return ok;
}
-static void clipObjects(Size sz, std::vector<Rect>& objects,
- std::vector<int>* a,
- std::vector<double>* b)
+void clipObjects(Size sz, std::vector<Rect>& objects,
+ std::vector<int>* a, std::vector<double>* b)
{
size_t i, j = 0, n = objects.size();
Rect win0 = Rect(0, 0, sz.width, sz.height);
namespace cv
{
+void clipObjects(Size sz, std::vector<Rect>& objects,
+ std::vector<int>* a, std::vector<double>* b);
+
class FeatureEvaluator
{
public:
//M*/
#include "precomp.hpp"
+#include "cascadedetect.hpp"
#include "opencv2/core/core_c.h"
#include "opencl_kernels_objdetect.hpp"
all_candidates.push_back(Rect(Point2d(locations[j]) * scale, scaled_win_size));
}
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;
}
#endif //HAVE_OPENCL
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
else
groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2);
+ clipObjects(imgSize, foundLocations, 0, &foundWeights);
}
void HOGDescriptor::detectMultiScale(InputArray img, std::vector<Rect>& foundLocations,
std::vector<float> 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);
+ }
+ }
+}