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EasyPR
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Reformat all source and header files using Clang-Format [Google].

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v1.6alpha
Micooz 11 years ago
parent d6ad832d50
commit 32b0560872
39 changed files with 1343 additions and 1441 deletions
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7
include/easypr/api.hpp
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@ -12,7 +12,7 @@ namespace easypr {
namespace api {
static bool plate_judge(const char* image,const char *model) {
static bool plate_judge(const char* image, const char* model) {
cv::Mat src = cv::imread(image);
assert(!src.empty());
@ -62,10 +62,7 @@ static Color get_plate_color(const char* image) {
return getPlateType(img, true);
}
}
}
#endif //EASYPR_API_HPP
#endif // EASYPR_API_HPP

18
include/easypr/core/chars_identify.h
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@ -1,5 +1,5 @@
#ifndef __CHARS_IDENTIFY_H__
#define __CHARS_IDENTIFY_H__
#ifndef EASYPR_CORE_CHARSIDENTIFY_H_
#define EASYPR_CORE_CHARSIDENTIFY_H_
#include <opencv2/opencv.hpp>
@ -7,18 +7,16 @@ namespace easypr {
class CharsIdentify {
public:
static CharsIdentify* instance();
static CharsIdentify* instance();
std::pair<std::string, std::string> identify(cv::Mat input);
std::pair<std::string, std::string> identify(cv::Mat input);
private:
CharsIdentify();
CharsIdentify();
static CharsIdentify* instance_;
//！使用的ANN模型
cv::Ptr<cv::ml::ANN_MLP> ann_;
static CharsIdentify* instance_;
cv::Ptr<cv::ml::ANN_MLP> ann_;
};
}
#endif
#endif // EASYPR_CORE_CHARSIDENTIFY_H_

6
include/easypr/core/chars_recognise.h
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@ -9,8 +9,8 @@
// Desciption:
// Defines CCharsRecognise
//////////////////////////////////////////////////////////////////////////
#ifndef __CHARS_RECOGNISE_H__
#define __CHARS_RECOGNISE_H__
#ifndef EASYPR_CORE_CHARSRECOGNISE_H_
#define EASYPR_CORE_CHARSRECOGNISE_H_
#include "easypr/core/chars_segment.h"
#include "easypr/core/chars_identify.h"
@ -61,4 +61,4 @@ class CCharsRecognise {
} /* \namespace easypr */
#endif /* endif __CHARS_RECOGNISE_H__ */
#endif // EASYPR_CORE_CHARSRECOGNISE_H_

6
include/easypr/core/chars_segment.h
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@ -9,8 +9,8 @@
// Desciption:
// Defines CCharsSegment
//////////////////////////////////////////////////////////////////////////
#ifndef __CHARS_SEGMENT_H__
#define __CHARS_SEGMENT_H__
#ifndef EASYPR_CORE_CHARSSEGMENT_H_
#define EASYPR_CORE_CHARSSEGMENT_H_
#include "easypr/core/core_func.h"
@ -93,4 +93,4 @@ class CCharsSegment {
} /* \namespace easypr */
#endif /* endif __CHARS_SEGMENT_H__ */
#endif // EASYPR_CORE_CHARSSEGMENT_H_

69
include/easypr/core/core_func.h
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@ -1,5 +1,5 @@
#ifndef __CORE_FUNC_H__
#define __CORE_FUNC_H__
#ifndef EASYPR_CORE_COREFUNC_H_
#define EASYPR_CORE_COREFUNC_H_
#include <opencv2/opencv.hpp>
@ -10,47 +10,46 @@ Namespace where all the C++ EasyPR functionality resides
*/
namespace easypr {
enum Color { BLUE, YELLOW, WHITE, UNKNOWN };
enum Color { BLUE, YELLOW, WHITE, UNKNOWN };
enum LocateType { SOBEL, COLOR, OTHER };
enum LocateType { SOBEL, COLOR, OTHER };
//! 根据一幅图像与颜色模板获取对应的二值图
//! 输入RGB图像, 颜色模板（蓝色、黄色）
//! 输出灰度图（只有0和255两个值，255代表匹配，0代表不匹配）
Mat colorMatch(const Mat& src, Mat& match, const Color r,
const bool adaptive_minsv);
//! 根据一幅图像与颜色模板获取对应的二值图
//! 输入RGB图像, 颜色模板（蓝色、黄色）
//! 输出灰度图（只有0和255两个值，255代表匹配，0代表不匹配）
Mat colorMatch(const Mat& src, Mat& match, const Color r,
const bool adaptive_minsv);
//! 判断一个车牌的颜色
//! 输入车牌mat与颜色模板
//! 返回true或fasle
bool plateColorJudge(const Mat& src, const Color r, const bool adaptive_minsv,
float& percent);
//! 判断一个车牌的颜色
//! 输入车牌mat与颜色模板
//! 返回true或fasle
bool plateColorJudge(const Mat& src, const Color r, const bool adaptive_minsv,
float& percent);
bool bFindLeftRightBound(Mat& bound_threshold, int& posLeft, int& posRight);
bool bFindLeftRightBound1(Mat& bound_threshold, int& posLeft, int& posRight);
bool bFindLeftRightBound2(Mat& bound_threshold, int& posLeft, int& posRight);
bool bFindLeftRightBound(Mat& bound_threshold, int& posLeft, int& posRight);
bool bFindLeftRightBound1(Mat& bound_threshold, int& posLeft, int& posRight);
bool bFindLeftRightBound2(Mat& bound_threshold, int& posLeft, int& posRight);
//去除车牌上方的钮钉
//计算每行元素的阶跃数，如果小于X认为是柳丁，将此行全部填0（涂黑）
// X的推荐值为，可根据实际调整
bool clearLiuDing(Mat& img);
void clearLiuDingOnly(Mat& img);
void clearLiuDing(Mat mask, int& top, int& bottom);
//去除车牌上方的钮钉
//计算每行元素的阶跃数，如果小于X认为是柳丁，将此行全部填0（涂黑）
// X的推荐值为，可根据实际调整
bool clearLiuDing(Mat& img);
void clearLiuDingOnly(Mat& img);
void clearLiuDing(Mat mask, int& top, int& bottom);
//! 获得车牌颜色
Color getPlateType(const Mat& src, const bool adaptive_minsv);
//! 获得车牌颜色
Color getPlateType(const Mat& src, const bool adaptive_minsv);
//! 直方图均衡
Mat histeq(Mat in);
Mat features(Mat in, int sizeData);
Rect GetCenterRect(Mat& in);
Mat CutTheRect(Mat& in, Rect& rect);
int ThresholdOtsu(Mat mat);
//! 直方图均衡
Mat histeq(Mat in);
Mat features(Mat in, int sizeData);
Rect GetCenterRect(Mat& in);
Mat CutTheRect(Mat& in, Rect& rect);
int ThresholdOtsu(Mat mat);
//! 获取垂直和水平方向直方图
Mat ProjectedHistogram(Mat img, int t);
//! 获取垂直和水平方向直方图
Mat ProjectedHistogram(Mat img, int t);
} /*! \namespace easypr*/
#endif
/* endif __CORE_FUNC_H__ */
#endif // EASYPR_CORE_COREFUNC_H_

37
include/easypr/core/feature.h
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@ -1,31 +1,30 @@
#ifndef __FEATURE_H__
#define __FEATURE_H__
#ifndef EASYPR_CORE_FEATURE_H_
#define EASYPR_CORE_FEATURE_H_
#include <opencv2/opencv.hpp>
namespace easypr {
//! 获得车牌的特征数
cv::Mat getTheFeatures(cv::Mat in);
//! 获得车牌的特征数
cv::Mat getTheFeatures(cv::Mat in);
//! EasyPR的getFeatures回调函数
//! 用于从车牌的image生成svm的训练特征features
typedef void(*svmCallback)(const cv::Mat& image, cv::Mat& features);
//! EasyPR的getFeatures回调函数
//! 用于从车牌的image生成svm的训练特征features
typedef void (*svmCallback)(const cv::Mat& image, cv::Mat& features);
//! EasyPR的getFeatures回调函数
//! 本函数是获取垂直和水平的直方图图值
void getHistogramFeatures(const cv::Mat& image, cv::Mat& features);
//! EasyPR的getFeatures回调函数
//! 本函数是获取垂直和水平的直方图图值
void getHistogramFeatures(const cv::Mat& image, cv::Mat& features);
//! 本函数是获取SIFT特征子
void getSIFTFeatures(const cv::Mat& image, cv::Mat& features);
//! 本函数是获取SIFT特征子
void getSIFTFeatures(const cv::Mat& image, cv::Mat& features);
//! 本函数是获取HOG特征子
void getHOGFeatures(const cv::Mat& image, cv::Mat& features);
//! 本函数是获取HOG特征子
void getHOGFeatures(const cv::Mat& image, cv::Mat& features);
//! 本函数是获取HSV空间量化的直方图特征子
void getHSVHistFeatures(const cv::Mat& image, cv::Mat& features);
//! 本函数是获取HSV空间量化的直方图特征子
void getHSVHistFeatures(const cv::Mat& image, cv::Mat& features);
} /*! \namespace easypr*/
} /*! \namespace easypr*/
#endif
/* endif __FEATURE_H__ */
#endif // EASYPR_CORE_FEATURE_H_

54
include/easypr/core/plate.h
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@ -7,8 +7,8 @@
// Desciption:
// An abstract class for car plate.
//////////////////////////////////////////////////////////////////////////
#ifndef __PLATE_H__
#define __PLATE_H__
#ifndef EASYPR_CORE_PLATE_H_
#define EASYPR_CORE_PLATE_H_
#include "core_func.h"
@ -17,40 +17,40 @@ Namespace where all the C++ EasyPR functionality resides
*/
namespace easypr {
class CPlate {
public:
bool bColored;
class CPlate {
public:
bool bColored;
//! 构造函数
CPlate();
//! 构造函数
CPlate();
//! 设置与读取变量
inline void setPlateMat(Mat param) { m_plateMat = param; }
inline Mat getPlateMat() const { return m_plateMat; }
//! 设置与读取变量
inline void setPlateMat(Mat param) { m_plateMat = param; }
inline Mat getPlateMat() const { return m_plateMat; }
inline void setPlatePos(RotatedRect param) { m_platePos = param; }
inline RotatedRect getPlatePos() const { return m_platePos; }
inline void setPlatePos(RotatedRect param) { m_platePos = param; }
inline RotatedRect getPlatePos() const { return m_platePos; }
inline void setPlateStr(String param) { m_plateStr = param; }
inline String getPlateStr() const { return m_plateStr; }
inline void setPlateStr(String param) { m_plateStr = param; }
inline String getPlateStr() const { return m_plateStr; }
inline void setPlateLocateType(LocateType param) { m_locateType = param; }
inline LocateType getPlateLocateType() const { return m_locateType; }
inline void setPlateLocateType(LocateType param) { m_locateType = param; }
inline LocateType getPlateLocateType() const { return m_locateType; }
private:
//! 车牌的图块
Mat m_plateMat;
private:
//! 车牌的图块
Mat m_plateMat;
//! 车牌在原图的位置
RotatedRect m_platePos;
//! 车牌在原图的位置
RotatedRect m_platePos;
//! 车牌字符串
String m_plateStr;
//! 车牌字符串
String m_plateStr;
//! 车牌定位的方法
LocateType m_locateType;
};
//! 车牌定位的方法
LocateType m_locateType;
};
} /*! \namespace easypr*/
#endif /* endif __PLATE_H__ */
#endif // EASYPR_CORE_PLATE_H_

6
include/easypr/core/plate_detect.h
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@ -10,8 +10,8 @@
// Desciption:
// Defines CPlateDetect
//////////////////////////////////////////////////////////////////////////
#ifndef __PLATE_DETECT_H__
#define __PLATE_DETECT_H__
#ifndef EASYPR_CORE_PLATEDETECT_H_
#define EASYPR_CORE_PLATEDETECT_H_
#include "easypr/core/plate_locate.h"
#include "easypr/core/plate_judge.h"
@ -99,4 +99,4 @@ class CPlateDetect {
} /*! \namespace easypr*/
#endif /* endif __PLATE_DETECT_H__ */
#endif // EASYPR_CORE_PLATEDETECT_H_

12
include/easypr/core/plate_judge.h
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@ -1,5 +1,5 @@
#ifndef __PLATE_JUDGE_H__
#define __PLATE_JUDGE_H__
#ifndef EASYPR_CORE_PLATEJUDGE_H_
#define EASYPR_CORE_PLATEJUDGE_H_
#include "easypr/core/plate.h"
#include "easypr/core/feature.h"
@ -10,8 +10,8 @@ namespace easypr {
class PlateJudge {
public:
static PlateJudge* instance();
//! 对多幅车牌进行SVM判断
//! 对多幅车牌进行SVM判断
int plateJudge(const std::vector<CPlate>&, std::vector<CPlate>&);
//! ³µÅÆÅжÏ
@ -26,9 +26,7 @@ class PlateJudge {
static PlateJudge* instance_;
cv::Ptr<ml::SVM> svm_;
};
}
#endif
#endif // EASYPR_CORE_PLATEJUDGE_H_

12
include/easypr/core/plate_locate.h
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@ -11,8 +11,8 @@
// Desciption:
// Defines CPlateLocate
//////////////////////////////////////////////////////////////////////////
#ifndef __PLATE_LOCATE_H__
#define __PLATE_LOCATE_H__
#ifndef EASYPR_CORE_PLATELOCATE_H_
#define EASYPR_CORE_PLATELOCATE_H_
#include "easypr/core/plate.h"
#include "easypr/core/core_func.h"
@ -38,8 +38,8 @@ class CPlateLocate {
std::vector<RotatedRect>& outRects);
//! 抗扭斜处理
int deskew(const Mat& src, const Mat& src_b, std::vector<RotatedRect>& inRects,
std::vector<CPlate>& outPlates);
int deskew(const Mat& src, const Mat& src_b,
std::vector<RotatedRect>& inRects, std::vector<CPlate>& outPlates);
//! 是否偏斜
//! 输入二值化图像，输出判断结果
@ -127,7 +127,7 @@ class CPlateLocate {
static const int TYPE = CV_8UC3;
//! verifySize所用常量
static const int DEFAULT_VERIFY_MIN = 1; // 3
static const int DEFAULT_VERIFY_MIN = 1; // 3
static const int DEFAULT_VERIFY_MAX = 24; // 20
//! 角度判断所用常量
@ -159,4 +159,4 @@ class CPlateLocate {
} /*! \namespace easypr*/
#endif /* endif __PLATE_LOCATE_H__ */
#endif // EASYPR_CORE_PLATELOCATE_H_

10
include/easypr/core/plate_recognize.h
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@ -9,8 +9,8 @@
// Desciption:
// Defines CPlateRecognize
//////////////////////////////////////////////////////////////////////////
#ifndef __PLATE_RECOGNIZE_H__
#define __PLATE_RECOGNIZE_H__
#ifndef EASYPR_CORE_PLATERECOGNIZE_H_
#define EASYPR_CORE_PLATERECOGNIZE_H_
#include "easypr/core/plate_detect.h"
#include "easypr/core/chars_recognise.h"
@ -31,11 +31,9 @@ class CPlateRecognize : public CPlateDetect, public CCharsRecognise {
inline void setLifemode(bool param) { CPlateDetect::setPDLifemode(param); }
//! 是否开启调试模式
inline void setDebug(bool param) {
CPlateDetect::setPDDebug(param);
}
inline void setDebug(bool param) { CPlateDetect::setPDDebug(param); }
};
} /* \namespace easypr */
#endif /* endif __PLATE_RECOGNITION_H__ */
#endif // EASYPR_CORE_PLATERECOGNIZE_H_

9
include/easypr/preprocess/deface.h
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@ -7,8 +7,8 @@
// 在发布数据到general_test数据集里，请先用这里的方法对图像进行处理
// EasyPR开源项目非常注重保护图片中驾驶人的隐私
#ifndef EASYPR_DEFACE_H
#define EASYPR_DEFACE_H
#ifndef EASYPR_CORE_DEFACE_H_
#define EASYPR_CORE_DEFACE_H_
#include <opencv2/opencv.hpp>
@ -20,10 +20,7 @@ int deface();
cv::Mat detectAndMaskFace(cv::Mat& img, cv::CascadeClassifier& cascade,
double scale);
}
}
#endif //EASYPR_DEFACE_H
#endif // EASYPR_CORE_DEFACE_H_

18
include/easypr/preprocess/gdts.h
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@ -2,22 +2,20 @@
// 在捐赠数据到GDTS数据集里，请先用这里的方法对图像进行预处理
// EasyPR开源项目非常注重保护原始图片的版权
// 所有的捐赠数据通过GDSL协议进行约定，保证使用人仅用于非商业性目的
#ifndef EASYPR_GDTS_H
#define EASYPR_GDTS_H
#ifndef EASYPR_CORE_GDTS_H_
#define EASYPR_CORE_GDTS_H_
#include <opencv2/opencv.hpp>
namespace easypr {
namespace preprocess {
namespace preprocess {
// EasyPR的图像预处理函数，进行模糊化与裁剪化处理
cv::Mat imageProcess(cv::Mat src);
int generate_gdts();
}
// EasyPR的图像预处理函数，进行模糊化与裁剪化处理
cv::Mat imageProcess(cv::Mat src);
int generate_gdts();
}
}
#endif //EASYPR_GDTS_H
#endif // EASYPR_CORE_GDTS_H_

20
include/easypr/preprocess/mc_data.h
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@ -1,17 +1,21 @@
// learn data_prepare :
// 生成learn data的cpp，learn data指的是train data，verify data， test data的总和，这个名字是我起的。
// learn data应该是贴好标签的数据，因此即便通过EasyPR自动化处理以后，也要人为的修正一下.
// 生成learn data的cpp，learn data指的是train data，verify data， test
// data的总和，这个名字是我起的。
// learn
// data应该是贴好标签的数据，因此即便通过EasyPR自动化处理以后，也要人为的修正一下.
// 你应该按照如下的步骤和顺序组织learn data：
// 1.用EasyPR对not label data进行处理，通过EasyPR辅助来对车牌图片进行标签（放到不同文件夹下）；
// 1.用EasyPR对not label
// data进行处理，通过EasyPR辅助来对车牌图片进行标签（放到不同文件夹下）；
// 2.标签分两种，一种是有车牌的，一种是无车牌的；
// 2.EasyPR生成标签以后，你还得需要肉眼确认下，将被不正确标签的数据转移到它们该去的文件夹下；
// 3.通过上面的步骤，正确的车牌图片和非车牌图片已经被分别放到两个文件下，假设是hasplate和noplate；
// 4.将这两个文件夹放到EasyPR目录train/data/plate_detect_svm/learn下面
// 5.运行EasyPR，选择“训练”目录下的“车牌检测（not divide）”，这个程序会自动把learn data分块，训练，测试
// 5.运行EasyPR，选择“训练”目录下的“车牌检测（not
// divide）”，这个程序会自动把learn data分块，训练，测试
#ifndef EASYPR_MC_DATA_H
#define EASYPR_MC_DATA_H
#ifndef EASYPR_CORE_MCDATA_H_
#define EASYPR_CORE_MCDATA_H_
#include <string>
@ -24,9 +28,7 @@ void create_learn_data(const char* raw_data_folder, const char* out_data_folder,
void tag_data(const char* source_folder, const char* has_plate_folder,
const char* no_plate_folder, const char* svm_model);
}
}
#endif //EASYPR_MC_DATA_H
#endif // EASYPR_CORE_MCDATA_H_

19
include/easypr/train/ann_train.h
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@ -5,22 +5,21 @@
namespace easypr {
class AnnTrain: public ITrain {
class AnnTrain : public ITrain {
public:
explicit AnnTrain(const char* chars_folder, const char* xml);
explicit AnnTrain(const char* chars_folder, const char* xml);
virtual void train();
virtual void train();
virtual void test();
virtual void test();
private:
virtual cv::Ptr<cv::ml::TrainData> tdata();
virtual cv::Ptr<cv::ml::TrainData> tdata();
cv::Ptr<cv::ml::ANN_MLP> ann_;
const char* ann_xml_;
const char* chars_folder_;
cv::Ptr<cv::ml::ANN_MLP> ann_;
const char* ann_xml_;
const char* chars_folder_;
};
}
#endif // EASYPR_TRAIN_ANNTRAIN_H_
#endif // EASYPR_TRAIN_ANNTRAIN_H_

31
include/easypr/train/svm_train.h
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@ -9,29 +9,28 @@ namespace easypr {
class SvmTrain : public ITrain {
public:
typedef struct {
std::string file;
SvmLabel label;
} TrainItem;
typedef struct {
std::string file;
SvmLabel label;
} TrainItem;
SvmTrain(const char* plates_folder, const char* xml);
SvmTrain(const char* plates_folder, const char* xml);
virtual void train();
virtual void train();
virtual void test();
virtual void test();
private:
void prepare();
void prepare();
virtual cv::Ptr<cv::ml::TrainData> tdata();
virtual cv::Ptr<cv::ml::TrainData> tdata();
cv::Ptr<cv::ml::SVM> svm_;
const char* plates_folder_;
const char* svm_xml_;
std::vector<TrainItem> train_file_list_;
std::vector<TrainItem> test_file_list_;
cv::Ptr<cv::ml::SVM> svm_;
const char* plates_folder_;
const char* svm_xml_;
std::vector<TrainItem> train_file_list_;
std::vector<TrainItem> test_file_list_;
};
}
#endif // EASYPR_TRAIN_SVMTRAIN_H_
#endif // EASYPR_TRAIN_SVMTRAIN_H_

14
include/easypr/train/train.h
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@ -7,17 +7,17 @@ namespace easypr {
class ITrain {
public:
ITrain();
ITrain();
virtual ~ITrain();
virtual ~ITrain();
virtual void train() = 0;
virtual void train() = 0;
virtual void test() = 0;
virtual void test() = 0;
private:
virtual cv::Ptr<cv::ml::TrainData> tdata() = 0;
virtual cv::Ptr<cv::ml::TrainData> tdata() = 0;
};
}
#endif // EASYPR_TRAIN_TRAIN_H_
#endif // EASYPR_TRAIN_TRAIN_H_

12
include/easypr/util/program_options.h
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@ -30,9 +30,7 @@ class Row {
public:
Row();
enum Field {
kShort, kLong, kDefault, kDescription
};
enum Field { kShort, kLong, kDefault, kDescription };
typedef std::initializer_list<Field> Order;
@ -232,7 +230,7 @@ class ParseItem {
/*
* dynamic type cast, support base data types including std::string
*/
template<typename T>
template <typename T>
T as() {
T r;
std::stringstream buf;
@ -334,7 +332,7 @@ class Parser {
ParseItem* get(const std::string& key);
inline void set_usage_subroutines(
const Generator::SubroutineCollection* subroutines) {
const Generator::SubroutineCollection* subroutines) {
subroutines_ = subroutines;
}
@ -353,8 +351,6 @@ class Parser {
ParameterList args_;
ParseResult* pr_;
};
}
#endif // UTIL_PROGRAM_OPTIONS_H
#endif // UTIL_PROGRAM_OPTIONS_H

6
include/easypr/util/util.h
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@ -1,5 +1,5 @@
#ifndef EASYPR_UTIL_H_
#define EASYPR_UTIL_H_
#ifndef EASYPR_UTIL_UTIL_H_
#define EASYPR_UTIL_UTIL_H_
#include <iostream>
#include <string>
@ -116,4 +116,4 @@ typedef Utils utils;
} // namespace easypr
#endif // EASYPR_UTIL_H_
#endif // EASYPR_UTIL_UTIL_H_

37
src/core/chars_identify.cpp
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@ -5,28 +5,27 @@
namespace easypr {
CharsIdentify* CharsIdentify::instance_ = nullptr;
CharsIdentify* CharsIdentify::instance_ = nullptr;
CharsIdentify* CharsIdentify::instance(){
if (!instance_){
instance_ = new CharsIdentify;
}
return instance_;
CharsIdentify* CharsIdentify::instance() {
if (!instance_) {
instance_ = new CharsIdentify;
}
return instance_;
}
CharsIdentify::CharsIdentify() {
ann_ = ml::ANN_MLP::load<ml::ANN_MLP>(kDefaultAnnPath);
}
CharsIdentify::CharsIdentify() {
ann_ = ml::ANN_MLP::load<ml::ANN_MLP>(kDefaultAnnPath);
}
std::pair<std::string, std::string> CharsIdentify::identify(cv::Mat input){
cv::Mat feature = features(input, kPredictSize);
auto index = static_cast<int>(ann_->predict(feature));
if (index < 34){
return std::make_pair(kChars[index], kChars[index]);
} else {
const char* key = kChars[index];
return std::make_pair(key, kCharsMap.at(key));
}
std::pair<std::string, std::string> CharsIdentify::identify(cv::Mat input) {
cv::Mat feature = features(input, kPredictSize);
auto index = static_cast<int>(ann_->predict(feature));
if (index < 34) {
return std::make_pair(kChars[index], kChars[index]);
} else {
const char* key = kChars[index];
return std::make_pair(key, kCharsMap.at(key));
}
}
}

30
src/core/chars_recognise.cpp
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@ -3,27 +3,21 @@
namespace easypr {
CCharsRecognise::CCharsRecognise() {
m_charsSegment = new CCharsSegment();
}
CCharsRecognise::CCharsRecognise() { m_charsSegment = new CCharsSegment(); }
CCharsRecognise::~CCharsRecognise() {
SAFE_RELEASE(m_charsSegment);
}
CCharsRecognise::~CCharsRecognise() { SAFE_RELEASE(m_charsSegment); }
std::string CCharsRecognise::charsRecognise(Mat plate) {
//³µÅÆ×Ö·û·½¿é¼¯ºÏ
std::vector<Mat> matChars;
std::string license;
std::string CCharsRecognise::charsRecognise(Mat plate) {
std::vector<Mat> matChars;
std::string license;
int result = m_charsSegment->charsSegment(plate, matChars);
if (result == 0) {
for(auto block : matChars){
auto character = CharsIdentify::instance()->identify(block);
license.append(character.second);
}
int result = m_charsSegment->charsSegment(plate, matChars);
if (result == 0) {
for (auto block : matChars) {
auto character = CharsIdentify::instance()->identify(block);
license.append(character.second);
}
return license;
}
return license;
}
}

27
src/core/chars_segment.cpp
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@ -71,10 +71,8 @@ Mat CCharsSegment::preprocessChar(Mat in) {
return out;
}
//! 字符分割与排序
int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec) {
if (!input.data) return 0x01;
int w = input.cols;
@ -167,7 +165,8 @@ int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec) {
// 对符合尺寸的图块按照从左到右进行排序;
// 直接使用stl的sort方法，更有效率
vector<Rect> sortedRect(vecRect);
std::sort(sortedRect.begin(), sortedRect.end(),[](const Rect& r1, const Rect& r2) { return r1.x < r2.x; });
std::sort(sortedRect.begin(), sortedRect.end(),
[](const Rect& r1, const Rect& r2) { return r1.x < r2.x; });
size_t specIndex = 0;
@ -196,30 +195,26 @@ int CCharsSegment::charsSegment(Mat input, vector<Mat>& resultVec) {
for (size_t i = 0; i < newSortedRect.size(); i++) {
Rect mr = newSortedRect[i];
//Mat auxRoi(img_threshold, mr);
// Mat auxRoi(img_threshold, mr);
// 使用灰度图来截取图块，然后依次对每个图块进行大津阈值来二值化
Mat auxRoi(input_grey, mr);
Mat newRoi;
if (BLUE == plateType) {
/* img_threshold = auxRoi.clone();
int w = input_grey.cols;
int h = input_grey.rows;
Mat tmp = input_grey(Rect_<double>(w * 0.1, h * 0.1, w * 0.8, h * 0.8));
int threadHoldV = ThresholdOtsu(tmp);*/
/* img_threshold = auxRoi.clone();
int w = input_grey.cols;
int h = input_grey.rows;
Mat tmp = input_grey(Rect_<double>(w * 0.1, h * 0.1, w * 0.8, h * 0.8));
int threadHoldV = ThresholdOtsu(tmp);*/
threshold(auxRoi, newRoi, 5, 255, CV_THRESH_BINARY + CV_THRESH_OTSU);
}
else if (YELLOW == plateType) {
} else if (YELLOW == plateType) {
threshold(auxRoi, newRoi, 5, 255, CV_THRESH_BINARY_INV + CV_THRESH_OTSU);
}
else if (WHITE == plateType) {
} else if (WHITE == plateType) {
threshold(auxRoi, newRoi, 5, 255, CV_THRESH_OTSU + CV_THRESH_BINARY_INV);
}
else {
} else {
threshold(auxRoi, newRoi, 5, 255, CV_THRESH_OTSU + CV_THRESH_BINARY);
}

1129
src/core/core_func.cpp
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File diff suppressed because it is too large Load Diff

96
src/core/feature.cpp
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@ -10,64 +10,64 @@ Namespace where all the C++ EasyPR functionality resides
*/
namespace easypr {
//! 获取垂直和水平的直方图图值
Mat getTheFeatures(Mat in) {
const int VERTICAL = 0;
const int HORIZONTAL = 1;
//! 获取垂直和水平的直方图图值
Mat getTheFeatures(Mat in) {
const int VERTICAL = 0;
const int HORIZONTAL = 1;
// Histogram features
Mat vhist = ProjectedHistogram(in, VERTICAL);
Mat hhist = ProjectedHistogram(in, HORIZONTAL);
// Histogram features
Mat vhist = ProjectedHistogram(in, VERTICAL);
Mat hhist = ProjectedHistogram(in, HORIZONTAL);
// Last 10 is the number of moments components
int numCols = vhist.cols + hhist.cols;
// Last 10 is the number of moments components
int numCols = vhist.cols + hhist.cols;
Mat out = Mat::zeros(1, numCols, CV_32F);
Mat out = Mat::zeros(1, numCols, CV_32F);
// Asign values to feature,样本特征为水平、垂直直方图
int j = 0;
for (int i = 0; i < vhist.cols; i++) {
out.at<float>(j) = vhist.at<float>(i);
j++;
}
for (int i = 0; i < hhist.cols; i++) {
out.at<float>(j) = hhist.at<float>(i);
j++;
}
return out;
// Asign values to feature,样本特征为水平、垂直直方图
int j = 0;
for (int i = 0; i < vhist.cols; i++) {
out.at<float>(j) = vhist.at<float>(i);
j++;
}
for (int i = 0; i < hhist.cols; i++) {
out.at<float>(j) = hhist.at<float>(i);
j++;
}
//! EasyPR的getFeatures回调函数
//! 本函数是获取垂直和水平的直方图图值
void getHistogramFeatures(const Mat& image, Mat& features) {
Mat grayImage;
cvtColor(image, grayImage, CV_RGB2GRAY);
return out;
}
// grayImage = histeq(grayImage);
//! EasyPR的getFeatures回调函数
//! 本函数是获取垂直和水平的直方图图值
void getHistogramFeatures(const Mat& image, Mat& features) {
Mat grayImage;
cvtColor(image, grayImage, CV_RGB2GRAY);
Mat img_threshold;
threshold(grayImage, img_threshold, 0, 255,
CV_THRESH_OTSU + CV_THRESH_BINARY);
features = getTheFeatures(img_threshold);
}
// grayImage = histeq(grayImage);
//! EasyPR的getFeatures回调函数
//! 本函数是获取SITF特征子
void getSIFTFeatures(const Mat& image, Mat& features) {
//待完善
}
Mat img_threshold;
threshold(grayImage, img_threshold, 0, 255,
CV_THRESH_OTSU + CV_THRESH_BINARY);
features = getTheFeatures(img_threshold);
}
//! EasyPR的getFeatures回调函数
//! 本函数是获取HOG特征子
void getHOGFeatures(const Mat& image, Mat& features) {
//待完善
}
//! EasyPR的getFeatures回调函数
//! 本函数是获取SITF特征子
void getSIFTFeatures(const Mat& image, Mat& features) {
//待完善
}
//! EasyPR的getFeatures回调函数
//! 本函数是获取HSV空间量化的直方图特征子
void getHSVHistFeatures(const Mat& image, Mat& features) {
// TODO
}
//! EasyPR的getFeatures回调函数
//! 本函数是获取HOG特征子
void getHOGFeatures(const Mat& image, Mat& features) {
//待完善
}
//! EasyPR的getFeatures回调函数
//! 本函数是获取HSV空间量化的直方图特征子
void getHSVHistFeatures(const Mat& image, Mat& features) {
// TODO
}
} /* \namespace easypr */

4
src/core/plate.cpp
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@ -5,8 +5,6 @@ Namespace where all the C++ EasyPR functionality resides
*/
namespace easypr {
CPlate::CPlate() {
bColored = true;
}
CPlate::CPlate() { bColored = true; }
} /*! \namespace easypr*/

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