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Fixed several documentation warnings and errors.

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This commit is contained in:
Andrey Kamaev
2012-04-12 17:41:55 +00:00
parent 9d764b4115
commit b6dac61e96
6 changed files with 11 additions and 14 deletions
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4
modules/gpu/doc/image_processing.rst
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@@ -499,7 +499,7 @@ gpu::buildWarpAffineMaps
------------------------
Builds transformation maps for affine transformation.
.. ocv:function:: void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
.. ocv:function:: void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null())
:param M: *2x3* transformation matrix.
@@ -543,7 +543,7 @@ gpu::buildWarpPerspectiveMaps
-----------------------------
Builds transformation maps for perspective transformation.
.. ocv:function:: void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null());
.. ocv:function:: void buildWarpAffineMaps(const Mat& M, bool inverse, Size dsize, GpuMat& xmap, GpuMat& ymap, Stream& stream = Stream::Null())
:param M: *3x3* transformation matrix.

2
modules/gpu/doc/introduction.rst
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@@ -43,7 +43,7 @@ Utilizing Multiple GPUs
-----------------------
In the current version, each of the OpenCV GPU algorithms can use only a single GPU. So, to utilize multiple GPUs, you have to manually distribute the work between GPUs.
Switching active devie can be done using :ocv:func:`gpu::setDevice()' function. For more details please read Cuda C Programing Guid.
Switching active devie can be done using :ocv:func:`gpu::setDevice()` function. For more details please read Cuda C Programing Guid.
While developing algorithms for multiple GPUs, note a data passing overhead. For primitive functions and small images, it can be significant, which may eliminate all the advantages of having multiple GPUs. But for high-level algorithms, consider using multi-GPU acceleration. For example, the Stereo Block Matching algorithm has been successfully parallelized using the following algorithm: