cap_libv4l depends on an external library (libv4l) yet is still larger
(1966 loc vs 1822 loc).
It was initially introduced copy pasting cap_v4l in order to offload
various color conversions to libv4l.
However nowadays we handle most of the needed color conversions inside
OpenCV. Our own implementation is better tested and (probably) also
better performing. (as it can optionally leverage IPP/ OpenCL)
Currently cap_v4l is better maintained and generally the code is in
better shape. There is however an API
difference in getting unconverted frames:
* on cap_libv4l one need to set `CV_CAP_MODE_GRAY=1` or
`CV_CAP_MODE_YUYV=1`
* on cap_v4l one needs to set `CV_CAP_PROP_CONVERT_RGB=0`
the latter is more flexible though as it also allows accessing undecoded
JPEG images.
fixes#4563
* dnn: Add a Vulkan based backend
This commit adds a new backend "DNN_BACKEND_VKCOM" and a
new target "DNN_TARGET_VULKAN". VKCOM means vulkan based
computation library.
This backend uses Vulkan API and SPIR-V shaders to do
the inference computation for layers. The layer types
that implemented in DNN_BACKEND_VKCOM include:
Conv, Concat, ReLU, LRN, PriorBox, Softmax, MaxPooling,
AvePooling, Permute
This is just a beginning work for Vulkan in OpenCV DNN,
more layer types will be supported and performance
tuning is on the way.
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
* dnn/vulkan: Add FindVulkan.cmake to detect Vulkan SDK
In order to build dnn with Vulkan support, need installing
Vulkan SDK and setting environment variable "VULKAN_SDK" and
add "-DWITH_VULKAN=ON" to cmake command.
You can download Vulkan SDK from:
https://vulkan.lunarg.com/sdk/home#linux
For how to install, see
https://vulkan.lunarg.com/doc/sdk/latest/linux/getting_started.htmlhttps://vulkan.lunarg.com/doc/sdk/latest/windows/getting_started.htmlhttps://vulkan.lunarg.com/doc/sdk/latest/mac/getting_started.html
respectively for linux, windows and mac.
To run the vulkan backend, also need installing mesa driver.
On Ubuntu, use this command 'sudo apt-get install mesa-vulkan-drivers'
To test, use command '$BUILD_DIR/bin/opencv_test_dnn --gtest_filter=*VkCom*'
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
* dnn/Vulkan: dynamically load Vulkan runtime
No compile-time dependency on Vulkan library.
If Vulkan runtime is unavailable, fallback to CPU path.
Use environment "OPENCL_VULKAN_RUNTIME" to specify path to your
own vulkan runtime library.
Signed-off-by: Wu Zhiwen <zhiwen.wu@intel.com>
* dnn/Vulkan: Add a python script to compile GLSL shaders to SPIR-V shaders
The SPIR-V shaders are in format of text-based 32-bit hexadecimal
numbers, and inserted into .cpp files as unsigned int32 array.
* dnn/Vulkan: Put Vulkan headers into 3rdparty directory and some other fixes
Vulkan header files are copied from
https://github.com/KhronosGroup/Vulkan-Docs/tree/master/include/vulkan
to 3rdparty/include
Fix the Copyright declaration issue.
Refine OpenCVDetectVulkan.cmake
* dnn/Vulkan: Add vulkan backend tests into existing ones.
Also fixed some test failures.
- Don't use bool variable as uniform for shader
- Fix dispathed group number beyond max issue
- Bypass "group > 1" convolution. This should be support in future.
* dnn/Vulkan: Fix multiple initialization in one thread.
* cmake: don't ignore Python from PATH environment variable
- this breaks selection between 32/64-bit Python
- this breaks Anaconda/Conda environments
- it is not the CMake default behavior, expected by many projects
* cmake: add Python version check, fallback path on CMake 3.12+
* cmake: drop Python 2.6, allow version selection for Python 3.x
Avoids passing -Wall, which will enable literally every warning in
Clang. Avoids passing -ffunction-sections etc to clang-cl, which each
generate warnings.
When cross compiling with clang, the internal C++ headers are not found
when adding sysroot to -isystem, that is redundant anyway because it
will look for headers insider --sysroot path with same quality as it
would do with -isystem otherwise
Fixes errors like
FAILED: 3rdparty/openexr/CMakeFiles/IlmImf.dir/Iex/IexBaseExc.cpp.o
....
In file included from
TOPDIR/build/tmp/work/cortexa7t2hf-neon-vfpv4-bec-linux-musleabi/opencv/3.4.3+gitAUTOINC+b38c50b3d0_1f6d6f0626_bdb7bb85f3_34e4206aef_fccf7cd6a4-r0/git/3rdparty/openexr/Iex/IexBaseExc.cpp:43:
In file included from
TOPDIR/build/tmp/work/cortexa7t2hf-neon-vfpv4-bec-linux-musleabi/opencv/3.4.3+gitAUTOINC+b38c50b3d0_1f6d6f0626_bdb7bb85f3_34e4206aef_fccf7cd6a4-r0/git/3rdparty/openexr/Iex/IexBaseExc.h:48:
In file included from
TOPDIR/build/tmp/work/cortexa7t2hf-neon-vfpv4-bec-linux-musleabi/opencv/3.4.3+gitAUTOINC+b38c50b3d0_1f6d6f0626_bdb7bb85f3_34e4206aef_fccf7cd6a4-r0/recipe-sysroot/usr/lib//arm-bec-linux-musleabi/8.2.0/../../../include/c++/8.2.0/string:52:
In file included from
TOPDIR/build/tmp/work/cortexa7t2hf-neon-vfpv4-bec-linux-musleabi/opencv/3.4.3+gitAUTOINC+b38c50b3d0_1f6d6f0626_bdb7bb85f3_34e4206aef_fccf7cd6a4-r0/recipe-sysroot/usr/lib//arm-bec-linux-musleabi/8.2.0/../../../include/c++/8.2.0/bits/basic_string.h:6391:
In file included from
TOPDIR/build/tmp/work/cortexa7t2hf-neon-vfpv4-bec-linux-musleabi/opencv/3.4.3+gitAUTOINC+b38c50b3d0_1f6d6f0626_bdb7bb85f3_34e4206aef_fccf7cd6a4-r0/recipe-sysroot/usr/lib//arm-bec-linux-musleabi/8.2.0/../../../include/c++/8.2.0/ext/string_conversions.h:41:
TOPDIR/build/tmp/work/cortexa7t2hf-neon-vfpv4-bec-linux-musleabi/opencv/3.4.3+gitAUTOINC+b38c50b3d0_1f6d6f0626_bdb7bb85f3_34e4206aef_fccf7cd6a4-r0/recipe-sysroot/usr/lib//arm-bec-linux-musleabi/8.2.0/../../../include/c++/8.2.0/cstdlib:75:15:
fatal error: 'stdlib.h' file not found
^~~~~~~~~~
1 error generated.
Signed-off-by: Khem Raj <raj.khem@gmail.com>
* trying to fix the custom AVX2 builder test failures (false alarms)
* fixed compile error with CPU_BASELINE=AVX2 on x86; raised tolerance thresholds in a couple of tests
* fixed compile error with CPU_BASELINE=AVX2 on x86; raised tolerance thresholds in a couple of tests
* fixed compile error with CPU_BASELINE=AVX2 on x86; raised tolerance thresholds in a couple of tests
* seemingly disabled false alarm warning in surf.cpp; increased tolerance thresholds in the tests for SolvePnP and in DNN/ENet
* Add HPX backend for OpenCV implementation
Adds hpx backend for cv::parallel_for_() calls respecting the nstripes chunking parameter. C++ code for the backend is added to modules/core/parallel.cpp. Also, the necessary changes to cmake files are introduced.
Backend can operate in 2 versions (selectable by cmake build option WITH_HPX_STARTSTOP): hpx (runtime always on) and hpx_startstop (start and stop the backend for each cv::parallel_for_() call)
* WIP: Conditionally include hpx_main.hpp to tests in core module
Header hpx_main.hpp is included to both core/perf/perf_main.cpp and core/test/test_main.cpp.
The changes to cmake files for linking hpx library to above mentioned test executalbles are proposed but have issues.
* Add coditional iclusion of hpx_main.hpp to cpp cpu modules
* Remove start/stop version of hpx backend
* created new decoder and encoder for PFM
pfm file format stores binary RGB or grayscale float images.
* added test for pfm codec
* replaced large with short licence header
* little/big-endian-check is now compile time
* fixed width/height confusion, improved big/little endian recognition, fixed scaling issue and Improved signature check
* adapted tests to handle float images well
* removed data-dependency: lena.pfm
the lena image is now loaded is pam and converted to pfm.
* fixed bug in endianess detection macro
* Added endianess detection for android and win
* removed fancy endianess detection
endianess detection will be implemented in cmake scripts soonish.
* fixed minor warnings
* fixed stupid elif defined bug
* silenced some implicit cast warnings
* replaced std::to_string with std::stringstream solution
std::to_string variant did not build on android.
* replaced new endianess macros with existing ones
* improved readability
this enables the usage of current sensors, while dropping support for
legacy devices, see:
https://github.com/IntelRealSense/librealsense#overview
Given limited resources, and that the legacy sensors where not that
great, I think we should focus on v2.
