potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 00:03:03 +04:00
Code Packages Releases Wiki Activity

Merge pull request #17088 from mpashchenkov:mp/ocv-gapi-kernel-laplacian

Browse Source 
G-API: Laplacian and bilateralFilter standard kernels

* Added Laplacian kernel and tests

* Added: Laplacian kernel, Bilateral kernel (CPU, GPU); Performance and accuracy tests for this kernels

* Changed tolerance for GPU test

* boner

* Some changes with alignment; Tests's parameters are the same as for OCV

* Cut tests

* Compressed tests

* Minor changes (rsrt bb)

* Returned types
This commit is contained in:
Maxim Pashchenkov
2020-04-23 20:47:55 +03:00
committed by GitHub
parent 189fc43765
commit 51a42c0647
12 changed files with 368 additions and 27 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/gapi/include/opencv2/gapi/imgproc.hpp
+76
View File
@@ -90,6 +90,20 @@ namespace imgproc {
}
};
G_TYPED_KERNEL(GLaplacian, <GMat(GMat,int, int, double, double, int)>,
"org.opencv.imgproc.filters.laplacian") {
static GMatDesc outMeta(GMatDesc in, int ddepth, int, double, double, int) {
return in.withDepth(ddepth);
}
};
G_TYPED_KERNEL(GBilateralFilter, <GMat(GMat,int, double, double, int)>,
"org.opencv.imgproc.filters.bilateralfilter") {
static GMatDesc outMeta(GMatDesc in, int, double, double, int) {
return in;
}
};
G_TYPED_KERNEL(GEqHist, <GMat(GMat)>, "org.opencv.imgproc.equalizeHist"){
static GMatDesc outMeta(GMatDesc in) {
return in.withType(CV_8U, 1);
@@ -643,6 +657,68 @@ GAPI_EXPORTS std::tuple<GMat, GMat> SobelXY(const GMat& src, int ddepth, int ord
int borderType = BORDER_DEFAULT,
const Scalar& borderValue = Scalar(0));
/** @brief Calculates the Laplacian of an image.
The function calculates the Laplacian of the source image by adding up the second x and y
derivatives calculated using the Sobel operator:
\f[\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\f]
This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image
with the following \f$3 \times 3\f$ aperture:
\f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]
@note Function textual ID is "org.opencv.imgproc.filters.laplacian"
@param src Source image.
@param ddepth Desired depth of the destination image.
@param ksize Aperture size used to compute the second-derivative filters. See #getDerivKernels for
details. The size must be positive and odd.
@param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
applied. See #getDerivKernels for details.
@param delta Optional delta value that is added to the results prior to storing them in dst .
@param borderType Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
@return Destination image of the same size and the same number of channels as src.
@sa Sobel, Scharr
*/
GAPI_EXPORTS GMat Laplacian(const GMat& src, int ddepth, int ksize = 1,
double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT);
/** @brief Applies the bilateral filter to an image.
The function applies bilateral filtering to the input image, as described in
http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html
bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is
very slow compared to most filters.
_Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\<
10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very
strong effect, making the image look "cartoonish".
_Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time
applications, and perhaps d=9 for offline applications that need heavy noise filtering.
This filter does not work inplace.
@note Function textual ID is "org.opencv.imgproc.filters.bilateralfilter"
@param src Source 8-bit or floating-point, 1-channel or 3-channel image.
@param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
it is computed from sigmaSpace.
@param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
in larger areas of semi-equal color.
@param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
farther pixels will influence each other as long as their colors are close enough (see sigmaColor
). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
proportional to sigmaSpace.
@param borderType border mode used to extrapolate pixels outside of the image, see #BorderTypes
@return Destination image of the same size and type as src.
*/
GAPI_EXPORTS GMat bilateralFilter(const GMat& src, int d, double sigmaColor, double sigmaSpace,
int borderType = BORDER_DEFAULT);
/** @brief Finds edges in an image using the Canny algorithm.
The function finds edges in the input image and marks them in the output map edges using the