add cpp inference

.gitignore

 # Byte-compiled / optimized / DLL files
 __pycache__/
+.ipynb_checkpoints/
 *.py[cod]
 *$py.class
 

deploy/cpp_infer/CMakeLists.txt

+project(ocr_system CXX C)
+option(WITH_MKL        "Compile demo with MKL/OpenBlas support, default use MKL."       ON)
+option(WITH_GPU        "Compile demo with GPU/CPU, default use CPU."                    OFF)
+option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static."   ON)
+option(USE_TENSORRT "Compile demo with TensorRT."   OFF)
+
+
+macro(safe_set_static_flag)
+    foreach(flag_var
+        CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
+        CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
+      if(${flag_var} MATCHES "/MD")
+        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
+      endif(${flag_var} MATCHES "/MD")
+    endforeach(flag_var)
+endmacro()
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -g -fpermissive")
+set(CMAKE_STATIC_LIBRARY_PREFIX "")
+message("flags" ${CMAKE_CXX_FLAGS})
+set(CMAKE_CXX_FLAGS_RELEASE "-O3")
+
+if(NOT DEFINED PADDLE_LIB)
+  message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib")
+endif()
+if(NOT DEFINED DEMO_NAME)
+  message(FATAL_ERROR "please set DEMO_NAME with -DDEMO_NAME=demo_name")
+endif()
+
+# user ze
+# find_package(OpenCV)
+
+
+set(OPENCV_DIR "/paddle/libs/opencv-3.4.7/opencv3")
+find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+include_directories("${PADDLE_LIB}/paddle/include")
+include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
+include_directories("${PADDLE_LIB}/third_party/install/glog/include")
+include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
+include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
+include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
+include_directories("${PADDLE_LIB}/third_party/boost")
+include_directories("${PADDLE_LIB}/third_party/eigen3")
+
+include_directories("${CMAKE_SOURCE_DIR}/")
+
+if (USE_TENSORRT AND WITH_GPU)
+      include_directories("${TENSORRT_ROOT}/include")
+      link_directories("${TENSORRT_ROOT}/lib")
+endif()
+
+link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
+
+link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
+link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
+link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
+link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
+link_directories("${PADDLE_LIB}/paddle/lib")
+
+
+add_executable(${DEMO_NAME} src/main.cpp src/ocr_det.cpp src/ocr_rec.cpp src/preprocess_op.cpp src/clipper.cpp src/postprocess_op.cpp )
+
+if(WITH_MKL)
+  include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
+  set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
+               ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+  set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
+  if(EXISTS ${MKLDNN_PATH})
+    include_directories("${MKLDNN_PATH}/include")
+    set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
+  endif()
+else()
+  set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
+endif()
+
+# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
+if(WITH_STATIC_LIB)
+  set(DEPS
+      ${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
+else()
+  set(DEPS
+      ${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
+endif()
+
+# user ze
+# set(EXTERNAL_LIB "-lrt -ldl -lpthread -lm -lopencv_world")
+# gry
+set(EXTERNAL_LIB "-lrt -ldl -lpthread -lm")
+
+set(DEPS ${DEPS}
+    ${MATH_LIB} ${MKLDNN_LIB}
+    glog gflags protobuf z xxhash
+    ${EXTERNAL_LIB} ${OpenCV_LIBS})
+
+if(WITH_GPU)
+  if (USE_TENSORRT)
+    set(DEPS ${DEPS}
+        ${TENSORRT_ROOT}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS}
+        ${TENSORRT_ROOT}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
+  endif()
+  set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
+  set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX} )
+  set(DEPS ${DEPS} ${CUDA_LIB}/libcublas${CMAKE_SHARED_LIBRARY_SUFFIX} )
+  set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX} )
+endif()
+
+target_link_libraries(${DEMO_NAME} ${DEPS})

deploy/cpp_infer/include/clipper.h

+/*******************************************************************************
+*                                                                              *
+* Author    :  Angus Johnson                                                   *
+* Version   :  6.4.2                                                           *
+* Date      :  27 February 2017                                                *
+* Website   :  http://www.angusj.com                                           *
+* Copyright :  Angus Johnson 2010-2017                                         *
+*                                                                              *
+* License:                                                                     *
+* Use, modification & distribution is subject to Boost Software License Ver 1. *
+* http://www.boost.org/LICENSE_1_0.txt                                         *
+*                                                                              *
+* Attributions:                                                                *
+* The code in this library is an extension of Bala Vatti's clipping algorithm: *
+* "A generic solution to polygon clipping"                                     *
+* Communications of the ACM, Vol 35, Issue 7 (July 1992) pp 56-63.             *
+* http://portal.acm.org/citation.cfm?id=129906                                 *
+*                                                                              *
+* Computer graphics and geometric modeling: implementation and algorithms      *
+* By Max K. Agoston                                                            *
+* Springer; 1 edition (January 4, 2005)                                        *
+* http://books.google.com/books?q=vatti+clipping+agoston                       *
+*                                                                              *
+* See also:                                                                    *
+* "Polygon Offsetting by Computing Winding Numbers"                            *
+* Paper no. DETC2005-85513 pp. 565-575                                         *
+* ASME 2005 International Design Engineering Technical Conferences             *
+* and Computers and Information in Engineering Conference (IDETC/CIE2005)      *
+* September 24-28, 2005 , Long Beach, California, USA                          *
+* http://www.me.berkeley.edu/~mcmains/pubs/DAC05OffsetPolygon.pdf              *
+*                                                                              *
+*******************************************************************************/
+
+#ifndef clipper_hpp
+#define clipper_hpp
+
+#define CLIPPER_VERSION "6.4.2"
+
+// use_int32: When enabled 32bit ints are used instead of 64bit ints. This
+// improve performance but coordinate values are limited to the range +/- 46340
+//#define use_int32
+
+// use_xyz: adds a Z member to IntPoint. Adds a minor cost to perfomance.
+//#define use_xyz
+
+// use_lines: Enables line clipping. Adds a very minor cost to performance.
+#define use_lines
+
+// use_deprecated: Enables temporary support for the obsolete functions
+//#define use_deprecated
+
+#include <cstdlib>
+#include <cstring>
+#include <functional>
+#include <list>
+#include <ostream>
+#include <queue>
+#include <set>
+#include <stdexcept>
+#include <vector>
+
+namespace ClipperLib {
+
+enum ClipType { ctIntersection, ctUnion, ctDifference, ctXor };
+enum PolyType { ptSubject, ptClip };
+// By far the most widely used winding rules for polygon filling are
+// EvenOdd & NonZero (GDI, GDI+, XLib, OpenGL, Cairo, AGG, Quartz, SVG, Gr32)
+// Others rules include Positive, Negative and ABS_GTR_EQ_TWO (only in OpenGL)
+// see http://glprogramming.com/red/chapter11.html
+enum PolyFillType { pftEvenOdd, pftNonZero, pftPositive, pftNegative };
+
+#ifdef use_int32
+typedef int cInt;
+static cInt const loRange = 0x7FFF;
+static cInt const hiRange = 0x7FFF;
+#else
+typedef signed long long cInt;
+static cInt const loRange = 0x3FFFFFFF;
+static cInt const hiRange = 0x3FFFFFFFFFFFFFFFLL;
+typedef signed long long long64; // used by Int128 class
+typedef unsigned long long ulong64;
+
+#endif
+
+struct IntPoint {
+  cInt X;
+  cInt Y;
+#ifdef use_xyz
+  cInt Z;
+  IntPoint(cInt x = 0, cInt y = 0, cInt z = 0) : X(x), Y(y), Z(z){};
+#else
+  IntPoint(cInt x = 0, cInt y = 0) : X(x), Y(y){};
+#endif
+
+  friend inline bool operator==(const IntPoint &a, const IntPoint &b) {
+    return a.X == b.X && a.Y == b.Y;
+  }
+  friend inline bool operator!=(const IntPoint &a, const IntPoint &b) {
+    return a.X != b.X || a.Y != b.Y;
+  }
+};
+//------------------------------------------------------------------------------
+
+typedef std::vector<IntPoint> Path;
+typedef std::vector<Path> Paths;
+
+inline Path &operator<<(Path &poly, const IntPoint &p) {
+  poly.push_back(p);
+  return poly;
+}
+inline Paths &operator<<(Paths &polys, const Path &p) {
+  polys.push_back(p);
+  return polys;
+}
+
+std::ostream &operator<<(std::ostream &s, const IntPoint &p);
+std::ostream &operator<<(std::ostream &s, const Path &p);
+std::ostream &operator<<(std::ostream &s, const Paths &p);
+
+struct DoublePoint {
+  double X;
+  double Y;
+  DoublePoint(double x = 0, double y = 0) : X(x), Y(y) {}
+  DoublePoint(IntPoint ip) : X((double)ip.X), Y((double)ip.Y) {}
+};
+//------------------------------------------------------------------------------
+
+#ifdef use_xyz
+typedef void (*ZFillCallback)(IntPoint &e1bot, IntPoint &e1top, IntPoint &e2bot,
+                              IntPoint &e2top, IntPoint &pt);
+#endif
+
+enum InitOptions {
+  ioReverseSolution = 1,
+  ioStrictlySimple = 2,
+  ioPreserveCollinear = 4
+};
+enum JoinType { jtSquare, jtRound, jtMiter };
+enum EndType {
+  etClosedPolygon,
+  etClosedLine,
+  etOpenButt,
+  etOpenSquare,
+  etOpenRound
+};
+
+class PolyNode;
+typedef std::vector<PolyNode *> PolyNodes;
+
+class PolyNode {
+public:
+  PolyNode();
+  virtual ~PolyNode(){};
+  Path Contour;
+  PolyNodes Childs;
+  PolyNode *Parent;
+  PolyNode *GetNext() const;
+  bool IsHole() const;
+  bool IsOpen() const;
+  int ChildCount() const;
+
+private:
+  // PolyNode& operator =(PolyNode& other);
+  unsigned Index; // node index in Parent.Childs
+  bool m_IsOpen;
+  JoinType m_jointype;
+  EndType m_endtype;
+  PolyNode *GetNextSiblingUp() const;
+  void AddChild(PolyNode &child);
+  friend class Clipper; // to access Index
+  friend class ClipperOffset;
+};
+
+class PolyTree : public PolyNode {
+public:
+  ~PolyTree() { Clear(); };
+  PolyNode *GetFirst() const;
+  void Clear();
+  int Total() const;
+
+private:
+  // PolyTree& operator =(PolyTree& other);
+  PolyNodes AllNodes;
+  friend class Clipper; // to access AllNodes
+};
+
+bool Orientation(const Path &poly);
+double Area(const Path &poly);
+int PointInPolygon(const IntPoint &pt, const Path &path);
+
+void SimplifyPolygon(const Path &in_poly, Paths &out_polys,
+                     PolyFillType fillType = pftEvenOdd);
+void SimplifyPolygons(const Paths &in_polys, Paths &out_polys,
+                      PolyFillType fillType = pftEvenOdd);
+void SimplifyPolygons(Paths &polys, PolyFillType fillType = pftEvenOdd);
+
+void CleanPolygon(const Path &in_poly, Path &out_poly, double distance = 1.415);
+void CleanPolygon(Path &poly, double distance = 1.415);
+void CleanPolygons(const Paths &in_polys, Paths &out_polys,
+                   double distance = 1.415);
+void CleanPolygons(Paths &polys, double distance = 1.415);
+
+void MinkowskiSum(const Path &pattern, const Path &path, Paths &solution,
+                  bool pathIsClosed);
+void MinkowskiSum(const Path &pattern, const Paths &paths, Paths &solution,
+                  bool pathIsClosed);
+void MinkowskiDiff(const Path &poly1, const Path &poly2, Paths &solution);
+
+void PolyTreeToPaths(const PolyTree &polytree, Paths &paths);
+void ClosedPathsFromPolyTree(const PolyTree &polytree, Paths &paths);
+void OpenPathsFromPolyTree(PolyTree &polytree, Paths &paths);
+
+void ReversePath(Path &p);
+void ReversePaths(Paths &p);
+
+struct IntRect {
+  cInt left;
+  cInt top;
+  cInt right;
+  cInt bottom;
+};
+
+// enums that are used internally ...
+enum EdgeSide { esLeft = 1, esRight = 2 };
+
+// forward declarations (for stuff used internally) ...
+struct TEdge;
+struct IntersectNode;
+struct LocalMinimum;
+struct OutPt;
+struct OutRec;
+struct Join;
+
+typedef std::vector<OutRec *> PolyOutList;
+typedef std::vector<TEdge *> EdgeList;
+typedef std::vector<Join *> JoinList;
+typedef std::vector<IntersectNode *> IntersectList;
+
+//------------------------------------------------------------------------------
+
+// ClipperBase is the ancestor to the Clipper class. It should not be
+// instantiated directly. This class simply abstracts the conversion of sets of
+// polygon coordinates into edge objects that are stored in a LocalMinima list.
+class ClipperBase {
+public:
+  ClipperBase();
+  virtual ~ClipperBase();
+  virtual bool AddPath(const Path &pg, PolyType PolyTyp, bool Closed);
+  bool AddPaths(const Paths &ppg, PolyType PolyTyp, bool Closed);
+  virtual void Clear();
+  IntRect GetBounds();
+  bool PreserveCollinear() { return m_PreserveCollinear; };
+  void PreserveCollinear(bool value) { m_PreserveCollinear = value; };
+
+protected:
+  void DisposeLocalMinimaList();
+  TEdge *AddBoundsToLML(TEdge *e, bool IsClosed);
+  virtual void Reset();
+  TEdge *ProcessBound(TEdge *E, bool IsClockwise);
+  void InsertScanbeam(const cInt Y);
+  bool PopScanbeam(cInt &Y);
+  bool LocalMinimaPending();
+  bool PopLocalMinima(cInt Y, const LocalMinimum *&locMin);
+  OutRec *CreateOutRec();
+  void DisposeAllOutRecs();
+  void DisposeOutRec(PolyOutList::size_type index);
+  void SwapPositionsInAEL(TEdge *edge1, TEdge *edge2);
+  void DeleteFromAEL(TEdge *e);
+  void UpdateEdgeIntoAEL(TEdge *&e);
+
+  typedef std::vector<LocalMinimum> MinimaList;
+  MinimaList::iterator m_CurrentLM;
+  MinimaList m_MinimaList;
+
+  bool m_UseFullRange;
+  EdgeList m_edges;
+  bool m_PreserveCollinear;
+  bool m_HasOpenPaths;
+  PolyOutList m_PolyOuts;
+  TEdge *m_ActiveEdges;
+
+  typedef std::priority_queue<cInt> ScanbeamList;
+  ScanbeamList m_Scanbeam;
+};
+//------------------------------------------------------------------------------
+
+class Clipper : public virtual ClipperBase {
+public:
+  Clipper(int initOptions = 0);
+  bool Execute(ClipType clipType, Paths &solution,
+               PolyFillType fillType = pftEvenOdd);
+  bool Execute(ClipType clipType, Paths &solution, PolyFillType subjFillType,
+               PolyFillType clipFillType);
+  bool Execute(ClipType clipType, PolyTree &polytree,
+               PolyFillType fillType = pftEvenOdd);
+  bool Execute(ClipType clipType, PolyTree &polytree, PolyFillType subjFillType,
+               PolyFillType clipFillType);
+  bool ReverseSolution() { return m_ReverseOutput; };
+  void ReverseSolution(bool value) { m_ReverseOutput = value; };
+  bool StrictlySimple() { return m_StrictSimple; };
+  void StrictlySimple(bool value) { m_StrictSimple = value; };
+// set the callback function for z value filling on intersections (otherwise Z
+// is 0)
+#ifdef use_xyz
+  void ZFillFunction(ZFillCallback zFillFunc);
+#endif
+protected:
+  virtual bool ExecuteInternal();
+
+private:
+  JoinList m_Joins;
+  JoinList m_GhostJoins;
+  IntersectList m_IntersectList;
+  ClipType m_ClipType;
+  typedef std::list<cInt> MaximaList;
+  MaximaList m_Maxima;
+  TEdge *m_SortedEdges;
+  bool m_ExecuteLocked;
+  PolyFillType m_ClipFillType;
+  PolyFillType m_SubjFillType;
+  bool m_ReverseOutput;
+  bool m_UsingPolyTree;
+  bool m_StrictSimple;
+#ifdef use_xyz
+  ZFillCallback m_ZFill; // custom callback
+#endif
+  void SetWindingCount(TEdge &edge);
+  bool IsEvenOddFillType(const TEdge &edge) const;
+  bool IsEvenOddAltFillType(const TEdge &edge) const;
+  void InsertLocalMinimaIntoAEL(const cInt botY);
+  void InsertEdgeIntoAEL(TEdge *edge, TEdge *startEdge);
+  void AddEdgeToSEL(TEdge *edge);
+  bool PopEdgeFromSEL(TEdge *&edge);
+  void CopyAELToSEL();
+  void DeleteFromSEL(TEdge *e);
+  void SwapPositionsInSEL(TEdge *edge1, TEdge *edge2);
+  bool IsContributing(const TEdge &edge) const;
+  bool IsTopHorz(const cInt XPos);
+  void DoMaxima(TEdge *e);
+  void ProcessHorizontals();
+  void ProcessHorizontal(TEdge *horzEdge);
+  void AddLocalMaxPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
+  OutPt *AddLocalMinPoly(TEdge *e1, TEdge *e2, const IntPoint &pt);
+  OutRec *GetOutRec(int idx);
+  void AppendPolygon(TEdge *e1, TEdge *e2);
+  void IntersectEdges(TEdge *e1, TEdge *e2, IntPoint &pt);
+  OutPt *AddOutPt(TEdge *e, const IntPoint &pt);
+  OutPt *GetLastOutPt(TEdge *e);
+  bool ProcessIntersections(const cInt topY);
+  void BuildIntersectList(const cInt topY);
+  void ProcessIntersectList();
+  void ProcessEdgesAtTopOfScanbeam(const cInt topY);
+  void BuildResult(Paths &polys);
+  void BuildResult2(PolyTree &polytree);
+  void SetHoleState(TEdge *e, OutRec *outrec);
+  void DisposeIntersectNodes();
+  bool FixupIntersectionOrder();
+  void FixupOutPolygon(OutRec &outrec);
+  void FixupOutPolyline(OutRec &outrec);
+  bool IsHole(TEdge *e);
+  bool FindOwnerFromSplitRecs(OutRec &outRec, OutRec *&currOrfl);
+  void FixHoleLinkage(OutRec &outrec);
+  void AddJoin(OutPt *op1, OutPt *op2, const IntPoint offPt);
+  void ClearJoins();
+  void ClearGhostJoins();
+  void AddGhostJoin(OutPt *op, const IntPoint offPt);
+  bool JoinPoints(Join *j, OutRec *outRec1, OutRec *outRec2);
+  void JoinCommonEdges();
+  void DoSimplePolygons();
+  void FixupFirstLefts1(OutRec *OldOutRec, OutRec *NewOutRec);
+  void FixupFirstLefts2(OutRec *InnerOutRec, OutRec *OuterOutRec);
+  void FixupFirstLefts3(OutRec *OldOutRec, OutRec *NewOutRec);
+#ifdef use_xyz
+  void SetZ(IntPoint &pt, TEdge &e1, TEdge &e2);
+#endif
+};
+//------------------------------------------------------------------------------
+
+class ClipperOffset {
+public:
+  ClipperOffset(double miterLimit = 2.0, double roundPrecision = 0.25);
+  ~ClipperOffset();
+  void AddPath(const Path &path, JoinType joinType, EndType endType);
+  void AddPaths(const Paths &paths, JoinType joinType, EndType endType);
+  void Execute(Paths &solution, double delta);
+  void Execute(PolyTree &solution, double delta);
+  void Clear();
+  double MiterLimit;
+  double ArcTolerance;
+
+private:
+  Paths m_destPolys;
+  Path m_srcPoly;
+  Path m_destPoly;
+  std::vector<DoublePoint> m_normals;
+  double m_delta, m_sinA, m_sin, m_cos;
+  double m_miterLim, m_StepsPerRad;
+  IntPoint m_lowest;
+  PolyNode m_polyNodes;
+
+  void FixOrientations();
+  void DoOffset(double delta);
+  void OffsetPoint(int j, int &k, JoinType jointype);
+  void DoSquare(int j, int k);
+  void DoMiter(int j, int k, double r);
+  void DoRound(int j, int k);
+};
+//------------------------------------------------------------------------------
+
+class clipperException : public std::exception {
+public:
+  clipperException(const char *description) : m_descr(description) {}
+  virtual ~clipperException() throw() {}
+  virtual const char *what() const throw() { return m_descr.c_str(); }
+
+private:
+  std::string m_descr;
+};
+//------------------------------------------------------------------------------
+
+} // ClipperLib namespace
+
+#endif // clipper_hpp

deploy/cpp_infer/include/ocr_det.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "paddle_api.h"
+#include "paddle_inference_api.h"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <include/postprocess_op.h>
+#include <include/preprocess_op.h>
+
+namespace PaddleOCR {
+
+class DBDetector {
+public:
+  explicit DBDetector(const std::string &model_dir, bool use_gpu = false,
+                      const int gpu_id = 0, const int max_side_len = 960) {
+    LoadModel(model_dir, use_gpu);
+    this->max_side_len_ = max_side_len;
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string &model_dir, bool use_gpu,
+                 const int min_subgraph_size = 3, const int batch_size = 1,
+                 const int gpu_id = 0);
+
+  // Run predictor
+  void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes);
+
+private:
+  std::shared_ptr<PaddlePredictor> predictor_;
+
+  int max_side_len_ = 960;
+
+  std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
+  std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
+  bool is_scale_ = true;
+
+  // pre-process
+  ResizeImgType0 resize_op_;
+  Normalize normalize_op_;
+  Permute permute_op_;
+
+  // post-process
+  PostProcessor post_processor_;
+};
+
+} // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/include/ocr_rec.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "paddle_api.h"
+#include "paddle_inference_api.h"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <include/postprocess_op.h>
+#include <include/preprocess_op.h>
+
+namespace PaddleOCR {
+
+class CRNNRecognizer {
+public:
+  explicit CRNNRecognizer(const std::string &model_dir,
+                          const string label_path = "./tools/ppocr_keys_v1.txt",
+                          bool use_gpu = false, const int gpu_id = 0) {
+    LoadModel(model_dir, use_gpu);
+
+    this->label_list_ = ReadDict(label_path);
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string &model_dir, bool use_gpu,
+                 const int gpu_id = 0, const int min_subgraph_size = 3,
+                 const int batch_size = 1);
+
+  void Run(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat &img);
+
+private:
+  std::shared_ptr<PaddlePredictor> predictor_;
+
+  std::vector<std::string> label_list_;
+
+  std::vector<float> mean_ = {0.5f, 0.5f, 0.5f};
+  std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
+  bool is_scale_ = true;
+
+  // pre-process
+  CrnnResizeImg resize_op_;
+  Normalize normalize_op_;
+  Permute permute_op_;
+
+  // post-process
+  PostProcessor post_processor_;
+
+  cv::Mat get_rotate_crop_image(const cv::Mat &srcimage,
+                                std::vector<std::vector<int>> box);
+
+  std::vector<std::string> ReadDict(const std::string &path);
+
+  template <class ForwardIterator>
+  inline size_t argmax(ForwardIterator first, ForwardIterator last) {
+    return std::distance(first, std::max_element(first, last));
+  }
+
+}; // class CrnnRecognizer
+
+} // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/include/postprocess_op.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include "include/clipper.h"
+
+using namespace std;
+
+namespace PaddleOCR {
+
+inline std::vector<std::string> ReadDict(std::string path) {
+  std::ifstream in(path);
+  std::string filename;
+  std::string line;
+  std::vector<std::string> m_vec;
+  if (in) {
+    while (getline(in, line)) {
+      m_vec.push_back(line);
+    }
+  } else {
+    std::cout << "no such file" << std::endl;
+  }
+  return m_vec;
+}
+
+template <class ForwardIterator>
+inline size_t Argmax(ForwardIterator first, ForwardIterator last) {
+  return std::distance(first, std::max_element(first, last));
+}
+
+class PostProcessor {
+public:
+  void GetContourArea(float **box, float unclip_ratio, float &distance);
+
+  cv::RotatedRect unclip(float **box);
+
+  float **Mat2Vec(cv::Mat mat);
+
+  void quickSort_vector(std::vector<std::vector<int>> &box, int l, int r,
+                        int axis);
+
+  std::vector<std::vector<int>>
+  order_points_clockwise(std::vector<std::vector<int>> pts);
+
+  float **get_mini_boxes(cv::RotatedRect box, float &ssid);
+
+  float box_score_fast(float **box_array, cv::Mat pred);
+
+  std::vector<std::vector<std::vector<int>>>
+  boxes_from_bitmap(const cv::Mat pred, const cv::Mat bitmap);
+
+  std::vector<std::vector<std::vector<int>>>
+  filter_tag_det_res(std::vector<std::vector<std::vector<int>>> boxes,
+                     float ratio_h, float ratio_w, cv::Mat srcimg);
+
+  template <class ForwardIterator>
+  inline size_t argmax(ForwardIterator first, ForwardIterator last) {
+    return std::distance(first, std::max_element(first, last));
+  }
+
+  // CRNN
+
+private:
+  void quickSort(float **s, int l, int r);
+
+  inline int _max(int a, int b) { return a >= b ? a : b; }
+
+  inline int _min(int a, int b) { return a >= b ? b : a; }
+
+  template <class T> inline T clamp(T x, T min, T max) {
+    if (x > max)
+      return max;
+    if (x < min)
+      return min;
+    return x;
+  }
+  inline float clampf(float x, float min, float max) {
+    if (x > max)
+      return max;
+    if (x < min)
+      return min;
+    return x;
+  }
+};
+
+} // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/include/preprocess_op.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+using namespace std;
+using namespace paddle;
+
+namespace PaddleOCR {
+
+class Normalize {
+public:
+  virtual void Run(cv::Mat *im, const std::vector<float> &mean,
+                   const std::vector<float> &scale, const bool is_scale = true);
+};
+
+// RGB -> CHW
+class Permute {
+public:
+  virtual void Run(const cv::Mat *im, float *data);
+};
+
+// RGB -> CHW
+class ResizeImgType0 {
+public:
+  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
+                   float &ratio_h, float &ratio_w);
+};
+
+class CrnnResizeImg {
+public:
+  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio,
+                   const std::vector<int> rec_image_shape = {3, 32, 320});
+};
+
+} // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/src/main.cpp

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <include/ocr_det.h>
+#include <include/ocr_rec.h>
+
+using namespace std;
+using namespace cv;
+using namespace PaddleOCR;
+
+int main(int argc, char **argv) {
+  if (argc < 4) {
+    std::cerr << "[ERROR] usage: " << argv[0]
+              << " det_model_file rec_model_file image_path\n";
+    exit(1);
+  }
+  std::string det_model_file = argv[1];
+  std::string rec_model_file = argv[2];
+  std::string img_path = argv[3];
+
+  auto start = std::chrono::system_clock::now();
+
+  cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
+
+  DBDetector det(det_model_file);
+  CRNNRecognizer rec(rec_model_file);
+
+  std::vector<std::vector<std::vector<int>>> boxes;
+  det.Run(srcimg, boxes);
+
+  rec.Run(boxes, srcimg);
+
+  auto end = std::chrono::system_clock::now();
+  auto duration =
+      std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+  std::cout << "花费了"
+            << double(duration.count()) *
+                   std::chrono::microseconds::period::num /
+                   std::chrono::microseconds::period::den
+            << "秒" << std::endl;
+
+  return 0;
+}

deploy/cpp_infer/src/ocr_det.cpp

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "paddle_api.h"
+#include "paddle_inference_api.h"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <include/ocr_det.h>
+
+namespace PaddleOCR {
+
+void DBDetector::LoadModel(const std::string &model_dir, bool use_gpu,
+                           const int gpu_id, const int min_subgraph_size,
+                           const int batch_size) {
+  AnalysisConfig config;
+  config.SetModel(model_dir + "/model", model_dir + "/params");
+
+  // for cpu
+  config.DisableGpu();
+  config.EnableMKLDNN(); // 开启MKLDNN加速
+  config.SetCpuMathLibraryNumThreads(10);
+
+  // 使用ZeroCopyTensor，此处必须设置为false
+  config.SwitchUseFeedFetchOps(false);
+  // 若输入为多个，此处必须设置为true
+  config.SwitchSpecifyInputNames(true);
+  // config.SwitchIrDebug(true); //
+  // 可视化调试选项，若开启，则会在每个图优化过程后生成dot文件
+  // config.SwitchIrOptim(false);// 默认为true。如果设置为false，关闭所有优化
+  config.EnableMemoryOptim(); // 开启内存/显存复用
+
+  this->predictor_ = CreatePaddlePredictor(config);
+  //   predictor_ = std::move(CreatePaddlePredictor(config)); // PaddleDetection
+  //   usage
+}
+
+void DBDetector::Run(cv::Mat &img,
+                     std::vector<std::vector<std::vector<int>>> &boxes) {
+  float ratio_h{};
+  float ratio_w{};
+
+  cv::Mat srcimg;
+  cv::Mat resize_img;
+  img.copyTo(srcimg);
+  this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w);
+
+  this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
+                          this->is_scale_);
+
+  float *input = new float[1 * 3 * resize_img.rows * resize_img.cols];
+  this->permute_op_.Run(&resize_img, input);
+
+  auto input_names = this->predictor_->GetInputNames();
+  auto input_t = this->predictor_->GetInputTensor(input_names[0]);
+  input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
+  input_t->copy_from_cpu(input);
+
+  this->predictor_->ZeroCopyRun();
+
+  std::vector<float> out_data;
+  auto output_names = this->predictor_->GetOutputNames();
+  auto output_t = this->predictor_->GetOutputTensor(output_names[0]);
+  std::vector<int> output_shape = output_t->shape();
+  int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
+                                std::multiplies<int>());
+
+  out_data.resize(out_num);
+  output_t->copy_to_cpu(out_data.data());
+
+  int n2 = output_shape[2];
+  int n3 = output_shape[3];
+  int n = n2 * n3;
+
+  float *pred = new float[n];
+  unsigned char *cbuf = new unsigned char[n];
+
+  for (int i = 0; i < n; i++) {
+    pred[i] = float(out_data[i]);
+    cbuf[i] = (unsigned char)((out_data[i]) * 255);
+  }
+
+  cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char *)cbuf);
+  cv::Mat pred_map(n2, n3, CV_32F, (float *)pred);
+
+  const double threshold = 0.3 * 255;
+  const double maxvalue = 255;
+  cv::Mat bit_map;
+  cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
+
+  boxes = post_processor_.boxes_from_bitmap(pred_map, bit_map);
+
+  boxes = post_processor_.filter_tag_det_res(boxes, ratio_h, ratio_w, srcimg);
+
+  //// visualization
+  cv::Point rook_points[boxes.size()][4];
+  for (int n = 0; n < boxes.size(); n++) {
+    for (int m = 0; m < boxes[0].size(); m++) {
+      rook_points[n][m] = cv::Point(int(boxes[n][m][0]), int(boxes[n][m][1]));
+    }
+  }
+
+  cv::Mat img_vis;
+  srcimg.copyTo(img_vis);
+  for (int n = 0; n < boxes.size(); n++) {
+    const cv::Point *ppt[1] = {rook_points[n]};
+    int npt[] = {4};
+    cv::polylines(img_vis, ppt, npt, 1, 1, CV_RGB(0, 255, 0), 2, 8, 0);
+  }
+
+  imwrite("./det_res.png", img_vis);
+
+  std::cout << "The detection visualized image saved in ./det_res.png"
+            << std::endl;
+
+  delete[] input;
+  delete[] pred;
+  delete[] cbuf;
+}
+
+} // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/src/ocr_rec.cpp

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "paddle_api.h"
+#include "paddle_inference_api.h"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <include/ocr_rec.h>
+
+namespace PaddleOCR {
+
+void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
+                         cv::Mat &img) {
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  cv::Mat crop_img;
+  cv::Mat resize_img;
+
+  std::cout << "The predicted text is :" << std::endl;
+  int index = 0;
+  for (int i = boxes.size() - 1; i >= 0; i--) {
+    crop_img = get_rotate_crop_image(srcimg, boxes[i]);
+
+    float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
+
+    this->resize_op_.Run(crop_img, resize_img, wh_ratio);
+
+    this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
+                            this->is_scale_);
+
+    float *input = new float[1 * 3 * resize_img.rows * resize_img.cols];
+
+    this->permute_op_.Run(&resize_img, input);
+
+    auto input_names = this->predictor_->GetInputNames();
+    auto input_t = this->predictor_->GetInputTensor(input_names[0]);
+    input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
+    input_t->copy_from_cpu(input);
+
+    this->predictor_->ZeroCopyRun();
+
+    std::vector<int64_t> rec_idx;
+    auto output_names = this->predictor_->GetOutputNames();
+    auto output_t = this->predictor_->GetOutputTensor(output_names[0]);
+    auto rec_idx_lod = output_t->lod();
+    auto shape_out = output_t->shape();
+    int out_num = std::accumulate(shape_out.begin(), shape_out.end(), 1,
+                                  std::multiplies<int>());
+
+    rec_idx.resize(out_num);
+    output_t->copy_to_cpu(rec_idx.data());
+
+    std::vector<int> pred_idx;
+    for (int n = int(rec_idx_lod[0][0]); n < int(rec_idx_lod[0][1]); n++) {
+      pred_idx.push_back(int(rec_idx[n]));
+    }
+
+    if (pred_idx.size() < 1e-3)
+      continue;
+
+    index += 1;
+    std::cout << index << "\t";
+    for (int n = 0; n < pred_idx.size(); n++) {
+      std::cout << label_list_[pred_idx[n]];
+    }
+
+    std::vector<float> predict_batch;
+    auto output_t_1 = this->predictor_->GetOutputTensor(output_names[1]);
+
+    auto predict_lod = output_t_1->lod();
+    auto predict_shape = output_t_1->shape();
+    int out_num_1 = std::accumulate(predict_shape.begin(), predict_shape.end(),
+                                    1, std::multiplies<int>());
+
+    predict_batch.resize(out_num_1);
+    output_t_1->copy_to_cpu(predict_batch.data());
+
+    int argmax_idx;
+    int blank = predict_shape[1];
+    float score = 0.f;
+    int count = 0;
+    float max_value = 0.0f;
+
+    for (int n = predict_lod[0][0]; n < predict_lod[0][1] - 1; n++) {
+      argmax_idx = int(argmax(&predict_batch[n * predict_shape[1]],
+                              &predict_batch[(n + 1) * predict_shape[1]]));
+      max_value =
+          float(*std::max_element(&predict_batch[n * predict_shape[1]],
+                                  &predict_batch[(n + 1) * predict_shape[1]]));
+      if (blank - 1 - argmax_idx > 1e-5) {
+        score += max_value;
+        count += 1;
+      }
+    }
+    score /= count;
+    std::cout << "\tscore: " << score << std::endl;
+
+    delete[] input;
+  }
+}
+
+void CRNNRecognizer::LoadModel(const std::string &model_dir, bool use_gpu,
+                               const int gpu_id, const int min_subgraph_size,
+                               const int batch_size) {
+  AnalysisConfig config;
+  config.SetModel(model_dir + "/model", model_dir + "/params");
+
+  // for cpu
+  config.DisableGpu();
+  config.EnableMKLDNN(); // 开启MKLDNN加速
+  config.SetCpuMathLibraryNumThreads(10);
+
+  // 使用ZeroCopyTensor，此处必须设置为false
+  config.SwitchUseFeedFetchOps(false);
+  // 若输入为多个，此处必须设置为true
+  config.SwitchSpecifyInputNames(true);
+  // config.SwitchIrDebug(true); //
+  // 可视化调试选项，若开启，则会在每个图优化过程后生成dot文件
+  // config.SwitchIrOptim(false);// 默认为true。如果设置为false，关闭所有优化
+  config.EnableMemoryOptim(); // 开启内存/显存复用
+
+  this->predictor_ = CreatePaddlePredictor(config);
+}
+
+cv::Mat
+CRNNRecognizer::get_rotate_crop_image(const cv::Mat &srcimage,
+                                      std::vector<std::vector<int>> box) {
+  cv::Mat image;
+  srcimage.copyTo(image);
+  std::vector<std::vector<int>> points = box;
+
+  int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
+  int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
+  int left = int(*std::min_element(x_collect, x_collect + 4));
+  int right = int(*std::max_element(x_collect, x_collect + 4));
+  int top = int(*std::min_element(y_collect, y_collect + 4));
+  int bottom = int(*std::max_element(y_collect, y_collect + 4));
+
+  cv::Mat img_crop;
+  image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
+
+  for (int i = 0; i < points.size(); i++) {
+    points[i][0] -= left;
+    points[i][1] -= top;
+  }
+
+  int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
+                                pow(points[0][1] - points[1][1], 2)));
+  int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
+                                 pow(points[0][1] - points[3][1], 2)));
+
+  cv::Point2f pts_std[4];
+  pts_std[0] = cv::Point2f(0., 0.);
+  pts_std[1] = cv::Point2f(img_crop_width, 0.);
+  pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
+  pts_std[3] = cv::Point2f(0.f, img_crop_height);
+
+  cv::Point2f pointsf[4];
+  pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
+  pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
+  pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
+  pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
+
+  cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
+
+  cv::Mat dst_img;
+  cv::warpPerspective(img_crop, dst_img, M,
+                      cv::Size(img_crop_width, img_crop_height),
+                      cv::BORDER_REPLICATE);
+
+  if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
+    cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
+    cv::transpose(dst_img, srcCopy);
+    cv::flip(srcCopy, srcCopy, 0);
+    return srcCopy;
+  } else {
+    return dst_img;
+  }
+}
+
+std::vector<std::string> CRNNRecognizer::ReadDict(const std::string &path) {
+  std::ifstream in(path);
+  std::string filename;
+  std::string line;
+  std::vector<std::string> m_vec;
+  if (in) {
+    while (getline(in, line)) {
+      m_vec.push_back(line);
+    }
+  } else {
+    std::cout << "no such file" << std::endl;
+  }
+  return m_vec;
+}
+
+} // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/src/postprocess_op.cpp

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <include/postprocess_op.h>
+
+namespace PaddleOCR {
+
+void PostProcessor::GetContourArea(float **box, float unclip_ratio,
+                                   float &distance) {
+  int pts_num = 4;
+  float area = 0.0f;
+  float dist = 0.0f;
+  for (int i = 0; i < pts_num; i++) {
+    area += box[i][0] * box[(i + 1) % pts_num][1] -
+            box[i][1] * box[(i + 1) % pts_num][0];
+    dist += sqrtf((box[i][0] - box[(i + 1) % pts_num][0]) *
+                      (box[i][0] - box[(i + 1) % pts_num][0]) +
+                  (box[i][1] - box[(i + 1) % pts_num][1]) *
+                      (box[i][1] - box[(i + 1) % pts_num][1]));
+  }
+  area = fabs(float(area / 2.0));
+
+  distance = area * unclip_ratio / dist;
+}
+
+cv::RotatedRect PostProcessor::unclip(float **box) {
+  float unclip_ratio = 2.0;
+  float distance = 1.0;
+
+  GetContourArea(box, unclip_ratio, distance);
+
+  ClipperLib::ClipperOffset offset;
+  ClipperLib::Path p;
+  p << ClipperLib::IntPoint(int(box[0][0]), int(box[0][1]))
+    << ClipperLib::IntPoint(int(box[1][0]), int(box[1][1]))
+    << ClipperLib::IntPoint(int(box[2][0]), int(box[2][1]))
+    << ClipperLib::IntPoint(int(box[3][0]), int(box[3][1]));
+  offset.AddPath(p, ClipperLib::jtRound, ClipperLib::etClosedPolygon);
+
+  ClipperLib::Paths soln;
+  offset.Execute(soln, distance);
+  std::vector<cv::Point2f> points;
+
+  for (int j = 0; j < soln.size(); j++) {
+    for (int i = 0; i < soln[soln.size() - 1].size(); i++) {
+      points.emplace_back(soln[j][i].X, soln[j][i].Y);
+    }
+  }
+  cv::RotatedRect res = cv::minAreaRect(points);
+
+  return res;
+}
+
+float **PostProcessor::Mat2Vec(cv::Mat mat) {
+  auto **array = new float *[mat.rows];
+  for (int i = 0; i < mat.rows; ++i)
+    array[i] = new float[mat.cols];
+  for (int i = 0; i < mat.rows; ++i) {
+    for (int j = 0; j < mat.cols; ++j) {
+      array[i][j] = mat.at<float>(i, j);
+    }
+  }
+
+  return array;
+}
+
+void PostProcessor::quickSort(float **s, int l, int r) {
+  if (l < r) {
+    int i = l, j = r;
+    float x = s[l][0];
+    float *xp = s[l];
+    while (i < j) {
+      while (i < j && s[j][0] >= x)
+        j--;
+      if (i < j)
+        std::swap(s[i++], s[j]);
+      while (i < j && s[i][0] < x)
+        i++;
+      if (i < j)
+        std::swap(s[j--], s[i]);
+    }
+    s[i] = xp;
+    quickSort(s, l, i - 1);
+    quickSort(s, i + 1, r);
+  }
+}
+
+void PostProcessor::quickSort_vector(std::vector<std::vector<int>> &box, int l,
+                                     int r, int axis) {
+  if (l < r) {
+    int i = l, j = r;
+    int x = box[l][axis];
+    std::vector<int> xp(box[l]);
+    while (i < j) {
+      while (i < j && box[j][axis] >= x)
+        j--;
+      if (i < j)
+        std::swap(box[i++], box[j]);
+      while (i < j && box[i][axis] < x)
+        i++;
+      if (i < j)
+        std::swap(box[j--], box[i]);
+    }
+    box[i] = xp;
+    quickSort_vector(box, l, i - 1, axis);
+    quickSort_vector(box, i + 1, r, axis);
+  }
+}
+
+std::vector<std::vector<int>>
+PostProcessor::order_points_clockwise(std::vector<std::vector<int>> pts) {
+  std::vector<std::vector<int>> box = pts;
+  quickSort_vector(box, 0, int(box.size() - 1), 0);
+  std::vector<std::vector<int>> leftmost = {box[0], box[1]};
+  std::vector<std::vector<int>> rightmost = {box[2], box[3]};
+
+  if (leftmost[0][1] > leftmost[1][1])
+    std::swap(leftmost[0], leftmost[1]);
+
+  if (rightmost[0][1] > rightmost[1][1])
+    std::swap(rightmost[0], rightmost[1]);
+
+  std::vector<std::vector<int>> rect = {leftmost[0], rightmost[0], rightmost[1],
+                                        leftmost[1]};
+  return rect;
+}
+
+float **PostProcessor::get_mini_boxes(cv::RotatedRect box, float &ssid) {
+  ssid = box.size.width >= box.size.height ? box.size.height : box.size.width;
+
+  cv::Mat points;
+  cv::boxPoints(box, points);
+  // sorted box points
+  auto array = Mat2Vec(points);
+  quickSort(array, 0, 3);
+
+  float *idx1 = array[0], *idx2 = array[1], *idx3 = array[2], *idx4 = array[3];
+  if (array[3][1] <= array[2][1]) {
+    idx2 = array[3];
+    idx3 = array[2];
+  } else {
+    idx2 = array[2];
+    idx3 = array[3];
+  }
+  if (array[1][1] <= array[0][1]) {
+    idx1 = array[1];
+    idx4 = array[0];
+  } else {
+    idx1 = array[0];
+    idx4 = array[1];
+  }
+
+  array[0] = idx1;
+  array[1] = idx2;
+  array[2] = idx3;
+  array[3] = idx4;
+
+  return array;
+}
+
+float PostProcessor::box_score_fast(float **box_array, cv::Mat pred) {
+  auto array = box_array;
+  int width = pred.cols;
+  int height = pred.rows;
+
+  float box_x[4] = {array[0][0], array[1][0], array[2][0], array[3][0]};
+  float box_y[4] = {array[0][1], array[1][1], array[2][1], array[3][1]};
+
+  int xmin = clamp(int(std::floor(*(std::min_element(box_x, box_x + 4)))), 0,
+                   width - 1);
+  int xmax = clamp(int(std::ceil(*(std::max_element(box_x, box_x + 4)))), 0,
+                   width - 1);
+  int ymin = clamp(int(std::floor(*(std::min_element(box_y, box_y + 4)))), 0,
+                   height - 1);
+  int ymax = clamp(int(std::ceil(*(std::max_element(box_y, box_y + 4)))), 0,
+                   height - 1);
+
+  cv::Mat mask;
+  mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
+
+  cv::Point root_point[4];
+  root_point[0] = cv::Point(int(array[0][0]) - xmin, int(array[0][1]) - ymin);
+  root_point[1] = cv::Point(int(array[1][0]) - xmin, int(array[1][1]) - ymin);
+  root_point[2] = cv::Point(int(array[2][0]) - xmin, int(array[2][1]) - ymin);
+  root_point[3] = cv::Point(int(array[3][0]) - xmin, int(array[3][1]) - ymin);
+  const cv::Point *ppt[1] = {root_point};
+  int npt[] = {4};
+  cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
+
+  cv::Mat croppedImg;
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
+      .copyTo(croppedImg);
+
+  auto score = cv::mean(croppedImg, mask)[0];
+  return score;
+}
+
+std::vector<std::vector<std::vector<int>>>
+PostProcessor::boxes_from_bitmap(const cv::Mat pred, const cv::Mat bitmap) {
+  const int min_size = 3;
+  const int max_candidates = 1000;
+  const float box_thresh = 0.5;
+
+  int width = bitmap.cols;
+  int height = bitmap.rows;
+
+  std::vector<std::vector<cv::Point>> contours;
+  std::vector<cv::Vec4i> hierarchy;
+
+  cv::findContours(bitmap, contours, hierarchy, cv::RETR_LIST,
+                   cv::CHAIN_APPROX_SIMPLE);
+
+  int num_contours =
+      contours.size() >= max_candidates ? max_candidates : contours.size();
+
+  std::vector<std::vector<std::vector<int>>> boxes;
+
+  for (int _i = 0; _i < num_contours; _i++) {
+    float ssid;
+    cv::RotatedRect box = cv::minAreaRect(contours[_i]);
+    auto array = get_mini_boxes(box, ssid);
+
+    auto box_for_unclip = array;
+    // end get_mini_box
+
+    if (ssid < min_size) {
+      continue;
+    }
+
+    float score;
+    score = box_score_fast(array, pred);
+    // end box_score_fast
+    if (score < box_thresh)
+      continue;
+
+    // start for unclip
+    cv::RotatedRect points = unclip(box_for_unclip);
+    // end for unclip
+
+    cv::RotatedRect clipbox = points;
+    auto cliparray = get_mini_boxes(clipbox, ssid);
+
+    if (ssid < min_size + 2)
+      continue;
+
+    int dest_width = pred.cols;
+    int dest_height = pred.rows;
+    std::vector<std::vector<int>> intcliparray;
+
+    for (int num_pt = 0; num_pt < 4; num_pt++) {
+      std::vector<int> a{int(clampf(roundf(cliparray[num_pt][0] / float(width) *
+                                           float(dest_width)),
+                                    0, float(dest_width))),
+                         int(clampf(roundf(cliparray[num_pt][1] /
+                                           float(height) * float(dest_height)),
+                                    0, float(dest_height)))};
+      intcliparray.push_back(a);
+    }
+    boxes.push_back(intcliparray);
+
+  } // end for
+  return boxes;
+}
+
+std::vector<std::vector<std::vector<int>>> PostProcessor::filter_tag_det_res(
+    std::vector<std::vector<std::vector<int>>> boxes, float ratio_h,
+    float ratio_w, cv::Mat srcimg) {
+  int oriimg_h = srcimg.rows;
+  int oriimg_w = srcimg.cols;
+
+  std::vector<std::vector<std::vector<int>>> root_points;
+  for (int n = 0; n < boxes.size(); n++) {
+    boxes[n] = order_points_clockwise(boxes[n]);
+    for (int m = 0; m < boxes[0].size(); m++) {
+      boxes[n][m][0] /= ratio_w;
+      boxes[n][m][1] /= ratio_h;
+
+      boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w - 1));
+      boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h - 1));
+    }
+  }
+
+  for (int n = 0; n < boxes.size(); n++) {
+    int rect_width, rect_height;
+    rect_width = int(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) +
+                          pow(boxes[n][0][1] - boxes[n][1][1], 2)));
+    rect_height = int(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) +
+                           pow(boxes[n][0][1] - boxes[n][3][1], 2)));
+    if (rect_width <= 10 || rect_height <= 10)
+      continue;
+    root_points.push_back(boxes[n]);
+  }
+  return root_points;
+}
+
+} // namespace PaddleOCR

deploy/cpp_infer/src/preprocess_op.cpp

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "paddle_api.h"
+#include "paddle_inference_api.h"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <include/preprocess_op.h>
+
+namespace PaddleOCR {
+
+void Permute::Run(const cv::Mat *im, float *data) {
+  int rh = im->rows;
+  int rw = im->cols;
+  int rc = im->channels();
+  for (int i = 0; i < rc; ++i) {
+    cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
+  }
+}
+
+void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
+                    const std::vector<float> &scale, const bool is_scale) {
+  double e = 1.0;
+  if (is_scale) {
+    e /= 255.0;
+  }
+  (*im).convertTo(*im, CV_32FC3, e);
+  for (int h = 0; h < im->rows; h++) {
+    for (int w = 0; w < im->cols; w++) {
+      im->at<cv::Vec3f>(h, w)[0] =
+          (im->at<cv::Vec3f>(h, w)[0] - mean[0]) * scale[0];
+      im->at<cv::Vec3f>(h, w)[1] =
+          (im->at<cv::Vec3f>(h, w)[1] - mean[1]) * scale[1];
+      im->at<cv::Vec3f>(h, w)[2] =
+          (im->at<cv::Vec3f>(h, w)[2] - mean[2]) * scale[2];
+    }
+  }
+}
+
+void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
+                         int max_size_len, float &ratio_h, float &ratio_w) {
+  int w = img.cols;
+  int h = img.rows;
+
+  float ratio = 1.f;
+  int max_wh = w >= h ? w : h;
+  if (max_wh > max_size_len) {
+    if (h > w) {
+      ratio = float(max_size_len) / float(h);
+    } else {
+      ratio = float(max_size_len) / float(w);
+    }
+  }
+
+  int resize_h = int(float(h) * ratio);
+  int resize_w = int(float(w) * ratio);
+  if (resize_h % 32 == 0)
+    resize_h = resize_h;
+  else if (resize_h / 32 < 1 + 1e-5)
+    resize_h = 32;
+  else
+    resize_h = (resize_h / 32 - 1) * 32;
+
+  if (resize_w % 32 == 0)
+    resize_w = resize_w;
+  else if (resize_w / 32 < 1)
+    resize_w = 32;
+  else
+    resize_w = (resize_w / 32 - 1) * 32;
+
+  cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
+
+  ratio_h = float(resize_h) / float(h);
+  ratio_w = float(resize_w) / float(w);
+}
+
+void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio,
+                        const std::vector<int> rec_image_shape) {
+  int imgC, imgH, imgW;
+  imgC = rec_image_shape[0];
+  imgH = rec_image_shape[1];
+  imgW = rec_image_shape[2];
+
+  imgW = int(32 * wh_ratio);
+
+  float ratio = float(img.cols) / float(img.rows);
+  int resize_w, resize_h;
+  if (ceilf(imgH * ratio) > imgW)
+    resize_w = imgW;
+  else
+    resize_w = int(ceilf(imgH * ratio));
+
+  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
+             cv::INTER_LINEAR);
+}
+
+} // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/tools/build.sh

+
+LIB_DIR=/paddle/code/gry/Paddle/build/fluid_inference_install_dir/
+CUDA_LIB_DIR=/usr/local/cuda/lib64
+CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
+TENSORRT_ROOT_DIR=YOUR_TENSORRT_ROOT_DIR
+
+BUILD_DIR=build
+rm -rf ${BUILD_DIR}
+mkdir ${BUILD_DIR}
+cd ${BUILD_DIR}
+cmake .. \
+    -DPADDLE_LIB=${LIB_DIR} \
+    -DWITH_MKL=ON \
+    -DDEMO_NAME=ocr_system \
+    -DWITH_GPU=OFF \
+    -DWITH_STATIC_LIB=OFF \
+    -DUSE_TENSORRT=OFF \
+    -DCUDNN_LIB=${CUDNN_LIB_DIR} \
+    -DCUDA_LIB=${CUDA_LIB_DIR} \
+    -DTENSORRT_ROOT=YOUR_TENSORRT_ROOT_DIR
+
+make -j
+
+

deploy/cpp_infer/tools/run.sh

+
+./build/ocr_system ./inference/det_db/ ./inference/rec_crnn/ ../../doc/imgs/11.jpg