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Merge pull request #27890 from Kumataro:fix26899

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core: support 16 bit LUT #27890

Close https://github.com/opencv/opencv/issues/26899

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [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.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Kumataro
2025-10-16 18:03:02 +09:00
committed by GitHub
parent c88b3cb11f
commit d0d9bd20ed
4 changed files with 176 additions and 125 deletions
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modules/core/test/test_arithm.cpp
+75 -46
View File
@@ -3221,11 +3221,12 @@ INSTANTIATE_TEST_CASE_P(Core_CartPolar, Core_PolarToCart_inplace,
)
);
CV_ENUM(LutMatType, CV_8U, CV_16U, CV_16F, CV_32S, CV_32F, CV_64F)
CV_ENUM(LutIdxType, CV_8U, CV_8S, CV_16U, CV_16S)
CV_ENUM(LutMatType, CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F, CV_16F)
struct Core_LUT: public testing::TestWithParam<LutMatType>
struct Core_LUT: public testing::TestWithParam< std::tuple<LutIdxType, LutMatType> >
{
template<typename T, int ch, bool same_cn>
template<typename Ti, typename T, int ch, bool same_cn>
cv::Mat referenceWithType(cv::Mat input, cv::Mat table)
{
cv::Mat ref(input.size(), CV_MAKE_TYPE(table.depth(), ch));
@@ -3235,7 +3236,7 @@ struct Core_LUT: public testing::TestWithParam<LutMatType>
{
if(ch == 1)
{
ref.at<T>(i, j) = table.at<T>(input.at<uchar>(i, j));
ref.at<T>(i, j) = table.at<T>(input.at<Ti>(i, j));
}
else
{
@@ -3244,11 +3245,11 @@ struct Core_LUT: public testing::TestWithParam<LutMatType>
{
if (same_cn)
{
val[k] = table.at<Vec<T, ch>>(input.at<Vec<uchar, ch>>(i, j)[k])[k];
val[k] = table.at<Vec<T, ch>>(input.at<Vec<Ti, ch>>(i, j)[k])[k];
}
else
{
val[k] = table.at<T>(input.at<Vec<uchar, ch>>(i, j)[k]);
val[k] = table.at<T>(input.at<Vec<Ti, ch>>(i, j)[k]);
}
}
ref.at<Vec<T, ch>>(i, j) = val;
@@ -3261,86 +3262,114 @@ struct Core_LUT: public testing::TestWithParam<LutMatType>
template<int ch = 1, bool same_cn = false>
cv::Mat reference(cv::Mat input, cv::Mat table)
{
if (table.depth() == CV_8U)
cv::Mat ret = cv::Mat();
if ((input.depth() == CV_8U) || (input.depth() == CV_8S)) // Index type for LUT operation
{
return referenceWithType<uchar, ch, same_cn>(input, table);
switch(table.depth()) // Value type for LUT operation
{
case CV_8U: ret = referenceWithType<uint8_t, uint8_t, ch, same_cn>(input, table); break;
case CV_8S: ret = referenceWithType<uint8_t, int8_t, ch, same_cn>(input, table); break;
case CV_16U: ret = referenceWithType<uint8_t, uint16_t, ch, same_cn>(input, table); break;
case CV_16S: ret = referenceWithType<uint8_t, int16_t, ch, same_cn>(input, table); break;
case CV_32S: ret = referenceWithType<uint8_t, int32_t, ch, same_cn>(input, table); break;
case CV_32F: ret = referenceWithType<uint8_t, float, ch, same_cn>(input, table); break;
case CV_64F: ret = referenceWithType<uint8_t, double, ch, same_cn>(input, table); break;
case CV_16F: ret = referenceWithType<uint8_t, uint16_t, ch, same_cn>(input, table); break;
default: ret = cv::Mat(); break;
}
}
else if (table.depth() == CV_16U)
else if ((input.depth() == CV_16U) || (input.depth() == CV_16S))
{
return referenceWithType<ushort, ch, same_cn>(input, table);
}
else if (table.depth() == CV_16F)
{
return referenceWithType<ushort, ch, same_cn>(input, table);
}
else if (table.depth() == CV_32S)
{
return referenceWithType<int, ch, same_cn>(input, table);
}
else if (table.depth() == CV_32F)
{
return referenceWithType<float, ch, same_cn>(input, table);
}
else if (table.depth() == CV_64F)
{
return referenceWithType<double, ch, same_cn>(input, table);
switch(table.depth()) // Value type for LUT operation
{
case CV_8U: ret = referenceWithType<uint16_t, uint8_t, ch, same_cn>(input, table); break;
case CV_8S: ret = referenceWithType<uint16_t, int8_t, ch, same_cn>(input, table); break;
case CV_16U: ret = referenceWithType<uint16_t, uint16_t, ch, same_cn>(input, table); break;
case CV_16S: ret = referenceWithType<uint16_t, int16_t, ch, same_cn>(input, table); break;
case CV_32S: ret = referenceWithType<uint16_t, int32_t, ch, same_cn>(input, table); break;
case CV_32F: ret = referenceWithType<uint16_t, float, ch, same_cn>(input, table); break;
case CV_64F: ret = referenceWithType<uint16_t, double, ch, same_cn>(input, table); break;
case CV_16F: ret = referenceWithType<uint16_t, uint16_t, ch, same_cn>(input, table); break;
default: ret = cv::Mat(); break;
}
}
return cv::Mat();
return ret;
}
};
TEST_P(Core_LUT, accuracy)
{
int type = GetParam();
cv::Mat input(117, 113, CV_8UC1);
randu(input, 0, 256);
int idx_type = get<0>(GetParam());
int value_type = get<1>(GetParam());
cv::Mat table(1, 256, CV_MAKE_TYPE(type, 1));
randu(table, 0, getMaxVal(type));
ASSERT_TRUE((idx_type == CV_8U) || (idx_type == CV_8S) || (idx_type == CV_16U ) || (idx_type == CV_16S));
const int tableSize = ((idx_type == CV_8U) || (idx_type == CV_8S)) ? 256: 65536;
cv::Mat input(117, 113, CV_MAKE_TYPE(idx_type, 1));
randu(input, getMinVal(idx_type), getMaxVal(idx_type));
cv::Mat table(1, tableSize, CV_MAKE_TYPE(value_type, 1));
randu(table, getMinVal(value_type), getMaxVal(value_type));
cv::Mat output;
cv::LUT(input, table, output);
ASSERT_NO_THROW(cv::LUT(input, table, output));
ASSERT_FALSE(output.empty());
cv::Mat gt = reference(input, table);
ASSERT_FALSE(gt.empty());
ASSERT_EQ(0, cv::norm(output, gt, cv::NORM_INF));
}
TEST_P(Core_LUT, accuracy_multi)
{
int type = (int)GetParam();
cv::Mat input(117, 113, CV_8UC3);
randu(input, 0, 256);
int idx_type = get<0>(GetParam());
int value_type = get<1>(GetParam());
cv::Mat table(1, 256, CV_MAKE_TYPE(type, 1));
randu(table, 0, getMaxVal(type));
ASSERT_TRUE((idx_type == CV_8U) || (idx_type == CV_8S) || (idx_type == CV_16U) || (idx_type == CV_16S));
const int tableSize = ((idx_type == CV_8U) || (idx_type == CV_8S) ) ? 256: 65536;
cv::Mat input(117, 113, CV_MAKE_TYPE(idx_type, 3));
randu(input, getMinVal(idx_type), getMaxVal(idx_type));
cv::Mat table(1, tableSize, CV_MAKE_TYPE(value_type, 1));
randu(table, getMinVal(value_type), getMaxVal(value_type));
cv::Mat output;
cv::LUT(input, table, output);
ASSERT_NO_THROW(cv::LUT(input, table, output));
ASSERT_FALSE(output.empty());
cv::Mat gt = reference<3>(input, table);
ASSERT_FALSE(gt.empty());
ASSERT_EQ(0, cv::norm(output, gt, cv::NORM_INF));
}
TEST_P(Core_LUT, accuracy_multi2)
{
int type = (int)GetParam();
cv::Mat input(117, 113, CV_8UC3);
randu(input, 0, 256);
int idx_type = get<0>(GetParam());
int value_type = get<1>(GetParam());
cv::Mat table(1, 256, CV_MAKE_TYPE(type, 3));
randu(table, 0, getMaxVal(type));
ASSERT_TRUE((idx_type == CV_8U) || (idx_type == CV_8S) || (idx_type == CV_16U) || (idx_type == CV_16S));
const int tableSize = ((idx_type == CV_8U) || (idx_type == CV_8S)) ? 256: 65536;
cv::Mat input(117, 113, CV_MAKE_TYPE(idx_type, 3));
randu(input, getMinVal(idx_type), getMaxVal(idx_type));
cv::Mat table(1, tableSize, CV_MAKE_TYPE(value_type, 3));
randu(table, getMinVal(value_type), getMaxVal(value_type));
cv::Mat output;
cv::LUT(input, table, output);
ASSERT_NO_THROW(cv::LUT(input, table, output));
ASSERT_FALSE(output.empty());
cv::Mat gt = reference<3, true>(input, table);
ASSERT_FALSE(gt.empty());
ASSERT_EQ(0, cv::norm(output, gt, cv::NORM_INF));
}
INSTANTIATE_TEST_CASE_P(/**/, Core_LUT, LutMatType::all());
INSTANTIATE_TEST_CASE_P(/**/, Core_LUT, testing::Combine( LutIdxType::all(), LutMatType::all()));
}} // namespace