This is consistent pattern with other files. Also is needed for next
commit to always include x86util.asm
Signed-off-by: Kacper Michajłow <kasper93@gmail.com>
This filter uses AVFrame.opaque to build a linked list of AVFrames. This
is very wrong, as AVFrame.opaque is intended to store caller's private
data and may not be touched by filters. What's worse, the filter leaks
the opaque values to the outside.
Use an AVFifo instead of a linked list to implement the same logic.
GET_UTF8 advances the pointer past the newline byte before the
newline check, so shape_text_hb receives text that includes the
newline character. Since HarfBuzz does not treat U+000A as
default-ignorable, it gets shaped into a .notdef glyph.
Fixes#21565
Reported-by: scriptituk <info@scriptit.uk>
Signed-off-by: Zhao Zhili <zhilizhao@tencent.com>
Otherwise, this will indefinitely persist the last couple of mapped frames
(including any extra decoded frames) in memory, even though they will never be
used again, causing a gradual memory leak until filter uninit.
Signed-off-by: Niklas Haas <git@haasn.dev>
Sponsored-by: nxtedition AB
The loop condition in the DEFINE_REMAP macro:
stereo < 1 + s->out_stereo > STEREO_2D
is parsed by C as:
(stereo < (1 + s->out_stereo)) > STEREO_2D
Since STEREO_2D is 0 and relational operators return 0 or 1, the
outer comparison against 0 is a no-op for STEREO_2D and STEREO_SBS.
But for STEREO_TB (value 2) the loop runs 3 iterations instead of 2,
producing an out-of-bounds stereo pass.
Add parentheses so the comparison is evaluated first:
stereo < 1 + (s->out_stereo > STEREO_2D)
This gives 1 iteration for 2D and 2 for any stereo format (SBS or TB),
matching the actual number of stereo views.
Signed-off-by: marcos ashton <marcosashiglesias@gmail.com>
Add support for CUDA and ROCm (AMD GPU) devices in the LibTorch DNN
backend.
This works for both NVIDIA CUDA and AMD ROCm, as PyTorch exposes ROCm
through the CUDA-compatible API.
Usage:
./ffmpeg -i input.mp4 -vf scale=224:224,format=rgb24,dnn_processing=dnn_backend=torch:model=sr_model_torch.pt:device=cuda output.mp4
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
Signed-off-by: younengxiao <steven.xiao@amd.com>
sscanf() returns EOF (-1) on input failure, which is non-zero and
passes a bare truthy check. When this happens, the %n directive is
never processed, so len stays uninitialized. Using that value to
advance the arg pointer causes an out-of-bounds read and crash.
Check for >= 1 instead, matching the fix applied to the other
sscanf() call in init() by commit b5b6391d64.
Fixes: https://code.ffmpeg.org/FFmpeg/FFmpeg/issues/22451
Signed-off-by: marcos ashton <marcosashiglesias@gmail.com>
ctx->options.async does not exist on DnnContext; the correct
field is ctx->async directly on the context struct.
Signed-off-by: younengxiao <steven.xiao@amd.com>
Why: the change is done to comply with lilv expectations of hosts.
Added call lilv_instance_activate in the config_output function to abide by lilv documentation that states it must be called before lilv_instance_run:
"This MUST be called before calling lilv_instance_run()" - documentation source (https://github.com/lv2/lilv/blob/main/include/lilv/lilv.h)
Added call lilv_instance_deactivate in the uninit function to abide by lv2 documentation:
"If a host calls activate(), it MUST call deactivate() at some point in the future" - documentation source (https://gitlab.com/lv2/lv2/-/blob/main/include/lv2/core/lv2.h)
Added instance_activated integer to LV2Context struct to track if instance was activated and only do lilv_instance_deactivate if was activated to abide by lv2 documentation:
"Hosts MUST NOT call deactivate() unless activate() was previously called." - documentation source (https://gitlab.com/lv2/lv2/-/blob/main/include/lv2/core/lv2.h)
Regarding the patcheck warning (possibly constant :instance_activated):
This is a false positive since the struct member is zero-initialized.
Fixes: trac issue #11661 (https://trac.ffmpeg.org/ticket/11661)
Reported-by: Dave Flater
Signed-off-by: Karl Mogensen <karlmogensen0@gmail.com>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
If s->stop is set, the return value would be overwritten
before being checked. This bug was introduced in the switch
to AV_TX in 014ace8f98.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
Added in e995cf1bcc,
yet this filter does not have any dsp function using MMX:
it only has generic x86 assembly, no SIMD at all,
so this emms_c() was always unnecessary.
Reviewed-by: Kacper Michajłow <kasper93@gmail.com>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
Fixes: signed integer overflow: 536870944 * 16 cannot be represented in type 'int'
Fixes: #21587
Found-by: HAORAN FANG
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
There are two options which use non-zero default value: async and
batch_size of openvino. init_model_ov checks and set batch_size to
one when batch_size is equal to zero, so the only option affected
by missing default value is async. Now async works as expected.
This commit update deinterlace_d3d12 filter options name.
Currently it follows the options name with "deinterlace_vaapi",
In this commit, it will follow filters such as "yadif" and "w3fdif".
Sample command lines:
1. Software decode with hwupload:
ffmpeg -init_hw_device d3d12va=d3d12 -i interlaced.ts \
-vf "format=nv12,hwupload,deinterlace_d3d12=method=default,hwdownload,format=nv12" \
-c:v libx264 output.mp4
2. Full hardware pipeline:
ffmpeg -hwaccel d3d12va -hwaccel_output_format d3d12 -i interlaced.ts \
-vf "deinterlace_d3d12=method=custom:mode=field" \
-c:v h264_d3d12va output.mp4
Signed-off-by: younengxiao <steven.xiao@amd.com>
This patch implements the DNNAsyncExecModule for the LibTorch backend,
enabling non-blocking inference using the common infrastructure instead
of custom threading (th_async_module_submit) to align with the
TensorFlow and OpenVINO backends.
The implementation uses ff_dnn_start_inference_async which provides
unified async logic across all DNN backends, eliminating the need for
backend-specific threading code.
Verified with:
ffmpeg -f lavfi -i testsrc=duration=5:size=320x240:rate=30 -vf dnn_processing=dnn_backend=torch:model=model.pt -y output.mp4
Signed-off-by: Raja Rathour <imraja729@gmail.com>
Also deduplicate printing json and summary output.
Reviewed-by: Kyle Swanson <k@ylo.ph>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
Descriptor buffers were a neat attempt at organizing descriptors.
Simple, robust, reliable.
Unfortunately, driver support never caught on, and neither did validation
layer support.
Now they're being replaced by descriptor heaps, which promises to be
the future. We'll see how it goes.
