opencv 3.0 cascade_gpu example source code

opencv 2.49 example is here
http://study.marearts.com/2014/09/opencv-face-detection-using-adaboost.html

#include < iostream>    
#include "opencv2\objdetect\objdetect.hpp"
#include "opencv2\highgui\highgui.hpp"
#include "opencv2\imgproc\imgproc.hpp"
#include "opencv2\cudaobjdetect.hpp"
#include "opencv2\cudaimgproc.hpp"

#ifdef _DEBUG               
#pragma comment(lib, "opencv_core300d.lib")       
#pragma comment(lib, "opencv_highgui300d.lib")    
#pragma comment(lib, "opencv_imgcodecs300d.lib")  
#pragma comment(lib, "opencv_objdetect300d.lib")  
#pragma comment(lib, "opencv_imgproc300d.lib")  
#pragma comment(lib, "opencv_cudaobjdetect300d.lib")  
#else       
#pragma comment(lib, "opencv_core300.lib")       
#pragma comment(lib, "opencv_highgui300.lib")    
#pragma comment(lib, "opencv_imgcodecs300.lib")    
#pragma comment(lib, "opencv_objdetect300.lib")  
#pragma comment(lib, "opencv_imgproc300.lib")  
#pragma comment(lib, "opencv_cudaobjdetect300.lib")  
#endif        

using namespace std;
using namespace cv;


void main()
{
 //for time measure  
 float TakeTime;
 unsigned long Atime, Btime;

 //window  
 namedWindow("origin");

 //load image  
 Mat img = imread("sh.jpg");
 Mat grayImg; //adaboost detection is gray input only.  
 cvtColor(img, grayImg, CV_BGR2GRAY);

 //load xml file  
 string trainface = ".\\haarcascade_frontalface_alt.xml";

 //declaration  
 Ptr< cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(trainface);

 
 /////////////////////////////////////////////  
 
 //gpu case face detection code  
 cuda::GpuMat faceBuf_gpu;
 cuda::GpuMat GpuImg;
 vector< Rect> faces;

 GpuImg.upload(grayImg);
 Atime = getTickCount();

 cascade_gpu->detectMultiScale(GpuImg, faceBuf_gpu);
 cascade_gpu->convert(faceBuf_gpu, faces);
 Btime = getTickCount();
 TakeTime = (Btime - Atime) / getTickFrequency();
 printf("detected face(gpu version) =%d / %lf sec take.\n", faces.size(), TakeTime);
 Mat faces_downloaded;
 if (faces.size() >= 1)
 {
  for (size_t i = 0; i < faces.size(); ++i)
   rectangle(img, faces[i], Scalar(255));
 } 

 /////////////////////////////////////////////////  
 //result display  
 imshow("origin", img);
 waitKey(0);
}

OpenCV face detection using adaboost example source code and cpu vs gpu detection speed compare (CascadeClassifier, CascadeClassifier_GPU, detectMultiScale)

OpenCV has AdaBoost algorithm function.
And gpu version also is provided.

For using detection, we prepare the trained xml file.
Although we can train some target using adaboost algorithm in opencv functions, there are several trained xml files in the opencv folder. (mostly in opencv/sources/data/haarcascades )

I will use "haarcascade_frontalface_alt.xml" file for face detection example.

gpu and cpu both versions use xml file.

more detail refer to this source code.
The source code is included 2 version of cpu and gpu.

result is ..
gpu is faster than cpu version (but exactly they may not be same condition..)
blue boxes are result of cpu.
red boxes are results of gpu.
The results are not important because it can be different by parameters values.

<code start>

<code end>

Github
https://github.com/MareArts/AdaBoost-Face-Detection-test-using-OpenCV


#Tags
cvtColor, CascadeClassifier, CascadeClassifier_GPU, detectMultiScale,