potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-30 15:53:03 +04:00
Code Packages Releases Wiki Activity

All tests writing temporary files are updated to use cv::tempfile() function

Browse Source
This commit is contained in:
Andrey Kamaev
2012-06-25 11:24:06 +00:00
parent ec3a7665b0
commit d9c74f63e1
26 changed files with 500 additions and 438 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/features2d/include/opencv2/features2d/features2d.hpp
+39 -39
View File
@@ -328,7 +328,7 @@ public:
float patternScale = 22.0f,
int nOctaves = 4,
const vector<int>& selectedPairs = vector<int>());
FREAK( const FREAK& rhs );
FREAK( const FREAK& rhs );
FREAK& operator=( const FREAK& );
virtual ~FREAK();
@@ -349,51 +349,51 @@ public:
vector<int> selectPairs( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints,
const double corrThresh = 0.7, bool verbose = true );
AlgorithmInfo* info() const;
AlgorithmInfo* info() const;
enum
{
NB_SCALES = 64, NB_PAIRS = 512, NB_ORIENPAIRS = 45
};
enum
{
NB_SCALES = 64, NB_PAIRS = 512, NB_ORIENPAIRS = 45
};
protected:
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
void buildPattern();
uchar meanIntensity( const Mat& image, const Mat& integral, const float kp_x, const float kp_y,
void buildPattern();
uchar meanIntensity( const Mat& image, const Mat& integral, const float kp_x, const float kp_y,
const unsigned int scale, const unsigned int rot, const unsigned int point ) const;
bool orientationNormalized; //true if the orientation is normalized, false otherwise
bool orientationNormalized; //true if the orientation is normalized, false otherwise
bool scaleNormalized; //true if the scale is normalized, false otherwise
double patternScale; //scaling of the pattern
int nOctaves; //number of octaves
bool extAll; // true if all pairs need to be extracted for pairs selection
double patternScale0;
int nOctaves0;
vector<int> selectedPairs0;
struct PatternPoint
{
float x; // x coordinate relative to center
float y; // x coordinate relative to center
float sigma; // Gaussian smoothing sigma
};
struct PatternPoint
{
float x; // x coordinate relative to center
float y; // x coordinate relative to center
float sigma; // Gaussian smoothing sigma
};
struct DescriptionPair
{
uchar i; // index of the first point
uchar j; // index of the second point
};
struct DescriptionPair
{
uchar i; // index of the first point
uchar j; // index of the second point
};
struct OrientationPair
{
uchar i; // index of the first point
uchar j; // index of the second point
int weight_dx; // dx/(norm_sq))*4096
int weight_dy; // dy/(norm_sq))*4096
};
vector<PatternPoint> patternLookup; // look-up table for the pattern points (position+sigma of all points at all scales and orientation)
struct OrientationPair
{
uchar i; // index of the first point
uchar j; // index of the second point
int weight_dx; // dx/(norm_sq))*4096
int weight_dy; // dy/(norm_sq))*4096
};
vector<PatternPoint> patternLookup; // look-up table for the pattern points (position+sigma of all points at all scales and orientation)
int patternSizes[NB_SCALES]; // size of the pattern at a specific scale (used to check if a point is within image boundaries)
DescriptionPair descriptionPairs[NB_PAIRS];
OrientationPair orientationPairs[NB_ORIENPAIRS];
@@ -603,7 +603,7 @@ public:
// TODO implement read/write
virtual bool empty() const;
AlgorithmInfo* info() const;
protected:
@@ -641,8 +641,8 @@ protected:
class CV_EXPORTS AdjusterAdapter: public FeatureDetector
{
public:
/** pure virtual interface
*/
/** pure virtual interface
*/
virtual ~AdjusterAdapter() {}
/** too few features were detected so, adjust the detector params accordingly
* \param min the minimum number of desired features
@@ -682,7 +682,7 @@ public:
/** \param adjuster an AdjusterAdapter that will do the detection and parameter adjustment
* \param max_features the maximum desired number of features
* \param max_iters the maximum number of times to try to adjust the feature detector params
* for the FastAdjuster this can be high, but with Star or Surf this can get time consuming
* for the FastAdjuster this can be high, but with Star or Surf this can get time consuming
* \param min_features the minimum desired features
*/
DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features=400, int max_features=500, int max_iters=5 );
@@ -693,8 +693,8 @@ protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
private:
DynamicAdaptedFeatureDetector& operator=(const DynamicAdaptedFeatureDetector&);
DynamicAdaptedFeatureDetector(const DynamicAdaptedFeatureDetector&);
DynamicAdaptedFeatureDetector& operator=(const DynamicAdaptedFeatureDetector&);
DynamicAdaptedFeatureDetector(const DynamicAdaptedFeatureDetector&);
int escape_iters_;
int min_features_, max_features_;
@@ -792,7 +792,7 @@ public:
virtual bool empty() const;
protected:
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
Ptr<DescriptorExtractor> descriptorExtractor;
};
@@ -962,7 +962,7 @@ class CV_EXPORTS_W DescriptorMatcher : public Algorithm
public:
virtual ~DescriptorMatcher();
/*
/*
* Add descriptors to train descriptor collection.
* descriptors Descriptors to add. Each descriptors[i] is a descriptors set from one image.
*/
@@ -1078,7 +1078,7 @@ protected:
static bool isMaskedOut( const vector<Mat>& masks, int queryIdx );
static Mat clone_op( Mat m ) { return m.clone(); }
void checkMasks( const vector<Mat>& masks, int queryDescriptorsCount ) const;
void checkMasks( const vector<Mat>& masks, int queryDescriptorsCount ) const;
// Collection of descriptors from train images.
vector<Mat> trainDescCollection;
modules/features2d/test/test_fast.cpp
+14 -14
View File
@@ -48,8 +48,8 @@ class CV_FastTest : public cvtest::BaseTest
{
public:
CV_FastTest();
~CV_FastTest();
protected:
~CV_FastTest();
protected:
void run(int);
};
@@ -58,13 +58,13 @@ CV_FastTest::~CV_FastTest() {}
void CV_FastTest::run( int )
{
Mat image1 = imread(string(ts->get_data_path()) + "inpaint/orig.jpg");
Mat image2 = imread(string(ts->get_data_path()) + "cameracalibration/chess9.jpg");
Mat image1 = imread(string(ts->get_data_path()) + "inpaint/orig.jpg");
Mat image2 = imread(string(ts->get_data_path()) + "cameracalibration/chess9.jpg");
string xml = string(ts->get_data_path()) + "fast/result.xml";
if (image1.empty() || image2.empty())
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
@@ -73,20 +73,20 @@ void CV_FastTest::run( int )
cvtColor(image2, gray2, CV_BGR2GRAY);
vector<KeyPoint> keypoints1;
vector<KeyPoint> keypoints2;
vector<KeyPoint> keypoints2;
FAST(gray1, keypoints1, 30);
FAST(gray2, keypoints2, 30);
for(size_t i = 0; i < keypoints1.size(); ++i)
{
const KeyPoint& kp = keypoints1[i];
cv::circle(image1, kp.pt, cvRound(kp.size/2), CV_RGB(255, 0, 0));
cv::circle(image1, kp.pt, cvRound(kp.size/2), CV_RGB(255, 0, 0));
}
for(size_t i = 0; i < keypoints2.size(); ++i)
{
const KeyPoint& kp = keypoints2[i];
cv::circle(image2, kp.pt, cvRound(kp.size/2), CV_RGB(255, 0, 0));
cv::circle(image2, kp.pt, cvRound(kp.size/2), CV_RGB(255, 0, 0));
}
Mat kps1(1, (int)(keypoints1.size() * sizeof(KeyPoint)), CV_8U, &keypoints1[0]);
@@ -99,14 +99,14 @@ void CV_FastTest::run( int )
fs << "exp_kps1" << kps1;
fs << "exp_kps2" << kps2;
fs.release();
}
}
if (!fs.isOpened())
fs.open(xml, FileStorage::READ);
Mat exp_kps1, exp_kps2;
Mat exp_kps1, exp_kps2;
read( fs["exp_kps1"], exp_kps1, Mat() );
read( fs["exp_kps2"], exp_kps2, Mat() );
read( fs["exp_kps2"], exp_kps2, Mat() );
fs.release();
if ( 0 != norm(exp_kps1, kps1, NORM_L2) || 0 != norm(exp_kps2, kps2, NORM_L2))
@@ -114,7 +114,7 @@ void CV_FastTest::run( int )
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
return;
}
/* cv::namedWindow("Img1"); cv::imshow("Img1", image1);
cv::namedWindow("Img2"); cv::imshow("Img2", image2);
cv::waitKey(0);*/
modules/features2d/test/test_mser.cpp
+5 -5
View File
@@ -50,8 +50,8 @@ using namespace cv;
class CV_MserTest : public cvtest::BaseTest
{
public:
CV_MserTest();
protected:
CV_MserTest();
protected:
void run(int);
int LoadBoxes(const char* path, vector<CvBox2D>& boxes);
int SaveBoxes(const char* path, const vector<CvBox2D>& boxes);
@@ -71,7 +71,7 @@ int CV_MserTest::LoadBoxes(const char* path, vector<CvBox2D>& boxes)
{
return 0;
}
while (!feof(f))
{
CvBox2D box;
@@ -175,12 +175,12 @@ void CV_MserTest::run(int)
{
RotatedRect box = fitEllipse(msers[i]);
box.angle=(float)CV_PI/2-box.angle;
boxes.push_back(box);
boxes.push_back(box);
}
string boxes_path = string(ts->get_data_path()) + "mser/boxes.txt";
string calc_boxes_path = string(ts->get_data_path()) + "mser/boxes.calc.txt";
if (!LoadBoxes(boxes_path.c_str(),boxes_orig))
{
SaveBoxes(boxes_path.c_str(),boxes);
modules/features2d/test/test_nearestneighbors.cpp
+4 -4
View File
@@ -128,7 +128,7 @@ void NearestNeighborTest::run( int /*start_from*/ ) {
randu( desc, Scalar(minValue), Scalar(maxValue) );
createModel( desc );
tempCode = checkGetPoins( desc );
if( tempCode != cvtest::TS::OK )
{
@@ -149,9 +149,9 @@ void NearestNeighborTest::run( int /*start_from*/ ) {
ts->printf( cvtest::TS::LOG, "bad accuracy of Find \n" );
code = tempCode;
}
releaseModel();
ts->set_failed_test_info( code );
}
@@ -398,7 +398,7 @@ void CV_FlannSavedIndexTest::createModel(const cv::Mat &data)
}
string filename = tempfile();
index->save( filename );
createIndex( data, SavedIndexParams(filename.c_str()));
remove( filename.c_str() );
}