add windows vs project
This commit is contained in:
jackyu
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//
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// Created by 庾金科 on 21/10/2017.
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//
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#ifndef SWIFTPR_CNNRECOGNIZER_H
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#define SWIFTPR_CNNRECOGNIZER_H
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#include "Recognizer.h"
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namespace pr{
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class CNNRecognizer: public GeneralRecognizer{
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public:
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const int CHAR_INPUT_W = 14;
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const int CHAR_INPUT_H = 30;
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CNNRecognizer(std::string prototxt,std::string caffemodel);
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label recognizeCharacter(cv::Mat character);
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private:
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cv::dnn::Net net;
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};
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}
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#endif //SWIFTPR_CNNRECOGNIZER_H
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//
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// Created by 庾金科 on 22/09/2017.
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//
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#ifndef SWIFTPR_FASTDESKEW_H
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#define SWIFTPR_FASTDESKEW_H
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#include <math.h>
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#include <opencv2/opencv.hpp>
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namespace pr{
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cv::Mat fastdeskew(cv::Mat skewImage,int blockSize);
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// cv::Mat spatialTransformer(cv::Mat skewImage);
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}//namepace pr
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#endif //SWIFTPR_FASTDESKEW_H
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//
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// Created by 庾金科 on 22/09/2017.
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//
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#ifndef SWIFTPR_FINEMAPPING_H
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#define SWIFTPR_FINEMAPPING_H
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#include <opencv2/opencv.hpp>
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#include <opencv2/dnn.hpp>
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#include <string>
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namespace pr{
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class FineMapping{
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public:
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FineMapping();
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FineMapping(std::string prototxt,std::string caffemodel);
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static cv::Mat FineMappingVertical(cv::Mat InputProposal,int sliceNum=15,int upper=0,int lower=-50,int windows_size=17);
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cv::Mat FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding);
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private:
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cv::dnn::Net net;
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};
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}
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#endif //SWIFTPR_FINEMAPPING_H
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//
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// Created by ��� on 22/10/2017.
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//
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#ifndef SWIFTPR_PIPLINE_H
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#define SWIFTPR_PIPLINE_H
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#include "PlateDetection.h"
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#include "PlateSegmentation.h"
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#include "CNNRecognizer.h"
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#include "PlateInfo.h"
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#include "FastDeskew.h"
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#include "FineMapping.h"
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#include "Recognizer.h"
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namespace pr{
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class PipelinePR{
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public:
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GeneralRecognizer *generalRecognizer;
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PlateDetection *plateDetection;
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PlateSegmentation *plateSegmentation;
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FineMapping *fineMapping;
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PipelinePR(std::string detector_filename,
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std::string finemapping_prototxt, std::string finemapping_caffemodel,
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std::string segmentation_prototxt, std::string segmentation_caffemodel,
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std::string charRecognization_proto, std::string charRecognization_caffemodel
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) {
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plateDetection = new PlateDetection(detector_filename);
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fineMapping = new FineMapping(finemapping_prototxt, finemapping_caffemodel);
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plateSegmentation = new PlateSegmentation(segmentation_prototxt, segmentation_caffemodel);
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generalRecognizer = new CNNRecognizer(charRecognization_proto, charRecognization_caffemodel);
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}
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~PipelinePR() {
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delete plateDetection;
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delete fineMapping;
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delete plateSegmentation;
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delete generalRecognizer;
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}
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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" };
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std::vector<std::string> plateRes;
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std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage) {
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std::vector<PlateInfo> results;
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std::vector<pr::PlateInfo> plates;
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plateDetection->plateDetectionRough(plateImage, plates);
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for (pr::PlateInfo plateinfo : plates) {
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cv::Mat image_finemapping = plateinfo.getPlateImage();
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image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
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image_finemapping = pr::fastdeskew(image_finemapping, 5);
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image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2, 5);
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cv::resize(image_finemapping, image_finemapping, cv::Size(136, 36));
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plateinfo.setPlateImage(image_finemapping);
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std::vector<cv::Rect> rects;
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plateSegmentation->segmentPlatePipline(plateinfo, 1, rects);
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plateSegmentation->ExtractRegions(plateinfo, rects);
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cv::copyMakeBorder(image_finemapping, image_finemapping, 0, 0, 0, 20, cv::BORDER_REPLICATE);
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plateinfo.setPlateImage(image_finemapping);
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generalRecognizer->SegmentBasedSequenceRecognition(plateinfo);
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plateinfo.decodePlateNormal(chars_code);
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results.push_back(plateinfo);
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std::cout << plateinfo.getPlateName() << std::endl;
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}
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// for (auto str:results) {
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// std::cout << str << std::endl;
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// }
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return results;
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}
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};
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}
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#endif //SWIFTPR_PIPLINE_H
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//
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// Created by 庾金科 on 20/09/2017.
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//
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#ifndef SWIFTPR_PLATEDETECTION_H
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#define SWIFTPR_PLATEDETECTION_H
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#include <opencv2/opencv.hpp>
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#include "PlateInfo.h"
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#include <vector>
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namespace pr{
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class PlateDetection{
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public:
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PlateDetection(std::string filename_cascade);
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PlateDetection();
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void LoadModel(std::string filename_cascade);
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void plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo> &plateInfos,int min_w=36,int max_w=800);
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// std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat InputImage,int min_w= 60,int max_h = 400);
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// std::vector<pr::PlateInfo> plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage);
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private:
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cv::CascadeClassifier cascade;
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};
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}// namespace pr
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#endif //SWIFTPR_PLATEDETECTION_H
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//
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// Created by 庾金科 on 20/09/2017.
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//
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#ifndef SWIFTPR_PLATEINFO_H
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#define SWIFTPR_PLATEINFO_H
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#include <opencv2/opencv.hpp>
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namespace pr {
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typedef std::vector<cv::Mat> Character;
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enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN};
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enum CharType {CHINESE,LETTER,LETTER_NUMS};
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class PlateInfo {
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public:
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std::vector<std::pair<CharType,cv::Mat>> plateChars;
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std::vector<std::pair<CharType,cv::Mat>> plateCoding;
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float confidence = 0;
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PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect, PlateColor plateType) {
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licensePlate = plateData;
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name = plateName;
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ROI = plateRect;
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Type = plateType;
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}
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PlateInfo(const cv::Mat &plateData, cv::Rect plateRect, PlateColor plateType) {
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licensePlate = plateData;
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ROI = plateRect;
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Type = plateType;
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}
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PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) {
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licensePlate = plateData;
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ROI = plateRect;
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}
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PlateInfo() {
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}
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cv::Mat getPlateImage() {
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return licensePlate;
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}
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void setPlateImage(cv::Mat plateImage){
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licensePlate = plateImage;
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}
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cv::Rect getPlateRect() {
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return ROI;
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}
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void setPlateRect(cv::Rect plateRect) {
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ROI = plateRect;
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}
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cv::String getPlateName() {
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return name;
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}
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void setPlateName(cv::String plateName) {
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name = plateName;
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}
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int getPlateType() {
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return Type;
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}
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void appendPlateChar(const std::pair<CharType,cv::Mat> &plateChar)
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{
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plateChars.push_back(plateChar);
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}
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void appendPlateCoding(const std::pair<CharType,cv::Mat> &charProb){
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plateCoding.push_back(charProb);
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}
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// cv::Mat getPlateChars(int id) {
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// if(id<PlateChars.size())
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// return PlateChars[id];
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// }
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std::string decodePlateNormal(std::vector<std::string> mappingTable) {
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std::string decode;
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for(auto plate:plateCoding) {
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float *prob = (float *)plate.second.data;
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if(plate.first == CHINESE) {
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decode += mappingTable[std::max_element(prob,prob+31) - prob];
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confidence+=*std::max_element(prob,prob+31);
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// std::cout<<*std::max_element(prob,prob+31)<<std::endl;
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}
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if(plate.first == LETTER) {
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decode += mappingTable[std::max_element(prob+41,prob+65)- prob];
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confidence+=*std::max_element(prob+41,prob+65);
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}
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if(plate.first == LETTER_NUMS) {
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decode += mappingTable[std::max_element(prob+31,prob+65)- prob];
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confidence+=*std::max_element(prob+31,prob+65);
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// std::cout<<*std::max_element(prob+31,prob+65)<<std::endl;
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}
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}
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name = decode;
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confidence/=7;
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return decode;
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}
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private:
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cv::Mat licensePlate;
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cv::Rect ROI;
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std::string name;
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PlateColor Type;
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};
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}
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#endif //SWIFTPR_PLATEINFO_H
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//
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// Created by 庾金科 on 16/10/2017.
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//
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#ifndef SWIFTPR_PLATESEGMENTATION_H
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#define SWIFTPR_PLATESEGMENTATION_H
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#include "opencv2/opencv.hpp"
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#include <opencv2/dnn.hpp>
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#include "PlateInfo.h"
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namespace pr{
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class PlateSegmentation{
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public:
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const int PLATE_NORMAL = 6;
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const int PLATE_NORMAL_GREEN = 7;
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const int DEFAULT_WIDTH = 20;
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PlateSegmentation(std::string phototxt,std::string caffemodel);
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PlateSegmentation(){}
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void segmentPlatePipline(PlateInfo &plateInfo,int stride,std::vector<cv::Rect> &Char_rects);
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void segmentPlateBySlidingWindows(cv::Mat &plateImage,int windowsWidth,int stride,cv::Mat &respones);
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void templateMatchFinding(const cv::Mat &respones,int windowsWidth,std::pair<float,std::vector<int>> &candidatePts);
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void refineRegion(cv::Mat &plateImage,const std::vector<int> &candidatePts,const int padding,std::vector<cv::Rect> &rects);
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void ExtractRegions(PlateInfo &plateInfo,std::vector<cv::Rect> &rects);
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cv::Mat classifyResponse(const cv::Mat &cropped);
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private:
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cv::dnn::Net net;
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// RefineRegion()
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};
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}//namespace pr
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#endif //SWIFTPR_PLATESEGMENTATION_H
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@@ -0,0 +1,21 @@
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//
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// Created by 庾金科 on 20/10/2017.
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//
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#ifndef SWIFTPR_RECOGNIZER_H
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#define SWIFTPR_RECOGNIZER_H
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#include "PlateInfo.h"
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#include "opencv2/dnn.hpp"
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namespace pr{
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typedef cv::Mat label;
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class GeneralRecognizer{
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public:
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virtual label recognizeCharacter(cv::Mat character) = 0;
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void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
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};
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}
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#endif //SWIFTPR_RECOGNIZER_H
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@@ -0,0 +1,107 @@
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//
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// Created by 庾金科 on 26/10/2017.
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//
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#ifndef SWIFTPR_NIBLACKTHRESHOLD_H
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#define SWIFTPR_NIBLACKTHRESHOLD_H
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#include <opencv2/opencv.hpp>
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using namespace cv;
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enum LocalBinarizationMethods{
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BINARIZATION_NIBLACK = 0, //!< Classic Niblack binarization. See @cite Niblack1985 .
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BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
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BINARIZATION_WOLF = 2, //!< Wolf's technique. See @cite Wolf2004 .
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BINARIZATION_NICK = 3 //!< NICK technique. See @cite Khurshid2009 .
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};
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void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
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int type, int blockSize, double k, int binarizationMethod )
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{
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// Input grayscale image
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Mat src = _src.getMat();
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CV_Assert(src.channels() == 1);
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CV_Assert(blockSize % 2 == 1 && blockSize > 1);
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if (binarizationMethod == BINARIZATION_SAUVOLA) {
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CV_Assert(src.depth() == CV_8U);
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}
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type &= THRESH_MASK;
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// Compute local threshold (T = mean + k * stddev)
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// using mean and standard deviation in the neighborhood of each pixel
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// (intermediate calculations are done with floating-point precision)
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Mat test;
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Mat thresh;
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{
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// note that: Var[X] = E[X^2] - E[X]^2
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Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
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double srcMin, stddevMax;
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boxFilter(src, mean, CV_32F, Size(blockSize, blockSize),
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Point(-1,-1), true, BORDER_REPLICATE);
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sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize),
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Point(-1,-1), true, BORDER_REPLICATE);
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variance = sqmean - mean.mul(mean);
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sqrt(variance, stddev);
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switch (binarizationMethod)
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{
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case BINARIZATION_NIBLACK:
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thresh = mean + stddev * static_cast<float>(k);
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break;
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case BINARIZATION_SAUVOLA:
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thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
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break;
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case BINARIZATION_WOLF:
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minMaxIdx(src, &srcMin,NULL);
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minMaxIdx(stddev, NULL, &stddevMax);
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thresh = mean - static_cast<float>(k) * (mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
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break;
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case BINARIZATION_NICK:
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sqrt(variance + sqmean, sqrtVarianceMeanSum);
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thresh = mean + static_cast<float>(k) * sqrtVarianceMeanSum;
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break;
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default:
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CV_Error( CV_StsBadArg, "Unknown binarization method" );
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break;
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}
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thresh.convertTo(thresh, src.depth());
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thresh.convertTo(test, src.depth());
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//
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// cv::imshow("imagex",test);
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// cv::waitKey(0);
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}
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// Prepare output image
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_dst.create(src.size(), src.type());
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Mat dst = _dst.getMat();
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CV_Assert(src.data != dst.data); // no inplace processing
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// Apply thresholding: ( pixel > threshold ) ? foreground : background
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Mat mask;
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switch (type)
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{
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case THRESH_BINARY: // dst = (src > thresh) ? maxval : 0
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case THRESH_BINARY_INV: // dst = (src > thresh) ? 0 : maxval
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compare(src, thresh, mask, (type == THRESH_BINARY ? CMP_GT : CMP_LE));
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dst.setTo(0);
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dst.setTo(maxValue, mask);
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break;
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case THRESH_TRUNC: // dst = (src > thresh) ? thresh : src
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compare(src, thresh, mask, CMP_GT);
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src.copyTo(dst);
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thresh.copyTo(dst, mask);
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break;
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case THRESH_TOZERO: // dst = (src > thresh) ? src : 0
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case THRESH_TOZERO_INV: // dst = (src > thresh) ? 0 : src
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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
|
||||
Reference in New Issue
Block a user