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Merge pull request #26836 from chacha21:thresholding_compute_threshold_only

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Add cv::THRESH_DRYRUN flag to get adaptive threshold values without thresholding #26836

A first proposal for #26777

Adds a `cv::THRESH_DRYRUN` flag to let cv::threshold() compute the threshold (useful for OTSU/TRIANGLE), but without actually running the thresholding. This flags is a proposal instead of a new function cv::computeThreshold()

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [X] There is a reference to the original bug report and related work
- [X] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Pierre Chatelier 2025-01-24 12:25:21 +01:00 committed by GitHub
parent ab77e1cfc8
commit 3cbb4acd2d
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GPG Key ID: B5690EEEBB952194
5 changed files with 92 additions and 7 deletions
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3
modules/imgproc/include/opencv2/imgproc.hpp
View File

@ -329,7 +329,8 @@ enum ThresholdTypes {
THRESH_TOZERO_INV = 4, //!< \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
THRESH_MASK = 7,
THRESH_OTSU = 8, //!< flag, use Otsu algorithm to choose the optimal threshold value
THRESH_TRIANGLE = 16 //!< flag, use Triangle algorithm to choose the optimal threshold value
THRESH_TRIANGLE = 16, //!< flag, use Triangle algorithm to choose the optimal threshold value
THRESH_DRYRUN = 128 //!< flag, compute threshold only (useful for OTSU/TRIANGLE) but does not actually run thresholding
};
//! adaptive threshold algorithm

4
modules/imgproc/include/opencv2/imgproc/types_c.h
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@ -610,9 +610,11 @@ enum
CV_THRESH_MASK =7,
CV_THRESH_OTSU =8, /**< use Otsu algorithm to choose the optimal threshold value;
combine the flag with one of the above CV_THRESH_* values */
CV_THRESH_TRIANGLE =16 /**< use Triangle algorithm to choose the optimal threshold value;
CV_THRESH_TRIANGLE =16, /**< use Triangle algorithm to choose the optimal threshold value;
combine the flag with one of the above CV_THRESH_* values, but not
with CV_THRESH_OTSU */
CV_THRESH_DRYRUN =128 /**< compute threshold only (useful for OTSU/TRIANGLE) but does not
actually run thresholding */
};
/** Adaptive threshold methods */

23
modules/imgproc/src/thresh.cpp
View File

@ -1404,8 +1404,11 @@ static bool ocl_threshold( InputArray _src, OutputArray _dst, double & thresh, d
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type),
kercn = ocl::predictOptimalVectorWidth(_src, _dst), ktype = CV_MAKE_TYPE(depth, kercn);
bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
const bool isDisabled = ((thresh_type & THRESH_DRYRUN) != 0);
thresh_type &= ~THRESH_DRYRUN;
if ( !(thresh_type == THRESH_BINARY || thresh_type == THRESH_BINARY_INV || thresh_type == THRESH_TRUNC ||
if ( isDisabled ||
!(thresh_type == THRESH_BINARY || thresh_type == THRESH_BINARY_INV || thresh_type == THRESH_TRUNC ||
thresh_type == THRESH_TOZERO || thresh_type == THRESH_TOZERO_INV) ||
(!doubleSupport && depth == CV_64F))
return false;
@ -1544,10 +1547,14 @@ double cv::threshold( InputArray _src, OutputArray _dst, double thresh, double m
CV_OCL_RUN_(_src.dims() <= 2 && _dst.isUMat(),
ocl_threshold(_src, _dst, thresh, maxval, type), thresh)
const bool isDisabled = ((type & THRESH_DRYRUN) != 0);
type &= ~THRESH_DRYRUN;
Mat src = _src.getMat();
if (!isDisabled)
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
Mat dst = isDisabled ? cv::Mat() : _dst.getMat();
int automatic_thresh = (type & ~cv::THRESH_MASK);
type &= THRESH_MASK;
@ -1574,6 +1581,9 @@ double cv::threshold( InputArray _src, OutputArray _dst, double thresh, double m
{
int ithresh = cvFloor(thresh);
thresh = ithresh;
if (isDisabled)
return thresh;
int imaxval = cvRound(maxval);
if( type == THRESH_TRUNC )
imaxval = ithresh;
@ -1605,6 +1615,9 @@ double cv::threshold( InputArray _src, OutputArray _dst, double thresh, double m
{
int ithresh = cvFloor(thresh);
thresh = ithresh;
if (isDisabled)
return thresh;
int imaxval = cvRound(maxval);
if( type == THRESH_TRUNC )
imaxval = ithresh;
@ -1632,6 +1645,9 @@ double cv::threshold( InputArray _src, OutputArray _dst, double thresh, double m
{
int ithresh = cvFloor(thresh);
thresh = ithresh;
if (isDisabled)
return thresh;
int imaxval = cvRound(maxval);
if (type == THRESH_TRUNC)
imaxval = ithresh;
@ -1663,6 +1679,9 @@ double cv::threshold( InputArray _src, OutputArray _dst, double thresh, double m
else
CV_Error( cv::Error::StsUnsupportedFormat, "" );
if (isDisabled)
return thresh;
parallel_for_(Range(0, dst.rows),
ThresholdRunner(src, dst, thresh, maxval, type),
dst.total()/(double)(1<<16));

44
modules/imgproc/test/ocl/test_imgproc.cpp
View File

@ -386,6 +386,40 @@ OCL_TEST_P(Threshold, Mat)
}
}
struct Threshold_Dryrun :
public ImgprocTestBase
{
int thresholdType;
virtual void SetUp()
{
type = GET_PARAM(0);
thresholdType = GET_PARAM(2);
useRoi = GET_PARAM(3);
}
};
OCL_TEST_P(Threshold_Dryrun, Mat)
{
for (int j = 0; j < test_loop_times; j++)
{
random_roi();
double maxVal = randomDouble(20.0, 127.0);
double thresh = randomDouble(0.0, maxVal);
const int _thresholdType = thresholdType | THRESH_DRYRUN;
src_roi.copyTo(dst_roi);
usrc_roi.copyTo(udst_roi);
OCL_OFF(cv::threshold(src_roi, dst_roi, thresh, maxVal, _thresholdType));
OCL_ON(cv::threshold(usrc_roi, udst_roi, thresh, maxVal, _thresholdType));
OCL_EXPECT_MATS_NEAR(dst, 0);
}
}
/////////////////////////////////////////// CLAHE //////////////////////////////////////////////////
PARAM_TEST_CASE(CLAHETest, Size, double, bool)
@ -483,6 +517,16 @@ OCL_INSTANTIATE_TEST_CASE_P(Imgproc, Threshold, Combine(
ThreshOp(THRESH_TOZERO), ThreshOp(THRESH_TOZERO_INV)),
Bool()));
OCL_INSTANTIATE_TEST_CASE_P(Imgproc, Threshold_Dryrun, Combine(
Values(CV_8UC1, CV_8UC2, CV_8UC3, CV_8UC4,
CV_16SC1, CV_16SC2, CV_16SC3, CV_16SC4,
CV_32FC1, CV_32FC2, CV_32FC3, CV_32FC4),
Values(0),
Values(ThreshOp(THRESH_BINARY),
ThreshOp(THRESH_BINARY_INV), ThreshOp(THRESH_TRUNC),
ThreshOp(THRESH_TOZERO), ThreshOp(THRESH_TOZERO_INV)),
Bool()));
OCL_INSTANTIATE_TEST_CASE_P(Imgproc, CLAHETest, Combine(
Values(Size(4, 4), Size(32, 8), Size(8, 64)),
Values(0.0, 10.0, 62.0, 300.0),

19
modules/imgproc/test/test_thresh.cpp
View File

@ -502,6 +502,25 @@ BIGDATA_TEST(Imgproc_Threshold, huge)
ASSERT_EQ((uint64)nz, n / 2);
}
TEST(Imgproc_Threshold, threshold_dryrun)
{
Size sz(16, 16);
Mat input_original(sz, CV_8U, Scalar::all(2));
Mat input = input_original.clone();
std::vector<int> threshTypes = {THRESH_BINARY, THRESH_BINARY_INV, THRESH_TRUNC, THRESH_TOZERO, THRESH_TOZERO_INV};
std::vector<int> threshFlags = {0, THRESH_OTSU, THRESH_TRIANGLE};
for(int threshType : threshTypes)
{
for(int threshFlag : threshFlags)
{
const int _threshType = threshType | threshFlag | THRESH_DRYRUN;
cv::threshold(input, input, 2.0, 0.0, _threshType);
EXPECT_MAT_NEAR(input, input_original, 0);
}
}
}
TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_16085)
{
Size sz(16, 16);