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fixed brocken tests by classifier loger from r9027

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This commit is contained in:
Marina Kolpakova 2012-07-15 04:11:46 +00:00
parent e4e00498a8
commit 336dcbcff7
3 changed files with 69 additions and 58 deletions
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12
modules/gpu/src/cascadeclassifier.cpp
View File

@ -97,13 +97,15 @@ void cv::gpu::CascadeClassifier_GPU_LBP::allocateBuffers(cv::Size frame)
Ncv32u bufSize;
ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
integralBuffer.create(1, bufSize, CV_8UC1);
}
candidates.create(1 , frame.width >> 1, CV_32SC4);
candidates.create(1 , frame.width >> 1, CV_32SC4);
}
}
bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const { return stage_mat.empty(); }
Size cv::gpu::CascadeClassifier_GPU_LBP::getClassifierSize() const { return NxM; }
bool cv::gpu::CascadeClassifier_GPU_LBP::empty() const
{
return stage_mat.empty();
}
bool cv::gpu::CascadeClassifier_GPU_LBP::load(const string& classifierAsXml)
{
@ -301,7 +303,6 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
else
objects.create(1 , image.cols >> 4, CV_32SC4);
// GpuMat candidates(1 , defaultObjSearchNum, CV_32SC4);
// used for debug
// candidates.setTo(cv::Scalar::all(0));
// objects.setTo(cv::Scalar::all(0));
@ -314,7 +315,6 @@ int cv::gpu::CascadeClassifier_GPU_LBP::detectMultiScale(const GpuMat& image, Gp
GpuMat dclassified(1, 1, CV_32S);
cudaSafeCall( cudaMemcpy(dclassified.ptr(), &classified, sizeof(int), cudaMemcpyHostToDevice) );
//int step = 2;
// cv::gpu::device::lbp::bindIntegral(integral);
Size scaledImageSize(image.cols, image.rows);

6
modules/gpu/test/test_objdetect.cpp
View File

@ -357,14 +357,18 @@ TEST_P(LBP_classify, Accuracy)
{
cv::Rect r = faces[i];
#if defined (LOG_CASCADE_STATISTIC)
std::cout << r.x << " " << r.y << " " << r.width << " " << r.height << std::endl;
#endif
cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
}
#if defined (LOG_CASCADE_STATISTIC)
cv::imshow("Res", markedImage); cv::waitKey();
#endif
}
INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, LBP_classify,
INSTANTIATE_TEST_CASE_P(GPU_ObjDetect, LBP_classify,
testing::Combine(ALL_DEVICES, testing::Values<int>(0)));
} // namespace

109
modules/objdetect/src/cascadedetect.cpp
View File

@ -49,64 +49,64 @@
struct Logger
{
enum { STADIES_NUM = 20 };
enum { STADIES_NUM = 20 };
int gid;
cv::Mat mask;
cv::Size sz0;
int step;
int gid;
cv::Mat mask;
cv::Size sz0;
int step;
Logger() : gid (0), step(2) {}
void setImage(const cv::Mat& image)
{
Logger() : gid (0), step(2) {}
void setImage(const cv::Mat& image)
{
if (gid == 0)
sz0 = image.size();
sz0 = image.size();
mask.create(image.rows, image.cols * (STADIES_NUM + 1) + STADIES_NUM, CV_8UC1);
mask = cv::Scalar(0);
cv::Mat roi = mask(cv::Rect(cv::Point(0,0), image.size()));
image.copyTo(roi);
mask.create(image.rows, image.cols * (STADIES_NUM + 1) + STADIES_NUM, CV_8UC1);
mask = cv::Scalar(0);
cv::Mat roi = mask(cv::Rect(cv::Point(0,0), image.size()));
image.copyTo(roi);
printf("%d) Size = (%d, %d)\n", gid, image.cols, image.rows);
printf("%d) Size = (%d, %d)\n", gid, image.cols, image.rows);
for(int i = 0; i < STADIES_NUM; ++i)
{
int x = image.cols + i * (image.cols + 1);
cv::line(mask, cv::Point(x, 0), cv::Point(x, mask.rows-1), cv::Scalar(255));
}
for(int i = 0; i < STADIES_NUM; ++i)
{
int x = image.cols + i * (image.cols + 1);
cv::line(mask, cv::Point(x, 0), cv::Point(x, mask.rows-1), cv::Scalar(255));
}
if (sz0.width/image.cols > 2 && sz0.height/image.rows > 2)
step = 1;
}
if (sz0.width/image.cols > 2 && sz0.height/image.rows > 2)
step = 1;
}
void setPoint(const cv::Point& p, int passed_stadies)
{
int cols = mask.cols / (STADIES_NUM + 1);
void setPoint(const cv::Point& p, int passed_stadies)
{
int cols = mask.cols / (STADIES_NUM + 1);
passed_stadies = -passed_stadies;
passed_stadies = (passed_stadies == -1) ? STADIES_NUM : passed_stadies;
unsigned char* ptr = mask.ptr<unsigned char>(p.y) + cols + 1 + p.x;
for(int i = 0; i < passed_stadies; ++i, ptr += cols + 1)
{
*ptr = 255;
passed_stadies = -passed_stadies;
passed_stadies = (passed_stadies == -1) ? STADIES_NUM : passed_stadies;
if (step == 2)
{
ptr[1] = 255;
ptr[mask.step] = 255;
ptr[mask.step + 1] = 255;
}
}
};
unsigned char* ptr = mask.ptr<unsigned char>(p.y) + cols + 1 + p.x;
for(int i = 0; i < passed_stadies; ++i, ptr += cols + 1)
{
*ptr = 255;
void write()
{
char buf[4096];
sprintf(buf, "%04d.png", gid++);
cv::imwrite(buf, mask);
}
if (step == 2)
{
ptr[1] = 255;
ptr[mask.step] = 255;
ptr[mask.step + 1] = 255;
}
}
};
void write()
{
char buf[4096];
sprintf(buf, "%04d.png", gid++);
cv::imwrite(buf, mask);
}
} logger;
@ -978,7 +978,10 @@ struct CascadeClassifierInvoker
double gypWeight;
int result = classifier->runAt(evaluator, Point(x, y), gypWeight);
logger.setPoint(Point(x, y), result);
#if defined (LOG_CASCADE_STATISTIC)
logger.setPoint(Point(x, y), result);
#endif
if( rejectLevels )
{
if( result == 1 )
@ -1019,8 +1022,9 @@ bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Siz
if( !featureEvaluator->setImage( image, data.origWinSize ) )
return false;
#if defined (LOG_CASCADE_STATISTIC)
logger.setImage(image);
#endif
Mat currentMask;
if (!maskGenerator.empty()) {
@ -1044,8 +1048,11 @@ bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Siz
}
candidates.insert( candidates.end(), concurrentCandidates.begin(), concurrentCandidates.end() );
logger.write();
return true;
#if defined (LOG_CASCADE_STATISTIC)
logger.write();
#endif
return true;
}
bool CascadeClassifier::isOldFormatCascade() const