mirror of
https://github.com/opencv/opencv.git
synced 2025-06-07 09:25:45 +08:00
Merge remote-tracking branch 'upstream/3.4' into merge-3.4
This commit is contained in:
Alexander Alekhin
commit
0cbaaba4b1
@ -129,9 +129,9 @@ endif()
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if(INF_ENGINE_TARGET)
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if(NOT INF_ENGINE_RELEASE)
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message(WARNING "InferenceEngine version has not been set, 2020.2 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
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message(WARNING "InferenceEngine version has not been set, 2020.3 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
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endif()
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set(INF_ENGINE_RELEASE "2020020000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)")
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set(INF_ENGINE_RELEASE "2020030000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2020.1.0.2 -> 2020010002)")
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set_target_properties(${INF_ENGINE_TARGET} PROPERTIES
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INTERFACE_COMPILE_DEFINITIONS "HAVE_INF_ENGINE=1;INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}"
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)
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@ -79,9 +79,10 @@ get_mkl_version(${MKL_INCLUDE_DIRS}/mkl_version.h)
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#determine arch
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if(CMAKE_CXX_SIZEOF_DATA_PTR EQUAL 8)
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set(MKL_X64 1)
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set(MKL_ARCH "intel64")
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set(MKL_ARCH_LIST "intel64")
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if(MSVC)
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list(APPEND MKL_ARCH_LIST "win-x64")
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endif()
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include(CheckTypeSize)
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CHECK_TYPE_SIZE(int _sizeof_int)
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if (_sizeof_int EQUAL 4)
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@ -90,14 +91,19 @@ if(CMAKE_CXX_SIZEOF_DATA_PTR EQUAL 8)
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set(MKL_ARCH_SUFFIX "ilp64")
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endif()
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else()
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set(MKL_ARCH "ia32")
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set(MKL_ARCH_LIST "ia32")
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set(MKL_ARCH_SUFFIX "c")
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endif()
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if(MKL_VERSION_STR VERSION_GREATER "11.3.0" OR MKL_VERSION_STR VERSION_EQUAL "11.3.0")
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set(mkl_lib_find_paths
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${MKL_ROOT_DIR}/lib
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${MKL_ROOT_DIR}/lib/${MKL_ARCH} ${MKL_ROOT_DIR}/../tbb/lib/${MKL_ARCH})
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${MKL_ROOT_DIR}/lib)
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foreach(MKL_ARCH ${MKL_ARCH_LIST})
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list(APPEND mkl_lib_find_paths
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${MKL_ROOT_DIR}/lib/${MKL_ARCH}
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${MKL_ROOT_DIR}/../tbb/lib/${MKL_ARCH}
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${MKL_ROOT_DIR}/${MKL_ARCH})
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endforeach()
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set(mkl_lib_list "mkl_intel_${MKL_ARCH_SUFFIX}")
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@ -121,7 +127,7 @@ endif()
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set(MKL_LIBRARIES "")
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foreach(lib ${mkl_lib_list})
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find_library(${lib} ${lib} ${mkl_lib_find_paths})
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find_library(${lib} NAMES ${lib} ${lib}_dll HINTS ${mkl_lib_find_paths})
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mark_as_advanced(${lib})
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if(NOT ${lib})
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mkl_fail()
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@ -30,7 +30,7 @@ Installing CMake
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-# Install the dmg package and launch it from Applications. That will give you the UI app of CMake
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-# From the CMake app window, choose menu Tools --> Install For Command Line Use.
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-# From the CMake app window, choose menu Tools --> How to Install For Command Line Use. Then, follow the instructions from the pop-up there.
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-# Install folder will be /usr/bin/ by default, submit it by choosing Install command line links.
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@ -66,7 +66,7 @@ git clone https://github.com/opencv/opencv_contrib.git
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Building OpenCV from Source Using CMake
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---------------------------------------
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-# Create a temporary directory, which we denote as `<cmake_build_dir>`, where you want to put
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-# Create a temporary directory, which we denote as `build_opencv`, where you want to put
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the generated Makefiles, project files as well the object files and output binaries and enter
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there.
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@ -87,8 +87,8 @@ Building OpenCV from Source Using CMake
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or cmake-gui
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- set full path to OpenCV source code, e.g. `/home/user/opencv`
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- set full path to `<cmake_build_dir>`, e.g. `/home/user/build_opencv`
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- set the OpenCV source code path to, e.g. `/home/user/opencv`
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- set the binary build path to your CMake build directory, e.g. `/home/user/build_opencv`
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- set optional parameters
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- run: "Configure"
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- run: "Generate"
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@ -66,10 +66,18 @@
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namespace cv
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{
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//! @addtogroup core_eigen
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/** @addtogroup core_eigen
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These functions are provided for OpenCV-Eigen interoperability. They convert `Mat`
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objects to corresponding `Eigen::Matrix` objects and vice-versa. Consult the [Eigen
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documentation](https://eigen.tuxfamily.org/dox/group__TutorialMatrixClass.html) for
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information about the `Matrix` template type.
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@note Using these functions requires the `Eigen/Dense` or similar header to be
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included before this header.
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*/
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//! @{
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#ifdef OPENCV_EIGEN_TENSOR_SUPPORT
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#if defined(OPENCV_EIGEN_TENSOR_SUPPORT) || defined(CV_DOXYGEN)
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/** @brief Converts an Eigen::Tensor to a cv::Mat.
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The method converts an Eigen::Tensor with shape (H x W x C) to a cv::Mat where:
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@ -2248,7 +2248,7 @@ struct Net::Impl : public detail::NetImplBase
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auto ieInpNode = inputNodes[i].dynamicCast<InfEngineNgraphNode>();
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CV_Assert(oid < ieInpNode->node->get_output_size());
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#if INF_ENGINE_VER_MAJOR_GT(2020030000)
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#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_3)
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inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid)));
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#else
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inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid, false)));
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@ -82,7 +82,7 @@ public:
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return type_info;
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}
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#if INF_ENGINE_VER_MAJOR_GT(2020020000)
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#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_3)
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NgraphCustomOp(const ngraph::OutputVector& inputs,
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#else
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NgraphCustomOp(const ngraph::NodeVector& inputs,
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@ -112,7 +112,7 @@ public:
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std::shared_ptr<ngraph::Node> copy_with_new_args(const ngraph::NodeVector& new_args) const override
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{
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#if INF_ENGINE_VER_MAJOR_GT(2020020000)
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#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_3)
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return std::make_shared<NgraphCustomOp>(ngraph::as_output_vector(new_args), params);
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#else
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return std::make_shared<NgraphCustomOp>(new_args, params);
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@ -239,7 +239,9 @@ private:
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class InfEngineNgraphExtension : public InferenceEngine::IExtension
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{
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public:
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#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2)
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virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {}
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#endif
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virtual void Unload() noexcept {}
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virtual void Release() noexcept {}
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virtual void GetVersion(const InferenceEngine::Version*&) const noexcept {}
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@ -283,7 +285,7 @@ InfEngineNgraphNode::InfEngineNgraphNode(const std::vector<Ptr<BackendNode> >& n
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{"internals", shapesToStr(internals)}
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};
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#if INF_ENGINE_VER_MAJOR_GT(2020020000)
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#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2020_3)
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ngraph::OutputVector inp_nodes;
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#else
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ngraph::NodeVector inp_nodes;
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@ -25,10 +25,11 @@
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#define INF_ENGINE_RELEASE_2019R3 2019030000
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#define INF_ENGINE_RELEASE_2020_1 2020010000
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#define INF_ENGINE_RELEASE_2020_2 2020020000
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#define INF_ENGINE_RELEASE_2020_3 2020030000
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#ifndef INF_ENGINE_RELEASE
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#warning("IE version have not been provided via command-line. Using 2020.2 by default")
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#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2020_2
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#warning("IE version have not been provided via command-line. Using 2020.3 by default")
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#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2020_3
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#endif
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#define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
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@ -226,7 +227,9 @@ private:
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class InfEngineExtension : public InferenceEngine::IExtension
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{
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public:
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#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2)
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virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {}
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#endif
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virtual void Unload() noexcept {}
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virtual void Release() noexcept {}
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virtual void GetVersion(const InferenceEngine::Version*&) const noexcept {}
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@ -1,25 +1,81 @@
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'''
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Text detection model: https://github.com/argman/EAST
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Download link: https://www.dropbox.com/s/r2ingd0l3zt8hxs/frozen_east_text_detection.tar.gz?dl=1
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Text recognition model taken from here: https://github.com/meijieru/crnn.pytorch
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How to convert from pb to onnx:
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Using classes from here: https://github.com/meijieru/crnn.pytorch/blob/master/models/crnn.py
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import torch
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import models.crnn as CRNN
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model = CRNN(32, 1, 37, 256)
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model.load_state_dict(torch.load('crnn.pth'))
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dummy_input = torch.randn(1, 1, 32, 100)
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torch.onnx.export(model, dummy_input, "crnn.onnx", verbose=True)
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'''
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# Import required modules
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import numpy as np
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import cv2 as cv
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import math
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import argparse
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############ Add argument parser for command line arguments ############
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parser = argparse.ArgumentParser(description='Use this script to run TensorFlow implementation (https://github.com/argman/EAST) of EAST: An Efficient and Accurate Scene Text Detector (https://arxiv.org/abs/1704.03155v2)')
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parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
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parser.add_argument('--model', required=True,
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help='Path to a binary .pb file of model contains trained weights.')
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parser = argparse.ArgumentParser(
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description="Use this script to run TensorFlow implementation (https://github.com/argman/EAST) of "
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"EAST: An Efficient and Accurate Scene Text Detector (https://arxiv.org/abs/1704.03155v2)"
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"The OCR model can be obtained from converting the pretrained CRNN model to .onnx format from the github repository https://github.com/meijieru/crnn.pytorch")
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parser.add_argument('--input',
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help='Path to input image or video file. Skip this argument to capture frames from a camera.')
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parser.add_argument('--model', '-m', required=True,
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help='Path to a binary .pb file contains trained detector network.')
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parser.add_argument('--ocr', default="crnn.onnx",
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help="Path to a binary .pb or .onnx file contains trained recognition network", )
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parser.add_argument('--width', type=int, default=320,
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help='Preprocess input image by resizing to a specific width. It should be multiple by 32.')
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parser.add_argument('--height',type=int, default=320,
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parser.add_argument('--height', type=int, default=320,
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help='Preprocess input image by resizing to a specific height. It should be multiple by 32.')
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parser.add_argument('--thr',type=float, default=0.5,
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parser.add_argument('--thr', type=float, default=0.5,
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help='Confidence threshold.')
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parser.add_argument('--nms',type=float, default=0.4,
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parser.add_argument('--nms', type=float, default=0.4,
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help='Non-maximum suppression threshold.')
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args = parser.parse_args()
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############ Utility functions ############
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def decode(scores, geometry, scoreThresh):
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def fourPointsTransform(frame, vertices):
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vertices = np.asarray(vertices)
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outputSize = (100, 32)
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targetVertices = np.array([
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[0, outputSize[1] - 1],
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[0, 0],
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[outputSize[0] - 1, 0],
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[outputSize[0] - 1, outputSize[1] - 1]], dtype="float32")
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rotationMatrix = cv.getPerspectiveTransform(vertices, targetVertices)
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result = cv.warpPerspective(frame, rotationMatrix, outputSize)
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return result
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def decodeText(scores):
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text = ""
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alphabet = "0123456789abcdefghijklmnopqrstuvwxyz"
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for i in range(scores.shape[0]):
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c = np.argmax(scores[i][0])
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if c != 0:
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text += alphabet[c - 1]
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else:
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text += '-'
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# adjacent same letters as well as background text must be removed to get the final output
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char_list = []
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for i in range(len(text)):
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if text[i] != '-' and (not (i > 0 and text[i] == text[i - 1])):
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char_list.append(text[i])
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return ''.join(char_list)
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def decodeBoundingBoxes(scores, geometry, scoreThresh):
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detections = []
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confidences = []
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@ -47,7 +103,7 @@ def decode(scores, geometry, scoreThresh):
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score = scoresData[x]
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# If score is lower than threshold score, move to next x
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if(score < scoreThresh):
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if (score < scoreThresh):
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continue
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# Calculate offset
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@ -66,24 +122,27 @@ def decode(scores, geometry, scoreThresh):
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# Find points for rectangle
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p1 = (-sinA * h + offset[0], -cosA * h + offset[1])
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p3 = (-cosA * w + offset[0], sinA * w + offset[1])
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center = (0.5*(p1[0]+p3[0]), 0.5*(p1[1]+p3[1]))
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detections.append((center, (w,h), -1*angle * 180.0 / math.pi))
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p3 = (-cosA * w + offset[0], sinA * w + offset[1])
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center = (0.5 * (p1[0] + p3[0]), 0.5 * (p1[1] + p3[1]))
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detections.append((center, (w, h), -1 * angle * 180.0 / math.pi))
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confidences.append(float(score))
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# Return detections and confidences
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return [detections, confidences]
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def main():
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# Read and store arguments
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confThreshold = args.thr
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nmsThreshold = args.nms
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inpWidth = args.width
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inpHeight = args.height
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model = args.model
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modelDetector = args.model
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modelRecognition = args.ocr
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# Load network
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net = cv.dnn.readNet(model)
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detector = cv.dnn.readNet(modelDetector)
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recognizer = cv.dnn.readNet(modelRecognition)
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# Create a new named window
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kWinName = "EAST: An Efficient and Accurate Scene Text Detector"
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@ -95,6 +154,7 @@ def main():
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# Open a video file or an image file or a camera stream
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cap = cv.VideoCapture(args.input if args.input else 0)
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||||
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tickmeter = cv.TickMeter()
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||||
while cv.waitKey(1) < 0:
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||||
# Read frame
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||||
hasFrame, frame = cap.read()
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@ -111,19 +171,20 @@ def main():
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||||
# Create a 4D blob from frame.
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blob = cv.dnn.blobFromImage(frame, 1.0, (inpWidth, inpHeight), (123.68, 116.78, 103.94), True, False)
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||||
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||||
# Run the model
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||||
net.setInput(blob)
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outs = net.forward(outNames)
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t, _ = net.getPerfProfile()
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||||
label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
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||||
# Run the detection model
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detector.setInput(blob)
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||||
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||||
tickmeter.start()
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outs = detector.forward(outNames)
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||||
tickmeter.stop()
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||||
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||||
# Get scores and geometry
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||||
scores = outs[0]
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||||
geometry = outs[1]
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[boxes, confidences] = decode(scores, geometry, confThreshold)
|
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[boxes, confidences] = decodeBoundingBoxes(scores, geometry, confThreshold)
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||||
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||||
# Apply NMS
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indices = cv.dnn.NMSBoxesRotated(boxes, confidences, confThreshold,nmsThreshold)
|
||||
indices = cv.dnn.NMSBoxesRotated(boxes, confidences, confThreshold, nmsThreshold)
|
||||
for i in indices:
|
||||
# get 4 corners of the rotated rect
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||||
vertices = cv.boxPoints(boxes[i[0]])
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@ -131,16 +192,40 @@ def main():
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||||
for j in range(4):
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vertices[j][0] *= rW
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||||
vertices[j][1] *= rH
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||||
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||||
|
||||
# get cropped image using perspective transform
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||||
if modelRecognition:
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||||
cropped = fourPointsTransform(frame, vertices)
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||||
cropped = cv.cvtColor(cropped, cv.COLOR_BGR2GRAY)
|
||||
|
||||
# Create a 4D blob from cropped image
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||||
blob = cv.dnn.blobFromImage(cropped, size=(100, 32), mean=127.5, scalefactor=1 / 127.5)
|
||||
recognizer.setInput(blob)
|
||||
|
||||
# Run the recognition model
|
||||
tickmeter.start()
|
||||
result = recognizer.forward()
|
||||
tickmeter.stop()
|
||||
|
||||
# decode the result into text
|
||||
wordRecognized = decodeText(result)
|
||||
cv.putText(frame, wordRecognized, (int(vertices[1][0]), int(vertices[1][1])), cv.FONT_HERSHEY_SIMPLEX,
|
||||
0.5, (255, 0, 0))
|
||||
|
||||
for j in range(4):
|
||||
p1 = (vertices[j][0], vertices[j][1])
|
||||
p2 = (vertices[(j + 1) % 4][0], vertices[(j + 1) % 4][1])
|
||||
cv.line(frame, p1, p2, (0, 255, 0), 1)
|
||||
|
||||
# Put efficiency information
|
||||
label = 'Inference time: %.2f ms' % (tickmeter.getTimeMilli())
|
||||
cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
|
||||
|
||||
# Display the frame
|
||||
cv.imshow(kWinName,frame)
|
||||
cv.imshow(kWinName, frame)
|
||||
tickmeter.reset()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user