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Commit149e4f7

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add n-dimensional transpose to core
1 parent8fa429c commit149e4f7

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3 files changed

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3 files changed

+146
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‎modules/core/include/opencv2/core.hpp‎

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Original file line numberDiff line numberDiff line change
@@ -1739,6 +1739,13 @@ should be done separately if needed.
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*/
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CV_EXPORTS_Wvoidtranspose(InputArray src, OutputArray dst);
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1742+
/** @brief Transpose for n-dimensional matrices.
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* @param src input array.
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* @param order new order of dimensions.
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* @param dst output array of the same type as src.
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*/
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CV_EXPORTS_Wvoidtranspose(const Mat& src,const std::vector<int>& order, OutputArray dst);
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/** @brief Performs the matrix transformation of every array element.
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The function cv::transform performs the matrix transformation of every

‎modules/core/src/matrix_transform.cpp‎

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@@ -4,6 +4,7 @@
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#include"precomp.hpp"
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#include"opencl_kernels_core.hpp"
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#include"opencv2/core/detail/dispatch_helper.impl.hpp"
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namespacecv {
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@@ -282,6 +283,69 @@ void transpose( InputArray _src, OutputArray _dst )
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}
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284285

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template<typename T>
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structtransposeImpl
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{
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voidoperator()(const cv::Mat& inp, cv::Mat& out,const std::vector<int>& order)const
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{
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int continuous_idx =0;
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for (int i =static_cast<int>(order.size()) -1; i >=0; --i)
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{
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if (order[i] != i)
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{
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continuous_idx = i +1;
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break;
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}
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}
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size_t continuous_size = continuous_idx ==0 ? out.total() : out.step1(continuous_idx -1);
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size_t outer_size = out.total() / continuous_size;
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std::vector<size_t>steps(order.size());
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for (int i =0; i <static_cast<int>(steps.size()); ++i)
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{
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steps[order[i]] = out.step1(i);
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}
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auto* src = inp.ptr<const T>();
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auto* dst = out.ptr<T>();
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size_t dst_offset =0;
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for (size_t i =0; i < outer_size; ++i)
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{
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std::memcpy(dst + dst_offset, src, out.elemSize() * continuous_size);
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src += continuous_size;
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for (int j = continuous_idx -1; j >=0; --j)
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{
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dst_offset += steps[j];
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if ((dst_offset / steps[j]) % inp.size[j] !=0)
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{
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break;
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}
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dst_offset -= steps[j] * inp.size[j];
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}
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}
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}
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};
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voidtranspose(const Mat& inp,const std::vector<int>& order, OutputArray _out)
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{
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CV_Assert(inp.isContinuous() && inp.channels() ==1);
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std::vector<int>newShape(order.size());
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for (size_t i =0; i < order.size(); ++i)
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{
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newShape[i] = inp.size[order[i]];
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}
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_out.create(static_cast<int>(newShape.size()), newShape.data(), inp.type());
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Mat out = _out.getMat();
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CV_Assert(out.isContinuous());// does OutputArray::create always returns continuous matrix?
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cv::detail::depthDispatch<transposeImpl>(inp.depth(), inp, out, order);
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}
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285349
#if CV_SIMD128
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template<typename V> CV_ALWAYS_INLINEvoidflipHoriz_single(const uchar* src,size_t sstep, uchar* dst,size_t dstep, Size size,size_t esz )
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{

‎modules/core/test/test_arithm.cpp‎

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@@ -3,6 +3,7 @@
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// of this distribution and at http://opencv.org/license.html.
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#include"test_precomp.hpp"
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#include"ref_reduce_arg.impl.hpp"
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#include<algorithm>
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namespaceopencv_test {namespace {
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@@ -2128,6 +2129,80 @@ TEST(Core_minMaxIdx, regression_9207_1)
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}
21292130

21302131

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classTransposeND :publictesting::TestWithParam< tuple<std::vector<int>, perf::MatType> >
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{
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public:
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std::vector<int> m_shape;
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int m_type;
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voidSetUp()
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{
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std::tie(m_shape, m_type) =GetParam();
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}
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};
2143+
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2145+
TEST_P(TransposeND, basic)
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{
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setRNGSeed(42);
2148+
Matinp(m_shape, m_type);
2149+
randu(inp,0,255);
2150+
2151+
std::vector<int>order(m_shape.size());
2152+
std::iota(order.begin(), order.end(),0);
2153+
auto transposer = [&order] (const std::vector<int>& id)
2154+
{
2155+
std::vector<int>ret(id.size());
2156+
for (size_t i =0; i < id.size(); ++i)
2157+
{
2158+
ret[i] = id[order[i]];
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}
2160+
return ret;
2161+
};
2162+
auto advancer = [&inp] (std::vector<int>& id)
2163+
{
2164+
for (int j = id.size() -1; j >=0; --j)
2165+
{
2166+
++id[j];
2167+
if (id[j] != inp.size[j])
2168+
{
2169+
break;
2170+
}
2171+
id[j] =0;
2172+
}
2173+
};
2174+
2175+
do
2176+
{
2177+
Mat out;
2178+
cv::transpose(inp, order, out);
2179+
std::vector<int>id(order.size());
2180+
for (size_t i =0; i < inp.total(); ++i)
2181+
{
2182+
auto new_id =transposer(id);
2183+
switch (inp.type())
2184+
{
2185+
case CV_8UC1:
2186+
CV_Assert(inp.at<uint8_t>(id.data()) == out.at<uint8_t>(new_id.data()));
2187+
break;
2188+
case CV_32FC1:
2189+
CV_Assert(inp.at<float>(id.data()) == out.at<float>(new_id.data()));
2190+
break;
2191+
default:
2192+
CV_Error(Error::StsBadArg,format("Unsupported type: %d", inp.type()));
2193+
}
2194+
advancer(id);
2195+
}
2196+
}while (std::next_permutation(order.begin(), order.end()));
2197+
}
2198+
2199+
2200+
INSTANTIATE_TEST_CASE_P(Arithm, TransposeND, testing::Combine(
2201+
testing::Values(std::vector<int>{2,3,4}, std::vector<int>{5,10}),
2202+
testing::Values(perf::MatType(CV_8UC1), CV_32FC1)
2203+
));
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TEST(Core_minMaxIdx, regression_9207_2)
21322207
{
21332208
constint rows =13;

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