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initial commit
This commit is contained in:
Joachim
@ -0,0 +1,905 @@
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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/opencv_modules.hpp"
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#if defined(HAVE_OPENCV_GAPI)
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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/highgui.hpp> // windows
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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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constexpr char kParserAbout[] =
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"Use this script to run the face beautification algorithm with G-API.";
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constexpr char kParserOptions[] =
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"{ help h || print the help message. }"
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"{ facepath f || a path to a Face detection model file (.xml).}"
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"{ facedevice |GPU| the face detection computation device.}"
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"{ landmpath l || a path to a Landmarks detection model file (.xml).}"
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"{ landmdevice |CPU| the landmarks detection computation device.}"
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"{ input i || a path to an input. Skip to capture from a camera.}"
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"{ boxes b |false| set true to draw face Boxes in the \"Input\" window.}"
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"{ landmarks m |false| set true to draw landMarks in the \"Input\" window.}"
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"{ streaming s |true| set false to disable stream pipelining.}"
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"{ performance p |false| set true to disable output displaying.}";
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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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} // namespace config
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namespace
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{
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//! [vec_ROI]
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using VectorROI = std::vector<cv::Rect>;
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//! [vec_ROI]
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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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//! [toDbl]
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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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//! [toDbl]
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struct Avg {
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struct Elapsed {
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explicit Elapsed(double ms) : ss(ms / 1000.),
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mm(toIntRounded(ss / 60)) {}
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const double ss;
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const int mm;
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};
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using MS = std::chrono::duration<double, std::ratio<1, 1000>>;
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using TS = std::chrono::time_point<std::chrono::high_resolution_clock>;
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TS started;
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void start() { started = now(); }
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TS now() const { return std::chrono::high_resolution_clock::now(); }
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double tick() const { return std::chrono::duration_cast<MS>(now() - started).count(); }
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Elapsed elapsed() const { return Elapsed{tick()}; }
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double fps(std::size_t n) const { return static_cast<double>(n) / (tick() / 1000.); }
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};
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std::ostream& operator<<(std::ostream &os, const Avg::Elapsed &e) {
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os << e.mm << ':' << (e.ss - 60*e.mm);
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return os;
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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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// 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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inline Contour getEyeEllipse(const cv::Point &ptLeft,
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const cv::Point &ptRight);
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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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//! [net_decl]
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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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//! [net_decl]
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// Function kernels
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G_TYPED_KERNEL(GBilatFilter, <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, <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, <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, <cv::GMat(cv::GMat,cv::GArray<Contour>,bool,
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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, <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, <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, <TplPtsFaceElements_Jaw(cv::GArray<cv::GMat>,
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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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//! [kern_m_decl]
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using TplFaces_FaceElements = std::tuple<cv::GArray<Contour>, cv::GArray<Contour>>;
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G_TYPED_KERNEL_M(GGetContours, <TplFaces_FaceElements (cv::GArray<Landmarks>, 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(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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//! [kern_m_decl]
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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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//! [fd_pp]
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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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// We can cut detections by the `conf` field
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// to avoid mistakes of the detector.
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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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// These are normalized coordinates and are between 0 and 1;
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// to get the real pixel coordinates we should multiply it by
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// the image sizes respectively to the directions:
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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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//! [fd_pp]
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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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// 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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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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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);
|
||||
|
||||
for (size_t i = 0; i < numFaces; i++)
|
||||
{
|
||||
const float *data = vctDetectResults[i].ptr<float>();
|
||||
// The face elements points:
|
||||
ptsFaceElems.clear();
|
||||
for (int j = 0; j < kNumFaceElems * 2; j += 2)
|
||||
{
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||||
cv::Point pt = cv::Point(toIntRounded(data[j] * vctRects[i].width),
|
||||
toIntRounded(data[j+1] * vctRects[i].height)) + vctRects[i].tl();
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||||
ptsFaceElems.push_back(pt);
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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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||||
cv::Point pt = cv::Point(toIntRounded(data[j] * vctRects[i].width),
|
||||
toIntRounded(data[j+1] * vctRects[i].height)) + vctRects[i].tl();
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||||
cntJaw.push_back(pt);
|
||||
}
|
||||
vctCntJaw.push_back(cntJaw);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// This kernel is the facial landmarks detection post-processing for every face
|
||||
// detected before; output is a tuple of vectors of detected face contours and
|
||||
// facial elements contours:
|
||||
//! [ld_pp_cnts]
|
||||
//! [kern_m_impl]
|
||||
GAPI_OCV_KERNEL(GCPUGetContours, GGetContours)
|
||||
{
|
||||
static void run(const std::vector<Landmarks> &vctPtsFaceElems, // 18 landmarks of the facial elements
|
||||
const std::vector<Contour> &vctCntJaw, // 17 landmarks of a jaw
|
||||
std::vector<Contour> &vctElemsContours,
|
||||
std::vector<Contour> &vctFaceContours)
|
||||
{
|
||||
//! [kern_m_impl]
|
||||
size_t numFaces = vctCntJaw.size();
|
||||
CV_Assert(numFaces == vctPtsFaceElems.size());
|
||||
CV_Assert(vctElemsContours.size() == 0ul);
|
||||
CV_Assert(vctFaceContours.size() == 0ul);
|
||||
// vctFaceElemsContours will store all the face elements' contours found
|
||||
// in an input image, namely 4 elements (two eyes, nose, mouth) for every detected face:
|
||||
vctElemsContours.reserve(numFaces * 4);
|
||||
// vctFaceElemsContours will store all the faces' contours found in an input image:
|
||||
vctFaceContours.reserve(numFaces);
|
||||
|
||||
Contour cntFace, cntLeftEye, cntRightEye, cntNose, cntMouth;
|
||||
cntNose.reserve(4);
|
||||
|
||||
for (size_t i = 0ul; i < numFaces; i++)
|
||||
{
|
||||
// The face elements contours
|
||||
|
||||
// A left eye:
|
||||
// Approximating the lower eye contour by half-ellipse (using eye points) and storing in cntLeftEye:
|
||||
cntLeftEye = getEyeEllipse(vctPtsFaceElems[i][1], vctPtsFaceElems[i][0]);
|
||||
// Pushing the left eyebrow clock-wise:
|
||||
cntLeftEye.insert(cntLeftEye.end(), {vctPtsFaceElems[i][12], vctPtsFaceElems[i][13],
|
||||
vctPtsFaceElems[i][14]});
|
||||
|
||||
// A right eye:
|
||||
// Approximating the lower eye contour by half-ellipse (using eye points) and storing in vctRightEye:
|
||||
cntRightEye = getEyeEllipse(vctPtsFaceElems[i][2], vctPtsFaceElems[i][3]);
|
||||
// Pushing the right eyebrow clock-wise:
|
||||
cntRightEye.insert(cntRightEye.end(), {vctPtsFaceElems[i][15], vctPtsFaceElems[i][16],
|
||||
vctPtsFaceElems[i][17]});
|
||||
|
||||
// A nose:
|
||||
// Storing the nose points clock-wise
|
||||
cntNose.clear();
|
||||
cntNose.insert(cntNose.end(), {vctPtsFaceElems[i][4], vctPtsFaceElems[i][7],
|
||||
vctPtsFaceElems[i][5], vctPtsFaceElems[i][6]});
|
||||
|
||||
// A mouth:
|
||||
// Approximating the mouth contour by two half-ellipses (using mouth points) and storing in vctMouth:
|
||||
cntMouth = getPatchedEllipse(vctPtsFaceElems[i][8], vctPtsFaceElems[i][9],
|
||||
vctPtsFaceElems[i][10], vctPtsFaceElems[i][11]);
|
||||
|
||||
// Storing all the elements in a vector:
|
||||
vctElemsContours.insert(vctElemsContours.end(), {cntLeftEye, cntRightEye, cntNose, cntMouth});
|
||||
|
||||
// The face contour:
|
||||
// Approximating the forehead contour by half-ellipse (using jaw points) and storing in vctFace:
|
||||
cntFace = getForeheadEllipse(vctCntJaw[i][0], vctCntJaw[i][16], vctCntJaw[i][8]);
|
||||
// The ellipse is drawn clock-wise, but jaw contour points goes vice versa, so it's necessary to push
|
||||
// cntJaw from the end to the begin using a reverse iterator:
|
||||
std::copy(vctCntJaw[i].crbegin(), vctCntJaw[i].crend(), std::back_inserter(cntFace));
|
||||
// Storing the face contour in another vector:
|
||||
vctFaceContours.push_back(cntFace);
|
||||
}
|
||||
}
|
||||
};
|
||||
//! [ld_pp_cnts]
|
||||
|
||||
// GAPI subgraph functions
|
||||
inline cv::GMat unsharpMask(const cv::GMat &src,
|
||||
const int sigma,
|
||||
const float strength);
|
||||
inline cv::GMat mask3C(const cv::GMat &src,
|
||||
const cv::GMat &mask);
|
||||
} // namespace custom
|
||||
|
||||
|
||||
// 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:
|
||||
//! [ld_pp_incl]
|
||||
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 / CV_PI);
|
||||
}
|
||||
//! [ld_pp_incl]
|
||||
|
||||
// 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.
|
||||
//! [ld_pp_fhd]
|
||||
inline Contour custom::getForeheadEllipse(const cv::Point &ptJawLeft,
|
||||
const cv::Point &ptJawRight,
|
||||
const cv::Point &ptJawLower)
|
||||
{
|
||||
Contour cntForehead;
|
||||
// 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;
|
||||
}
|
||||
//! [ld_pp_fhd]
|
||||
|
||||
// Approximates the lower eye contour by half-ellipse using eye points and some
|
||||
// geometry and then returns points of the contour.
|
||||
//! [ld_pp_eye]
|
||||
inline Contour custom::getEyeEllipse(const cv::Point &ptLeft, const cv::Point &ptRight)
|
||||
{
|
||||
Contour cntEyeBottom;
|
||||
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;
|
||||
}
|
||||
//! [ld_pp_eye]
|
||||
|
||||
//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
|
||||
std::copy(cntMouthTop.cbegin(), cntMouthTop.cend(), std::back_inserter(cntMouth));
|
||||
return cntMouth;
|
||||
}
|
||||
|
||||
//! [unsh]
|
||||
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));
|
||||
}
|
||||
//! [unsh]
|
||||
|
||||
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::namedWindow(config::kWinFaceBeautification, cv::WINDOW_NORMAL);
|
||||
cv::namedWindow(config::kWinInput, cv::WINDOW_NORMAL);
|
||||
|
||||
cv::CommandLineParser parser(argc, argv, config::kParserOptions);
|
||||
parser.about(config::kParserAbout);
|
||||
if (argc == 1 || parser.has("help"))
|
||||
{
|
||||
parser.printMessage();
|
||||
return 0;
|
||||
}
|
||||
|
||||
// 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");
|
||||
|
||||
// Declaring a graph
|
||||
// The version of a pipeline expression with a lambda-based
|
||||
// constructor is used to keep all temporary objects in a dedicated scope.
|
||||
//! [ppl]
|
||||
cv::GComputation pipeline([=]()
|
||||
{
|
||||
//! [net_usg_fd]
|
||||
cv::GMat gimgIn; // input
|
||||
|
||||
cv::GMat faceOut = cv::gapi::infer<custom::FaceDetector>(gimgIn);
|
||||
//! [net_usg_fd]
|
||||
GArrayROI garRects = custom::GFacePostProc::on(faceOut, gimgIn, config::kConfThresh); // post-proc
|
||||
|
||||
//! [net_usg_ld]
|
||||
cv::GArray<cv::GMat> landmOut = cv::gapi::infer<custom::LandmDetector>(garRects, gimgIn);
|
||||
//! [net_usg_ld]
|
||||
cv::GArray<Landmarks> garElems; // |
|
||||
cv::GArray<Contour> garJaws; // |output arrays
|
||||
std::tie(garElems, garJaws) = custom::GLandmPostProc::on(landmOut, garRects); // post-proc
|
||||
cv::GArray<Contour> garElsConts; // face elements
|
||||
cv::GArray<Contour> garFaceConts; // whole faces
|
||||
std::tie(garElsConts, garFaceConts) = custom::GGetContours::on(garElems, garJaws); // interpolation
|
||||
|
||||
//! [msk_ppline]
|
||||
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); // |draw masks
|
||||
// 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); // |
|
||||
//! [msk_ppline]
|
||||
|
||||
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;
|
||||
return cv::GComputation(cv::GIn(gimgIn), cv::GOut(gimgBeautif,
|
||||
cv::gapi::copy(gimgIn),
|
||||
garFaceConts,
|
||||
garElsConts,
|
||||
garRects));
|
||||
});
|
||||
//! [ppl]
|
||||
// Declaring IE params for networks
|
||||
//! [net_param]
|
||||
auto faceParams = cv::gapi::ie::Params<custom::FaceDetector>
|
||||
{
|
||||
/*std::string*/ faceXmlPath,
|
||||
/*std::string*/ faceBinPath,
|
||||
/*std::string*/ faceDevice
|
||||
};
|
||||
auto landmParams = cv::gapi::ie::Params<custom::LandmDetector>
|
||||
{
|
||||
/*std::string*/ landmXmlPath,
|
||||
/*std::string*/ landmBinPath,
|
||||
/*std::string*/ landmDevice
|
||||
};
|
||||
//! [net_param]
|
||||
//! [netw]
|
||||
auto networks = cv::gapi::networks(faceParams, landmParams);
|
||||
//! [netw]
|
||||
// Declaring custom and fluid kernels have been used:
|
||||
//! [kern_pass_1]
|
||||
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);
|
||||
//! [kern_pass_1]
|
||||
|
||||
Avg avg;
|
||||
size_t frames = 0;
|
||||
|
||||
// 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");
|
||||
// The flag to involve stream pipelining:
|
||||
const bool flgStreaming = parser.get<bool>("streaming");
|
||||
// The flag to display the output images or not:
|
||||
const bool flgPerformance = parser.get<bool>("performance");
|
||||
// Now we are ready to compile the pipeline to a stream with specified
|
||||
// kernels, networks and image format expected to process
|
||||
if (flgStreaming == true)
|
||||
{
|
||||
//! [str_comp]
|
||||
cv::GStreamingCompiled stream = pipeline.compileStreaming(cv::compile_args(kernels, networks));
|
||||
//! [str_comp]
|
||||
// Setting the source for the stream:
|
||||
//! [str_src]
|
||||
if (parser.has("input"))
|
||||
{
|
||||
stream.setSource(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(parser.get<cv::String>("input")));
|
||||
}
|
||||
//! [str_src]
|
||||
else
|
||||
{
|
||||
stream.setSource(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(0));
|
||||
}
|
||||
// Declaring output variables
|
||||
// Streaming:
|
||||
cv::Mat imgShow;
|
||||
cv::Mat imgBeautif;
|
||||
std::vector<Contour> vctFaceConts, vctElsConts;
|
||||
VectorROI vctRects;
|
||||
if (flgPerformance == true)
|
||||
{
|
||||
auto out_vector = cv::gout(imgBeautif, imgShow, vctFaceConts,
|
||||
vctElsConts, vctRects);
|
||||
stream.start();
|
||||
avg.start();
|
||||
while (stream.running())
|
||||
{
|
||||
stream.pull(std::move(out_vector));
|
||||
frames++;
|
||||
}
|
||||
}
|
||||
else // flgPerformance == false
|
||||
{
|
||||
//! [str_loop]
|
||||
auto out_vector = cv::gout(imgBeautif, imgShow, vctFaceConts,
|
||||
vctElsConts, vctRects);
|
||||
stream.start();
|
||||
avg.start();
|
||||
while (stream.running())
|
||||
{
|
||||
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;
|
||||
}
|
||||
frames++;
|
||||
// Drawing face boxes and landmarks if necessary:
|
||||
if (flgLandmarks == true)
|
||||
{
|
||||
cv::polylines(imgShow, vctFaceConts, config::kClosedLine,
|
||||
config::kClrYellow);
|
||||
cv::polylines(imgShow, vctElsConts, config::kClosedLine,
|
||||
config::kClrYellow);
|
||||
}
|
||||
if (flgBoxes == true)
|
||||
for (auto rect : vctRects)
|
||||
cv::rectangle(imgShow, rect, config::kClrGreen);
|
||||
cv::imshow(config::kWinInput, imgShow);
|
||||
cv::imshow(config::kWinFaceBeautification, imgBeautif);
|
||||
}
|
||||
//! [str_loop]
|
||||
}
|
||||
std::cout << "Processed " << frames << " frames in " << avg.elapsed()
|
||||
<< " (" << avg.fps(frames) << " FPS)" << std::endl;
|
||||
}
|
||||
else // serial mode:
|
||||
{
|
||||
//! [bef_cap]
|
||||
#include <opencv2/videoio.hpp>
|
||||
cv::GCompiled cc;
|
||||
cv::VideoCapture cap;
|
||||
if (parser.has("input"))
|
||||
{
|
||||
cap.open(parser.get<cv::String>("input"));
|
||||
}
|
||||
//! [bef_cap]
|
||||
else if (!cap.open(0))
|
||||
{
|
||||
std::cout << "No input available" << std::endl;
|
||||
return 1;
|
||||
}
|
||||
if (flgPerformance == true)
|
||||
{
|
||||
while (true)
|
||||
{
|
||||
cv::Mat img;
|
||||
cv::Mat imgShow;
|
||||
cv::Mat imgBeautif;
|
||||
std::vector<Contour> vctFaceConts, vctElsConts;
|
||||
VectorROI vctRects;
|
||||
cap >> img;
|
||||
if (img.empty())
|
||||
{
|
||||
break;
|
||||
}
|
||||
frames++;
|
||||
if (!cc)
|
||||
{
|
||||
cc = pipeline.compile(cv::descr_of(img), cv::compile_args(kernels, networks));
|
||||
avg.start();
|
||||
}
|
||||
cc(cv::gin(img), cv::gout(imgBeautif, imgShow, vctFaceConts,
|
||||
vctElsConts, vctRects));
|
||||
}
|
||||
}
|
||||
else // flgPerformance == false
|
||||
{
|
||||
//! [bef_loop]
|
||||
while (cv::waitKey(1) < 0)
|
||||
{
|
||||
cv::Mat img;
|
||||
cv::Mat imgShow;
|
||||
cv::Mat imgBeautif;
|
||||
std::vector<Contour> vctFaceConts, vctElsConts;
|
||||
VectorROI vctRects;
|
||||
cap >> img;
|
||||
if (img.empty())
|
||||
{
|
||||
cv::waitKey();
|
||||
break;
|
||||
}
|
||||
frames++;
|
||||
//! [apply]
|
||||
pipeline.apply(cv::gin(img), cv::gout(imgBeautif, imgShow,
|
||||
vctFaceConts,
|
||||
vctElsConts, vctRects),
|
||||
cv::compile_args(kernels, networks));
|
||||
//! [apply]
|
||||
if (frames == 1)
|
||||
{
|
||||
// Start timer only after 1st frame processed -- compilation
|
||||
// happens on-the-fly here
|
||||
avg.start();
|
||||
}
|
||||
// Drawing face boxes and landmarks if necessary:
|
||||
if (flgLandmarks == true)
|
||||
{
|
||||
cv::polylines(imgShow, vctFaceConts, config::kClosedLine,
|
||||
config::kClrYellow);
|
||||
cv::polylines(imgShow, vctElsConts, config::kClosedLine,
|
||||
config::kClrYellow);
|
||||
}
|
||||
if (flgBoxes == true)
|
||||
for (auto rect : vctRects)
|
||||
cv::rectangle(imgShow, rect, config::kClrGreen);
|
||||
cv::imshow(config::kWinInput, imgShow);
|
||||
cv::imshow(config::kWinFaceBeautification, imgBeautif);
|
||||
}
|
||||
}
|
||||
//! [bef_loop]
|
||||
std::cout << "Processed " << frames << " frames in " << avg.elapsed()
|
||||
<< " (" << avg.fps(frames) << " FPS)" << std::endl;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
#else
|
||||
#include <iostream>
|
||||
int main()
|
||||
{
|
||||
std::cerr << "This tutorial code requires G-API module "
|
||||
"with Inference Engine backend to run"
|
||||
<< std::endl;
|
||||
return 1;
|
||||
}
|
||||
#endif // HAVE_OPECV_GAPI
|
||||
Reference in New Issue
Block a user