feat.新增支持部分模拟器

Refactor Gui

.gitattributes

@@ -1,2 +0,0 @@
-*.dll filter=lfs diff=lfs merge=lfs -text
-*.lib filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -425,3 +425,6 @@ FodyWeavers.xsd
 # JetBrains Rider
 .idea/
 *.sln.iml
+
+screen.png
+adb_screen.png

3rdPart/bin/OcrLiteNcnn.dll

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:32b8b36100090e37d2a892783ef9922fd890767d0546400ed737bf2d57698dfd
-size 4745216

3rdPart/bin/opencv_world3415.dll

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3efb02b69038309da6adc09ed9b4688c966cf88871d433a49687c3840aba9343
-size 55650816

3rdPart/include/OcrLiteNcnn/AngleNet.h

@@ -1,37 +0,0 @@
-#ifndef __OCR_ANGLENET_H__
-#define __OCR_ANGLENET_H__
-
-#include "OcrStruct.h"
-#include <opencv/cv.hpp>
-#include <memory>
-
-namespace ocr {
-    class AngleNet {
-    public:
-
-        ~AngleNet();
-
-        void setNumThread(int numOfThread);
-
-        void setGpuIndex(int gpuIndex);
-
-        bool initModel(const std::string& pathStr);
-
-        std::vector<Angle> getAngles(std::vector<cv::Mat>& partImgs, const char* path,
-            const char* imgName, bool doAngle, bool mostAngle);
-
-    private:
-        bool isOutputAngleImg = false;
-        int numThread;
-        std::shared_ptr<ncnn::Net> pNet = std::make_shared<ncnn::Net>();
-        const float meanValues[3] = { 127.5, 127.5, 127.5 };
-        const float normValues[3] = { 1.0 / 127.5, 1.0 / 127.5, 1.0 / 127.5 };
-
-        const int dstWidth = 192;
-        const int dstHeight = 32;
-
-        Angle getAngle(cv::Mat& src);
-    };
-}
-
-#endif //__OCR_ANGLENET_H__

3rdPart/include/OcrLiteNcnn/CrnnNet.h

@@ -1,39 +0,0 @@
-#ifndef __OCR_CRNNNET_H__
-#define __OCR_CRNNNET_H__
-
-#include "OcrStruct.h"
-#include <opencv/cv.hpp>
-#include <memory>
-
-namespace ocr {
-    class CrnnNet {
-    public:
-
-        ~CrnnNet();
-
-        void setNumThread(int numOfThread);
-
-        void setGpuIndex(int gpuIndex);
-
-        bool initModel(const std::string& pathStr, const std::string& keysPath);
-
-        std::vector<TextLine> getTextLines(std::vector<cv::Mat>& partImg, const char* path, const char* imgName);
-
-    private:
-        bool isOutputDebugImg = false;
-        int numThread;
-        std::shared_ptr<ncnn::Net> pNet = std::make_shared<ncnn::Net>();
-
-        const float meanValues[3] = { 127.5, 127.5, 127.5 };
-        const float normValues[3] = { 1.0 / 127.5, 1.0 / 127.5, 1.0 / 127.5 };
-        const int dstHeight = 32;
-
-        std::vector<std::string> keys;
-
-        TextLine scoreToTextLine(const std::vector<float>& outputData, int h, int w);
-
-        TextLine getTextLine(const cv::Mat& src);
-    };
-}
-
-#endif //__OCR_CRNNNET_H__

3rdPart/include/OcrLiteNcnn/DbNet.h

@@ -1,31 +0,0 @@
-#ifndef __OCR_DBNET_H__
-#define __OCR_DBNET_H__
-
-#include "OcrStruct.h"
-#include <opencv/cv.hpp>
-#include <memory>
-
-namespace ocr {
-    class DbNet {
-    public:
-        ~DbNet();
-
-        void setNumThread(int numOfThread);
-
-        void setGpuIndex(int gpuIndex);
-
-        bool initModel(const std::string& pathStr);
-
-        std::vector<TextBox> getTextBoxes(cv::Mat& src, ScaleParam& s, float boxScoreThresh,
-            float boxThresh, float unClipRatio);
-
-    private:
-        int numThread;
-        std::shared_ptr<ncnn::Net> pNet = std::make_shared<ncnn::Net>();
-        const float meanValues[3] = { 0.485 * 255, 0.456 * 255, 0.406 * 255 };
-        const float normValues[3] = { 1.0 / 0.229 / 255.0, 1.0 / 0.224 / 255.0, 1.0 / 0.225 / 255.0 };
-    };
-}
-
-
-#endif //__OCR_DBNET_H__

3rdPart/include/OcrLiteNcnn/OcrLite.h

@@ -1,75 +0,0 @@
-#ifndef __OCR_LITE_H__
-#define __OCR_LITE_H__
-
-#include "opencv2/core.hpp"
-#include "OcrStruct.h"
-#include "DbNet.h"
-#include "AngleNet.h"
-#include "CrnnNet.h"
-
-namespace ocr {
-	class OCRLITE_EXPORT OcrLite {
-	public:
-		OcrLite();
-
-		~OcrLite();
-
-		void setNumThread(int numOfThread);
-
-		void initLogger(bool isConsole, bool isPartImg, bool isResultImg);
-
-		void enableResultTxt(const char* path, const char* imgName);
-
-		void setGpuIndex(int gpuIndex);
-
-		bool initModels(const std::string& detPath, const std::string& clsPath,
-			const std::string& recPath, const std::string& keysPath);
-
-		void Logger(const char* format, ...);
-
-		/*
-		* padding：图像预处理，在图片外周添加白边，用于提升识别率，文字框没有正确框住所有文字时，增加此值。
-		* maxSideLen ：按图片最长边的长度，此值为0代表不缩放，例：1024，如果图片长边大于1024则把图像整体缩小到1024再进行图像分割计算，如果图片长边小于1024则不缩放，如果图片长边小于32，则缩放到32。
-		* boxScoreThresh：文字框置信度门限，文字框没有正确框住所有文字时，减小此值。
-		* boxThresh：请自行试验。
-		* unClipRatio：单个文字框大小倍率，越大时单个文字框越大。此项与图片的大小相关，越大的图片此值应该越大。
-		* doAngle：启用(1)/禁用(0) 文字方向检测，只有图片倒置的情况下(旋转90~270度的图片)，才需要启用文字方向检测。
-		* mostAngle：启用(1)/禁用(0) 角度投票(整张图片以最大可能文字方向来识别)，当禁用文字方向检测时，此项也不起作用。
-		*/
-		OcrResult detect(const char* path, const char* imgName,
-			int padding, int maxSideLen,
-			float boxScoreThresh, float boxThresh, float unClipRatio, bool doAngle, bool mostAngle);
-
-		/*
-		* padding：图像预处理，在图片外周添加白边，用于提升识别率，文字框没有正确框住所有文字时，增加此值。
-		* maxSideLen ：按图片最长边的长度，此值为0代表不缩放，例：1024，如果图片长边大于1024则把图像整体缩小到1024再进行图像分割计算，如果图片长边小于1024则不缩放，如果图片长边小于32，则缩放到32。
-		* boxScoreThresh：文字框置信度门限，文字框没有正确框住所有文字时，减小此值。
-		* boxThresh：请自行试验。
-		* unClipRatio：单个文字框大小倍率，越大时单个文字框越大。此项与图片的大小相关，越大的图片此值应该越大。
-		* doAngle：启用(1)/禁用(0) 文字方向检测，只有图片倒置的情况下(旋转90~270度的图片)，才需要启用文字方向检测。
-		* mostAngle：启用(1)/禁用(0) 角度投票(整张图片以最大可能文字方向来识别)，当禁用文字方向检测时，此项也不起作用。
-		*/
-		OcrResult detect(const cv::Mat& mat,
-			int padding, int maxSideLen,
-			float boxScoreThresh, float boxThresh, float unClipRatio, bool doAngle, bool mostAngle);
-	private:
-		bool isOutputConsole = false;
-		bool isOutputPartImg = false;
-		bool isOutputResultTxt = false;
-		bool isOutputResultImg = false;
-		FILE* resultTxt;
-		DbNet dbNet;
-		AngleNet angleNet;
-		CrnnNet crnnNet;
-
-		std::vector<cv::Mat> getPartImages(cv::Mat& src, std::vector<TextBox>& textBoxes,
-			const char* path, const char* imgName);
-
-		OcrResult detect(const char* path, const char* imgName,
-			cv::Mat& src, cv::Rect& originRect, ScaleParam& scale,
-			float boxScoreThresh = 0.6f, float boxThresh = 0.3f,
-			float unClipRatio = 2.0f, bool doAngle = true, bool mostAngle = true);
-	};
-}
-
-#endif //__OCR_LITE_H__

3rdPart/include/OcrLiteNcnn/OcrStruct.h

@@ -1,65 +0,0 @@
-#ifndef __OCR_STRUCT_H__
-#define __OCR_STRUCT_H__
-
-#include "opencv2/core.hpp"
-#include <vector>
-
-#ifdef __C_API__
-#define OCRLITE_EXPORT __declspec(dllexport)
-#else
-#define OCRLITE_EXPORT
-#endif
-
-namespace ncnn {
-    class Net;
-}
-
-namespace ocr {
-    struct OCRLITE_EXPORT ScaleParam {
-        int srcWidth;
-        int srcHeight;
-        int dstWidth;
-        int dstHeight;
-        float ratioWidth;
-        float ratioHeight;
-    };
-
-    struct OCRLITE_EXPORT TextBox {
-        std::vector<cv::Point> boxPoint;
-        float score;
-    };
-
-    struct OCRLITE_EXPORT Angle {
-        int index;
-        float score;
-        double time;
-    };
-
-    struct OCRLITE_EXPORT TextLine {
-        std::string text;
-        std::vector<float> charScores;
-        double time;
-    };
-
-    struct OCRLITE_EXPORT TextBlock {
-        std::vector<cv::Point> boxPoint;
-        float boxScore;
-        int angleIndex;
-        float angleScore;
-        double angleTime;
-        std::string text;
-        std::vector<float> charScores;
-        double crnnTime;
-        double blockTime;
-    };
-
-    struct OCRLITE_EXPORT OcrResult {
-        double dbNetTime;
-        std::vector<TextBlock> textBlocks;
-        cv::Mat boxImg;
-        double detectTime;
-        std::string strRes;
-    };
-}
-
-#endif //__OCR_STRUCT_H__

3rdPart/include/OcrLiteNcnn/OcrUtils.h

@@ -1,66 +0,0 @@
-#ifndef __OCR_UTILS_H__
-#define __OCR_UTILS_H__
-
-#include <opencv2/core.hpp>
-#include "OcrStruct.h"
-
-#include <sys/stat.h>
-/*#define __ENABLE_CONSOLE__ true
-#define Logger(format, ...) {\
-  if(__ENABLE_CONSOLE__) printf(format,##__VA_ARGS__); \
-}*/
-
-namespace ocr {
-    OCRLITE_EXPORT double getCurrentTime();
-
-    OCRLITE_EXPORT inline bool isFileExists(const std::string& name) {
-        struct stat buffer;
-        return (stat(name.c_str(), &buffer) == 0);
-    }
-
-    OCRLITE_EXPORT std::wstring strToWstr(std::string str);
-
-    OCRLITE_EXPORT ScaleParam getScaleParam(cv::Mat& src, const float scale);
-
-    OCRLITE_EXPORT ScaleParam getScaleParam(cv::Mat& src, const int targetSize);
-
-    OCRLITE_EXPORT std::vector<cv::Point2f> getBox(const cv::RotatedRect& rect);
-
-    OCRLITE_EXPORT int getThickness(cv::Mat& boxImg);
-
-    OCRLITE_EXPORT void drawTextBox(cv::Mat& boxImg, cv::RotatedRect& rect, int thickness);
-
-    OCRLITE_EXPORT void drawTextBox(cv::Mat& boxImg, const std::vector<cv::Point>& box, int thickness);
-
-    OCRLITE_EXPORT void drawTextBoxes(cv::Mat& boxImg, std::vector<TextBox>& textBoxes, int thickness);
-
-    OCRLITE_EXPORT cv::Mat matRotateClockWise180(cv::Mat src);
-
-    OCRLITE_EXPORT cv::Mat matRotateClockWise90(cv::Mat src);
-
-    OCRLITE_EXPORT cv::Mat getRotateCropImage(const cv::Mat& src, std::vector<cv::Point> box);
-
-    OCRLITE_EXPORT cv::Mat adjustTargetImg(cv::Mat& src, int dstWidth, int dstHeight);
-
-    OCRLITE_EXPORT std::vector<cv::Point> getMinBoxes(const std::vector<cv::Point>& inVec, float& minSideLen, float& allEdgeSize);
-
-    OCRLITE_EXPORT float boxScoreFast(const cv::Mat& inMat, const std::vector<cv::Point>& inBox);
-
-    OCRLITE_EXPORT std::vector<cv::Point> unClip(const std::vector<cv::Point>& inBox, float perimeter, float unClipRatio);
-
-    OCRLITE_EXPORT std::vector<int> getAngleIndexes(std::vector<Angle>& angles);
-
-    OCRLITE_EXPORT void saveImg(cv::Mat& img, const char* imgPath);
-
-    OCRLITE_EXPORT std::string getSrcImgFilePath(const char* path, const char* imgName);
-
-    OCRLITE_EXPORT std::string getResultTxtFilePath(const char* path, const char* imgName);
-
-    OCRLITE_EXPORT std::string getResultImgFilePath(const char* path, const char* imgName);
-
-    OCRLITE_EXPORT std::string getDebugImgFilePath(const char* path, const char* imgName, int i, const char* tag);
-
-    OCRLITE_EXPORT void printGpuInfo();
-}
-
-#endif //__OCR_UTILS_H__

3rdPart/include/meojson/json_exception.h

@@ -1,27 +0,0 @@
-#pragma once
-
-#include <exception>
-#include <string>
-
-namespace json
-{
-    class exception : public std::exception
-    {
-    public:
-        exception() = default;
-        exception(const std::string &msg);
-
-        exception(const exception &) = default;
-        exception &operator=(const exception &) = default;
-        exception(exception &&) = default;
-        exception &operator=(exception &&) = default;
-
-        virtual ~exception() noexcept override = default;
-
-        virtual const char *what() const noexcept override;
-
-    private:
-        std::string m_msg;
-    };
-
-} // namespace json

3rdPart/include/opencv2/opencv_modules.hpp

@@ -1,32 +0,0 @@
-/*
- *      ** File generated automatically, do not modify **
- *
- * This file defines the list of modules available in current build configuration
- *
- *
-*/
-
-// This definition means that OpenCV is built with enabled non-free code.
-// For example, patented algorithms for non-profit/non-commercial use only.
-/* #undef OPENCV_ENABLE_NONFREE */
-
-#define HAVE_OPENCV_CALIB3D
-#define HAVE_OPENCV_CORE
-#define HAVE_OPENCV_DNN
-#define HAVE_OPENCV_FEATURES2D
-#define HAVE_OPENCV_FLANN
-#define HAVE_OPENCV_HIGHGUI
-#define HAVE_OPENCV_IMGCODECS
-#define HAVE_OPENCV_IMGPROC
-#define HAVE_OPENCV_ML
-#define HAVE_OPENCV_OBJDETECT
-#define HAVE_OPENCV_PHOTO
-#define HAVE_OPENCV_SHAPE
-#define HAVE_OPENCV_STITCHING
-#define HAVE_OPENCV_SUPERRES
-#define HAVE_OPENCV_VIDEO
-#define HAVE_OPENCV_VIDEOIO
-#define HAVE_OPENCV_VIDEOSTAB
-#define HAVE_OPENCV_WORLD
-
-

3rdPart/lib/OcrLiteNcnn.lib

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5cc75be243439729d3faca7b20e9c0fe0e4b8d6139fffbbb05383735f54136af
-size 27804

3rdPart/lib/libmeojson.lib

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ce622e0ee7151279d492776a768946324a4739cb4810d5b77aae4daa413a945c
-size 4833204

3rdPart/lib/opencv_world3415.lib

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:673035c2d9da9fd08b6912fa23c7d06388f310818730f95ef8d953f8f271529a
-size 2467276

3rdPart/resource/OcrLiteNcnn/models/angle_op.param

@@ -1,111 +0,0 @@
-7767517
-109 125
-Input                    input                    0 1 input
-Convolution              339                      1 1 input 341 0=24 1=3 3=2 4=1 5=1 6=648 9=1
-Pooling                  342                      1 1 341 342 1=3 2=2 12=1 3=1 5=1
-Split                    splitncnn_0              1 2 342 342_splitncnn_0 342_splitncnn_1
-ConvolutionDepthWise     343                      1 1 342_splitncnn_1 343 0=24 1=3 3=2 4=1 5=1 6=216 7=24
-Convolution              345                      1 1 343 347 0=24 1=1 5=1 6=576 9=1
-Convolution              348                      1 1 342_splitncnn_0 350 0=24 1=1 5=1 6=576 9=1
-ConvolutionDepthWise     351                      1 1 350 351 0=24 1=3 3=2 4=1 5=1 6=216 7=24
-Convolution              353                      1 1 351 355 0=24 1=1 5=1 6=576 9=1
-Concat                   356                      2 1 347 355 356
-ShuffleChannel           361                      1 1 356 361 0=2
-Slice                    362                      1 2 361 362 363 -23300=2,24,-233
-Convolution              364                      1 1 363 366 0=24 1=1 5=1 6=576 9=1
-ConvolutionDepthWise     367                      1 1 366 367 0=24 1=3 4=1 5=1 6=216 7=24
-Convolution              369                      1 1 367 371 0=24 1=1 5=1 6=576 9=1
-Concat                   372                      2 1 362 371 372
-ShuffleChannel           377                      1 1 372 377 0=2
-Slice                    378                      1 2 377 378 379 -23300=2,24,-233
-Convolution              380                      1 1 379 382 0=24 1=1 5=1 6=576 9=1
-ConvolutionDepthWise     383                      1 1 382 383 0=24 1=3 4=1 5=1 6=216 7=24
-Convolution              385                      1 1 383 387 0=24 1=1 5=1 6=576 9=1
-Concat                   388                      2 1 378 387 388
-ShuffleChannel           393                      1 1 388 393 0=2
-Slice                    394                      1 2 393 394 395 -23300=2,24,-233
-Convolution              396                      1 1 395 398 0=24 1=1 5=1 6=576 9=1
-ConvolutionDepthWise     399                      1 1 398 399 0=24 1=3 4=1 5=1 6=216 7=24
-Convolution              401                      1 1 399 403 0=24 1=1 5=1 6=576 9=1
-Concat                   404                      2 1 394 403 404
-ShuffleChannel           409                      1 1 404 409 0=2
-Split                    splitncnn_1              1 2 409 409_splitncnn_0 409_splitncnn_1
-ConvolutionDepthWise     410                      1 1 409_splitncnn_1 410 0=48 1=3 3=2 4=1 5=1 6=432 7=48
-Convolution              412                      1 1 410 414 0=48 1=1 5=1 6=2304 9=1
-Convolution              415                      1 1 409_splitncnn_0 417 0=48 1=1 5=1 6=2304 9=1
-ConvolutionDepthWise     418                      1 1 417 418 0=48 1=3 3=2 4=1 5=1 6=432 7=48
-Convolution              420                      1 1 418 422 0=48 1=1 5=1 6=2304 9=1
-Concat                   423                      2 1 414 422 423
-ShuffleChannel           428                      1 1 423 428 0=2
-Slice                    429                      1 2 428 429 430 -23300=2,48,-233
-Convolution              431                      1 1 430 433 0=48 1=1 5=1 6=2304 9=1
-ConvolutionDepthWise     434                      1 1 433 434 0=48 1=3 4=1 5=1 6=432 7=48
-Convolution              436                      1 1 434 438 0=48 1=1 5=1 6=2304 9=1
-Concat                   439                      2 1 429 438 439
-ShuffleChannel           444                      1 1 439 444 0=2
-Slice                    445                      1 2 444 445 446 -23300=2,48,-233
-Convolution              447                      1 1 446 449 0=48 1=1 5=1 6=2304 9=1
-ConvolutionDepthWise     450                      1 1 449 450 0=48 1=3 4=1 5=1 6=432 7=48
-Convolution              452                      1 1 450 454 0=48 1=1 5=1 6=2304 9=1
-Concat                   455                      2 1 445 454 455
-ShuffleChannel           460                      1 1 455 460 0=2
-Slice                    461                      1 2 460 461 462 -23300=2,48,-233
-Convolution              463                      1 1 462 465 0=48 1=1 5=1 6=2304 9=1
-ConvolutionDepthWise     466                      1 1 465 466 0=48 1=3 4=1 5=1 6=432 7=48
-Convolution              468                      1 1 466 470 0=48 1=1 5=1 6=2304 9=1
-Concat                   471                      2 1 461 470 471
-ShuffleChannel           476                      1 1 471 476 0=2
-Slice                    477                      1 2 476 477 478 -23300=2,48,-233
-Convolution              479                      1 1 478 481 0=48 1=1 5=1 6=2304 9=1
-ConvolutionDepthWise     482                      1 1 481 482 0=48 1=3 4=1 5=1 6=432 7=48
-Convolution              484                      1 1 482 486 0=48 1=1 5=1 6=2304 9=1
-Concat                   487                      2 1 477 486 487
-ShuffleChannel           492                      1 1 487 492 0=2
-Slice                    493                      1 2 492 493 494 -23300=2,48,-233
-Convolution              495                      1 1 494 497 0=48 1=1 5=1 6=2304 9=1
-ConvolutionDepthWise     498                      1 1 497 498 0=48 1=3 4=1 5=1 6=432 7=48
-Convolution              500                      1 1 498 502 0=48 1=1 5=1 6=2304 9=1
-Concat                   503                      2 1 493 502 503
-ShuffleChannel           508                      1 1 503 508 0=2
-Slice                    509                      1 2 508 509 510 -23300=2,48,-233
-Convolution              511                      1 1 510 513 0=48 1=1 5=1 6=2304 9=1
-ConvolutionDepthWise     514                      1 1 513 514 0=48 1=3 4=1 5=1 6=432 7=48
-Convolution              516                      1 1 514 518 0=48 1=1 5=1 6=2304 9=1
-Concat                   519                      2 1 509 518 519
-ShuffleChannel           524                      1 1 519 524 0=2
-Slice                    525                      1 2 524 525 526 -23300=2,48,-233
-Convolution              527                      1 1 526 529 0=48 1=1 5=1 6=2304 9=1
-ConvolutionDepthWise     530                      1 1 529 530 0=48 1=3 4=1 5=1 6=432 7=48
-Convolution              532                      1 1 530 534 0=48 1=1 5=1 6=2304 9=1
-Concat                   535                      2 1 525 534 535
-ShuffleChannel           540                      1 1 535 540 0=2
-Split                    splitncnn_2              1 2 540 540_splitncnn_0 540_splitncnn_1
-ConvolutionDepthWise     541                      1 1 540_splitncnn_1 541 0=96 1=3 3=2 4=1 5=1 6=864 7=96
-Convolution              543                      1 1 541 545 0=96 1=1 5=1 6=9216 9=1
-Convolution              546                      1 1 540_splitncnn_0 548 0=96 1=1 5=1 6=9216 9=1
-ConvolutionDepthWise     549                      1 1 548 549 0=96 1=3 3=2 4=1 5=1 6=864 7=96
-Convolution              551                      1 1 549 553 0=96 1=1 5=1 6=9216 9=1
-Concat                   554                      2 1 545 553 554
-ShuffleChannel           559                      1 1 554 559 0=2
-Slice                    560                      1 2 559 560 561 -23300=2,96,-233
-Convolution              562                      1 1 561 564 0=96 1=1 5=1 6=9216 9=1
-ConvolutionDepthWise     565                      1 1 564 565 0=96 1=3 4=1 5=1 6=864 7=96
-Convolution              567                      1 1 565 569 0=96 1=1 5=1 6=9216 9=1
-Concat                   570                      2 1 560 569 570
-ShuffleChannel           575                      1 1 570 575 0=2
-Slice                    576                      1 2 575 576 577 -23300=2,96,-233
-Convolution              578                      1 1 577 580 0=96 1=1 5=1 6=9216 9=1
-ConvolutionDepthWise     581                      1 1 580 581 0=96 1=3 4=1 5=1 6=864 7=96
-Convolution              583                      1 1 581 585 0=96 1=1 5=1 6=9216 9=1
-Concat                   586                      2 1 576 585 586
-ShuffleChannel           591                      1 1 586 591 0=2
-Slice                    592                      1 2 591 592 593 -23300=2,96,-233
-Convolution              594                      1 1 593 596 0=96 1=1 5=1 6=9216 9=1
-ConvolutionDepthWise     597                      1 1 596 597 0=96 1=3 4=1 5=1 6=864 7=96
-Convolution              599                      1 1 597 601 0=96 1=1 5=1 6=9216 9=1
-Concat                   602                      2 1 592 601 602
-ShuffleChannel           607                      1 1 602 607 0=2
-Convolution              608                      1 1 607 610 0=256 1=1 5=1 6=49152 9=1
-Reduction                611                      1 1 610 611 0=3 1=0 -23303=2,2,3
-InnerProduct             612                      1 1 611 612 0=2 1=1 2=512
-Softmax                  out                      1 1 612 out

3rdPart/resource/OcrLiteNcnn/models/crnn_lite_op.param

@@ -1,203 +0,0 @@
-7767517
-201 244
-Input                    input                    0 1 input
-Convolution              423                      1 1 input 425 0=8 1=3 3=2 4=1 5=1 6=216 9=1
-Split                    splitncnn_0              1 2 425 425_splitncnn_0 425_splitncnn_1
-Convolution              426                      1 1 425_splitncnn_1 428 0=4 1=1 5=1 6=32 9=1
-Split                    splitncnn_1              1 2 428 428_splitncnn_0 428_splitncnn_1
-ConvolutionDepthWise     429                      1 1 428_splitncnn_1 431 0=4 1=3 4=1 5=1 6=36 7=4 9=1
-Concat                   432                      2 1 428_splitncnn_0 431 432
-ConvolutionDepthWise     433                      1 1 432 433 0=8 1=3 13=2 4=1 5=1 6=72 7=8
-Split                    splitncnn_2              1 2 433 433_splitncnn_0 433_splitncnn_1
-Pooling                  441                      1 1 433_splitncnn_1 445 0=1 4=1
-InnerProduct             446                      1 1 445 447 0=2 1=1 2=16 9=1
-InnerProduct             448                      1 1 447 448 0=8 1=1 2=16
-Reshape                  456                      1 1 448 456 0=1 1=1 2=8
-Clip                     457                      1 1 456 457 0=0.000000e+00 1=1.000000e+00
-BinaryOp                 458                      2 1 433_splitncnn_0 457 458 0=2
-Convolution              459                      1 1 458 459 0=4 1=1 5=1 6=32
-Split                    splitncnn_3              1 2 459 459_splitncnn_0 459_splitncnn_1
-ConvolutionDepthWise     461                      1 1 459_splitncnn_1 461 0=4 1=3 4=1 5=1 6=36 7=4
-Concat                   463                      2 1 459_splitncnn_0 461 463
-ConvolutionDepthWise     464                      1 1 425_splitncnn_0 466 0=8 1=3 13=2 4=1 5=1 6=72 7=8 9=1
-Convolution              467                      1 1 466 467 0=8 1=1 5=1 6=64
-BinaryOp                 469                      2 1 463 467 469
-Split                    splitncnn_4              1 2 469 469_splitncnn_0 469_splitncnn_1
-Convolution              470                      1 1 469_splitncnn_1 472 0=28 1=1 5=1 6=224 9=1
-Split                    splitncnn_5              1 2 472 472_splitncnn_0 472_splitncnn_1
-ConvolutionDepthWise     473                      1 1 472_splitncnn_1 475 0=28 1=3 4=1 5=1 6=252 7=28 9=1
-Concat                   476                      2 1 472_splitncnn_0 475 476
-ConvolutionDepthWise     477                      1 1 476 477 0=56 1=3 13=2 4=1 5=1 6=504 7=56
-Convolution              479                      1 1 477 479 0=6 1=1 5=1 6=336
-Split                    splitncnn_6              1 2 479 479_splitncnn_0 479_splitncnn_1
-ConvolutionDepthWise     481                      1 1 479_splitncnn_1 481 0=6 1=3 4=1 5=1 6=54 7=6
-Concat                   483                      2 1 479_splitncnn_0 481 483
-ConvolutionDepthWise     484                      1 1 469_splitncnn_0 486 0=8 1=3 13=2 4=1 5=1 6=72 7=8 9=1
-Convolution              487                      1 1 486 487 0=12 1=1 5=1 6=96
-BinaryOp                 489                      2 1 483 487 489
-Split                    splitncnn_7              1 2 489 489_splitncnn_0 489_splitncnn_1
-Convolution              490                      1 1 489_splitncnn_1 492 0=22 1=1 5=1 6=264 9=1
-Split                    splitncnn_8              1 2 492 492_splitncnn_0 492_splitncnn_1
-ConvolutionDepthWise     493                      1 1 492_splitncnn_1 495 0=22 1=3 4=1 5=1 6=198 7=22 9=1
-Concat                   496                      2 1 492_splitncnn_0 495 496
-Convolution              497                      1 1 496 497 0=6 1=1 5=1 6=264
-Split                    splitncnn_9              1 2 497 497_splitncnn_0 497_splitncnn_1
-ConvolutionDepthWise     499                      1 1 497_splitncnn_1 499 0=6 1=3 4=1 5=1 6=54 7=6
-Concat                   501                      2 1 497_splitncnn_0 499 501
-BinaryOp                 502                      2 1 501 489_splitncnn_0 502
-Split                    splitncnn_10             1 2 502 502_splitncnn_0 502_splitncnn_1
-Convolution              503                      1 1 502_splitncnn_1 505 0=40 1=1 5=1 6=480 9=1
-Split                    splitncnn_11             1 2 505 505_splitncnn_0 505_splitncnn_1
-ConvolutionDepthWise     506                      1 1 505_splitncnn_1 508 0=40 1=3 4=1 5=1 6=360 7=40 9=1
-Concat                   509                      2 1 505_splitncnn_0 508 509
-ConvolutionDepthWise     510                      1 1 509 510 0=80 1=5 13=2 4=2 5=1 6=2000 7=80
-Split                    splitncnn_12             1 2 510 510_splitncnn_0 510_splitncnn_1
-Pooling                  518                      1 1 510_splitncnn_1 522 0=1 4=1
-InnerProduct             523                      1 1 522 524 0=20 1=1 2=1600 9=1
-InnerProduct             525                      1 1 524 525 0=80 1=1 2=1600
-Reshape                  533                      1 1 525 533 0=1 1=1 2=80
-Clip                     534                      1 1 533 534 0=0.000000e+00 1=1.000000e+00
-BinaryOp                 535                      2 1 510_splitncnn_0 534 535 0=2
-Convolution              536                      1 1 535 536 0=10 1=1 5=1 6=800
-Split                    splitncnn_13             1 2 536 536_splitncnn_0 536_splitncnn_1
-ConvolutionDepthWise     538                      1 1 536_splitncnn_1 538 0=10 1=3 4=1 5=1 6=90 7=10
-Concat                   540                      2 1 536_splitncnn_0 538 540
-ConvolutionDepthWise     541                      1 1 502_splitncnn_0 543 0=12 1=3 13=2 4=1 5=1 6=108 7=12 9=1
-Convolution              544                      1 1 543 544 0=20 1=1 5=1 6=240
-BinaryOp                 546                      2 1 540 544 546
-Split                    splitncnn_14             1 2 546 546_splitncnn_0 546_splitncnn_1
-Convolution              547                      1 1 546_splitncnn_1 549 0=60 1=1 5=1 6=1200 9=1
-Split                    splitncnn_15             1 2 549 549_splitncnn_0 549_splitncnn_1
-ConvolutionDepthWise     550                      1 1 549_splitncnn_1 552 0=60 1=3 4=1 5=1 6=540 7=60 9=1
-Concat                   553                      2 1 549_splitncnn_0 552 553
-Split                    splitncnn_16             1 2 553 553_splitncnn_0 553_splitncnn_1
-Pooling                  560                      1 1 553_splitncnn_1 564 0=1 4=1
-InnerProduct             565                      1 1 564 566 0=30 1=1 2=3600 9=1
-InnerProduct             567                      1 1 566 567 0=120 1=1 2=3600
-Reshape                  575                      1 1 567 575 0=1 1=1 2=120
-Clip                     576                      1 1 575 576 0=0.000000e+00 1=1.000000e+00
-BinaryOp                 577                      2 1 553_splitncnn_0 576 577 0=2
-Convolution              578                      1 1 577 578 0=10 1=1 5=1 6=1200
-Split                    splitncnn_17             1 2 578 578_splitncnn_0 578_splitncnn_1
-ConvolutionDepthWise     580                      1 1 578_splitncnn_1 580 0=10 1=3 4=1 5=1 6=90 7=10
-Concat                   582                      2 1 578_splitncnn_0 580 582
-BinaryOp                 583                      2 1 582 546_splitncnn_0 583
-Split                    splitncnn_18             1 2 583 583_splitncnn_0 583_splitncnn_1
-Convolution              584                      1 1 583_splitncnn_1 586 0=60 1=1 5=1 6=1200 9=1
-Split                    splitncnn_19             1 2 586 586_splitncnn_0 586_splitncnn_1
-ConvolutionDepthWise     587                      1 1 586_splitncnn_1 589 0=60 1=3 4=1 5=1 6=540 7=60 9=1
-Concat                   590                      2 1 586_splitncnn_0 589 590
-Split                    splitncnn_20             1 2 590 590_splitncnn_0 590_splitncnn_1
-Pooling                  597                      1 1 590_splitncnn_1 601 0=1 4=1
-InnerProduct             602                      1 1 601 603 0=30 1=1 2=3600 9=1
-InnerProduct             604                      1 1 603 604 0=120 1=1 2=3600
-Reshape                  612                      1 1 604 612 0=1 1=1 2=120
-Clip                     613                      1 1 612 613 0=0.000000e+00 1=1.000000e+00
-BinaryOp                 614                      2 1 590_splitncnn_0 613 614 0=2
-Convolution              615                      1 1 614 615 0=10 1=1 5=1 6=1200
-Split                    splitncnn_21             1 2 615 615_splitncnn_0 615_splitncnn_1
-ConvolutionDepthWise     617                      1 1 615_splitncnn_1 617 0=10 1=3 4=1 5=1 6=90 7=10
-Concat                   619                      2 1 615_splitncnn_0 617 619
-BinaryOp                 620                      2 1 619 583_splitncnn_0 620
-Split                    splitncnn_22             1 2 620 620_splitncnn_0 620_splitncnn_1
-Convolution              621                      1 1 620_splitncnn_1 623 0=36 1=1 5=1 6=720 9=1
-Split                    splitncnn_23             1 2 623 623_splitncnn_0 623_splitncnn_1
-ConvolutionDepthWise     624                      1 1 623_splitncnn_1 626 0=36 1=3 4=1 5=1 6=324 7=36 9=1
-Concat                   627                      2 1 623_splitncnn_0 626 627
-Split                    splitncnn_24             1 2 627 627_splitncnn_0 627_splitncnn_1
-Pooling                  634                      1 1 627_splitncnn_1 638 0=1 4=1
-InnerProduct             639                      1 1 638 640 0=18 1=1 2=1296 9=1
-InnerProduct             641                      1 1 640 641 0=72 1=1 2=1296
-Reshape                  649                      1 1 641 649 0=1 1=1 2=72
-Clip                     650                      1 1 649 650 0=0.000000e+00 1=1.000000e+00
-BinaryOp                 651                      2 1 627_splitncnn_0 650 651 0=2
-Convolution              652                      1 1 651 652 0=12 1=1 5=1 6=864
-Split                    splitncnn_25             1 2 652 652_splitncnn_0 652_splitncnn_1
-ConvolutionDepthWise     654                      1 1 652_splitncnn_1 654 0=12 1=3 4=1 5=1 6=108 7=12
-Concat                   656                      2 1 652_splitncnn_0 654 656
-ConvolutionDepthWise     657                      1 1 620_splitncnn_0 659 0=20 1=3 4=1 5=1 6=180 7=20 9=1
-Convolution              660                      1 1 659 660 0=24 1=1 5=1 6=480
-BinaryOp                 662                      2 1 656 660 662
-Split                    splitncnn_26             1 2 662 662_splitncnn_0 662_splitncnn_1
-Convolution              663                      1 1 662_splitncnn_1 665 0=36 1=1 5=1 6=864 9=1
-Split                    splitncnn_27             1 2 665 665_splitncnn_0 665_splitncnn_1
-ConvolutionDepthWise     666                      1 1 665_splitncnn_1 668 0=36 1=3 4=1 5=1 6=324 7=36 9=1
-Concat                   669                      2 1 665_splitncnn_0 668 669
-Split                    splitncnn_28             1 2 669 669_splitncnn_0 669_splitncnn_1
-Pooling                  676                      1 1 669_splitncnn_1 680 0=1 4=1
-InnerProduct             681                      1 1 680 682 0=18 1=1 2=1296 9=1
-InnerProduct             683                      1 1 682 683 0=72 1=1 2=1296
-Reshape                  691                      1 1 683 691 0=1 1=1 2=72
-Clip                     692                      1 1 691 692 0=0.000000e+00 1=1.000000e+00
-BinaryOp                 693                      2 1 669_splitncnn_0 692 693 0=2
-Convolution              694                      1 1 693 694 0=12 1=1 5=1 6=864
-Split                    splitncnn_29             1 2 694 694_splitncnn_0 694_splitncnn_1
-ConvolutionDepthWise     696                      1 1 694_splitncnn_1 696 0=12 1=3 4=1 5=1 6=108 7=12
-Concat                   698                      2 1 694_splitncnn_0 696 698
-BinaryOp                 699                      2 1 698 662_splitncnn_0 699
-Split                    splitncnn_30             1 2 699 699_splitncnn_0 699_splitncnn_1
-Convolution              700                      1 1 699_splitncnn_1 702 0=144 1=1 5=1 6=3456 9=1
-Split                    splitncnn_31             1 2 702 702_splitncnn_0 702_splitncnn_1
-ConvolutionDepthWise     703                      1 1 702_splitncnn_1 705 0=144 1=3 4=1 5=1 6=1296 7=144 9=1
-Concat                   706                      2 1 702_splitncnn_0 705 706
-ConvolutionDepthWise     707                      1 1 706 707 0=288 1=5 13=2 4=2 5=1 6=7200 7=288
-Split                    splitncnn_32             1 2 707 707_splitncnn_0 707_splitncnn_1
-Pooling                  715                      1 1 707_splitncnn_1 719 0=1 4=1
-InnerProduct             720                      1 1 719 721 0=72 1=1 2=20736 9=1
-InnerProduct             722                      1 1 721 722 0=288 1=1 2=20736
-Reshape                  730                      1 1 722 730 0=1 1=1 2=288
-Clip                     731                      1 1 730 731 0=0.000000e+00 1=1.000000e+00
-BinaryOp                 732                      2 1 707_splitncnn_0 731 732 0=2
-Convolution              733                      1 1 732 733 0=24 1=1 5=1 6=6912
-Split                    splitncnn_33             1 2 733 733_splitncnn_0 733_splitncnn_1
-ConvolutionDepthWise     735                      1 1 733_splitncnn_1 735 0=24 1=3 4=1 5=1 6=216 7=24
-Concat                   737                      2 1 733_splitncnn_0 735 737
-ConvolutionDepthWise     738                      1 1 699_splitncnn_0 740 0=24 1=3 13=2 4=1 5=1 6=216 7=24 9=1
-Convolution              741                      1 1 740 741 0=48 1=1 5=1 6=1152
-BinaryOp                 743                      2 1 737 741 743
-Split                    splitncnn_34             1 2 743 743_splitncnn_0 743_splitncnn_1
-Convolution              744                      1 1 743_splitncnn_1 746 0=144 1=1 5=1 6=6912 9=1
-Split                    splitncnn_35             1 2 746 746_splitncnn_0 746_splitncnn_1
-ConvolutionDepthWise     747                      1 1 746_splitncnn_1 749 0=144 1=3 4=1 5=1 6=1296 7=144 9=1
-Concat                   750                      2 1 746_splitncnn_0 749 750
-Split                    splitncnn_36             1 2 750 750_splitncnn_0 750_splitncnn_1
-Pooling                  757                      1 1 750_splitncnn_1 761 0=1 4=1
-InnerProduct             762                      1 1 761 763 0=72 1=1 2=20736 9=1
-InnerProduct             764                      1 1 763 764 0=288 1=1 2=20736
-Reshape                  772                      1 1 764 772 0=1 1=1 2=288
-Clip                     773                      1 1 772 773 0=0.000000e+00 1=1.000000e+00
-BinaryOp                 774                      2 1 750_splitncnn_0 773 774 0=2
-Convolution              775                      1 1 774 775 0=24 1=1 5=1 6=6912
-Split                    splitncnn_37             1 2 775 775_splitncnn_0 775_splitncnn_1
-ConvolutionDepthWise     777                      1 1 775_splitncnn_1 777 0=24 1=3 4=1 5=1 6=216 7=24
-Concat                   779                      2 1 775_splitncnn_0 777 779
-BinaryOp                 780                      2 1 779 743_splitncnn_0 780
-Split                    splitncnn_38             1 2 780 780_splitncnn_0 780_splitncnn_1
-Convolution              781                      1 1 780_splitncnn_1 783 0=144 1=1 5=1 6=6912 9=1
-Split                    splitncnn_39             1 2 783 783_splitncnn_0 783_splitncnn_1
-ConvolutionDepthWise     784                      1 1 783_splitncnn_1 786 0=144 1=3 4=1 5=1 6=1296 7=144 9=1
-Concat                   787                      2 1 783_splitncnn_0 786 787
-Split                    splitncnn_40             1 2 787 787_splitncnn_0 787_splitncnn_1
-Pooling                  794                      1 1 787_splitncnn_1 798 0=1 4=1
-InnerProduct             799                      1 1 798 800 0=72 1=1 2=20736 9=1
-InnerProduct             801                      1 1 800 801 0=288 1=1 2=20736
-Reshape                  809                      1 1 801 809 0=1 1=1 2=288
-Clip                     810                      1 1 809 810 0=0.000000e+00 1=1.000000e+00
-BinaryOp                 811                      2 1 787_splitncnn_0 810 811 0=2
-Convolution              812                      1 1 811 812 0=24 1=1 5=1 6=6912
-Split                    splitncnn_41             1 2 812 812_splitncnn_0 812_splitncnn_1
-ConvolutionDepthWise     814                      1 1 812_splitncnn_1 814 0=24 1=3 4=1 5=1 6=216 7=24
-Concat                   816                      2 1 812_splitncnn_0 814 816
-BinaryOp                 817                      2 1 816 780_splitncnn_0 817
-Convolution              818                      1 1 817 820 0=288 1=1 5=1 6=13824 9=1
-Pooling                  821                      1 1 820 821 1=2 11=1 2=2 12=1 5=1
-Reshape          822                      1 1 821 822 0=-1 1=288 2=-233
-Permute          823                      1 1 822 823 0=1
-Split            splitncnn_42             1 2 823 823_splitncnn_0 823_splitncnn_1
-LSTM             857                      1 1 823_splitncnn_1 860 0=48 1=55296 2=0
-LSTM             883                      1 1 860 886 0=48 1=9216 2=0
-LSTM             943                      1 1 823_splitncnn_0 948 0=48 1=110592 2=2
-LSTM             994                      1 1 948 999 0=48 1=36864 2=2
-Concat           1000                     2 1 886 999 1000 0=1
-Reshape          1014                     1 1 1000 1014 0=144 1=-1
-InnerProduct     out                      1 1 1014 out 0=5531 1=1 2=796464

3rdPart/resource/OcrLiteNcnn/models/dbnet_op.param

@@ -1,144 +0,0 @@
-7767517
-142 163
-Input                    input0                   0 1 input0
-Convolution              346                      1 1 input0 346 0=16 1=3 3=2 4=1 5=1 6=432
-HardSwish                353                      1 1 346 353 0=1.666667e-01
-Split                    splitncnn_0              1 2 353 353_splitncnn_0 353_splitncnn_1
-ConvolutionDepthWise     354                      1 1 353_splitncnn_1 356 0=16 1=3 4=1 5=1 6=144 7=16 9=1
-Convolution              357                      1 1 356 357 0=16 1=1 5=1 6=256
-BinaryOp                 359                      2 1 353_splitncnn_0 357 359
-Convolution              360                      1 1 359 362 0=64 1=1 5=1 6=1024 9=1
-ConvolutionDepthWise     363                      1 1 362 365 0=64 1=3 3=2 4=1 5=1 6=576 7=64 9=1
-Convolution              366                      1 1 365 366 0=24 1=1 5=1 6=1536
-Split                    splitncnn_1              1 2 366 366_splitncnn_0 366_splitncnn_1
-Convolution              368                      1 1 366_splitncnn_1 370 0=72 1=1 5=1 6=1728 9=1
-ConvolutionDepthWise     371                      1 1 370 373 0=72 1=3 4=1 5=1 6=648 7=72 9=1
-Convolution              374                      1 1 373 374 0=24 1=1 5=1 6=1728
-BinaryOp                 376                      2 1 366_splitncnn_0 374 376
-Split                    splitncnn_2              1 2 376 376_splitncnn_0 376_splitncnn_1
-Convolution              377                      1 1 376_splitncnn_1 379 0=72 1=1 5=1 6=1728 9=1
-ConvolutionDepthWise     380                      1 1 379 380 0=72 1=5 3=2 4=2 5=1 6=1800 7=72
-Split                    splitncnn_3              1 2 380 380_splitncnn_0 380_splitncnn_1
-Pooling                  388                      1 1 380_splitncnn_1 392 0=1 4=1
-InnerProduct             393                      1 1 392 394 0=24 1=1 2=1728 9=1
-InnerProduct             395                      1 1 394 395 0=72 1=1 2=1728
-HardSigmoid              400                      1 1 395 400 0=1.666667e-01
-BinaryOp                 409                      2 1 380_splitncnn_0 400 409 0=2
-ReLU                     410                      1 1 409 410
-Convolution              411                      1 1 410 411 0=32 1=1 5=1 6=2304
-Split                    splitncnn_4              1 2 411 411_splitncnn_0 411_splitncnn_1
-Convolution              413                      1 1 411_splitncnn_1 415 0=96 1=1 5=1 6=3072 9=1
-ConvolutionDepthWise     416                      1 1 415 416 0=96 1=5 4=2 5=1 6=2400 7=96
-Split                    splitncnn_5              1 2 416 416_splitncnn_0 416_splitncnn_1
-Pooling                  424                      1 1 416_splitncnn_1 428 0=1 4=1
-InnerProduct             429                      1 1 428 430 0=24 1=1 2=2304 9=1
-InnerProduct             431                      1 1 430 431 0=96 1=1 2=2304
-HardSigmoid              436                      1 1 431 436 0=1.666667e-01
-BinaryOp                 445                      2 1 416_splitncnn_0 436 445 0=2
-ReLU                     446                      1 1 445 446
-Convolution              447                      1 1 446 447 0=32 1=1 5=1 6=3072
-BinaryOp                 449                      2 1 411_splitncnn_0 447 449
-Split                    splitncnn_6              1 2 449 449_splitncnn_0 449_splitncnn_1
-Convolution              450                      1 1 449_splitncnn_1 452 0=96 1=1 5=1 6=3072 9=1
-ConvolutionDepthWise     453                      1 1 452 453 0=96 1=5 4=2 5=1 6=2400 7=96
-Split                    splitncnn_7              1 2 453 453_splitncnn_0 453_splitncnn_1
-Pooling                  461                      1 1 453_splitncnn_1 465 0=1 4=1
-InnerProduct             466                      1 1 465 467 0=24 1=1 2=2304 9=1
-InnerProduct             468                      1 1 467 468 0=96 1=1 2=2304
-HardSigmoid              473                      1 1 468 473 0=1.666667e-01
-BinaryOp                 482                      2 1 453_splitncnn_0 473 482 0=2
-ReLU                     483                      1 1 482 483
-Convolution              484                      1 1 483 484 0=32 1=1 5=1 6=3072
-BinaryOp                 486                      2 1 449_splitncnn_0 484 486
-Split                    splitncnn_8              1 2 486 486_splitncnn_0 486_splitncnn_1
-Convolution              487                      1 1 486_splitncnn_1 487 0=192 1=1 5=1 6=6144
-HardSwish                494                      1 1 487 494 0=1.666667e-01
-ConvolutionDepthWise     495                      1 1 494 495 0=192 1=3 3=2 4=1 5=1 6=1728 7=192
-HardSwish                502                      1 1 495 502 0=1.666667e-01
-Convolution              503                      1 1 502 503 0=64 1=1 5=1 6=12288
-Split                    splitncnn_9              1 2 503 503_splitncnn_0 503_splitncnn_1
-Convolution              505                      1 1 503_splitncnn_1 505 0=160 1=1 5=1 6=10240
-HardSwish                512                      1 1 505 512 0=1.666667e-01
-ConvolutionDepthWise     513                      1 1 512 513 0=160 1=3 4=1 5=1 6=1440 7=160
-HardSwish                520                      1 1 513 520 0=1.666667e-01
-Convolution              521                      1 1 520 521 0=64 1=1 5=1 6=10240
-BinaryOp                 523                      2 1 503_splitncnn_0 521 523
-Split                    splitncnn_10             1 2 523 523_splitncnn_0 523_splitncnn_1
-Convolution              524                      1 1 523_splitncnn_1 524 0=144 1=1 5=1 6=9216
-HardSwish                531                      1 1 524 531 0=1.666667e-01
-ConvolutionDepthWise     532                      1 1 531 532 0=144 1=3 4=1 5=1 6=1296 7=144
-HardSwish                539                      1 1 532 539 0=1.666667e-01
-Convolution              540                      1 1 539 540 0=64 1=1 5=1 6=9216
-BinaryOp                 542                      2 1 523_splitncnn_0 540 542
-Split                    splitncnn_11             1 2 542 542_splitncnn_0 542_splitncnn_1
-Convolution              543                      1 1 542_splitncnn_1 543 0=144 1=1 5=1 6=9216
-HardSwish                550                      1 1 543 550 0=1.666667e-01
-ConvolutionDepthWise     551                      1 1 550 551 0=144 1=3 4=1 5=1 6=1296 7=144
-HardSwish                558                      1 1 551 558 0=1.666667e-01
-Convolution              559                      1 1 558 559 0=64 1=1 5=1 6=9216
-BinaryOp                 561                      2 1 542_splitncnn_0 559 561
-Convolution              562                      1 1 561 562 0=384 1=1 5=1 6=24576
-HardSwish                569                      1 1 562 569 0=1.666667e-01
-ConvolutionDepthWise     570                      1 1 569 570 0=384 1=3 4=1 5=1 6=3456 7=384
-Split                    splitncnn_12             1 2 570 570_splitncnn_0 570_splitncnn_1
-Pooling                  578                      1 1 570_splitncnn_1 582 0=1 4=1
-InnerProduct             583                      1 1 582 584 0=96 1=1 2=36864 9=1
-InnerProduct             585                      1 1 584 585 0=384 1=1 2=36864
-HardSigmoid              590                      1 1 585 590 0=1.666667e-01
-BinaryOp                 599                      2 1 570_splitncnn_0 590 599 0=2
-HardSwish                605                      1 1 599 605 0=1.666667e-01
-Convolution              606                      1 1 605 606 0=88 1=1 5=1 6=33792
-Split                    splitncnn_13             1 2 606 606_splitncnn_0 606_splitncnn_1
-Convolution              608                      1 1 606_splitncnn_1 608 0=528 1=1 5=1 6=46464
-HardSwish                615                      1 1 608 615 0=1.666667e-01
-ConvolutionDepthWise     616                      1 1 615 616 0=528 1=3 4=1 5=1 6=4752 7=528
-Split                    splitncnn_14             1 2 616 616_splitncnn_0 616_splitncnn_1
-Pooling                  624                      1 1 616_splitncnn_1 628 0=1 4=1
-InnerProduct             629                      1 1 628 630 0=136 1=1 2=71808 9=1
-InnerProduct             631                      1 1 630 631 0=528 1=1 2=71808
-HardSigmoid              636                      1 1 631 636 0=1.666667e-01
-BinaryOp                 645                      2 1 616_splitncnn_0 636 645 0=2
-HardSwish                651                      1 1 645 651 0=1.666667e-01
-Convolution              652                      1 1 651 652 0=88 1=1 5=1 6=46464
-BinaryOp                 654                      2 1 606_splitncnn_0 652 654
-Split                    splitncnn_15             1 2 654 654_splitncnn_0 654_splitncnn_1
-Convolution              655                      1 1 654_splitncnn_1 655 0=528 1=1 5=1 6=46464
-HardSwish                662                      1 1 655 662 0=1.666667e-01
-ConvolutionDepthWise     663                      1 1 662 663 0=528 1=5 3=2 4=2 5=1 6=13200 7=528
-Split                    splitncnn_16             1 2 663 663_splitncnn_0 663_splitncnn_1
-Pooling                  671                      1 1 663_splitncnn_1 675 0=1 4=1
-InnerProduct             676                      1 1 675 677 0=136 1=1 2=71808 9=1
-InnerProduct             678                      1 1 677 678 0=528 1=1 2=71808
-HardSigmoid              683                      1 1 678 683 0=1.666667e-01
-BinaryOp                 692                      2 1 663_splitncnn_0 683 692 0=2
-HardSwish                698                      1 1 692 698 0=1.666667e-01
-Convolution              699                      1 1 698 699 0=120 1=1 5=1 6=63360
-Convolution              701                      1 1 699 703 0=24 1=1 5=1 6=2880 9=1
-Split                    splitncnn_17             1 2 703 703_splitncnn_0 703_splitncnn_1
-Convolution              704                      1 1 654_splitncnn_0 706 0=24 1=1 5=1 6=2112 9=1
-Interp                   723                      1 1 703_splitncnn_1 723 0=1 1=2.000000e+00 2=2.000000e+00
-BinaryOp                 724                      2 1 723 706 724
-Convolution              725                      1 1 724 727 0=24 1=3 4=1 5=1 6=5184 9=1
-Split                    splitncnn_18             1 2 727 727_splitncnn_0 727_splitncnn_1
-Convolution              728                      1 1 486_splitncnn_0 730 0=24 1=1 5=1 6=768 9=1
-Interp                   747                      1 1 727_splitncnn_1 747 0=1 1=2.000000e+00 2=2.000000e+00
-BinaryOp                 748                      2 1 747 730 748
-Convolution              749                      1 1 748 751 0=24 1=3 4=1 5=1 6=5184 9=1
-Split                    splitncnn_19             1 2 751 751_splitncnn_0 751_splitncnn_1
-Convolution              752                      1 1 376_splitncnn_0 754 0=24 1=1 5=1 6=576 9=1
-Interp                   771                      1 1 751_splitncnn_1 771 0=1 1=2.000000e+00 2=2.000000e+00
-BinaryOp                 772                      2 1 771 754 772
-Convolution              773                      1 1 772 775 0=24 1=3 4=1 5=1 6=5184 9=1
-Interp                   792                      1 1 751_splitncnn_0 792 0=1 1=2.000000e+00 2=2.000000e+00
-Interp                   803                      1 1 727_splitncnn_0 803 0=1 1=4.000000e+00 2=4.000000e+00
-Interp                   814                      1 1 703_splitncnn_0 814 0=1 1=8.000000e+00 2=8.000000e+00
-Concat                   815                      4 1 775 792 803 814 815
-Convolution              816                      1 1 815 818 0=96 1=3 4=1 5=1 6=82944 9=1
-Split                    splitncnn_20             1 2 818 818_splitncnn_0 818_splitncnn_1
-Convolution              819                      1 1 818_splitncnn_1 821 0=24 1=3 4=1 5=1 6=20736 9=1
-Deconvolution            822                      1 1 821 824 0=24 1=2 3=2 5=1 6=2304 9=1
-Deconvolution            825                      1 1 824 826 0=1 1=2 3=2 5=1 6=96 9=4
-Convolution              827                      1 1 818_splitncnn_0 829 0=24 1=3 4=1 5=1 6=20736 9=1
-Deconvolution            830                      1 1 829 832 0=24 1=2 3=2 5=1 6=2304 9=1
-Deconvolution            833                      1 1 832 834 0=1 1=2 3=2 5=1 6=96 9=4
-Concat                   out1                     2 1 826 834 out1

3rdparty/include/OcrLiteOnnx/AngleNet.h

@@ -0,0 +1,41 @@
+#ifndef __OCR_ANGLENET_H__
+#define __OCR_ANGLENET_H__
+
+#include "OcrStruct.h"
+#include "onnxruntime_cxx_api.h"
+#include <opencv/cv.hpp>
+
+class AngleNet {
+public:
+    AngleNet();
+
+    ~AngleNet();
+
+    void setNumThread(int numOfThread);
+
+    void initModel(const std::string &pathStr);
+
+    std::vector<Angle> getAngles(std::vector<cv::Mat> &partImgs, const char *path,
+                                 const char *imgName, bool doAngle, bool mostAngle);
+
+private:
+    bool isOutputAngleImg = false;
+
+    Ort::Session *session;
+    Ort::Env env = Ort::Env(ORT_LOGGING_LEVEL_ERROR, "AngleNet");
+    Ort::SessionOptions sessionOptions = Ort::SessionOptions();
+    int numThread = 0;
+
+    char *inputName;
+    char *outputName;
+
+    const float meanValues[3] = {127.5, 127.5, 127.5};
+    const float normValues[3] = {1.0 / 127.5, 1.0 / 127.5, 1.0 / 127.5};
+    const int dstWidth = 192;
+    const int dstHeight = 32;
+
+    Angle getAngle(cv::Mat &src);
+};
+
+
+#endif //__OCR_ANGLENET_H__

3rdparty/include/OcrLiteOnnx/CrnnNet.h

@@ -0,0 +1,43 @@
+#ifndef __OCR_CRNNNET_H__
+#define __OCR_CRNNNET_H__
+
+#include "OcrStruct.h"
+#include "onnxruntime_cxx_api.h"
+#include <opencv/cv.hpp>
+
+class CrnnNet {
+public:
+
+    CrnnNet();
+
+    ~CrnnNet();
+
+    void setNumThread(int numOfThread);
+
+    void initModel(const std::string &pathStr, const std::string &keysPath);
+
+    std::vector<TextLine> getTextLines(std::vector<cv::Mat> &partImg, const char *path, const char *imgName);
+
+private:
+    bool isOutputDebugImg = false;
+    Ort::Session *session;
+    Ort::Env env = Ort::Env(ORT_LOGGING_LEVEL_ERROR, "CrnnNet");
+    Ort::SessionOptions sessionOptions = Ort::SessionOptions();
+    int numThread = 0;
+
+    char *inputName;
+    char *outputName;
+
+    const float meanValues[3] = {127.5, 127.5, 127.5};
+    const float normValues[3] = {1.0 / 127.5, 1.0 / 127.5, 1.0 / 127.5};
+    const int dstHeight = 32;
+
+    std::vector<std::string> keys;
+
+    TextLine scoreToTextLine(const std::vector<float> &outputData, int h, int w);
+
+    TextLine getTextLine(const cv::Mat &src);
+};
+
+
+#endif //__OCR_CRNNNET_H__

3rdparty/include/OcrLiteOnnx/DbNet.h

@@ -0,0 +1,34 @@
+#ifndef __OCR_DBNET_H__
+#define __OCR_DBNET_H__
+
+#include "OcrStruct.h"
+#include "onnxruntime_cxx_api.h"
+#include <opencv/cv.hpp>
+
+class DbNet {
+public:
+    DbNet();
+
+    ~DbNet();
+
+    void setNumThread(int numOfThread);
+
+    void initModel(const std::string &pathStr);
+
+    std::vector<TextBox> getTextBoxes(cv::Mat &src, ScaleParam &s, float boxScoreThresh,
+                                      float boxThresh, float unClipRatio);
+
+private:
+    Ort::Session *session;
+    Ort::Env env = Ort::Env(ORT_LOGGING_LEVEL_ERROR, "DbNet");
+    Ort::SessionOptions sessionOptions = Ort::SessionOptions();
+    int numThread = 0;
+    char *inputName;
+    char *outputName;
+
+    const float meanValues[3] = {0.485 * 255, 0.456 * 255, 0.406 * 255};
+    const float normValues[3] = {1.0 / 0.229 / 255.0, 1.0 / 0.224 / 255.0, 1.0 / 0.225 / 255.0};
+};
+
+
+#endif //__OCR_DBNET_H__

3rdparty/include/OcrLiteOnnx/OcrLite.h

@@ -0,0 +1,56 @@
+#ifndef __OCR_LITE_H__
+#define __OCR_LITE_H__
+
+#include "opencv2/core.hpp"
+#include "onnxruntime_cxx_api.h"
+#include "OcrStruct.h"
+#include "DbNet.h"
+#include "AngleNet.h"
+#include "CrnnNet.h"
+
+class OcrLite {
+public:
+    OcrLite();
+
+    ~OcrLite();
+
+    void setNumThread(int numOfThread);
+
+    void initLogger(bool isConsole, bool isPartImg, bool isResultImg);
+
+    void enableResultTxt(const char *path, const char *imgName);
+
+    void initModels(const std::string &detPath, const std::string &clsPath,
+                    const std::string &recPath, const std::string &keysPath);
+
+    void Logger(const char *format, ...);
+
+    OcrResult detect(const char *path, const char *imgName,
+                     int padding, int maxSideLen,
+                     float boxScoreThresh, float boxThresh, float unClipRatio, bool doAngle, bool mostAngle);
+
+
+	OcrResult detect(const cv::Mat& mat,
+		int padding, int maxSideLen,
+		float boxScoreThresh, float boxThresh, float unClipRatio, bool doAngle, bool mostAngle);
+
+private:
+    bool isOutputConsole = false;
+    bool isOutputPartImg = false;
+    bool isOutputResultTxt = false;
+    bool isOutputResultImg = false;
+    FILE *resultTxt;
+    DbNet dbNet;
+    AngleNet angleNet;
+    CrnnNet crnnNet;
+
+    std::vector<cv::Mat> getPartImages(cv::Mat &src, std::vector<TextBox> &textBoxes,
+                                       const char *path, const char *imgName);
+
+    OcrResult detect(const char *path, const char *imgName,
+                     cv::Mat &src, cv::Rect &originRect, ScaleParam &scale,
+                     float boxScoreThresh = 0.6f, float boxThresh = 0.3f,
+                     float unClipRatio = 2.0f, bool doAngle = true, bool mostAngle = true);
+};
+
+#endif //__OCR_LITE_H__

3rdparty/include/OcrLiteOnnx/OcrLiteCaller.h

@@ -0,0 +1,35 @@
+#pragma once
+
+#include <memory>
+#include <string>
+
+#include "OcrLitePort.h"
+#include "OcrStruct.h"
+
+namespace cv
+{
+	class Mat;
+}
+class OcrLite;
+
+class OCRLITE_PORT OcrLiteCaller
+{
+public:
+	OcrLiteCaller();
+	~OcrLiteCaller() = default;
+	OcrLiteCaller(const OcrLite&) = delete;
+	OcrLiteCaller(OcrLite&&) = delete;
+
+	void setNumThread(int numOfThread);
+	void initModels(const std::string& detPath, const std::string& clsPath,
+		const std::string& recPath, const std::string& keysPath);
+
+	OcrResult detect(const cv::Mat& mat,
+		int padding, int maxSideLen,
+		float boxScoreThresh, float boxThresh, float unClipRatio, bool doAngle, bool mostAngle);
+
+	OcrLiteCaller& operator=(const OcrLiteCaller&) = delete;
+	OcrLiteCaller& operator=(OcrLiteCaller&&) = delete;
+private:
+	std::shared_ptr<OcrLite> m_ocrlite_ptr;
+};

3rdparty/include/OcrLiteOnnx/OcrLitePort.h

@@ -0,0 +1,37 @@
+#pragma once
+
+#pragma once
+
+// The way how the function is called
+#if !defined(OCRLITE_CALL)
+#if defined(_WIN32)
+#define OCRLITE_CALL __stdcall
+#else
+#define OCRLITE_CALL
+#endif /* _WIN32 */
+#endif /* ISSCALL */
+
+#if defined _WIN32 || defined __CYGWIN__
+#define OCRLITE_EXPORT __declspec(dllexport)
+#define OCRLITE_IMPORT __declspec(dllimport)
+#define OCRLITE_LOCAL
+#else   // ! defined _WIN32 || defined __CYGWIN__
+#if __GNUC__ >= 4
+#define OCRLITE_EXPORT __attribute__ ((visibility ("default")))
+#define OCRLITE_IMPORT __attribute__ ((visibility ("default")))
+#define OCRLITE_LOCAL  __attribute__ ((visibility ("hidden")))
+#else   // ! __GNUC__ >= 4
+#define OCRLITE_EXPORT
+#define OCRLITE_IMPORT
+#endif  // End __GNUC__ >= 4
+#endif  // End defined _WIN32 || defined __CYGWIN__
+
+#ifdef __CLIB__
+#define OCRLITE_PORT OCRLITE_EXPORT
+#else
+#define OCRLITE_PORT OCRLITE_IMPORT
+#endif // OCRLITE_PORT
+
+#define OCR_API OCRLITE_PORT OCRLITE_CALL
+
+#define OCR_LOCAL OCRLITE_LOCAL OCRLITE_CALL

3rdparty/include/OcrLiteOnnx/OcrStruct.h

@@ -0,0 +1,55 @@
+#ifndef __OCR_STRUCT_H__
+#define __OCR_STRUCT_H__
+
+#include "opencv2/core.hpp"
+#include <vector>
+
+#include "OcrLitePort.h"
+
+struct ScaleParam {
+    int srcWidth;
+    int srcHeight;
+    int dstWidth;
+    int dstHeight;
+    float ratioWidth;
+    float ratioHeight;
+};
+
+struct TextBox {
+    std::vector<cv::Point> boxPoint;
+    float score;
+};
+
+struct Angle {
+    int index;
+    float score;
+    double time;
+};
+
+struct TextLine {
+    std::string text;
+    std::vector<float> charScores;
+    double time;
+};
+
+struct OCRLITE_PORT TextBlock {
+    std::vector<cv::Point> boxPoint;
+    float boxScore;
+    int angleIndex;
+    float angleScore;
+    double angleTime;
+    std::string text;
+    std::vector<float> charScores;
+    double crnnTime;
+    double blockTime;
+};
+
+struct OCRLITE_PORT OcrResult {
+    double dbNetTime;
+    std::vector<TextBlock> textBlocks;
+    cv::Mat boxImg;
+    double detectTime;
+    std::string strRes;
+};
+
+#endif //__OCR_STRUCT_H__

3rdparty/include/OcrLiteOnnx/OcrUtils.h

@@ -0,0 +1,103 @@
+#ifndef __OCR_UTILS_H__
+#define __OCR_UTILS_H__
+
+#include <opencv2/core.hpp>
+#include "OcrStruct.h"
+#include "onnxruntime_cxx_api.h"
+#include <numeric>
+#include <sys/stat.h>
+
+template<typename T, typename... Ts>
+static std::unique_ptr<T> makeUnique(Ts &&... params) {
+    return std::unique_ptr<T>(new T(std::forward<Ts>(params)...));
+}
+
+template<typename T>
+static double getMean(std::vector<T> &input) {
+    auto sum = accumulate(input.begin(), input.end(), 0.0);
+    return sum / input.size();
+}
+
+template<typename T>
+static double getStdev(std::vector<T> &input, double mean) {
+    if (input.size() <= 1) return 0;
+    double accum = 0.0;
+    for_each(input.begin(), input.end(), [&](const double d) {
+        accum += (d - mean) * (d - mean);
+    });
+    double stdev = sqrt(accum / (input.size() - 1));
+    return stdev;
+}
+
+double getCurrentTime();
+
+inline bool isFileExists(const std::string &name) {
+    struct stat buffer;
+    return (stat(name.c_str(), &buffer) == 0);
+}
+
+#ifdef _WIN32
+#define my_strtol wcstol
+#define my_strrchr wcsrchr
+#define my_strcasecmp _wcsicmp
+#define my_strdup _strdup
+#else
+#define my_strtol strtol
+#define my_strrchr strrchr
+#define my_strcasecmp strcasecmp
+#define my_strdup strdup
+#endif
+
+std::wstring strToWstr(std::string str);
+
+ScaleParam getScaleParam(cv::Mat &src, const float scale);
+
+ScaleParam getScaleParam(cv::Mat &src, const int targetSize);
+
+std::vector<cv::Point2f> getBox(const cv::RotatedRect &rect);
+
+int getThickness(cv::Mat &boxImg);
+
+void drawTextBox(cv::Mat &boxImg, cv::RotatedRect &rect, int thickness);
+
+void drawTextBox(cv::Mat &boxImg, const std::vector<cv::Point> &box, int thickness);
+
+void drawTextBoxes(cv::Mat &boxImg, std::vector<TextBox> &textBoxes, int thickness);
+
+cv::Mat matRotateClockWise180(cv::Mat src);
+
+cv::Mat matRotateClockWise90(cv::Mat src);
+
+cv::Mat getRotateCropImage(const cv::Mat &src, std::vector<cv::Point> box);
+
+cv::Mat adjustTargetImg(cv::Mat &src, int dstWidth, int dstHeight);
+
+std::vector<cv::Point> getMinBoxes(const std::vector<cv::Point> &inVec, float &minSideLen, float &allEdgeSize);
+
+float boxScoreFast(const cv::Mat &inMat, const std::vector<cv::Point> &inBox);
+
+std::vector<cv::Point> unClip(const std::vector<cv::Point> &inBox, float perimeter, float unClipRatio);
+
+std::vector<float> substractMeanNormalize(cv::Mat &src, const float *meanVals, const float *normVals);
+
+std::vector<int> getAngleIndexes(std::vector<Angle> &angles);
+
+std::vector<char *> getInputNames(Ort::Session *session);
+
+std::vector<char *> getOutputNames(Ort::Session *session);
+
+void getInputName(Ort::Session *session, char *&inputName);
+
+void getOutputName(Ort::Session *session, char *&outputName);
+
+void saveImg(cv::Mat &img, const char *imgPath);
+
+std::string getSrcImgFilePath(const char *path, const char *imgName);
+
+std::string getResultTxtFilePath(const char *path, const char *imgName);
+
+std::string getResultImgFilePath(const char *path, const char *imgName);
+
+std::string getDebugImgFilePath(const char *path, const char *imgName, int i, const char *tag);
+
+#endif //__OCR_UTILS_H__

3rdparty/include/OcrLiteOnnx/getopt.h

@@ -0,0 +1,47 @@
+/*
+ * getopt - POSIX like getopt for Windows console Application
+ *
+ * win-c - Windows Console Library
+ * Copyright (c) 2015 Koji Takami
+ * Released under the MIT license
+ * https://github.com/takamin/win-c/blob/master/LICENSE
+ */
+#ifndef _GETOPT_H_
+#define _GETOPT_H_
+
+#ifndef __CLIB__
+
+#ifdef __cplusplus
+extern "C" {
+#endif // __cplusplus
+
+int getopt(int argc, char *const argv[],
+           const char *optstring);
+
+extern char *optarg;
+extern int optind, opterr, optopt;
+
+#define no_argument 0
+#define required_argument 1
+#define optional_argument 2
+
+struct option {
+    const char *name;
+    int has_arg;
+    int *flag;
+    int val;
+};
+
+int getopt_long(int argc, char *const argv[],
+                const char *optstring,
+                const struct option *longopts, int *longindex);
+/****************************************************************************
+    int getopt_long_only(int argc, char* const argv[],
+            const char* optstring,
+            const struct option* longopts, int* longindex);
+****************************************************************************/
+#ifdef __cplusplus
+}
+#endif
+#endif // __cplusplus
+#endif // _GETOPT_H_

3rdparty/include/OcrLiteOnnx/main.h

@@ -0,0 +1,56 @@
+#ifndef __MAIN_H__
+#define __MAIN_H__
+#ifndef __CLIB__
+
+#include "getopt.h"
+
+static const struct option long_options[] = {
+        {"models",         required_argument, NULL, 'd'},
+        {"det",            required_argument, NULL, '1'},
+        {"cls",            required_argument, NULL, '2'},
+        {"rec",            required_argument, NULL, '3'},
+        {"keys",           required_argument, NULL, '4'},
+        {"image",          required_argument, NULL, 'i'},
+        {"numThread",      required_argument, NULL, 't'},
+        {"padding",        required_argument, NULL, 'p'},
+        {"maxSideLen",     required_argument, NULL, 's'},
+        {"boxScoreThresh", required_argument, NULL, 'b'},
+        {"boxThresh",      required_argument, NULL, 'o'},
+        {"unClipRatio",    required_argument, NULL, 'u'},
+        {"doAngle",        required_argument, NULL, 'a'},
+        {"mostAngle",      required_argument, NULL, 'A'},
+        {"version",        no_argument,       NULL, 'v'},
+        {"help",           no_argument,       NULL, 'h'},
+        {"loopCount",      required_argument, NULL, 'l'},
+        {NULL,             no_argument,       NULL, 0}
+};
+
+const char *usageMsg = "(-d --models) (-1 --det) (-2 --cls) (-3 --rec) (-4 --keys) (-i --image)\n"\
+                       "[-t --numThread] [-p --padding] [-s --maxSideLen]\n" \
+                       "[-b --boxScoreThresh] [-o --boxThresh] [-u --unClipRatio]\n" \
+                       "[-a --noAngle] [-A --mostAngle]\n\n";
+
+const char *requiredMsg = "-d --models: models directory.\n" \
+                          "-1 --det: model file name of det.\n" \
+                          "-2 --cls: model file name of cls.\n" \
+                          "-3 --rec: model file name of rec.\n" \
+						  "-4 --keys: keys file name.\n" \
+                          "-i --image: path of target image.\n\n";
+
+const char *optionalMsg = "-t --numThread: value of numThread(int), default: 4\n" \
+                          "-p --padding: value of padding(int), default: 50\n" \
+                          "-s --maxSideLen: Long side of picture for resize(int), default: 1024\n" \
+                          "-b --boxScoreThresh: value of boxScoreThresh(float), default: 0.6\n" \
+                          "-o --boxThresh: value of boxThresh(float), default: 0.3\n" \
+                          "-u --unClipRatio: value of unClipRatio(float), default: 2.0\n" \
+                          "-a --doAngle: Enable(1)/Disable(0) Angle Net, default: Enable\n" \
+                          "-A --mostAngle: Enable(1)/Disable(0) Most Possible AngleIndex, default: Enable\n\n";
+
+const char *otherMsg = "-v --version: show version\n" \
+                       "-h --help: print this help\n\n";
+
+const char *example1Msg = "Example1: %s --models models --det det.onnx --cls cls.onnx --rec rec.onnx --keys keys.txt --image 1.jpg\n";
+const char *example2Msg = "Example2: %s -d models -1 det.onnx -2 cls.onnx -3 rec.onnx -4 keys.txt -i 1.jpg -t 4 -p 50 -s 0 -b 0.6 -o 0.3 -u 2.0 -a 1 -A 1\n";
+
+#endif
+#endif //__MAIN_H__

3rdparty/include/OcrLiteOnnx/version.h

@@ -1,8 +1,6 @@
 #ifndef __OCR_VERSION_H__
 #define __OCR_VERSION_H__
 
-namespace ocr {
-	static const char* VERSION = "1.5.1.20210128";
-}
+#define VERSION "1.5.1.20210128"
 
 #endif //__OCR_VERSION_H__

3rdparty/include/meojson/json_array.h

@@ -18,18 +18,27 @@ namespace json
         using const_reverse_iterator = raw_array::const_reverse_iterator;
 
         array() = default;
-        array(const array &rhs) = default;
-        array(array &&rhs) noexcept = default;
-        array(const raw_array &arr);
-        array(raw_array &&arr) noexcept;
+        array(const array& rhs) = default;
+        array(array&& rhs) noexcept = default;
+        array(const raw_array& arr);
+        array(raw_array&& arr) noexcept;
         array(std::initializer_list<raw_array::value_type> init_list);
+        template<typename ArrayType>
+        array(ArrayType arr) {
+            static_assert(
+                std::is_constructible<json::value, typename ArrayType::value_type>::value,
+                "Parameter can't be used to construct a json::value");
+            for (auto&& ele : arr) {
+                _array_data.emplace_back(std::move(ele));
+            }
+        }
 
         ~array() noexcept = default;
 
         bool empty() const noexcept { return _array_data.empty(); }
         size_t size() const noexcept { return _array_data.size(); }
         bool exist(size_t pos) const { return _array_data.size() < pos; }
-        const value &at(size_t pos) const;
+        const value& at(size_t pos) const;
         const std::string to_string() const;
         const std::string format(std::string shift_str = "    ", size_t basic_shift_count = 0) const;
 
@@ -43,7 +52,7 @@ namespace json
         const double get(size_t pos, double default_value) const;
         const long double get(size_t pos, long double default_value) const;
         const std::string get(size_t pos, std::string default_value) const;
-        const std::string get(size_t pos, const char * default_value) const;
+        const std::string get(size_t pos, const char* default_value) const;
 
         template <typename... Args>
         decltype(auto) emplace_back(Args &&... args)
@@ -59,19 +68,23 @@ namespace json
 
         iterator begin() noexcept;
         iterator end() noexcept;
+        const_iterator begin() const noexcept;
+        const_iterator end() const noexcept;
         const_iterator cbegin() const noexcept;
         const_iterator cend() const noexcept;
 
         reverse_iterator rbegin() noexcept;
         reverse_iterator rend() noexcept;
+        const_reverse_iterator rbegin() const noexcept;
+        const_reverse_iterator rend() const noexcept;
         const_reverse_iterator crbegin() const noexcept;
         const_reverse_iterator crend() const noexcept;
 
-        const value &operator[](size_t pos) const;
-        value &operator[](size_t pos);
+        const value& operator[](size_t pos) const;
+        value& operator[](size_t pos);
 
-        array &operator=(const array &) = default;
-        array &operator=(array &&) noexcept = default;
+        array& operator=(const array&) = default;
+        array& operator=(array&&) noexcept = default;
 
         // const raw_array &raw_data() const;
 
@@ -79,6 +92,6 @@ namespace json
         raw_array _array_data;
     };
 
-    std::ostream &operator<<(std::ostream &out, const array &arr);
+    std::ostream& operator<<(std::ostream& out, const array& arr);
 
 } // namespace json

3rdparty/include/meojson/json_aux.h

@@ -0,0 +1,97 @@
+#pragma once
+
+#include <string>
+#include "json_value.h"
+
+namespace json
+{
+	static std::string unescape_string(std::string&& str)
+	{
+		std::string replace_str;
+		std::string escape_str = std::move(str);
+
+		for (size_t pos = 0; pos < escape_str.size(); ++pos)
+		{
+			switch (escape_str[pos]) {
+			case '\"':
+				replace_str = R"(\")";
+				break;
+			case '\\':
+				replace_str = R"(\\)";
+				break;
+			case '\b':
+				replace_str = R"(\b)";
+				break;
+			case '\f':
+				replace_str = R"(\f)";
+				break;
+			case '\n':
+				replace_str = R"(\n)";
+				break;
+			case '\r':
+				replace_str = R"(\r)";
+				break;
+			case '\t':
+				replace_str = R"(\t)";
+				break;
+			default:
+				continue;
+				break;
+			}
+			escape_str.replace(pos, 1, replace_str);
+			++pos;
+		}
+		return escape_str;
+	}
+
+	static std::string unescape_string(const std::string& str)
+	{
+		return unescape_string(std::string(str));
+	}
+
+	static std::string escape_string(std::string&& str)
+	{
+		std::string escape_str = std::move(str);
+
+		for (size_t pos = 0; pos + 1 < escape_str.size(); ++pos)
+		{
+			if (escape_str[pos] != '\\') {
+				continue;
+			}
+			std::string replace_str;
+			switch (escape_str[pos+1]) {
+			case '"':
+				replace_str = "\"";
+				break;
+			case '\\':
+				replace_str = "\\";
+				break;
+			case 'b':
+				replace_str = "\b";
+				break;
+			case 'f':
+				replace_str = "\f";
+				break;
+			case 'n':
+				replace_str = "\n";
+				break;
+			case 'r':
+				replace_str = "\r";
+				break;
+			case 't':
+				replace_str = "\r";
+				break;
+			default:
+				return std::string();
+				break;
+			}
+			escape_str.replace(pos, 2, replace_str);
+		}
+		return escape_str;
+	}
+
+	static std::string escape_string(const std::string& str)
+	{
+		return escape_string(std::string(str));
+	}
+}

3rdparty/include/meojson/json_exception.h

@@ -0,0 +1,27 @@
+#pragma once
+
+#include <exception>
+#include <string>
+
+namespace json
+{
+    class exception : public std::exception
+    {
+    public:
+        exception() = default;
+        exception(const std::string& msg);
+
+        exception(const exception&) = default;
+        exception& operator=(const exception&) = default;
+        exception(exception&&) = default;
+        exception& operator=(exception&&) = default;
+
+        virtual ~exception() noexcept override = default;
+
+        virtual const char* what() const noexcept override;
+
+    private:
+        std::string m_msg;
+    };
+
+} // namespace json

3rdparty/include/meojson/json_object.h

@@ -21,7 +21,16 @@ namespace json
 		object(const raw_object& raw_obj);
 		object(raw_object&& raw_obj);
 		object(std::initializer_list<raw_object::value_type> init_list);
-
+		template<typename MapType>
+		object(MapType map) {
+			static_assert(
+				std::is_constructible<raw_object::value_type, typename MapType::value_type>::value,
+				"Parameter can't be used to construct a json::object::raw_object::value_type");
+			for (auto&& ele : map) {
+				_object_data.emplace(std::move(ele));
+			}
+		}
+		
 		~object() = default;
 
 		bool empty() const noexcept { return _object_data.empty(); }
@@ -57,6 +66,8 @@ namespace json
 
 		iterator begin() noexcept;
 		iterator end() noexcept;
+		const_iterator begin() const noexcept;
+		const_iterator end() const noexcept;
 		const_iterator cbegin() const noexcept;
 		const_iterator cend() const noexcept;
 

3rdparty/include/meojson/json_parser.h

@@ -15,12 +15,12 @@ namespace json
     public:
         ~parser() noexcept = default;
 
-        static std::optional<value> parse(const std::string &content);
+        static std::optional<value> parse(const std::string& content);
 
     private:
         parser(
-            const std::string::const_iterator &cbegin,
-            const std::string::const_iterator &cend) noexcept
+            const std::string::const_iterator& cbegin,
+            const std::string::const_iterator& cend) noexcept
             : _cur(cbegin), _end(cend) {}
 
         std::optional<value> parse();
@@ -44,4 +44,7 @@ namespace json
         std::string::const_iterator _cur;
         std::string::const_iterator _end;
     };
+
+    std::optional<value> parse(const std::string& content);
+
 } // namespace json

3rdparty/include/meojson/json_value.h

@@ -29,8 +29,8 @@ namespace json
 
     public:
         value();
-        value(const value &rhs);
-        value(value &&rhs) noexcept;
+        value(const value& rhs);
+        value(value&& rhs) noexcept;
 
         value(bool b);
 
@@ -44,24 +44,24 @@ namespace json
         value(double num);
         value(long double num);
 
-        value(const char *str);
-        value(const std::string &str);
-        value(std::string &&str);
+        value(const char* str);
+        value(const std::string& str);
+        value(std::string&& str);
 
-        value(const array &arr);
-        value(array &&arr);
+        value(const array& arr);
+        value(array&& arr);
         // value(std::initializer_list<value> init_list); // for array
 
-        value(const object &obj);
-        value(object &&obj);
+        value(const object& obj);
+        value(object&& obj);
         // error: conversion from ‘<brace-enclosed initializer list>’ to ‘json::value’ is ambiguous
         // value(std::initializer_list<std::pair<std::string, value>> init_list); // for object
 
         // Constructed from raw data
         template <typename... Args>
-        value(value_type type, Args &&... args)
+        value(value_type type, Args &&...args)
             : _type(type),
-              _raw_data(std::forward<Args>(args)...)
+            _raw_data(std::forward<Args>(args)...)
         {
             static_assert(
                 std::is_constructible<std::string, Args...>::value,
@@ -85,15 +85,17 @@ namespace json
         bool exist(const std::string& key) const;
         bool exist(size_t pos) const;
         value_type type() const noexcept { return _type; }
-        const value &at(size_t pos) const;
-        const value &at(const std::string &key) const;
+        const value& at(size_t pos) const;
+        const value& at(const std::string& key) const;
 
-        template<typename Type>
-        decltype(auto) get(const std::string& key, Type default_value) {
+        template <typename Type>
+        decltype(auto) get(const std::string& key, Type default_value) const
+        {
             return is_object() ? as_object().get(key, default_value) : default_value;
         }
-        template<typename Type>
-        decltype(auto) get(size_t pos, Type default_value) {
+        template <typename Type>
+        decltype(auto) get(size_t pos, Type default_value) const
+        {
             return is_array() ? as_array().get(pos, default_value) : default_value;
         }
 
@@ -108,8 +110,8 @@ namespace json
         const double as_double() const;
         const long double as_long_double() const;
         const std::string as_string() const;
-        const array & as_array() const;
-        const object & as_object() const;
+        const array& as_array() const;
+        const object& as_object() const;
 
         array& as_array();
         object& as_object();
@@ -118,18 +120,29 @@ namespace json
         const std::string to_string() const;
         const std::string format(std::string shift_str = "    ", size_t basic_shift_count = 0) const;
 
-        value &operator=(const value &rhs);
-        value &operator=(value &&) noexcept;
+        value& operator=(const value& rhs);
+        value& operator=(value&&) noexcept;
 
-        const value &operator[](size_t pos) const;
-        value &operator[](size_t pos);
-        value &operator[](const std::string &key);
-        value &operator[](std::string &&key);
-        explicit operator bool() const noexcept { return valid(); }
+        const value& operator[](size_t pos) const;
+        value& operator[](size_t pos);
+        value& operator[](const std::string& key);
+        value& operator[](std::string&& key);
+        //explicit operator bool() const noexcept { return valid(); }
+
+        explicit operator bool() const { return as_boolean(); }
+        explicit operator int() const { return as_integer(); }
+        explicit operator long() const { return as_long(); }
+        explicit operator unsigned long() const { return as_unsigned_long(); }
+        explicit operator long long() const { return as_long_long(); }
+        explicit operator unsigned long long() const { return as_unsigned_long_long(); }
+        explicit operator float() const { return as_float(); }
+        explicit operator double() const { return as_double(); }
+        explicit operator long double() const { return as_long_double(); }
+        explicit operator std::string() const { return as_string(); }
 
     private:
         template <typename T>
-        static std::unique_ptr<T> copy_unique_ptr(const std::unique_ptr<T> &t)
+        static std::unique_ptr<T> copy_unique_ptr(const std::unique_ptr<T>& t)
         {
             return t == nullptr ? nullptr : std::make_unique<T>(*t);
         }
@@ -141,7 +154,7 @@ namespace json
     };
 
     const value invalid_value();
-    std::ostream &operator<<(std::ostream &out, const value &val);
+    std::ostream& operator<<(std::ostream& out, const value& val);
     // std::istream &operator>>(std::istream &in, value &val);
 
 } // namespace json

3rdparty/include/opencv2/aruco.hpp

@@ -0,0 +1,604 @@
+/*
+By downloading, copying, installing or using the software you agree to this
+license. If you do not agree to this license, do not download, install,
+copy or use the software.
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are
+disclaimed. In no event shall copyright holders or contributors be liable for
+any direct, indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+*/
+
+#ifndef __OPENCV_ARUCO_HPP__
+#define __OPENCV_ARUCO_HPP__
+
+#include <opencv2/core.hpp>
+#include <vector>
+#include "opencv2/aruco/dictionary.hpp"
+
+/**
+ * @defgroup aruco ArUco Marker Detection
+ * This module is dedicated to square fiducial markers (also known as Augmented Reality Markers)
+ * These markers are useful for easy, fast and robust camera pose estimation.ç
+ *
+ * The main functionalities are:
+ * - Detection of markers in an image
+ * - Pose estimation from a single marker or from a board/set of markers
+ * - Detection of ChArUco board for high subpixel accuracy
+ * - Camera calibration from both, ArUco boards and ChArUco boards.
+ * - Detection of ChArUco diamond markers
+ * The samples directory includes easy examples of how to use the module.
+ *
+ * The implementation is based on the ArUco Library by R. Muñoz-Salinas and S. Garrido-Jurado @cite Aruco2014.
+ *
+ * Markers can also be detected based on the AprilTag 2 @cite wang2016iros fiducial detection method.
+ *
+ * @sa S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez. 2014.
+ * "Automatic generation and detection of highly reliable fiducial markers under occlusion".
+ * Pattern Recogn. 47, 6 (June 2014), 2280-2292. DOI=10.1016/j.patcog.2014.01.005
+ *
+ * @sa http://www.uco.es/investiga/grupos/ava/node/26
+ *
+ * This module has been originally developed by Sergio Garrido-Jurado as a project
+ * for Google Summer of Code 2015 (GSoC 15).
+ *
+ *
+*/
+
+namespace cv {
+namespace aruco {
+
+//! @addtogroup aruco
+//! @{
+
+enum CornerRefineMethod{
+    CORNER_REFINE_NONE,     ///< Tag and corners detection based on the ArUco approach
+    CORNER_REFINE_SUBPIX,   ///< ArUco approach and refine the corners locations using corner subpixel accuracy
+    CORNER_REFINE_CONTOUR,  ///< ArUco approach and refine the corners locations using the contour-points line fitting
+    CORNER_REFINE_APRILTAG, ///< Tag and corners detection based on the AprilTag 2 approach @cite wang2016iros
+};
+
+/**
+ * @brief Parameters for the detectMarker process:
+ * - adaptiveThreshWinSizeMin: minimum window size for adaptive thresholding before finding
+ *   contours (default 3).
+ * - adaptiveThreshWinSizeMax: maximum window size for adaptive thresholding before finding
+ *   contours (default 23).
+ * - adaptiveThreshWinSizeStep: increments from adaptiveThreshWinSizeMin to adaptiveThreshWinSizeMax
+ *   during the thresholding (default 10).
+ * - adaptiveThreshConstant: constant for adaptive thresholding before finding contours (default 7)
+ * - minMarkerPerimeterRate: determine minimum perimeter for marker contour to be detected. This
+ *   is defined as a rate respect to the maximum dimension of the input image (default 0.03).
+ * - maxMarkerPerimeterRate:  determine maximum perimeter for marker contour to be detected. This
+ *   is defined as a rate respect to the maximum dimension of the input image (default 4.0).
+ * - polygonalApproxAccuracyRate: minimum accuracy during the polygonal approximation process to
+ *   determine which contours are squares. (default 0.03)
+ * - minCornerDistanceRate: minimum distance between corners for detected markers relative to its
+ *   perimeter (default 0.05)
+ * - minDistanceToBorder: minimum distance of any corner to the image border for detected markers
+ *   (in pixels) (default 3)
+ * - minMarkerDistanceRate: minimum mean distance beetween two marker corners to be considered
+ *   similar, so that the smaller one is removed. The rate is relative to the smaller perimeter
+ *   of the two markers (default 0.05).
+ * - cornerRefinementMethod: corner refinement method. (CORNER_REFINE_NONE, no refinement.
+ *   CORNER_REFINE_SUBPIX, do subpixel refinement. CORNER_REFINE_CONTOUR use contour-Points,
+ *   CORNER_REFINE_APRILTAG  use the AprilTag2 approach). (default CORNER_REFINE_NONE)
+ * - cornerRefinementWinSize: window size for the corner refinement process (in pixels) (default 5).
+ * - cornerRefinementMaxIterations: maximum number of iterations for stop criteria of the corner
+ *   refinement process (default 30).
+ * - cornerRefinementMinAccuracy: minimum error for the stop cristeria of the corner refinement
+ *   process (default: 0.1)
+ * - markerBorderBits: number of bits of the marker border, i.e. marker border width (default 1).
+ * - perspectiveRemovePixelPerCell: number of bits (per dimension) for each cell of the marker
+ *   when removing the perspective (default 4).
+ * - perspectiveRemoveIgnoredMarginPerCell: width of the margin of pixels on each cell not
+ *   considered for the determination of the cell bit. Represents the rate respect to the total
+ *   size of the cell, i.e. perspectiveRemovePixelPerCell (default 0.13)
+ * - maxErroneousBitsInBorderRate: maximum number of accepted erroneous bits in the border (i.e.
+ *   number of allowed white bits in the border). Represented as a rate respect to the total
+ *   number of bits per marker (default 0.35).
+ * - minOtsuStdDev: minimun standard deviation in pixels values during the decodification step to
+ *   apply Otsu thresholding (otherwise, all the bits are set to 0 or 1 depending on mean higher
+ *   than 128 or not) (default 5.0)
+ * - errorCorrectionRate error correction rate respect to the maximun error correction capability
+ *   for each dictionary. (default 0.6).
+ * - aprilTagMinClusterPixels: reject quads containing too few pixels. (default 5)
+ * - aprilTagMaxNmaxima: how many corner candidates to consider when segmenting a group of pixels into a quad. (default 10)
+ * - aprilTagCriticalRad: Reject quads where pairs of edges have angles that are close to straight or close to
+ *   180 degrees. Zero means that no quads are rejected. (In radians) (default 10*PI/180)
+ * - aprilTagMaxLineFitMse:  When fitting lines to the contours, what is the maximum mean squared error
+ *   allowed?  This is useful in rejecting contours that are far from being quad shaped; rejecting
+ *   these quads "early" saves expensive decoding processing. (default 10.0)
+ * - aprilTagMinWhiteBlackDiff: When we build our model of black & white pixels, we add an extra check that
+ *   the white model must be (overall) brighter than the black model.  How much brighter? (in pixel values, [0,255]). (default 5)
+ * - aprilTagDeglitch:  should the thresholded image be deglitched? Only useful for very noisy images. (default 0)
+ * - aprilTagQuadDecimate: Detection of quads can be done on a lower-resolution image, improving speed at a
+ *   cost of pose accuracy and a slight decrease in detection rate. Decoding the binary payload is still
+ *   done at full resolution. (default 0.0)
+ * - aprilTagQuadSigma: What Gaussian blur should be applied to the segmented image (used for quad detection?)
+ *   Parameter is the standard deviation in pixels.  Very noisy images benefit from non-zero values (e.g. 0.8). (default 0.0)
+ * - detectInvertedMarker: to check if there is a white marker. In order to generate a "white" marker just
+ *   invert a normal marker by using a tilde, ~markerImage. (default false)
+ */
+struct CV_EXPORTS_W DetectorParameters {
+
+    DetectorParameters();
+
+    CV_WRAP static Ptr<DetectorParameters> create();
+
+    CV_PROP_RW int adaptiveThreshWinSizeMin;
+    CV_PROP_RW int adaptiveThreshWinSizeMax;
+    CV_PROP_RW int adaptiveThreshWinSizeStep;
+    CV_PROP_RW double adaptiveThreshConstant;
+    CV_PROP_RW double minMarkerPerimeterRate;
+    CV_PROP_RW double maxMarkerPerimeterRate;
+    CV_PROP_RW double polygonalApproxAccuracyRate;
+    CV_PROP_RW double minCornerDistanceRate;
+    CV_PROP_RW int minDistanceToBorder;
+    CV_PROP_RW double minMarkerDistanceRate;
+    CV_PROP_RW int cornerRefinementMethod;
+    CV_PROP_RW int cornerRefinementWinSize;
+    CV_PROP_RW int cornerRefinementMaxIterations;
+    CV_PROP_RW double cornerRefinementMinAccuracy;
+    CV_PROP_RW int markerBorderBits;
+    CV_PROP_RW int perspectiveRemovePixelPerCell;
+    CV_PROP_RW double perspectiveRemoveIgnoredMarginPerCell;
+    CV_PROP_RW double maxErroneousBitsInBorderRate;
+    CV_PROP_RW double minOtsuStdDev;
+    CV_PROP_RW double errorCorrectionRate;
+
+    // April :: User-configurable parameters.
+    CV_PROP_RW float aprilTagQuadDecimate;
+    CV_PROP_RW float aprilTagQuadSigma;
+
+    // April :: Internal variables
+    CV_PROP_RW int aprilTagMinClusterPixels;
+    CV_PROP_RW int aprilTagMaxNmaxima;
+    CV_PROP_RW float aprilTagCriticalRad;
+    CV_PROP_RW float aprilTagMaxLineFitMse;
+    CV_PROP_RW int aprilTagMinWhiteBlackDiff;
+    CV_PROP_RW int aprilTagDeglitch;
+
+    // to detect white (inverted) markers
+    CV_PROP_RW bool detectInvertedMarker;
+};
+
+
+
+/**
+ * @brief Basic marker detection
+ *
+ * @param image input image
+ * @param dictionary indicates the type of markers that will be searched
+ * @param corners vector of detected marker corners. For each marker, its four corners
+ * are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers,
+ * the dimensions of this array is Nx4. The order of the corners is clockwise.
+ * @param ids vector of identifiers of the detected markers. The identifier is of type int
+ * (e.g. std::vector<int>). For N detected markers, the size of ids is also N.
+ * The identifiers have the same order than the markers in the imgPoints array.
+ * @param parameters marker detection parameters
+ * @param rejectedImgPoints contains the imgPoints of those squares whose inner code has not a
+ * correct codification. Useful for debugging purposes.
+ * @param cameraMatrix optional input 3x3 floating-point camera matrix
+ * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
+ * @param distCoeff optional vector of distortion coefficients
+ * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
+ *
+ * Performs marker detection in the input image. Only markers included in the specific dictionary
+ * are searched. For each detected marker, it returns the 2D position of its corner in the image
+ * and its corresponding identifier.
+ * Note that this function does not perform pose estimation.
+ * @sa estimatePoseSingleMarkers,  estimatePoseBoard
+ *
+ */
+CV_EXPORTS_W void detectMarkers(InputArray image, const Ptr<Dictionary> &dictionary, OutputArrayOfArrays corners,
+                                OutputArray ids, const Ptr<DetectorParameters> &parameters = DetectorParameters::create(),
+                                OutputArrayOfArrays rejectedImgPoints = noArray(), InputArray cameraMatrix= noArray(), InputArray distCoeff= noArray());
+
+
+
+/**
+ * @brief Pose estimation for single markers
+ *
+ * @param corners vector of already detected markers corners. For each marker, its four corners
+ * are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers,
+ * the dimensions of this array should be Nx4. The order of the corners should be clockwise.
+ * @sa detectMarkers
+ * @param markerLength the length of the markers' side. The returning translation vectors will
+ * be in the same unit. Normally, unit is meters.
+ * @param cameraMatrix input 3x3 floating-point camera matrix
+ * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
+ * @param distCoeffs vector of distortion coefficients
+ * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
+ * @param rvecs array of output rotation vectors (@sa Rodrigues) (e.g. std::vector<cv::Vec3d>).
+ * Each element in rvecs corresponds to the specific marker in imgPoints.
+ * @param tvecs array of output translation vectors (e.g. std::vector<cv::Vec3d>).
+ * Each element in tvecs corresponds to the specific marker in imgPoints.
+ * @param _objPoints array of object points of all the marker corners
+ *
+ * This function receives the detected markers and returns their pose estimation respect to
+ * the camera individually. So for each marker, one rotation and translation vector is returned.
+ * The returned transformation is the one that transforms points from each marker coordinate system
+ * to the camera coordinate system.
+ * The marker corrdinate system is centered on the middle of the marker, with the Z axis
+ * perpendicular to the marker plane.
+ * The coordinates of the four corners of the marker in its own coordinate system are:
+ * (-markerLength/2, markerLength/2, 0), (markerLength/2, markerLength/2, 0),
+ * (markerLength/2, -markerLength/2, 0), (-markerLength/2, -markerLength/2, 0)
+ */
+CV_EXPORTS_W void estimatePoseSingleMarkers(InputArrayOfArrays corners, float markerLength,
+                                            InputArray cameraMatrix, InputArray distCoeffs,
+                                            OutputArray rvecs, OutputArray tvecs, OutputArray _objPoints = noArray());
+
+
+
+/**
+ * @brief Board of markers
+ *
+ * A board is a set of markers in the 3D space with a common coordinate system.
+ * The common form of a board of marker is a planar (2D) board, however any 3D layout can be used.
+ * A Board object is composed by:
+ * - The object points of the marker corners, i.e. their coordinates respect to the board system.
+ * - The dictionary which indicates the type of markers of the board
+ * - The identifier of all the markers in the board.
+ */
+class CV_EXPORTS_W Board {
+
+    public:
+    /**
+    * @brief Provide way to create Board by passing necessary data. Specially needed in Python.
+    *
+    * @param objPoints array of object points of all the marker corners in the board
+    * @param dictionary the dictionary of markers employed for this board
+    * @param ids vector of the identifiers of the markers in the board
+    *
+    */
+    CV_WRAP static Ptr<Board> create(InputArrayOfArrays objPoints, const Ptr<Dictionary> &dictionary, InputArray ids);
+    /// array of object points of all the marker corners in the board
+    /// each marker include its 4 corners in CCW order. For M markers, the size is Mx4.
+    CV_PROP std::vector< std::vector< Point3f > > objPoints;
+
+    /// the dictionary of markers employed for this board
+    CV_PROP Ptr<Dictionary> dictionary;
+
+    /// vector of the identifiers of the markers in the board (same size than objPoints)
+    /// The identifiers refers to the board dictionary
+    CV_PROP std::vector< int > ids;
+};
+
+
+
+/**
+ * @brief Planar board with grid arrangement of markers
+ * More common type of board. All markers are placed in the same plane in a grid arrangement.
+ * The board can be drawn using drawPlanarBoard() function (@sa drawPlanarBoard)
+ */
+class CV_EXPORTS_W GridBoard : public Board {
+
+    public:
+    /**
+     * @brief Draw a GridBoard
+     *
+     * @param outSize size of the output image in pixels.
+     * @param img output image with the board. The size of this image will be outSize
+     * and the board will be on the center, keeping the board proportions.
+     * @param marginSize minimum margins (in pixels) of the board in the output image
+     * @param borderBits width of the marker borders.
+     *
+     * This function return the image of the GridBoard, ready to be printed.
+     */
+    CV_WRAP void draw(Size outSize, OutputArray img, int marginSize = 0, int borderBits = 1);
+
+
+    /**
+     * @brief Create a GridBoard object
+     *
+     * @param markersX number of markers in X direction
+     * @param markersY number of markers in Y direction
+     * @param markerLength marker side length (normally in meters)
+     * @param markerSeparation separation between two markers (same unit as markerLength)
+     * @param dictionary dictionary of markers indicating the type of markers
+     * @param firstMarker id of first marker in dictionary to use on board.
+     * @return the output GridBoard object
+     *
+     * This functions creates a GridBoard object given the number of markers in each direction and
+     * the marker size and marker separation.
+     */
+    CV_WRAP static Ptr<GridBoard> create(int markersX, int markersY, float markerLength,
+                                         float markerSeparation, const Ptr<Dictionary> &dictionary, int firstMarker = 0);
+
+    /**
+      *
+      */
+    CV_WRAP Size getGridSize() const { return Size(_markersX, _markersY); }
+
+    /**
+      *
+      */
+    CV_WRAP float getMarkerLength() const { return _markerLength; }
+
+    /**
+      *
+      */
+    CV_WRAP float getMarkerSeparation() const { return _markerSeparation; }
+
+
+    private:
+    // number of markers in X and Y directions
+    int _markersX, _markersY;
+
+    // marker side length (normally in meters)
+    float _markerLength;
+
+    // separation between markers in the grid
+    float _markerSeparation;
+};
+
+
+
+/**
+ * @brief Pose estimation for a board of markers
+ *
+ * @param corners vector of already detected markers corners. For each marker, its four corners
+ * are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers, the
+ * dimensions of this array should be Nx4. The order of the corners should be clockwise.
+ * @param ids list of identifiers for each marker in corners
+ * @param board layout of markers in the board. The layout is composed by the marker identifiers
+ * and the positions of each marker corner in the board reference system.
+ * @param cameraMatrix input 3x3 floating-point camera matrix
+ * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
+ * @param distCoeffs vector of distortion coefficients
+ * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
+ * @param rvec Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board
+ * (see cv::Rodrigues). Used as initial guess if not empty.
+ * @param tvec Output vector (e.g. cv::Mat) corresponding to the translation vector of the board.
+ * @param useExtrinsicGuess defines whether initial guess for \b rvec and \b tvec will be used or not.
+ * Used as initial guess if not empty.
+ *
+ * This function receives the detected markers and returns the pose of a marker board composed
+ * by those markers.
+ * A Board of marker has a single world coordinate system which is defined by the board layout.
+ * The returned transformation is the one that transforms points from the board coordinate system
+ * to the camera coordinate system.
+ * Input markers that are not included in the board layout are ignored.
+ * The function returns the number of markers from the input employed for the board pose estimation.
+ * Note that returning a 0 means the pose has not been estimated.
+ */
+CV_EXPORTS_W int estimatePoseBoard(InputArrayOfArrays corners, InputArray ids, const Ptr<Board> &board,
+                                   InputArray cameraMatrix, InputArray distCoeffs, OutputArray rvec,
+                                   OutputArray tvec, bool useExtrinsicGuess = false);
+
+
+
+
+/**
+ * @brief Refind not detected markers based on the already detected and the board layout
+ *
+ * @param image input image
+ * @param board layout of markers in the board.
+ * @param detectedCorners vector of already detected marker corners.
+ * @param detectedIds vector of already detected marker identifiers.
+ * @param rejectedCorners vector of rejected candidates during the marker detection process.
+ * @param cameraMatrix optional input 3x3 floating-point camera matrix
+ * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
+ * @param distCoeffs optional vector of distortion coefficients
+ * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
+ * @param minRepDistance minimum distance between the corners of the rejected candidate and the
+ * reprojected marker in order to consider it as a correspondence.
+ * @param errorCorrectionRate rate of allowed erroneous bits respect to the error correction
+ * capability of the used dictionary. -1 ignores the error correction step.
+ * @param checkAllOrders Consider the four posible corner orders in the rejectedCorners array.
+ * If it set to false, only the provided corner order is considered (default true).
+ * @param recoveredIdxs Optional array to returns the indexes of the recovered candidates in the
+ * original rejectedCorners array.
+ * @param parameters marker detection parameters
+ *
+ * This function tries to find markers that were not detected in the basic detecMarkers function.
+ * First, based on the current detected marker and the board layout, the function interpolates
+ * the position of the missing markers. Then it tries to find correspondence between the reprojected
+ * markers and the rejected candidates based on the minRepDistance and errorCorrectionRate
+ * parameters.
+ * If camera parameters and distortion coefficients are provided, missing markers are reprojected
+ * using projectPoint function. If not, missing marker projections are interpolated using global
+ * homography, and all the marker corners in the board must have the same Z coordinate.
+ */
+CV_EXPORTS_W void refineDetectedMarkers(
+    InputArray image,const  Ptr<Board> &board, InputOutputArrayOfArrays detectedCorners,
+    InputOutputArray detectedIds, InputOutputArrayOfArrays rejectedCorners,
+    InputArray cameraMatrix = noArray(), InputArray distCoeffs = noArray(),
+    float minRepDistance = 10.f, float errorCorrectionRate = 3.f, bool checkAllOrders = true,
+    OutputArray recoveredIdxs = noArray(), const Ptr<DetectorParameters> &parameters = DetectorParameters::create());
+
+
+
+/**
+ * @brief Draw detected markers in image
+ *
+ * @param image input/output image. It must have 1 or 3 channels. The number of channels is not
+ * altered.
+ * @param corners positions of marker corners on input image.
+ * (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers, the dimensions of
+ * this array should be Nx4. The order of the corners should be clockwise.
+ * @param ids vector of identifiers for markers in markersCorners .
+ * Optional, if not provided, ids are not painted.
+ * @param borderColor color of marker borders. Rest of colors (text color and first corner color)
+ * are calculated based on this one to improve visualization.
+ *
+ * Given an array of detected marker corners and its corresponding ids, this functions draws
+ * the markers in the image. The marker borders are painted and the markers identifiers if provided.
+ * Useful for debugging purposes.
+ */
+CV_EXPORTS_W void drawDetectedMarkers(InputOutputArray image, InputArrayOfArrays corners,
+                                      InputArray ids = noArray(),
+                                      Scalar borderColor = Scalar(0, 255, 0));
+
+
+
+/**
+ * @brief Draw coordinate system axis from pose estimation
+ *
+ * @param image input/output image. It must have 1 or 3 channels. The number of channels is not
+ * altered.
+ * @param cameraMatrix input 3x3 floating-point camera matrix
+ * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
+ * @param distCoeffs vector of distortion coefficients
+ * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
+ * @param rvec rotation vector of the coordinate system that will be drawn. (@sa Rodrigues).
+ * @param tvec translation vector of the coordinate system that will be drawn.
+ * @param length length of the painted axis in the same unit than tvec (usually in meters)
+ *
+ * Given the pose estimation of a marker or board, this function draws the axis of the world
+ * coordinate system, i.e. the system centered on the marker/board. Useful for debugging purposes.
+ *
+ * @deprecated use cv::drawFrameAxes
+ */
+CV_EXPORTS_W void drawAxis(InputOutputArray image, InputArray cameraMatrix, InputArray distCoeffs,
+                           InputArray rvec, InputArray tvec, float length);
+
+
+
+/**
+ * @brief Draw a canonical marker image
+ *
+ * @param dictionary dictionary of markers indicating the type of markers
+ * @param id identifier of the marker that will be returned. It has to be a valid id
+ * in the specified dictionary.
+ * @param sidePixels size of the image in pixels
+ * @param img output image with the marker
+ * @param borderBits width of the marker border.
+ *
+ * This function returns a marker image in its canonical form (i.e. ready to be printed)
+ */
+CV_EXPORTS_W void drawMarker(const Ptr<Dictionary> &dictionary, int id, int sidePixels, OutputArray img,
+                             int borderBits = 1);
+
+
+
+/**
+ * @brief Draw a planar board
+ * @sa _drawPlanarBoardImpl
+ *
+ * @param board layout of the board that will be drawn. The board should be planar,
+ * z coordinate is ignored
+ * @param outSize size of the output image in pixels.
+ * @param img output image with the board. The size of this image will be outSize
+ * and the board will be on the center, keeping the board proportions.
+ * @param marginSize minimum margins (in pixels) of the board in the output image
+ * @param borderBits width of the marker borders.
+ *
+ * This function return the image of a planar board, ready to be printed. It assumes
+ * the Board layout specified is planar by ignoring the z coordinates of the object points.
+ */
+CV_EXPORTS_W void drawPlanarBoard(const Ptr<Board> &board, Size outSize, OutputArray img,
+                                  int marginSize = 0, int borderBits = 1);
+
+
+
+/**
+ * @brief Implementation of drawPlanarBoard that accepts a raw Board pointer.
+ */
+void _drawPlanarBoardImpl(Board *board, Size outSize, OutputArray img,
+                          int marginSize = 0, int borderBits = 1);
+
+
+
+/**
+ * @brief Calibrate a camera using aruco markers
+ *
+ * @param corners vector of detected marker corners in all frames.
+ * The corners should have the same format returned by detectMarkers (see #detectMarkers).
+ * @param ids list of identifiers for each marker in corners
+ * @param counter number of markers in each frame so that corners and ids can be split
+ * @param board Marker Board layout
+ * @param imageSize Size of the image used only to initialize the intrinsic camera matrix.
+ * @param cameraMatrix Output 3x3 floating-point camera matrix
+ * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS
+ * and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
+ * initialized before calling the function.
+ * @param distCoeffs Output vector of distortion coefficients
+ * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
+ * @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each board view
+ * (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding
+ * k-th translation vector (see the next output parameter description) brings the board pattern
+ * from the model coordinate space (in which object points are specified) to the world coordinate
+ * space, that is, a real position of the board pattern in the k-th pattern view (k=0.. *M* -1).
+ * @param tvecs Output vector of translation vectors estimated for each pattern view.
+ * @param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters.
+ * Order of deviations values:
+ * \f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
+ * s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero.
+ * @param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters.
+ * Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views,
+ * \f$R_i, T_i\f$ are concatenated 1x3 vectors.
+ * @param perViewErrors Output vector of average re-projection errors estimated for each pattern view.
+ * @param flags flags Different flags  for the calibration process (see #calibrateCamera for details).
+ * @param criteria Termination criteria for the iterative optimization algorithm.
+ *
+ * This function calibrates a camera using an Aruco Board. The function receives a list of
+ * detected markers from several views of the Board. The process is similar to the chessboard
+ * calibration in calibrateCamera(). The function returns the final re-projection error.
+ */
+CV_EXPORTS_AS(calibrateCameraArucoExtended) double calibrateCameraAruco(
+    InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr<Board> &board,
+    Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
+    OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
+    OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics,
+    OutputArray perViewErrors, int flags = 0,
+    TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON));
+
+
+/** @brief It's the same function as #calibrateCameraAruco but without calibration error estimation.
+ */
+CV_EXPORTS_W double calibrateCameraAruco(
+  InputArrayOfArrays corners, InputArray ids, InputArray counter, const Ptr<Board> &board,
+  Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
+  OutputArrayOfArrays rvecs = noArray(), OutputArrayOfArrays tvecs = noArray(), int flags = 0,
+  TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON));
+
+
+/**
+ * @brief Given a board configuration and a set of detected markers, returns the corresponding
+ * image points and object points to call solvePnP
+ *
+ * @param board Marker board layout.
+ * @param detectedCorners List of detected marker corners of the board.
+ * @param detectedIds List of identifiers for each marker.
+ * @param objPoints Vector of vectors of board marker points in the board coordinate space.
+ * @param imgPoints Vector of vectors of the projections of board marker corner points.
+*/
+CV_EXPORTS_W void getBoardObjectAndImagePoints(const Ptr<Board> &board, InputArrayOfArrays detectedCorners,
+  InputArray detectedIds, OutputArray objPoints, OutputArray imgPoints);
+
+
+//! @}
+}
+}
+
+#endif

3rdparty/include/opencv2/aruco/charuco.hpp

@@ -0,0 +1,354 @@
+/*
+By downloading, copying, installing or using the software you agree to this
+license. If you do not agree to this license, do not download, install,
+copy or use the software.
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are
+disclaimed. In no event shall copyright holders or contributors be liable for
+any direct, indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+*/
+
+#ifndef __OPENCV_CHARUCO_HPP__
+#define __OPENCV_CHARUCO_HPP__
+
+#include <opencv2/core.hpp>
+#include <vector>
+#include <opencv2/aruco.hpp>
+
+
+namespace cv {
+namespace aruco {
+
+//! @addtogroup aruco
+//! @{
+
+
+/**
+ * @brief ChArUco board
+ * Specific class for ChArUco boards. A ChArUco board is a planar board where the markers are placed
+ * inside the white squares of a chessboard. The benefits of ChArUco boards is that they provide
+ * both, ArUco markers versatility and chessboard corner precision, which is important for
+ * calibration and pose estimation.
+ * This class also allows the easy creation and drawing of ChArUco boards.
+ */
+class CV_EXPORTS_W CharucoBoard : public Board {
+
+    public:
+    // vector of chessboard 3D corners precalculated
+    CV_PROP std::vector< Point3f > chessboardCorners;
+
+    // for each charuco corner, nearest marker id and nearest marker corner id of each marker
+    CV_PROP std::vector< std::vector< int > > nearestMarkerIdx;
+    CV_PROP std::vector< std::vector< int > > nearestMarkerCorners;
+
+    /**
+     * @brief Draw a ChArUco board
+     *
+     * @param outSize size of the output image in pixels.
+     * @param img output image with the board. The size of this image will be outSize
+     * and the board will be on the center, keeping the board proportions.
+     * @param marginSize minimum margins (in pixels) of the board in the output image
+     * @param borderBits width of the marker borders.
+     *
+     * This function return the image of the ChArUco board, ready to be printed.
+     */
+    CV_WRAP void draw(Size outSize, OutputArray img, int marginSize = 0, int borderBits = 1);
+
+
+    /**
+     * @brief Create a CharucoBoard object
+     *
+     * @param squaresX number of chessboard squares in X direction
+     * @param squaresY number of chessboard squares in Y direction
+     * @param squareLength chessboard square side length (normally in meters)
+     * @param markerLength marker side length (same unit than squareLength)
+     * @param dictionary dictionary of markers indicating the type of markers.
+     * The first markers in the dictionary are used to fill the white chessboard squares.
+     * @return the output CharucoBoard object
+     *
+     * This functions creates a CharucoBoard object given the number of squares in each direction
+     * and the size of the markers and chessboard squares.
+     */
+    CV_WRAP static Ptr<CharucoBoard> create(int squaresX, int squaresY, float squareLength,
+                                            float markerLength, const Ptr<Dictionary> &dictionary);
+
+    /**
+      *
+      */
+    CV_WRAP Size getChessboardSize() const { return Size(_squaresX, _squaresY); }
+
+    /**
+      *
+      */
+    CV_WRAP float getSquareLength() const { return _squareLength; }
+
+    /**
+      *
+      */
+    CV_WRAP float getMarkerLength() const { return _markerLength; }
+
+    private:
+    void _getNearestMarkerCorners();
+
+    // number of markers in X and Y directions
+    int _squaresX, _squaresY;
+
+    // size of chessboard squares side (normally in meters)
+    float _squareLength;
+
+    // marker side length (normally in meters)
+    float _markerLength;
+};
+
+
+
+
+/**
+ * @brief Interpolate position of ChArUco board corners
+ * @param markerCorners vector of already detected markers corners. For each marker, its four
+ * corners are provided, (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers, the
+ * dimensions of this array should be Nx4. The order of the corners should be clockwise.
+ * @param markerIds list of identifiers for each marker in corners
+ * @param image input image necesary for corner refinement. Note that markers are not detected and
+ * should be sent in corners and ids parameters.
+ * @param board layout of ChArUco board.
+ * @param charucoCorners interpolated chessboard corners
+ * @param charucoIds interpolated chessboard corners identifiers
+ * @param cameraMatrix optional 3x3 floating-point camera matrix
+ * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
+ * @param distCoeffs optional vector of distortion coefficients
+ * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
+ * @param minMarkers number of adjacent markers that must be detected to return a charuco corner
+ *
+ * This function receives the detected markers and returns the 2D position of the chessboard corners
+ * from a ChArUco board using the detected Aruco markers. If camera parameters are provided,
+ * the process is based in an approximated pose estimation, else it is based on local homography.
+ * Only visible corners are returned. For each corner, its corresponding identifier is
+ * also returned in charucoIds.
+ * The function returns the number of interpolated corners.
+ */
+CV_EXPORTS_W int interpolateCornersCharuco(InputArrayOfArrays markerCorners, InputArray markerIds,
+                                           InputArray image, const Ptr<CharucoBoard> &board,
+                                           OutputArray charucoCorners, OutputArray charucoIds,
+                                           InputArray cameraMatrix = noArray(),
+                                           InputArray distCoeffs = noArray(), int minMarkers = 2);
+
+
+
+
+/**
+ * @brief Pose estimation for a ChArUco board given some of their corners
+ * @param charucoCorners vector of detected charuco corners
+ * @param charucoIds list of identifiers for each corner in charucoCorners
+ * @param board layout of ChArUco board.
+ * @param cameraMatrix input 3x3 floating-point camera matrix
+ * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$
+ * @param distCoeffs vector of distortion coefficients
+ * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
+ * @param rvec Output vector (e.g. cv::Mat) corresponding to the rotation vector of the board
+ * (see cv::Rodrigues).
+ * @param tvec Output vector (e.g. cv::Mat) corresponding to the translation vector of the board.
+ * @param useExtrinsicGuess defines whether initial guess for \b rvec and \b tvec will be used or not.
+ *
+ * This function estimates a Charuco board pose from some detected corners.
+ * The function checks if the input corners are enough and valid to perform pose estimation.
+ * If pose estimation is valid, returns true, else returns false.
+ */
+CV_EXPORTS_W bool estimatePoseCharucoBoard(InputArray charucoCorners, InputArray charucoIds,
+                                           const Ptr<CharucoBoard> &board, InputArray cameraMatrix,
+                                           InputArray distCoeffs, OutputArray rvec, OutputArray tvec,
+                                           bool useExtrinsicGuess = false);
+
+
+
+
+/**
+ * @brief Draws a set of Charuco corners
+ * @param image input/output image. It must have 1 or 3 channels. The number of channels is not
+ * altered.
+ * @param charucoCorners vector of detected charuco corners
+ * @param charucoIds list of identifiers for each corner in charucoCorners
+ * @param cornerColor color of the square surrounding each corner
+ *
+ * This function draws a set of detected Charuco corners. If identifiers vector is provided, it also
+ * draws the id of each corner.
+ */
+CV_EXPORTS_W void drawDetectedCornersCharuco(InputOutputArray image, InputArray charucoCorners,
+                                             InputArray charucoIds = noArray(),
+                                             Scalar cornerColor = Scalar(255, 0, 0));
+
+
+
+/**
+ * @brief Calibrate a camera using Charuco corners
+ *
+ * @param charucoCorners vector of detected charuco corners per frame
+ * @param charucoIds list of identifiers for each corner in charucoCorners per frame
+ * @param board Marker Board layout
+ * @param imageSize input image size
+ * @param cameraMatrix Output 3x3 floating-point camera matrix
+ * \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS
+ * and/or CV_CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
+ * initialized before calling the function.
+ * @param distCoeffs Output vector of distortion coefficients
+ * \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6],[s_1, s_2, s_3, s_4]])\f$ of 4, 5, 8 or 12 elements
+ * @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each board view
+ * (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding
+ * k-th translation vector (see the next output parameter description) brings the board pattern
+ * from the model coordinate space (in which object points are specified) to the world coordinate
+ * space, that is, a real position of the board pattern in the k-th pattern view (k=0.. *M* -1).
+ * @param tvecs Output vector of translation vectors estimated for each pattern view.
+ * @param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters.
+ * Order of deviations values:
+ * \f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
+ * s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero.
+ * @param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters.
+ * Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views,
+ * \f$R_i, T_i\f$ are concatenated 1x3 vectors.
+ * @param perViewErrors Output vector of average re-projection errors estimated for each pattern view.
+ * @param flags flags Different flags  for the calibration process (see #calibrateCamera for details).
+ * @param criteria Termination criteria for the iterative optimization algorithm.
+ *
+ * This function calibrates a camera using a set of corners of a  Charuco Board. The function
+ * receives a list of detected corners and its identifiers from several views of the Board.
+ * The function returns the final re-projection error.
+ */
+CV_EXPORTS_AS(calibrateCameraCharucoExtended) double calibrateCameraCharuco(
+    InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds, const Ptr<CharucoBoard> &board,
+    Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
+    OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
+    OutputArray stdDeviationsIntrinsics, OutputArray stdDeviationsExtrinsics,
+    OutputArray perViewErrors, int flags = 0,
+    TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON));
+
+/** @brief It's the same function as #calibrateCameraCharuco but without calibration error estimation.
+*/
+CV_EXPORTS_W double calibrateCameraCharuco(
+  InputArrayOfArrays charucoCorners, InputArrayOfArrays charucoIds, const Ptr<CharucoBoard> &board,
+  Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
+  OutputArrayOfArrays rvecs = noArray(), OutputArrayOfArrays tvecs = noArray(), int flags = 0,
+  TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON));
+
+
+
+/**
+ * @brief Detect ChArUco Diamond markers
+ *
+ * @param image input image necessary for corner subpixel.
+ * @param markerCorners list of detected marker corners from detectMarkers function.
+ * @param markerIds list of marker ids in markerCorners.
+ * @param squareMarkerLengthRate rate between square and marker length:
+ * squareMarkerLengthRate = squareLength/markerLength. The real units are not necessary.
+ * @param diamondCorners output list of detected diamond corners (4 corners per diamond). The order
+ * is the same than in marker corners: top left, top right, bottom right and bottom left. Similar
+ * format than the corners returned by detectMarkers (e.g std::vector<std::vector<cv::Point2f> > ).
+ * @param diamondIds ids of the diamonds in diamondCorners. The id of each diamond is in fact of
+ * type Vec4i, so each diamond has 4 ids, which are the ids of the aruco markers composing the
+ * diamond.
+ * @param cameraMatrix Optional camera calibration matrix.
+ * @param distCoeffs Optional camera distortion coefficients.
+ *
+ * This function detects Diamond markers from the previous detected ArUco markers. The diamonds
+ * are returned in the diamondCorners and diamondIds parameters. If camera calibration parameters
+ * are provided, the diamond search is based on reprojection. If not, diamond search is based on
+ * homography. Homography is faster than reprojection but can slightly reduce the detection rate.
+ */
+CV_EXPORTS_W void detectCharucoDiamond(InputArray image, InputArrayOfArrays markerCorners,
+                                       InputArray markerIds, float squareMarkerLengthRate,
+                                       OutputArrayOfArrays diamondCorners, OutputArray diamondIds,
+                                       InputArray cameraMatrix = noArray(),
+                                       InputArray distCoeffs = noArray());
+
+
+
+/**
+ * @brief Draw a set of detected ChArUco Diamond markers
+ *
+ * @param image input/output image. It must have 1 or 3 channels. The number of channels is not
+ * altered.
+ * @param diamondCorners positions of diamond corners in the same format returned by
+ * detectCharucoDiamond(). (e.g std::vector<std::vector<cv::Point2f> > ). For N detected markers,
+ * the dimensions of this array should be Nx4. The order of the corners should be clockwise.
+ * @param diamondIds vector of identifiers for diamonds in diamondCorners, in the same format
+ * returned by detectCharucoDiamond() (e.g. std::vector<Vec4i>).
+ * Optional, if not provided, ids are not painted.
+ * @param borderColor color of marker borders. Rest of colors (text color and first corner color)
+ * are calculated based on this one.
+ *
+ * Given an array of detected diamonds, this functions draws them in the image. The marker borders
+ * are painted and the markers identifiers if provided.
+ * Useful for debugging purposes.
+ */
+CV_EXPORTS_W void drawDetectedDiamonds(InputOutputArray image, InputArrayOfArrays diamondCorners,
+                                       InputArray diamondIds = noArray(),
+                                       Scalar borderColor = Scalar(0, 0, 255));
+
+
+
+
+/**
+ * @brief Draw a ChArUco Diamond marker
+ *
+ * @param dictionary dictionary of markers indicating the type of markers.
+ * @param ids list of 4 ids for each ArUco marker in the ChArUco marker.
+ * @param squareLength size of the chessboard squares in pixels.
+ * @param markerLength size of the markers in pixels.
+ * @param img output image with the marker. The size of this image will be
+ * 3*squareLength + 2*marginSize,.
+ * @param marginSize minimum margins (in pixels) of the marker in the output image
+ * @param borderBits width of the marker borders.
+ *
+ * This function return the image of a ChArUco marker, ready to be printed.
+ */
+// TODO cannot be exported yet; conversion from/to Vec4i is not wrapped in core
+CV_EXPORTS void drawCharucoDiamond(const Ptr<Dictionary> &dictionary, Vec4i ids, int squareLength,
+                                   int markerLength, OutputArray img, int marginSize = 0,
+                                   int borderBits = 1);
+
+
+/**
+ * @brief test whether the ChArUco markers are collinear
+ *
+ * @param _board layout of ChArUco board.
+ * @param _charucoIds list of identifiers for each corner in charucoCorners per frame.
+ * @return bool value, 1 (true) if detected corners form a line, 0 (false) if they do not.
+      solvePnP, calibration functions will fail if the corners are collinear (true).
+ *
+ * The number of ids in charucoIDs should be <= the number of chessboard corners in the board.  This functions checks whether the charuco corners are on a straight line (returns true, if so), or not (false).  Axis parallel, as well as diagonal and other straight lines detected.  Degenerate cases: for number of charucoIDs <= 2, the function returns true.
+ */
+CV_EXPORTS_W bool testCharucoCornersCollinear(const Ptr<CharucoBoard> &_board,
+                                              InputArray _charucoIds);
+
+//! @}
+}
+}
+
+#endif

3rdparty/include/opencv2/aruco/dictionary.hpp

@@ -0,0 +1,212 @@
+/*
+By downloading, copying, installing or using the software you agree to this
+license. If you do not agree to this license, do not download, install,
+copy or use the software.
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are
+disclaimed. In no event shall copyright holders or contributors be liable for
+any direct, indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+*/
+
+#ifndef __OPENCV_DICTIONARY_HPP__
+#define __OPENCV_DICTIONARY_HPP__
+
+#include <opencv2/core.hpp>
+
+namespace cv {
+namespace aruco {
+
+//! @addtogroup aruco
+//! @{
+
+
+/**
+ * @brief Dictionary/Set of markers. It contains the inner codification
+ *
+ * bytesList contains the marker codewords where
+ * - bytesList.rows is the dictionary size
+ * - each marker is encoded using `nbytes = ceil(markerSize*markerSize/8.)`
+ * - each row contains all 4 rotations of the marker, so its length is `4*nbytes`
+ *
+ * `bytesList.ptr(i)[k*nbytes + j]` is then the j-th byte of i-th marker, in its k-th rotation.
+ */
+class CV_EXPORTS_W Dictionary {
+
+    public:
+    CV_PROP_RW Mat bytesList;         // marker code information
+    CV_PROP_RW int markerSize;        // number of bits per dimension
+    CV_PROP_RW int maxCorrectionBits; // maximum number of bits that can be corrected
+
+
+    /**
+      */
+    Dictionary(const Mat &_bytesList = Mat(), int _markerSize = 0, int _maxcorr = 0);
+
+
+    /**
+    Dictionary(const Dictionary &_dictionary);
+    */
+
+
+    /**
+      */
+    Dictionary(const Ptr<Dictionary> &_dictionary);
+
+
+    /**
+     * @see generateCustomDictionary
+     */
+    CV_WRAP_AS(create) static Ptr<Dictionary> create(int nMarkers, int markerSize, int randomSeed=0);
+
+
+    /**
+     * @see generateCustomDictionary
+     */
+    CV_WRAP_AS(create_from) static Ptr<Dictionary> create(int nMarkers, int markerSize,
+            const Ptr<Dictionary> &baseDictionary, int randomSeed=0);
+
+    /**
+     * @see getPredefinedDictionary
+     */
+    CV_WRAP static Ptr<Dictionary> get(int dict);
+
+    /**
+     * @brief Given a matrix of bits. Returns whether if marker is identified or not.
+     * It returns by reference the correct id (if any) and the correct rotation
+     */
+    bool identify(const Mat &onlyBits, int &idx, int &rotation, double maxCorrectionRate) const;
+
+    /**
+      * @brief Returns the distance of the input bits to the specific id. If allRotations is true,
+      * the four posible bits rotation are considered
+      */
+    int getDistanceToId(InputArray bits, int id, bool allRotations = true) const;
+
+
+    /**
+     * @brief Draw a canonical marker image
+     */
+    CV_WRAP void drawMarker(int id, int sidePixels, OutputArray _img, int borderBits = 1) const;
+
+
+    /**
+      * @brief Transform matrix of bits to list of bytes in the 4 rotations
+      */
+    CV_WRAP static Mat getByteListFromBits(const Mat &bits);
+
+
+    /**
+      * @brief Transform list of bytes to matrix of bits
+      */
+    CV_WRAP static Mat getBitsFromByteList(const Mat &byteList, int markerSize);
+};
+
+
+
+
+/**
+ * @brief Predefined markers dictionaries/sets
+ * Each dictionary indicates the number of bits and the number of markers contained
+ * - DICT_ARUCO_ORIGINAL: standard ArUco Library Markers. 1024 markers, 5x5 bits, 0 minimum
+                          distance
+ */
+enum PREDEFINED_DICTIONARY_NAME {
+    DICT_4X4_50 = 0,
+    DICT_4X4_100,
+    DICT_4X4_250,
+    DICT_4X4_1000,
+    DICT_5X5_50,
+    DICT_5X5_100,
+    DICT_5X5_250,
+    DICT_5X5_1000,
+    DICT_6X6_50,
+    DICT_6X6_100,
+    DICT_6X6_250,
+    DICT_6X6_1000,
+    DICT_7X7_50,
+    DICT_7X7_100,
+    DICT_7X7_250,
+    DICT_7X7_1000,
+    DICT_ARUCO_ORIGINAL,
+    DICT_APRILTAG_16h5,     ///< 4x4 bits, minimum hamming distance between any two codes = 5, 30 codes
+    DICT_APRILTAG_25h9,     ///< 5x5 bits, minimum hamming distance between any two codes = 9, 35 codes
+    DICT_APRILTAG_36h10,    ///< 6x6 bits, minimum hamming distance between any two codes = 10, 2320 codes
+    DICT_APRILTAG_36h11     ///< 6x6 bits, minimum hamming distance between any two codes = 11, 587 codes
+};
+
+
+/**
+  * @brief Returns one of the predefined dictionaries defined in PREDEFINED_DICTIONARY_NAME
+  */
+CV_EXPORTS Ptr<Dictionary> getPredefinedDictionary(PREDEFINED_DICTIONARY_NAME name);
+
+
+/**
+  * @brief Returns one of the predefined dictionaries referenced by DICT_*.
+  */
+CV_EXPORTS_W Ptr<Dictionary> getPredefinedDictionary(int dict);
+
+
+/**
+  * @see generateCustomDictionary
+  */
+CV_EXPORTS_AS(custom_dictionary) Ptr<Dictionary> generateCustomDictionary(
+        int nMarkers,
+        int markerSize,
+        int randomSeed=0);
+
+
+/**
+  * @brief Generates a new customizable marker dictionary
+  *
+  * @param nMarkers number of markers in the dictionary
+  * @param markerSize number of bits per dimension of each markers
+  * @param baseDictionary Include the markers in this dictionary at the beginning (optional)
+  * @param randomSeed a user supplied seed for theRNG()
+  *
+  * This function creates a new dictionary composed by nMarkers markers and each markers composed
+  * by markerSize x markerSize bits. If baseDictionary is provided, its markers are directly
+  * included and the rest are generated based on them. If the size of baseDictionary is higher
+  * than nMarkers, only the first nMarkers in baseDictionary are taken and no new marker is added.
+  */
+CV_EXPORTS_AS(custom_dictionary_from) Ptr<Dictionary> generateCustomDictionary(
+        int nMarkers,
+        int markerSize,
+        const Ptr<Dictionary> &baseDictionary,
+        int randomSeed=0);
+
+
+
+//! @}
+}
+}
+
+#endif

3rdparty/include/opencv2/bgsegm.hpp

@@ -0,0 +1,380 @@
+/*
+By downloading, copying, installing or using the software you agree to this
+license. If you do not agree to this license, do not download, install,
+copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are
+disclaimed. In no event shall copyright holders or contributors be liable for
+any direct, indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+*/
+
+#ifndef __OPENCV_BGSEGM_HPP__
+#define __OPENCV_BGSEGM_HPP__
+
+#include "opencv2/video.hpp"
+
+#ifdef __cplusplus
+
+/** @defgroup bgsegm Improved Background-Foreground Segmentation Methods
+*/
+
+namespace cv
+{
+namespace bgsegm
+{
+
+//! @addtogroup bgsegm
+//! @{
+
+/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
+
+The class implements the algorithm described in @cite KB2001 .
+ */
+class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor
+{
+public:
+    CV_WRAP virtual int getHistory() const = 0;
+    CV_WRAP virtual void setHistory(int nframes) = 0;
+
+    CV_WRAP virtual int getNMixtures() const = 0;
+    CV_WRAP virtual void setNMixtures(int nmix) = 0;
+
+    CV_WRAP virtual double getBackgroundRatio() const = 0;
+    CV_WRAP virtual void setBackgroundRatio(double backgroundRatio) = 0;
+
+    CV_WRAP virtual double getNoiseSigma() const = 0;
+    CV_WRAP virtual void setNoiseSigma(double noiseSigma) = 0;
+};
+
+/** @brief Creates mixture-of-gaussian background subtractor
+
+@param history Length of the history.
+@param nmixtures Number of Gaussian mixtures.
+@param backgroundRatio Background ratio.
+@param noiseSigma Noise strength (standard deviation of the brightness or each color channel). 0
+means some automatic value.
+ */
+CV_EXPORTS_W Ptr<BackgroundSubtractorMOG>
+    createBackgroundSubtractorMOG(int history=200, int nmixtures=5,
+                                  double backgroundRatio=0.7, double noiseSigma=0);
+
+
+/** @brief Background Subtractor module based on the algorithm given in @cite Gold2012 .
+
+ Takes a series of images and returns a sequence of mask (8UC1)
+ images of the same size, where 255 indicates Foreground and 0 represents Background.
+ This class implements an algorithm described in "Visual Tracking of Human Visitors under
+ Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
+ A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
+ */
+class CV_EXPORTS_W BackgroundSubtractorGMG : public BackgroundSubtractor
+{
+public:
+    /** @brief Returns total number of distinct colors to maintain in histogram.
+    */
+    CV_WRAP virtual int getMaxFeatures() const = 0;
+    /** @brief Sets total number of distinct colors to maintain in histogram.
+    */
+    CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
+
+    /** @brief Returns the learning rate of the algorithm.
+
+    It lies between 0.0 and 1.0. It determines how quickly features are "forgotten" from
+    histograms.
+     */
+    CV_WRAP virtual double getDefaultLearningRate() const = 0;
+    /** @brief Sets the learning rate of the algorithm.
+    */
+    CV_WRAP virtual void setDefaultLearningRate(double lr) = 0;
+
+    /** @brief Returns the number of frames used to initialize background model.
+    */
+    CV_WRAP virtual int getNumFrames() const = 0;
+    /** @brief Sets the number of frames used to initialize background model.
+    */
+    CV_WRAP virtual void setNumFrames(int nframes) = 0;
+
+    /** @brief Returns the parameter used for quantization of color-space.
+
+    It is the number of discrete levels in each channel to be used in histograms.
+     */
+    CV_WRAP virtual int getQuantizationLevels() const = 0;
+    /** @brief Sets the parameter used for quantization of color-space
+    */
+    CV_WRAP virtual void setQuantizationLevels(int nlevels) = 0;
+
+    /** @brief Returns the prior probability that each individual pixel is a background pixel.
+    */
+    CV_WRAP virtual double getBackgroundPrior() const = 0;
+    /** @brief Sets the prior probability that each individual pixel is a background pixel.
+    */
+    CV_WRAP virtual void setBackgroundPrior(double bgprior) = 0;
+
+    /** @brief Returns the kernel radius used for morphological operations
+    */
+    CV_WRAP virtual int getSmoothingRadius() const = 0;
+    /** @brief Sets the kernel radius used for morphological operations
+    */
+    CV_WRAP virtual void setSmoothingRadius(int radius) = 0;
+
+    /** @brief Returns the value of decision threshold.
+
+    Decision value is the value above which pixel is determined to be FG.
+     */
+    CV_WRAP virtual double getDecisionThreshold() const = 0;
+    /** @brief Sets the value of decision threshold.
+    */
+    CV_WRAP virtual void setDecisionThreshold(double thresh) = 0;
+
+    /** @brief Returns the status of background model update
+    */
+    CV_WRAP virtual bool getUpdateBackgroundModel() const = 0;
+    /** @brief Sets the status of background model update
+    */
+    CV_WRAP virtual void setUpdateBackgroundModel(bool update) = 0;
+
+    /** @brief Returns the minimum value taken on by pixels in image sequence. Usually 0.
+    */
+    CV_WRAP virtual double getMinVal() const = 0;
+    /** @brief Sets the minimum value taken on by pixels in image sequence.
+    */
+    CV_WRAP virtual void setMinVal(double val) = 0;
+
+    /** @brief Returns the maximum value taken on by pixels in image sequence. e.g. 1.0 or 255.
+    */
+    CV_WRAP virtual double getMaxVal() const = 0;
+    /** @brief Sets the maximum value taken on by pixels in image sequence.
+    */
+    CV_WRAP virtual void setMaxVal(double val) = 0;
+};
+
+/** @brief Creates a GMG Background Subtractor
+
+@param initializationFrames number of frames used to initialize the background models.
+@param decisionThreshold Threshold value, above which it is marked foreground, else background.
+ */
+CV_EXPORTS_W Ptr<BackgroundSubtractorGMG> createBackgroundSubtractorGMG(int initializationFrames=120,
+                                                                        double decisionThreshold=0.8);
+
+/** @brief Background subtraction based on counting.
+
+  About as fast as MOG2 on a high end system.
+  More than twice faster than MOG2 on cheap hardware (benchmarked on Raspberry Pi3).
+
+  %Algorithm by Sagi Zeevi ( https://github.com/sagi-z/BackgroundSubtractorCNT )
+*/
+class CV_EXPORTS_W BackgroundSubtractorCNT  : public BackgroundSubtractor
+{
+public:
+    // BackgroundSubtractor interface
+    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
+    CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE = 0;
+
+    /** @brief Returns number of frames with same pixel color to consider stable.
+    */
+    CV_WRAP virtual int getMinPixelStability() const = 0;
+    /** @brief Sets the number of frames with same pixel color to consider stable.
+    */
+    CV_WRAP virtual void setMinPixelStability(int value) = 0;
+
+    /** @brief Returns maximum allowed credit for a pixel in history.
+    */
+    CV_WRAP virtual int getMaxPixelStability() const = 0;
+    /** @brief Sets the maximum allowed credit for a pixel in history.
+    */
+    CV_WRAP virtual void setMaxPixelStability(int value) = 0;
+
+    /** @brief Returns if we're giving a pixel credit for being stable for a long time.
+    */
+    CV_WRAP virtual bool getUseHistory() const = 0;
+    /** @brief Sets if we're giving a pixel credit for being stable for a long time.
+    */
+    CV_WRAP virtual void setUseHistory(bool value) = 0;
+
+    /** @brief Returns if we're parallelizing the algorithm.
+    */
+    CV_WRAP virtual bool getIsParallel() const = 0;
+    /** @brief Sets if we're parallelizing the algorithm.
+    */
+    CV_WRAP virtual void setIsParallel(bool value) = 0;
+};
+
+/** @brief Creates a CNT Background Subtractor
+
+@param minPixelStability number of frames with same pixel color to consider stable
+@param useHistory determines if we're giving a pixel credit for being stable for a long time
+@param maxPixelStability maximum allowed credit for a pixel in history
+@param isParallel determines if we're parallelizing the algorithm
+ */
+
+CV_EXPORTS_W Ptr<BackgroundSubtractorCNT>
+createBackgroundSubtractorCNT(int minPixelStability = 15,
+                              bool useHistory = true,
+                              int maxPixelStability = 15*60,
+                              bool isParallel = true);
+
+enum LSBPCameraMotionCompensation {
+    LSBP_CAMERA_MOTION_COMPENSATION_NONE = 0,
+    LSBP_CAMERA_MOTION_COMPENSATION_LK
+};
+
+/** @brief Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.
+
+This algorithm demonstrates better performance on CDNET 2014 dataset compared to other algorithms in OpenCV.
+ */
+class CV_EXPORTS_W BackgroundSubtractorGSOC : public BackgroundSubtractor
+{
+public:
+    // BackgroundSubtractor interface
+    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
+
+    CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE = 0;
+};
+
+/** @brief Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at @cite LGuo2016
+ */
+class CV_EXPORTS_W BackgroundSubtractorLSBP : public BackgroundSubtractor
+{
+public:
+    // BackgroundSubtractor interface
+    CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
+
+    CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE = 0;
+};
+
+/** @brief This is for calculation of the LSBP descriptors.
+ */
+class CV_EXPORTS_W BackgroundSubtractorLSBPDesc
+{
+public:
+    static void calcLocalSVDValues(OutputArray localSVDValues, const Mat& frame);
+
+    static void computeFromLocalSVDValues(OutputArray desc, const Mat& localSVDValues, const Point2i* LSBPSamplePoints);
+
+    static void compute(OutputArray desc, const Mat& frame, const Point2i* LSBPSamplePoints);
+};
+
+/** @brief Creates an instance of BackgroundSubtractorGSOC algorithm.
+
+Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.
+
+@param mc Whether to use camera motion compensation.
+@param nSamples Number of samples to maintain at each point of the frame.
+@param replaceRate Probability of replacing the old sample - how fast the model will update itself.
+@param propagationRate Probability of propagating to neighbors.
+@param hitsThreshold How many positives the sample must get before it will be considered as a possible replacement.
+@param alpha Scale coefficient for threshold.
+@param beta Bias coefficient for threshold.
+@param blinkingSupressionDecay Blinking supression decay factor.
+@param blinkingSupressionMultiplier Blinking supression multiplier.
+@param noiseRemovalThresholdFacBG Strength of the noise removal for background points.
+@param noiseRemovalThresholdFacFG Strength of the noise removal for foreground points.
+ */
+CV_EXPORTS_W Ptr<BackgroundSubtractorGSOC> createBackgroundSubtractorGSOC(int mc = LSBP_CAMERA_MOTION_COMPENSATION_NONE, int nSamples = 20, float replaceRate = 0.003f, float propagationRate = 0.01f, int hitsThreshold = 32, float alpha = 0.01f, float beta = 0.0022f, float blinkingSupressionDecay = 0.1f, float blinkingSupressionMultiplier = 0.1f, float noiseRemovalThresholdFacBG = 0.0004f, float noiseRemovalThresholdFacFG = 0.0008f);
+
+/** @brief Creates an instance of BackgroundSubtractorLSBP algorithm.
+
+Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at @cite LGuo2016
+
+@param mc Whether to use camera motion compensation.
+@param nSamples Number of samples to maintain at each point of the frame.
+@param LSBPRadius LSBP descriptor radius.
+@param Tlower Lower bound for T-values. See @cite LGuo2016 for details.
+@param Tupper Upper bound for T-values. See @cite LGuo2016 for details.
+@param Tinc Increase step for T-values. See @cite LGuo2016 for details.
+@param Tdec Decrease step for T-values. See @cite LGuo2016 for details.
+@param Rscale Scale coefficient for threshold values.
+@param Rincdec Increase/Decrease step for threshold values.
+@param noiseRemovalThresholdFacBG Strength of the noise removal for background points.
+@param noiseRemovalThresholdFacFG Strength of the noise removal for foreground points.
+@param LSBPthreshold Threshold for LSBP binary string.
+@param minCount Minimal number of matches for sample to be considered as foreground.
+ */
+CV_EXPORTS_W Ptr<BackgroundSubtractorLSBP> createBackgroundSubtractorLSBP(int mc = LSBP_CAMERA_MOTION_COMPENSATION_NONE, int nSamples = 20, int LSBPRadius = 16, float Tlower = 2.0f, float Tupper = 32.0f, float Tinc = 1.0f, float Tdec = 0.05f, float Rscale = 10.0f, float Rincdec = 0.005f, float noiseRemovalThresholdFacBG = 0.0004f, float noiseRemovalThresholdFacFG = 0.0008f, int LSBPthreshold = 8, int minCount = 2);
+
+/** @brief Synthetic frame sequence generator for testing background subtraction algorithms.
+
+ It will generate the moving object on top of the background.
+ It will apply some distortion to the background to make the test more complex.
+ */
+class CV_EXPORTS_W SyntheticSequenceGenerator : public Algorithm
+{
+private:
+    const double amplitude;
+    const double wavelength;
+    const double wavespeed;
+    const double objspeed;
+    unsigned timeStep;
+    Point2d pos;
+    Point2d dir;
+    Mat background;
+    Mat object;
+    RNG rng;
+
+public:
+    /** @brief Creates an instance of SyntheticSequenceGenerator.
+
+    @param background Background image for object.
+    @param object Object image which will move slowly over the background.
+    @param amplitude Amplitude of wave distortion applied to background.
+    @param wavelength Length of waves in distortion applied to background.
+    @param wavespeed How fast waves will move.
+    @param objspeed How fast object will fly over background.
+     */
+    CV_WRAP SyntheticSequenceGenerator(InputArray background, InputArray object, double amplitude, double wavelength, double wavespeed, double objspeed);
+
+    /** @brief Obtain the next frame in the sequence.
+
+    @param frame Output frame.
+    @param gtMask Output ground-truth (reference) segmentation mask object/background.
+     */
+    CV_WRAP void getNextFrame(OutputArray frame, OutputArray gtMask);
+};
+
+/** @brief Creates an instance of SyntheticSequenceGenerator.
+
+@param background Background image for object.
+@param object Object image which will move slowly over the background.
+@param amplitude Amplitude of wave distortion applied to background.
+@param wavelength Length of waves in distortion applied to background.
+@param wavespeed How fast waves will move.
+@param objspeed How fast object will fly over background.
+ */
+CV_EXPORTS_W Ptr<SyntheticSequenceGenerator> createSyntheticSequenceGenerator(InputArray background, InputArray object, double amplitude = 2.0, double wavelength = 20.0, double wavespeed = 0.2, double objspeed = 6.0);
+
+//! @}
+
+}
+}
+
+#endif
+#endif

3rdparty/include/opencv2/bioinspired.hpp

@@ -0,0 +1,60 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_BIOINSPIRED_HPP__
+#define __OPENCV_BIOINSPIRED_HPP__
+
+#include "opencv2/core.hpp"
+#include "opencv2/bioinspired/retina.hpp"
+#include "opencv2/bioinspired/retinafasttonemapping.hpp"
+#include "opencv2/bioinspired/transientareassegmentationmodule.hpp"
+
+/** @defgroup bioinspired Biologically inspired vision models and derivated tools
+
+The module provides biological visual systems models (human visual system and others). It also
+provides derivated objects that take advantage of those bio-inspired models.
+
+@ref bioinspired_retina
+
+*/
+
+#endif

3rdparty/include/opencv2/bioinspired/bioinspired.hpp

@@ -0,0 +1,48 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                          License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifdef __OPENCV_BUILD
+#error this is a compatibility header which should not be used inside the OpenCV library
+#endif
+
+#include "opencv2/bioinspired.hpp"

3rdparty/include/opencv2/bioinspired/retina.hpp

@@ -0,0 +1,454 @@
+/*#******************************************************************************
+ ** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+ **
+ ** By downloading, copying, installing or using the software you agree to this license.
+ ** If you do not agree to this license, do not download, install,
+ ** copy or use the software.
+ **
+ **
+ ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab.
+ ** Use: extract still images & image sequences features, from contours details to motion spatio-temporal features, etc. for high level visual scene analysis. Also contribute to image enhancement/compression such as tone mapping.
+ **
+ ** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications)
+ **
+ **  Creation - enhancement process 2007-2015
+ **      Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France
+ **
+ ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr).
+ ** Refer to the following research paper for more information:
+ ** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
+ ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book:
+ ** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
+ **
+ ** The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author :
+ ** _take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper:
+ ** ====> B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
+ ** _take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions.
+ ** ====> more informations in the above cited Jeanny Heraults's book.
+ **
+ **                          License Agreement
+ **               For Open Source Computer Vision Library
+ **
+ ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+ ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
+ **
+ **               For Human Visual System tools (bioinspired)
+ ** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
+ **
+ ** Third party copyrights are property of their respective owners.
+ **
+ ** Redistribution and use in source and binary forms, with or without modification,
+ ** are permitted provided that the following conditions are met:
+ **
+ ** * Redistributions of source code must retain the above copyright notice,
+ **    this list of conditions and the following disclaimer.
+ **
+ ** * Redistributions in binary form must reproduce the above copyright notice,
+ **    this list of conditions and the following disclaimer in the documentation
+ **    and/or other materials provided with the distribution.
+ **
+ ** * The name of the copyright holders may not be used to endorse or promote products
+ **    derived from this software without specific prior written permission.
+ **
+ ** This software is provided by the copyright holders and contributors "as is" and
+ ** any express or implied warranties, including, but not limited to, the implied
+ ** warranties of merchantability and fitness for a particular purpose are disclaimed.
+ ** In no event shall the Intel Corporation or contributors be liable for any direct,
+ ** indirect, incidental, special, exemplary, or consequential damages
+ ** (including, but not limited to, procurement of substitute goods or services;
+ ** loss of use, data, or profits; or business interruption) however caused
+ ** and on any theory of liability, whether in contract, strict liability,
+ ** or tort (including negligence or otherwise) arising in any way out of
+ ** the use of this software, even if advised of the possibility of such damage.
+ *******************************************************************************/
+
+#ifndef __OPENCV_BIOINSPIRED_RETINA_HPP__
+#define __OPENCV_BIOINSPIRED_RETINA_HPP__
+
+/**
+@file
+@date Jul 19, 2011
+@author Alexandre Benoit
+*/
+
+#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support
+
+
+namespace cv{
+namespace bioinspired{
+
+//! @addtogroup bioinspired
+//! @{
+
+enum {
+    RETINA_COLOR_RANDOM, //!< each pixel position is either R, G or B in a random choice
+    RETINA_COLOR_DIAGONAL,//!< color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR...
+    RETINA_COLOR_BAYER//!< standard bayer sampling
+};
+
+
+/** @brief retina model parameters structure
+
+    For better clarity, check explenations on the comments of methods : setupOPLandIPLParvoChannel and setupIPLMagnoChannel
+
+    Here is the default configuration file of the retina module. It gives results such as the first
+    retina output shown on the top of this page.
+
+    @code{xml}
+    <?xml version="1.0"?>
+    <opencv_storage>
+    <OPLandIPLparvo>
+        <colorMode>1</colorMode>
+        <normaliseOutput>1</normaliseOutput>
+        <photoreceptorsLocalAdaptationSensitivity>7.5e-01</photoreceptorsLocalAdaptationSensitivity>
+        <photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
+        <photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
+        <horizontalCellsGain>0.01</horizontalCellsGain>
+        <hcellsTemporalConstant>0.5</hcellsTemporalConstant>
+        <hcellsSpatialConstant>7.</hcellsSpatialConstant>
+        <ganglionCellsSensitivity>7.5e-01</ganglionCellsSensitivity></OPLandIPLparvo>
+    <IPLmagno>
+        <normaliseOutput>1</normaliseOutput>
+        <parasolCells_beta>0.</parasolCells_beta>
+        <parasolCells_tau>0.</parasolCells_tau>
+        <parasolCells_k>7.</parasolCells_k>
+        <amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
+        <V0CompressionParameter>9.5e-01</V0CompressionParameter>
+        <localAdaptintegration_tau>0.</localAdaptintegration_tau>
+        <localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
+    </opencv_storage>
+    @endcode
+
+    Here is the 'realistic" setup used to obtain the second retina output shown on the top of this page.
+
+    @code{xml}
+    <?xml version="1.0"?>
+    <opencv_storage>
+    <OPLandIPLparvo>
+      <colorMode>1</colorMode>
+      <normaliseOutput>1</normaliseOutput>
+      <photoreceptorsLocalAdaptationSensitivity>8.9e-01</photoreceptorsLocalAdaptationSensitivity>
+      <photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
+      <photoreceptorsSpatialConstant>5.3e-01</photoreceptorsSpatialConstant>
+      <horizontalCellsGain>0.3</horizontalCellsGain>
+      <hcellsTemporalConstant>0.5</hcellsTemporalConstant>
+      <hcellsSpatialConstant>7.</hcellsSpatialConstant>
+      <ganglionCellsSensitivity>8.9e-01</ganglionCellsSensitivity></OPLandIPLparvo>
+    <IPLmagno>
+      <normaliseOutput>1</normaliseOutput>
+      <parasolCells_beta>0.</parasolCells_beta>
+      <parasolCells_tau>0.</parasolCells_tau>
+      <parasolCells_k>7.</parasolCells_k>
+      <amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
+      <V0CompressionParameter>9.5e-01</V0CompressionParameter>
+      <localAdaptintegration_tau>0.</localAdaptintegration_tau>
+      <localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
+    </opencv_storage>
+    @endcode
+      */
+    struct RetinaParameters{
+        //! Outer Plexiform Layer (OPL) and Inner Plexiform Layer Parvocellular (IplParvo) parameters
+        struct OPLandIplParvoParameters{
+               OPLandIplParvoParameters():colorMode(true),
+                                 normaliseOutput(true),
+                                 photoreceptorsLocalAdaptationSensitivity(0.75f),
+                                 photoreceptorsTemporalConstant(0.9f),
+                                 photoreceptorsSpatialConstant(0.53f),
+                                 horizontalCellsGain(0.01f),
+                                 hcellsTemporalConstant(0.5f),
+                                 hcellsSpatialConstant(7.f),
+                                 ganglionCellsSensitivity(0.75f) { } // default setup
+               bool colorMode, normaliseOutput;
+               float photoreceptorsLocalAdaptationSensitivity, photoreceptorsTemporalConstant, photoreceptorsSpatialConstant, horizontalCellsGain, hcellsTemporalConstant, hcellsSpatialConstant, ganglionCellsSensitivity;
+        };
+        //! Inner Plexiform Layer Magnocellular channel (IplMagno)
+        struct IplMagnoParameters{
+            IplMagnoParameters():
+                          normaliseOutput(true),
+                          parasolCells_beta(0.f),
+                          parasolCells_tau(0.f),
+                          parasolCells_k(7.f),
+                          amacrinCellsTemporalCutFrequency(2.0f),
+                          V0CompressionParameter(0.95f),
+                          localAdaptintegration_tau(0.f),
+                          localAdaptintegration_k(7.f) { } // default setup
+            bool normaliseOutput;
+            float parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, V0CompressionParameter, localAdaptintegration_tau, localAdaptintegration_k;
+        };
+        OPLandIplParvoParameters OPLandIplParvo;
+        IplMagnoParameters IplMagno;
+    };
+
+
+
+/** @brief class which allows the Gipsa/Listic Labs model to be used with OpenCV.
+
+This retina model allows spatio-temporal image processing (applied on still images, video sequences).
+As a summary, these are the retina model properties:
+- It applies a spectral whithening (mid-frequency details enhancement)
+- high frequency spatio-temporal noise reduction
+- low frequency luminance to be reduced (luminance range compression)
+- local logarithmic luminance compression allows details to be enhanced in low light conditions
+
+USE : this model can be used basically for spatio-temporal video effects but also for :
+     _using the getParvo method output matrix : texture analysiswith enhanced signal to noise ratio and enhanced details robust against input images luminance ranges
+     _using the getMagno method output matrix : motion analysis also with the previously cited properties
+
+for more information, reer to the following papers :
+Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
+Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
+
+The retina filter includes the research contributions of phd/research collegues from which code has been redrawn by the author :
+take a look at the retinacolor.hpp module to discover Brice Chaix de Lavarene color mosaicing/demosaicing and the reference paper:
+B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
+take a look at imagelogpolprojection.hpp to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions.
+more informations in the above cited Jeanny Heraults's book.
+ */
+class CV_EXPORTS_W Retina : public Algorithm {
+
+public:
+
+
+    /** @brief Retreive retina input buffer size
+    @return the retina input buffer size
+     */
+    CV_WRAP virtual Size getInputSize()=0;
+
+    /** @brief Retreive retina output buffer size that can be different from the input if a spatial log
+    transformation is applied
+    @return the retina output buffer size
+     */
+    CV_WRAP virtual Size getOutputSize()=0;
+
+    /** @brief Try to open an XML retina parameters file to adjust current retina instance setup
+
+    - if the xml file does not exist, then default setup is applied
+    - warning, Exceptions are thrown if read XML file is not valid
+    @param retinaParameterFile the parameters filename
+    @param applyDefaultSetupOnFailure set to true if an error must be thrown on error
+
+    You can retrieve the current parameters structure using the method Retina::getParameters and update
+    it before running method Retina::setup.
+     */
+    CV_WRAP virtual void setup(String retinaParameterFile="", const bool applyDefaultSetupOnFailure=true)=0;
+
+    /** @overload
+    @param fs the open Filestorage which contains retina parameters
+    @param applyDefaultSetupOnFailure set to true if an error must be thrown on error
+    */
+    virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0;
+
+    /** @overload
+    @param newParameters a parameters structures updated with the new target configuration.
+    */
+    virtual void setup(RetinaParameters newParameters)=0;
+
+    /**
+    @return the current parameters setup
+    */
+    virtual RetinaParameters getParameters()=0;
+
+    /** @brief Outputs a string showing the used parameters setup
+    @return a string which contains formated parameters information
+     */
+    CV_WRAP virtual const String printSetup()=0;
+
+    /** @brief Write xml/yml formated parameters information
+    @param fs the filename of the xml file that will be open and writen with formatted parameters
+    information
+     */
+    CV_WRAP virtual void write( String fs ) const=0;
+
+    /** @overload */
+    virtual void write( FileStorage& fs ) const CV_OVERRIDE = 0;
+
+    /** @brief Setup the OPL and IPL parvo channels (see biologocal model)
+
+    OPL is referred as Outer Plexiform Layer of the retina, it allows the spatio-temporal filtering
+    which withens the spectrum and reduces spatio-temporal noise while attenuating global luminance
+    (low frequency energy) IPL parvo is the OPL next processing stage, it refers to a part of the
+    Inner Plexiform layer of the retina, it allows high contours sensitivity in foveal vision. See
+    reference papers for more informations.
+    for more informations, please have a look at the paper Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
+    @param colorMode specifies if (true) color is processed of not (false) to then processing gray
+    level image
+    @param normaliseOutput specifies if (true) output is rescaled between 0 and 255 of not (false)
+    @param photoreceptorsLocalAdaptationSensitivity the photoreceptors sensitivity renage is 0-1
+    (more log compression effect when value increases)
+    @param photoreceptorsTemporalConstant the time constant of the first order low pass filter of
+    the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is
+    frames, typical value is 1 frame
+    @param photoreceptorsSpatialConstant the spatial constant of the first order low pass filter of
+    the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is
+    pixels, typical value is 1 pixel
+    @param horizontalCellsGain gain of the horizontal cells network, if 0, then the mean value of
+    the output is zero, if the parameter is near 1, then, the luminance is not filtered and is
+    still reachable at the output, typicall value is 0
+    @param HcellsTemporalConstant the time constant of the first order low pass filter of the
+    horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is
+    frames, typical value is 1 frame, as the photoreceptors
+    @param HcellsSpatialConstant the spatial constant of the first order low pass filter of the
+    horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels,
+    typical value is 5 pixel, this value is also used for local contrast computing when computing
+    the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular
+    channel model)
+    @param ganglionCellsSensitivity the compression strengh of the ganglion cells local adaptation
+    output, set a value between 0.6 and 1 for best results, a high value increases more the low
+    value sensitivity... and the output saturates faster, recommended value: 0.7
+     */
+    CV_WRAP virtual void setupOPLandIPLParvoChannel(const bool colorMode=true, const bool normaliseOutput = true, const float photoreceptorsLocalAdaptationSensitivity=0.7f, const float photoreceptorsTemporalConstant=0.5f, const float photoreceptorsSpatialConstant=0.53f, const float horizontalCellsGain=0.f, const float HcellsTemporalConstant=1.f, const float HcellsSpatialConstant=7.f, const float ganglionCellsSensitivity=0.7f)=0;
+
+    /** @brief Set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel
+
+    this channel processes signals output from OPL processing stage in peripheral vision, it allows
+    motion information enhancement. It is decorrelated from the details channel. See reference
+    papers for more details.
+
+    @param normaliseOutput specifies if (true) output is rescaled between 0 and 255 of not (false)
+    @param parasolCells_beta the low pass filter gain used for local contrast adaptation at the
+    IPL level of the retina (for ganglion cells local adaptation), typical value is 0
+    @param parasolCells_tau the low pass filter time constant used for local contrast adaptation
+    at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical
+    value is 0 (immediate response)
+    @param parasolCells_k the low pass filter spatial constant used for local contrast adaptation
+    at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical
+    value is 5
+    @param amacrinCellsTemporalCutFrequency the time constant of the first order high pass fiter of
+    the magnocellular way (motion information channel), unit is frames, typical value is 1.2
+    @param V0CompressionParameter the compression strengh of the ganglion cells local adaptation
+    output, set a value between 0.6 and 1 for best results, a high value increases more the low
+    value sensitivity... and the output saturates faster, recommended value: 0.95
+    @param localAdaptintegration_tau specifies the temporal constant of the low pas filter
+    involved in the computation of the local "motion mean" for the local adaptation computation
+    @param localAdaptintegration_k specifies the spatial constant of the low pas filter involved
+    in the computation of the local "motion mean" for the local adaptation computation
+     */
+    CV_WRAP virtual void setupIPLMagnoChannel(const bool normaliseOutput = true, const float parasolCells_beta=0.f, const float parasolCells_tau=0.f, const float parasolCells_k=7.f, const float amacrinCellsTemporalCutFrequency=1.2f, const float V0CompressionParameter=0.95f, const float localAdaptintegration_tau=0.f, const float localAdaptintegration_k=7.f)=0;
+
+    /** @brief Method which allows retina to be applied on an input image,
+
+    after run, encapsulated retina module is ready to deliver its outputs using dedicated
+    acccessors, see getParvo and getMagno methods
+    @param inputImage the input Mat image to be processed, can be gray level or BGR coded in any
+    format (from 8bit to 16bits)
+     */
+    CV_WRAP virtual void run(InputArray inputImage)=0;
+
+    /** @brief Method which processes an image in the aim to correct its luminance correct
+    backlight problems, enhance details in shadows.
+
+    This method is designed to perform High Dynamic Range image tone mapping (compress \>8bit/pixel
+    images to 8bit/pixel). This is a simplified version of the Retina Parvocellular model
+    (simplified version of the run/getParvo methods call) since it does not include the
+    spatio-temporal filter modelling the Outer Plexiform Layer of the retina that performs spectral
+    whitening and many other stuff. However, it works great for tone mapping and in a faster way.
+
+    Check the demos and experiments section to see examples and the way to perform tone mapping
+    using the original retina model and the method.
+
+    @param inputImage the input image to process (should be coded in float format : CV_32F,
+    CV_32FC1, CV_32F_C3, CV_32F_C4, the 4th channel won't be considered).
+    @param outputToneMappedImage the output 8bit/channel tone mapped image (CV_8U or CV_8UC3 format).
+     */
+    CV_WRAP virtual void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)=0;
+
+    /** @brief Accessor of the details channel of the retina (models foveal vision).
+
+    Warning, getParvoRAW methods return buffers that are not rescaled within range [0;255] while
+    the non RAW method allows a normalized matrix to be retrieved.
+
+    @param retinaOutput_parvo the output buffer (reallocated if necessary), format can be :
+    -   a Mat, this output is rescaled for standard 8bits image processing use in OpenCV
+    -   RAW methods actually return a 1D matrix (encoding is R1, R2, ... Rn, G1, G2, ..., Gn, B1,
+    B2, ...Bn), this output is the original retina filter model output, without any
+    quantification or rescaling.
+    @see getParvoRAW
+     */
+    CV_WRAP virtual void getParvo(OutputArray retinaOutput_parvo)=0;
+
+    /** @brief Accessor of the details channel of the retina (models foveal vision).
+    @see getParvo
+     */
+    CV_WRAP virtual void getParvoRAW(OutputArray retinaOutput_parvo)=0;
+
+    /** @brief Accessor of the motion channel of the retina (models peripheral vision).
+
+    Warning, getMagnoRAW methods return buffers that are not rescaled within range [0;255] while
+    the non RAW method allows a normalized matrix to be retrieved.
+    @param retinaOutput_magno the output buffer (reallocated if necessary), format can be :
+    -   a Mat, this output is rescaled for standard 8bits image processing use in OpenCV
+    -   RAW methods actually return a 1D matrix (encoding is M1, M2,... Mn), this output is the
+    original retina filter model output, without any quantification or rescaling.
+    @see getMagnoRAW
+     */
+    CV_WRAP virtual void getMagno(OutputArray retinaOutput_magno)=0;
+
+    /** @brief Accessor of the motion channel of the retina (models peripheral vision).
+    @see getMagno
+     */
+    CV_WRAP virtual void getMagnoRAW(OutputArray retinaOutput_magno)=0;
+
+    /** @overload */
+    CV_WRAP virtual const Mat getMagnoRAW() const=0;
+    /** @overload */
+    CV_WRAP virtual const Mat getParvoRAW() const=0;
+
+    /** @brief Activate color saturation as the final step of the color demultiplexing process -\> this
+    saturation is a sigmoide function applied to each channel of the demultiplexed image.
+    @param saturateColors boolean that activates color saturation (if true) or desactivate (if false)
+    @param colorSaturationValue the saturation factor : a simple factor applied on the chrominance
+    buffers
+     */
+    CV_WRAP virtual void setColorSaturation(const bool saturateColors=true, const float colorSaturationValue=4.0f)=0;
+
+    /** @brief Clears all retina buffers
+
+    (equivalent to opening the eyes after a long period of eye close ;o) whatchout the temporal
+    transition occuring just after this method call.
+     */
+    CV_WRAP virtual void clearBuffers()=0;
+
+    /** @brief Activate/desactivate the Magnocellular pathway processing (motion information extraction), by
+    default, it is activated
+    @param activate true if Magnocellular output should be activated, false if not... if activated,
+    the Magnocellular output can be retrieved using the **getMagno** methods
+     */
+    CV_WRAP virtual void activateMovingContoursProcessing(const bool activate)=0;
+
+    /** @brief Activate/desactivate the Parvocellular pathway processing (contours information extraction), by
+    default, it is activated
+    @param activate true if Parvocellular (contours information extraction) output should be
+    activated, false if not... if activated, the Parvocellular output can be retrieved using the
+    Retina::getParvo methods
+     */
+    CV_WRAP virtual void activateContoursProcessing(const bool activate)=0;
+
+    /** @overload */
+    CV_WRAP static Ptr<Retina> create(Size inputSize);
+    /** @brief Constructors from standardized interfaces : retreive a smart pointer to a Retina instance
+
+    @param inputSize the input frame size
+    @param colorMode the chosen processing mode : with or without color processing
+    @param colorSamplingMethod specifies which kind of color sampling will be used :
+    -   cv::bioinspired::RETINA_COLOR_RANDOM: each pixel position is either R, G or B in a random choice
+    -   cv::bioinspired::RETINA_COLOR_DIAGONAL: color sampling is RGBRGBRGB..., line 2 BRGBRGBRG..., line 3, GBRGBRGBR...
+    -   cv::bioinspired::RETINA_COLOR_BAYER: standard bayer sampling
+    @param useRetinaLogSampling activate retina log sampling, if true, the 2 following parameters can
+    be used
+    @param reductionFactor only usefull if param useRetinaLogSampling=true, specifies the reduction
+    factor of the output frame (as the center (fovea) is high resolution and corners can be
+    underscaled, then a reduction of the output is allowed without precision leak
+    @param samplingStrength only usefull if param useRetinaLogSampling=true, specifies the strength of
+    the log scale that is applied
+     */
+    CV_WRAP static Ptr<Retina> create(Size inputSize, const bool colorMode,
+                                           int colorSamplingMethod=RETINA_COLOR_BAYER,
+                                           const bool useRetinaLogSampling=false,
+                                           const float reductionFactor=1.0f, const float samplingStrength=10.0f);
+};
+
+//! @}
+
+}
+}
+#endif /* __OPENCV_BIOINSPIRED_RETINA_HPP__ */

3rdparty/include/opencv2/bioinspired/retinafasttonemapping.hpp

@@ -0,0 +1,138 @@
+
+/*#******************************************************************************
+ ** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+ **
+ ** By downloading, copying, installing or using the software you agree to this license.
+ ** If you do not agree to this license, do not download, install,
+ ** copy or use the software.
+ **
+ **
+ ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models. Presented models originate from Jeanny Herault's original research and have been reused and adapted by the author&collaborators for computed vision applications since his thesis with Alice Caplier at Gipsa-Lab.
+ **
+ ** Maintainers : Listic lab (code author current affiliation & applications) and Gipsa Lab (original research origins & applications)
+ **
+ **  Creation - enhancement process 2007-2013
+ **      Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France
+ **
+ ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr).
+ ** Refer to the following research paper for more information:
+ ** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
+ ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book:
+ ** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
+ **
+ **
+ **
+ **
+ **
+ ** This class is based on image processing tools of the author and already used within the Retina class (this is the same code as method retina::applyFastToneMapping, but in an independent class, it is ligth from a memory requirement point of view). It implements an adaptation of the efficient tone mapping algorithm propose by David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite:
+ ** -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816
+ **
+ **
+ **                          License Agreement
+ **               For Open Source Computer Vision Library
+ **
+ ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+ ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
+ **
+ **               For Human Visual System tools (bioinspired)
+ ** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
+ **
+ ** Third party copyrights are property of their respective owners.
+ **
+ ** Redistribution and use in source and binary forms, with or without modification,
+ ** are permitted provided that the following conditions are met:
+ **
+ ** * Redistributions of source code must retain the above copyright notice,
+ **    this list of conditions and the following disclaimer.
+ **
+ ** * Redistributions in binary form must reproduce the above copyright notice,
+ **    this list of conditions and the following disclaimer in the documentation
+ **    and/or other materials provided with the distribution.
+ **
+ ** * The name of the copyright holders may not be used to endorse or promote products
+ **    derived from this software without specific prior written permission.
+ **
+ ** This software is provided by the copyright holders and contributors "as is" and
+ ** any express or implied warranties, including, but not limited to, the implied
+ ** warranties of merchantability and fitness for a particular purpose are disclaimed.
+ ** In no event shall the Intel Corporation or contributors be liable for any direct,
+ ** indirect, incidental, special, exemplary, or consequential damages
+ ** (including, but not limited to, procurement of substitute goods or services;
+ ** loss of use, data, or profits; or business interruption) however caused
+ ** and on any theory of liability, whether in contract, strict liability,
+ ** or tort (including negligence or otherwise) arising in any way out of
+ ** the use of this software, even if advised of the possibility of such damage.
+ *******************************************************************************/
+
+#ifndef __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__
+#define __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__
+
+/**
+@file
+@date May 26, 2013
+@author Alexandre Benoit
+ */
+
+#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support
+
+namespace cv{
+namespace bioinspired{
+
+//! @addtogroup bioinspired
+//! @{
+
+/** @brief  a wrapper class which allows the tone mapping algorithm of Meylan&al(2007) to be used with OpenCV.
+
+This algorithm is already implemented in thre Retina class (retina::applyFastToneMapping) but used it does not require all the retina model to be allocated. This allows a light memory use for low memory devices (smartphones, etc.
+As a summary, these are the model properties:
+- 2 stages of local luminance adaptation with a different local neighborhood for each.
+- first stage models the retina photorecetors local luminance adaptation
+- second stage models th ganglion cells local information adaptation
+- compared to the initial publication, this class uses spatio-temporal low pass filters instead of spatial only filters.
+  this can help noise robustness and temporal stability for video sequence use cases.
+
+for more information, read to the following papers :
+Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
+regarding spatio-temporal filter and the bigger retina model :
+Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
+*/
+class CV_EXPORTS_W RetinaFastToneMapping : public Algorithm
+{
+public:
+
+    /** @brief applies a luminance correction (initially High Dynamic Range (HDR) tone mapping)
+
+    using only the 2 local adaptation stages of the retina parvocellular channel : photoreceptors
+    level and ganlion cells level. Spatio temporal filtering is applied but limited to temporal
+    smoothing and eventually high frequencies attenuation. This is a lighter method than the one
+    available using the regular retina::run method. It is then faster but it does not include
+    complete temporal filtering nor retina spectral whitening. Then, it can have a more limited
+    effect on images with a very high dynamic range. This is an adptation of the original still
+    image HDR tone mapping algorithm of David Alleyson, Sabine Susstruck and Laurence Meylan's
+    work, please cite: -> Meylan L., Alleysson D., and Susstrunk S., A Model of Retinal Local
+    Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of
+    America, A, Vol. 24, N 9, September, 1st, 2007, pp. 2807-2816
+
+    @param inputImage the input image to process RGB or gray levels
+    @param outputToneMappedImage the output tone mapped image
+    */
+    CV_WRAP virtual void applyFastToneMapping(InputArray inputImage, OutputArray outputToneMappedImage)=0;
+
+    /** @brief updates tone mapping behaviors by adjusing the local luminance computation area
+
+    @param photoreceptorsNeighborhoodRadius the first stage local adaptation area
+    @param ganglioncellsNeighborhoodRadius the second stage local adaptation area
+    @param meanLuminanceModulatorK the factor applied to modulate the meanLuminance information
+    (default is 1, see reference paper)
+     */
+    CV_WRAP virtual void setup(const float photoreceptorsNeighborhoodRadius=3.f, const float ganglioncellsNeighborhoodRadius=1.f, const float meanLuminanceModulatorK=1.f)=0;
+
+    CV_WRAP static Ptr<RetinaFastToneMapping> create(Size inputSize);
+};
+
+
+//! @}
+
+}
+}
+#endif /* __OPENCV_BIOINSPIRED_RETINAFASTTONEMAPPING_HPP__ */

3rdparty/include/opencv2/bioinspired/transientareassegmentationmodule.hpp

@@ -0,0 +1,204 @@
+/*#******************************************************************************
+ ** IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+ **
+ ** By downloading, copying, installing or using the software you agree to this license.
+ ** If you do not agree to this license, do not download, install,
+ ** copy or use the software.
+ **
+ **
+ ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models.
+ ** TransientAreasSegmentationModule Use: extract areas that present spatio-temporal changes.
+ ** => It should be used at the output of the cv::bioinspired::Retina::getMagnoRAW() output that enhances spatio-temporal changes
+ **
+ ** Maintainers : Listic lab (code author current affiliation & applications)
+ **
+ **  Creation - enhancement process 2007-2015
+ **      Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France
+ **
+ ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr).
+ ** Refer to the following research paper for more information:
+ ** Strat, S.T.; Benoit, A.; Lambert, P., "Retina enhanced bag of words descriptors for video classification," Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European , vol., no., pp.1307,1311, 1-5 Sept. 2014 (http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6952461&isnumber=6951911)
+ ** Benoit A., Caplier A., Durette B., Herault, J., "USING HUMAN VISUAL SYSTEM MODELING FOR BIO-INSPIRED LOW LEVEL IMAGE PROCESSING", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773, DOI: http://dx.doi.org/10.1016/j.cviu.2010.01.011
+ ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book:
+ ** Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
+ **
+ **
+ **                          License Agreement
+ **               For Open Source Computer Vision Library
+ **
+ ** Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+ ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
+ **
+ **               For Human Visual System tools (bioinspired)
+ ** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
+ **
+ ** Third party copyrights are property of their respective owners.
+ **
+ ** Redistribution and use in source and binary forms, with or without modification,
+ ** are permitted provided that the following conditions are met:
+ **
+ ** * Redistributions of source code must retain the above copyright notice,
+ **    this list of conditions and the following disclaimer.
+ **
+ ** * Redistributions in binary form must reproduce the above copyright notice,
+ **    this list of conditions and the following disclaimer in the documentation
+ **    and/or other materials provided with the distribution.
+ **
+ ** * The name of the copyright holders may not be used to endorse or promote products
+ **    derived from this software without specific prior written permission.
+ **
+ ** This software is provided by the copyright holders and contributors "as is" and
+ ** any express or implied warranties, including, but not limited to, the implied
+ ** warranties of merchantability and fitness for a particular purpose are disclaimed.
+ ** In no event shall the Intel Corporation or contributors be liable for any direct,
+ ** indirect, incidental, special, exemplary, or consequential damages
+ ** (including, but not limited to, procurement of substitute goods or services;
+ ** loss of use, data, or profits; or business interruption) however caused
+ ** and on any theory of liability, whether in contract, strict liability,
+ ** or tort (including negligence or otherwise) arising in any way out of
+ ** the use of this software, even if advised of the possibility of such damage.
+ *******************************************************************************/
+
+#ifndef SEGMENTATIONMODULE_HPP_
+#define SEGMENTATIONMODULE_HPP_
+
+/**
+@file
+@date 2007-2013
+@author Alexandre BENOIT, benoit.alexandre.vision@gmail.com
+*/
+
+#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support
+
+namespace cv
+{
+namespace bioinspired
+{
+//! @addtogroup bioinspired
+//! @{
+
+/** @brief parameter structure that stores the transient events detector setup parameters
+*/
+struct SegmentationParameters{ // CV_EXPORTS_W_MAP to export to python native dictionnaries
+	// default structure instance construction with default values
+	SegmentationParameters():
+	    thresholdON(100),
+	    thresholdOFF(100),
+	    localEnergy_temporalConstant(0.5),
+	    localEnergy_spatialConstant(5),
+	    neighborhoodEnergy_temporalConstant(1),
+	    neighborhoodEnergy_spatialConstant(15),
+	    contextEnergy_temporalConstant(1),
+	    contextEnergy_spatialConstant(75){};
+	// all properties list
+	float thresholdON;
+	float thresholdOFF;
+	//! the time constant of the first order low pass filter, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 0.5 frame
+	float localEnergy_temporalConstant;
+	//! the spatial constant of the first order low pass filter, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 5 pixel
+	float localEnergy_spatialConstant;
+	//! local neighborhood energy filtering parameters : the aim is to get information about the energy neighborhood to perform a center surround energy analysis
+	float neighborhoodEnergy_temporalConstant;
+	float neighborhoodEnergy_spatialConstant;
+	//! context neighborhood energy filtering parameters : the aim is to get information about the energy on a wide neighborhood area to filtered out local effects
+	float contextEnergy_temporalConstant;
+	float contextEnergy_spatialConstant;
+};
+
+/** @brief class which provides a transient/moving areas segmentation module
+
+perform a locally adapted segmentation by using the retina magno input data Based on Alexandre
+BENOIT thesis: "Le système visuel humain au secours de la vision par ordinateur"
+
+3 spatio temporal filters are used:
+- a first one which filters the noise and local variations of the input motion energy
+- a second (more powerfull low pass spatial filter) which gives the neighborhood motion energy the
+segmentation consists in the comparison of these both outputs, if the local motion energy is higher
+to the neighborhood otion energy, then the area is considered as moving and is segmented
+- a stronger third low pass filter helps decision by providing a smooth information about the
+"motion context" in a wider area
+ */
+
+class CV_EXPORTS_W TransientAreasSegmentationModule: public Algorithm
+{
+public:
+
+
+    /** @brief return the sze of the manage input and output images
+    */
+    CV_WRAP virtual Size getSize()=0;
+
+    /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup
+
+    - if the xml file does not exist, then default setup is applied
+    - warning, Exceptions are thrown if read XML file is not valid
+    @param segmentationParameterFile : the parameters filename
+    @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
+     */
+    CV_WRAP virtual void setup(String segmentationParameterFile="", const bool applyDefaultSetupOnFailure=true)=0;
+
+    /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup
+
+    - if the xml file does not exist, then default setup is applied
+    - warning, Exceptions are thrown if read XML file is not valid
+    @param fs : the open Filestorage which contains segmentation parameters
+    @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
+    */
+    virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0;
+
+    /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup
+
+    - if the xml file does not exist, then default setup is applied
+    - warning, Exceptions are thrown if read XML file is not valid
+    @param newParameters : a parameters structures updated with the new target configuration
+     */
+    virtual void setup(SegmentationParameters newParameters)=0;
+
+    /** @brief return the current parameters setup
+    */
+    virtual SegmentationParameters getParameters()=0;
+
+    /** @brief parameters setup display method
+    @return a string which contains formatted parameters information
+    */
+    CV_WRAP virtual const String printSetup()=0;
+
+    /** @brief write xml/yml formated parameters information
+    @param fs : the filename of the xml file that will be open and writen with formatted parameters information
+    */
+    CV_WRAP virtual void write( String fs ) const=0;
+
+    /** @brief write xml/yml formated parameters information
+    @param fs : a cv::Filestorage object ready to be filled
+    */
+    virtual void write( cv::FileStorage& fs ) const CV_OVERRIDE = 0;
+
+    /** @brief main processing method, get result using methods getSegmentationPicture()
+    @param inputToSegment : the image to process, it must match the instance buffer size !
+    @param channelIndex : the channel to process in case of multichannel images
+    */
+    CV_WRAP virtual void run(InputArray inputToSegment, const int channelIndex=0)=0;
+
+    /** @brief access function
+    return the last segmentation result: a boolean picture which is resampled between 0 and 255 for a display purpose
+    */
+    CV_WRAP virtual void getSegmentationPicture(OutputArray transientAreas)=0;
+
+    /** @brief cleans all the buffers of the instance
+    */
+    CV_WRAP virtual void clearAllBuffers()=0;
+
+    /** @brief allocator
+    @param inputSize : size of the images input to segment (output will be the same size)
+     */
+    CV_WRAP static Ptr<TransientAreasSegmentationModule> create(Size inputSize);
+};
+
+//! @}
+
+}} // namespaces end : cv and bioinspired
+
+
+#endif
+
+

3rdparty/include/opencv2/calib3d.hpp

@@ -1510,8 +1510,8 @@ concatenated together.
 @param imageSize Size of the image used only to initialize the camera intrinsic matrix.
 @param cameraMatrix Input/output 3x3 floating-point camera intrinsic matrix
 \f$\cameramatrix{A}\f$ . If @ref CALIB_USE_INTRINSIC_GUESS
-and/or @ref CALIB_FIX_ASPECT_RATIO, @ref CALIB_FIX_PRINCIPAL_POINT or @ref CALIB_FIX_FOCAL_LENGTH
-are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
+and/or @ref CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
+initialized before calling the function.
 @param distCoeffs Input/output vector of distortion coefficients
 \f$\distcoeffs\f$.
 @param rvecs Output vector of rotation vectors (@ref Rodrigues ) estimated for each pattern view
@@ -1537,7 +1537,7 @@ the number of pattern views. \f$R_i, T_i\f$ are concatenated 1x3 vectors.
 fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
 center ( imageSize is used), and focal distances are computed in a least-squares fashion.
 Note, that if intrinsic parameters are known, there is no need to use this function just to
-estimate extrinsic parameters. Use @ref solvePnP instead.
+estimate extrinsic parameters. Use solvePnP instead.
 -   @ref CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global
 optimization. It stays at the center or at a different location specified when
  @ref CALIB_USE_INTRINSIC_GUESS is set too.
@@ -1547,23 +1547,24 @@ ratio fx/fy stays the same as in the input cameraMatrix . When
 ignored, only their ratio is computed and used further.
 -   @ref CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \f$(p_1, p_2)\f$ are set
 to zeros and stay zero.
--   @ref CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if
- @ref CALIB_USE_INTRINSIC_GUESS is set.
 -   @ref CALIB_FIX_K1,..., @ref CALIB_FIX_K6 The corresponding radial distortion
 coefficient is not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is
 set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
 -   @ref CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the
 backward compatibility, this extra flag should be explicitly specified to make the
-calibration function use the rational model and return 8 coefficients or more.
+calibration function use the rational model and return 8 coefficients. If the flag is not
+set, the function computes and returns only 5 distortion coefficients.
 -   @ref CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the
 backward compatibility, this extra flag should be explicitly specified to make the
-calibration function use the thin prism model and return 12 coefficients or more.
+calibration function use the thin prism model and return 12 coefficients. If the flag is not
+set, the function computes and returns only 5 distortion coefficients.
 -   @ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during
 the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
 supplied distCoeffs matrix is used. Otherwise, it is set to 0.
 -   @ref CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the
 backward compatibility, this extra flag should be explicitly specified to make the
-calibration function use the tilted sensor model and return 14 coefficients.
+calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
+set, the function computes and returns only 5 distortion coefficients.
 -   @ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during
 the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
 supplied distCoeffs matrix is used. Otherwise, it is set to 0.
@@ -1588,12 +1589,12 @@ The algorithm performs the following steps:
     zeros initially unless some of CALIB_FIX_K? are specified.
 
 -   Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
-    done using @ref solvePnP .
+    done using solvePnP .
 
 -   Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error,
     that is, the total sum of squared distances between the observed feature points imagePoints and
     the projected (using the current estimates for camera parameters and the poses) object points
-    objectPoints. See @ref projectPoints for details.
+    objectPoints. See projectPoints for details.
 
 @note
     If you use a non-square (i.e. non-N-by-N) grid and @ref findChessboardCorners for calibration,
@@ -2240,7 +2241,6 @@ final fundamental matrix. It can be set to something like 1-3, depending on the
 point localization, image resolution, and the image noise.
 @param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
 for the other points. The array is computed only in the RANSAC and LMedS methods.
-@param maxIters The maximum number of robust method iterations.
 
 This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 .
 @cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation:
@@ -2251,12 +2251,6 @@ where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding
 second images, respectively. The result of this function may be passed further to
 decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
  */
-CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
-                                 InputArray cameraMatrix, int method,
-                                 double prob, double threshold,
-                                 int maxIters, OutputArray mask = noArray() );
-
-/** @overload */
 CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
                                  InputArray cameraMatrix, int method = RANSAC,
                                  double prob = 0.999, double threshold = 1.0,
@@ -2280,7 +2274,6 @@ point localization, image resolution, and the image noise.
 confidence (probability) that the estimated matrix is correct.
 @param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
 for the other points. The array is computed only in the RANSAC and LMedS methods.
-@param maxIters The maximum number of robust method iterations.
 
 This function differs from the one above that it computes camera intrinsic matrix from focal length and
 principal point:
@@ -2292,13 +2285,6 @@ f & 0 & x_{pp}  \\
 0 & 0 & 1
 \end{bmatrix}\f]
  */
-CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
-                                 double focal, Point2d pp,
-                                 int method, double prob,
-                                 double threshold, int maxIters,
-                                 OutputArray mask = noArray() );
-
-/** @overload */
 CV_EXPORTS_W Mat findEssentialMat( InputArray points1, InputArray points2,
                                  double focal = 1.0, Point2d pp = Point2d(0, 0),
                                  int method = RANSAC, double prob = 0.999,

3rdparty/include/opencv2/ccalib.hpp

@@ -0,0 +1,157 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+ //
+ //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+ //
+ //  By downloading, copying, installing or using the software you agree to this license.
+ //  If you do not agree to this license, do not download, install,
+ //  copy or use the software.
+ //
+ //
+ //                           License Agreement
+ //                For Open Source Computer Vision Library
+ //
+ // Copyright (C) 2014, OpenCV Foundation, all rights reserved.
+ // Third party copyrights are property of their respective owners.
+ //
+ // Redistribution and use in source and binary forms, with or without modification,
+ // are permitted provided that the following conditions are met:
+ //
+ //   * Redistribution's of source code must retain the above copyright notice,
+ //     this list of conditions and the following disclaimer.
+ //
+ //   * Redistribution's in binary form must reproduce the above copyright notice,
+ //     this list of conditions and the following disclaimer in the documentation
+ //     and/or other materials provided with the distribution.
+ //
+ //   * The name of the copyright holders may not be used to endorse or promote products
+ //     derived from this software without specific prior written permission.
+ //
+ // This software is provided by the copyright holders and contributors "as is" and
+ // any express or implied warranties, including, but not limited to, the implied
+ // warranties of merchantability and fitness for a particular purpose are disclaimed.
+ // In no event shall the Intel Corporation or contributors be liable for any direct,
+ // indirect, incidental, special, exemplary, or consequential damages
+ // (including, but not limited to, procurement of substitute goods or services;
+ // loss of use, data, or profits; or business interruption) however caused
+ // and on any theory of liability, whether in contract, strict liability,
+ // or tort (including negligence or otherwise) arising in any way out of
+ // the use of this software, even if advised of the possibility of such damage.
+ //
+ //M*/
+
+#ifndef __OPENCV_CCALIB_HPP__
+#define __OPENCV_CCALIB_HPP__
+
+#include <opencv2/core.hpp>
+#include <opencv2/features2d.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/calib3d.hpp>
+
+#include <vector>
+
+/** @defgroup ccalib Custom Calibration Pattern for 3D reconstruction
+*/
+
+namespace cv{ namespace ccalib{
+
+//! @addtogroup ccalib
+//! @{
+
+class CV_EXPORTS CustomPattern : public Algorithm
+{
+public:
+	CustomPattern();
+	virtual ~CustomPattern();
+
+	bool create(InputArray pattern, const Size2f boardSize, OutputArray output = noArray());
+
+	bool findPattern(InputArray image, OutputArray matched_features, OutputArray pattern_points, const double ratio = 0.7,
+					 const double proj_error = 8.0, const bool refine_position = false, OutputArray out = noArray(),
+					 OutputArray H = noArray(), OutputArray pattern_corners = noArray());
+
+	bool isInitialized();
+
+	void getPatternPoints(std::vector<KeyPoint>& original_points);
+    /**<
+		Returns a vector<Point> of the original points.
+	*/
+	double getPixelSize();
+    /**<
+		Get the pixel size of the pattern
+	*/
+
+	bool setFeatureDetector(Ptr<FeatureDetector> featureDetector);
+	bool setDescriptorExtractor(Ptr<DescriptorExtractor> extractor);
+	bool setDescriptorMatcher(Ptr<DescriptorMatcher> matcher);
+
+	Ptr<FeatureDetector> getFeatureDetector();
+	Ptr<DescriptorExtractor> getDescriptorExtractor();
+	Ptr<DescriptorMatcher> getDescriptorMatcher();
+
+	double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints,
+				Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
+				OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs, int flags = 0,
+				TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON));
+    /**<
+		Calls the calirateCamera function with the same inputs.
+	*/
+
+	bool findRt(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs,
+                OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE);
+	bool findRt(InputArray image, InputArray cameraMatrix, InputArray distCoeffs,
+                OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int flags = SOLVEPNP_ITERATIVE);
+    /**<
+		Uses solvePnP to find the rotation and translation of the pattern
+		with respect to the camera frame.
+	*/
+
+	bool findRtRANSAC(InputArray objectPoints, InputArray imagePoints, InputArray cameraMatrix, InputArray distCoeffs,
+				OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100,
+				float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE);
+	bool findRtRANSAC(InputArray image, InputArray cameraMatrix, InputArray distCoeffs,
+				OutputArray rvec, OutputArray tvec, bool useExtrinsicGuess = false, int iterationsCount = 100,
+				float reprojectionError = 8.0, int minInliersCount = 100, OutputArray inliers = noArray(), int flags = SOLVEPNP_ITERATIVE);
+        /**<
+		Uses solvePnPRansac()
+	*/
+
+	void drawOrientation(InputOutputArray image, InputArray tvec, InputArray rvec, InputArray cameraMatrix,
+						 InputArray distCoeffs, double axis_length = 3, int axis_width = 2);
+    /**<
+		pattern_corners -> projected over the image position of the edges of the pattern.
+	*/
+
+private:
+
+	Mat img_roi;
+	std::vector<Point2f> obj_corners;
+	double pxSize;
+
+	bool initialized;
+
+	Ptr<FeatureDetector> detector;
+	Ptr<DescriptorExtractor> descriptorExtractor;
+	Ptr<DescriptorMatcher> descriptorMatcher;
+
+	std::vector<KeyPoint> keypoints;
+	std::vector<Point3f> points3d;
+	Mat descriptor;
+
+	bool init(Mat& image, const float pixel_size, OutputArray output = noArray());
+	bool findPatternPass(const Mat& image, std::vector<Point2f>& matched_features, std::vector<Point3f>& pattern_points,
+						 Mat& H, std::vector<Point2f>& scene_corners, const double pratio, const double proj_error,
+						 const bool refine_position = false, const Mat& mask = Mat(), OutputArray output = noArray());
+	void scaleFoundPoints(const double squareSize, const std::vector<KeyPoint>& corners, std::vector<Point3f>& pts3d);
+	void check_matches(std::vector<Point2f>& matched, const std::vector<Point2f>& pattern, std::vector<DMatch>& good, std::vector<Point3f>& pattern_3d, const Mat& H);
+
+	void keypoints2points(const std::vector<KeyPoint>& in, std::vector<Point2f>& out);
+	void updateKeypointsPos(std::vector<KeyPoint>& in, const std::vector<Point2f>& new_pos);
+	void refinePointsPos(const Mat& img, std::vector<Point2f>& p);
+	void refineKeypointsPos(const Mat& img, std::vector<KeyPoint>& kp);
+};
+
+//! @}
+
+}} // namespace ccalib, cv
+
+#endif

3rdparty/include/opencv2/ccalib/multicalib.hpp

@@ -0,0 +1,212 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2015, Baisheng Lai (laibaisheng@gmail.com), Zhejiang University,
+// all rights reserved.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_MULTICAMERACALIBRATION_HPP__
+#define __OPENCV_MULTICAMERACALIBRATION_HPP__
+
+#include "opencv2/ccalib/randpattern.hpp"
+#include "opencv2/ccalib/omnidir.hpp"
+#include <string>
+#include <iostream>
+
+namespace cv { namespace multicalib {
+
+//! @addtogroup ccalib
+//! @{
+
+#define HEAD -1
+#define INVALID -2
+
+/** @brief Class for multiple camera calibration that supports pinhole camera and omnidirection camera.
+For omnidirectional camera model, please refer to omnidir.hpp in ccalib module.
+It first calibrate each camera individually, then a bundle adjustment like optimization is applied to
+refine extrinsic parameters. So far, it only support "random" pattern for calibration,
+see randomPattern.hpp in ccalib module for details.
+Images that are used should be named by "cameraIdx-timestamp.*", several images with the same timestamp
+means that they are the same pattern that are photographed. cameraIdx should start from 0.
+
+For more details, please refer to paper
+    B. Li, L. Heng, K. Kevin  and M. Pollefeys, "A Multiple-Camera System
+    Calibration Toolbox Using A Feature Descriptor-Based Calibration
+    Pattern", in IROS 2013.
+*/
+
+class CV_EXPORTS MultiCameraCalibration
+{
+public:
+    enum {
+        PINHOLE,
+        OMNIDIRECTIONAL
+        //FISHEYE
+    };
+
+    // an edge connects a camera and pattern
+    struct edge
+    {
+        int cameraVertex;   // vertex index for camera in this edge
+        int photoVertex;    // vertex index for pattern in this edge
+        int photoIndex;     // photo index among photos for this camera
+        Mat transform;      // transform from pattern to camera
+
+        edge(int cv, int pv, int pi, Mat trans)
+        {
+            cameraVertex = cv;
+            photoVertex = pv;
+            photoIndex = pi;
+            transform = trans;
+        }
+    };
+
+    struct vertex
+    {
+        Mat pose;   // relative pose to the first camera. For camera vertex, it is the
+                    // transform from the first camera to this camera, for pattern vertex,
+                    // it is the transform from pattern to the first camera
+        int timestamp;  // timestamp of photo, only available for photo vertex
+
+        vertex(Mat po, int ts)
+        {
+            pose = po;
+            timestamp = ts;
+        }
+
+        vertex()
+        {
+            pose = Mat::eye(4, 4, CV_32F);
+            timestamp = -1;
+        }
+    };
+    /* @brief Constructor
+    @param cameraType camera type, PINHOLE or OMNIDIRECTIONAL
+    @param nCameras number of cameras
+    @fileName filename of string list that are used for calibration, the file is generated
+    by imagelist_creator from OpenCv samples. The first one in the list is the pattern filename.
+    @patternWidth the physical width of pattern, in user defined unit.
+    @patternHeight the physical height of pattern, in user defined unit.
+    @showExtration whether show extracted features and feature filtering.
+    @nMiniMatches minimal number of matched features for a frame.
+	@flags Calibration flags
+    @criteria optimization stopping criteria.
+    @detector feature detector that detect feature points in pattern and images.
+    @descriptor feature descriptor.
+    @matcher feature matcher.
+    */
+    MultiCameraCalibration(int cameraType, int nCameras, const std::string& fileName, float patternWidth,
+        float patternHeight, int verbose = 0, int showExtration = 0, int nMiniMatches = 20, int flags = 0,
+        TermCriteria criteria = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 200, 1e-7),
+        Ptr<FeatureDetector> detector = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.006f),
+        Ptr<DescriptorExtractor> descriptor = AKAZE::create(AKAZE::DESCRIPTOR_MLDB,0, 3, 0.006f),
+        Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-L1"));
+
+    /* @brief load images
+    */
+    void loadImages();
+
+    /* @brief initialize multiple camera calibration. It calibrates each camera individually.
+    */
+    void initialize();
+
+    /* @brief optimization extrinsic parameters
+    */
+    double optimizeExtrinsics();
+
+    /* @brief run multi-camera camera calibration, it runs loadImage(), initialize() and optimizeExtrinsics()
+    */
+    double run();
+
+    /* @brief write camera parameters to file.
+    */
+    void writeParameters(const std::string& filename);
+
+private:
+    std::vector<std::string> readStringList();
+
+    int getPhotoVertex(int timestamp);
+
+    void graphTraverse(const Mat& G, int begin, std::vector<int>& order, std::vector<int>& pre);
+
+    void findRowNonZero(const Mat& row, Mat& idx);
+
+    void computeJacobianExtrinsic(const Mat& extrinsicParams, Mat& JTJ_inv, Mat& JTE);
+
+    void computePhotoCameraJacobian(const Mat& rvecPhoto, const Mat& tvecPhoto, const Mat& rvecCamera,
+        const Mat& tvecCamera, Mat& rvecTran, Mat& tvecTran, const Mat& objectPoints, const Mat& imagePoints, const Mat& K,
+        const Mat& distort, const Mat& xi, Mat& jacobianPhoto, Mat& jacobianCamera, Mat& E);
+
+    void compose_motion(InputArray _om1, InputArray _T1, InputArray _om2, InputArray _T2, Mat& om3, Mat& T3, Mat& dom3dom1,
+        Mat& dom3dT1, Mat& dom3dom2, Mat& dom3dT2, Mat& dT3dom1, Mat& dT3dT1, Mat& dT3dom2, Mat& dT3dT2);
+
+    void JRodriguesMatlab(const Mat& src, Mat& dst);
+    void dAB(InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB);
+
+    double computeProjectError(Mat& parameters);
+
+    void vector2parameters(const Mat& parameters, std::vector<Vec3f>& rvecVertex, std::vector<Vec3f>& tvecVertexs);
+    void parameters2vector(const std::vector<Vec3f>& rvecVertex, const std::vector<Vec3f>& tvecVertex, Mat& parameters);
+
+    int _camType; //PINHOLE, FISHEYE or OMNIDIRECTIONAL
+    int _nCamera;
+    int _nMiniMatches;
+    int _flags;
+	int _verbose;
+    double _error;
+    float _patternWidth, _patternHeight;
+    TermCriteria _criteria;
+    std::string _filename;
+    int _showExtraction;
+    Ptr<FeatureDetector> _detector;
+    Ptr<DescriptorExtractor> _descriptor;
+    Ptr<DescriptorMatcher> _matcher;
+
+    std::vector<edge> _edgeList;
+    std::vector<vertex> _vertexList;
+    std::vector<std::vector<cv::Mat> > _objectPointsForEachCamera;
+    std::vector<std::vector<cv::Mat> > _imagePointsForEachCamera;
+    std::vector<cv::Mat> _cameraMatrix;
+    std::vector<cv::Mat> _distortCoeffs;
+    std::vector<cv::Mat> _xi;
+    std::vector<std::vector<Mat> > _omEachCamera, _tEachCamera;
+};
+
+//! @}
+
+}} // namespace multicalib, cv
+#endif

3rdparty/include/opencv2/ccalib/omnidir.hpp

@@ -0,0 +1,315 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2015, Baisheng Lai (laibaisheng@gmail.com), Zhejiang University,
+// all rights reserved.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_OMNIDIR_HPP__
+#define __OPENCV_OMNIDIR_HPP__
+
+#include "opencv2/core.hpp"
+#include "opencv2/core/affine.hpp"
+#include <vector>
+
+namespace cv
+{
+namespace omnidir
+{
+    //! @addtogroup ccalib
+    //! @{
+
+    enum {
+        CALIB_USE_GUESS             = 1,
+        CALIB_FIX_SKEW              = 2,
+        CALIB_FIX_K1                = 4,
+        CALIB_FIX_K2                = 8,
+        CALIB_FIX_P1                = 16,
+        CALIB_FIX_P2                = 32,
+        CALIB_FIX_XI                = 64,
+        CALIB_FIX_GAMMA             = 128,
+        CALIB_FIX_CENTER            = 256
+    };
+
+    enum{
+        RECTIFY_PERSPECTIVE         = 1,
+        RECTIFY_CYLINDRICAL         = 2,
+        RECTIFY_LONGLATI            = 3,
+        RECTIFY_STEREOGRAPHIC       = 4
+    };
+
+    enum{
+        XYZRGB  = 1,
+        XYZ     = 2
+    };
+/**
+ * This module was accepted as a GSoC 2015 project for OpenCV, authored by
+ * Baisheng Lai, mentored by Bo Li.
+ */
+
+    /** @brief Projects points for omnidirectional camera using CMei's model
+
+    @param objectPoints Object points in world coordinate, vector of vector of Vec3f or Mat of
+    1xN/Nx1 3-channel of type CV_32F and N is the number of points. 64F is also acceptable.
+    @param imagePoints Output array of image points, vector of vector of Vec2f or
+    1xN/Nx1 2-channel of type CV_32F. 64F is also acceptable.
+    @param rvec vector of rotation between world coordinate and camera coordinate, i.e., om
+    @param tvec vector of translation between pattern coordinate and camera coordinate
+    @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
+    @param D Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$.
+    @param xi The parameter xi for CMei's model
+    @param jacobian Optional output 2Nx16 of type CV_64F jacobian matrix, contains the derivatives of
+    image pixel points wrt parameters including \f$om, T, f_x, f_y, s, c_x, c_y, xi, k_1, k_2, p_1, p_2\f$.
+    This matrix will be used in calibration by optimization.
+
+    The function projects object 3D points of world coordinate to image pixels, parameter by intrinsic
+    and extrinsic parameters. Also, it optionally compute a by-product: the jacobian matrix containing
+    contains the derivatives of image pixel points wrt intrinsic and extrinsic parameters.
+     */
+    CV_EXPORTS_W void projectPoints(InputArray objectPoints, OutputArray imagePoints, InputArray rvec, InputArray tvec,
+                       InputArray K, double xi, InputArray D, OutputArray jacobian = noArray());
+
+    /** @overload */
+    CV_EXPORTS void projectPoints(InputArray objectPoints, OutputArray imagePoints, const Affine3d& affine,
+                        InputArray K, double xi, InputArray D, OutputArray jacobian = noArray());
+
+    /** @brief Undistort 2D image points for omnidirectional camera using CMei's model
+
+    @param distorted Array of distorted image points, vector of Vec2f
+    or 1xN/Nx1 2-channel Mat of type CV_32F, 64F depth is also acceptable
+    @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
+    @param D Distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$.
+    @param xi The parameter xi for CMei's model
+    @param R Rotation trainsform between the original and object space : 3x3 1-channel, or vector: 3x1/1x3
+    1-channel or 1x1 3-channel
+    @param undistorted array of normalized object points, vector of Vec2f/Vec2d or 1xN/Nx1 2-channel Mat with the same
+    depth of distorted points.
+     */
+    CV_EXPORTS_W void undistortPoints(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray xi, InputArray R);
+
+    /** @brief Computes undistortion and rectification maps for omnidirectional camera image transform by a rotation R.
+    It output two maps that are used for cv::remap(). If D is empty then zero distortion is used,
+    if R or P is empty then identity matrices are used.
+
+    @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$, with depth CV_32F or CV_64F
+    @param D Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$, with depth CV_32F or CV_64F
+    @param xi The parameter xi for CMei's model
+    @param R Rotation transform between the original and object space : 3x3 1-channel, or vector: 3x1/1x3, with depth CV_32F or CV_64F
+    @param P New camera matrix (3x3) or new projection matrix (3x4)
+    @param size Undistorted image size.
+    @param m1type Type of the first output map that can be CV_32FC1 or CV_16SC2 . See convertMaps()
+    for details.
+    @param map1 The first output map.
+    @param map2 The second output map.
+    @param flags Flags indicates the rectification type,  RECTIFY_PERSPECTIVE, RECTIFY_CYLINDRICAL, RECTIFY_LONGLATI and RECTIFY_STEREOGRAPHIC
+    are supported.
+     */
+    CV_EXPORTS_W void initUndistortRectifyMap(InputArray K, InputArray D, InputArray xi, InputArray R, InputArray P, const cv::Size& size,
+        int m1type, OutputArray map1, OutputArray map2, int flags);
+
+    /** @brief Undistort omnidirectional images to perspective images
+
+    @param distorted The input omnidirectional image.
+    @param undistorted The output undistorted image.
+    @param K Camera matrix \f$K = \vecthreethree{f_x}{s}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$.
+    @param D Input vector of distortion coefficients \f$(k_1, k_2, p_1, p_2)\f$.
+    @param xi The parameter xi for CMei's model.
+    @param flags Flags indicates the rectification type,  RECTIFY_PERSPECTIVE, RECTIFY_CYLINDRICAL, RECTIFY_LONGLATI and RECTIFY_STEREOGRAPHIC
+    @param Knew Camera matrix of the distorted image. If it is not assigned, it is just K.
+    @param new_size The new image size. By default, it is the size of distorted.
+    @param R Rotation matrix between the input and output images. By default, it is identity matrix.
+    */
+    CV_EXPORTS_W void undistortImage(InputArray distorted, OutputArray undistorted, InputArray K, InputArray D, InputArray xi, int flags,
+        InputArray Knew = cv::noArray(), const Size& new_size = Size(), InputArray R = Mat::eye(3, 3, CV_64F));
+
+    /** @brief Perform omnidirectional camera calibration, the default depth of outputs is CV_64F.
+
+    @param objectPoints Vector of vector of Vec3f object points in world (pattern) coordinate.
+    It also can be vector of Mat with size 1xN/Nx1 and type CV_32FC3. Data with depth of 64_F is also acceptable.
+    @param imagePoints Vector of vector of Vec2f corresponding image points of objectPoints. It must be the same
+    size and the same type with objectPoints.
+    @param size Image size of calibration images.
+    @param K Output calibrated camera matrix.
+    @param xi Output parameter xi for CMei's model
+    @param D Output distortion parameters \f$(k_1, k_2, p_1, p_2)\f$
+    @param rvecs Output rotations for each calibration images
+    @param tvecs Output translation for each calibration images
+    @param flags The flags that control calibrate
+    @param criteria Termination criteria for optimization
+    @param idx Indices of images that pass initialization, which are really used in calibration. So the size of rvecs is the
+    same as idx.total().
+    */
+    CV_EXPORTS_W double calibrate(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size size,
+        InputOutputArray K, InputOutputArray xi, InputOutputArray D, OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
+        int flags, TermCriteria criteria, OutputArray idx=noArray());
+
+    /** @brief Stereo calibration for omnidirectional camera model. It computes the intrinsic parameters for two
+    cameras and the extrinsic parameters between two cameras. The default depth of outputs is CV_64F.
+
+    @param objectPoints Object points in world (pattern) coordinate. Its type is vector<vector<Vec3f> >.
+    It also can be vector of Mat with size 1xN/Nx1 and type CV_32FC3. Data with depth of 64_F is also acceptable.
+    @param imagePoints1 The corresponding image points of the first camera, with type vector<vector<Vec2f> >.
+    It must be the same size and the same type as objectPoints.
+    @param imagePoints2 The corresponding image points of the second camera, with type vector<vector<Vec2f> >.
+    It must be the same size and the same type as objectPoints.
+    @param imageSize1 Image size of calibration images of the first camera.
+    @param imageSize2 Image size of calibration images of the second camera.
+    @param K1 Output camera matrix for the first camera.
+    @param xi1 Output parameter xi of Mei's model for the first camera
+    @param D1 Output distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the first camera
+    @param K2 Output camera matrix for the first camera.
+    @param xi2 Output parameter xi of CMei's model for the second camera
+    @param D2 Output distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the second camera
+    @param rvec Output rotation between the first and second camera
+    @param tvec Output translation between the first and second camera
+    @param rvecsL Output rotation for each image of the first camera
+    @param tvecsL Output translation for each image of the first camera
+    @param flags The flags that control stereoCalibrate
+    @param criteria Termination criteria for optimization
+    @param idx Indices of image pairs that pass initialization, which are really used in calibration. So the size of rvecs is the
+    same as idx.total().
+    @
+    */
+    CV_EXPORTS_W double stereoCalibrate(InputOutputArrayOfArrays objectPoints, InputOutputArrayOfArrays imagePoints1, InputOutputArrayOfArrays imagePoints2,
+        const Size& imageSize1, const Size& imageSize2, InputOutputArray K1, InputOutputArray xi1, InputOutputArray D1, InputOutputArray K2, InputOutputArray xi2,
+        InputOutputArray D2, OutputArray rvec, OutputArray tvec, OutputArrayOfArrays rvecsL, OutputArrayOfArrays tvecsL, int flags, TermCriteria criteria, OutputArray idx=noArray());
+
+    /** @brief Stereo rectification for omnidirectional camera model. It computes the rectification rotations for two cameras
+
+    @param R Rotation between the first and second camera
+    @param T Translation between the first and second camera
+    @param R1 Output 3x3 rotation matrix for the first camera
+    @param R2 Output 3x3 rotation matrix for the second camera
+    */
+    CV_EXPORTS_W void stereoRectify(InputArray R, InputArray T, OutputArray R1, OutputArray R2);
+
+    /** @brief Stereo 3D reconstruction from a pair of images
+
+    @param image1 The first input image
+    @param image2 The second input image
+    @param K1 Input camera matrix of the first camera
+    @param D1 Input distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the first camera
+    @param xi1 Input parameter xi for the first camera for CMei's model
+    @param K2 Input camera matrix of the second camera
+    @param D2 Input distortion parameters \f$(k_1, k_2, p_1, p_2)\f$ for the second camera
+    @param xi2 Input parameter xi for the second camera for CMei's model
+    @param R Rotation between the first and second camera
+    @param T Translation between the first and second camera
+    @param flag Flag of rectification type, RECTIFY_PERSPECTIVE or RECTIFY_LONGLATI
+    @param numDisparities The parameter 'numDisparities' in StereoSGBM, see StereoSGBM for details.
+    @param SADWindowSize The parameter 'SADWindowSize' in StereoSGBM, see StereoSGBM for details.
+    @param disparity Disparity map generated by stereo matching
+    @param image1Rec Rectified image of the first image
+    @param image2Rec rectified image of the second image
+    @param newSize Image size of rectified image, see omnidir::undistortImage
+    @param Knew New camera matrix of rectified image, see omnidir::undistortImage
+    @param pointCloud Point cloud of 3D reconstruction, with type CV_64FC3
+    @param pointType Point cloud type, it can be XYZRGB or XYZ
+    */
+    CV_EXPORTS_W void stereoReconstruct(InputArray image1, InputArray image2, InputArray K1, InputArray D1, InputArray xi1,
+        InputArray K2, InputArray D2, InputArray xi2, InputArray R, InputArray T, int flag, int numDisparities, int SADWindowSize,
+        OutputArray disparity, OutputArray image1Rec, OutputArray image2Rec, const Size& newSize = Size(), InputArray Knew = cv::noArray(),
+        OutputArray pointCloud = cv::noArray(), int pointType = XYZRGB);
+
+namespace internal
+{
+    void initializeCalibration(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, Size size, OutputArrayOfArrays omAll,
+        OutputArrayOfArrays tAll, OutputArray K, double& xi, OutputArray idx = noArray());
+
+    void initializeStereoCalibration(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
+        const Size& size1, const Size& size2, OutputArray om, OutputArray T, OutputArrayOfArrays omL, OutputArrayOfArrays tL, OutputArray K1, OutputArray D1, OutputArray K2, OutputArray D2,
+        double &xi1, double &xi2, int flags, OutputArray idx);
+
+    void computeJacobian(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, InputArray parameters, Mat& JTJ_inv, Mat& JTE, int flags,
+							double epsilon);
+
+    void computeJacobianStereo(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2,
+        InputArray parameters, Mat& JTJ_inv, Mat& JTE, int flags, double epsilon);
+
+    void encodeParameters(InputArray K, InputArrayOfArrays omAll, InputArrayOfArrays tAll, InputArray distoaration, double xi, OutputArray parameters);
+
+    void encodeParametersStereo(InputArray K1, InputArray K2, InputArray om, InputArray T, InputArrayOfArrays omL, InputArrayOfArrays tL,
+        InputArray D1, InputArray D2, double xi1, double xi2, OutputArray parameters);
+
+    void decodeParameters(InputArray paramsters, OutputArray K, OutputArrayOfArrays omAll, OutputArrayOfArrays tAll, OutputArray distoration, double& xi);
+
+    void decodeParametersStereo(InputArray parameters, OutputArray K1, OutputArray K2, OutputArray om, OutputArray T, OutputArrayOfArrays omL,
+        OutputArrayOfArrays tL, OutputArray D1, OutputArray D2, double& xi1, double& xi2);
+
+    void estimateUncertainties(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, InputArray parameters, Mat& errors, Vec2d& std_error, double& rms, int flags);
+
+    void estimateUncertaintiesStereo(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputArray parameters, Mat& errors,
+        Vec2d& std_error, double& rms, int flags);
+
+    double computeMeanReproErr(InputArrayOfArrays imagePoints, InputArrayOfArrays proImagePoints);
+
+    double computeMeanReproErr(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints, InputArray K, InputArray D, double xi, InputArrayOfArrays omAll,
+        InputArrayOfArrays tAll);
+
+    double computeMeanReproErrStereo(InputArrayOfArrays objectPoints, InputArrayOfArrays imagePoints1, InputArrayOfArrays imagePoints2, InputArray K1, InputArray K2,
+        InputArray D1, InputArray D2, double xi1, double xi2, InputArray om, InputArray T, InputArrayOfArrays omL, InputArrayOfArrays TL);
+
+    void subMatrix(const Mat& src, Mat& dst, const std::vector<int>& cols, const std::vector<int>& rows);
+
+    void flags2idx(int flags, std::vector<int>& idx, int n);
+
+    void flags2idxStereo(int flags, std::vector<int>& idx, int n);
+
+    void fillFixed(Mat&G, int flags, int n);
+
+    void fillFixedStereo(Mat& G, int flags, int n);
+
+    double findMedian(const Mat& row);
+
+    Vec3d findMedian3(InputArray mat);
+
+    void getInterset(InputArray idx1, InputArray idx2, OutputArray inter1, OutputArray inter2, OutputArray inter_ori);
+
+    void compose_motion(InputArray _om1, InputArray _T1, InputArray _om2, InputArray _T2, Mat& om3, Mat& T3, Mat& dom3dom1,
+        Mat& dom3dT1, Mat& dom3dom2, Mat& dom3dT2, Mat& dT3dom1, Mat& dT3dT1, Mat& dT3dom2, Mat& dT3dT2);
+
+    //void JRodriguesMatlab(const Mat& src, Mat& dst);
+
+    //void dAB(InputArray A, InputArray B, OutputArray dABdA, OutputArray dABdB);
+} // internal
+
+//! @}
+
+} // omnidir
+
+} //cv
+#endif

3rdparty/include/opencv2/ccalib/randpattern.hpp

@@ -0,0 +1,184 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2015, Baisheng Lai (laibaisheng@gmail.com), Zhejiang University,
+// all rights reserved.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_RANDOMPATTERN_HPP__
+#define __OPENCV_RANDOMPATTERN_HPP__
+
+#include "opencv2/features2d.hpp"
+#include "opencv2/highgui.hpp"
+
+namespace cv { namespace randpattern {
+
+
+//! @addtogroup ccalib
+//! @{
+
+/** @brief Class for finding features points and corresponding 3D in world coordinate of
+a "random" pattern, which can be to be used in calibration. It is useful when pattern is
+partly occluded or only a part of pattern can be observed in multiple cameras calibration.
+The pattern can be generated by RandomPatternGenerator class described in this file.
+
+Please refer to paper
+    B. Li, L. Heng, K. Kevin  and M. Pollefeys, "A Multiple-Camera System
+    Calibration Toolbox Using A Feature Descriptor-Based Calibration
+    Pattern", in IROS 2013.
+*/
+
+class CV_EXPORTS RandomPatternCornerFinder
+{
+public:
+
+    /* @brief Construct RandomPatternCornerFinder object
+
+    @param patternWidth the real width of "random" pattern in a user defined unit.
+    @param patternHeight the real height of "random" pattern in a user defined unit.
+    @param nMiniMatch number of minimal matches, otherwise that image is abandoned
+    @depth depth of output objectPoints and imagePoints, set it to be CV_32F or CV_64F.
+    @showExtraction whether show feature extraction, 0 for no and 1 for yes.
+    @detector feature detector to detect feature points in pattern and images.
+    @descriptor feature descriptor.
+    @matcher feature matcher.
+    */
+    RandomPatternCornerFinder(float patternWidth, float patternHeight,
+        int nminiMatch = 20, int depth = CV_32F, int verbose = 0, int showExtraction = 0,
+        Ptr<FeatureDetector> detector = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.005f),
+        Ptr<DescriptorExtractor> descriptor = AKAZE::create(AKAZE::DESCRIPTOR_MLDB,0, 3, 0.005f),
+        Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-L1"));
+
+    /* @brief Load pattern image and compute features for pattern
+    @param patternImage image for "random" pattern generated by RandomPatternGenerator, run it first.
+    */
+    void loadPattern(const cv::Mat &patternImage);
+
+    /* @brief Load pattern and features
+	@param patternImage image for "random" pattern generated by RandomPatternGenerator, run it first.
+	@param patternKeyPoints keyPoints created from a FeatureDetector.
+	@param patternDescriptors descriptors created from a DescriptorExtractor.
+	*/
+    void loadPattern(const cv::Mat &patternImage, const std::vector<cv::KeyPoint> &patternKeyPoints, const cv::Mat &patternDescriptors);
+
+    /* @brief Compute matched object points and image points which are used for calibration
+    The objectPoints (3D) and imagePoints (2D) are stored inside the class. Run getObjectPoints()
+    and getImagePoints() to get them.
+
+    @param inputImages vector of 8-bit grayscale images containing "random" pattern
+    that are used for calibration.
+    */
+    void computeObjectImagePoints(std::vector<cv::Mat> inputImages);
+
+    //void computeObjectImagePoints2(std::vector<cv::Mat> inputImages);
+
+    /* @brief Compute object and image points for a single image. It returns a vector<Mat> that
+    the first element stores the imagePoints and the second one stores the objectPoints.
+
+    @param inputImage single input image for calibration
+    */
+    std::vector<cv::Mat> computeObjectImagePointsForSingle(cv::Mat inputImage);
+
+    /* @brief Get object(3D) points
+    */
+    const std::vector<cv::Mat> &getObjectPoints();
+
+    /* @brief and image(2D) points
+    */
+    const std::vector<cv::Mat> &getImagePoints();
+
+private:
+
+    std::vector<cv::Mat> _objectPonits, _imagePoints;
+    float _patternWidth, _patternHeight;
+    cv::Size _patternImageSize;
+    int _nminiMatch;
+    int _depth;
+	int _verbose;
+
+    Ptr<FeatureDetector> _detector;
+    Ptr<DescriptorExtractor> _descriptor;
+    Ptr<DescriptorMatcher> _matcher;
+    Mat _descriptorPattern;
+    std::vector<cv::KeyPoint> _keypointsPattern;
+    Mat _patternImage;
+    int _showExtraction;
+
+    void keyPoints2MatchedLocation(const std::vector<cv::KeyPoint>& imageKeypoints,
+        const std::vector<cv::KeyPoint>& patternKeypoints, const std::vector<cv::DMatch> matchces,
+        cv::Mat& matchedImagelocation, cv::Mat& matchedPatternLocation);
+    void getFilteredLocation(cv::Mat& imageKeypoints, cv::Mat& patternKeypoints, const cv::Mat mask);
+    void getObjectImagePoints(const cv::Mat& imageKeypoints, const cv::Mat& patternKeypoints);
+    void crossCheckMatching( cv::Ptr<DescriptorMatcher>& descriptorMatcher,
+        const Mat& descriptors1, const Mat& descriptors2,
+        std::vector<DMatch>& filteredMatches12, int knn=1 );
+    void drawCorrespondence(const Mat& image1, const std::vector<cv::KeyPoint> keypoint1,
+        const Mat& image2, const std::vector<cv::KeyPoint> keypoint2, const std::vector<cv::DMatch> matchces,
+        const Mat& mask1, const Mat& mask2, const int step);
+};
+
+/* @brief Class to generate "random" pattern image that are used for RandomPatternCornerFinder
+Please refer to paper
+B. Li, L. Heng, K. Kevin  and M. Pollefeys, "A Multiple-Camera System
+Calibration Toolbox Using A Feature Descriptor-Based Calibration
+Pattern", in IROS 2013.
+*/
+class CV_EXPORTS RandomPatternGenerator
+{
+public:
+    /* @brief Construct RandomPatternGenerator
+
+    @param imageWidth image width of the generated pattern image
+    @param imageHeight image height of the generated pattern image
+    */
+    RandomPatternGenerator(int imageWidth, int imageHeight);
+
+    /* @brief Generate pattern
+    */
+    void generatePattern();
+    /* @brief Get pattern
+    */
+    cv::Mat getPattern();
+private:
+    cv::Mat _pattern;
+    int _imageWidth, _imageHeight;
+};
+
+//! @}
+
+}} //namespace randpattern, cv
+#endif

3rdparty/include/opencv2/core.hpp

@@ -50,6 +50,7 @@
 #endif
 
 #include "opencv2/core/cvdef.h"
+#include "opencv2/core/version.hpp"
 #include "opencv2/core/base.hpp"
 #include "opencv2/core/cvstd.hpp"
 #include "opencv2/core/traits.hpp"

3rdparty/include/opencv2/core/base.hpp

@@ -587,21 +587,6 @@ _AccTp normInf(const _Tp* a, const _Tp* b, int n)
  */
 CV_EXPORTS_W float cubeRoot(float val);
 
-/** @overload
-
-cubeRoot with argument of `double` type calls `std::cbrt(double)` (C++11) or falls back on `pow()` for C++98 compilation mode.
-*/
-static inline
-double cubeRoot(double val)
-{
-#ifdef CV_CXX11
-    return std::cbrt(val);
-#else
-    double v = pow(abs(val), 1/3.);  // pow doesn't support negative inputs with fractional exponents
-    return val >= 0 ? v : -v;
-#endif
-}
-
 /** @brief Calculates the angle of a 2D vector in degrees.
 
  The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured

3rdparty/include/opencv2/core/bindings_utils.hpp

@@ -7,9 +7,6 @@
 
 #include <opencv2/core/async.hpp>
 #include <opencv2/core/detail/async_promise.hpp>
-#include <opencv2/core/utils/logger.hpp>
-
-#include <stdexcept>
 
 namespace cv { namespace utils {
 //! @addtogroup core_utils
@@ -67,61 +64,6 @@ String dumpString(const String& argument)
     return cv::format("String: %s", argument.c_str());
 }
 
-CV_WRAP static inline
-String testOverloadResolution(int value, const Point& point = Point(42, 24))
-{
-    return format("overload (int=%d, point=(x=%d, y=%d))", value, point.x,
-                  point.y);
-}
-
-CV_WRAP static inline
-String testOverloadResolution(const Rect& rect)
-{
-    return format("overload (rect=(x=%d, y=%d, w=%d, h=%d))", rect.x, rect.y,
-                  rect.width, rect.height);
-}
-
-CV_WRAP static inline
-String dumpRect(const Rect& argument)
-{
-    return format("rect: (x=%d, y=%d, w=%d, h=%d)", argument.x, argument.y,
-                  argument.width, argument.height);
-}
-
-CV_WRAP static inline
-String dumpTermCriteria(const TermCriteria& argument)
-{
-    return format("term_criteria: (type=%d, max_count=%d, epsilon=%lf",
-                  argument.type, argument.maxCount, argument.epsilon);
-}
-
-CV_WRAP static inline
-String dumpRotatedRect(const RotatedRect& argument)
-{
-    return format("rotated_rect: (c_x=%f, c_y=%f, w=%f, h=%f, a=%f)",
-                  argument.center.x, argument.center.y, argument.size.width,
-                  argument.size.height, argument.angle);
-}
-
-CV_WRAP static inline
-String dumpRange(const Range& argument)
-{
-    if (argument == Range::all())
-    {
-        return "range: all";
-    }
-    else
-    {
-        return format("range: (s=%d, e=%d)", argument.start, argument.end);
-    }
-}
-
-CV_WRAP static inline
-void testRaiseGeneralException()
-{
-    throw std::runtime_error("exception text");
-}
-
 CV_WRAP static inline
 AsyncArray testAsyncArray(InputArray argument)
 {
@@ -145,26 +87,7 @@ AsyncArray testAsyncException()
     return p.getArrayResult();
 }
 
-//! @}  // core_utils
-}  // namespace cv::utils
-
-//! @cond IGNORED
-
-CV_WRAP static inline
-int setLogLevel(int level)
-{
-    // NB: Binding generators doesn't work with enums properly yet, so we define separate overload here
-    return cv::utils::logging::setLogLevel((cv::utils::logging::LogLevel)level);
-}
-
-CV_WRAP static inline
-int getLogLevel()
-{
-    return cv::utils::logging::getLogLevel();
-}
-
-//! @endcond IGNORED
-
-} // namespaces cv /  utils
+//! @}
+}} // namespace
 
 #endif // OPENCV_CORE_BINDINGS_UTILS_HPP