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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 diff --git a/Prj-Win/lpr/include/FastDeskew.h b/Prj-Win/lpr/include/FastDeskew.h new file mode 100644 index 0000000..08359e5 --- /dev/null +++ b/Prj-Win/lpr/include/FastDeskew.h @@ -0,0 +1,18 @@ +// +// Created by 庾金科 on 22/09/2017. +// + +#ifndef SWIFTPR_FASTDESKEW_H +#define SWIFTPR_FASTDESKEW_H + +#include +#include +namespace pr{ + + cv::Mat fastdeskew(cv::Mat skewImage,int blockSize); +// cv::Mat spatialTransformer(cv::Mat skewImage); + +}//namepace pr + + +#endif //SWIFTPR_FASTDESKEW_H diff --git a/Prj-Win/lpr/include/FineMapping.h b/Prj-Win/lpr/include/FineMapping.h new file mode 100644 index 0000000..352202e --- /dev/null +++ b/Prj-Win/lpr/include/FineMapping.h @@ -0,0 +1,32 @@ +// +// Created by 庾金科 on 22/09/2017. +// + +#ifndef SWIFTPR_FINEMAPPING_H +#define SWIFTPR_FINEMAPPING_H + +#include +#include + +#include +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 diff --git a/Prj-Win/lpr/include/Pipeline.h b/Prj-Win/lpr/include/Pipeline.h new file mode 100644 index 0000000..53e4580 --- /dev/null +++ b/Prj-Win/lpr/include/Pipeline.h @@ -0,0 +1,85 @@ +// +// 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 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" }; + + std::vector plateRes; + std::vector RunPiplineAsImage(cv::Mat plateImage) { + std::vector results; + std::vector 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 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 diff --git a/Prj-Win/lpr/include/PlateDetection.h b/Prj-Win/lpr/include/PlateDetection.h new file mode 100644 index 0000000..f4cf567 --- /dev/null +++ b/Prj-Win/lpr/include/PlateDetection.h @@ -0,0 +1,33 @@ +// +// Created by 庾金科 on 20/09/2017. +// + +#ifndef SWIFTPR_PLATEDETECTION_H +#define SWIFTPR_PLATEDETECTION_H + +#include +#include "PlateInfo.h" +#include +namespace pr{ + class PlateDetection{ + public: + PlateDetection(std::string filename_cascade); + PlateDetection(); + void LoadModel(std::string filename_cascade); + void plateDetectionRough(cv::Mat InputImage,std::vector &plateInfos,int min_w=36,int max_w=800); +// std::vector plateDetectionRough(cv::Mat InputImage,int min_w= 60,int max_h = 400); + + +// std::vector plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage); + + + + private: + cv::CascadeClassifier cascade; + + + }; + +}// namespace pr + +#endif //SWIFTPR_PLATEDETECTION_H diff --git a/Prj-Win/lpr/include/PlateInfo.h b/Prj-Win/lpr/include/PlateInfo.h new file mode 100644 index 0000000..ee1e5da --- /dev/null +++ b/Prj-Win/lpr/include/PlateInfo.h @@ -0,0 +1,127 @@ +// +// Created by 庾金科 on 20/09/2017. +// + +#ifndef SWIFTPR_PLATEINFO_H +#define SWIFTPR_PLATEINFO_H +#include +namespace pr { + + typedef std::vector Character; + + enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN}; + enum CharType {CHINESE,LETTER,LETTER_NUMS}; + + + class PlateInfo { + public: + std::vector> plateChars; + std::vector> plateCoding; + + float confidence = 0; + + + PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect, PlateColor plateType) { + licensePlate = plateData; + name = plateName; + ROI = plateRect; + Type = plateType; + } + PlateInfo(const cv::Mat &plateData, cv::Rect plateRect, PlateColor plateType) { + licensePlate = plateData; + ROI = plateRect; + Type = plateType; + } + PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) { + licensePlate = plateData; + ROI = plateRect; + } + PlateInfo() { + + } + + cv::Mat getPlateImage() { + return licensePlate; + } + + void setPlateImage(cv::Mat plateImage){ + licensePlate = plateImage; + } + + cv::Rect getPlateRect() { + return ROI; + } + + void setPlateRect(cv::Rect plateRect) { + ROI = plateRect; + } + cv::String getPlateName() { + return name; + + } + void setPlateName(cv::String plateName) { + name = plateName; + } + int getPlateType() { + return Type; + } + + void appendPlateChar(const std::pair &plateChar) + { + plateChars.push_back(plateChar); + } + + void appendPlateCoding(const std::pair &charProb){ + plateCoding.push_back(charProb); + } + + // cv::Mat getPlateChars(int id) { + // if(id mappingTable) { + std::string decode; + for(auto plate:plateCoding) { + float *prob = (float *)plate.second.data; + if(plate.first == CHINESE) { + + decode += mappingTable[std::max_element(prob,prob+31) - prob]; + confidence+=*std::max_element(prob,prob+31); + + +// std::cout<<*std::max_element(prob,prob+31)< +#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 &Char_rects); + + void segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones); + void templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair> &candidatePts); + void refineRegion(cv::Mat &plateImage,const std::vector &candidatePts,const int padding,std::vector &rects); + void ExtractRegions(PlateInfo &plateInfo,std::vector &rects); + cv::Mat classifyResponse(const cv::Mat &cropped); + private: + cv::dnn::Net net; + + +// RefineRegion() + + }; + +}//namespace pr + +#endif //SWIFTPR_PLATESEGMENTATION_H diff --git a/Prj-Win/lpr/include/Recognizer.h b/Prj-Win/lpr/include/Recognizer.h new file mode 100644 index 0000000..809b717 --- /dev/null +++ b/Prj-Win/lpr/include/Recognizer.h @@ -0,0 +1,21 @@ +// +// 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; + void SegmentBasedSequenceRecognition(PlateInfo &plateinfo); + + }; + +} +#endif //SWIFTPR_RECOGNIZER_H diff --git a/Prj-Win/lpr/include/niBlackThreshold.h b/Prj-Win/lpr/include/niBlackThreshold.h new file mode 100644 index 0000000..5ad7e14 --- /dev/null +++ b/Prj-Win/lpr/include/niBlackThreshold.h @@ -0,0 +1,107 @@ +// +// Created by 庾金科 on 26/10/2017. +// + +#ifndef SWIFTPR_NIBLACKTHRESHOLD_H +#define SWIFTPR_NIBLACKTHRESHOLD_H + + +#include +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(k); + + break; + case BINARIZATION_SAUVOLA: + thresh = mean.mul(1. + static_cast(k) * (stddev / 128.0 - 1.)); + break; + case BINARIZATION_WOLF: + minMaxIdx(src, &srcMin,NULL); + minMaxIdx(stddev, NULL, &stddevMax); + thresh = mean - static_cast(k) * (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax); + break; + case BINARIZATION_NICK: + sqrt(variance + sqmean, sqrtVarianceMeanSum); + thresh = mean + static_cast(k) * sqrtVarianceMeanSum; + break; + default: + CV_Error( CV_StsBadArg, "Unknown binarization method" ); + break; + } + thresh.convertTo(thresh, src.depth()); + + thresh.convertTo(test, src.depth()); +// +// cv::imshow("imagex",test); +// cv::waitKey(0); + + } + // 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 diff --git a/Prj-Win/lpr/model/cascade.xml b/Prj-Win/lpr/model/cascade.xml new file mode 100644 index 0000000..277775b --- /dev/null +++ b/Prj-Win/lpr/model/cascade.xml @@ -0,0 +1,12117 @@ + + + + BOOST + HAAR + 13 + 51 + + GAB + 9.9900001287460327e-001 + 5.0000000000000000e-001 + 9.4999999999999996e-001 + 1 + 100 + + 0 + 1 + ALL + 20 + + + <_> + 8 + -1.9158077239990234e+000 + + <_> + + 0 -1 344 -8.1478752195835114e-002 + + 6.2639594078063965e-001 -8.1564724445343018e-001 + <_> + + 0 -1 701 -1.2957378290593624e-002 + + 7.7114331722259521e-001 -4.9504086375236511e-001 + <_> + + 0 -1 687 -9.2470366507768631e-003 + + 8.1202191114425659e-001 -2.8070560097694397e-001 + <_> + + 0 -1 614 1.2374955229461193e-002 + + -2.5367051362991333e-001 7.3795551061630249e-001 + <_> + + 0 -1 299 -4.7858944162726402e-003 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<_> + 50 5 1 1 3. + 0 + diff --git a/Prj-Win/lpr/src/CNNRecognizer.cpp b/Prj-Win/lpr/src/CNNRecognizer.cpp new file mode 100644 index 0000000..909753c --- /dev/null +++ b/Prj-Win/lpr/src/CNNRecognizer.cpp @@ -0,0 +1,19 @@ +// +// Created by 庾金科 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(); + } +} \ No newline at end of file diff --git a/Prj-Win/lpr/src/FastDeskew.cpp b/Prj-Win/lpr/src/FastDeskew.cpp new file mode 100644 index 0000000..bf46f29 --- /dev/null +++ b/Prj-Win/lpr/src/FastDeskew.cpp @@ -0,0 +1,133 @@ +// +// Created by 庾金科 on 02/10/2017. +// + + + +#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 avgfilter(std::vector angle_list,int windowsSize) { + std::vector 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 seq){ + cv::Mat image(300,seq.size(),CV_8U); + image.setTo(0); + + for(int i = 0;i(skewPlate.rows*tan(cv::abs(angle)/180* 3.14) ); +// else +// extend_padding = static_cast(skewPlate.rows/tan(cv::abs(angle)/180* 3.14) ); + +// std::cout<<"extend:"<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 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(j, i)[4]; + float y2 = eigen.at(j, i)[5]; + int angle_cell = angle(x2,y2); + angle_list[(angle_cell + 180)%180]+=1.0; + + } + } + } + std::vector 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(maxPos),60.0f); + return deskewed; + } + + + +}//namespace pr diff --git a/Prj-Win/lpr/src/FineMapping.cpp b/Prj-Win/lpr/src/FineMapping.cpp new file mode 100644 index 0000000..6890eff --- /dev/null +++ b/Prj-Win/lpr/src/FineMapping.cpp @@ -0,0 +1,205 @@ +// +// 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 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) + { + +// if(FinedVertical.channels()==1) +// cv::cvtColor(FinedVertical,FinedVertical,cv::COLOR_GRAY2BGR); + 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(prob.at(0,0)*FinedVertical.cols); + int back = static_cast(prob.at(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 FitLineRansac(std::vector pts,int zeroadd = 0 ) + { + std::pair 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((-x * vy / vx) + y); + int righty = static_cast(((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); + +// 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(upper-lower); + diff/=static_cast(sliceNum-1); + cv::Mat binary_adaptive; + std::vector line_upper; + std::vector line_lower; + int contours_nums=0; + + for(int i = 0 ; i < sliceNum ; i++) + { + std::vector > 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(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; + } + } + } + + std:: cout<<"contours_nums "< > 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); + for(auto contour: contours) + { + cv::Rect bdbox =cv::boundingRect(contour); + float lwRatio = bdbox.height/static_cast(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; + } + } + } +// std:: cout<<"contours_nums "< A; + std::pair B; + A = FitLineRansac(line_upper, -2); + B = FitLineRansac(line_lower, 2); + 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 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 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; + + } + + +} + + diff --git a/Prj-Win/lpr/src/Pipeline.cpp b/Prj-Win/lpr/src/Pipeline.cpp new file mode 100644 index 0000000..fad9e19 --- /dev/null +++ b/Prj-Win/lpr/src/Pipeline.cpp @@ -0,0 +1 @@ +//// //// Created by �׽�� on 23/10/2017. //// // //#include "../include/Pipeline.h" // // //namespace pr { // // //std::vector 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 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 PipelinePR:: RunPiplineAsImage(cv::Mat plateImage) { // std::vector results; // std::vector 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 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 // // // //} \ No newline at end of file diff --git a/Prj-Win/lpr/src/PlateDetection.cpp b/Prj-Win/lpr/src/PlateDetection.cpp new file mode 100644 index 0000000..b207190 --- /dev/null +++ b/Prj-Win/lpr/src/PlateDetection.cpp @@ -0,0 +1,61 @@ +// +// 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 &plateInfos,int min_w,int max_w){ + + cv::Mat processImage; + + cv::cvtColor(InputImage,processImage,cv::COLOR_BGR2GRAY); + + + std::vector platesRegions; +// std::vector 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(plate.width*0.28); + int zeroadd_h = static_cast(plate.height*1.2); + int zeroadd_x = static_cast(plate.width*0.14); + int zeroadd_y = static_cast(plate.height*0.6); + plate.x-=zeroadd_x; + plate.y-=zeroadd_y; + plate.height += zeroadd_h; + plate.width += zeroadd_w; + cv::Mat plateImage = util::cropFromImage(InputImage,plate); + PlateInfo plateInfo(plateImage,plate); + plateInfos.push_back(plateInfo); + + } + } +// std::vector 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 diff --git a/Prj-Win/lpr/src/PlateSegmentation.cpp b/Prj-Win/lpr/src/PlateSegmentation.cpp new file mode 100644 index 0000000..94da476 --- /dev/null +++ b/Prj-Win/lpr/src/PlateSegmentation.cpp @@ -0,0 +1,402 @@ +// +// Created by 庾金科 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>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 &candidatePts,const int padding,std::vector &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,15,0.3,BINARIZATION_NIBLACK); + + std::vector> 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 rows>>1) + { final_dist =dist; + final_center = center; + final_bdbox = bdbox; + } + } + + //rebuild box + if(final_bdbox.height/ static_cast(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<0&&i+j+r> &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 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 = -1; + + 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 height = plateImage.rows - 1; + int width = plateImage.cols - 1; + + for(int i = 0 ; i < plateImage.cols - 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> images ; +// +// +// std::cout< &Char_rects){ + cv::Mat plateImage = plateInfo.getPlateImage(); // get src image . + cv::Mat plateImageGray; + cv::cvtColor(plateImage,plateImageGray,cv::COLOR_BGR2GRAY); + //do binarzation + // + std::pair> 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); + +// std::cout< &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 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 diff --git a/Prj-Win/lpr/src/Recognizer.cpp b/Prj-Win/lpr/src/Recognizer.cpp new file mode 100644 index 0000000..0cad4e6 --- /dev/null +++ b/Prj-Win/lpr/src/Recognizer.cpp @@ -0,0 +1,26 @@ +// +// Created by 庾金科 on 22/10/2017. +// + +#include "../include/Recognizer.h" + +namespace pr{ + void GeneralRecognizer::SegmentBasedSequenceRecognition(PlateInfo &plateinfo){ + + + for(auto char_instance:plateinfo.plateChars) + { + + + std::pair res; + cv::Mat code_table= recognizeCharacter(char_instance.second); + res.first = char_instance.first; + code_table.copyTo(res.second); + plateinfo.appendPlateCoding(res); + + } + + + + } +} \ No newline at end of file diff --git a/Prj-Win/lpr/src/util.h b/Prj-Win/lpr/src/util.h new file mode 100644 index 0000000..76a452f --- /dev/null +++ b/Prj-Win/lpr/src/util.h @@ -0,0 +1,79 @@ +// +// Created by 庾金科 on 04/04/2017. +// + +#include + +namespace util{ + + template void swap ( T& a, T& b ) + { + T c(a); a=b; b=c; + } + + template 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 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 diff --git a/Prj-Win/lpr/tests/test_batch.cpp b/Prj-Win/lpr/tests/test_batch.cpp new file mode 100644 index 0000000..316b0c6 --- /dev/null +++ b/Prj-Win/lpr/tests/test_batch.cpp @@ -0,0 +1,4 @@ +// +// Created by 庾金科 on 26/10/2017. +// + diff --git a/Prj-Win/lpr/tests/test_detection.cpp b/Prj-Win/lpr/tests/test_detection.cpp new file mode 100644 index 0000000..db73366 --- /dev/null +++ b/Prj-Win/lpr/tests/test_detection.cpp @@ -0,0 +1,34 @@ +// +// 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 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 ; + + +} diff --git a/Prj-Win/lpr/tests/test_fastdeskew.cpp b/Prj-Win/lpr/tests/test_fastdeskew.cpp new file mode 100644 index 0000000..faa0d93 --- /dev/null +++ b/Prj-Win/lpr/tests/test_fastdeskew.cpp @@ -0,0 +1,34 @@ +// +// Created by 庾金科 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 ; + + +} diff --git a/Prj-Win/lpr/tests/test_finemapping.cpp b/Prj-Win/lpr/tests/test_finemapping.cpp new file mode 100644 index 0000000..4893efd --- /dev/null +++ b/Prj-Win/lpr/tests/test_finemapping.cpp @@ -0,0 +1,25 @@ +// +// Created by 庾金科 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 ; + +} \ No newline at end of file diff --git a/Prj-Win/lpr/tests/test_pipeline.cpp b/Prj-Win/lpr/tests/test_pipeline.cpp new file mode 100644 index 0000000..fccdf1f --- /dev/null +++ b/Prj-Win/lpr/tests/test_pipeline.cpp @@ -0,0 +1,42 @@ +// +// Created by 庾金科 on 23/10/2017. +// + +#include "../include/Pipeline.h" +using namespace pr; + +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); + + std::vector 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< 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 +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< 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 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<2.0.0) + Theano(>0.9) or Tensorflow(>1.1.x) + Numpy (>1.10) + Scipy (0.19.1) + OpenCV(>3.0) -+ scikit-image (0.13.0) ++ Scikit-image (0.13.0) ++ PIL + +### CPP 依赖 + ++ Opencv 3.3 ### 设计流程 @@ -50,8 +63,9 @@ from hyperlpr import pipline as pp import cv2 image = cv2.imread("filename") image,res = pp.SimpleRecognizePlate(image) +print(res) ``` -### CPP编译 +### Linux/Mac 编译 + 仅需要的依赖OpenCV 3.3 (需要DNN框架) @@ -60,7 +74,7 @@ cd cpp_implementation mkdir build cd build cmake ../ -sudo make -j8 +sudo make -j ``` ### 可识别和待支持的车牌的类型 @@ -89,9 +103,9 @@ sudo make -j8 ![android](./demo_images/android.png) -### 最新版本下载 +### 识别测试APP -- 体验 Android 测试版：[https://fir.im/HyperLPR](https://fir.im/HyperLPR) +- 体验 Android APP：[https://fir.im/HyperLPR](https://fir.im/HyperLPR) ### 数据分享 From 3b955ff1e500d97394e8b97288d8b5b7f627ad3a Mon Sep 17 00:00:00 2001 From: jackyu <455501914@qq.com> Date: Fri, 17 Nov 2017 13:59:04 +0800 Subject: [PATCH 02/18] add e2e model. --- README.md | 14 ++++++++---- hyperlpr/e2e.py | 64 ++++++++++++++++++++++++++++++++++++++++++++++++++++ hyperlpr/e2emodel.py | 35 ++++++++++++++++++++++++++++ hyperlpr/pipline.py | 44 ++++++++++++++++++++++++++++++++---- 4 files changed, 148 insertions(+), 9 deletions(-) create mode 100755 hyperlpr/e2e.py create mode 100644 hyperlpr/e2emodel.py diff --git a/README.md b/README.md index 443f05d..65b7bf7 100644 --- a/README.md +++ b/README.md @@ -8,15 +8,18 @@ HyperLPR是一个使用深度学习针对对中文车牌识别的实现，与较 ### 更新 ++ 添加端到端的序列识别模型识别率大幅度提升 ++ 添加的端到端模型可以识别 新能源车牌，教练车牌，白色警用车牌 + 更新Windows版本的Visual Studio工程。（2017.11.15） -+ 增加cpp版本,目前仅支持标准蓝牌(需要依赖OpenCV 3.3)。 -+ 添加了简单的Android实现 (骁龙835 (*720*x*1280*) 200ms)。 ++ 增加cpp版本,目前仅支持标准蓝牌(需要依赖OpenCV 3.3) ++ 添加了简单的Android实现 (骁龙835 (*720*x*1280*) 200ms) ### 特性 + 速度快 720p ，单核 Intel 2.2G CPU (macbook Pro 2015)识别时间 <=140ms 。 ++ 基于端到端的车牌识别无需进行字符分割。 + 识别率高 EasyPR数据集上0-error达到 81.75%, 1-error识别率达到 94.1%。 + 轻量 总代码量不超1k行。 @@ -81,13 +84,14 @@ sudo make -j - [x] 单行蓝牌 - [x] 单行黄牌 -- [ ] 新能源车牌 +- [x] 新能源车牌 +- [x] 白色警用车牌 +- [x] 使馆/港澳车牌 +- [x] 教练车牌 - [ ] 双层黄牌 - [ ] 双层武警 - [ ] 双层军牌 - [ ] 农用车牌 -- [ ] 白色警用车牌 -- [ ] 使馆/港澳车牌 - [ ] 民航车牌 - [ ] 个性化车牌 diff --git a/hyperlpr/e2e.py b/hyperlpr/e2e.py new file mode 100755 index 0000000..76b96b8 --- /dev/null +++ b/hyperlpr/e2e.py @@ -0,0 +1,64 @@ +#coding=utf-8 +from keras import backend as K +from keras.models import load_model +from keras.layers import * +from captcha.image import ImageCaptcha +import numpy as np +import random +import string + +import cv2 +import e2emodel as model +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", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A", + u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L", u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X", + u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南",u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空" + ]; +pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5",) +import time + + + +def fastdecode(y_pred): + results = "" + confidence = 0.0 + table_pred = y_pred.reshape(-1, len(chars)+1) + + res = table_pred.argmax(axis=1) + + for i,one in enumerate(res): + if one0 and ptype<5: + if ptype>0 and ptype<4: plate = cv2.bitwise_not(plate) # demo = verticalEdgeDetection(plate) @@ -137,7 +137,7 @@ def RecognizePlateJson(image): image_rgb = fv.finemappingVertical(image_rgb) cache.verticalMappingToFolder(image_rgb) # print time.time() - t1,"校正" - + print "e2e:",e2e.recognizeOne(image_rgb)[0] image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY) @@ -184,6 +184,40 @@ def RecognizePlateJson(image): +def SimpleRecognizePlateByE2E(image): + t0 = time.time() + images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1) + res_set = [] + for j,plate in enumerate(images): + plate, rect, origin_plate =plate + # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY) + plate =cv2.resize(plate,(136,36*2)) + res,confidence = e2e.recognizeOne(origin_plate) + print "res",res + + t1 = time.time() + ptype = td.SimplePredict(plate) + if ptype>0 and ptype<5: + # pass + plate = cv2.bitwise_not(plate) + image_rgb = fm.findContoursAndDrawBoundingBox(plate) + image_rgb = fv.finemappingVertical(image_rgb) + image_rgb = fv.finemappingVertical(image_rgb) + cache.verticalMappingToFolder(image_rgb) + cv2.imwrite("./"+str(j)+".jpg",image_rgb) + res,confidence = e2e.recognizeOne(image_rgb) + print res,confidence + res_set.append([[],res,confidence]) + + if confidence>0.7: + image = drawRectBox(image, rect, res+" "+str(round(confidence,3))) + return image,res_set + + + + + + def SimpleRecognizePlate(image): t0 = time.time() @@ -200,8 +234,10 @@ def SimpleRecognizePlate(image): plate = cv2.bitwise_not(plate) image_rgb = fm.findContoursAndDrawBoundingBox(plate) + image_rgb = fv.finemappingVertical(image_rgb) cache.verticalMappingToFolder(image_rgb) + print "e2e:", e2e.recognizeOne(image_rgb) image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_RGB2GRAY) # image_gray = horizontalSegmentation(image_gray) From 75ccffadaad4cdab11375943d81194249cc22074 Mon Sep 17 00:00:00 2001 From: jackyu <455501914@qq.com> Date: Fri, 17 Nov 2017 13:59:33 +0800 Subject: [PATCH 03/18] add e2e model. --- model/ocr_plate_all_w_rnn_2.h5 | Bin 0 -> 15281736 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 model/ocr_plate_all_w_rnn_2.h5 diff --git a/model/ocr_plate_all_w_rnn_2.h5 b/model/ocr_plate_all_w_rnn_2.h5 new file mode 100644 index 0000000000000000000000000000000000000000..0909207d53c03e3acfbed51e08960bd70d9e5fc1 GIT binary patch literal 15281736 zcmV+W{{#SuNJK^r3K|Lk000002nYZK01yBG000000000000030|NsC0|Ns9;F6jUO 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