This commit is contained in:
Ilya Lavrenov 2014-03-03 19:37:47 +04:00
parent af8aa8a2fa
commit 099ea91823
30 changed files with 78 additions and 78 deletions

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@ -126,7 +126,7 @@ PERF_TEST_P(PointsNum, DISABLED_SolvePnPRansac, testing::Values(4, 3*9, 7*13))
Mat tvec;
#ifdef HAVE_TBB
// limit concurrency to get determenistic result
// limit concurrency to get deterministic result
cv::Ptr<tbb::task_scheduler_init> one_thread = new tbb::task_scheduler_init(1);
#endif

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@ -271,7 +271,7 @@ TEST(DISABLED_Calib3d_SolvePnPRansac, concurrency)
Mat tvec1, tvec2;
{
// limit concurrency to get determenistic result
// limit concurrency to get deterministic result
cv::theRNG().state = 20121010;
cv::Ptr<tbb::task_scheduler_init> one_thread = new tbb::task_scheduler_init(1);
solvePnPRansac(object, image, camera_mat, dist_coef, rvec1, tvec1);

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@ -362,14 +362,14 @@ bool ImageLogPolProjection::_initLogPolarCortexSampling(const double reductionFa
//std::cout<<"ImageLogPolProjection::Starting cortex projection"<<std::endl;
// compute transformation, get theta and Radius in reagrd of the output sampled pixel
double diagonalLenght=sqrt((double)(_outputNBcolumns*_outputNBcolumns+_outputNBrows*_outputNBrows));
double diagonalLength=sqrt((double)(_outputNBcolumns*_outputNBcolumns+_outputNBrows*_outputNBrows));
for (unsigned int radiusIndex=0;radiusIndex<_outputNBcolumns;++radiusIndex)
for(unsigned int orientationIndex=0;orientationIndex<_outputNBrows;++orientationIndex)
{
double x=1.0+sinh(radiusAxis[radiusIndex])*cos(orientationAxis[orientationIndex]);
double y=sinh(radiusAxis[radiusIndex])*sin(orientationAxis[orientationIndex]);
// get the input picture coordinate
double R=diagonalLenght*sqrt(x*x+y*y)/(5.0+sqrt(x*x+y*y));
double R=diagonalLength*sqrt(x*x+y*y)/(5.0+sqrt(x*x+y*y));
double theta=atan2(y,x);
// convert input polar coord into cartesian/C compatble coordinate
unsigned int columnIndex=(unsigned int)(cos(theta)*R)+halfInputColumns;

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@ -229,7 +229,7 @@ Mat subspaceReconstruct(InputArray _W, InputArray _mean, InputArray _src)
string error_message = format("Wrong mean shape for the given eigenvector matrix. Expected %d, but was %d.", W.cols, mean.total());
CV_Error(CV_StsBadArg, error_message);
}
// initalize temporary matrices
// initialize temporary matrices
Mat X, Y;
// copy data & make sure we are using the correct type
src.convertTo(Y, W.type());

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@ -357,27 +357,27 @@ namespace cv
for (unsigned int i=0;i<this->size();++i)
{
double curentValue=(double)*(bufferPTR++);
double currentValue=(double)*(bufferPTR++);
// updating "closest to the high threshold" pixel value
double highValueTest=maxThreshold-curentValue;
double highValueTest=maxThreshold-currentValue;
if (highValueTest>0)
{
if (deltaH>highValueTest)
{
deltaH=highValueTest;
updatedHighValue=curentValue;
updatedHighValue=currentValue;
}
}
// updating "closest to the low threshold" pixel value
double lowValueTest=curentValue-minThreshold;
double lowValueTest=currentValue-minThreshold;
if (lowValueTest>0)
{
if (deltaL>lowValueTest)
{
deltaL=lowValueTest;
updatedLowValue=curentValue;
updatedLowValue=currentValue;
}
}
}

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@ -105,7 +105,7 @@ CVAPI(void) cvResetImageROI( IplImage* image );
/* Retrieves image ROI */
CVAPI(CvRect) cvGetImageROI( const IplImage* image );
/* Allocates and initalizes CvMat header */
/* Allocates and initializes CvMat header */
CVAPI(CvMat*) cvCreateMatHeader( int rows, int cols, int type );
#define CV_AUTOSTEP 0x7fffffff

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@ -2897,7 +2897,7 @@ cvCreateImage( CvSize size, int depth, int channels )
}
// initalize IplImage header, allocated by the user
// initialize IplImage header, allocated by the user
CV_IMPL IplImage*
cvInitImageHeader( IplImage * image, CvSize size, int depth,
int channels, int origin, int align )

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@ -11,9 +11,9 @@ FAST
----
Detects corners using the FAST algorithm
.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
.. ocv:function:: void FAST( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression=true )
.. ocv:function:: void FASTX( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression, int type )
.. ocv:function:: void FASTX( InputArray image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSuppression, int type )
:param image: grayscale image where keypoints (corners) are detected.
@ -21,7 +21,7 @@ Detects corners using the FAST algorithm
:param threshold: threshold on difference between intensity of the central pixel and pixels of a circle around this pixel.
:param nonmaxSupression: if true, non-maximum suppression is applied to detected corners (keypoints).
:param nonmaxSuppression: if true, non-maximum suppression is applied to detected corners (keypoints).
:param type: one of the three neighborhoods as defined in the paper: ``FastFeatureDetector::TYPE_9_16``, ``FastFeatureDetector::TYPE_7_12``, ``FastFeatureDetector::TYPE_5_8``

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@ -566,10 +566,10 @@ protected:
//! detects corners using FAST algorithm by E. Rosten
CV_EXPORTS void FAST( InputArray image, CV_OUT vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSupression=true );
int threshold, bool nonmaxSuppression=true );
CV_EXPORTS void FASTX( InputArray image, CV_OUT vector<KeyPoint>& keypoints,
int threshold, bool nonmaxSupression, int type );
int threshold, bool nonmaxSuppression, int type );
class CV_EXPORTS_W FastFeatureDetector : public FeatureDetector
{

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@ -128,7 +128,7 @@ public:
Point2f center;
Scalar ellipse; // 3 elements a, b, c: ax^2+2bxy+cy^2=1
Size_<float> axes; // half lenght of elipse axes
Size_<float> axes; // half length of ellipse axes
Size_<float> boundingBox; // half sizes of bounding box which sides are parallel to the coordinate axes
};

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@ -24,7 +24,7 @@ Class used for corner detection using the FAST algorithm. ::
// all features have same size
static const int FEATURE_SIZE = 7;
explicit FAST_GPU(int threshold, bool nonmaxSupression = true,
explicit FAST_GPU(int threshold, bool nonmaxSuppression = true,
double keypointsRatio = 0.05);
void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
@ -39,7 +39,7 @@ Class used for corner detection using the FAST algorithm. ::
void release();
bool nonmaxSupression;
bool nonmaxSuppression;
int threshold;
@ -61,11 +61,11 @@ gpu::FAST_GPU::FAST_GPU
-------------------------------------
Constructor.
.. ocv:function:: gpu::FAST_GPU::FAST_GPU(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05)
.. ocv:function:: gpu::FAST_GPU::FAST_GPU(int threshold, bool nonmaxSuppression = true, double keypointsRatio = 0.05)
:param threshold: Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel.
:param nonmaxSupression: If it is true, non-maximum suppression is applied to detected corners (keypoints).
:param nonmaxSuppression: If it is true, non-maximum suppression is applied to detected corners (keypoints).
:param keypointsRatio: Inner buffer size for keypoints store is determined as (keypointsRatio * image_width * image_height).
@ -115,7 +115,7 @@ Releases inner buffer memory.
gpu::FAST_GPU::calcKeyPointsLocation
-------------------------------------
Find keypoints and compute it's response if ``nonmaxSupression`` is true.
Find keypoints and compute it's response if ``nonmaxSuppression`` is true.
.. ocv:function:: int gpu::FAST_GPU::calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask)
@ -185,7 +185,7 @@ Class for extracting ORB features and descriptors from an image. ::
int descriptorSize() const;
void setParams(size_t n_features, const ORB::CommonParams& detector_params);
void setFastParams(int threshold, bool nonmaxSupression = true);
void setFastParams(int threshold, bool nonmaxSuppression = true);
void release();

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@ -1580,7 +1580,7 @@ public:
// all features have same size
static const int FEATURE_SIZE = 7;
explicit FAST_GPU(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05);
explicit FAST_GPU(int threshold, bool nonmaxSuppression = true, double keypointsRatio = 0.05);
//! finds the keypoints using FAST detector
//! supports only CV_8UC1 images
@ -1596,19 +1596,19 @@ public:
//! release temporary buffer's memory
void release();
bool nonmaxSupression;
bool nonmaxSuppression;
int threshold;
//! max keypoints = keypointsRatio * img.size().area()
double keypointsRatio;
//! find keypoints and compute it's response if nonmaxSupression is true
//! find keypoints and compute it's response if nonmaxSuppression is true
//! return count of detected keypoints
int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask);
//! get final array of keypoints
//! performs nonmax supression if needed
//! performs nonmax suppression if needed
//! return final count of keypoints
int getKeyPoints(GpuMat& keypoints);
@ -1670,10 +1670,10 @@ public:
//! returns the descriptor size in bytes
inline int descriptorSize() const { return kBytes; }
inline void setFastParams(int threshold, bool nonmaxSupression = true)
inline void setFastParams(int threshold, bool nonmaxSuppression = true)
{
fastDetector_.threshold = threshold;
fastDetector_.nonmaxSupression = nonmaxSupression;
fastDetector_.nonmaxSuppression = nonmaxSuppression;
}
//! release temporary buffer's memory

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@ -49,9 +49,9 @@ using namespace perf;
//////////////////////////////////////////////////////////////////////
// FAST
DEF_PARAM_TEST(Image_Threshold_NonMaxSupression, string, int, bool);
DEF_PARAM_TEST(Image_Threshold_NonMaxSuppression, string, int, bool);
PERF_TEST_P(Image_Threshold_NonMaxSupression, Features2D_FAST,
PERF_TEST_P(Image_Threshold_NonMaxSuppression, Features2D_FAST,
Combine(Values<string>("gpu/perf/aloe.png"),
Values(20),
Bool()))

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@ -1746,7 +1746,7 @@ PERF_TEST_P(Image, ImgProc_HoughLinesP,
const float rho = 1.0f;
const float theta = static_cast<float>(CV_PI / 180.0);
const int threshold = 100;
const int minLineLenght = 50;
const int minLineLength = 50;
const int maxLineGap = 5;
const cv::Mat image = cv::imread(fileName, cv::IMREAD_GRAYSCALE);
@ -1761,7 +1761,7 @@ PERF_TEST_P(Image, ImgProc_HoughLinesP,
cv::gpu::GpuMat d_lines;
cv::gpu::HoughLinesBuf d_buf;
TEST_CYCLE() cv::gpu::HoughLinesP(d_mask, d_lines, d_buf, rho, theta, minLineLenght, maxLineGap);
TEST_CYCLE() cv::gpu::HoughLinesP(d_mask, d_lines, d_buf, rho, theta, minLineLength, maxLineGap);
cv::Mat gpu_lines(d_lines);
cv::Vec4i* begin = gpu_lines.ptr<cv::Vec4i>();
@ -1773,7 +1773,7 @@ PERF_TEST_P(Image, ImgProc_HoughLinesP,
{
std::vector<cv::Vec4i> cpu_lines;
TEST_CYCLE() cv::HoughLinesP(mask, cpu_lines, rho, theta, threshold, minLineLenght, maxLineGap);
TEST_CYCLE() cv::HoughLinesP(mask, cpu_lines, rho, theta, threshold, minLineLength, maxLineGap);
SANITY_CHECK(cpu_lines);
}

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@ -74,7 +74,7 @@ PERF_TEST_P(Image, HoughLinesP, testing::Values(std::string("im1_1280x800.jpg"))
const float rho = 1.f;
const float theta = 1.f;
const int threshold = 40;
const int minLineLenght = 20;
const int minLineLength = 20;
const int maxLineGap = 5;
cv::Mat image = cv::imread(fileName, cv::IMREAD_GRAYSCALE);
@ -85,11 +85,11 @@ PERF_TEST_P(Image, HoughLinesP, testing::Values(std::string("im1_1280x800.jpg"))
cv::gpu::GpuMat d_lines;
cv::gpu::HoughLinesBuf d_buf;
cv::gpu::HoughLinesP(d_image, d_lines, d_buf, rho, theta, minLineLenght, maxLineGap);
cv::gpu::HoughLinesP(d_image, d_lines, d_buf, rho, theta, minLineLength, maxLineGap);
TEST_CYCLE()
{
cv::gpu::HoughLinesP(d_image, d_lines, d_buf, rho, theta, minLineLenght, maxLineGap);
cv::gpu::HoughLinesP(d_image, d_lines, d_buf, rho, theta, minLineLength, maxLineGap);
}
}
else
@ -98,11 +98,11 @@ PERF_TEST_P(Image, HoughLinesP, testing::Values(std::string("im1_1280x800.jpg"))
cv::Canny(image, mask, 50, 100);
std::vector<cv::Vec4i> lines;
cv::HoughLinesP(mask, lines, rho, theta, threshold, minLineLenght, maxLineGap);
cv::HoughLinesP(mask, lines, rho, theta, threshold, minLineLength, maxLineGap);
TEST_CYCLE()
{
cv::HoughLinesP(mask, lines, rho, theta, threshold, minLineLenght, maxLineGap);
cv::HoughLinesP(mask, lines, rho, theta, threshold, minLineLength, maxLineGap);
}
}

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@ -318,9 +318,9 @@ namespace cv { namespace gpu { namespace device
}
///////////////////////////////////////////////////////////////////////////
// nonmaxSupression
// nonmaxSuppression
__global__ void nonmaxSupression(const short2* kpLoc, int count, const PtrStepSzi scoreMat, short2* locFinal, float* responseFinal)
__global__ void nonmaxSuppression(const short2* kpLoc, int count, const PtrStepSzi scoreMat, short2* locFinal, float* responseFinal)
{
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 110)
@ -356,7 +356,7 @@ namespace cv { namespace gpu { namespace device
#endif
}
int nonmaxSupression_gpu(const short2* kpLoc, int count, PtrStepSzi score, short2* loc, float* response)
int nonmaxSuppression_gpu(const short2* kpLoc, int count, PtrStepSzi score, short2* loc, float* response)
{
void* counter_ptr;
cudaSafeCall( cudaGetSymbolAddress(&counter_ptr, g_counter) );
@ -368,7 +368,7 @@ namespace cv { namespace gpu { namespace device
cudaSafeCall( cudaMemset(counter_ptr, 0, sizeof(unsigned int)) );
nonmaxSupression<<<grid, block>>>(kpLoc, count, score, loc, response);
nonmaxSuppression<<<grid, block>>>(kpLoc, count, score, loc, response);
cudaSafeCall( cudaGetLastError() );
cudaSafeCall( cudaDeviceSynchronize() );

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@ -59,8 +59,8 @@ int cv::gpu::FAST_GPU::getKeyPoints(GpuMat&) { throw_nogpu(); return 0; }
#else /* !defined (HAVE_CUDA) */
cv::gpu::FAST_GPU::FAST_GPU(int _threshold, bool _nonmaxSupression, double _keypointsRatio) :
nonmaxSupression(_nonmaxSupression), threshold(_threshold), keypointsRatio(_keypointsRatio), count_(0)
cv::gpu::FAST_GPU::FAST_GPU(int _threshold, bool _nonmaxSuppression, double _keypointsRatio) :
nonmaxSuppression(_nonmaxSuppression), threshold(_threshold), keypointsRatio(_keypointsRatio), count_(0)
{
}
@ -114,7 +114,7 @@ namespace cv { namespace gpu { namespace device
namespace fast
{
int calcKeypoints_gpu(PtrStepSzb img, PtrStepSzb mask, short2* kpLoc, int maxKeypoints, PtrStepSzi score, int threshold);
int nonmaxSupression_gpu(const short2* kpLoc, int count, PtrStepSzi score, short2* loc, float* response);
int nonmaxSuppression_gpu(const short2* kpLoc, int count, PtrStepSzi score, short2* loc, float* response);
}
}}}
@ -129,13 +129,13 @@ int cv::gpu::FAST_GPU::calcKeyPointsLocation(const GpuMat& img, const GpuMat& ma
ensureSizeIsEnough(1, maxKeypoints, CV_16SC2, kpLoc_);
if (nonmaxSupression)
if (nonmaxSuppression)
{
ensureSizeIsEnough(img.size(), CV_32SC1, score_);
score_.setTo(Scalar::all(0));
}
count_ = calcKeypoints_gpu(img, mask, kpLoc_.ptr<short2>(), maxKeypoints, nonmaxSupression ? score_ : PtrStepSzi(), threshold);
count_ = calcKeypoints_gpu(img, mask, kpLoc_.ptr<short2>(), maxKeypoints, nonmaxSuppression ? score_ : PtrStepSzi(), threshold);
count_ = std::min(count_, maxKeypoints);
return count_;
@ -150,8 +150,8 @@ int cv::gpu::FAST_GPU::getKeyPoints(GpuMat& keypoints)
ensureSizeIsEnough(ROWS_COUNT, count_, CV_32FC1, keypoints);
if (nonmaxSupression)
return nonmaxSupression_gpu(kpLoc_.ptr<short2>(), count_, score_, keypoints.ptr<short2>(LOCATION_ROW), keypoints.ptr<float>(RESPONSE_ROW));
if (nonmaxSuppression)
return nonmaxSuppression_gpu(kpLoc_.ptr<short2>(), count_, score_, keypoints.ptr<short2>(LOCATION_ROW), keypoints.ptr<float>(RESPONSE_ROW));
GpuMat locRow(1, count_, kpLoc_.type(), keypoints.ptr(0));
kpLoc_.colRange(0, count_).copyTo(locRow);

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@ -52,20 +52,20 @@ using namespace cvtest;
namespace
{
IMPLEMENT_PARAM_CLASS(FAST_Threshold, int)
IMPLEMENT_PARAM_CLASS(FAST_NonmaxSupression, bool)
IMPLEMENT_PARAM_CLASS(FAST_NonmaxSuppression, bool)
}
PARAM_TEST_CASE(FAST, cv::gpu::DeviceInfo, FAST_Threshold, FAST_NonmaxSupression)
PARAM_TEST_CASE(FAST, cv::gpu::DeviceInfo, FAST_Threshold, FAST_NonmaxSuppression)
{
cv::gpu::DeviceInfo devInfo;
int threshold;
bool nonmaxSupression;
bool nonmaxSuppression;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
threshold = GET_PARAM(1);
nonmaxSupression = GET_PARAM(2);
nonmaxSuppression = GET_PARAM(2);
cv::gpu::setDevice(devInfo.deviceID());
}
@ -77,7 +77,7 @@ GPU_TEST_P(FAST, Accuracy)
ASSERT_FALSE(image.empty());
cv::gpu::FAST_GPU fast(threshold);
fast.nonmaxSupression = nonmaxSupression;
fast.nonmaxSuppression = nonmaxSuppression;
if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS))
{
@ -97,7 +97,7 @@ GPU_TEST_P(FAST, Accuracy)
fast(loadMat(image), cv::gpu::GpuMat(), keypoints);
std::vector<cv::KeyPoint> keypoints_gold;
cv::FAST(image, keypoints_gold, threshold, nonmaxSupression);
cv::FAST(image, keypoints_gold, threshold, nonmaxSuppression);
ASSERT_KEYPOINTS_EQ(keypoints_gold, keypoints);
}
@ -106,7 +106,7 @@ GPU_TEST_P(FAST, Accuracy)
INSTANTIATE_TEST_CASE_P(GPU_Features2D, FAST, testing::Combine(
ALL_DEVICES,
testing::Values(FAST_Threshold(25), FAST_Threshold(50)),
testing::Values(FAST_NonmaxSupression(false), FAST_NonmaxSupression(true))));
testing::Values(FAST_NonmaxSuppression(false), FAST_NonmaxSuppression(true))));
/////////////////////////////////////////////////////////////////////////////////////////////////
// ORB

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@ -1536,7 +1536,7 @@ CvWindow::CvWindow(QString name, int arg2)
setWindowTitle(name);
setObjectName(name);
setFocus( Qt::PopupFocusReason ); //#1695 arrow keys are not recieved without the explicit focus
setFocus( Qt::PopupFocusReason ); //#1695 arrow keys are not received without the explicit focus
resize(400, 300);
setMinimumSize(1, 1);

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@ -569,7 +569,7 @@ static int icvCreateTrackbar (const char* trackbar_name,
//pad size maxvalue in pixel
Point qdSize;
char valueinchar[strlen(trackbar_name)+1 +1 +1+nbDigit+1];//lenght+\n +space +(+nbDigit+)
char valueinchar[strlen(trackbar_name)+1 +1 +1+nbDigit+1];//length+\n +space +(+nbDigit+)
sprintf(valueinchar, "%s (%d)",trackbar_name, trackbar->maxval);
SInt16 baseline;
CFStringRef text = CFStringCreateWithCString(NULL,valueinchar,kCFStringEncodingASCII);

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@ -101,7 +101,7 @@ Harris edge detector.
.. ocv:pyfunction:: cv2.cornerHarris(src, blockSize, ksize, k[, dst[, borderType]]) -> dst
.. ocv:cfunction:: void cvCornerHarris( const CvArr* image, CvArr* harris_responce, int block_size, int aperture_size=3, double k=0.04 )
.. ocv:cfunction:: void cvCornerHarris( const CvArr* image, CvArr* harris_response, int block_size, int aperture_size=3, double k=0.04 )
.. ocv:pyoldfunction:: cv.CornerHarris(image, harris_dst, blockSize, aperture_size=3, k=0.04) -> None

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@ -304,7 +304,7 @@ CVAPI(int) cvFindContours( CvArr* image, CvMemStorage* storage, CvSeq** first_c
int method CV_DEFAULT(CV_CHAIN_APPROX_SIMPLE),
CvPoint offset CV_DEFAULT(cvPoint(0,0)));
/* Initalizes contour retrieving process.
/* Initializes contour retrieving process.
Calls cvStartFindContours.
Calls cvFindNextContour until null pointer is returned
or some other condition becomes true.
@ -334,7 +334,7 @@ CVAPI(CvSeq*) cvApproxChains( CvSeq* src_seq, CvMemStorage* storage,
int minimal_perimeter CV_DEFAULT(0),
int recursive CV_DEFAULT(0));
/* Initalizes Freeman chain reader.
/* Initializes Freeman chain reader.
The reader is used to iteratively get coordinates of all the chain points.
If the Freeman codes should be read as is, a simple sequence reader should be used */
CVAPI(void) cvStartReadChainPoints( CvChain* chain, CvChainPtReader* reader );
@ -573,7 +573,7 @@ CVAPI(void) cvCornerMinEigenVal( const CvArr* image, CvArr* eigenval,
/* Harris corner detector:
Calculates det(M) - k*(trace(M)^2), where M is 2x2 gradient covariation matrix for each pixel */
CVAPI(void) cvCornerHarris( const CvArr* image, CvArr* harris_responce,
CVAPI(void) cvCornerHarris( const CvArr* image, CvArr* harris_response,
int block_size, int aperture_size CV_DEFAULT(3),
double k CV_DEFAULT(0.04) );

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@ -220,7 +220,7 @@ CvSeq* icvApproximateChainTC89( CvChain* chain, int header_size,
current = temp.next;
/* Pass 2.
Performs non-maxima supression */
Performs non-maxima suppression */
do
{
int k2 = current->k >> 1;

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@ -171,7 +171,7 @@ void cv::Canny( InputArray _src, OutputArray _dst,
#define CANNY_PUSH(d) *(d) = uchar(2), *stack_top++ = (d)
#define CANNY_POP(d) (d) = *--stack_top
// calculate magnitude and angle of gradient, perform non-maxima supression.
// calculate magnitude and angle of gradient, perform non-maxima suppression.
// fill the map with one of the following values:
// 0 - the pixel might belong to an edge
// 1 - the pixel can not belong to an edge

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@ -217,7 +217,7 @@ __kernel void gpuRunHaarClassifierCascadePacked(
(SumL[M0(n0.x)+lcl_off] - SumL[M1(n0.x)+lcl_off] - SumL[M0(n0.y)+lcl_off] + SumL[M1(n0.y)+lcl_off]) * as_float(n1.z) +
(SumL[M0(n0.z)+lcl_off] - SumL[M1(n0.z)+lcl_off] - SumL[M0(n0.w)+lcl_off] + SumL[M1(n0.w)+lcl_off]) * as_float(n1.w) +
(SumL[M0(n1.x)+lcl_off] - SumL[M1(n1.x)+lcl_off] - SumL[M0(n1.y)+lcl_off] + SumL[M1(n1.y)+lcl_off]) * as_float(n2.x);
//accumulate stage responce
//accumulate stage response
stage_sum += (classsum >= nodethreshold) ? as_float(n2.w) : as_float(n2.z);
}
result = (stage_sum >= stagethreshold);

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@ -372,7 +372,7 @@ public:
// processing time (in this case there should be possibility to interrupt such a function
FAIL_HANG=-13,
// unexpected responce on passing bad arguments to the tested function
// unexpected response on passing bad arguments to the tested function
// (the function crashed, proceed succesfully (while it should not), or returned
// error code that is different from what is expected)
FAIL_BAD_ARG_CHECK=-14,

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@ -397,7 +397,7 @@ This 3D Widget defines a cone. ::
{
public:
//! create default cone, oriented along x-axis with center of its base located at origin
WCone(double lenght, double radius, int resolution = 6.0, const Color &color = Color::white());
WCone(double length, double radius, int resolution = 6.0, const Color &color = Color::white());
//! creates repositioned cone
WCone(double radius, const Point3d& center, const Point3d& tip, int resolution = 6.0, const Color &color = Color::white());

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@ -214,12 +214,12 @@ public class ManagerActivity extends Activity
}
});
mPackageChangeReciever = new BroadcastReceiver() {
mPackageChangeReceiver = new BroadcastReceiver() {
@Override
public void onReceive(Context context, Intent intent) {
Log.d("OpenCVManager/Reciever", "Bradcast message " + intent.getAction() + " reciever");
Log.d("OpenCVManager/Reciever", "Filling package list on broadcast message");
Log.d("OpenCVManager/Receiver", "Broadcast message " + intent.getAction() + " receiver");
Log.d("OpenCVManager/Receiver", "Filling package list on broadcast message");
if (!bindService(new Intent("org.opencv.engine.BIND"), new OpenCVEngineServiceConnection(), Context.BIND_AUTO_CREATE))
{
TextView EngineVersionView = (TextView)findViewById(R.id.EngineVersionValue);
@ -235,14 +235,14 @@ public class ManagerActivity extends Activity
filter.addAction(Intent.ACTION_PACKAGE_REMOVED);
filter.addAction(Intent.ACTION_PACKAGE_REPLACED);
registerReceiver(mPackageChangeReciever, filter);
registerReceiver(mPackageChangeReceiver, filter);
}
@Override
protected void onDestroy() {
super.onDestroy();
if (mPackageChangeReciever != null)
unregisterReceiver(mPackageChangeReciever);
if (mPackageChangeReceiver != null)
unregisterReceiver(mPackageChangeReceiver);
}
@Override
@ -273,7 +273,7 @@ public class ManagerActivity extends Activity
protected int ManagerApiLevel = 0;
protected String ManagerVersion;
protected BroadcastReceiver mPackageChangeReciever = null;
protected BroadcastReceiver mPackageChangeReceiver = null;
protected class OpenCVEngineServiceConnection implements ServiceConnection
{

View File

@ -12,7 +12,7 @@ static void help()
printf("\nShow off image morphology: erosion, dialation, open and close\n"
"Call:\n morphology2 [image]\n"
"This program also shows use of rect, elipse and cross kernels\n\n");
"This program also shows use of rect, ellipse and cross kernels\n\n");
printf( "Hot keys: \n"
"\tESC - quit the program\n"
"\tr - use rectangle structuring element\n"

View File

@ -13,7 +13,7 @@ static void help()
printf("\nShow off image morphology: erosion, dialation, open and close\n"
"Call:\n morphology2 [image]\n"
"This program also shows use of rect, elipse and cross kernels\n\n");
"This program also shows use of rect, ellipse and cross kernels\n\n");
printf( "Hot keys: \n"
"\tESC - quit the program\n"
"\tr - use rectangle structuring element\n"