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#include "FiveWin.ch"

function Main()

   Test()

return nil

#pragma BEGINDUMP

#include <hbapi.h>
#include <opencv.hpp>

using namespace cv;

HB_FUNC( TEST )
{
   cv::Mat img = imread( "007.jpg" );
   
   namedWindow( "image", WINDOW_AUTOSIZE );
   imshow( "image", img );
   waitKey( 0 );
}

#pragma ENDDUMP

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@set oldpath=%Path%
@set oldinclude=%INCLUDE%
call "%ProgramFiles(x86)%\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x86_amd64
c:\harbour\bin\win\msvc64\hbmk2 cv1.hbp -comp=msvc64
@set Path=%oldpath%
@set INCLUDE=%oldinclude%

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cv1.prg

-cflag=-TP

-Ic:\fwh64\include
-Ic:\opencv\build\include
-Ic:\opencv\build\include\opencv2

-Lc:\fwh64\lib

-lfiveh64
-lfivehc64
-lgdiplus
-lversion
-lOleDlg
-lopencv_world454

-gui

xhb.hbc
hbct.hbc
hbwin.hbc
hbmzip.hbc
hbziparc.hbc
hbfoxpro.hbc

opencvlib.hbp
 

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-hbimplib opencv_world454.dll

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#include "FiveWin.ch"

#define WINDOW_AUTOSIZE 1

function Main()

   local hMat := cv_ImRead( "007.jpg" )

   cv_namedWindow( "window title", WINDOW_AUTOSIZE )
   cv_ImShow( "window title", hMat )
   cv_WaitKey()

return nil

#pragma BEGINDUMP

#include <hbapi.h>
#include <opencv.hpp>

using namespace cv;

static cv::Mat mat;

HB_FUNC( CV_IMREAD )
{
   mat = imread( hb_parc( 1 ) );

   hb_retptr( &mat );
}

HB_FUNC( CV_NAMEDWINDOW )
{
   namedWindow( hb_parc( 1 ), hb_parnl( 2 ) );
}

HB_FUNC( CV_IMSHOW )
{
   imshow( hb_parc( 1 ), * ( ( cv::Mat * ) hb_parptr( 2 ) ) );
}

HB_FUNC( CV_WAITKEY )
{
   waitKey( hb_parnl( 1 ) );
}

#pragma ENDDUMP

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#include "FiveWin.ch"

#define WINDOW_AUTOSIZE 1

function Main()

   local hMat := cv_ImRead( "007.jpg" )

   cv_namedWindow( "window title", WINDOW_AUTOSIZE )
   cv_ImShow( "window title", hMat )
   cv_WaitKey()

return nil

#pragma BEGINDUMP

#include <hbapi.h>
#include <opencv.hpp>

// using namespace cv;

static cv::Mat mat;

HB_FUNC( CV_IMREAD )
{
   mat = cv::imread( hb_parc( 1 ) );

   hb_retptr( &mat );
}

HB_FUNC( CV_NAMEDWINDOW )
{
   cv::namedWindow( hb_parc( 1 ), hb_parnl( 2 ) );
}

HB_FUNC( CV_IMSHOW )
{
   cv::imshow( hb_parc( 1 ), * ( ( cv::Mat * ) hb_parptr( 2 ) ) );
}

HB_FUNC( CV_WAITKEY )
{
   cv::waitKey( hb_parnl( 1 ) );
}

#pragma ENDDUMP

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@set oldpath=%Path%
@set oldinclude=%INCLUDE%
call "%ProgramFiles(x86)%\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x86_amd64
cl /EHsc test1.cpp -Ic:\opencv\build\include -Ic:\opencv_contrib\modules\ximgproc\include c:\opencv\build\x64\vc15\lib\opencv_world454.lib
@set Path=%oldpath%
@set INCLUDE=%oldinclude%

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@set oldpath=%Path%
@set oldinclude=%INCLUDE%
call "%ProgramFiles(x86)%\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x86_amd64
cmake -DOPENCV_EXTRA_MODULES_PATH=c:\opencv_contrib\modules c:\opencv\sources
msbuild OpenCV.sln /p:Configuration=Release /t:Clean,Build
@set Path=%oldpath%
@set INCLUDE=%oldinclude%

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@set oldpath=%Path%
@set oldinclude=%INCLUDE%
call "%ProgramFiles(x86)%\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x86_amd64
cl /EHsc test1.cpp -Ic:\opencv\build\include -Ic:\opencv_contrib\modules\ximgproc\include c:\opencv\build\x64\vc15\lib\opencv_world454.lib c:\opencv_fwh\lib\Release\opencv_ximgproc454.lib
@set Path=%oldpath%
@set INCLUDE=%oldinclude%

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@set oldpath=%Path%
@set oldinclude=%INCLUDE%
call "%ProgramFiles(x86)%\Microsoft Visual Studio\2019\Community\VC\Auxiliary\Build\vcvarsall.bat" x86_amd64
cl /EHsc test1.cpp -Ic:\opencv\build\include -Ic:\opencv_contrib\modules\ximgproc\include -Ic:\opencv_contrib\modules\shape\include c:\opencv\build\x64\vc15\lib\opencv_world454.lib c:\opencv_fwh\lib\Release\opencv_ximgproc454.lib c:\opencv_fwh\lib\Release\opencv_shape454.lib
@set Path=%oldpath%
@set INCLUDE=%oldinclude%

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// -*- coding:utf-8; mode:c++; mode:auto-fill; fill-column:80; -*-

/// @file      cascade-classifier.cpp
/// @brief     OpenCV object recognition example.
/// @author    J. Arrieta <juan.arrieta@nablazerolabs.com>
/// @date      October 04, 2017
/// @copyright (c) 2017 Nabla Zero Labs
/// @license   MIT License.
///
/// I wrote this example program for my later reference.
///
/// Compilation:
///
///     clang++ cascade-classifier.cpp -o cascade-classifier \
///     -std=c++1z -Wall -Wextra -Ofast -march=native \
///     -lopencv_objdetect -lopencv_highgui \
///     -lopencv_imgproc -lopencv_core -lopencv_videoio
///
/// The Haar cascade XML description is provided as a command-line argument; the
/// examples I used are in GitHub:
///
///     https://github.com/opencv/opencv/tree/m ... arcascades
///

// C++ Standard Library
#include <cstdlib>
#include <iostream>
#include <vector>

// OpenCV
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui_c.h>

#define     CV_HAAR_SCALE_IMAGE   2

int main(int argc, char* argv[]) {
  if (argc != 2) {
    std::cerr << "usage: " << argv[0] << " classifier.xml\n";
    std::exit(EXIT_FAILURE);
  }

  // Load a classifier from its XML description
  cv::CascadeClassifier classifier(argv[1]);

  // Prepare a display window
  const char* const window_name{"Facial Recognition Window"};

  cv::namedWindow(window_name, CV_WINDOW_AUTOSIZE);

  // Prepare a video capture device
  cv::VideoCapture capture(0); // `0` means "default video capture"
  if (! capture.isOpened()) {
    std::cerr << "cannot open video capture device\n";
    std::exit(EXIT_FAILURE);
  }

  // Prepare an image where to store the video frames, and an image to store a
  // grayscale version
  cv::Mat image;
  cv::Mat grayscale_image;

  // Prepare a vector where the detected features will be stored
  std::vector<cv::Rect> features;

  // Main loop
  while (capture.read(image) && (! image.empty())) {
    // Create a normalized, gray-scale version of the captured image
    cv::cvtColor(image, grayscale_image, CV_BGR2GRAY);
    cv::equalizeHist(grayscale_image, grayscale_image);

    // Detect the features in the normalized, gray-scale version of the
    // image. You don't need to clear the previously-found features because the
    // detectMultiScale method will clear before adding new features.
    classifier.detectMultiScale(grayscale_image, features, 1.1, 2,
                                0 | CV_HAAR_SCALE_IMAGE, cv::Size(30, 30));

    // Draw each feature as a separate green rectangle
    for (auto&& feature : features) {
      cv::rectangle(image, feature, cv::Scalar(0, 255, 0), 2);
    }

    // Show the captured image and the detected features
    cv::imshow(window_name, image);

    // Wait for input or process the next frame
    switch (cv::waitKey(10)) {
      case 'q':
        std::exit(EXIT_SUCCESS);
      case 'Q':
        std::exit(EXIT_SUCCESS);
      default:
        break;
    }
  }
  return EXIT_SUCCESS;
}

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import numpy as np
import cv2
f_cascade = cv2.CascadeClassifier("face.xml")
e_cascade = cv2.CascadeClassifier("eye.xml")
image = cv2.imread("actor.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = f_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
    img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = img[y:y+h, x:x+w]
    eyes = e_cascade.detectMultiScale(roi_gray)
    for (ex,ey,ew,eh) in eyes:
        cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
cv2.imshow('img',image)
cv2.waitKey(0)
cv2.destroyAllWindows()