For openGL interoperability, nvdec uses cuGraphicsGLRegisterImage API
which is to register openGL texture image.
Meanwhile nvenc uses cuGraphicsGLRegisterBuffer API to registure openGL buffer object.
That means two kinds of graphics resources are registered per memory
when nvdec/nvenc are configured at the same time.
The graphics resource registration brings possibly high overhead
so the registration should be performed only once per resource
from optimization point of view.
gst_query_get_n_allocation_pools > 0 does not guarantee that
the N th internal array has GstBufferPool object. So users should
check the returned GstBufferPool object from
gst_query_parse_nth_allocation_pool.
Async CUDA operation with default stream (NULL CUstream) is not much
beneficial than blocking operation since all CUDA operations which belong
to the CUDA context will be synchronized with the default stream's operation.
Note that CUDA stream will share all resources of the corresponding CUDA context
but which can help parallel operation similar to the relation between thread and process
The internal decoding state must be GST_NVDEC_STATE_PARSE before
calling CuvidParseVideoData(). Otherwise, nvdec will be confused
on decode callback as if the frame is decoding only frame and
the input timestamp of corresponding frame will be ignored.
Eventually one decoded frame will have non-increased PTS.
The destroy callback can be called just before the fìnalization of
GstMiniObject. So the nvdec object might be destroyed already.
Instead, store the GstCudaContext with increased ref to safely
unregister the CUDA resource.
The gst_cuda_result macro function is more helpful for debugging
than previous cuda_OK because gst_cuda_result prints the function
and line number. If the CUDA API return was not CUDA_SUCCESS,
gst_cuda_result will print WARNING level debug message with
error name, error text strings.
... and drop CUvideoctxlock usage. The CUvideoctxlock basically
has the identical role of cuda context push/pop but nvdec specific
way. Since we can share the CUDA context among encoders and decoders,
use CUDA context directly for accessing GPU API.
Encoded bitstream might not have valid framerate. If upstream
provided non-variable-framerate (i.e., fps_n > 0 and fps_d > 0)
use upstream framerate instead of parsed one.
By adding system memory support for nvdec, both en/decoder
in the nvcodec plugin are able to be usable regardless of
OpenGL dependency. Besides, the direct use of system memory
might have less overhead than OpenGL memory depending on use cases.
(e.g., transcoding using S/W encoder)
Any plugin which returned FALSE from plugin_init will be blacklisted
so the plugin will be unusable even if an user install required runtime
dependency next time. So that's the reason why nvcodec returns TRUE always.
This commit is to remove possible misreading code.
This commit includes h265 main-10 profile support if the device can
decode it.
Note that since h264 10bits decoding is not supported by nvidia GPU for now,
the additional code path for h264 high-10 profile is a preparation for
the future Nvidia's enhancement.
GstVideoDecoder::drain/flush can be called at very initial state
with stream-start and flush-stop event, respectively.
Draning with NULL CUvideoparser seems to unsafe and that eventually
failed to handle it.
Only the default device has been used by NVDEC so far.
This commit make it possible to use registered device id.
To simplify device id selection, GstNvDecCudaContext usage is removed.
By this commit, each codec has its own element factory so the
nvdec element factory is removed. Also, if there are more than one device,
additional nvdec element factory will be created per
device like nvh264device{device-id}dec, so that the element factory
can expose the exact capability of the device for the codec.
Callbacks of CUvideoparser is called on the streaming thread.
So the use of async queue has no benefit.
Make control flow straightforward instead of long while/switch loop.
... and put them into new nvcodec plugin.
* nvcodec plugin
Now each nvenc and nvdec element is moved to be a part of nvcodec plugin
for better interoperability.
Additionally, cuda runtime API header dependencies
(i.e., cuda_runtime_api.h and cuda_gl_interop.h) are removed.
Note that cuda runtime APIs have prefix "cuda". Since 1.16 release with
Windows support, only "cuda.h" and "cudaGL.h" dependent symbols have
been used except for some defined types. However, those types could be
replaced with other types which were defined by "cuda.h".
* dynamic library loading
CUDA library will be opened with g_module_open() instead of build-time linking.
On Windows, nvcuda.dll is installed to system path by CUDA Toolkit
installer, and on *nix, user should ensure that libcuda.so.1 can be
loadable (i.e., via LD_LIBRARY_PATH or default dlopen path)
Therefore, NVIDIA_VIDEO_CODEC_SDK_PATH env build time dependency for Windows
is removed.
