// Created by 庾金科 on 22/09/2017.
-//
-
 #include "FineMapping.h"
 namespace pr{
 
-    const int FINEMAPPING_H = 50;
-    const int FINEMAPPING_W = 120;
+    const int FINEMAPPING_H = 60 ;
+    const int FINEMAPPING_W = 140;
     const int PADDING_UP_DOWN = 30;
     void drawRect(cv::Mat image,cv::Rect rect)
     {
@@ -65,31 +61,21 @@ namespace pr{
     }
 
     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);
-
-//            proposal = PreInputProposal;
-
         // this will improve some sen
         cv::Mat kernal = cv::getStructuringElement(cv::MORPH_ELLIPSE,cv::Size(1,3));
-//        cv::erode(proposal,proposal,kernal);
-
-
         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;
@@ -106,7 +92,6 @@ namespace pr{
                 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);
@@ -115,9 +100,6 @@ namespace pr{
                 }
             }
         }
-
-        std:: cout<<"contours_nums "<<contours_nums<<std::endl;
-
         if(contours_nums<41)
         {
             cv::bitwise_not(InputProposal,InputProposal);
@@ -130,14 +112,11 @@ namespace pr{
             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::imshow("image",binary_adaptive);
-//            cv::waitKey(0);
                 cv::Mat draw;
                 binary_adaptive.copyTo(draw);
                 cv::findContours(binary_adaptive,contours,cv::RETR_EXTERNAL,cv::CHAIN_APPROX_SIMPLE);
@@ -158,30 +137,19 @@ namespace pr{
                     }
                 }
             }
-//            std:: cout<<"contours_nums "<<contours_nums<<std::endl;
         }
-
             cv::Mat rgb;
-            cv::copyMakeBorder(PreInputProposal, rgb, 30, 30, 0, 0, cv::BORDER_REPLICATE);
-//        cv::imshow("rgb",rgb);
-//        cv::waitKey(0);
-//
-
-
+            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, -2);
-            B = FitLineRansac(line_lower, 2);
+            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;
-//        pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
-//        pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
-//        mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
-//        image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
             std::vector<cv::Point2f> corners(4);
             corners[0] = cv::Point2f(cols - 1, rightyA);
             corners[1] = cv::Point2f(0, leftyA);
@@ -196,10 +164,7 @@ namespace pr{
             cv::Mat quad = cv::Mat::zeros(36, 136, CV_8UC3);
             cv::warpPerspective(rgb, quad, transform, quad.size());
         return quad;
-
     }
-
-
 }
 
 

Prj-Win/lpr/src/Pipeline.cpp

@@ -1 +1,85 @@
-////
//// Created by �׽�� on 23/10/2017.
////
//
//#include "../include/Pipeline.h"
//
//
//namespace pr {
//
//	//std::vector<std::string> chars_code{ "��","��","��","��","��","��","��","��","��","��","��","��","��","��","��","³","ԥ","��","��","��","��","��","��","��","��","��","��","��","��","��","��","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" };
//
//	std::vector<std::string> chars_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" };
//
//
//    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) {
//        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);
//    }
//
//    PipelinePR::~PipelinePR() {
//
//        delete plateDetection;
//        delete fineMapping;
//        delete plateSegmentation;
//        delete generalRecognizer;
//
//    }
//
//    std::vector<PlateInfo> PipelinePR:: RunPiplineAsImage(cv::Mat plateImage) {
//        std::vector<PlateInfo> results;
//        std::vector<pr::PlateInfo> plates;
//        plateDetection->plateDetectionRough(plateImage,plates);
//
//        for (pr::PlateInfo plateinfo:plates) {
//
//            cv::Mat image_finemapping = plateinfo.getPlateImage();
//            image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
//            image_finemapping = pr::fastdeskew(image_finemapping, 5);
//            image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2, 5);
//            cv::resize(image_finemapping, image_finemapping, cv::Size(136, 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(chars_code);
//            results.push_back(plateinfo);
//            std::cout << plateinfo.getPlateName() << std::endl;
//
//
//        }
//
////        for (auto str:results) {
////            std::cout << str << std::endl;
////        }
//        return results;
//
//    }//namespace pr
//
//
//
//}
+//
+// Created by Jack Yu on 23/10/2017.
+//
+
+#include "../include/Pipeline.h"
+
+
+namespace pr {
+
+
+
+    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
+
+
+
+}

Prj-Win/lpr/src/PlateDetection.cpp

@@ -1,45 +1,24 @@
-//
-// Created by 庾金科 on 20/09/2017.
-//
 #include "../include/PlateDetection.h"
-
 #include "util.h"
-
 namespace pr{
-
-
     PlateDetection::PlateDetection(std::string filename_cascade){
         cascade.load(filename_cascade);
 
     };
-
-
     void PlateDetection::plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w,int max_w){
-
         cv::Mat processImage;
-
-         cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY);
-
-
+        cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY);
         std::vector<cv::Rect> platesRegions;
-//        std::vector<PlateInfo> plates;
         cv::Size minSize(min_w,min_w/4);
         cv::Size maxSize(max_w,max_w/4);
-//        cv::imshow("input",InputImage);
-//                cv::waitKey(0);
         cascade.detectMultiScale( processImage, platesRegions,
                                   1.1, 3, cv::CASCADE_SCALE_IMAGE,minSize,maxSize);
         for(auto plate:platesRegions)
         {
-            // extend rects
-//            x -= w * 0.14
-//            w += w * 0.28
-//            y -= h * 0.6
-//            h += h * 1.1;
-            int zeroadd_w  = static_cast<int>(plate.width*0.28);
-            int zeroadd_h = static_cast<int>(plate.height*1.2);
-            int zeroadd_x = static_cast<int>(plate.width*0.14);
-            int zeroadd_y = static_cast<int>(plate.height*0.6);
+            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);
             plate.x-=zeroadd_x;
             plate.y-=zeroadd_y;
             plate.height += zeroadd_h;
@@ -50,12 +29,4 @@ namespace pr{
 
         }
     }
-//    std::vector<pr::PlateInfo> PlateDetection::plateDetectionRough(cv::Mat InputImage,cv::Rect roi,int min_w,int max_w){
-//        cv::Mat roi_region = util::cropFromImage(InputImage,roi);
-//        return plateDetectionRough(roi_region,min_w,max_w);
-//    }
-
-
-
-
 }//namespace pr

Prj-Win/lpr/src/PlateSegmentation.cpp

@@ -1,5 +1,5 @@
 //
-// Created by 庾金科 on 16/10/2017.
+// Created by Jack  Yu on 16/10/2017.
 //
 
 #include "../include/PlateSegmentation.h"
@@ -94,7 +94,7 @@ namespace pr{
                 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,15,0.3,BINARIZATION_NIBLACK);
+                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);
@@ -110,7 +110,7 @@ namespace pr{
                     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 && bdbox.height > rows>>1)
+                    if(dist<final_dist &&  bdbox.height > rows>>1)
                     {   final_dist =dist;
                         final_center = center;
                         final_bdbox = bdbox;
@@ -220,7 +220,7 @@ namespace pr{
 
 
         int cp_list[7];
-        float loss_selected = -1;
+        float loss_selected = -10;
 
         for(int start = 0 ; start < 20 ; start+=2)
             for(int  width = windowsWidth-5; width < windowsWidth+5 ; width++ ){
@@ -246,13 +246,10 @@ namespace pr{
 
                     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]);
-
-
-
+//                    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)
                     {
@@ -284,15 +281,15 @@ namespace pr{
     void PlateSegmentation::segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones){
 
 
-        cv::resize(plateImage,plateImage,cv::Size(136,36));
+//        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;
-
-        for(int i = 0 ; i < plateImage.cols - windowsWidth +1 ; i +=stride)
+        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);
@@ -348,6 +345,11 @@ namespace pr{
         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;
 

Prj-Win/lpr/src/Recognizer.cpp

@@ -1,26 +1,23 @@
 //
-// Created by 庾金科 on 22/10/2017.
+// 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;
-            cv::Mat code_table= recognizeCharacter(char_instance.second);
-            res.first = char_instance.first;
-            code_table.copyTo(res.second);
-            plateinfo.appendPlateCoding(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-Win/lpr/src/SegmentationFreeRecognizer.cpp

@@ -0,0 +1,89 @@
+//
+// 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-Win/lpr/src/util.h

@@ -1,20 +1,16 @@
 //
-// Created by 庾金科 on 04/04/2017.
+// 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){
@@ -57,23 +53,16 @@ namespace util{
         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);
-
+//      return cv::compareHist(histA,histB,CV_COMP_CORREL);
+		return cv::compareHist(histA, histB, 0);
     }
-
-
-
-
-
 }//namespace util

Prj-Win/lpr/tests/test_fastdeskew.cpp

@@ -1,5 +1,5 @@
 //
-// Created by 庾金科 on 02/10/2017.
+// Created by Jack Yu on 02/10/2017.
 //
 
 

Prj-Win/lpr/tests/test_finemapping.cpp

@@ -1,5 +1,5 @@
 //
-// Created by 庾金科 on 24/09/2017.
+// Created by Jack Yu on 24/09/2017.
 //
 
 #include "FineMapping.h"
@@ -22,4 +22,4 @@ int main()
 
     return 0 ;
 
-}
+}

Prj-Win/lpr/tests/test_pipeline.cpp

@@ -1,42 +1,229 @@
-//
-// Created by 庾金科 on 23/10/2017.
+//
+// Created by Jack Yu on 23/10/2017.
 //
 
 #include "../include/Pipeline.h"
-using namespace pr;
+#include<fstream>
+#include<vector>
 
-void TEST_PIPELINE(){
 
-    pr::PipelinePR prc("../lpr/model/cascade.xml",
-                      "../lpr/model/HorizonalFinemapping.prototxt","../lpr/model/HorizonalFinemapping.caffemodel",
-                      "../lpr/model/Segmentation.prototxt","../lpr/model/Segmentation.caffemodel",
-                      "../lpr/model/CharacterRecognization.prototxt","../lpr/model/CharacterRecognization.caffemodel"
-                    );
 
-    cv::Mat image = cv::imread("../6.jpg");
-    cv::imshow("image",image);
-    cv::waitKey(0);
+using namespace std;
 
-    std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(image);
-    float conf = 0 ;
-    for(auto st:res) {
-        if(st.confidence>0.1) {
-            std::cout << st.getPlateName() << " " << st.confidence << std::endl;
-            conf += st.confidence;
-        }
-    }
-    std::cout<<conf<<std::endl;
-	system("pause");
 
+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];
 }
-int main()
+
+
+void TEST_CAM()
 {
+	cv::VideoCapture capture("test1.mp4");
+	cv::Mat frame;
+	pr::PipelinePR prc("../lpr/model/cascade.xml",
+		"../lpr/model/HorizonalFinemapping.prototxt", "../lpr/model/HorizonalFinemapping.caffemodel",
+		"../lpr/model/Segmentation.prototxt", "../lpr/model/Segmentation.caffemodel",
+		"../lpr/model/CharacterRecognization.prototxt", "../lpr/model/CharacterRecognization.caffemodel",
+		"../lpr/model/SegmentationFree.prototxt", "../lpr/model/SegmentationFree.caffemodel"
+	);
+	while (1) {
+		//读取下一帧
+		if (!capture.read(frame)) {
+			std::cout << "读取视频失败" << std::endl;
+			exit(1);
+		}
+		//
+		//        cv::transpose(frame,frame);
+		//        cv::flip(frame,frame,2);
+
+		//        cv::resize(frame,frame,cv::Size(frame.cols/2,frame.rows/2));
+
+
+		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);
+	}
+}
+
+
+void TEST_ACC() {
+
+	pr::PipelinePR prc("../lpr/model/cascade.xml",
+		"../lpr/model/HorizonalFinemapping.prototxt", "../lpr/model/HorizonalFinemapping.caffemodel",
+		"../lpr/model/Segmentation.prototxt", "../lpr/model/Segmentation.caffemodel",
+		"../lpr/model/CharacterRecognization.prototxt", "../lpr/model/CharacterRecognization.caffemodel",
+		"../lpr/model/SegmentationFree.prototxt", "../lpr/model/SegmentationFree.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("../lpr/model/cascade.xml",
+		"../lpr/model/HorizonalFinemapping.prototxt", "../lpr/model/HorizonalFinemapping.caffemodel",
+		"../lpr/model/Segmentation.prototxt", "../lpr/model/Segmentation.caffemodel",
+		"../lpr/model/CharacterRecognization.prototxt", "../lpr/model/CharacterRecognization.caffemodel",
+		"../lpr/model/SegmentationFree.prototxt", "../lpr/model/SegmentationFree.caffemodel"
+	);
 
-   TEST_PIPELINE();
+	cv::Mat image = cv::imread("../lpr/res/test.jpg");
 
 
-    return 0 ;
+	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("../lpr/model/cascade.xml",
+		"../lpr/model/HorizonalFinemapping.prototxt", "../lpr/model/HorizonalFinemapping.caffemodel",
+		"../lpr/model/Segmentation.prototxt", "../lpr/model/Segmentation.caffemodel",
+		"../lpr/model/CharacterRecognization.prototxt", "../lpr/model/CharacterRecognization.caffemodel",
+		"../lpr/model/SegmentationFree.prototxt", "../lpr/model/SegmentationFree.caffemodel"
+	);
+	while (1) {
+		//读取下一帧
+		if (!capture.read(frame)) {
+			std::cout << "读取视频失败" << std::endl;
+			exit(1);
+		}
+		//
+		//        cv::transpose(frame,frame);
+		//        cv::flip(frame,frame,2);
+
+		//        cv::resize(frame,frame,cv::Size(frame.cols/2,frame.rows/2));
+
+
+
+		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-Win/lpr/tests/test_recognization.cpp

@@ -1,5 +1,5 @@
 //
-// Created by 庾金科 on 23/10/2017.
+// Created by Jack Yu on 23/10/2017.
 //
 
 #include "../include/CNNRecognizer.h"
@@ -16,6 +16,7 @@ void getMaxClass(cv::Mat &probBlob, int *classId, double *classProb)
     cv::Point classNumber;
 
     cv::minMaxLoc(probBlob, NULL, classProb, NULL, &classNumber);
+
     *classId = classNumber.x;
 }
 

Prj-Win/lpr/tests/test_segmentation.cpp

@@ -1,5 +1,5 @@
 //
-// Created by 庾金科 on 16/10/2017.
+// Created by Jack Yu on 16/10/2017.
 //
 
 
@@ -40,4 +40,4 @@ int main(){
     TEST_SLIDINGWINDOWS_EVAL();
 
     return  0;
-}
+}

Prj-Win/lpr/tests/test_segmentationFree.cpp

@@ -0,0 +1,54 @@
+//
+// 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;
+
+}