Add resizeOnnx

.gitignore

@@ -1,3 +1,7 @@
+# ignore dot files/directories
+.*
+!.gitignore
+
 *.autosave
 *.pyc
 *.user

modules/cudawarping/include/opencv2/cudawarping.hpp

@@ -113,6 +113,33 @@ supported for now.
  */
 CV_EXPORTS_W void resize(InputArray src, OutputArray dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR, Stream& stream = Stream::Null());
 
+/** @brief onnx resize op
+https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize
+https://github.com/onnx/onnx/blob/main/onnx/reference/ops/op_resize.py
+
+Not support `tf_crop_resize` yet.
+
+To get a similar result to `cv::resize`, give dsize and:
+    INTER_NEAREST : ASYMMETRIC + NEAREST_FLOOR
+    INTER_LINEAR  : HALF_PIXEL
+    INTER_CUBIC   : HALF_PIXEL + cubicCoeff(-0.75)
+
+@param src input image.
+@param dst output image; it has the size dsize (when it is non-zero) or the size computed from src.size(), scale; the type of dst is the same as of src.
+@param dsize output image size; if it equals to zero, it is computed as:
+\f[\texttt{dsize = Size(int(scale.x * src.cols), int(scale.y * src.rows))}\f]
+Either dsize or scale must be non-zero.
+@param scale scale factor; use same definition as ONNX, if scale > 1, it's upsampling.
+@param interpolation interpolation flags, see #InterpolationFlags and #ResizeONNXFlags
+@param cubicCoeff cubic sampling coefficient, range \f[[-1.0, 0)\f]
+@param stream Stream for the asynchronous version.
+
+@sa resize, resizeOnnx
+ */
+CV_EXPORTS_W void resizeOnnx(InputArray src, OutputArray dst, Size dsize,
+    Point2d scale = Point2d(), int interpolation = INTER_LINEAR,
+    float cubicCoeff = -0.75f, Stream& stream = Stream::Null());
+
 /** @brief Applies an affine transformation to an image.
 
 @param src Source image. CV_8U , CV_16U , CV_32S , or CV_32F depth and 1, 3, or 4 channels are

modules/cudawarping/perf/perf_warping.cpp

@@ -223,6 +223,48 @@ PERF_TEST_P(Sz_Depth_Cn_Scale, ResizeArea,
     }
 }
 
+//////////////////////////////////////////////////////////////////////
+// ResizeOnnx
+
+PERF_TEST_P(Sz_Depth_Cn_Scale, ResizeOnnxLinearAntialias,
+            Combine(CUDA_TYPICAL_MAT_SIZES,
+                    Values(CV_8U, CV_16U, CV_32F),
+                    CUDA_CHANNELS_1_3_4,
+                    Values(0.8, 0.5, 0.3)))
+{
+    declare.time(10.0);
+
+    const cv::Size size = GET_PARAM(0);
+    const int depth = GET_PARAM(1);
+    const int channels = GET_PARAM(2);
+    const int interpolation = cv::INTER_LINEAR | cv::INTER_ANTIALIAS;
+    const double f = GET_PARAM(3);
+    const Point2d scale = Point2d(f, f);
+
+    const int type = CV_MAKE_TYPE(depth, channels);
+
+    cv::Mat src(size, type);
+    declare.in(src, WARMUP_RNG);
+
+    if (PERF_RUN_CUDA())
+    {
+        const cv::cuda::GpuMat d_src(src);
+        cv::cuda::GpuMat dst;
+
+        TEST_CYCLE() cv::cuda::resizeOnnx(d_src, dst, cv::Size(), scale, interpolation);
+
+        CUDA_SANITY_CHECK(dst, 1);
+    }
+    else
+    {
+        cv::Mat dst;
+
+        TEST_CYCLE() cv::resizeOnnx(src, dst, cv::Size(), scale, interpolation);
+
+        CPU_SANITY_CHECK(dst);
+    }
+}
+
 //////////////////////////////////////////////////////////////////////
 // WarpAffine