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Commit fad96b95 authored by yao's avatar yao
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add results verification to facedetect and hog samples

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samples/ocl/facedetect.cpp
View file @ fad96b95
//This sample is inherited from facedetect.cpp in smaple/c
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
......@@ -9,78 +7,84 @@
using namespace std;
using namespace cv;
#define LOOP_NUM 10
const static Scalar colors[] = { CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)} ;
static void help()
int64 work_begin = 0;
int64 work_end = 0;
static void workBegin()
{
work_begin = getTickCount();
}
static void workEnd()
{
cout << "\nThis program demonstrates the cascade recognizer.\n"
"This classifier can recognize many ~rigid objects, it's most known use is for faces.\n"
"Usage:\n"
"./facedetect [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n"
" [--scale=<image scale greater or equal to 1, try 1.3 for example>\n"
" [filename|camera_index]\n\n"
"see facedetect.cmd for one call:\n"
"./facedetect --cascade=\"../../data/haarcascades/haarcascade_frontalface_alt.xml\" --scale=1.3 \n"
"Hit any key to quit.\n"
"Using OpenCV version " << CV_VERSION << "\n" << endl;
work_end += (getTickCount() - work_begin);
}
struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } };
void detectAndDraw( Mat& img,
cv::ocl::OclCascadeClassifier& cascade, CascadeClassifier& nestedCascade,
double scale);
static double getTime(){
return work_end /((double)cvGetTickFrequency() * 1000.);
}
void detect( Mat& img, vector<Rect>& faces,
cv::ocl::OclCascadeClassifierBuf& cascade,
double scale, bool calTime);
String cascadeName = "../../../data/haarcascades/haarcascade_frontalface_alt.xml";
void detectCPU( Mat& img, vector<Rect>& faces,
CascadeClassifier& cascade,
double scale, bool calTime);
void Draw(Mat& img, vector<Rect>& faces, double scale);
// This function test if gpu_rst matches cpu_rst.
// If the two vectors are not equal, it will return the difference in vector size
// Else if will return (total diff of each cpu and gpu rects covered pixels)/(total cpu rects covered pixels)
double checkRectSimilarity(Size sz, std::vector<Rect>& cpu_rst, std::vector<Rect>& gpu_rst);
int main( int argc, const char** argv )
{
CvCapture* capture = 0;
Mat frame, frameCopy, image;
const String scaleOpt = "--scale=";
size_t scaleOptLen = scaleOpt.length();
const String cascadeOpt = "--cascade=";
size_t cascadeOptLen = cascadeOpt.length();
String inputName;
help();
cv::ocl::OclCascadeClassifier cascade;
CascadeClassifier nestedCascade;
double scale = 1;
for( int i = 1; i < argc; i++ )
const char* keys =
"{ h | help | false | print help message }"
"{ i | input | | specify input image }"
"{ t | template | ../../../data/haarcascades/haarcascade_frontalface_alt.xml | specify template file }"
"{ c | scale | 1.0 | scale image }"
"{ s | use_cpu | false | use cpu or gpu to process the image }";
CommandLineParser cmd(argc, argv, keys);
if (cmd.get<bool>("help"))
{
cout << "Processing " << i << " " << argv[i] << endl;
if( cascadeOpt.compare( 0, cascadeOptLen, argv[i], cascadeOptLen ) == 0 )
{
cascadeName.assign( argv[i] + cascadeOptLen );
cout << " from which we have cascadeName= " << cascadeName << endl;
}
else if( scaleOpt.compare( 0, scaleOptLen, argv[i], scaleOptLen ) == 0 )
{
if( !sscanf( argv[i] + scaleOpt.length(), "%lf", &scale ) || scale < 1 )
scale = 1;
cout << " from which we read scale = " << scale << endl;
}
else if( argv[i][0] == '-' )
{
cerr << "WARNING: Unknown option %s" << argv[i] << endl;
}
else
inputName.assign( argv[i] );
cout << "Avaible options:" << endl;
cmd.printParams();
return 0;
}
CvCapture* capture = 0;
Mat frame, frameCopy, image;
if( !cascade.load( cascadeName ) )
bool useCPU = cmd.get<bool>("s");
string inputName = cmd.get<string>("i");
string cascadeName = cmd.get<string>("t");
double scale = cmd.get<double>("c");
cv::ocl::OclCascadeClassifierBuf cascade;
CascadeClassifier cpu_cascade;
if( !cascade.load( cascadeName ) || !cpu_cascade.load(cascadeName) )
{
cerr << "ERROR: Could not load classifier cascade" << endl;
cerr << "Usage: facedetect [--cascade=<cascade_path>]\n"
" [--scale[=<image scale>\n"
" [filename|camera_index]\n" << endl ;
return -1;
}
if( inputName.empty() || (isdigit(inputName.c_str()[0]) && inputName.c_str()[1] == '\0') )
if( inputName.empty() )
{
capture = cvCaptureFromCAM( inputName.empty() ? 0 : inputName.c_str()[0] - '0' );
int c = inputName.empty() ? 0 : inputName.c_str()[0] - '0' ;
if(!capture) cout << "Capture from CAM " << c << " didn't work" << endl;
capture = cvCaptureFromCAM(0);
if(!capture)
cout << "Capture from CAM 0 didn't work" << endl;
}
else if( inputName.size() )
{
......@@ -88,26 +92,30 @@ int main( int argc, const char** argv )
if( image.empty() )
{
capture = cvCaptureFromAVI( inputName.c_str() );
if(!capture) cout << "Capture from AVI didn't work" << endl;
if(!capture)
cout << "Capture from AVI didn't work" << endl;
return -1;
}
}
else
{
image = imread( "lena.jpg", 1 );
if(image.empty()) cout << "Couldn't read lena.jpg" << endl;
if(image.empty())
cout << "Couldn't read lena.jpg" << endl;
return -1;
}
cvNamedWindow( "result", 1 );
std::vector<cv::ocl::Info> oclinfo;
int devnums = cv::ocl::getDevice(oclinfo);
if(devnums<1)
if( devnums < 1 )
{
std::cout << "no device found\n";
return -1;
}
//if you want to use undefault device, set it here
//setDevice(oclinfo[0]);
//setBinpath(CLBINPATH);
ocl::setBinpath("./");
if( capture )
{
cout << "In capture ..." << endl;
......@@ -115,15 +123,20 @@ int main( int argc, const char** argv )
{
IplImage* iplImg = cvQueryFrame( capture );
frame = iplImg;
vector<Rect> faces;
if( frame.empty() )
break;
if( iplImg->origin == IPL_ORIGIN_TL )
frame.copyTo( frameCopy );
else
flip( frame, frameCopy, 0 );
detectAndDraw( frameCopy, cascade, nestedCascade, scale );
if(useCPU){
detectCPU(frameCopy, faces, cpu_cascade, scale, false);
}
else{
detect(frameCopy, faces, cascade, scale, false);
}
Draw(frameCopy, faces, scale);
if( waitKey( 10 ) >= 0 )
goto _cleanup_;
}
......@@ -136,42 +149,34 @@ _cleanup_:
else
{
cout << "In image read" << endl;
if( !image.empty() )
{
detectAndDraw( image, cascade, nestedCascade, scale );
waitKey(0);
}
else if( !inputName.empty() )
vector<Rect> faces;
vector<Rect> ref_rst;
double accuracy = 0.;
for(int i = 0; i <= LOOP_NUM;i ++)
{
/* assume it is a text file containing the
list of the image filenames to be processed - one per line */
FILE* f = fopen( inputName.c_str(), "rt" );
if( f )
cout << "loop" << i << endl;
if(useCPU){
detectCPU(image, faces, cpu_cascade, scale, i==0?false:true);
}
else{
detect(image, faces, cascade, scale, i==0?false:true);
if(i == 0){
detectCPU(image, ref_rst, cpu_cascade, scale, false);
accuracy = checkRectSimilarity(image.size(), ref_rst, faces);
}
}
if (i == LOOP_NUM)
{
char buf[1000+1];
while( fgets( buf, 1000, f ) )
{
int len = (int)strlen(buf), c;
while( len > 0 && isspace(buf[len-1]) )
len--;
buf[len] = '\0';
cout << "file " << buf << endl;
image = imread( buf, 1 );
if( !image.empty() )
{
detectAndDraw( image, cascade, nestedCascade, scale );
c = waitKey(0);
if( c == 27 || c == 'q' || c == 'Q' )
break;
}
else
{
cerr << "Aw snap, couldn't read image " << buf << endl;
}
}
fclose(f);
if (useCPU)
cout << "average CPU time (noCamera) : ";
else
cout << "average GPU time (noCamera) : ";
cout << getTime() / LOOP_NUM << " ms" << endl;
cout << "accuracy value: " << accuracy <<endl;
}
}
Draw(image, faces, scale);
waitKey(0);
}
cvDestroyWindow("result");
......@@ -179,44 +184,44 @@ _cleanup_:
return 0;
}
void detectAndDraw( Mat& img,
cv::ocl::OclCascadeClassifier& cascade, CascadeClassifier&,
double scale)
void detect( Mat& img, vector<Rect>& faces,
cv::ocl::OclCascadeClassifierBuf& cascade,
double scale, bool calTime)
{
int i = 0;
double t = 0;
vector<Rect> faces;
const static Scalar colors[] = { CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)} ;
cv::ocl::oclMat image(img);
cv::ocl::oclMat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
if(calTime) workBegin();
cv::ocl::cvtColor( image, gray, CV_BGR2GRAY );
cv::ocl::resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
cv::ocl::equalizeHist( smallImg, smallImg );
CvSeq* _objects;
MemStorage storage(cvCreateMemStorage(0));
t = (double)cvGetTickCount();
_objects = cascade.oclHaarDetectObjects( smallImg, storage, 1.1,
cascade.detectMultiScale( smallImg, faces, 1.1,
3, 0
|CV_HAAR_SCALE_IMAGE
, Size(30,30), Size(0, 0) );
vector<CvAvgComp> vecAvgComp;
Seq<CvAvgComp>(_objects).copyTo(vecAvgComp);
faces.resize(vecAvgComp.size());
std::transform(vecAvgComp.begin(), vecAvgComp.end(), faces.begin(), getRect());
t = (double)cvGetTickCount() - t;
printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
if(calTime) workEnd();
}
void detectCPU( Mat& img, vector<Rect>& faces,
CascadeClassifier& cascade,
double scale, bool calTime)
{
if(calTime) workBegin();
Mat cpu_gray, cpu_smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
cvtColor(img, cpu_gray, CV_BGR2GRAY);
resize(cpu_gray, cpu_smallImg, cpu_smallImg.size(), 0, 0, INTER_LINEAR);
equalizeHist(cpu_smallImg, cpu_smallImg);
cascade.detectMultiScale(cpu_smallImg, faces, 1.1,
3, 0 | CV_HAAR_SCALE_IMAGE,
Size(30, 30), Size(0, 0));
if(calTime) workEnd();
}
void Draw(Mat& img, vector<Rect>& faces, double scale)
{
int i = 0;
for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
Mat smallImgROI;
Point center;
Scalar color = colors[i%8];
int radius;
......@@ -227,3 +232,42 @@ void detectAndDraw( Mat& img,
}
cv::imshow( "result", img );
}
double checkRectSimilarity(Size sz, std::vector<Rect>& ob1, std::vector<Rect>& ob2)
{
double final_test_result = 0.0;
size_t sz1 = ob1.size();
size_t sz2 = ob2.size();
if(sz1 != sz2)
return sz1 > sz2 ? (double)(sz1 - sz2) : (double)(sz2 - sz1);
else
{
cv::Mat cpu_result(sz, CV_8UC1);
cpu_result.setTo(0);
for(vector<Rect>::const_iterator r = ob1.begin(); r != ob1.end(); r++)
{
cv::Mat cpu_result_roi(cpu_result, *r);
cpu_result_roi.setTo(1);
cpu_result.copyTo(cpu_result);
}
int cpu_area = cv::countNonZero(cpu_result > 0);
cv::Mat gpu_result(sz, CV_8UC1);
gpu_result.setTo(0);
for(vector<Rect>::const_iterator r2 = ob2.begin(); r2 != ob2.end(); r2++)
{
cv::Mat gpu_result_roi(gpu_result, *r2);
gpu_result_roi.setTo(1);
gpu_result.copyTo(gpu_result);
}
cv::Mat result_;
multiply(cpu_result, gpu_result, result_);
int result = cv::countNonZero(result_ > 0);
final_test_result = 1.0 - (double)result/(double)cpu_area;
}
return final_test_result;
}
samples/ocl/hog.cpp
View file @ fad96b95
......@@ -45,7 +45,6 @@ public:
bool gamma_corr;
};
class App
{
public:
......@@ -64,6 +63,13 @@ public:
string message() const;
// This function test if gpu_rst matches cpu_rst.
// If the two vectors are not equal, it will return the difference in vector size
// Else if will return
// (total diff of each cpu and gpu rects covered pixels)/(total cpu rects covered pixels)
double checkRectSimilarity(Size sz,
std::vector<Rect>& cpu_rst,
std::vector<Rect>& gpu_rst);
private:
App operator=(App&);
......@@ -290,6 +296,7 @@ void App::run()
ocl::oclMat gpu_img;
// Iterate over all frames
bool verify = false;
while (running && !frame.empty())
{
workBegin();
......@@ -316,7 +323,18 @@ void App::run()
gpu_img.upload(img);
gpu_hog.detectMultiScale(gpu_img, found, hit_threshold, win_stride,
Size(0, 0), scale, gr_threshold);
}
if (!verify)
{
// verify if GPU output same objects with CPU at 1st run
verify = true;
vector<Rect> ref_rst;
cvtColor(img, img, CV_BGRA2BGR);
cpu_hog.detectMultiScale(img, ref_rst, hit_threshold, win_stride,
Size(0, 0), scale, gr_threshold-2);
double accuracy = checkRectSimilarity(img.size(), ref_rst, found);
cout << "\naccuracy value: " << accuracy << endl;
}
}
else cpu_hog.detectMultiScale(img, found, hit_threshold, win_stride,
Size(0, 0), scale, gr_threshold);
hogWorkEnd();
......@@ -457,3 +475,45 @@ inline string App::workFps() const
return ss.str();
}
double App::checkRectSimilarity(Size sz,
std::vector<Rect>& ob1,
std::vector<Rect>& ob2)
{
double final_test_result = 0.0;
size_t sz1 = ob1.size();
size_t sz2 = ob2.size();
if(sz1 != sz2)
return sz1 > sz2 ? (double)(sz1 - sz2) : (double)(sz2 - sz1);
else
{
cv::Mat cpu_result(sz, CV_8UC1);
cpu_result.setTo(0);
for(vector<Rect>::const_iterator r = ob1.begin(); r != ob1.end(); r++)
{
cv::Mat cpu_result_roi(cpu_result, *r);
cpu_result_roi.setTo(1);
cpu_result.copyTo(cpu_result);
}
int cpu_area = cv::countNonZero(cpu_result > 0);
cv::Mat gpu_result(sz, CV_8UC1);
gpu_result.setTo(0);
for(vector<Rect>::const_iterator r2 = ob2.begin(); r2 != ob2.end(); r2++)
{
cv::Mat gpu_result_roi(gpu_result, *r2);
gpu_result_roi.setTo(1);
gpu_result.copyTo(gpu_result);
}
cv::Mat result_;
multiply(cpu_result, gpu_result, result_);
int result = cv::countNonZero(result_ > 0);
final_test_result = 1.0 - (double)result/(double)cpu_area;
}
return final_test_result;
}
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