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fix legacy constants

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pull/21378/head
Suleyman TURKMEN 3 years ago
parent 5f249a3e67
commit 0e6a2c0491
17 changed files with 75 additions and 83 deletions
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  1. 4
      modules/core/perf/perf_reduce.cpp
  2. 2
      modules/core/src/matmul.dispatch.cpp
  3. 40
      modules/core/src/matrix_operations.cpp
  4. 16
      modules/core/test/ocl/test_arithm.cpp
  5. 34
      modules/core/test/test_mat.cpp
  6. 2
      modules/core/test/test_math.cpp
  7. 3
      modules/core/test/test_precomp.hpp
  8. 4
      modules/core/test/test_umat.cpp
  9. 2
      modules/imgcodecs/src/grfmt_exr.cpp
  10. 12
      modules/imgcodecs/src/grfmt_jpeg.cpp
  11. 28
      modules/imgcodecs/src/grfmt_pam.cpp
  12. 2
      modules/imgcodecs/src/grfmt_webp.cpp
  13. 2
      modules/imgcodecs/src/loadsave.cpp
  14. 3
      modules/imgcodecs/src/precomp.hpp
  15. 2
      modules/ml/src/em.cpp
  16. 1
      modules/ml/test/test_precomp.hpp
  17. 1
      modules/objdetect/src/hog.cpp

4
modules/core/perf/perf_reduce.cpp
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@ -23,7 +23,7 @@ PERF_TEST_P(Size_MatType_ROp, reduceR,
int reduceOp = get<2>(GetParam());
int ddepth = -1;
if( CV_MAT_DEPTH(matType) < CV_32S && (reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG) )
if( CV_MAT_DEPTH(matType) < CV_32S && (reduceOp == REDUCE_SUM || reduceOp == REDUCE_AVG) )
ddepth = CV_32S;
Mat src(sz, matType);
@ -51,7 +51,7 @@ PERF_TEST_P(Size_MatType_ROp, reduceC,
int reduceOp = get<2>(GetParam());
int ddepth = -1;
if( CV_MAT_DEPTH(matType)< CV_32S && (reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG) )
if( CV_MAT_DEPTH(matType)< CV_32S && (reduceOp == REDUCE_SUM || reduceOp == REDUCE_AVG) )
ddepth = CV_32S;
Mat src(sz, matType);

2
modules/core/src/matmul.dispatch.cpp
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@ -804,7 +804,7 @@ void calcCovarMatrix( InputArray _src, OutputArray _covar, InputOutputArray _mea
else
{
ctype = std::max(CV_MAT_DEPTH(ctype >= 0 ? ctype : type), CV_32F);
reduce( _src, _mean, takeRows ? 0 : 1, CV_REDUCE_AVG, ctype );
reduce( _src, _mean, takeRows ? 0 : 1, REDUCE_AVG, ctype );
mean = _mean.getMat();
}

40
modules/core/src/matrix_operations.cpp
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@ -616,7 +616,7 @@ static bool ocl_reduce(InputArray _src, OutputArray _dst,
if (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F))
return false;
if (op == CV_REDUCE_AVG)
if (op == REDUCE_AVG)
{
if (sdepth < CV_32S && ddepth < CV_32S)
ddepth = CV_32S;
@ -654,7 +654,7 @@ static bool ocl_reduce(InputArray _src, OutputArray _dst,
_dst.create(dsize, dtype);
UMat dst = _dst.getUMat();
if (op0 == CV_REDUCE_AVG)
if (op0 == REDUCE_AVG)
k.args(ocl::KernelArg::ReadOnly(src),
ocl::KernelArg::WriteOnlyNoSize(dst), 1.0f / src.cols);
else
@ -690,7 +690,7 @@ static bool ocl_reduce(InputArray _src, OutputArray _dst,
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnly(src),
temparg = ocl::KernelArg::WriteOnlyNoSize(dst);
if (op0 == CV_REDUCE_AVG)
if (op0 == REDUCE_AVG)
k.args(srcarg, temparg, 1.0f / (dim == 0 ? src.rows : src.cols));
else
k.args(srcarg, temparg);
@ -717,8 +717,8 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
int ddepth = CV_MAT_DEPTH(dtype);
CV_Assert( cn == CV_MAT_CN(dtype) );
CV_Assert( op == CV_REDUCE_SUM || op == CV_REDUCE_MAX ||
op == CV_REDUCE_MIN || op == CV_REDUCE_AVG );
CV_Assert( op == REDUCE_SUM || op == REDUCE_MAX ||
op == REDUCE_MIN || op == REDUCE_AVG );
CV_OCL_RUN(_dst.isUMat(),
ocl_reduce(_src, _dst, dim, op, op0, stype, dtype))
@ -732,9 +732,9 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
_dst.create(dim == 0 ? 1 : src.rows, dim == 0 ? src.cols : 1, dtype);
Mat dst = _dst.getMat(), temp = dst;
if( op == CV_REDUCE_AVG )
if( op == REDUCE_AVG )
{
op = CV_REDUCE_SUM;
op = REDUCE_SUM;
if( sdepth < CV_32S && ddepth < CV_32S )
{
temp.create(dst.rows, dst.cols, CV_32SC(cn));
@ -745,7 +745,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
ReduceFunc func = 0;
if( dim == 0 )
{
if( op == CV_REDUCE_SUM )
if( op == REDUCE_SUM )
{
if(sdepth == CV_8U && ddepth == CV_32S)
func = GET_OPTIMIZED(reduceSumR8u32s);
@ -768,7 +768,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceSumR64f64f;
}
else if(op == CV_REDUCE_MAX)
else if(op == REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMaxR8u);
@ -781,7 +781,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMaxR64f;
}
else if(op == CV_REDUCE_MIN)
else if(op == REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMinR8u);
@ -797,7 +797,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
}
else
{
if(op == CV_REDUCE_SUM)
if(op == REDUCE_SUM)
{
if(sdepth == CV_8U && ddepth == CV_32S)
func = GET_OPTIMIZED(reduceSumC8u32s);
@ -820,7 +820,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceSumC64f64f;
}
else if(op == CV_REDUCE_MAX)
else if(op == REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMaxC8u);
@ -833,7 +833,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMaxC64f;
}
else if(op == CV_REDUCE_MIN)
else if(op == REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = GET_OPTIMIZED(reduceMinC8u);
@ -854,7 +854,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
func( src, temp );
if( op0 == CV_REDUCE_AVG )
if( op0 == REDUCE_AVG )
temp.convertTo(dst, dst.type(), 1./(dim == 0 ? src.rows : src.cols));
}
@ -868,9 +868,9 @@ template<typename T> static void sort_( const Mat& src, Mat& dst, int flags )
{
AutoBuffer<T> buf;
int n, len;
bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
bool sortRows = (flags & 1) == SORT_EVERY_ROW;
bool inplace = src.data == dst.data;
bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
bool sortDescending = (flags & SORT_DESCENDING) != 0;
if( sortRows )
n = src.rows, len = src.cols;
@ -940,8 +940,8 @@ static bool ipp_sort(const Mat& src, Mat& dst, int flags)
{
CV_INSTRUMENT_REGION_IPP();
bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
bool sortRows = (flags & 1) == SORT_EVERY_ROW;
bool sortDescending = (flags & SORT_DESCENDING) != 0;
bool inplace = (src.data == dst.data);
int depth = src.depth();
IppDataType type = ippiGetDataType(depth);
@ -1013,8 +1013,8 @@ template<typename T> static void sortIdx_( const Mat& src, Mat& dst, int flags )
{
AutoBuffer<T> buf;
AutoBuffer<int> ibuf;
bool sortRows = (flags & 1) == CV_SORT_EVERY_ROW;
bool sortDescending = (flags & CV_SORT_DESCENDING) != 0;
bool sortRows = (flags & 1) == SORT_EVERY_ROW;
bool sortDescending = (flags & SORT_DESCENDING) != 0;
CV_Assert( src.data != dst.data );

16
modules/core/test/ocl/test_arithm.cpp
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@ -1819,8 +1819,8 @@ OCL_TEST_P(ReduceSum, Mat)
{
generateTestData();
OCL_OFF(cv::reduce(src_roi, dst_roi, dim, CV_REDUCE_SUM, dtype));
OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, CV_REDUCE_SUM, dtype));
OCL_OFF(cv::reduce(src_roi, dst_roi, dim, REDUCE_SUM, dtype));
OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, REDUCE_SUM, dtype));
double eps = ddepth <= CV_32S ? 1 : 7e-4;
OCL_EXPECT_MATS_NEAR(dst, eps);
@ -1835,8 +1835,8 @@ OCL_TEST_P(ReduceMax, Mat)
{
generateTestData();
OCL_OFF(cv::reduce(src_roi, dst_roi, dim, CV_REDUCE_MAX, dtype));
OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, CV_REDUCE_MAX, dtype));
OCL_OFF(cv::reduce(src_roi, dst_roi, dim, REDUCE_MAX, dtype));
OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, REDUCE_MAX, dtype));
OCL_EXPECT_MATS_NEAR(dst, 0);
}
@ -1850,8 +1850,8 @@ OCL_TEST_P(ReduceMin, Mat)
{
generateTestData();
OCL_OFF(cv::reduce(src_roi, dst_roi, dim, CV_REDUCE_MIN, dtype));
OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, CV_REDUCE_MIN, dtype));
OCL_OFF(cv::reduce(src_roi, dst_roi, dim, REDUCE_MIN, dtype));
OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, REDUCE_MIN, dtype));
OCL_EXPECT_MATS_NEAR(dst, 0);
}
@ -1865,8 +1865,8 @@ OCL_TEST_P(ReduceAvg, Mat)
{
generateTestData();
OCL_OFF(cv::reduce(src_roi, dst_roi, dim, CV_REDUCE_AVG, dtype));
OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, CV_REDUCE_AVG, dtype));
OCL_OFF(cv::reduce(src_roi, dst_roi, dim, REDUCE_AVG, dtype));
OCL_ON(cv::reduce(usrc_roi, udst_roi, dim, REDUCE_AVG, dtype));
double eps = ddepth <= CV_32S ? 1 : 6e-6;
OCL_EXPECT_MATS_NEAR(dst, eps);

34
modules/core/test/test_mat.cpp
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@ -93,7 +93,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat
{
int srcType = src.type();
bool support = false;
if( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG )
if( opType == REDUCE_SUM || opType == REDUCE_AVG )
{
if( srcType == CV_8U && (dstType == CV_32S || dstType == CV_32F || dstType == CV_64F) )
support = true;
@ -106,7 +106,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat
if( srcType == CV_64F && dstType == CV_64F)
support = true;
}
else if( opType == CV_REDUCE_MAX )
else if( opType == REDUCE_MAX )
{
if( srcType == CV_8U && dstType == CV_8U )
support = true;
@ -115,7 +115,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat
if( srcType == CV_64F && dstType == CV_64F )
support = true;
}
else if( opType == CV_REDUCE_MIN )
else if( opType == REDUCE_MIN )
{
if( srcType == CV_8U && dstType == CV_8U)
support = true;
@ -128,7 +128,7 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat
return cvtest::TS::OK;
double eps = 0.0;
if ( opType == CV_REDUCE_SUM || opType == CV_REDUCE_AVG )
if ( opType == REDUCE_SUM || opType == REDUCE_AVG )
{
if ( dstType == CV_32F )
eps = 1.e-5;
@ -152,10 +152,10 @@ int Core_ReduceTest::checkOp( const Mat& src, int dstType, int opType, const Mat
if( check )
{
char msg[100];
const char* opTypeStr = opType == CV_REDUCE_SUM ? "CV_REDUCE_SUM" :
opType == CV_REDUCE_AVG ? "CV_REDUCE_AVG" :
opType == CV_REDUCE_MAX ? "CV_REDUCE_MAX" :
opType == CV_REDUCE_MIN ? "CV_REDUCE_MIN" : "unknown operation type";
const char* opTypeStr = opType == REDUCE_SUM ? "REDUCE_SUM" :
opType == REDUCE_AVG ? "REDUCE_AVG" :
opType == REDUCE_MAX ? "REDUCE_MAX" :
opType == REDUCE_MIN ? "REDUCE_MIN" : "unknown operation type";
string srcTypeStr, dstTypeStr;
getMatTypeStr( src.type(), srcTypeStr );
getMatTypeStr( dstType, dstTypeStr );
@ -195,19 +195,19 @@ int Core_ReduceTest::checkCase( int srcType, int dstType, int dim, Size sz )
CV_Assert( 0 );
// 1. sum
tempCode = checkOp( src, dstType, CV_REDUCE_SUM, sum, dim );
tempCode = checkOp( src, dstType, REDUCE_SUM, sum, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 2. avg
tempCode = checkOp( src, dstType, CV_REDUCE_AVG, avg, dim );
tempCode = checkOp( src, dstType, REDUCE_AVG, avg, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 3. max
tempCode = checkOp( src, dstType, CV_REDUCE_MAX, max, dim );
tempCode = checkOp( src, dstType, REDUCE_MAX, max, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
// 4. min
tempCode = checkOp( src, dstType, CV_REDUCE_MIN, min, dim );
tempCode = checkOp( src, dstType, REDUCE_MIN, min, dim );
code = tempCode != cvtest::TS::OK ? tempCode : code;
return code;
@ -315,7 +315,7 @@ TEST(Core_PCA, accuracy)
Mat rBackPrjTestPoints = rPCA.backProject( rPrjTestPoints );
Mat avg(1, sz.width, CV_32FC1 );
cv::reduce( rPoints, avg, 0, CV_REDUCE_AVG );
cv::reduce( rPoints, avg, 0, REDUCE_AVG );
Mat Q = rPoints - repeat( avg, rPoints.rows, 1 ), Qt = Q.t(), eval, evec;
Q = Qt * Q;
Q = Q /(float)rPoints.rows;
@ -1559,10 +1559,10 @@ TEST(Reduce, regression_should_fail_bug_4594)
cv::Mat src = cv::Mat::eye(4, 4, CV_8U);
std::vector<int> dst;
EXPECT_THROW(cv::reduce(src, dst, 0, CV_REDUCE_MIN, CV_32S), cv::Exception);
EXPECT_THROW(cv::reduce(src, dst, 0, CV_REDUCE_MAX, CV_32S), cv::Exception);
EXPECT_NO_THROW(cv::reduce(src, dst, 0, CV_REDUCE_SUM, CV_32S));
EXPECT_NO_THROW(cv::reduce(src, dst, 0, CV_REDUCE_AVG, CV_32S));
EXPECT_THROW(cv::reduce(src, dst, 0, REDUCE_MIN, CV_32S), cv::Exception);
EXPECT_THROW(cv::reduce(src, dst, 0, REDUCE_MAX, CV_32S), cv::Exception);
EXPECT_NO_THROW(cv::reduce(src, dst, 0, REDUCE_SUM, CV_32S));
EXPECT_NO_THROW(cv::reduce(src, dst, 0, REDUCE_AVG, CV_32S));
}
TEST(Mat, push_back_vector)

2
modules/core/test/test_math.cpp
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@ -3018,7 +3018,7 @@ TEST(CovariationMatrixVectorOfMatWithMean, accuracy)
cv::randu(src,cv::Scalar(-128), cv::Scalar(128));
cv::Mat goldMean;
cv::reduce(src,goldMean,0 ,CV_REDUCE_AVG, CV_32F);
cv::reduce(src,goldMean,0 ,REDUCE_AVG, CV_32F);
cv::calcCovarMatrix(src,gold,goldMean,singleMatFlags,CV_32F);

3
modules/core/test/test_precomp.hpp
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@ -6,9 +6,6 @@
#include "opencv2/ts.hpp"
#include "opencv2/ts/ocl_test.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/core/cvdef.h"
#include "opencv2/core/private.hpp"
#include "opencv2/core/hal/hal.hpp"

4
modules/core/test/test_umat.cpp
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@ -1398,8 +1398,8 @@ TEST(UMat, testTempObjects_Mat_issue_8693)
randu(srcUMat, -1.f, 1.f);
srcUMat.copyTo(srcMat);
reduce(srcUMat, srcUMat, 0, CV_REDUCE_SUM);
reduce(srcMat, srcMat, 0, CV_REDUCE_SUM);
reduce(srcUMat, srcUMat, 0, REDUCE_SUM);
reduce(srcMat, srcMat, 0, REDUCE_SUM);
srcUMat.convertTo(srcUMat, CV_64FC1);
srcMat.convertTo(srcMat, CV_64FC1);

2
modules/imgcodecs/src/grfmt_exr.cpp
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@ -637,7 +637,7 @@ bool ExrEncoder::write( const Mat& img, const std::vector<int>& params )
for( size_t i = 0; i < params.size(); i += 2 )
{
if( params[i] == CV_IMWRITE_EXR_TYPE )
if( params[i] == IMWRITE_EXR_TYPE )
{
switch( params[i+1] )
{

12
modules/imgcodecs/src/grfmt_jpeg.cpp
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@ -643,23 +643,23 @@ bool JpegEncoder::write( const Mat& img, const std::vector<int>& params )
for( size_t i = 0; i < params.size(); i += 2 )
{
if( params[i] == CV_IMWRITE_JPEG_QUALITY )
if( params[i] == IMWRITE_JPEG_QUALITY )
{
quality = params[i+1];
quality = MIN(MAX(quality, 0), 100);
}
if( params[i] == CV_IMWRITE_JPEG_PROGRESSIVE )
if( params[i] == IMWRITE_JPEG_PROGRESSIVE )
{
progressive = params[i+1];
}
if( params[i] == CV_IMWRITE_JPEG_OPTIMIZE )
if( params[i] == IMWRITE_JPEG_OPTIMIZE )
{
optimize = params[i+1];
}
if( params[i] == CV_IMWRITE_JPEG_LUMA_QUALITY )
if( params[i] == IMWRITE_JPEG_LUMA_QUALITY )
{
if (params[i+1] >= 0)
{
@ -674,7 +674,7 @@ bool JpegEncoder::write( const Mat& img, const std::vector<int>& params )
}
}
if( params[i] == CV_IMWRITE_JPEG_CHROMA_QUALITY )
if( params[i] == IMWRITE_JPEG_CHROMA_QUALITY )
{
if (params[i+1] >= 0)
{
@ -682,7 +682,7 @@ bool JpegEncoder::write( const Mat& img, const std::vector<int>& params )
}
}
if( params[i] == CV_IMWRITE_JPEG_RST_INTERVAL )
if( params[i] == IMWRITE_JPEG_RST_INTERVAL )
{
rst_interval = params[i+1];
rst_interval = MIN(MAX(rst_interval, 0), 65535L);

28
modules/imgcodecs/src/grfmt_pam.cpp
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@ -111,12 +111,12 @@ static bool rgb_convert (void *src, void *target, int width, int target_channels
int target_depth);
const static struct pam_format formats[] = {
{CV_IMWRITE_PAM_FORMAT_NULL, "", NULL, {0, 0, 0, 0} },
{CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE, "BLACKANDWHITE", NULL, {0, 0, 0, 0} },
{CV_IMWRITE_PAM_FORMAT_GRAYSCALE, "GRAYSCALE", NULL, {0, 0, 0, 0} },
{CV_IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA, "GRAYSCALE_ALPHA", NULL, {0, 0, 0, 0} },
{CV_IMWRITE_PAM_FORMAT_RGB, "RGB", rgb_convert, {0, 1, 2, 0} },
{CV_IMWRITE_PAM_FORMAT_RGB_ALPHA, "RGB_ALPHA", NULL, {0, 1, 2, 0} },
{IMWRITE_PAM_FORMAT_NULL, "", NULL, {0, 0, 0, 0} },
{IMWRITE_PAM_FORMAT_BLACKANDWHITE, "BLACKANDWHITE", NULL, {0, 0, 0, 0} },
{IMWRITE_PAM_FORMAT_GRAYSCALE, "GRAYSCALE", NULL, {0, 0, 0, 0} },
{IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA, "GRAYSCALE_ALPHA", NULL, {0, 0, 0, 0} },
{IMWRITE_PAM_FORMAT_RGB, "RGB", rgb_convert, {0, 1, 2, 0} },
{IMWRITE_PAM_FORMAT_RGB_ALPHA, "RGB_ALPHA", NULL, {0, 1, 2, 0} },
};
#define PAM_FORMATS_NO (sizeof (fields) / sizeof ((fields)[0]))
@ -341,7 +341,7 @@ PAMDecoder::PAMDecoder()
m_offset = -1;
m_buf_supported = true;
bit_mode = false;
selected_fmt = CV_IMWRITE_PAM_FORMAT_NULL;
selected_fmt = IMWRITE_PAM_FORMAT_NULL;
m_maxval = 0;
m_channels = 0;
m_sampledepth = 0;
@ -462,14 +462,14 @@ bool PAMDecoder::readHeader()
if (flds_endhdr && flds_height && flds_width && flds_depth && flds_maxval)
{
if (selected_fmt == CV_IMWRITE_PAM_FORMAT_NULL)
if (selected_fmt == IMWRITE_PAM_FORMAT_NULL)
{
if (m_channels == 1 && m_maxval == 1)
selected_fmt = CV_IMWRITE_PAM_FORMAT_BLACKANDWHITE;
selected_fmt = IMWRITE_PAM_FORMAT_BLACKANDWHITE;
else if (m_channels == 1 && m_maxval < 256)
selected_fmt = CV_IMWRITE_PAM_FORMAT_GRAYSCALE;
selected_fmt = IMWRITE_PAM_FORMAT_GRAYSCALE;
else if (m_channels == 3 && m_maxval < 256)
selected_fmt = CV_IMWRITE_PAM_FORMAT_RGB;
selected_fmt = IMWRITE_PAM_FORMAT_RGB;
}
m_type = CV_MAKETYPE(m_sampledepth, m_channels);
m_offset = m_strm.getPos();
@ -512,7 +512,7 @@ bool PAMDecoder::readData(Mat& img)
if( m_offset < 0 || !m_strm.isOpened())
return false;
if (selected_fmt != CV_IMWRITE_PAM_FORMAT_NULL)
if (selected_fmt != IMWRITE_PAM_FORMAT_NULL)
fmt = &formats[selected_fmt];
else {
/* default layout handling */
@ -662,8 +662,8 @@ bool PAMEncoder::write( const Mat& img, const std::vector<int>& params )
/* parse save file type */
for( size_t i = 0; i < params.size(); i += 2 )
if( params[i] == CV_IMWRITE_PAM_TUPLETYPE ) {
if ( params[i+1] > CV_IMWRITE_PAM_FORMAT_NULL &&
if( params[i] == IMWRITE_PAM_TUPLETYPE ) {
if ( params[i+1] > IMWRITE_PAM_FORMAT_NULL &&
params[i+1] < (int) PAM_FORMATS_NO)
fmt = &formats[params[i+1]];
}

2
modules/imgcodecs/src/grfmt_webp.cpp
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@ -243,7 +243,7 @@ bool WebPEncoder::write(const Mat& img, const std::vector<int>& params)
if (params.size() > 1)
{
if (params[0] == CV_IMWRITE_WEBP_QUALITY)
if (params[0] == IMWRITE_WEBP_QUALITY)
{
comp_lossless = false;
quality = static_cast<float>(params[1]);

2
modules/imgcodecs/src/loadsave.cpp
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@ -562,7 +562,7 @@ imreadmulti_(const String& filename, int flags, std::vector<Mat>& mats, int star
if ((flags & IMREAD_ANYDEPTH) == 0)
type = CV_MAKETYPE(CV_8U, CV_MAT_CN(type));
if ((flags & CV_LOAD_IMAGE_COLOR) != 0 ||
if ((flags & IMREAD_COLOR) != 0 ||
((flags & IMREAD_ANYCOLOR) != 0 && CV_MAT_CN(type) > 1))
type = CV_MAKETYPE(CV_MAT_DEPTH(type), 3);
else

3
modules/imgcodecs/src/precomp.hpp
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@ -43,11 +43,8 @@
#define __IMGCODECS_H_
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgcodecs/legacy/constants_c.h"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/private.hpp"
#include "opencv2/imgproc.hpp"
#include <stdlib.h>

2
modules/ml/src/em.cpp
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@ -656,7 +656,7 @@ public:
// Update weights
// not normalized first
reduce(trainProbs, weights, 0, CV_REDUCE_SUM);
reduce(trainProbs, weights, 0, REDUCE_SUM);
// Update means
means.create(nclusters, dim, CV_64FC1);

1
modules/ml/test/test_precomp.hpp
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@ -4,7 +4,6 @@
#include "opencv2/ts.hpp"
#include <opencv2/ts/cuda_test.hpp> // EXPECT_MAT_NEAR
#include "opencv2/ml.hpp"
#include "opencv2/core/core_c.h"
#include <fstream>
using std::ifstream;

1
modules/objdetect/src/hog.cpp
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@ -42,7 +42,6 @@
#include "precomp.hpp"
#include "cascadedetect.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/core/hal/intrin.hpp"
#include "opencl_kernels_objdetect.hpp"

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