av_mallocz() is superfluous as get_packet_defaults() is called immediately
after it's allocated, which will initialize the entire struct to default
values.
Signed-off-by: James Almer <jamrial@gmail.com>
If a copy callback is provided by the caller, the packet passed to it
was zeroed instead of initialized with default values.
Signed-off-by: James Almer <jamrial@gmail.com>
Libavcodec can now handle the AV1CodecConfigurationRecord structure
as-is when passed as extradata, so the standard behavior of
read-box-into-extradata should suffice, just like with AVC and HEVC.
The SVQ1 decoder does not need mpegvideo or rl.c, but it uses stuff
from h263data.c. But since 61fe481586
h263data.c called ff_rl_init() and this of course led to build errors
when the SVQ1 decoder is enabled and mpegvideo disabled.
Fix this by moving ff_h263_init_rl_inter() to h263.c.
Fixes ticket #9224.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
MSA2 optimizations are attached to MSA macros in generic_macros_msa.h.
It's difficult to do runtime check for them. Remove this part of code
can make it more robust. H264 1080p decoding: 5.13x==>5.12x.
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Using mask to avoid judgment, H264 4K decoding speed
improved about 0.1fps tested on 3A4000
Signed-off-by: Shiyou Yin <yinshiyou-hf@loongson.cn>
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
1. Refined function get_cabac_inline_mips.
2. Optimize function get_cabac_bypass and get_cabac_bypass_sign.
Speed of decoding h264: 4.89x ==> 5.05x(tested on 3A4000).
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
The MSA optimization has been refined in commit 93218c2 and ce0a52e.
It is better than MMI version now.
Speed of decoding H264: 4.83x ==> 4.89x (tested on 3A4000).
Signed-off-by: Michael Niedermayer <michael@niedermayer.cc>
Initializing zlib in the way we do here is threadsafe, see
https://www.zlib.net/zlib_faq.html#faq21
Reviewed-by: Tomas Härdin <tjoppen@acc.umu.se>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
Initializing zlib in the way we do here is threadsafe, see
https://www.zlib.net/zlib_faq.html#faq21
Reviewed-by: Tomas Härdin <tjoppen@acc.umu.se>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
It is not documented to be safe to call inflateEnd() on a z_stream
that has not been successfully initialized via inflateInit(); so
record whether it has been successfully initialized.
Reviewed-by: Tomas Härdin <tjoppen@acc.umu.se>
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
This will give us more room to improve the implementation later.
Suggested-by: Anton Khirnov <anton@khirnov.net>
Signed-off-by: James Almer <jamrial@gmail.com>
Here the packet size is known before allocating the packet because
the encoder provides said information (and works with internal buffers
itself), so one use this information to avoid the implicit use of another
intermediate buffer for the packet data; and by switching to
ff_get_encode_buffer() one can also allow user-supplied buffers.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
Signed-off-by: Rick Kern <kernrj@gmail.com>
Said RL VLC is only used by the decoder, ergo don't initialize it for
the encoder and move the whole code and the RL VLC table itself to
dvdec.c.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
It can and therefore we switch from a heap allocated VLC table to
a VLC initialized via the mechanism for static VLCs, but without
an actual static VLC.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
It is init-threadsafe since b9c1ab8907
and except on MIPS even before that due to its use of ff_thread_once()
for static initialization.
Signed-off-by: Andreas Rheinhardt <andreas.rheinhardt@gmail.com>
From the comment it's not available on old version. It works now
by testing on macOS 11.2.1. There is no document about since when.
So trying to set the configuration and ignore the error for hevc.
Signed-off-by: Rick Kern <kernrj@gmail.com>
classification is done on every detection bounding box in frame's side data,
which are the results of object detection (filter dnn_detect).
Please refer to commit log of dnn_detect for the material for detection,
and see below for classification.
- download material for classifcation:
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.bin
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.xml
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.label
- run command as:
./ffmpeg -i cici.jpg -vf dnn_detect=dnn_backend=openvino:model=face-detection-adas-0001.xml:input=data:output=detection_out:confidence=0.6:labels=face-detection-adas-0001.label,dnn_classify=dnn_backend=openvino:model=emotions-recognition-retail-0003.xml:input=data:output=prob_emotion:confidence=0.3:labels=emotions-recognition-retail-0003.label:target=face,showinfo -f null -
We'll see the detect&classify result as below:
[Parsed_showinfo_2 @ 0x55b7d25e77c0] side data - detection bounding boxes:
[Parsed_showinfo_2 @ 0x55b7d25e77c0] source: face-detection-adas-0001.xml, emotions-recognition-retail-0003.xml
[Parsed_showinfo_2 @ 0x55b7d25e77c0] index: 0, region: (1005, 813) -> (1086, 905), label: face, confidence: 10000/10000.
[Parsed_showinfo_2 @ 0x55b7d25e77c0] classify: label: happy, confidence: 6757/10000.
[Parsed_showinfo_2 @ 0x55b7d25e77c0] index: 1, region: (888, 839) -> (967, 926), label: face, confidence: 6917/10000.
[Parsed_showinfo_2 @ 0x55b7d25e77c0] classify: label: anger, confidence: 4320/10000.
Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Different function type of model requires different parameters, for
example, object detection detects lots of objects (cat/dog/...) in
the frame, and classifcation needs to know which object (cat or dog)
it is going to classify.
The current interface needs to add a new function with more parameters
to support new requirement, with this change, we can just add a new
struct (for example DNNExecClassifyParams) based on DNNExecBaseParams,
and so we can continue to use the current interface execute_model just
with params changed.
There's one task item for one function call from dnn interface,
there's one request item for one call to openvino. For classify,
one task might need multiple inference for classification on every
bounding box, so add InferenceItem.
