Micooz
9 years ago
parent
fc49dbdee1
commit
5d3cd897f0
@ -1,69 +1,54 @@
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#ifndef EASYPR_CONFIG_H_
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#define EASYPR_CONFIG_H_
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namespace easypr {
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static const char* kDefaultSvmPath = "resources/model/svm.xml";
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static const char* kDefaultAnnPath = "resources/model/ann.xml";
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typedef enum {
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kForward = 1, // correspond to "has plate"
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kInverse = 0 // correspond to "no plate"
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} SvmLabel;
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static const float kSvmPercentage = 0.7f;
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static const int kPredictSize = 10;
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static const int kNeurons = 40;
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static const char *kChars[] = {
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"0", "1", "2",
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"3", "4", "5",
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"6", "7", "8",
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"9",
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/* 10 */
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"A", "B", "C",
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"D", "E", "F",
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"G", "H", /* {"I", "I"} */
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"J", "K", "L",
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"M", "N", /* {"O", "O"} */
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"P", "Q", "R",
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"S", "T", "U",
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"V", "W", "X",
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"Y", "Z",
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/* 24 */
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"zh_cuan" , "zh_e" , "zh_gan" ,
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"zh_gan1" , "zh_gui" , "zh_gui1" ,
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"zh_hei" , "zh_hu" , "zh_ji" ,
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"zh_jin" , "zh_jing" , "zh_jl" ,
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"zh_liao" , "zh_lu" , "zh_meng" ,
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"zh_min" , "zh_ning" , "zh_qing" ,
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"zh_qiong", "zh_shan" , "zh_su" ,
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"zh_sx" , "zh_wan" , "zh_xiang",
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"zh_xin" , "zh_yu" , "zh_yu1" ,
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"zh_yue" , "zh_yun" , "zh_zang" ,
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"zh_zhe"
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/* 31 */
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};
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static const std::map<std::string, std::string> kCharsMap = {
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{"zh_cuan" , "川"}, {"zh_e" , "鄂"}, {"zh_gan" , "赣"},
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{"zh_gan1" , "甘"}, {"zh_gui" , "贵"}, {"zh_gui1" , "桂"},
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{"zh_hei" , "黑"}, {"zh_hu" , "沪"}, {"zh_ji" , "冀"},
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{"zh_jin" , "津"}, {"zh_jing" , "京"}, {"zh_jl" , "吉"},
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{"zh_liao" , "辽"}, {"zh_lu" , "鲁"}, {"zh_meng" , "蒙"},
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{"zh_min" , "闽"}, {"zh_ning" , "宁"}, {"zh_qing" , "青"},
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{"zh_qiong", "琼"}, {"zh_shan" , "陕"}, {"zh_su" , "苏"},
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{"zh_sx" , "晋"}, {"zh_wan" , "皖"}, {"zh_xiang", "湘"},
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{"zh_xin" , "新"}, {"zh_yu" , "豫"}, {"zh_yu1" , "渝"},
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{"zh_yue" , "粤"}, {"zh_yun" , "云"}, {"zh_zang" , "藏"},
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{"zh_zhe" , "浙"}
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/* 31 */
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};
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static const int kCharsTotalNumber = 65;
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static bool kDebug = false;
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}
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#ifndef EASYPR_CONFIG_H_
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#define EASYPR_CONFIG_H_
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namespace easypr {
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static const char* kDefaultSvmPath = "resources/model/svm.xml";
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static const char* kDefaultAnnPath = "resources/model/ann.xml";
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typedef enum {
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kForward = 1, // correspond to "has plate"
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kInverse = 0 // correspond to "no plate"
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} SvmLabel;
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static const float kSvmPercentage = 0.7f;
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static const int kPredictSize = 10;
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static const int kNeurons = 40;
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static const char *kChars[] = {
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"0", "1", "2",
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"3", "4", "5",
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"6", "7", "8",
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"9",
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/* 10 */
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"A", "B", "C",
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"D", "E", "F",
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"G", "H", /* {"I", "I"} */
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"J", "K", "L",
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"M", "N", /* {"O", "O"} */
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"P", "Q", "R",
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"S", "T", "U",
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"V", "W", "X",
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"Y", "Z",
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/* 24 */
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"zh_cuan" , "zh_e" , "zh_gan" ,
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"zh_gan1" , "zh_gui" , "zh_gui1" ,
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"zh_hei" , "zh_hu" , "zh_ji" ,
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"zh_jin" , "zh_jing" , "zh_jl" ,
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"zh_liao" , "zh_lu" , "zh_meng" ,
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"zh_min" , "zh_ning" , "zh_qing" ,
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"zh_qiong", "zh_shan" , "zh_su" ,
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"zh_sx" , "zh_wan" , "zh_xiang",
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"zh_xin" , "zh_yu" , "zh_yu1" ,
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"zh_yue" , "zh_yun" , "zh_zang" ,
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"zh_zhe"
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/* 31 */
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};
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static const int kCharsTotalNumber = 65;
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static bool kDebug = false;
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}
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#endif // EASYPR_CONFIG_H_
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@ -1,30 +1,36 @@
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#ifndef EASYPR_CORE_FEATURE_H_
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#define EASYPR_CORE_FEATURE_H_
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#include <opencv2/opencv.hpp>
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namespace easypr {
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//! 获得车牌的特征数
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cv::Mat getTheFeatures(cv::Mat in);
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//! EasyPR的getFeatures回调函数
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//! 用于从车牌的image生成svm的训练特征features
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typedef void (*svmCallback)(const cv::Mat& image, cv::Mat& features);
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//! EasyPR的getFeatures回调函数
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//! 本函数是获取垂直和水平的直方图图值
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void getHistogramFeatures(const cv::Mat& image, cv::Mat& features);
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//! 本函数是获取SIFT特征子
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void getSIFTFeatures(const cv::Mat& image, cv::Mat& features);
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//! 本函数是获取HOG特征子
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void getHOGFeatures(const cv::Mat& image, cv::Mat& features);
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//! 本函数是获取HSV空间量化的直方图特征子
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void getHSVHistFeatures(const cv::Mat& image, cv::Mat& features);
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} /*! \namespace easypr*/
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#ifndef EASYPR_CORE_FEATURE_H_
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#define EASYPR_CORE_FEATURE_H_
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#include <opencv2/opencv.hpp>
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namespace easypr {
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//! 获得车牌的特征数
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cv::Mat getTheFeatures(cv::Mat in);
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//! EasyPR的getFeatures回调函数
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//! 用于从车牌的image生成svm的训练特征features
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typedef void (*svmCallback)(const cv::Mat& image, cv::Mat& features);
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//! EasyPR的getFeatures回调函数
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//! 本函数是获取垂直和水平的直方图图值
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void getHistogramFeatures(const cv::Mat& image, cv::Mat& features);
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//! 本函数是获取SIFT特征子
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void getSIFTFeatures(const cv::Mat& image, cv::Mat& features);
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//! 本函数是获取HOG特征子
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void getHOGFeatures(const cv::Mat& image, cv::Mat& features);
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//! 本函数是获取HSV空间量化的直方图特征子
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void getHSVHistFeatures(const cv::Mat& image, cv::Mat& features);
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} /*! \namespace easypr*/
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#endif // EASYPR_CORE_FEATURE_H_
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@ -1,56 +1,61 @@
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//////////////////////////////////////////////////////////////////////////
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// Name: plate Header
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// Version: 1.0
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// Date: 2015-03-12
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// Author: liuruoze
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// Copyright: liuruoze
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// Desciption:
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// An abstract class for car plate.
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//////////////////////////////////////////////////////////////////////////
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#ifndef EASYPR_CORE_PLATE_H_
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#define EASYPR_CORE_PLATE_H_
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#include "core_func.h"
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/*! \namespace easypr
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Namespace where all the C++ EasyPR functionality resides
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*/
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namespace easypr {
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class CPlate {
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public:
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bool bColored;
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//! 构造函数
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CPlate();
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//! 设置与读取变量
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inline void setPlateMat(Mat param) { m_plateMat = param; }
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inline Mat getPlateMat() const { return m_plateMat; }
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inline void setPlatePos(RotatedRect param) { m_platePos = param; }
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inline RotatedRect getPlatePos() const { return m_platePos; }
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inline void setPlateStr(String param) { m_plateStr = param; }
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inline String getPlateStr() const { return m_plateStr; }
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inline void setPlateLocateType(LocateType param) { m_locateType = param; }
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inline LocateType getPlateLocateType() const { return m_locateType; }
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private:
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//! 车牌的图块
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Mat m_plateMat;
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//! 车牌在原图的位置
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RotatedRect m_platePos;
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//! 车牌字符串
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String m_plateStr;
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//! 车牌定位的方法
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LocateType m_locateType;
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};
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} /*! \namespace easypr*/
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//////////////////////////////////////////////////////////////////////////
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// Name: plate Header
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// Version: 1.0
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// Date: 2015-03-12
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// Author: liuruoze
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// Copyright: liuruoze
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// Desciption:
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// An abstract class for car plate.
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//////////////////////////////////////////////////////////////////////////
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#ifndef EASYPR_CORE_PLATE_H_
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#define EASYPR_CORE_PLATE_H_
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#include "core_func.h"
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/*! \namespace easypr
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Namespace where all the C++ EasyPR functionality resides
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*/
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namespace easypr {
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class CPlate {
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public:
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bool bColored;
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//! 构造函数
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CPlate();
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//! 设置与读取变量
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inline void setPlateMat(Mat param) { m_plateMat = param; }
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inline Mat getPlateMat() const { return m_plateMat; }
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inline void setPlatePos(RotatedRect param) { m_platePos = param; }
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inline RotatedRect getPlatePos() const { return m_platePos; }
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inline void setPlateStr(String param) { m_plateStr = param; }
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inline String getPlateStr() const { return m_plateStr; }
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inline void setPlateLocateType(LocateType param) { m_locateType = param; }
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inline LocateType getPlateLocateType() const { return m_locateType; }
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private:
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//! 车牌的图块
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Mat m_plateMat;
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//! 车牌在原图的位置
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RotatedRect m_platePos;
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//! 车牌字符串
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String m_plateStr;
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//! 车牌定位的方法
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LocateType m_locateType;
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};
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} /*! \namespace easypr*/
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#endif // EASYPR_CORE_PLATE_H_
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@ -1,25 +1,25 @@
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#ifndef EASYPR_TRAIN_ANNTRAIN_H_
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#define EASYPR_TRAIN_ANNTRAIN_H_
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#include "easypr/train/train.h"
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namespace easypr {
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class AnnTrain : public ITrain {
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public:
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explicit AnnTrain(const char* chars_folder, const char* xml);
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virtual void train();
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virtual void test();
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private:
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virtual cv::Ptr<cv::ml::TrainData> tdata();
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cv::Ptr<cv::ml::ANN_MLP> ann_;
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const char* ann_xml_;
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const char* chars_folder_;
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};
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}
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#ifndef EASYPR_TRAIN_ANNTRAIN_H_
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#define EASYPR_TRAIN_ANNTRAIN_H_
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#include "easypr/train/train.h"
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namespace easypr {
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class AnnTrain : public ITrain {
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public:
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explicit AnnTrain(const char* chars_folder, const char* xml);
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virtual void train();
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virtual void test();
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private:
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virtual cv::Ptr<cv::ml::TrainData> tdata();
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cv::Ptr<cv::ml::ANN_MLP> ann_;
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const char* ann_xml_;
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const char* chars_folder_;
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};
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}
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#endif // EASYPR_TRAIN_ANNTRAIN_H_
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,73 +1,80 @@
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// 这个文件定义了EasyPR里所有特征生成的函数
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// 所属命名空间为easypr
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// 这个部分中的特征由easypr的开发者修改
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#include "easypr/core/feature.h"
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#include "easypr/core/core_func.h"
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/*! \namespace easypr
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Namespace where all the C++ EasyPR functionality resides
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*/
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namespace easypr {
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//! 获取垂直和水平的直方图图值
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Mat getTheFeatures(Mat in) {
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const int VERTICAL = 0;
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const int HORIZONTAL = 1;
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// Histogram features
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Mat vhist = ProjectedHistogram(in, VERTICAL);
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Mat hhist = ProjectedHistogram(in, HORIZONTAL);
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// Last 10 is the number of moments components
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int numCols = vhist.cols + hhist.cols;
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Mat out = Mat::zeros(1, numCols, CV_32F);
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// Asign values to feature,样本特征为水平、垂直直方图
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int j = 0;
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for (int i = 0; i < vhist.cols; i++) {
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out.at<float>(j) = vhist.at<float>(i);
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j++;
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}
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for (int i = 0; i < hhist.cols; i++) {
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out.at<float>(j) = hhist.at<float>(i);
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j++;
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}
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return out;
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}
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//! EasyPR的getFeatures回调函数
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//! 本函数是获取垂直和水平的直方图图值
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void getHistogramFeatures(const Mat& image, Mat& features) {
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Mat grayImage;
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cvtColor(image, grayImage, CV_RGB2GRAY);
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//grayImage = histeq(grayImage);
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Mat img_threshold;
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threshold(grayImage, img_threshold, 0, 255,
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CV_THRESH_OTSU + CV_THRESH_BINARY);
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features = getTheFeatures(img_threshold);
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}
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//! EasyPR的getFeatures回调函数
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//! 本函数是获取SITF特征子
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void getSIFTFeatures(const Mat& image, Mat& features) {
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//待完善
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}
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//! EasyPR的getFeatures回调函数
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//! 本函数是获取HOG特征子
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void getHOGFeatures(const Mat& image, Mat& features) {
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//待完善
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}
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//! EasyPR的getFeatures回调函数
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//! 本函数是获取HSV空间量化的直方图特征子
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void getHSVHistFeatures(const Mat& image, Mat& features) {
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// TODO
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}
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} /* \namespace easypr */
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// 这个文件定义了EasyPR里所有特征生成的函数
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// 所属命名空间为easypr
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// 这个部分中的特征由easypr的开发者修改
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#include "easypr/core/feature.h"
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#include "easypr/core/core_func.h"
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namespace easypr {
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//! 获取垂直和水平的直方图图值
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Mat getTheFeatures(Mat in) {
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const int VERTICAL = 0;
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const int HORIZONTAL = 1;
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// Histogram features
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Mat vhist = ProjectedHistogram(in, VERTICAL);
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Mat hhist = ProjectedHistogram(in, HORIZONTAL);
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// Last 10 is the number of moments components
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int numCols = vhist.cols + hhist.cols;
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Mat out = Mat::zeros(1, numCols, CV_32F);
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// Asign values to feature,样本特征为水平、垂直直方图
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int j = 0;
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for (int i = 0; i < vhist.cols; i++) {
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out.at<float>(j) = vhist.at<float>(i);
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j++;
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}
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for (int i = 0; i < hhist.cols; i++) {
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out.at<float>(j) = hhist.at<float>(i);
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j++;
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}
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return out;
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}
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//! EasyPR的getFeatures回调函数
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//! 本函数是获取垂直和水平的直方图图值
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void getHistogramFeatures(const Mat& image, Mat& features) {
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Mat grayImage;
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cvtColor(image, grayImage, CV_RGB2GRAY);
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||||
//grayImage = histeq(grayImage);
|
||||
|
||||
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
|
||||
}
|
||||
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,123 +1,123 @@
|
||||
#include "easypr/train/ann_train.h"
|
||||
#include "easypr/config.h"
|
||||
#include "easypr/core/chars_identify.h"
|
||||
#include "easypr/core/core_func.h"
|
||||
#include "easypr/util/util.h"
|
||||
|
||||
namespace easypr {
|
||||
|
||||
AnnTrain::AnnTrain(const char* chars_folder, const char* xml)
|
||||
: chars_folder_(chars_folder), ann_xml_(xml) {
|
||||
ann_ = cv::ml::ANN_MLP::create();
|
||||
}
|
||||
|
||||
void AnnTrain::train() {
|
||||
cv::Mat layers(1, 3, CV_32SC1);
|
||||
layers.at<int>(0) = 120; // the input layer
|
||||
layers.at<int>(1) = kNeurons; // the neurons
|
||||
layers.at<int>(2) = kCharsTotalNumber; // the output layer
|
||||
|
||||
ann_->setLayerSizes(layers);
|
||||
ann_->setActivationFunction(cv::ml::ANN_MLP::SIGMOID_SYM, 1, 1);
|
||||
ann_->setTrainMethod(cv::ml::ANN_MLP::TrainingMethods::BACKPROP);
|
||||
ann_->setBackpropWeightScale(0.1);
|
||||
ann_->setBackpropMomentumScale(0.1);
|
||||
|
||||
auto traindata = tdata();
|
||||
std::cout << "Training ANN model, please wait..." << std::endl;
|
||||
long start = utils::getTimestamp();
|
||||
ann_->train(traindata);
|
||||
long end = utils::getTimestamp();
|
||||
std::cout << "Training done. Time elapse: " << (end - start) << "ms"
|
||||
<< std::endl;
|
||||
|
||||
ann_->save(ann_xml_);
|
||||
std::cout << "Your ANN Model was saved to " << ann_xml_ << std::endl;
|
||||
}
|
||||
|
||||
void AnnTrain::test() {
|
||||
assert(chars_folder_);
|
||||
|
||||
for (int i = 0; i < kCharsTotalNumber; ++i) {
|
||||
auto char_key = kChars[i];
|
||||
char sub_folder[512] = {0};
|
||||
|
||||
sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
|
||||
fprintf(stdout, ">> Testing characters %s in %s \n", char_key, sub_folder);
|
||||
|
||||
auto chars_files = utils::getFiles(sub_folder);
|
||||
int corrects = 0, sum = 0;
|
||||
std::vector<std::pair<std::string, std::string>> error_files;
|
||||
|
||||
for (auto file : chars_files) {
|
||||
auto img = cv::imread(file, 0); // a grayscale image
|
||||
std::pair<std::string, std::string> ch =
|
||||
CharsIdentify::instance()->identify(img);
|
||||
if (ch.first == char_key) {
|
||||
++corrects;
|
||||
} else {
|
||||
error_files.push_back(
|
||||
std::make_pair(utils::getFileName(file), ch.second));
|
||||
}
|
||||
++sum;
|
||||
}
|
||||
fprintf(stdout, ">> [sum: %d, correct: %d, rate: %.2f]\n", sum, corrects,
|
||||
(float)corrects / (sum == 0 ? 1 : sum));
|
||||
std::string error_string;
|
||||
auto end = error_files.end();
|
||||
if (error_files.size() >= 10) {
|
||||
end -= static_cast<size_t>(error_files.size() * (1 - 0.1));
|
||||
}
|
||||
for (auto k = error_files.begin(); k != end; ++k) {
|
||||
auto kv = *k;
|
||||
error_string.append(" ").append(kv.first).append(": ").append(
|
||||
kv.second);
|
||||
if (k != end - 1) {
|
||||
error_string.append(",\n");
|
||||
} else {
|
||||
error_string.append("\n ...");
|
||||
}
|
||||
}
|
||||
fprintf(stdout, ">> [\n%s\n ]\n", error_string.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
cv::Ptr<cv::ml::TrainData> AnnTrain::tdata() {
|
||||
assert(chars_folder_);
|
||||
|
||||
cv::Mat samples;
|
||||
std::vector<int> labels;
|
||||
|
||||
std::cout << "Collecting chars in " << chars_folder_ << std::endl;
|
||||
|
||||
for (int i = 0; i < kCharsTotalNumber; ++i) {
|
||||
auto char_key = kChars[i];
|
||||
char sub_folder[512] = {0};
|
||||
|
||||
sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
|
||||
std::cout << " >> Featuring characters " << char_key << " in "
|
||||
<< sub_folder << std::endl;
|
||||
|
||||
auto chars_files = utils::getFiles(sub_folder);
|
||||
for (auto file : chars_files) {
|
||||
auto img = cv::imread(file, 0); // a grayscale image
|
||||
auto fps = features(img, kPredictSize);
|
||||
|
||||
samples.push_back(fps);
|
||||
labels.push_back(i);
|
||||
}
|
||||
}
|
||||
|
||||
cv::Mat samples_;
|
||||
samples.convertTo(samples_, CV_32F);
|
||||
cv::Mat train_classes =
|
||||
cv::Mat::zeros((int)labels.size(), kCharsTotalNumber, CV_32F);
|
||||
|
||||
for (int i = 0; i < train_classes.rows; ++i) {
|
||||
train_classes.at<float>(i, labels[i]) = 1.f;
|
||||
}
|
||||
|
||||
return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE,
|
||||
train_classes);
|
||||
}
|
||||
}
|
||||
#include "easypr/train/ann_train.h"
|
||||
#include "easypr/config.h"
|
||||
#include "easypr/core/chars_identify.h"
|
||||
#include "easypr/core/core_func.h"
|
||||
#include "easypr/util/util.h"
|
||||
|
||||
namespace easypr {
|
||||
|
||||
AnnTrain::AnnTrain(const char* chars_folder, const char* xml)
|
||||
: chars_folder_(chars_folder), ann_xml_(xml) {
|
||||
ann_ = cv::ml::ANN_MLP::create();
|
||||
}
|
||||
|
||||
void AnnTrain::train() {
|
||||
cv::Mat layers(1, 3, CV_32SC1);
|
||||
layers.at<int>(0) = 120; // the input layer
|
||||
layers.at<int>(1) = kNeurons; // the neurons
|
||||
layers.at<int>(2) = kCharsTotalNumber; // the output layer
|
||||
|
||||
ann_->setLayerSizes(layers);
|
||||
ann_->setActivationFunction(cv::ml::ANN_MLP::SIGMOID_SYM, 1, 1);
|
||||
ann_->setTrainMethod(cv::ml::ANN_MLP::TrainingMethods::BACKPROP);
|
||||
ann_->setBackpropWeightScale(0.1);
|
||||
ann_->setBackpropMomentumScale(0.1);
|
||||
|
||||
auto traindata = tdata();
|
||||
std::cout << "Training ANN model, please wait..." << std::endl;
|
||||
long start = utils::getTimestamp();
|
||||
ann_->train(traindata);
|
||||
long end = utils::getTimestamp();
|
||||
std::cout << "Training done. Time elapse: " << (end - start) << "ms"
|
||||
<< std::endl;
|
||||
|
||||
ann_->save(ann_xml_);
|
||||
std::cout << "Your ANN Model was saved to " << ann_xml_ << std::endl;
|
||||
}
|
||||
|
||||
void AnnTrain::test() {
|
||||
assert(chars_folder_);
|
||||
|
||||
for (int i = 0; i < kCharsTotalNumber; ++i) {
|
||||
auto char_key = kChars[i];
|
||||
char sub_folder[512] = {0};
|
||||
|
||||
sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
|
||||
fprintf(stdout, ">> Testing characters %s in %s \n", char_key, sub_folder);
|
||||
|
||||
auto chars_files = utils::getFiles(sub_folder);
|
||||
int corrects = 0, sum = 0;
|
||||
std::vector<std::pair<std::string, std::string>> error_files;
|
||||
|
||||
for (auto file : chars_files) {
|
||||
auto img = cv::imread(file, 0); // a grayscale image
|
||||
std::pair<std::string, std::string> ch =
|
||||
CharsIdentify::instance()->identify(img);
|
||||
if (ch.first == char_key) {
|
||||
++corrects;
|
||||
} else {
|
||||
error_files.push_back(
|
||||
std::make_pair(utils::getFileName(file), ch.second));
|
||||
}
|
||||
++sum;
|
||||
}
|
||||
fprintf(stdout, ">> [sum: %d, correct: %d, rate: %.2f]\n", sum, corrects,
|
||||
(float)corrects / (sum == 0 ? 1 : sum));
|
||||
std::string error_string;
|
||||
auto end = error_files.end();
|
||||
if (error_files.size() >= 10) {
|
||||
end -= static_cast<size_t>(error_files.size() * (1 - 0.1));
|
||||
}
|
||||
for (auto k = error_files.begin(); k != end; ++k) {
|
||||
auto kv = *k;
|
||||
error_string.append(" ").append(kv.first).append(": ").append(
|
||||
kv.second);
|
||||
if (k != end - 1) {
|
||||
error_string.append(",\n");
|
||||
} else {
|
||||
error_string.append("\n ...");
|
||||
}
|
||||
}
|
||||
fprintf(stdout, ">> [\n%s\n ]\n", error_string.c_str());
|
||||
}
|
||||
}
|
||||
|
||||
cv::Ptr<cv::ml::TrainData> AnnTrain::tdata() {
|
||||
assert(chars_folder_);
|
||||
|
||||
cv::Mat samples;
|
||||
std::vector<int> labels;
|
||||
|
||||
std::cout << "Collecting chars in " << chars_folder_ << std::endl;
|
||||
|
||||
for (int i = 0; i < kCharsTotalNumber; ++i) {
|
||||
auto char_key = kChars[i];
|
||||
char sub_folder[512] = {0};
|
||||
|
||||
sprintf(sub_folder, "%s/%s", chars_folder_, char_key);
|
||||
std::cout << " >> Featuring characters " << char_key << " in "
|
||||
<< sub_folder << std::endl;
|
||||
|
||||
auto chars_files = utils::getFiles(sub_folder);
|
||||
for (auto file : chars_files) {
|
||||
auto img = cv::imread(file, 0); // a grayscale image
|
||||
auto fps = features(img, kPredictSize);
|
||||
|
||||
samples.push_back(fps);
|
||||
labels.push_back(i);
|
||||
}
|
||||
}
|
||||
|
||||
cv::Mat samples_;
|
||||
samples.convertTo(samples_, CV_32F);
|
||||
cv::Mat train_classes =
|
||||
cv::Mat::zeros((int)labels.size(), kCharsTotalNumber, CV_32F);
|
||||
|
||||
for (int i = 0; i < train_classes.rows; ++i) {
|
||||
train_classes.at<float>(i, labels[i]) = 1.f;
|
||||
}
|
||||
|
||||
return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE,
|
||||
train_classes);
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,8 +1,8 @@
|
||||
#include "easypr/train/train.h"
|
||||
|
||||
namespace easypr {
|
||||
|
||||
ITrain::ITrain() {}
|
||||
|
||||
ITrain::~ITrain() {}
|
||||
}
|
||||
#include "easypr/train/train.h"
|
||||
|
||||
namespace easypr {
|
||||
|
||||
ITrain::ITrain() {}
|
||||
|
||||
ITrain::~ITrain() {}
|
||||
}
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
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Reference in new issue