add cpp inference
This commit is contained in:
littletomatodonkey
parent
a987ec70e8
commit
297edfaa03
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@ -1,5 +1,6 @@
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# Byte-compiled / optimized / DLL files
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__pycache__/
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.ipynb_checkpoints/
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*.py[cod]
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*$py.class
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@ -0,0 +1,110 @@
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project(ocr_system CXX C)
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option(WITH_MKL "Compile demo with MKL/OpenBlas support, default use MKL." ON)
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option(WITH_GPU "Compile demo with GPU/CPU, default use CPU." OFF)
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option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static." ON)
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option(USE_TENSORRT "Compile demo with TensorRT." OFF)
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macro(safe_set_static_flag)
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foreach(flag_var
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CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
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CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
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if(${flag_var} MATCHES "/MD")
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string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
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endif(${flag_var} MATCHES "/MD")
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endforeach(flag_var)
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endmacro()
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -g -fpermissive")
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set(CMAKE_STATIC_LIBRARY_PREFIX "")
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message("flags" ${CMAKE_CXX_FLAGS})
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set(CMAKE_CXX_FLAGS_RELEASE "-O3")
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if(NOT DEFINED PADDLE_LIB)
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message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib")
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endif()
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if(NOT DEFINED DEMO_NAME)
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message(FATAL_ERROR "please set DEMO_NAME with -DDEMO_NAME=demo_name")
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endif()
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# user ze
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# find_package(OpenCV)
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set(OPENCV_DIR "/paddle/libs/opencv-3.4.7/opencv3")
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find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
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include_directories(${OpenCV_INCLUDE_DIRS})
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include_directories("${PADDLE_LIB}/paddle/include")
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include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
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include_directories("${PADDLE_LIB}/third_party/install/glog/include")
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include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
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include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
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include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
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include_directories("${PADDLE_LIB}/third_party/boost")
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include_directories("${PADDLE_LIB}/third_party/eigen3")
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include_directories("${CMAKE_SOURCE_DIR}/")
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if (USE_TENSORRT AND WITH_GPU)
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include_directories("${TENSORRT_ROOT}/include")
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link_directories("${TENSORRT_ROOT}/lib")
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endif()
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link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
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link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
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link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
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link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
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link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
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link_directories("${PADDLE_LIB}/paddle/lib")
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add_executable(${DEMO_NAME} src/main.cpp src/ocr_det.cpp src/ocr_rec.cpp src/preprocess_op.cpp src/clipper.cpp src/postprocess_op.cpp )
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if(WITH_MKL)
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include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
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set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
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${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
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set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
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if(EXISTS ${MKLDNN_PATH})
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include_directories("${MKLDNN_PATH}/include")
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set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
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endif()
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else()
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set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
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endif()
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# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
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if(WITH_STATIC_LIB)
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set(DEPS
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${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
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else()
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set(DEPS
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${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
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endif()
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# user ze
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# set(EXTERNAL_LIB "-lrt -ldl -lpthread -lm -lopencv_world")
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# gry
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set(EXTERNAL_LIB "-lrt -ldl -lpthread -lm")
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set(DEPS ${DEPS}
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${MATH_LIB} ${MKLDNN_LIB}
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glog gflags protobuf z xxhash
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${EXTERNAL_LIB} ${OpenCV_LIBS})
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if(WITH_GPU)
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if (USE_TENSORRT)
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set(DEPS ${DEPS}
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${TENSORRT_ROOT}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
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set(DEPS ${DEPS}
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${TENSORRT_ROOT}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
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endif()
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set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
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set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX} )
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set(DEPS ${DEPS} ${CUDA_LIB}/libcublas${CMAKE_SHARED_LIBRARY_SUFFIX} )
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set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX} )
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endif()
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target_link_libraries(${DEMO_NAME} ${DEPS})
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@ -0,0 +1,423 @@
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/*******************************************************************************
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* *
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* Author : Angus Johnson *
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* Version : 6.4.2 *
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* Date : 27 February 2017 *
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* Website : http://www.angusj.com *
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* Copyright : Angus Johnson 2010-2017 *
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* *
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* License: *
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* Use, modification & distribution is subject to Boost Software License Ver 1. *
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* http://www.boost.org/LICENSE_1_0.txt *
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* *
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* Attributions: *
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* The code in this library is an extension of Bala Vatti's clipping algorithm: *
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* "A generic solution to polygon clipping" *
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* Communications of the ACM, Vol 35, Issue 7 (July 1992) pp 56-63. *
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* http://portal.acm.org/citation.cfm?id=129906 *
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* *
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* Computer graphics and geometric modeling: implementation and algorithms *
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* By Max K. Agoston *
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* Springer; 1 edition (January 4, 2005) *
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* http://books.google.com/books?q=vatti+clipping+agoston *
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* *
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* See also: *
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* "Polygon Offsetting by Computing Winding Numbers" *
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* Paper no. DETC2005-85513 pp. 565-575 *
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* ASME 2005 International Design Engineering Technical Conferences *
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* and Computers and Information in Engineering Conference (IDETC/CIE2005) *
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* September 24-28, 2005 , Long Beach, California, USA *
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* http://www.me.berkeley.edu/~mcmains/pubs/DAC05OffsetPolygon.pdf *
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* *
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*******************************************************************************/
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#ifndef clipper_hpp
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#define clipper_hpp
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#define CLIPPER_VERSION "6.4.2"
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// use_int32: When enabled 32bit ints are used instead of 64bit ints. This
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// improve performance but coordinate values are limited to the range +/- 46340
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//#define use_int32
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// use_xyz: adds a Z member to IntPoint. Adds a minor cost to perfomance.
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//#define use_xyz
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// use_lines: Enables line clipping. Adds a very minor cost to performance.
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#define use_lines
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// use_deprecated: Enables temporary support for the obsolete functions
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//#define use_deprecated
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#include <cstdlib>
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#include <cstring>
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#include <functional>
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#include <list>
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#include <ostream>
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#include <queue>
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#include <set>
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#include <stdexcept>
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#include <vector>
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namespace ClipperLib {
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enum ClipType { ctIntersection, ctUnion, ctDifference, ctXor };
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enum PolyType { ptSubject, ptClip };
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// By far the most widely used winding rules for polygon filling are
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// EvenOdd & NonZero (GDI, GDI+, XLib, OpenGL, Cairo, AGG, Quartz, SVG, Gr32)
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// Others rules include Positive, Negative and ABS_GTR_EQ_TWO (only in OpenGL)
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// see http://glprogramming.com/red/chapter11.html
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enum PolyFillType { pftEvenOdd, pftNonZero, pftPositive, pftNegative };
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#ifdef use_int32
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typedef int cInt;
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static cInt const loRange = 0x7FFF;
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static cInt const hiRange = 0x7FFF;
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#else
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typedef signed long long cInt;
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static cInt const loRange = 0x3FFFFFFF;
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static cInt const hiRange = 0x3FFFFFFFFFFFFFFFLL;
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typedef signed long long long64; // used by Int128 class
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typedef unsigned long long ulong64;
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#endif
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struct IntPoint {
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cInt X;
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cInt Y;
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#ifdef use_xyz
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cInt Z;
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IntPoint(cInt x = 0, cInt y = 0, cInt z = 0) : X(x), Y(y), Z(z){};
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#else
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IntPoint(cInt x = 0, cInt y = 0) : X(x), Y(y){};
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#endif
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friend inline bool operator==(const IntPoint &a, const IntPoint &b) {
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return a.X == b.X && a.Y == b.Y;
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}
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friend inline bool operator!=(const IntPoint &a, const IntPoint &b) {
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return a.X != b.X || a.Y != b.Y;
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}
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};
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//------------------------------------------------------------------------------
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typedef std::vector<IntPoint> Path;
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typedef std::vector<Path> Paths;
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inline Path &operator<<(Path &poly, const IntPoint &p) {
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poly.push_back(p);
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return poly;
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}
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inline Paths &operator<<(Paths &polys, const Path &p) {
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polys.push_back(p);
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return polys;
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}
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std::ostream &operator<<(std::ostream &s, const IntPoint &p);
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std::ostream &operator<<(std::ostream &s, const Path &p);
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std::ostream &operator<<(std::ostream &s, const Paths &p);
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struct DoublePoint {
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double X;
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double Y;
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DoublePoint(double x = 0, double y = 0) : X(x), Y(y) {}
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DoublePoint(IntPoint ip) : X((double)ip.X), Y((double)ip.Y) {}
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};
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//------------------------------------------------------------------------------
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#ifdef use_xyz
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typedef void (*ZFillCallback)(IntPoint &e1bot, IntPoint &e1top, IntPoint &e2bot,
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IntPoint &e2top, IntPoint &pt);
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#endif
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enum InitOptions {
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ioReverseSolution = 1,
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ioStrictlySimple = 2,
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ioPreserveCollinear = 4
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};
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enum JoinType { jtSquare, jtRound, jtMiter };
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enum EndType {
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etClosedPolygon,
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etClosedLine,
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etOpenButt,
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etOpenSquare,
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etOpenRound
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};
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class PolyNode;
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typedef std::vector<PolyNode *> PolyNodes;
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class PolyNode {
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public:
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PolyNode();
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virtual ~PolyNode(){};
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Path Contour;
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PolyNodes Childs;
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PolyNode *Parent;
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PolyNode *GetNext() const;
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bool IsHole() const;
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bool IsOpen() const;
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int ChildCount() const;
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private:
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// PolyNode& operator =(PolyNode& other);
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unsigned Index; // node index in Parent.Childs
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bool m_IsOpen;
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JoinType m_jointype;
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EndType m_endtype;
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PolyNode *GetNextSiblingUp() const;
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void AddChild(PolyNode &child);
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friend class Clipper; // to access Index
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friend class ClipperOffset;
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};
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class PolyTree : public PolyNode {
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public:
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~PolyTree() { Clear(); };
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PolyNode *GetFirst() const;
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void Clear();
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int Total() const;
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private:
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// PolyTree& operator =(PolyTree& other);
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PolyNodes AllNodes;
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friend class Clipper; // to access AllNodes
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};
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bool Orientation(const Path &poly);
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double Area(const Path &poly);
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int PointInPolygon(const IntPoint &pt, const Path &path);
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void SimplifyPolygon(const Path &in_poly, Paths &out_polys,
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PolyFillType fillType = pftEvenOdd);
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void SimplifyPolygons(const Paths &in_polys, Paths &out_polys,
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PolyFillType fillType = pftEvenOdd);
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void SimplifyPolygons(Paths &polys, PolyFillType fillType = pftEvenOdd);
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void CleanPolygon(const Path &in_poly, Path &out_poly, double distance = 1.415);
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void CleanPolygon(Path &poly, double distance = 1.415);
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void CleanPolygons(const Paths &in_polys, Paths &out_polys,
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double distance = 1.415);
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void CleanPolygons(Paths &polys, double distance = 1.415);
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void MinkowskiSum(const Path &pattern, const Path &path, Paths &solution,
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bool pathIsClosed);
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void MinkowskiSum(const Path &pattern, const Paths &paths, Paths &solution,
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bool pathIsClosed);
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void MinkowskiDiff(const Path &poly1, const Path &poly2, Paths &solution);
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void PolyTreeToPaths(const PolyTree &polytree, Paths &paths);
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void ClosedPathsFromPolyTree(const PolyTree &polytree, Paths &paths);
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void OpenPathsFromPolyTree(PolyTree &polytree, Paths &paths);
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void ReversePath(Path &p);
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void ReversePaths(Paths &p);
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struct IntRect {
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cInt left;
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cInt top;
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cInt right;
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cInt bottom;
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};
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// enums that are used internally ...
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enum EdgeSide { esLeft = 1, esRight = 2 };
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// forward declarations (for stuff used internally) ...
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struct TEdge;
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struct IntersectNode;
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struct LocalMinimum;
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struct OutPt;
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struct OutRec;
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struct Join;
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typedef std::vector<OutRec *> PolyOutList;
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typedef std::vector<TEdge *> EdgeList;
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typedef std::vector<Join *> JoinList;
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typedef std::vector<IntersectNode *> IntersectList;
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//------------------------------------------------------------------------------
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// ClipperBase is the ancestor to the Clipper class. It should not be
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// instantiated directly. This class simply abstracts the conversion of sets of
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// polygon coordinates into edge objects that are stored in a LocalMinima list.
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class ClipperBase {
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public:
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ClipperBase();
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virtual ~ClipperBase();
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virtual bool AddPath(const Path &pg, PolyType PolyTyp, bool Closed);
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bool AddPaths(const Paths &ppg, PolyType PolyTyp, bool Closed);
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virtual void Clear();
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IntRect GetBounds();
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bool PreserveCollinear() { return m_PreserveCollinear; };
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void PreserveCollinear(bool value) { m_PreserveCollinear = value; };
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protected:
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void DisposeLocalMinimaList();
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TEdge *AddBoundsToLML(TEdge *e, bool IsClosed);
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virtual void Reset();
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TEdge *ProcessBound(TEdge *E, bool IsClockwise);
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void InsertScanbeam(const cInt Y);
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bool PopScanbeam(cInt &Y);
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bool LocalMinimaPending();
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bool PopLocalMinima(cInt Y, const LocalMinimum *&locMin);
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OutRec *CreateOutRec();
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void DisposeAllOutRecs();
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void DisposeOutRec(PolyOutList::size_type index);
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void SwapPositionsInAEL(TEdge *edge1, TEdge *edge2);
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void DeleteFromAEL(TEdge *e);
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void UpdateEdgeIntoAEL(TEdge *&e);
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typedef std::vector<LocalMinimum> MinimaList;
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MinimaList::iterator m_CurrentLM;
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MinimaList m_MinimaList;
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bool m_UseFullRange;
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EdgeList m_edges;
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bool m_PreserveCollinear;
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bool m_HasOpenPaths;
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PolyOutList m_PolyOuts;
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TEdge *m_ActiveEdges;
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typedef std::priority_queue<cInt> ScanbeamList;
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ScanbeamList m_Scanbeam;
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};
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//------------------------------------------------------------------------------
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class Clipper : public virtual ClipperBase {
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public:
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Clipper(int initOptions = 0);
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bool Execute(ClipType clipType, Paths &solution,
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PolyFillType fillType = pftEvenOdd);
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bool Execute(ClipType clipType, Paths &solution, PolyFillType subjFillType,
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PolyFillType clipFillType);
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bool Execute(ClipType clipType, PolyTree &polytree,
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PolyFillType fillType = pftEvenOdd);
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bool Execute(ClipType clipType, PolyTree &polytree, PolyFillType subjFillType,
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PolyFillType clipFillType);
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bool ReverseSolution() { return m_ReverseOutput; };
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void ReverseSolution(bool value) { m_ReverseOutput = value; };
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bool StrictlySimple() { return m_StrictSimple; };
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void StrictlySimple(bool value) { m_StrictSimple = value; };
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// set the callback function for z value filling on intersections (otherwise Z
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// is 0)
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#ifdef use_xyz
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void ZFillFunction(ZFillCallback zFillFunc);
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#endif
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protected:
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virtual bool ExecuteInternal();
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private:
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JoinList m_Joins;
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JoinList m_GhostJoins;
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IntersectList m_IntersectList;
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ClipType m_ClipType;
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typedef std::list<cInt> MaximaList;
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MaximaList m_Maxima;
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TEdge *m_SortedEdges;
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bool m_ExecuteLocked;
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||||
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
|
||||
|
|
@ -0,0 +1,71 @@
|
|||
// 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
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
// 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
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
// 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
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
// 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
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,67 @@
|
|||
// 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;
|
||||
}
|
||||
|
|
@ -0,0 +1,142 @@
|
|||
// 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
|
||||
|
|
@ -0,0 +1,218 @@
|
|||
// 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
|
||||
|
|
@ -0,0 +1,307 @@
|
|||
// 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
|
||||
|
|
@ -0,0 +1,119 @@
|
|||
// 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
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
|
||||
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
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,2 @@
|
|||
|
||||
./build/ocr_system ./inference/det_db/ ./inference/rec_crnn/ ../../doc/imgs/11.jpg
|
||||
Loading…
Reference in New Issue