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revise ocl samples, add tvl1 sample

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This commit is contained in:
yao
2013-06-19 16:36:23 +08:00
parent 2c198f6cd6
commit f1c549fabf
7 changed files with 924 additions and 645 deletions
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159
samples/ocl/facedetect.cpp
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@@ -7,55 +7,67 @@
using namespace std;
using namespace cv;
#define LOOP_NUM 10
#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)} ;
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)
} ;
int64 work_begin = 0;
int64 work_end = 0;
string outputName;
static void workBegin()
{
static void workBegin()
{
work_begin = getTickCount();
}
static void workEnd()
{
work_end += (getTickCount() - work_begin);
}
static double getTime(){
static double getTime()
{
return work_end /((double)cvGetTickFrequency() * 1000.);
}
void detect( Mat& img, vector<Rect>& faces,
cv::ocl::OclCascadeClassifierBuf& cascade,
double scale, bool calTime);
void detectCPU( Mat& img, vector<Rect>& faces,
CascadeClassifier& cascade,
double scale, bool calTime);
void detect( Mat& img, vector<Rect>& faces,
ocl::OclCascadeClassifierBuf& cascade,
double scale, bool calTime);
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);
double checkRectSimilarity(Size sz, vector<Rect>& cpu_rst, vector<Rect>& gpu_rst);
int main( int argc, const char** argv )
{
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 }"
"{ t | template | haarcascade_frontalface_alt.xml |"
" specify template file path }"
"{ c | scale | 1.0 | scale image }"
"{ s | use_cpu | false | use cpu or gpu to process the image }";
"{ s | use_cpu | false | use cpu or gpu to process the image }"
"{ o | output | facedetect_output.jpg |"
" specify output image save path(only works when input is images) }";
CommandLineParser cmd(argc, argv, keys);
if (cmd.get<bool>("help"))
@@ -69,9 +81,10 @@ int main( int argc, const char** argv )
bool useCPU = cmd.get<bool>("s");
string inputName = cmd.get<string>("i");
outputName = cmd.get<string>("o");
string cascadeName = cmd.get<string>("t");
double scale = cmd.get<double>("c");
cv::ocl::OclCascadeClassifierBuf cascade;
ocl::OclCascadeClassifierBuf cascade;
CascadeClassifier cpu_cascade;
if( !cascade.load( cascadeName ) || !cpu_cascade.load(cascadeName) )
@@ -83,7 +96,7 @@ int main( int argc, const char** argv )
if( inputName.empty() )
{
capture = cvCaptureFromCAM(0);
if(!capture)
if(!capture)
cout << "Capture from CAM 0 didn't work" << endl;
}
else if( inputName.size() )
@@ -92,7 +105,7 @@ int main( int argc, const char** argv )
if( image.empty() )
{
capture = cvCaptureFromAVI( inputName.c_str() );
if(!capture)
if(!capture)
cout << "Capture from AVI didn't work" << endl;
return -1;
}
@@ -100,14 +113,15 @@ int main( int argc, const char** argv )
else
{
image = imread( "lena.jpg", 1 );
if(image.empty())
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);
vector<ocl::Info> oclinfo;
int devnums = ocl::getDevice(oclinfo);
if( devnums < 1 )
{
std::cout << "no device found\n";
@@ -130,19 +144,23 @@ int main( int argc, const char** argv )
frame.copyTo( frameCopy );
else
flip( frame, frameCopy, 0 );
if(useCPU){
if(useCPU)
{
detectCPU(frameCopy, faces, cpu_cascade, scale, false);
}
else{
detect(frameCopy, faces, cascade, scale, false);
else
{
detect(frameCopy, faces, cascade, scale, false);
}
Draw(frameCopy, faces, scale);
if( waitKey( 10 ) >= 0 )
goto _cleanup_;
}
waitKey(0);
_cleanup_:
cvReleaseCapture( &capture );
}
@@ -152,18 +170,21 @@ _cleanup_:
vector<Rect> faces;
vector<Rect> ref_rst;
double accuracy = 0.;
for(int i = 0; i <= LOOP_NUM;i ++)
for(int i = 0; i <= LOOP_NUM; i ++)
{
cout << "loop" << i << endl;
if(useCPU){
detectCPU(image, faces, cpu_cascade, scale, i==0?false:true);
if(useCPU)
{
detectCPU(image, faces, cpu_cascade, scale, i==0?false:true);
}
else{
else
{
detect(image, faces, cascade, scale, i==0?false:true);
if(i == 0){
if(i == 0)
{
detectCPU(image, ref_rst, cpu_cascade, scale, false);
accuracy = checkRectSimilarity(image.size(), ref_rst, faces);
}
}
}
if (i == LOOP_NUM)
{
@@ -180,31 +201,31 @@ _cleanup_:
}
cvDestroyWindow("result");
return 0;
}
void detect( Mat& img, vector<Rect>& faces,
cv::ocl::OclCascadeClassifierBuf& cascade,
double scale, bool calTime)
void detect( Mat& img, vector<Rect>& faces,
ocl::OclCascadeClassifierBuf& cascade,
double scale, bool calTime)
{
cv::ocl::oclMat image(img);
cv::ocl::oclMat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
ocl::oclMat image(img);
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 );
ocl::cvtColor( image, gray, CV_BGR2GRAY );
ocl::resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
ocl::equalizeHist( smallImg, smallImg );
cascade.detectMultiScale( smallImg, faces, 1.1,
3, 0
|CV_HAAR_SCALE_IMAGE
, Size(30,30), Size(0, 0) );
3, 0
|CV_HAAR_SCALE_IMAGE
, Size(30,30), Size(0, 0) );
if(calTime) workEnd();
}
void detectCPU( Mat& img, vector<Rect>& faces,
CascadeClassifier& cascade,
double scale, bool calTime)
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 );
@@ -212,11 +233,12 @@ void detectCPU( Mat& img, vector<Rect>& faces,
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();
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;
@@ -230,31 +252,38 @@ void Draw(Mat& img, vector<Rect>& faces, double scale)
radius = cvRound((r->width + r->height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
}
cv::imshow( "result", img );
imshow( "result", img );
imwrite( outputName, img );
}
double checkRectSimilarity(Size sz, std::vector<Rect>& ob1, std::vector<Rect>& ob2)
double checkRectSimilarity(Size sz, vector<Rect>& ob1, 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);
if(sz1==0 && sz2==0)
return 0;
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);
{
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);
int cpu_area = countNonZero(cpu_result > 0);
cv::Mat gpu_result(sz, CV_8UC1);
Mat gpu_result(sz, CV_8UC1);
gpu_result.setTo(0);
for(vector<Rect>::const_iterator r2 = ob2.begin(); r2 != ob2.end(); r2++)
{
@@ -263,11 +292,13 @@ double checkRectSimilarity(Size sz, std::vector<Rect>& ob1, std::vector<Rect>& o
gpu_result.copyTo(gpu_result);
}
cv::Mat result_;
Mat result_;
multiply(cpu_result, gpu_result, result_);
int result = cv::countNonZero(result_ > 0);
final_test_result = 1.0 - (double)result/(double)cpu_area;
int result = countNonZero(result_ > 0);
if(cpu_area!=0 && result!=0)
final_test_result = 1.0 - (double)result/(double)cpu_area;
else if(cpu_area==0 && result!=0)
final_test_result = -1;
}
return final_test_result;
}