Update graphics/opencv to 3.0.0.
Major changes (besides bugfixes):
- opencv_contrib (http://github.com/itseez/opencv_contrib) repository
has been added.
- a subset of Intel IPP (IPPCV) is given to us and our users free
of charge, free of licensing fees, for commercial and non-commerical
use.
- T-API (transparent API) has been introduced, this is transparent GPU
acceleration layer using OpenCL. It does not add any compile-time or
runtime dependency of OpenCL. When OpenCL is available, it's detected
and used, but it can be disabled at compile time or at runtime.
- ~40 OpenCV functions have been accelerated using NEON intrinsics and
because these are mostly basic functions, some higher-level functions
got accelerated as well.
- There is also new OpenCV HAL layer that will simplifies creation
of NEON-optimized code and that should form a base for the open-source
and proprietary OpenCV accelerators.
- The documentation is now in Doxygen: http://docs.opencv.org/master/
- We cleaned up API of many high-level algorithms from features2d, calib3d,
objdetect etc. They now follow the uniform "abstract interface - hidden
implementation" pattern and make extensive use of smart pointers (Ptr<>).
- Greatly improved and extended Python & Java bindings (also, see below on
the Python bindings), newly introduced Matlab bindings
- Improved Android support - now OpenCV Manager is in Java and supports
both 2.4 and 3.0.
- Greatly improved WinRT support, including video capturing and
multi-threading capabilities. Thanks for Microsoft team for this!
- Big thanks to Google who funded several successive GSoC programs and
let OpenCV in. The results of many successful GSoC 2013 and 2014 projects
have been integrated in opencv 3.0 and opencv_contrib (earlier results
are also available in OpenCV 2.4.x). We can name:
- text detection
- many computational photography algorithms (HDR, inpainting, edge-aware
filters, superpixels,...)
- tracking and optical flow algorithms
- new features, including line descriptors, KAZE/AKAZE
- general use optimization (hill climbing, linear programming)
- greatly improved Python support, including Python 3.0 support, many new
tutorials & samples on how to use OpenCV with Python.
- 2d shape matching module and 3d surface matching module
- RGB-D module
- VTK-based 3D visualization module
For full changelog see:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
For 2.4 to 3.0 transition, see the transition guide:
http://docs.opencv.org/master/db/dfa/tutorial_transition_guide.html
2015-10-08 19:45:59 +02:00
|
|
|
$NetBSD: patch-modules_ts_src_ts.cpp,v 1.3 2015/10/08 17:45:59 fhajny Exp $
|
2013-06-12 12:13:58 +02:00
|
|
|
|
2013-06-19 13:54:04 +02:00
|
|
|
Avoid std::foo conflict.
|
2013-06-12 12:13:58 +02:00
|
|
|
|
Update graphics/opencv to 3.0.0.
Major changes (besides bugfixes):
- opencv_contrib (http://github.com/itseez/opencv_contrib) repository
has been added.
- a subset of Intel IPP (IPPCV) is given to us and our users free
of charge, free of licensing fees, for commercial and non-commerical
use.
- T-API (transparent API) has been introduced, this is transparent GPU
acceleration layer using OpenCL. It does not add any compile-time or
runtime dependency of OpenCL. When OpenCL is available, it's detected
and used, but it can be disabled at compile time or at runtime.
- ~40 OpenCV functions have been accelerated using NEON intrinsics and
because these are mostly basic functions, some higher-level functions
got accelerated as well.
- There is also new OpenCV HAL layer that will simplifies creation
of NEON-optimized code and that should form a base for the open-source
and proprietary OpenCV accelerators.
- The documentation is now in Doxygen: http://docs.opencv.org/master/
- We cleaned up API of many high-level algorithms from features2d, calib3d,
objdetect etc. They now follow the uniform "abstract interface - hidden
implementation" pattern and make extensive use of smart pointers (Ptr<>).
- Greatly improved and extended Python & Java bindings (also, see below on
the Python bindings), newly introduced Matlab bindings
- Improved Android support - now OpenCV Manager is in Java and supports
both 2.4 and 3.0.
- Greatly improved WinRT support, including video capturing and
multi-threading capabilities. Thanks for Microsoft team for this!
- Big thanks to Google who funded several successive GSoC programs and
let OpenCV in. The results of many successful GSoC 2013 and 2014 projects
have been integrated in opencv 3.0 and opencv_contrib (earlier results
are also available in OpenCV 2.4.x). We can name:
- text detection
- many computational photography algorithms (HDR, inpainting, edge-aware
filters, superpixels,...)
- tracking and optical flow algorithms
- new features, including line descriptors, KAZE/AKAZE
- general use optimization (hill climbing, linear programming)
- greatly improved Python support, including Python 3.0 support, many new
tutorials & samples on how to use OpenCV with Python.
- 2d shape matching module and 3d surface matching module
- RGB-D module
- VTK-based 3D visualization module
For full changelog see:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
For 2.4 to 3.0 transition, see the transition guide:
http://docs.opencv.org/master/db/dfa/tutorial_transition_guide.html
2015-10-08 19:45:59 +02:00
|
|
|
--- modules/ts/src/ts.cpp.orig 2015-06-03 17:21:34.000000000 +0000
|
2013-06-12 12:13:58 +02:00
|
|
|
+++ modules/ts/src/ts.cpp
|
Update graphics/opencv to 3.0.0.
Major changes (besides bugfixes):
- opencv_contrib (http://github.com/itseez/opencv_contrib) repository
has been added.
- a subset of Intel IPP (IPPCV) is given to us and our users free
of charge, free of licensing fees, for commercial and non-commerical
use.
- T-API (transparent API) has been introduced, this is transparent GPU
acceleration layer using OpenCL. It does not add any compile-time or
runtime dependency of OpenCL. When OpenCL is available, it's detected
and used, but it can be disabled at compile time or at runtime.
- ~40 OpenCV functions have been accelerated using NEON intrinsics and
because these are mostly basic functions, some higher-level functions
got accelerated as well.
- There is also new OpenCV HAL layer that will simplifies creation
of NEON-optimized code and that should form a base for the open-source
and proprietary OpenCV accelerators.
- The documentation is now in Doxygen: http://docs.opencv.org/master/
- We cleaned up API of many high-level algorithms from features2d, calib3d,
objdetect etc. They now follow the uniform "abstract interface - hidden
implementation" pattern and make extensive use of smart pointers (Ptr<>).
- Greatly improved and extended Python & Java bindings (also, see below on
the Python bindings), newly introduced Matlab bindings
- Improved Android support - now OpenCV Manager is in Java and supports
both 2.4 and 3.0.
- Greatly improved WinRT support, including video capturing and
multi-threading capabilities. Thanks for Microsoft team for this!
- Big thanks to Google who funded several successive GSoC programs and
let OpenCV in. The results of many successful GSoC 2013 and 2014 projects
have been integrated in opencv 3.0 and opencv_contrib (earlier results
are also available in OpenCV 2.4.x). We can name:
- text detection
- many computational photography algorithms (HDR, inpainting, edge-aware
filters, superpixels,...)
- tracking and optical flow algorithms
- new features, including line descriptors, KAZE/AKAZE
- general use optimization (hill climbing, linear programming)
- greatly improved Python support, including Python 3.0 support, many new
tutorials & samples on how to use OpenCV with Python.
- 2d shape matching module and 3d surface matching module
- RGB-D module
- VTK-based 3D visualization module
For full changelog see:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
For 2.4 to 3.0 transition, see the transition guide:
http://docs.opencv.org/master/db/dfa/tutorial_transition_guide.html
2015-10-08 19:45:59 +02:00
|
|
|
@@ -235,7 +235,7 @@ void BaseTest::safe_run( int start_from
|
|
|
|
|
}
|
|
|
|
|
catch (const TS::FailureCode& fc)
|
|
|
|
|
{
|
|
|
|
|
- std::string errorStr = TS::str_from_code(fc);
|
|
|
|
|
+ ::std::string errorStr = TS::str_from_code(fc);
|
|
|
|
|
ts->printf(TS::LOG, "General failure:\n\t%s (%d)\n", errorStr.c_str(), fc);
|
|
|
|
|
|
|
|
|
|
ts->set_failed_test_info( fc );
|
|
|
|
|
@@ -558,7 +558,7 @@ void TS::vprintf( int streams, const cha
|
2013-06-12 12:13:58 +02:00
|
|
|
for( int i = 0; i < MAX_IDX; i++ )
|
|
|
|
|
if( (streams & (1 << i)) )
|
|
|
|
|
{
|
|
|
|
|
- output_buf[i] += std::string(str);
|
|
|
|
|
+ output_buf[i] += ::std::string(str);
|
|
|
|
|
// in the new GTest-based framework we do not use
|
|
|
|
|
// any output files (except for the automatically generated xml report).
|
|
|
|
|
// if a test fails, all the buffers are printed, so we do not want to duplicate the information and
|
Update graphics/opencv to 3.0.0.
Major changes (besides bugfixes):
- opencv_contrib (http://github.com/itseez/opencv_contrib) repository
has been added.
- a subset of Intel IPP (IPPCV) is given to us and our users free
of charge, free of licensing fees, for commercial and non-commerical
use.
- T-API (transparent API) has been introduced, this is transparent GPU
acceleration layer using OpenCL. It does not add any compile-time or
runtime dependency of OpenCL. When OpenCL is available, it's detected
and used, but it can be disabled at compile time or at runtime.
- ~40 OpenCV functions have been accelerated using NEON intrinsics and
because these are mostly basic functions, some higher-level functions
got accelerated as well.
- There is also new OpenCV HAL layer that will simplifies creation
of NEON-optimized code and that should form a base for the open-source
and proprietary OpenCV accelerators.
- The documentation is now in Doxygen: http://docs.opencv.org/master/
- We cleaned up API of many high-level algorithms from features2d, calib3d,
objdetect etc. They now follow the uniform "abstract interface - hidden
implementation" pattern and make extensive use of smart pointers (Ptr<>).
- Greatly improved and extended Python & Java bindings (also, see below on
the Python bindings), newly introduced Matlab bindings
- Improved Android support - now OpenCV Manager is in Java and supports
both 2.4 and 3.0.
- Greatly improved WinRT support, including video capturing and
multi-threading capabilities. Thanks for Microsoft team for this!
- Big thanks to Google who funded several successive GSoC programs and
let OpenCV in. The results of many successful GSoC 2013 and 2014 projects
have been integrated in opencv 3.0 and opencv_contrib (earlier results
are also available in OpenCV 2.4.x). We can name:
- text detection
- many computational photography algorithms (HDR, inpainting, edge-aware
filters, superpixels,...)
- tracking and optical flow algorithms
- new features, including line descriptors, KAZE/AKAZE
- general use optimization (hill climbing, linear programming)
- greatly improved Python support, including Python 3.0 support, many new
tutorials & samples on how to use OpenCV with Python.
- 2d shape matching module and 3d surface matching module
- RGB-D module
- VTK-based 3D visualization module
For full changelog see:
http://code.opencv.org/projects/opencv/wiki/ChangeLog
For 2.4 to 3.0 transition, see the transition guide:
http://docs.opencv.org/master/db/dfa/tutorial_transition_guide.html
2015-10-08 19:45:59 +02:00
|
|
|
@@ -613,7 +613,7 @@ void smoothBorder(Mat& img, const Scalar
|
2013-06-19 13:54:04 +02:00
|
|
|
Scalar s;
|
|
|
|
|
uchar *p = NULL;
|
|
|
|
|
int n = 100/delta;
|
|
|
|
|
- int nR = std::min(n, (img.rows+1)/2), nC = std::min(n, (img.cols+1)/2);
|
|
|
|
|
+ int nR = ::std::min(n, (img.rows+1)/2), nC = ::std::min(n, (img.cols+1)/2);
|
|
|
|
|
|
|
|
|
|
int r, c, i;
|
|
|
|
|
for(r=0; r<nR; r++)
|
