opencv/modules/features2d/test/test_descriptors_regression.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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#include "test_precomp.hpp"
using namespace std;
using namespace cv;
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using namespace testing;
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const string FEATURES2D_DIR = "features2d";
const string IMAGE_FILENAME = "tsukuba.png";
const string DESCRIPTOR_DIR = FEATURES2D_DIR + "/descriptor_extractors";
/****************************************************************************************\
* Regression tests for descriptor extractors. *
\****************************************************************************************/
static void writeMatInBin( const Mat& mat, const string& filename )
{
FILE* f = fopen( filename.c_str(), "wb");
if( f )
{
CV_Assert(4 == sizeof(int));
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int type = mat.type();
fwrite( (void*)&mat.rows, sizeof(int), 1, f );
fwrite( (void*)&mat.cols, sizeof(int), 1, f );
fwrite( (void*)&type, sizeof(int), 1, f );
int dataSize = (int)(mat.step * mat.rows);
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fwrite( (void*)&dataSize, sizeof(int), 1, f );
fwrite( (void*)mat.ptr(), 1, dataSize, f );
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fclose(f);
}
}
static Mat readMatFromBin( const string& filename )
{
FILE* f = fopen( filename.c_str(), "rb" );
if( f )
{
CV_Assert(4 == sizeof(int));
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int rows, cols, type, dataSize;
size_t elements_read1 = fread( (void*)&rows, sizeof(int), 1, f );
size_t elements_read2 = fread( (void*)&cols, sizeof(int), 1, f );
size_t elements_read3 = fread( (void*)&type, sizeof(int), 1, f );
size_t elements_read4 = fread( (void*)&dataSize, sizeof(int), 1, f );
CV_Assert(elements_read1 == 1 && elements_read2 == 1 && elements_read3 == 1 && elements_read4 == 1);
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int step = dataSize / rows / CV_ELEM_SIZE(type);
CV_Assert(step >= cols);
Mat returnMat = Mat(rows, step, type).colRange(0, cols);
size_t elements_read = fread( returnMat.ptr(), 1, dataSize, f );
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CV_Assert(elements_read == (size_t)(dataSize));
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fclose(f);
return returnMat;
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}
return Mat();
}
template<class Distance>
class CV_DescriptorExtractorTest : public cvtest::BaseTest
{
public:
typedef typename Distance::ValueType ValueType;
typedef typename Distance::ResultType DistanceType;
CV_DescriptorExtractorTest( const string _name, DistanceType _maxDist, const Ptr<DescriptorExtractor>& _dextractor,
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Distance d = Distance(), Ptr<FeatureDetector> _detector = Ptr<FeatureDetector>()):
name(_name), maxDist(_maxDist), dextractor(_dextractor), distance(d) , detector(_detector) {}
~CV_DescriptorExtractorTest()
{
}
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protected:
virtual void createDescriptorExtractor() {}
void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
{
if( validDescriptors.size != calcDescriptors.size || validDescriptors.type() != calcDescriptors.type() )
{
ts->printf(cvtest::TS::LOG, "Valid and computed descriptors matrices must have the same size and type.\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
CV_Assert( DataType<ValueType>::type == validDescriptors.type() );
int dimension = validDescriptors.cols;
DistanceType curMaxDist = 0;
size_t exact_count = 0, failed_count = 0;
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for( int y = 0; y < validDescriptors.rows; y++ )
{
DistanceType dist = distance( validDescriptors.ptr<ValueType>(y), calcDescriptors.ptr<ValueType>(y), dimension );
if (dist == 0)
exact_count++;
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if( dist > curMaxDist )
{
if (dist > maxDist)
failed_count++;
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curMaxDist = dist;
}
#if 0
if (dist > 0)
{
std::cout << "i=" << y << " fail_count=" << failed_count << " dist=" << dist << std::endl;
std::cout << "valid: " << validDescriptors.row(y) << std::endl;
std::cout << " calc: " << calcDescriptors.row(y) << std::endl;
}
#endif
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}
float exact_percents = (100 * (float)exact_count / validDescriptors.rows);
float failed_percents = (100 * (float)failed_count / validDescriptors.rows);
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stringstream ss;
ss << "Exact count (dist == 0): " << exact_count << " (" << (int)exact_percents << "%)" << std::endl
<< "Failed count (dist > " << maxDist << "): " << failed_count << " (" << (int)failed_percents << "%)" << std::endl
<< "Max distance between valid and computed descriptors (" << validDescriptors.size() << "): " << curMaxDist;
EXPECT_LE(failed_percents, 20.0f);
std::cout << ss.str() << std::endl;
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}
void emptyDataTest()
{
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assert( dextractor );
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// One image.
Mat image;
vector<KeyPoint> keypoints;
Mat descriptors;
try
{
dextractor->compute( image, keypoints, descriptors );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "compute() on empty image and empty keypoints must not generate exception (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
RNG rng;
image = cvtest::randomMat(rng, Size(50, 50), CV_8UC3, 0, 255, false);
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try
{
dextractor->compute( image, keypoints, descriptors );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "compute() on nonempty image and empty keypoints must not generate exception (1).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
// Several images.
vector<Mat> images;
vector<vector<KeyPoint> > keypointsCollection;
vector<Mat> descriptorsCollection;
try
{
dextractor->compute( images, keypointsCollection, descriptorsCollection );
}
catch(...)
{
ts->printf( cvtest::TS::LOG, "compute() on empty images and empty keypoints collection must not generate exception (2).\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
}
}
void regressionTest()
{
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assert( dextractor );
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// Read the test image.
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
Mat img = imread( imgFilename );
if( img.empty() )
{
ts->printf( cvtest::TS::LOG, "Image %s can not be read.\n", imgFilename.c_str() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
const std::string keypoints_filename = string(ts->get_data_path()) +
(detector.empty()
? (FEATURES2D_DIR + "/" + std::string("keypoints.xml.gz"))
: (DESCRIPTOR_DIR + "/" + name + "_keypoints.xml.gz"));
FileStorage fs(keypoints_filename, FileStorage::READ);
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vector<KeyPoint> keypoints;
EXPECT_TRUE(fs.isOpened()) << "Keypoint testdata is missing. Re-computing and re-writing keypoints testdata...";
if (!fs.isOpened())
{
fs.open(keypoints_filename, FileStorage::WRITE);
ASSERT_TRUE(fs.isOpened()) << "File for writting keypoints can not be opened.";
if (detector.empty())
{
Ptr<ORB> fd = ORB::create();
fd->detect(img, keypoints);
}
else
{
detector->detect(img, keypoints);
}
write(fs, "keypoints", keypoints);
fs.release();
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}
else
{
read(fs.getFirstTopLevelNode(), keypoints);
fs.release();
}
if(!detector.empty())
{
vector<KeyPoint> calcKeypoints;
detector->detect(img, calcKeypoints);
// TODO validate received keypoints
int diff = abs((int)calcKeypoints.size() - (int)keypoints.size());
if (diff > 0)
{
std::cout << "Keypoints difference: " << diff << std::endl;
EXPECT_LE(diff, (int)(keypoints.size() * 0.03f));
}
}
ASSERT_FALSE(keypoints.empty());
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{
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Mat calcDescriptors;
double t = (double)getTickCount();
dextractor->compute(img, keypoints, calcDescriptors);
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t = getTickCount() - t;
ts->printf(cvtest::TS::LOG, "\nAverage time of computing one descriptor = %g ms.\n", t/((double)getTickFrequency()*1000.)/calcDescriptors.rows);
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if (calcDescriptors.rows != (int)keypoints.size())
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{
ts->printf( cvtest::TS::LOG, "Count of computed descriptors and keypoints count must be equal.\n" );
ts->printf( cvtest::TS::LOG, "Count of keypoints is %d.\n", (int)keypoints.size() );
ts->printf( cvtest::TS::LOG, "Count of computed descriptors is %d.\n", calcDescriptors.rows );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
if (calcDescriptors.cols != dextractor->descriptorSize() || calcDescriptors.type() != dextractor->descriptorType())
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{
ts->printf( cvtest::TS::LOG, "Incorrect descriptor size or descriptor type.\n" );
ts->printf( cvtest::TS::LOG, "Expected size is %d.\n", dextractor->descriptorSize() );
ts->printf( cvtest::TS::LOG, "Calculated size is %d.\n", calcDescriptors.cols );
ts->printf( cvtest::TS::LOG, "Expected type is %d.\n", dextractor->descriptorType() );
ts->printf( cvtest::TS::LOG, "Calculated type is %d.\n", calcDescriptors.type() );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_OUTPUT );
return;
}
// TODO read and write descriptor extractor parameters and check them
Mat validDescriptors = readDescriptors();
EXPECT_FALSE(validDescriptors.empty()) << "Descriptors testdata is missing. Re-writing descriptors testdata...";
if (!validDescriptors.empty())
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{
compareDescriptors(validDescriptors, calcDescriptors);
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}
else
{
ASSERT_TRUE(writeDescriptors(calcDescriptors)) << "Descriptors can not be written.";
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}
}
}
void run(int)
{
createDescriptorExtractor();
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if( !dextractor )
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{
ts->printf(cvtest::TS::LOG, "Descriptor extractor is empty.\n");
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
emptyDataTest();
regressionTest();
ts->set_failed_test_info( cvtest::TS::OK );
}
virtual Mat readDescriptors()
{
Mat res = readMatFromBin( string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
return res;
}
virtual bool writeDescriptors( Mat& descs )
{
writeMatInBin( descs, string(ts->get_data_path()) + DESCRIPTOR_DIR + "/" + string(name) );
return true;
}
string name;
const DistanceType maxDist;
Ptr<DescriptorExtractor> dextractor;
Distance distance;
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Ptr<FeatureDetector> detector;
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private:
CV_DescriptorExtractorTest& operator=(const CV_DescriptorExtractorTest&) { return *this; }
};
/****************************************************************************************\
* Tests registrations *
\****************************************************************************************/
TEST( Features2d_DescriptorExtractor_BRISK, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-brisk",
(CV_DescriptorExtractorTest<Hamming>::DistanceType)2.f,
BRISK::create() );
test.safe_run();
}
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TEST( Features2d_DescriptorExtractor_ORB, regression )
{
// TODO adjust the parameters below
CV_DescriptorExtractorTest<Hamming> test( "descriptor-orb",
#if CV_NEON
(CV_DescriptorExtractorTest<Hamming>::DistanceType)25.f,
#else
(CV_DescriptorExtractorTest<Hamming>::DistanceType)12.f,
#endif
ORB::create() );
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test.safe_run();
}
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TEST( Features2d_DescriptorExtractor_KAZE, regression )
{
CV_DescriptorExtractorTest< L2<float> > test( "descriptor-kaze", 0.03f,
KAZE::create(),
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L2<float>(), KAZE::create() );
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test.safe_run();
}
TEST( Features2d_DescriptorExtractor_AKAZE, regression )
{
CV_DescriptorExtractorTest<Hamming> test( "descriptor-akaze",
(CV_DescriptorExtractorTest<Hamming>::DistanceType)(486*0.05f),
AKAZE::create(),
Hamming(), AKAZE::create());
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test.safe_run();
Merge pull request #8869 from hrnr:akaze_part1 [GSOC] Speeding-up AKAZE, part #1 (#8869) * ts: expand arguments before stringifications in CV_ENUM and CV_FLAGS added protective macros to always force macro expansion of arguments. This allows using CV_ENUM and CV_FLAGS with macro arguments. * feature2d: unify perf test use the same test for all detectors/descriptors we have. * added AKAZE tests * features2d: extend perf tests * add BRISK, KAZE, MSER * run all extract tests on AKAZE keypoints, so that the test si more comparable for the speed of extraction * feature2d: rework opencl perf tests use the same configuration as cpu tests * feature2d: fix descriptors allocation for AKAZE and KAZE fix crash when descriptors are UMat * feature2d: name enum to fix build with older gcc * Revert "ts: expand arguments before stringifications in CV_ENUM and CV_FLAGS" This reverts commit 19538cac1e45b0cec98190cf06a5ecb07d9b596e. This wasn't a great idea after all. There is a lot of flags implemented as #define, that we don't want to expand. * feature2d: fix expansion problems with CV_ENUM in perf * expand arguments before passing them to CV_ENUM. This does not need modifications of CV_ENUM. * added include guards to `perf_feature2d.hpp` * feature2d: fix crash in AKAZE when using KAZE descriptors * out-of-bound access in Get_MSURF_Descriptor_64 * this happened reliably when running on provided keypoints (not computed by the same instance) * feature2d: added regression tests for AKAZE * test with both MLDB and KAZE keypoints * feature2d: do not compute keypoints orientation twice * always compute keypoints orientation, when computing keypoints * do not recompute keypoint orientation when computing descriptors this allows to test detection and extraction separately * features2d: fix crash in AKAZE * out-of-bound reads near the image edge * same as the bug in KAZE descriptors * feature2d: refactor invariance testing * split detectors and descriptors tests * rewrite to google test to simplify debugging * add tests for AKAZE and one test for ORB * stitching: add tests with AKAZE feature finder * added basic stitching cpu and ocl tests * fix bug in AKAZE wrapper for stitching pipeline causing lots of ! OPENCV warning: getUMat()/getMat() call chain possible problem. ! Base object is dead, while nested/derived object is still alive or processed. ! Please check lifetime of UMat/Mat objects!
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}
TEST( Features2d_DescriptorExtractor_AKAZE_DESCRIPTOR_KAZE, regression )
{
CV_DescriptorExtractorTest< L2<float> > test( "descriptor-akaze-with-kaze-desc", 0.03f,
AKAZE::create(AKAZE::DESCRIPTOR_KAZE),
L2<float>(), AKAZE::create(AKAZE::DESCRIPTOR_KAZE));
test.safe_run();
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}
TEST( Features2d_DescriptorExtractor, batch )
{
string path = string(cvtest::TS::ptr()->get_data_path() + "detectors_descriptors_evaluation/images_datasets/graf");
vector<Mat> imgs, descriptors;
vector<vector<KeyPoint> > keypoints;
int i, n = 6;
Ptr<ORB> orb = ORB::create();
for( i = 0; i < n; i++ )
{
string imgname = format("%s/img%d.png", path.c_str(), i+1);
Mat img = imread(imgname, 0);
imgs.push_back(img);
}
orb->detect(imgs, keypoints);
orb->compute(imgs, keypoints, descriptors);
ASSERT_EQ((int)keypoints.size(), n);
ASSERT_EQ((int)descriptors.size(), n);
for( i = 0; i < n; i++ )
{
EXPECT_GT((int)keypoints[i].size(), 100);
EXPECT_GT(descriptors[i].rows, 100);
}
}
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class DescriptorImage : public TestWithParam<std::string>
{
protected:
virtual void SetUp() {
pattern = GetParam();
}
std::string pattern;
};
TEST_P(DescriptorImage, no_crash)
{
vector<String> fnames;
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glob(cvtest::TS::ptr()->get_data_path() + pattern, fnames, false);
sort(fnames.begin(), fnames.end());
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Ptr<AKAZE> akaze_mldb = AKAZE::create(AKAZE::DESCRIPTOR_MLDB);
Ptr<AKAZE> akaze_mldb_upright = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT);
Ptr<AKAZE> akaze_mldb_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 256);
Ptr<AKAZE> akaze_mldb_upright_256 = AKAZE::create(AKAZE::DESCRIPTOR_MLDB_UPRIGHT, 256);
Ptr<AKAZE> akaze_kaze = AKAZE::create(AKAZE::DESCRIPTOR_KAZE);
Ptr<AKAZE> akaze_kaze_upright = AKAZE::create(AKAZE::DESCRIPTOR_KAZE_UPRIGHT);
Ptr<ORB> orb = ORB::create();
Ptr<KAZE> kaze = KAZE::create();
Ptr<BRISK> brisk = BRISK::create();
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size_t n = fnames.size();
vector<KeyPoint> keypoints;
Mat descriptors;
orb->setMaxFeatures(5000);
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for(size_t i = 0; i < n; i++ )
{
printf("%d. image: %s:\n", (int)i, fnames[i].c_str());
if( strstr(fnames[i].c_str(), "MP.png") != 0 )
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{
printf("\tskip\n");
continue;
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}
bool checkCount = strstr(fnames[i].c_str(), "templ.png") == 0;
Mat img = imread(fnames[i], -1);
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printf("\t%dx%d\n", img.cols, img.rows);
#define TEST_DETECTOR(name, descriptor) \
keypoints.clear(); descriptors.release(); \
printf("\t" name "\n"); fflush(stdout); \
descriptor->detectAndCompute(img, noArray(), keypoints, descriptors); \
printf("\t\t\t(%d keypoints, descriptor size = %d)\n", (int)keypoints.size(), descriptors.cols); fflush(stdout); \
if (checkCount) \
{ \
EXPECT_GT((int)keypoints.size(), 0); \
} \
ASSERT_EQ(descriptors.rows, (int)keypoints.size());
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TEST_DETECTOR("AKAZE:MLDB", akaze_mldb);
TEST_DETECTOR("AKAZE:MLDB_UPRIGHT", akaze_mldb_upright);
TEST_DETECTOR("AKAZE:MLDB_256", akaze_mldb_256);
TEST_DETECTOR("AKAZE:MLDB_UPRIGHT_256", akaze_mldb_upright_256);
TEST_DETECTOR("AKAZE:KAZE", akaze_kaze);
TEST_DETECTOR("AKAZE:KAZE_UPRIGHT", akaze_kaze_upright);
TEST_DETECTOR("KAZE", kaze);
TEST_DETECTOR("ORB", orb);
TEST_DETECTOR("BRISK", brisk);
}
}
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INSTANTIATE_TEST_CASE_P(Features2d, DescriptorImage,
testing::Values(
"shared/lena.png",
"shared/box*.png",
"shared/fruits*.png",
"shared/airplane.png",
"shared/graffiti.png",
"shared/1_itseez-0001*.png",
"shared/pic*.png",
"shared/templ.png"
)
);