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clip the found objects in HOG as well (http://code.opencv.org/issues/3825); added test to check CascadeClassifier on small images (http://code.opencv.org/issues/3710)

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This commit is contained in:
Vadim Pisarevsky 2015-05-28 21:02:27 +03:00
parent 8c3c6b31fc
commit 882c0321f4
4 changed files with 39 additions and 5 deletions
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5
modules/objdetect/src/cascadedetect.cpp
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@ -1587,9 +1587,8 @@ bool CascadeClassifier::read(const FileNode &root)
return ok; return ok;
} }
static void clipObjects(Size sz, std::vector<Rect>& objects, void clipObjects(Size sz, std::vector<Rect>& objects,
std::vector<int>* a, std::vector<int>* a, std::vector<double>* b)
std::vector<double>* b)
{ {
size_t i, j = 0, n = objects.size(); size_t i, j = 0, n = objects.size();
Rect win0 = Rect(0, 0, sz.width, sz.height); Rect win0 = Rect(0, 0, sz.width, sz.height);

3
modules/objdetect/src/cascadedetect.hpp
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@ -5,6 +5,9 @@
namespace cv namespace cv
{ {
void clipObjects(Size sz, std::vector<Rect>& objects,
std::vector<int>* a, std::vector<double>* b);
class FeatureEvaluator class FeatureEvaluator
{ {
public: public:

6
modules/objdetect/src/hog.cpp
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@ -41,6 +41,7 @@
//M*/ //M*/
#include "precomp.hpp" #include "precomp.hpp"
#include "cascadedetect.hpp"
#include "opencv2/core/core_c.h" #include "opencv2/core/core_c.h"
#include "opencl_kernels_objdetect.hpp" #include "opencl_kernels_objdetect.hpp"
@ -1822,7 +1823,9 @@ static bool ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locat
all_candidates.push_back(Rect(Point2d(locations[j]) * scale, scaled_win_size)); all_candidates.push_back(Rect(Point2d(locations[j]) * scale, scaled_win_size));
} }
found_locations.assign(all_candidates.begin(), all_candidates.end()); found_locations.assign(all_candidates.begin(), all_candidates.end());
cv::groupRectangles(found_locations, (int)group_threshold, 0.2); groupRectangles(found_locations, (int)group_threshold, 0.2);
clipObjects(imgSize, found_locations, 0, 0);
return true; return true;
} }
#endif //HAVE_OPENCL #endif //HAVE_OPENCL
@ -1878,6 +1881,7 @@ void HOGDescriptor::detectMultiScale(
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize); groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
else else
groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2); groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2);
clipObjects(imgSize, foundLocations, 0, &foundWeights);
} }
void HOGDescriptor::detectMultiScale(InputArray img, std::vector<Rect>& foundLocations, void HOGDescriptor::detectMultiScale(InputArray img, std::vector<Rect>& foundLocations,

28
modules/objdetect/test/test_cascadeandhog.cpp
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@ -1360,4 +1360,32 @@ TEST(Objdetect_HOGDetector_Strict, accuracy)
std::vector<float> descriptors; std::vector<float> descriptors;
reference_hog.compute(image, descriptors); reference_hog.compute(image, descriptors);
} }
}
TEST(Objdetect_CascadeDetector, small_img)
{
String root = cvtest::TS::ptr()->get_data_path() + "cascadeandhog/cascades/";
String cascades[] =
{
root + "haarcascade_frontalface_alt.xml",
root + "lbpcascade_frontalface.xml",
String()
};
vector<Rect> objects;
RNG rng((uint64)-1);
for( int i = 0; !cascades[i].empty(); i++ )
{
printf("%d. %s\n", i, cascades[i].c_str());
CascadeClassifier cascade(cascades[i]);
for( int j = 0; j < 100; j++ )
{
int width = rng.uniform(1, 100);
int height = rng.uniform(1, 100);
Mat img(height, width, CV_8U);
randu(img, 0, 256);
cascade.detectMultiScale(img, objects);
} }
}
}