Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

784 lines
32 KiB

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level
// directory of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2018-2019 Intel Corporation
#include <opencv2/gapi.hpp>
#include <opencv2/gapi/core.hpp>
#include <opencv2/gapi/imgproc.hpp>
#include <opencv2/gapi/fluid/core.hpp>
#include <opencv2/gapi/infer.hpp>
#include <opencv2/gapi/infer/ie.hpp>
#include <opencv2/gapi/cpu/gcpukernel.hpp>
#include "opencv2/gapi/streaming/cap.hpp"
#include <opencv2/videoio.hpp>
#include <opencv2/highgui.hpp>
#include <iomanip>
namespace config
{
constexpr char kWinFaceBeautification[] = "FaceBeautificator";
constexpr char kWinInput[] = "Input";
const cv::Scalar kClrWhite (255, 255, 255);
const cv::Scalar kClrGreen ( 0, 255, 0);
const cv::Scalar kClrYellow( 0, 255, 255);
constexpr float kConfThresh = 0.7f;
const cv::Size kGKernelSize(5, 5);
constexpr double kGSigma = 0.0;
constexpr int kBSize = 9;
constexpr double kBSigmaCol = 30.0;
constexpr double kBSigmaSp = 30.0;
constexpr int kUnshSigma = 3;
constexpr float kUnshStrength = 0.7f;
constexpr int kAngDelta = 1;
constexpr bool kClosedLine = true;
const size_t kNumPointsInHalfEllipse = 180 / config::kAngDelta + 1;
} // namespace config
namespace
{
using VectorROI = std::vector<cv::Rect>;
using GArrayROI = cv::GArray<cv::Rect>;
using Contour = std::vector<cv::Point>;
using Landmarks = std::vector<cv::Point>;
// Wrapper function
template<typename Tp> inline int toIntRounded(const Tp x)
{
return static_cast<int>(std::lround(x));
}
template<typename Tp> inline double toDouble(const Tp x)
{
return static_cast<double>(x);
}
std::string getWeightsPath(const std::string &mdlXMLPath) // mdlXMLPath =
// "The/Full/Path.xml"
{
size_t size = mdlXMLPath.size();
CV_Assert(mdlXMLPath.substr(size - 4, size) // The last 4 symbols
== ".xml"); // must be ".xml"
std::string mdlBinPath(mdlXMLPath);
return mdlBinPath.replace(size - 3, 3, "bin"); // return
// "The/Full/Path.bin"
}
} // anonymous namespace
namespace custom
{
using TplPtsFaceElements_Jaw = std::tuple<cv::GArray<Landmarks>,
cv::GArray<Contour>>;
using TplFaces_FaceElements = std::tuple<cv::GArray<Contour>,
cv::GArray<Contour>>;
// Wrapper-functions
inline int getLineInclinationAngleDegrees(const cv::Point &ptLeft,
const cv::Point &ptRight);
inline Contour getForeheadEllipse(const cv::Point &ptJawLeft,
const cv::Point &ptJawRight,
const cv::Point &ptJawMiddle,
const size_t capacity);
inline Contour getEyeEllipse(const cv::Point &ptLeft,
const cv::Point &ptRight,
const size_t capacity);
inline Contour getPatchedEllipse(const cv::Point &ptLeft,
const cv::Point &ptRight,
const cv::Point &ptUp,
const cv::Point &ptDown);
// Networks
G_API_NET(FaceDetector, <cv::GMat(cv::GMat)>, "face_detector");
G_API_NET(LandmDetector, <cv::GMat(cv::GMat)>, "landm_detector");
// Function kernels
G_TYPED_KERNEL(GBilatFilter,
<cv::GMat(cv::GMat,int,double,double)>,
"custom.faceb12n.bilateralFilter")
{
static cv::GMatDesc outMeta(cv::GMatDesc in, int,double,double)
{
return in;
}
};
G_TYPED_KERNEL(GLaplacian,
<cv::GMat(cv::GMat,int)>,
"custom.faceb12n.Laplacian")
{
static cv::GMatDesc outMeta(cv::GMatDesc in, int)
{
return in;
}
};
G_TYPED_KERNEL(GFillPolyGContours,
<cv::GMat(cv::GMat,cv::GArray<Contour>)>,
"custom.faceb12n.fillPolyGContours")
{
static cv::GMatDesc outMeta(cv::GMatDesc in, cv::GArrayDesc)
{
return in.withType(CV_8U, 1);
}
};
G_TYPED_KERNEL(GPolyLines,
<cv::GMat(cv::GMat,cv::GArray<Contour>,bool,cv::Scalar)>,
"custom.faceb12n.polyLines")
{
static cv::GMatDesc outMeta(cv::GMatDesc in, cv::GArrayDesc,bool,cv::Scalar)
{
return in;
}
};
G_TYPED_KERNEL(GRectangle,
<cv::GMat(cv::GMat,GArrayROI,cv::Scalar)>,
"custom.faceb12n.rectangle")
{
static cv::GMatDesc outMeta(cv::GMatDesc in, cv::GArrayDesc,cv::Scalar)
{
return in;
}
};
G_TYPED_KERNEL(GFacePostProc,
<GArrayROI(cv::GMat,cv::GMat,float)>,
"custom.faceb12n.faceDetectPostProc")
{
static cv::GArrayDesc outMeta(const cv::GMatDesc&,const cv::GMatDesc&,float)
{
return cv::empty_array_desc();
}
};
G_TYPED_KERNEL_M(GLandmPostProc,
<TplPtsFaceElements_Jaw(cv::GArray<cv::GMat>,GArrayROI)>,
"custom.faceb12n.landmDetectPostProc")
{
static std::tuple<cv::GArrayDesc,cv::GArrayDesc> outMeta(
const cv::GArrayDesc&,const cv::GArrayDesc&)
{
return std::make_tuple(cv::empty_array_desc(), cv::empty_array_desc());
}
};
G_TYPED_KERNEL_M(GGetContours,
<TplFaces_FaceElements(cv::GArray<Landmarks>,
cv::GArray<Contour>)>,
"custom.faceb12n.getContours")
{
static std::tuple<cv::GArrayDesc,cv::GArrayDesc> outMeta(
const cv::GArrayDesc&,const cv::GArrayDesc&)
{
return std::make_tuple(cv::empty_array_desc(), cv::empty_array_desc());
}
};
// OCV_Kernels
// This kernel applies Bilateral filter to an input src with default
// "cv::bilateralFilter" border argument
GAPI_OCV_KERNEL(GCPUBilateralFilter, custom::GBilatFilter)
{
static void run(const cv::Mat &src,
const int diameter,
const double sigmaColor,
const double sigmaSpace,
cv::Mat &out)
{
cv::bilateralFilter(src, out, diameter, sigmaColor, sigmaSpace);
}
};
// This kernel applies Laplace operator to an input src with default
// "cv::Laplacian" arguments
GAPI_OCV_KERNEL(GCPULaplacian, custom::GLaplacian)
{
static void run(const cv::Mat &src,
const int ddepth,
cv::Mat &out)
{
cv::Laplacian(src, out, ddepth);
}
};
// This kernel draws given white filled contours "cnts" on a clear Mat "out"
// (defined by a Scalar(0)) with standard "cv::fillPoly" arguments.
// It should be used to create a mask.
// The input Mat seems unused inside the function "run", but it is used deeper
// in the kernel to define an output size.
GAPI_OCV_KERNEL(GCPUFillPolyGContours, custom::GFillPolyGContours)
{
static void run(const cv::Mat &,
const std::vector<Contour> &cnts,
cv::Mat &out)
{
out = cv::Scalar(0);
cv::fillPoly(out, cnts, config::kClrWhite);
}
};
// This kernel draws given contours on an input src with default "cv::polylines"
// arguments
GAPI_OCV_KERNEL(GCPUPolyLines, custom::GPolyLines)
{
static void run(const cv::Mat &src,
const std::vector<Contour> &cnts,
const bool isClosed,
const cv::Scalar &color,
cv::Mat &out)
{
src.copyTo(out);
cv::polylines(out, cnts, isClosed, color);
}
};
// This kernel draws given rectangles on an input src with default
// "cv::rectangle" arguments
GAPI_OCV_KERNEL(GCPURectangle, custom::GRectangle)
{
static void run(const cv::Mat &src,
const VectorROI &vctFaceBoxes,
const cv::Scalar &color,
cv::Mat &out)
{
src.copyTo(out);
for (const cv::Rect &box : vctFaceBoxes)
{
cv::rectangle(out, box, color);
}
}
};
// A face detector outputs a blob with the shape: [1, 1, N, 7], where N is
// the number of detected bounding boxes. Structure of an output for every
// detected face is the following:
// [image_id, label, conf, x_min, y_min, x_max, y_max]; all the seven elements
// are floating point. For more details please visit:
// https://github.com/opencv/open_model_zoo/blob/master/intel_models/face-detection-adas-0001
// This kernel is the face detection output blob parsing that returns a vector
// of detected faces' rects:
GAPI_OCV_KERNEL(GCPUFacePostProc, GFacePostProc)
{
static void run(const cv::Mat &inDetectResult,
const cv::Mat &inFrame,
const float faceConfThreshold,
VectorROI &outFaces)
{
const int kObjectSize = 7;
const int imgCols = inFrame.size().width;
const int imgRows = inFrame.size().height;
const cv::Rect borders({0, 0}, inFrame.size());
outFaces.clear();
const int numOfDetections = inDetectResult.size[2];
const float *data = inDetectResult.ptr<float>();
for (int i = 0; i < numOfDetections; i++)
{
const float faceId = data[i * kObjectSize + 0];
if (faceId < 0.f) // indicates the end of detections
{
break;
}
const float faceConfidence = data[i * kObjectSize + 2];
if (faceConfidence > faceConfThreshold)
{
const float left = data[i * kObjectSize + 3];
const float top = data[i * kObjectSize + 4];
const float right = data[i * kObjectSize + 5];
const float bottom = data[i * kObjectSize + 6];
cv::Point tl(toIntRounded(left * imgCols),
toIntRounded(top * imgRows));
cv::Point br(toIntRounded(right * imgCols),
toIntRounded(bottom * imgRows));
outFaces.push_back(cv::Rect(tl, br) & borders);
}
}
}
};
// This kernel is the facial landmarks detection output Mat parsing for every
// detected face; returns a tuple containing a vector of vectors of
// face elements' Points and a vector of vectors of jaw's Points:
GAPI_OCV_KERNEL(GCPULandmPostProc, GLandmPostProc)
{
static void run(const std::vector<cv::Mat> &vctDetectResults,
const VectorROI &vctRects,
std::vector<Landmarks> &vctPtsFaceElems,
std::vector<Contour> &vctCntJaw)
{
// There are 35 landmarks given by the default detector for each face
// in a frame; the first 18 of them are face elements (eyes, eyebrows,
// a nose, a mouth) and the last 17 - a jaw contour. The detector gives
// floating point values for landmarks' normed coordinates relatively
// to an input ROI (not the original frame).
// For more details please visit:
// https://github.com/opencv/open_model_zoo/blob/master/intel_models/facial-landmarks-35-adas-0002
static constexpr int kNumFaceElems = 18;
static constexpr int kNumTotal = 35;
const size_t numFaces = vctRects.size();
CV_Assert(vctPtsFaceElems.size() == 0ul);
CV_Assert(vctCntJaw.size() == 0ul);
vctPtsFaceElems.reserve(numFaces);
vctCntJaw.reserve(numFaces);
Landmarks ptsFaceElems;
Contour cntJaw;
ptsFaceElems.reserve(kNumFaceElems);
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)
{
cv::Point pt =
cv::Point(toIntRounded(data[j] * vctRects[i].width),
toIntRounded(data[j+1] * vctRects[i].height))
+ vctRects[i].tl();
ptsFaceElems.push_back(pt);
}
vctPtsFaceElems.push_back(ptsFaceElems);
// The jaw contour points:
cntJaw.clear();
for(int j = kNumFaceElems * 2; j < kNumTotal * 2; j += 2)
{
cv::Point pt =
cv::Point(toIntRounded(data[j] * vctRects[i].width),
toIntRounded(data[j+1] * vctRects[i].height))
+ vctRects[i].tl();
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:
GAPI_OCV_KERNEL(GCPUGetContours, GGetContours)
{
static void run(const std::vector<Landmarks> &vctPtsFaceElems,
const std::vector<Contour> &vctCntJaw,
std::vector<Contour> &vctElemsContours,
std::vector<Contour> &vctFaceContours)
{
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
// on 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 on
// 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],
config::kNumPointsInHalfEllipse + 3);
// Pushing the left eyebrow clock-wise:
cntLeftEye.insert(cntLeftEye.cend(), {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],
config::kNumPointsInHalfEllipse + 3);
// Pushing the right eyebrow clock-wise:
cntRightEye.insert(cntRightEye.cend(), {vctPtsFaceElems[i][15],
vctPtsFaceElems[i][16],
vctPtsFaceElems[i][17]});
// A nose:
// Storing the nose points clock-wise
cntNose.clear();
cntNose.insert(cntNose.cend(), {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.cend(), {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],
config::kNumPointsInHalfEllipse +
vctCntJaw[i].size());
// 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);
}
}
};
// 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:
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;
}