Fixes: out of array access
Fixes: 7aj_swaprect_odd17_nv12.nut / 7aj_generate_swaprect_odd17_nv12.py
Fixes: VRAXYvKtmKa8
Found-by: Adrian Junge (vurlo) <adjun37@gmail.com>
(cherry picked from commit a7e38b617b)
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Fixes: out of array access
Fixes: 8aj_floodfill_dynamic_size.pgm / 8aj_generate_floodfill_dynamic_size_pgm.py
Fixes: 3MleMXjGZvu3
Found-by: Adrian Junge (vurlo) <adjun37@gmail.com>
(cherry picked from commit 24c322fdb2)
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Two runtime paths could compute out-of-range source coordinates for
degenerate projection geometry, causing heap-buffer-overflow reads
Fixes: out of array read
Fixes: assertion failure
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
(cherry picked from commit a73d648f6e)
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Fixes: out of array read
Fixes: assertion failure
Fixes: mQzloVqnivHQ
Found-by: Anthony Hurtado
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
(cherry picked from commit b3712addc9)
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Fixes: out of array access
Fixes: JbvzNObhorBp
Fixes: 030e140145 (lavfi: add quirc filter)
Found-by: Adrian Junge (vurlo)
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
(cherry picked from commit 4da9812e25)
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
The hand written boundary mirroring reflected an out of range index only
once, which is insufficient when the image dimension is smaller than the
filter half width (filt_w/2 == 8). A 1x1 input made the index reach 8
and -7, reading out of the src[]/temp[] arrays. Use avpriv_mirror(),
which mirrors repeatedly and stays in range for any dimension.
Fixes: out of array access
Fixes: repro.sh
Fixes: HuQn51lLiVJX
Fixes: 38aea9b041 (avfilter: add vif filter)
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
(cherry picked from commit 56309e476a)
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Fixes: error: no type named 'system_error' in namespace 'std'
Signed-off-by: Kacper Michajłow <kasper93@gmail.com>
(cherry picked from commit c6498178bb)
Since the input and output format can differ (e.g. 444 -> 420), we need to
reference the correct subsampling for the partially applied filter.
Keep track of this in the CUDATex itself.
Signed-off-by: Niklas Haas <git@haasn.dev>
(cherry picked from commit 01972b4f85)
Signed-off-by: Marvin Scholz <epirat07@gmail.com>
Instead of going via an AVFrame at all. This will allow us to fix the
intermediate chroma plane size for split downscaling.
Signed-off-by: Niklas Haas <git@haasn.dev>
(cherry picked from commit 420a9e90b8)
Signed-off-by: Marvin Scholz <epirat07@gmail.com>
Instead of re-creating this object every frame.
Signed-off-by: Niklas Haas <git@haasn.dev>
(cherry picked from commit e79e9f06ba)
Signed-off-by: Marvin Scholz <epirat07@gmail.com>
At this point, s->hwctx and CudaFunctions * are available.
Signed-off-by: Niklas Haas <git@haasn.dev>
(cherry picked from commit 4289a29bb0)
Signed-off-by: Marvin Scholz <epirat07@gmail.com>
I want to disentangle the internal logic from AVFrame, because some
intermediate states (e.g. for partially subsampled chroma with simultaneous
scaling) may not directly map to a valid AVPixelFormat.
Signed-off-by: Niklas Haas <git@haasn.dev>
(cherry picked from commit fef976b197)
Signed-off-by: Marvin Scholz <epirat07@gmail.com>
Make the next commit a bit easier to review.
Signed-off-by: Niklas Haas <git@haasn.dev>
(cherry picked from commit 61750318db)
Signed-off-by: Marvin Scholz <epirat07@gmail.com>
This is already done by cudascale_filter_frame().
Signed-off-by: Niklas Haas <git@haasn.dev>
(cherry picked from commit 0c3f04a97c)
Signed-off-by: Marvin Scholz <epirat07@gmail.com>
NVDEC and CUVID now output AV_PIX_FMT_P012 (12-bit 4:2:0), AV_PIX_FMT_P212
(12-bit 4:2:2) and AV_PIX_FMT_YUV444P10MSB / AV_PIX_FMT_YUV444P12MSB
(10/12-bit 4:4:4) for high-bit-depth content, but these CUDA filters
rejected the formats in their supported-format lists, breaking pipelines
such as "-hwaccel cuda ... -vf scale_cuda" on 12-bit input.
These formats use 16-bit sample storage, and the filters select their CUDA
kernel by byte-storage size and plane layout, not by the number of valid
bits, so they can reuse the existing 16-bit kernels:
- scale_cuda: P012/P212 -> "semiplanar16", YUV444P10MSB/YUV444P12MSB ->
"planar16".
- transpose_cuda: the ushort/ushort2 kernels are chosen from the pixel
descriptor (byte size + channel count); just allow the new formats.
- thumbnail_cuda: P012 reuses the P010/P016 path and the MSB 4:4:4 formats
reuse the YUV444P16 path; P012 is added to the 4:2:0 chroma-histogram
scaling as well. (thumbnail has no 4:2:2 path, so P212 is not added.)
The CUDA deinterlacers (bwdif_cuda, yadif_cuda) already accept any format
with <= 2 bytes per sample and <= 2 channels, so they need no change. The
8-bit-only filters (overlay_cuda, pad_cuda, bilateral_cuda, chromakey_cuda,
colorspace_cuda) do not support high bit depths and are left untouched.
Signed-off-by: Diego de Souza <ddesouza@nvidia.com>
This may be faster or slower than the existing specialized kernels,
so I opted not to prefer it by default. I also deliberately didn't expose
additional filter function capabilites yet.
The main motivating reason here is to get correct anti-aliasing behavior
when downscaling, which is currently completely broken.
Signed-off-by: Niklas Haas <git@haasn.dev>
Useful for GPU-based filters, which may also need to compute filter weights.
Since we cannot cross-link to internal functions, we need to recompile this
helper inside libavfilter.c.
Signed-off-by: Niklas Haas <git@haasn.dev>
This fallback function is used if external MMX is available,
while inline MMX and intrinsics for emitting emms are unavailable.
It is implemented as an avpriv function, which has several
drawbacks for shared builds:
1. The function is so small (3 bytes; 16 with padding)
that the overhead of exporting and importing it dwarfs
the gains from code deduplication.
2. A call to an external library has more overhead than
a library-internal one.
3. It may cause linking failures when a libavutil not exporting
avpriv_emms_asm() is paired with a library needing it
(if inline assembly and intrinsics were unavailable when building
the dependent library). I am not aware of this ever happening.
4. We would be forced to keep avpriv_emms_asm() around for ABI stability
even after it is no longer needed.
This commit therefore uses the STLIBOBJS, SHLIBOBJS approach
to duplicating it into each library on its own if needed.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
libnpp and the corresponding filters have been deprecated
in commit 994a368451
on 2025-09-26. By the time of our next release,
a year will have passed, so they are removed immediately.
Note: Passing --enable-libnpp to configure results in
a warning about the deprecation and is otherwise a no-op.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
The ass filter exposes libass' shaping mode selection so callers can
request complex shaping for scripts such as Arabic. The subtitles filter
uses the same renderer path but did not expose the option.
This left the zero-initialized shaping field to select
ASS_SHAPING_SIMPLE implicitly.
Expose the same shaping option for subtitles and default it to auto,
matching the ass filter. This allows subtitles=...:shaping=complex to
render Arabic lam-alef correctly when libass is built with HarfBuzz
support.
Signed-off-by: Jun Zhao <barryjzhao@tencent.com>
This patch adds ONNX Runtime as a new DNN backend for FFmpeg's dnn_processing
filter, enabling hardware-accelerated neural network inference on multiple
GPU and NPU platforms.
Execution Providers Supported:
- CPU execution provider (default)
- CUDA execution provider (NVIDIA GPUs)
- DirectML execution provider (AMD/Intel/NVIDIA GPUs on Windows)
- VitisAI execution provider (AMD Ryzen AI NPU)
The options for dnn_processing with dnn_backend=onnx:
- device: execution provider — cpu, cuda, dml, or vitisai (default: cpu)
- device_id: GPU device index (default: 0)
- threads_per_operation: inference thread count for CPU EP (default: 0, auto)
- input: input tensor name. When omitted the backend resolves it from loaded session
- output: output tensor name. When omitted the backend resolves it from loaded session
Example usage:
# CPU inference
ffmpeg -i input.mp4 -vf "format=rgb24,dnn_processing=dnn_backend=onnx:model=model.onnx:input=image_in:output=image_out" output.mp4
# CUDA GPU inference
ffmpeg -i input.mp4 -vf "dnn_processing=dnn_backend=onnx:model=model.onnx:device=cuda:device_id=0" output.mp4
# DirectML GPU inference (Windows)
ffmpeg -i input.mp4 -vf "dnn_processing=dnn_backend=onnx:model=model.onnx:device=dml:device_id=0" output.mp4
# VitisAI NPU inference
ffmpeg -i input.mp4 -vf "dnn_processing=dnn_backend=onnx:model=model.onnx:device=vitisai" output.mp4
Note: depending on the model, you may need a format filter (e.g. format=rgb24 or format=grayf32) before dnn_processing to convert the frames to the pixel format the model's input tensor expects.
Signed-off-by: younengxiao <steven.xiao@amd.com>
Reviewed-by: Guo Yejun <yejun.guo@intel.com>
