Do not restrict allowed maximum resolution depending on the
initial resolution. If new resolution is larger than previous one,
just re-init encode session.
Register openGL resource only once per memory. Also if upstream
provides the registered information, reuse the information
instead of doing it again. This can improve performance dramatically
depending on system since the resource registration might cause
high overhead.
YV12 format is supported by Nvidia NVENC without manual conversion.
So nvenc is exposing YV12 format at sinkpad template but there is some
missing point around uploading the memory to GPU.
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 add support CUDA context sharing similar to glcontext sharing.
Multiple CUDA context per GPU is not the best practice. The context
sharing method is very similar to that of glcontext. The difference
is that there can be multiple context object on a pipeline since
the CUDA context is created per GPU id. For example, a pipeline
has nvh264dec (uses GPU #0) and nvh264device0dec (uses GPU #1),
then two CUDA context will propagated to all pipeline.
During GstVideoInfo conversion from GstCaps, interlace-mode is
inferred to progressive so unspecified interlace-mode should not cause any
negotiation issue. Simly set GST_PAD_FLAG_ACCEPT_INTERSECT flag
on sinkpad to fix issue.
Encoding thread is terminated without any notification so
upstream streaming thread is locked because there is nothing
to pop from GAsyncQueue. If downstream returns error,
we need put SHUTDOWN_COOKIE to GAsyncQueue for chain function
can wakeup.
* By this commit, if there are more than one device,
nvenc element factory will be created per
device like nvh264device{device-id}enc and nvh265device{device-id}enc
in addition to nvh264enc and nvh265enc, so that the element factory
can expose the exact capability of the device for the codec.
* Each element factory will have fixed cuda-device-id
which is determined during plugin initialization
depending on the capability of corresponding device.
(e.g., when only the second device can encode h265 among two GPU,
then nvh265enc will choose "1" (zero-based numbering)
as it's target cuda-device-id. As we have element factory
per GPU device, "cuda-device-id" property is changed to read-only.
* nvh265enc gains ability to encoding
4:4:4 8bits, 4:2:0 10 bits formats and up to 8K resolution
depending on device capability.
Additionally, I420 GLMemory input is supported by nvenc.
... 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.
