Merge pull request #15888 from OrestChura:facebeautification_gapi_sample
Introducing the sample of Face Beautification algorithm implemented via Graph-API * Introducing the sample of Face Beautification algorithm implemented via Graph-API - 'gapi/samples/face_beautification.cpp' added - FIXME added in 'gcpukernel.hpp' * INF_ENGINE fix - preprocessing clauses added not to run the sample without Inference Engine * INF_ENGINE fix 2 - warnings removed * Fixes - checking IE version cut as there is no dependency - some alignments fixed - the comment about preprocessing commands fixed * ie::backend() issue fix (according to dmatveev) - as the sample needs the cv::gapi::ie::backend() to be defined regardless of having IE or not, there is its throw-error definition in `giebackend.cpp` now (by dmatveev) - for the same reason, #includes in `giebackend.hpp` are fixed - HAVE_INF_ENGINE check is removed from the samplepull/15935/head
Orest Chura
5 years ago
committed by
Alexander Alekhin
parent
fc41c18c6f
commit
af230ec133
4 changed files with 801 additions and 3 deletions
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level
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// directory of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2018-2019 Intel Corporation
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#include <opencv2/gapi.hpp> |
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#include <opencv2/gapi/core.hpp> |
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#include <opencv2/gapi/imgproc.hpp> |
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#include <opencv2/gapi/fluid/core.hpp> |
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#include <opencv2/gapi/infer.hpp> |
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#include <opencv2/gapi/infer/ie.hpp> |
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#include <opencv2/gapi/cpu/gcpukernel.hpp> |
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#include "opencv2/gapi/streaming/cap.hpp" |
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#include <opencv2/videoio.hpp> |
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#include <opencv2/highgui.hpp> |
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#include <iomanip> |
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namespace config |
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{ |
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constexpr char kWinFaceBeautification[] = "FaceBeautificator"; |
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constexpr char kWinInput[] = "Input"; |
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const cv::Scalar kClrWhite (255, 255, 255); |
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const cv::Scalar kClrGreen ( 0, 255, 0); |
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const cv::Scalar kClrYellow( 0, 255, 255); |
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constexpr float kConfThresh = 0.7f; |
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const cv::Size kGKernelSize(5, 5); |
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constexpr double kGSigma = 0.0; |
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constexpr int kBSize = 9; |
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constexpr double kBSigmaCol = 30.0; |
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constexpr double kBSigmaSp = 30.0; |
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constexpr int kUnshSigma = 3; |
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constexpr float kUnshStrength = 0.7f; |
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constexpr int kAngDelta = 1; |
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constexpr bool kClosedLine = true; |
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const size_t kNumPointsInHalfEllipse = 180 / config::kAngDelta + 1; |
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} // namespace config
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namespace |
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{ |
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using VectorROI = std::vector<cv::Rect>; |
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using GArrayROI = cv::GArray<cv::Rect>; |
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using Contour = std::vector<cv::Point>; |
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using Landmarks = std::vector<cv::Point>; |
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// Wrapper function
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template<typename Tp> inline int toIntRounded(const Tp x) |
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{ |
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return static_cast<int>(std::lround(x)); |
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} |
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template<typename Tp> inline double toDouble(const Tp x) |
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{ |
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return static_cast<double>(x); |
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} |
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std::string getWeightsPath(const std::string &mdlXMLPath) // mdlXMLPath =
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// "The/Full/Path.xml"
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{ |
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size_t size = mdlXMLPath.size(); |
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CV_Assert(mdlXMLPath.substr(size - 4, size) // The last 4 symbols
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== ".xml"); // must be ".xml"
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std::string mdlBinPath(mdlXMLPath); |
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return mdlBinPath.replace(size - 3, 3, "bin"); // return
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// "The/Full/Path.bin"
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} |
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} // anonymous namespace
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namespace custom |
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{ |
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using TplPtsFaceElements_Jaw = std::tuple<cv::GArray<Landmarks>, |
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cv::GArray<Contour>>; |
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using TplFaces_FaceElements = std::tuple<cv::GArray<Contour>, |
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cv::GArray<Contour>>; |
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// Wrapper-functions
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inline int getLineInclinationAngleDegrees(const cv::Point &ptLeft, |
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const cv::Point &ptRight); |
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inline Contour getForeheadEllipse(const cv::Point &ptJawLeft, |
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const cv::Point &ptJawRight, |
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const cv::Point &ptJawMiddle, |
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const size_t capacity); |
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inline Contour getEyeEllipse(const cv::Point &ptLeft, |
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const cv::Point &ptRight, |
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const size_t capacity); |
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inline Contour getPatchedEllipse(const cv::Point &ptLeft, |
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const cv::Point &ptRight, |
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const cv::Point &ptUp, |
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const cv::Point &ptDown); |
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// Networks
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G_API_NET(FaceDetector, <cv::GMat(cv::GMat)>, "face_detector"); |
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G_API_NET(LandmDetector, <cv::GMat(cv::GMat)>, "landm_detector"); |
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// Function kernels
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G_TYPED_KERNEL(GBilatFilter, |
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<cv::GMat(cv::GMat,int,double,double)>, |
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"custom.faceb12n.bilateralFilter") |
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{ |
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static cv::GMatDesc outMeta(cv::GMatDesc in, int,double,double) |
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{ |
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return in; |
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} |
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}; |
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G_TYPED_KERNEL(GLaplacian, |
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<cv::GMat(cv::GMat,int)>, |
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"custom.faceb12n.Laplacian") |
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{ |
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static cv::GMatDesc outMeta(cv::GMatDesc in, int) |
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{ |
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return in; |
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} |
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}; |
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G_TYPED_KERNEL(GFillPolyGContours, |
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<cv::GMat(cv::GMat,cv::GArray<Contour>)>, |
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"custom.faceb12n.fillPolyGContours") |
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{ |
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static cv::GMatDesc outMeta(cv::GMatDesc in, cv::GArrayDesc) |
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{ |
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return in.withType(CV_8U, 1); |
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} |
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}; |
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G_TYPED_KERNEL(GPolyLines, |
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<cv::GMat(cv::GMat,cv::GArray<Contour>,bool,cv::Scalar)>, |
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"custom.faceb12n.polyLines") |
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{ |
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static cv::GMatDesc outMeta(cv::GMatDesc in, cv::GArrayDesc,bool,cv::Scalar) |
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{ |
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return in; |
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} |
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}; |
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G_TYPED_KERNEL(GRectangle, |
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<cv::GMat(cv::GMat,GArrayROI,cv::Scalar)>, |
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"custom.faceb12n.rectangle") |
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{ |
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static cv::GMatDesc outMeta(cv::GMatDesc in, cv::GArrayDesc,cv::Scalar) |
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{ |
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return in; |
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} |
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}; |
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G_TYPED_KERNEL(GFacePostProc, |
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<GArrayROI(cv::GMat,cv::GMat,float)>, |
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"custom.faceb12n.faceDetectPostProc") |
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{ |
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static cv::GArrayDesc outMeta(const cv::GMatDesc&,const cv::GMatDesc&,float) |
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{ |
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return cv::empty_array_desc(); |
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} |
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}; |
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G_TYPED_KERNEL_M(GLandmPostProc, |
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<TplPtsFaceElements_Jaw(cv::GArray<cv::GMat>,GArrayROI)>, |
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"custom.faceb12n.landmDetectPostProc") |
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{ |
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static std::tuple<cv::GArrayDesc,cv::GArrayDesc> outMeta( |
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const cv::GArrayDesc&,const cv::GArrayDesc&) |
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{ |
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return std::make_tuple(cv::empty_array_desc(), cv::empty_array_desc()); |
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} |
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}; |
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G_TYPED_KERNEL_M(GGetContours, |
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<TplFaces_FaceElements(cv::GArray<Landmarks>, |
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cv::GArray<Contour>)>, |
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"custom.faceb12n.getContours") |
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{ |
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static std::tuple<cv::GArrayDesc,cv::GArrayDesc> outMeta( |
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const cv::GArrayDesc&,const cv::GArrayDesc&) |
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{ |
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return std::make_tuple(cv::empty_array_desc(), cv::empty_array_desc()); |
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} |
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}; |
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// OCV_Kernels
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// This kernel applies Bilateral filter to an input src with default
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// "cv::bilateralFilter" border argument
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GAPI_OCV_KERNEL(GCPUBilateralFilter, custom::GBilatFilter) |
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{ |
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static void run(const cv::Mat &src, |
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const int diameter, |
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const double sigmaColor, |
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const double sigmaSpace, |
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cv::Mat &out) |
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{ |
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cv::bilateralFilter(src, out, diameter, sigmaColor, sigmaSpace); |
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} |
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}; |
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// This kernel applies Laplace operator to an input src with default
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// "cv::Laplacian" arguments
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GAPI_OCV_KERNEL(GCPULaplacian, custom::GLaplacian) |
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{ |
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static void run(const cv::Mat &src, |
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const int ddepth, |
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cv::Mat &out) |
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{ |
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cv::Laplacian(src, out, ddepth); |
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} |
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}; |
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// This kernel draws given white filled contours "cnts" on a clear Mat "out"
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// (defined by a Scalar(0)) with standard "cv::fillPoly" arguments.
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// It should be used to create a mask.
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// The input Mat seems unused inside the function "run", but it is used deeper
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// in the kernel to define an output size.
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GAPI_OCV_KERNEL(GCPUFillPolyGContours, custom::GFillPolyGContours) |
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{ |
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static void run(const cv::Mat &, |
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const std::vector<Contour> &cnts, |
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cv::Mat &out) |
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{ |
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out = cv::Scalar(0); |
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cv::fillPoly(out, cnts, config::kClrWhite); |
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} |
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}; |
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// This kernel draws given contours on an input src with default "cv::polylines"
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// arguments
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GAPI_OCV_KERNEL(GCPUPolyLines, custom::GPolyLines) |
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{ |
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static void run(const cv::Mat &src, |
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const std::vector<Contour> &cnts, |
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const bool isClosed, |
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const cv::Scalar &color, |
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cv::Mat &out) |
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{ |
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src.copyTo(out); |
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cv::polylines(out, cnts, isClosed, color); |
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} |
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}; |
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// This kernel draws given rectangles on an input src with default
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// "cv::rectangle" arguments
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GAPI_OCV_KERNEL(GCPURectangle, custom::GRectangle) |
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{ |
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static void run(const cv::Mat &src, |
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const VectorROI &vctFaceBoxes, |
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const cv::Scalar &color, |
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cv::Mat &out) |
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{ |
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src.copyTo(out); |
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for (const cv::Rect &box : vctFaceBoxes) |
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{ |
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cv::rectangle(out, box, color); |
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} |
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} |
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}; |
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// A face detector outputs a blob with the shape: [1, 1, N, 7], where N is
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// the number of detected bounding boxes. Structure of an output for every
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// detected face is the following:
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// [image_id, label, conf, x_min, y_min, x_max, y_max]; all the seven elements
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// are floating point. For more details please visit:
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// https://github.com/opencv/open_model_zoo/blob/master/intel_models/face-detection-adas-0001
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// This kernel is the face detection output blob parsing that returns a vector
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// of detected faces' rects:
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GAPI_OCV_KERNEL(GCPUFacePostProc, GFacePostProc) |
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{ |
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static void run(const cv::Mat &inDetectResult, |
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const cv::Mat &inFrame, |
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const float faceConfThreshold, |
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VectorROI &outFaces) |
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{ |
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const int kObjectSize = 7; |
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const int imgCols = inFrame.size().width; |
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const int imgRows = inFrame.size().height; |
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const cv::Rect borders({0, 0}, inFrame.size()); |
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outFaces.clear(); |
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const int numOfDetections = inDetectResult.size[2]; |
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const float *data = inDetectResult.ptr<float>(); |
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for (int i = 0; i < numOfDetections; i++) |
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{ |
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const float faceId = data[i * kObjectSize + 0]; |
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if (faceId < 0.f) // indicates the end of detections
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{ |
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break; |
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} |
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const float faceConfidence = data[i * kObjectSize + 2]; |
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if (faceConfidence > faceConfThreshold) |
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{ |
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const float left = data[i * kObjectSize + 3]; |
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const float top = data[i * kObjectSize + 4]; |
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const float right = data[i * kObjectSize + 5]; |
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const float bottom = data[i * kObjectSize + 6]; |
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cv::Point tl(toIntRounded(left * imgCols), |
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toIntRounded(top * imgRows)); |
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cv::Point br(toIntRounded(right * imgCols), |
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toIntRounded(bottom * imgRows)); |
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outFaces.push_back(cv::Rect(tl, br) & borders); |
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} |
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} |
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} |
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}; |
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// This kernel is the facial landmarks detection output Mat parsing for every
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// detected face; returns a tuple containing a vector of vectors of
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// face elements' Points and a vector of vectors of jaw's Points:
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GAPI_OCV_KERNEL(GCPULandmPostProc, GLandmPostProc) |
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{ |
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static void run(const std::vector<cv::Mat> &vctDetectResults, |
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const VectorROI &vctRects, |
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std::vector<Landmarks> &vctPtsFaceElems, |
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std::vector<Contour> &vctCntJaw) |
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{ |
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// There are 35 landmarks given by the default detector for each face
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// in a frame; the first 18 of them are face elements (eyes, eyebrows,
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// a nose, a mouth) and the last 17 - a jaw contour. The detector gives
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// floating point values for landmarks' normed coordinates relatively
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// to an input ROI (not the original frame).
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// For more details please visit:
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// https://github.com/opencv/open_model_zoo/blob/master/intel_models/facial-landmarks-35-adas-0002
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static constexpr int kNumFaceElems = 18; |
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static constexpr int kNumTotal = 35; |
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const size_t numFaces = vctRects.size(); |
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CV_Assert(vctPtsFaceElems.size() == 0ul); |
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CV_Assert(vctCntJaw.size() == 0ul); |
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vctPtsFaceElems.reserve(numFaces); |
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vctCntJaw.reserve(numFaces); |
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Landmarks ptsFaceElems; |
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Contour cntJaw; |
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ptsFaceElems.reserve(kNumFaceElems); |
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cntJaw.reserve(kNumTotal - kNumFaceElems); |
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for (size_t i = 0; i < numFaces; i++) |
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{ |
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const float *data = vctDetectResults[i].ptr<float>(); |
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// The face elements points:
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ptsFaceElems.clear(); |
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for (int j = 0; j < kNumFaceElems * 2; j += 2) |
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{ |
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cv::Point pt = |
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cv::Point(toIntRounded(data[j] * vctRects[i].width), |
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toIntRounded(data[j+1] * vctRects[i].height)) |
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+ vctRects[i].tl(); |
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ptsFaceElems.push_back(pt); |
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} |
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vctPtsFaceElems.push_back(ptsFaceElems); |
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// The jaw contour points:
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cntJaw.clear(); |
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for(int j = kNumFaceElems * 2; j < kNumTotal * 2; j += 2) |
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{ |
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cv::Point pt = |
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cv::Point(toIntRounded(data[j] * vctRects[i].width), |
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toIntRounded(data[j+1] * vctRects[i].height)) |
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+ vctRects[i].tl(); |
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cntJaw.push_back(pt); |
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} |
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vctCntJaw.push_back(cntJaw); |
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} |
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} |
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}; |
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// This kernel is the facial landmarks detection post-processing for every face
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// detected before; output is a tuple of vectors of detected face contours and
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// facial elements contours:
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GAPI_OCV_KERNEL(GCPUGetContours, GGetContours) |
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{ |
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static void run(const std::vector<Landmarks> &vctPtsFaceElems, |
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const std::vector<Contour> &vctCntJaw, |
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std::vector<Contour> &vctElemsContours, |
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std::vector<Contour> &vctFaceContours) |
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{ |
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size_t numFaces = vctCntJaw.size(); |
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CV_Assert(numFaces == vctPtsFaceElems.size()); |
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CV_Assert(vctElemsContours.size() == 0ul); |
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CV_Assert(vctFaceContours.size() == 0ul); |
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// vctFaceElemsContours will store all the face elements' contours found
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// on an input image, namely 4 elements (two eyes, nose, mouth)
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// for every detected face
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vctElemsContours.reserve(numFaces * 4); |
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// vctFaceElemsContours will store all the faces' contours found on
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// an input image
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vctFaceContours.reserve(numFaces); |
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Contour cntFace, cntLeftEye, cntRightEye, cntNose, cntMouth; |
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cntNose.reserve(4); |
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for (size_t i = 0ul; i < numFaces; i++) |
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{ |
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// The face elements contours
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// A left eye:
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// Approximating the lower eye contour by half-ellipse
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// (using eye points) and storing in cntLeftEye:
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cntLeftEye = getEyeEllipse(vctPtsFaceElems[i][1], |
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vctPtsFaceElems[i][0], |
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config::kNumPointsInHalfEllipse + 3); |
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// Pushing the left eyebrow clock-wise:
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cntLeftEye.insert(cntLeftEye.cend(), {vctPtsFaceElems[i][12], |
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vctPtsFaceElems[i][13], |
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vctPtsFaceElems[i][14]}); |
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// A right eye:
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// Approximating the lower eye contour by half-ellipse
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// (using eye points) and storing in vctRightEye:
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cntRightEye = getEyeEllipse(vctPtsFaceElems[i][2], |
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vctPtsFaceElems[i][3], |
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config::kNumPointsInHalfEllipse + 3); |
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// Pushing the right eyebrow clock-wise:
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cntRightEye.insert(cntRightEye.cend(), {vctPtsFaceElems[i][15], |
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vctPtsFaceElems[i][16], |
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vctPtsFaceElems[i][17]}); |
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// A nose:
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// Storing the nose points clock-wise
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cntNose.clear(); |
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cntNose.insert(cntNose.cend(), {vctPtsFaceElems[i][4], |
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vctPtsFaceElems[i][7], |
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vctPtsFaceElems[i][5], |
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vctPtsFaceElems[i][6]}); |
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// A mouth:
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// Approximating the mouth contour by two half-ellipses
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// (using mouth points) and storing in vctMouth:
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cntMouth = getPatchedEllipse(vctPtsFaceElems[i][8], |
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vctPtsFaceElems[i][9], |
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vctPtsFaceElems[i][10], |
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vctPtsFaceElems[i][11]); |
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// Storing all the elements in a vector:
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vctElemsContours.insert(vctElemsContours.cend(), {cntLeftEye, |
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cntRightEye, |
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cntNose, |
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cntMouth}); |
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// The face contour:
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// Approximating the forehead contour by half-ellipse
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// (using jaw points) and storing in vctFace:
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cntFace = getForeheadEllipse(vctCntJaw[i][0], vctCntJaw[i][16], |
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vctCntJaw[i][8], |
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config::kNumPointsInHalfEllipse + |
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vctCntJaw[i].size()); |
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// The ellipse is drawn clock-wise, but jaw contour points goes
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// vice versa, so it's necessary to push cntJaw from the end
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// to the begin using a reverse iterator:
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std::copy(vctCntJaw[i].crbegin(), vctCntJaw[i].crend(), |
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std::back_inserter(cntFace)); |
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// Storing the face contour in another vector:
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vctFaceContours.push_back(cntFace); |
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} |
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} |
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}; |
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// GAPI subgraph functions
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inline cv::GMat unsharpMask(const cv::GMat &src, |
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const int sigma, |
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const float strength); |
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inline cv::GMat mask3C(const cv::GMat &src, |
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const cv::GMat &mask); |
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} // namespace custom
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|
||||
|
||||
// Functions implementation:
|
||||
// Returns an angle (in degrees) between a line given by two Points and
|
||||
// the horison. Note that the result depends on the arguments order:
|
||||
inline int custom::getLineInclinationAngleDegrees(const cv::Point &ptLeft, |
||||
const cv::Point &ptRight) |
||||
{ |
||||
const cv::Point residual = ptRight - ptLeft; |
||||
if (residual.y == 0 && residual.x == 0) |
||||
return 0; |
||||
else |
||||
return toIntRounded(atan2(toDouble(residual.y), toDouble(residual.x)) |
||||
* 180.0 / M_PI); |
||||
} |
||||
|
||||
// Approximates a forehead by half-ellipse using jaw points and some geometry
|
||||
// and then returns points of the contour; "capacity" is used to reserve enough
|
||||
// memory as there will be other points inserted.
|
||||
inline Contour custom::getForeheadEllipse(const cv::Point &ptJawLeft, |
||||
const cv::Point &ptJawRight, |
||||
const cv::Point &ptJawLower, |
||||
const size_t capacity = 0) |
||||
{ |
||||
Contour cntForehead; |
||||
cntForehead.reserve(std::max(capacity, config::kNumPointsInHalfEllipse)); |
||||
// The point amid the top two points of a jaw:
|
||||
const cv::Point ptFaceCenter((ptJawLeft + ptJawRight) / 2); |
||||
// This will be the center of the ellipse.
|
||||
|
||||
// The angle between the jaw and the vertical:
|
||||
const int angFace = getLineInclinationAngleDegrees(ptJawLeft, ptJawRight); |
||||
// This will be the inclination of the ellipse
|
||||
|
||||
// Counting the half-axis of the ellipse:
|
||||
const double jawWidth = cv::norm(ptJawLeft - ptJawRight); |
||||
// A forehead width equals the jaw width, and we need a half-axis:
|
||||
const int axisX = toIntRounded(jawWidth / 2.0); |
||||
|
||||
const double jawHeight = cv::norm(ptFaceCenter - ptJawLower); |
||||
// According to research, in average a forehead is approximately 2/3 of
|
||||
// a jaw:
|
||||
const int axisY = toIntRounded(jawHeight * 2 / 3.0); |
||||
|
||||
// We need the upper part of an ellipse:
|
||||
static constexpr int kAngForeheadStart = 180; |
||||
static constexpr int kAngForeheadEnd = 360; |
||||
cv::ellipse2Poly(ptFaceCenter, cv::Size(axisX, axisY), angFace, |
||||
kAngForeheadStart, kAngForeheadEnd, config::kAngDelta, |
||||
cntForehead); |
||||
return cntForehead; |
||||
} |
||||
|
||||
// Approximates the lower eye contour by half-ellipse using eye points and some
|
||||
// geometry and then returns points of the contour; "capacity" is used
|
||||
// to reserve enough memory as there will be other points inserted.
|
||||
inline Contour custom::getEyeEllipse(const cv::Point &ptLeft, |
||||
const cv::Point &ptRight, |
||||
const size_t capacity = 0) |
||||
{ |
||||
Contour cntEyeBottom; |
||||
cntEyeBottom.reserve(std::max(capacity, config::kNumPointsInHalfEllipse)); |
||||
const cv::Point ptEyeCenter((ptRight + ptLeft) / 2); |
||||
const int angle = getLineInclinationAngleDegrees(ptLeft, ptRight); |
||||
const int axisX = toIntRounded(cv::norm(ptRight - ptLeft) / 2.0); |
||||
// According to research, in average a Y axis of an eye is approximately
|
||||
// 1/3 of an X one.
|
||||
const int axisY = axisX / 3; |
||||
// We need the lower part of an ellipse:
|
||||
static constexpr int kAngEyeStart = 0; |
||||
static constexpr int kAngEyeEnd = 180; |
||||
cv::ellipse2Poly(ptEyeCenter, cv::Size(axisX, axisY), angle, kAngEyeStart, |
||||
kAngEyeEnd, config::kAngDelta, cntEyeBottom); |
||||
return cntEyeBottom; |
||||
} |
||||
|
||||
//This function approximates an object (a mouth) by two half-ellipses using
|
||||
// 4 points of the axes' ends and then returns points of the contour:
|
||||
inline Contour custom::getPatchedEllipse(const cv::Point &ptLeft, |
||||
const cv::Point &ptRight, |
||||
const cv::Point &ptUp, |
||||
const cv::Point &ptDown) |
||||
{ |
||||
// Shared characteristics for both half-ellipses:
|
||||
const cv::Point ptMouthCenter((ptLeft + ptRight) / 2); |
||||
const int angMouth = getLineInclinationAngleDegrees(ptLeft, ptRight); |
||||
const int axisX = toIntRounded(cv::norm(ptRight - ptLeft) / 2.0); |
||||
|
||||
// The top half-ellipse:
|
||||
Contour cntMouthTop; |
||||
const int axisYTop = toIntRounded(cv::norm(ptMouthCenter - ptUp)); |
||||
// We need the upper part of an ellipse:
|
||||
static constexpr int angTopStart = 180; |
||||
static constexpr int angTopEnd = 360; |
||||
cv::ellipse2Poly(ptMouthCenter, cv::Size(axisX, axisYTop), angMouth, |
||||
angTopStart, angTopEnd, config::kAngDelta, cntMouthTop); |
||||
|
||||
// The bottom half-ellipse:
|
||||
Contour cntMouth; |
||||
const int axisYBot = toIntRounded(cv::norm(ptMouthCenter - ptDown)); |
||||
// We need the lower part of an ellipse:
|
||||
static constexpr int angBotStart = 0; |
||||
static constexpr int angBotEnd = 180; |
||||
cv::ellipse2Poly(ptMouthCenter, cv::Size(axisX, axisYBot), angMouth, |
||||
angBotStart, angBotEnd, config::kAngDelta, cntMouth); |
||||
|
||||
// Pushing the upper part to vctOut
|
||||
cntMouth.reserve(cntMouth.size() + cntMouthTop.size()); |
||||
std::copy(cntMouthTop.cbegin(), cntMouthTop.cend(), |
||||
std::back_inserter(cntMouth)); |
||||
return cntMouth; |
||||
} |
||||
|
||||
inline cv::GMat custom::unsharpMask(const cv::GMat &src, |
||||
const int sigma, |
||||
const float strength) |
||||
{ |
||||
cv::GMat blurred = cv::gapi::medianBlur(src, sigma); |
||||
cv::GMat laplacian = custom::GLaplacian::on(blurred, CV_8U); |
||||
return (src - (laplacian * strength)); |
||||
} |
||||
|
||||
inline cv::GMat custom::mask3C(const cv::GMat &src, |
||||
const cv::GMat &mask) |
||||
{ |
||||
std::tuple<cv::GMat,cv::GMat,cv::GMat> tplIn = cv::gapi::split3(src); |
||||
cv::GMat masked0 = cv::gapi::mask(std::get<0>(tplIn), mask); |
||||
cv::GMat masked1 = cv::gapi::mask(std::get<1>(tplIn), mask); |
||||
cv::GMat masked2 = cv::gapi::mask(std::get<2>(tplIn), mask); |
||||
return cv::gapi::merge3(masked0, masked1, masked2); |
||||
} |
||||
|
||||
|
||||
int main(int argc, char** argv) |
||||
{ |
||||
cv::CommandLineParser parser(argc, argv, |
||||
"{ help h || print the help message. }" |
||||
|
||||
"{ facepath f || a path to a Face detection model file (.xml).}" |
||||
"{ facedevice |GPU| the face detection computation device.}" |
||||
|
||||
"{ landmpath l || a path to a Landmarks detection model file (.xml).}" |
||||
"{ landmdevice |CPU| the landmarks detection computation device.}" |
||||
|
||||
"{ input i || a path to an input. Skip to capture from a camera.}" |
||||
"{ boxes b |false| set true to draw face Boxes in the \"Input\" window.}" |
||||
"{ landmarks m |false| set true to draw landMarks in the \"Input\" window.}" |
||||
); |
||||
parser.about("Use this script to run the face beautification" |
||||
" algorithm on G-API."); |
||||
if (argc == 1 || parser.has("help")) |
||||
{ |
||||
parser.printMessage(); |
||||
return 0; |
||||
} |
||||
|
||||
cv::namedWindow(config::kWinFaceBeautification, cv::WINDOW_NORMAL); |
||||
cv::namedWindow(config::kWinInput, cv::WINDOW_NORMAL); |
||||
|
||||
// Parsing input arguments
|
||||
const std::string faceXmlPath = parser.get<std::string>("facepath"); |
||||
const std::string faceBinPath = getWeightsPath(faceXmlPath); |
||||
const std::string faceDevice = parser.get<std::string>("facedevice"); |
||||
|
||||
const std::string landmXmlPath = parser.get<std::string>("landmpath"); |
||||
const std::string landmBinPath = getWeightsPath(landmXmlPath); |
||||
const std::string landmDevice = parser.get<std::string>("landmdevice"); |
||||
|
||||
// The flags for drawing/not drawing face boxes or/and landmarks in the
|
||||
// \"Input\" window:
|
||||
const bool flgBoxes = parser.get<bool>("boxes"); |
||||
const bool flgLandmarks = parser.get<bool>("landmarks"); |
||||
// To provide this opportunity, it is necessary to check the flags when
|
||||
// compiling a graph
|
||||
|
||||
// Declaring a graph
|
||||
// Streaming-API version of a pipeline expression with a lambda-based
|
||||
// constructor is used to keep all temporary objects in a dedicated scope.
|
||||
cv::GComputation pipeline([=]() |
||||
{ |
||||
cv::GMat gimgIn; |
||||
// Infering
|
||||
cv::GMat faceOut = cv::gapi::infer<custom::FaceDetector>(gimgIn); |
||||
GArrayROI garRects = custom::GFacePostProc::on(faceOut, gimgIn, |
||||
config::kConfThresh); |
||||
cv::GArray<Landmarks> garElems; |
||||
cv::GArray<Contour> garJaws; |
||||
cv::GArray<cv::GMat> landmOut = cv::gapi::infer<custom::LandmDetector>( |
||||
garRects, gimgIn); |
||||
std::tie(garElems, garJaws) = custom::GLandmPostProc::on(landmOut, |
||||
garRects); |
||||
cv::GArray<Contour> garElsConts; |
||||
cv::GArray<Contour> garFaceConts; |
||||
std::tie(garElsConts, garFaceConts) = custom::GGetContours::on(garElems, |
||||
garJaws); |
||||
// Masks drawing
|
||||
// All masks are created as CV_8UC1
|
||||
cv::GMat mskSharp = custom::GFillPolyGContours::on(gimgIn, |
||||
garElsConts); |
||||
cv::GMat mskSharpG = cv::gapi::gaussianBlur(mskSharp, |
||||
config::kGKernelSize, |
||||
config::kGSigma); |
||||
cv::GMat mskBlur = custom::GFillPolyGContours::on(gimgIn, |
||||
garFaceConts); |
||||
cv::GMat mskBlurG = cv::gapi::gaussianBlur(mskBlur, |
||||
config::kGKernelSize, |
||||
config::kGSigma); |
||||
// The first argument in mask() is Blur as we want to subtract from
|
||||
// BlurG the next step:
|
||||
cv::GMat mskBlurFinal = mskBlurG - cv::gapi::mask(mskBlurG, |
||||
mskSharpG); |
||||
cv::GMat mskFacesGaussed = mskBlurFinal + mskSharpG; |
||||
cv::GMat mskFacesWhite = cv::gapi::threshold(mskFacesGaussed, 0, 255, |
||||
cv::THRESH_BINARY); |
||||
cv::GMat mskNoFaces = cv::gapi::bitwise_not(mskFacesWhite); |
||||
cv::GMat gimgBilat = custom::GBilatFilter::on(gimgIn, |
||||
config::kBSize, |
||||
config::kBSigmaCol, |
||||
config::kBSigmaSp); |
||||
cv::GMat gimgSharp = custom::unsharpMask(gimgIn, |
||||
config::kUnshSigma, |
||||
config::kUnshStrength); |
||||
// Applying the masks
|
||||
// Custom function mask3C() should be used instead of just gapi::mask()
|
||||
// as mask() provides CV_8UC1 source only (and we have CV_8U3C)
|
||||
cv::GMat gimgBilatMasked = custom::mask3C(gimgBilat, mskBlurFinal); |
||||
cv::GMat gimgSharpMasked = custom::mask3C(gimgSharp, mskSharpG); |
||||
cv::GMat gimgInMasked = custom::mask3C(gimgIn, mskNoFaces); |
||||
cv::GMat gimgBeautif = gimgBilatMasked + gimgSharpMasked + |
||||
gimgInMasked; |
||||
// Drawing face boxes and landmarks if necessary:
|
||||
cv::GMat gimgTemp; |
||||
if (flgLandmarks == true) |
||||
{ |
||||
cv::GMat gimgTemp2 = custom::GPolyLines::on(gimgIn, garFaceConts, |
||||
config::kClosedLine, |
||||
config::kClrYellow); |
||||
gimgTemp = custom::GPolyLines::on(gimgTemp2, garElsConts, |
||||
config::kClosedLine, |
||||
config::kClrYellow); |
||||
} |
||||
else |
||||
{ |
||||
gimgTemp = gimgIn; |
||||
} |
||||
cv::GMat gimgShow; |
||||
if (flgBoxes == true) |
||||
{ |
||||
gimgShow = custom::GRectangle::on(gimgTemp, garRects, |
||||
config::kClrGreen); |
||||
} |
||||
else |
||||
{ |
||||
// This action is necessary because an output node must be a result of
|
||||
// some operations applied to an input node, so it handles the case
|
||||
// when it should be nothing to draw
|
||||
gimgShow = cv::gapi::copy(gimgTemp); |
||||
} |
||||
return cv::GComputation(cv::GIn(gimgIn), |
||||
cv::GOut(gimgBeautif, gimgShow)); |
||||
}); |
||||
// Declaring IE params for networks
|
||||
auto faceParams = cv::gapi::ie::Params<custom::FaceDetector> |
||||
{ |
||||
faceXmlPath, |
||||
faceBinPath, |
||||
faceDevice |
||||
}; |
||||
auto landmParams = cv::gapi::ie::Params<custom::LandmDetector> |
||||
{ |
||||
landmXmlPath, |
||||
landmBinPath, |
||||
landmDevice |
||||
}; |
||||
auto networks = cv::gapi::networks(faceParams, landmParams); |
||||
// Declaring custom and fluid kernels have been used:
|
||||
auto customKernels = cv::gapi::kernels<custom::GCPUBilateralFilter, |
||||
custom::GCPULaplacian, |
||||
custom::GCPUFillPolyGContours, |
||||
custom::GCPUPolyLines, |
||||
custom::GCPURectangle, |
||||
custom::GCPUFacePostProc, |
||||
custom::GCPULandmPostProc, |
||||
custom::GCPUGetContours>(); |
||||
auto kernels = cv::gapi::combine(cv::gapi::core::fluid::kernels(), |
||||
customKernels); |
||||
// Now we are ready to compile the pipeline to a stream with specified
|
||||
// kernels, networks and image format expected to process
|
||||
auto stream = pipeline.compileStreaming(cv::GMatDesc{CV_8U,3, |
||||
cv::Size(1280,720)}, |
||||
cv::compile_args(kernels, |
||||
networks)); |
||||
// Setting the source for the stream:
|
||||
if (parser.has("input")) |
||||
{ |
||||
stream.setSource(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource> |
||||
(parser.get<cv::String>("input"))); |
||||
} |
||||
else |
||||
{ |
||||
stream.setSource(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource> |
||||
(0)); |
||||
} |
||||
// Declaring output variables
|
||||
cv::Mat imgShow; |
||||
cv::Mat imgBeautif; |
||||
// Streaming:
|
||||
stream.start(); |
||||
while (stream.running()) |
||||
{ |
||||
auto out_vector = cv::gout(imgBeautif, imgShow); |
||||
if (!stream.try_pull(std::move(out_vector))) |
||||
{ |
||||
// Use a try_pull() to obtain data.
|
||||
// If there's no data, let UI refresh (and handle keypress)
|
||||
if (cv::waitKey(1) >= 0) break; |
||||
else continue; |
||||
} |
||||
cv::imshow(config::kWinInput, imgShow); |
||||
cv::imshow(config::kWinFaceBeautification, imgBeautif); |
||||
} |
||||
return 0; |
||||
} |
||||
Loading…
Reference in new issue