src change
jackyu1127
5 years ago
parent
24ecdd93ad
commit
632c7de2ed
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//
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// Created by Jack Yu on 21/10/2017.
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//
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#ifndef HYPERPR_CNNRECOGNIZER_H
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#define HYPERPR_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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} // namespace pr
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#endif // HYPERPR_CNNRECOGNIZER_H
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//
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// Created by Jack Yu on 22/09/2017.
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//
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#ifndef HYPERPR_FASTDESKEW_H
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#define HYPERPR_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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} // namespace pr
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#endif // HYPERPR_FASTDESKEW_H
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//
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// Created by Jack Yu on 22/09/2017.
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//
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#ifndef HYPERPR_FINEMAPPING_H
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#define HYPERPR_FINEMAPPING_H
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#include <opencv2/dnn.hpp>
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#include <opencv2/opencv.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,
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int upper = 0, int lower = -50,
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int windows_size = 17);
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cv::Mat FineMappingHorizon(cv::Mat FinedVertical, int leftPadding,
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int rightPadding);
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private:
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cv::dnn::Net net;
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};
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} // namespace pr
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#endif // HYPERPR_FINEMAPPING_H
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//
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// Created by Jack Yu on 22/10/2017.
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//
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#ifndef HYPERPR_PIPLINE_H
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#define HYPERPR_PIPLINE_H
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#include "CNNRecognizer.h"
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#include "FastDeskew.h"
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#include "FineMapping.h"
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#include "PlateDetection.h"
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#include "PlateInfo.h"
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#include "PlateSegmentation.h"
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#include "Recognizer.h"
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#include "SegmentationFreeRecognizer.h"
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namespace pr {
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const std::vector<std::string> CH_PLATE_CODE{
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"京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙",
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"皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵",
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"云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4",
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"5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G",
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"H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U",
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"V", "W", "X", "Y", "Z", "港", "学", "使", "警", "澳", "挂", "军",
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"北", "南", "广", "沈", "兰", "成", "济", "海", "民", "航", "空"};
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const int SEGMENTATION_FREE_METHOD = 0;
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const int SEGMENTATION_BASED_METHOD = 1;
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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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SegmentationFreeRecognizer *segmentationFreeRecognizer;
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PipelinePR(std::string detector_filename, std::string finemapping_prototxt,
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std::string finemapping_caffemodel,
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std::string segmentation_prototxt,
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std::string segmentation_caffemodel,
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std::string charRecognization_proto,
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std::string charRecognization_caffemodel,
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std::string segmentationfree_proto,
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std::string segmentationfree_caffemodel);
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~PipelinePR();
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std::vector<std::string> plateRes;
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std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage, int method);
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};
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} // namespace pr
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#endif // HYPERPR_PIPLINE_H
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//
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// Created by Jack Yu on 20/09/2017.
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//
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#ifndef HYPERPR_PLATEDETECTION_H
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#define HYPERPR_PLATEDETECTION_H
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#include <PlateInfo.h>
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#include <opencv2/opencv.hpp>
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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,
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std::vector<pr::PlateInfo> &plateInfos,
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int min_w = 36, int max_w = 800);
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// std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat
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// InputImage,int min_w= 60,int max_h = 400);
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// std::vector<pr::PlateInfo>
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// 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 // HYPERPR_PLATEDETECTION_H
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//
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// Created by Jack Yu on 20/09/2017.
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//
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#ifndef HYPERPR_PLATEINFO_H
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#define HYPERPR_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, INVALID };
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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,
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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,
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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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cv::Mat getPlateImage() { return licensePlate; }
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void setPlateImage(cv::Mat plateImage) { licensePlate = plateImage; }
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cv::Rect getPlateRect() { return ROI; }
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void setPlateRect(cv::Rect plateRect) { ROI = plateRect; }
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cv::String getPlateName() { return name; }
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void setPlateName(cv::String plateName) { name = plateName; }
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int getPlateType() { return Type; }
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void appendPlateChar(const std::pair<CharType, cv::Mat> &plateChar) {
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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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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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}
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else 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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else 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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} else if (plate.first == INVALID) {
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decode += '*';
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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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} // namespace pr
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#endif // HYPERPR_PLATEINFO_H
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#ifndef HYPERPR_PLATESEGMENTATION_H
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#define HYPERPR_PLATESEGMENTATION_H
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#include "PlateInfo.h"
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#include "opencv2/opencv.hpp"
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#include <opencv2/dnn.hpp>
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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,
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std::vector<cv::Rect> &Char_rects);
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void segmentPlateBySlidingWindows(cv::Mat &plateImage, int windowsWidth,
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int stride, cv::Mat &respones);
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void templateMatchFinding(const cv::Mat &respones, int windowsWidth,
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std::pair<float, std::vector<int>> &candidatePts);
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void refineRegion(cv::Mat &plateImage, const std::vector<int> &candidatePts,
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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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};
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} // namespace pr
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#endif // HYPERPR_PLATESEGMENTATION_H
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//
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// Created by Jack Yu on 20/10/2017.
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//
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#ifndef HYPERPR_RECOGNIZER_H
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#define HYPERPR_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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// virtual cv::Mat SegmentationFreeForSinglePlate(cv::Mat plate) =
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// 0;
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void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
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void SegmentationFreeSequenceRecognition(PlateInfo &plateInfo);
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};
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} // namespace pr
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#endif // HYPERPR_RECOGNIZER_H
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//
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// Created by Jack Yu on 28/11/2017.
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//
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#ifndef HYPERPR_SEGMENTATIONFREERECOGNIZER_H
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#define HYPERPR_SEGMENTATIONFREERECOGNIZER_H
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#include "Recognizer.h"
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namespace pr {
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class SegmentationFreeRecognizer {
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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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const int CHAR_LEN = 84;
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SegmentationFreeRecognizer(std::string prototxt, std::string caffemodel);
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std::pair<std::string, float>
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SegmentationFreeForSinglePlate(cv::Mat plate,
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std::vector<std::string> mapping_table);
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private:
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cv::dnn::Net net;
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};
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} // namespace pr
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#endif // HYPERPR_SEGMENTATIONFREERECOGNIZER_H
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//
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// Created by Jack Yu on 26/10/2017.
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//
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#ifndef HYPERPR_NIBLACKTHRESHOLD_H
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#define HYPERPR_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 =
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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,
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int binarizationMethod) {
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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), Point(-1, -1),
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true, BORDER_REPLICATE);
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sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize), Point(-1, -1),
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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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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 =
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mean - static_cast<float>(k) *
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(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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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));
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dst.setTo(0);
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src.copyTo(dst, mask);
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break;
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default:
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CV_Error(CV_StsBadArg, "Unknown threshold type");
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break;
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}
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}
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#endif // HYPERPR_NIBLACKTHRESHOLD_H
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