third_party/opencv
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge remote-tracking branch 'upstream/3.4' into merge-3.4

Browse Source
This commit is contained in:
Alexander Alekhin 2022-02-19 21:04:35 +00:00
commit 5a86592e93
6 changed files with 57 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
modules/core/include/opencv2/core/vsx_utils.hpp
View File

@ -684,7 +684,8 @@ VSX_IMPL_LOAD_L8(vec_double2, double)
#endif
// absolute difference
#ifndef vec_absd
#ifndef _ARCH_PWR9
# undef vec_absd
# define vec_absd(a, b) vec_sub(vec_max(a, b), vec_min(a, b))
#endif

3
modules/imgproc/src/histogram.cpp
View File

@ -909,7 +909,8 @@ static bool ipp_calchist(const Mat &image, Mat &hist, int histSize, const float*
#endif
// IPP_DISABLE_HISTOGRAM - https://github.com/opencv/opencv/issues/11544
if (uniform && (ranges[0][1] - ranges[0][0]) != histSize)
// and https://github.com/opencv/opencv/issues/21595
if ((uniform && (ranges[0][1] - ranges[0][0]) != histSize) || abs(ranges[0][0]) != cvFloor(ranges[0][0]))
return false;
Mat ihist = hist;

32
modules/imgproc/test/test_histograms.cpp
View File

@ -1993,6 +1993,38 @@ TEST(Imgproc_Hist_Calc, badarg)
EXPECT_NO_THROW(cv::calcBackProject(&img, 1, channels, hist, backProj, NULL, 1, true));
}
TEST(Imgproc_Hist_Calc, IPP_ranges_with_equal_exponent_21595)
{
const int channels[] = { 0 };
float range1[] = { -0.5f, 1.5f };
const float* ranges[] = { range1 };
const int hist_size[] = { 2 };
uint8_t m[1][6] = { { 0, 1, 0, 1 , 1, 1 } };
cv::Mat images_u = Mat(1, 6, CV_8UC1, m);
cv::Mat histogram_u;
cv::calcHist(&images_u, 1, channels, noArray(), histogram_u, 1, hist_size, ranges);
ASSERT_EQ(histogram_u.at<float>(0), 2.f) << "0 not counts correctly, res: " << histogram_u.at<float>(0);
ASSERT_EQ(histogram_u.at<float>(1), 4.f) << "1 not counts correctly, res: " << histogram_u.at<float>(0);
}
TEST(Imgproc_Hist_Calc, IPP_ranges_with_nonequal_exponent_21595)
{
const int channels[] = { 0 };
float range1[] = { -1.3f, 1.5f };
const float* ranges[] = { range1 };
const int hist_size[] = { 3 };
uint8_t m[1][6] = { { 0, 1, 0, 1 , 1, 1 } };
cv::Mat images_u = Mat(1, 6, CV_8UC1, m);
cv::Mat histogram_u;
cv::calcHist(&images_u, 1, channels, noArray(), histogram_u, 1, hist_size, ranges);
ASSERT_EQ(histogram_u.at<float>(0), 0.f) << "not equal to zero, res: " << histogram_u.at<float>(0);
ASSERT_EQ(histogram_u.at<float>(1), 2.f) << "0 not counts correctly, res: " << histogram_u.at<float>(1);
ASSERT_EQ(histogram_u.at<float>(2), 4.f) << "1 not counts correctly, res: " << histogram_u.at<float>(2);
}
}} // namespace
/* End Of File */

22
modules/objdetect/include/opencv2/objdetect.hpp
View File

@ -383,7 +383,7 @@ public:
};
enum DescriptorStorageFormat { DESCR_FORMAT_COL_BY_COL, DESCR_FORMAT_ROW_BY_ROW };
/**@brief Creates the HOG descriptor and detector with default params.
/**@brief Creates the HOG descriptor and detector with default parameters.
aqual to HOGDescriptor(Size(64,128), Size(16,16), Size(8,8), Size(8,8), 9 )
*/
@ -419,6 +419,8 @@ public:
{}
/** @overload
Creates the HOG descriptor and detector and loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
@param filename The file name containing HOGDescriptor properties and coefficients for the linear SVM classifier.
*/
CV_WRAP HOGDescriptor(const String& filename)
@ -457,19 +459,19 @@ public:
*/
CV_WRAP virtual void setSVMDetector(InputArray svmdetector);
/** @brief Reads HOGDescriptor parameters from a cv::FileNode.
/** @brief Reads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file node.
@param fn File node
*/
virtual bool read(FileNode& fn);
/** @brief Stores HOGDescriptor parameters in a cv::FileStorage.
/** @brief Stores HOGDescriptor parameters and coefficients for the linear SVM classifier in a file storage.
@param fs File storage
@param objname Object name
*/
virtual void write(FileStorage& fs, const String& objname) const;
/** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file.
@param filename Path of the file to read.
/** @brief loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file
@param filename Name of the file to read.
@param objname The optional name of the node to read (if empty, the first top-level node will be used).
*/
CV_WRAP virtual bool load(const String& filename, const String& objname = String());
@ -542,13 +544,14 @@ public:
@param winStride Window stride. It must be a multiple of block stride.
@param padding Padding
@param scale Coefficient of the detection window increase.
@param finalThreshold Final threshold
@param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered
by many rectangles. 0 means not to perform grouping.
@param useMeanshiftGrouping indicates grouping algorithm
*/
CV_WRAP virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
CV_OUT std::vector<double>& foundWeights, double hitThreshold = 0,
Size winStride = Size(), Size padding = Size(), double scale = 1.05,
double finalThreshold = 2.0,bool useMeanshiftGrouping = false) const;
double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
/** @brief Detects objects of different sizes in the input image. The detected objects are returned as a list
of rectangles.
@ -560,13 +563,14 @@ public:
@param winStride Window stride. It must be a multiple of block stride.
@param padding Padding
@param scale Coefficient of the detection window increase.
@param finalThreshold Final threshold
@param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered
by many rectangles. 0 means not to perform grouping.
@param useMeanshiftGrouping indicates grouping algorithm
*/
virtual void detectMultiScale(InputArray img, CV_OUT std::vector<Rect>& foundLocations,
double hitThreshold = 0, Size winStride = Size(),
Size padding = Size(), double scale = 1.05,
double finalThreshold = 2.0, bool useMeanshiftGrouping = false) const;
double groupThreshold = 2.0, bool useMeanshiftGrouping = false) const;
/** @brief Computes gradients and quantized gradient orientations.
@param img Matrix contains the image to be computed

12
modules/objdetect/src/hog.cpp
View File

@ -1886,7 +1886,7 @@ static bool ocl_detectMultiScale(InputArray _img, std::vector<Rect> &found_locat
void HOGDescriptor::detectMultiScale(
InputArray _img, std::vector<Rect>& foundLocations, std::vector<double>& foundWeights,
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
double scale0, double groupThreshold, bool useMeanshiftGrouping) const
{
CV_INSTRUMENT_REGION();
@ -1912,7 +1912,7 @@ void HOGDescriptor::detectMultiScale(
CV_OCL_RUN(_img.dims() <= 2 && _img.type() == CV_8UC1 && scale0 > 1 && winStride.width % blockStride.width == 0 &&
winStride.height % blockStride.height == 0 && padding == Size(0,0) && _img.isUMat(),
ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, finalThreshold, oclSvmDetector,
ocl_detectMultiScale(_img, foundLocations, levelScale, hitThreshold, winStride, groupThreshold, oclSvmDetector,
blockSize, cellSize, nbins, blockStride, winSize, gammaCorrection, L2HysThreshold, (float)getWinSigma(), free_coef, signedGradient));
std::vector<Rect> allCandidates;
@ -1933,21 +1933,21 @@ void HOGDescriptor::detectMultiScale(
std::copy(tempWeights.begin(), tempWeights.end(), back_inserter(foundWeights));
if ( useMeanshiftGrouping )
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, finalThreshold, winSize);
groupRectangles_meanshift(foundLocations, foundWeights, foundScales, groupThreshold, winSize);
else
groupRectangles(foundLocations, foundWeights, (int)finalThreshold, 0.2);
groupRectangles(foundLocations, foundWeights, (int)groupThreshold, 0.2);
clipObjects(imgSize, foundLocations, 0, &foundWeights);
}
void HOGDescriptor::detectMultiScale(InputArray img, std::vector<Rect>& foundLocations,
double hitThreshold, Size winStride, Size padding,
double scale0, double finalThreshold, bool useMeanshiftGrouping) const
double scale0, double groupThreshold, bool useMeanshiftGrouping) const
{
CV_INSTRUMENT_REGION();
std::vector<double> foundWeights;
detectMultiScale(img, foundLocations, foundWeights, hitThreshold, winStride,
padding, scale0, finalThreshold, useMeanshiftGrouping);
padding, scale0, groupThreshold, useMeanshiftGrouping);
}
std::vector<float> HOGDescriptor::getDefaultPeopleDetector()

4
modules/stitching/src/seam_finders.cpp
View File

@ -587,8 +587,8 @@ void DpSeamFinder::computeGradients(const Mat &image1, const Mat &image2)
bool DpSeamFinder::hasOnlyOneNeighbor(int comp)
{
std::set<std::pair<int, int> >::iterator begin, end;
begin = lower_bound(edges_.begin(), edges_.end(), std::make_pair(comp, std::numeric_limits<int>::min()));
end = upper_bound(edges_.begin(), edges_.end(), std::make_pair(comp, std::numeric_limits<int>::max()));
begin = edges_.lower_bound(std::make_pair(comp, std::numeric_limits<int>::min()));
end = edges_.upper_bound(std::make_pair(comp, std::numeric_limits<int>::max()));
return ++begin == end;
}