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• dnn/vf_dnn_detect.c : add tensorflow output parse support

  6 mai 2021, par Ting Fu

  dnn/vf_dnn_detect.c : add tensorflow output parse support
  Testing model is tensorflow offical model in github repo, please refer
  
  https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
  
  to download the detect model as you need.
  
  For example, local testing was carried on with 'ssd_mobilenet_v2_coco_2018_03_29.tar.gz', and
  
  used one image of dog in
  
  https://github.com/tensorflow/models/blob/master/research/object_detection/test_images/image1.jpg
  Testing command is :
  
  ./ffmpeg -i image1.jpg -vf dnn_detect=dnn_backend=tensorflow:input=image_tensor:output=\
  
  "num_detections&detection_scores&detection_classes&detection_boxes":model=ssd_mobilenet_v2_coco.pb,\
  
  showinfo -f null -
  We will see the result similar as below :
  
  [Parsed_showinfo_1 @ 0x33e65f0]   side data - detection bounding boxes :
  
  [Parsed_showinfo_1 @ 0x33e65f0] source : ssd_mobilenet_v2_coco.pb
  
  [Parsed_showinfo_1 @ 0x33e65f0] index : 0,       region : (382, 60) -> (1005, 593), label : 18, confidence : 9834/10000.
  
  [Parsed_showinfo_1 @ 0x33e65f0] index : 1,       region : (12, 8) -> (328, 549), label : 18, confidence : 8555/10000.
  
  [Parsed_showinfo_1 @ 0x33e65f0] index : 2,       region : (293, 7) -> (682, 458), label : 1, confidence : 8033/10000.
  
  [Parsed_showinfo_1 @ 0x33e65f0] index : 3,       region : (342, 0) -> (690, 325), label : 1, confidence : 5878/10000.
  There are two boxes of dog with cores 94.05% & 93.45% and two boxes of person with scores 80.33% & 58.78%.
  Signed-off-by : Ting Fu <ting.fu@intel.com>
  
  Signed-off-by : Guo, Yejun <yejun.guo@intel.com>
  

  • [DH] libavfilter/vf_dnn_detect.c

• FFmpeg : canvas and crop work separately but result in black screen when combined

  25 janvier, par didi00

  I'm working on a video processing pipeline with FFmpeg, where I :

  


  

  • Create a black canvas using the color filter.

  


  • Crop a region from my video input.

  


  • Overlay the cropped region onto the black canvas.

  


  


  Both the canvas and the crop display correctly when tested individually. However, when I attempt to combine them (overlay the crop onto the canvas), the result is a black screen.
What Works :

  


  Black Canvas Alone :

  


  ffmpeg -filter_complex "color=c=black:s=1920x1080[out]" -map "[out]" -f nut - | ffplay 
-


  


  This shows a plain black screen, as expected.

  


  Cropped Region Alone :

  


  ffmpeg -f v4l2 -input_format yuyv422 -framerate 60 -video_size 1920x1080 -i /dev/video0 
\ -vf "crop=1024:192:0:0" -f nut - | ffplay -


  


  This shows the cropped region of the video correctly.

  


  When I combine these steps to overlay the crop onto the black canvas, I get a black screen :

  


  ffmpeg -f v4l2 -input_format yuyv422 -framerate 60 -video_size 1920x1080 -i /dev/video0 
\-filter_complex "color=c=black:s=1920x1080,format=yuv420p[background]; \
[0:v]crop=1024:192:0:0,format=yuv420p[region0]; \
[background][region0]overlay=x=0:y=0[out]" \
-map "[out]" -f nut - | ffplay -


  


  Environment :

  


  

  • OS : Linux (Debian-based)

  


  • FFmpeg Version : [Insert version, e.g., 4.x or 5.x]

  


  • Capture Card Format : yuyv422

  


  


  Question :

  


  Why does the pipeline result in a black screen when combining the canvas and the crop, even though both work separately ? Is this an issue with pixel format compatibility, or is there something I'm overlooking in the overlay filter setup ?

  


• make ffmpeg chose Nvidia CUDA over Intel QSV (Windows 10 with two video adapters)

  3 avril 2024, par Bart Lederman

  I just set up a 'new' PC with built-in Intel video and an Nvida card : mostly to speed up video processing with ffmpeg and other programs. At first the built-in Intel was disabled, running only the Nvidia card. ffmpeg worked as expected, the CPU could be used for decoding and encoding.

  


  However : VirtualDub, a program I use frequently, has a problem with Nvidia cards (at least on Windows 10). The display gets screwed up, previews don't work, and all sorts of other problems occur. I tried all of the various discussion boards, and nobody has a good solution. (The problem is apparently split between VirtualDub and Nvidia, as all other programs such as VideoLan, Avidemux, HandBrake, OBS studio, etc, all appear to work fine.)

  


  So I re-enabled the on-board Intel adapter, and made that my primary and only video with a monitor. The Nvidia card is still there, but with no monitor attached. I really only need it for hardware acceleration.

  


  HandBrake and OBS Studio found the card and used it with no problem.

  


  However, my batch file that specified cuda for both decoding and encoding failed to run. The ffmpeg command that includes -hwaccel cuda resulted in :

  


  [h264 @ 000002783beaa700] Hardware is lacking required capabilities 
[h264 @ 000002783beaa700] Failed setup for format cuda: hwaccel initialisation returned error.


  


  I also tried -hwaccel nvenc, which is rejected. It's apparently not a synonym in this version of ffmpeg :

  


  ffmpeg version 4.3.1-2021-01-01-full_build-www.gyan.dev Copyright (c) 2000-2021 the FFmpeg developers built with gcc 10.2.0 (Rev5, Built by MSYS2 project) configuration: --enable-gpl --enable-version3 --enable-static --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-lzma --enable-libsnappy --enable-zlib --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libdav1d --enable-libzvbi --enable-librav1e --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxvid --enable-libaom --enable-libopenjpeg --enable-libvpx --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-d3d11va --enable-dxva2 --enable-libmfx --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint libavutil 56. 51.100 / 56. 51.100 libavcodec 58. 91.100 / 58. 91.100 libavformat 58. 45.100 / 58. 45.100 libavdevice 58. 10.100 / 58. 10.100 libavfilter 7. 85.100 / 7. 85.100 libswscale 5. 7.100 / 5. 7.100 libswresample 3. 7.100 / 3. 7.100 libpostproc 55. 7.100 / 55. 7.100


  


  When I use QSV acceleration on my other PC I have to do this :

  


  -init_hw_device qsv=qsv -hwaccel qsv


  


  so I tried

  


  -init_hw_device cuda=cuda -hwaccel cuda


  


  but that didn't work either.

  


  I've seen comments about the ability to select the GPU if there is more than one board installed, using the -gpu option. However, when I try to use -gpu 0 or -gpu 1 I get :

  


  Codec AVOption gpu (Selects which NVENC capable GPU to use. First GPU is 0, second is 1, and so on.) specified for input file #0 (xxx.avi) is not a decoding option.

  


  I looked at :

  


  https://github.com/FFmpeg/FFmpeg/commit/527a1e213167123d24d014bc0b956ef43d9d6542

  


  to get more information on -init_hw_device, but I'm sorry to say that what's on that page makes no sense to me at all. There are no examples, and no explanation of how to actually select a device.

  


  I looked at :

  


  https://docs.nvidia.com/video-technologies/video-codec-sdk/ffmpeg-with-nvidia-gpu/

  


  which has an 'example' of -init_hw_device, and I did a cut and paste of what they had there to my batch file, but it was rejected.

  


  I also looked at :

  


  How to to burn subtitles based image on video using ’overlay_cuda’, ffmpeg video filter

  


  which has two examples of how to initialize a cuda device, and they don't work for me either. -init_hw_device cuda=cuda is accepted without error, but then -hwaccel cuda still fails. Trying to use the hw accelerated filter scale_cuda also fails.

  


  So how do I get the Nvidia card to decode video when it's not the only graphics adapter ? I was able to decode video when only the Nvidia card was active, there "must" be a way to get to it now. I just need to know how to tell ffmpeg to use the card that is there. Since it has no problem finding the card for encoding, shouldn't it also still be able to find the card for decoding and filters ? Or am I really the first person ever to have both Intel and Nvidia graphics adapters working on my system and trying to use ffmpeg with hardware acceleration ?

  


  =====================

  


  Latest update.

  


  I had tried the examples on the Nvidia FFmpeg transcoding guide web page, and as mentioned previously I still got errors. I did a cut and paste from that web page to my command window, and ffmpeg still did not find the correct graphics adapter.

  


  However, I do have a work-around. I don't particularly like it, but it works.

  


  First : Windows (10) does not understand the concept of a graphics adapter that doesn't have a monitor attached to it. Even though graphics processors (specifically Nvidia) are available without the actual video output and are used in supercomputers and elsewhere to do high speed stream processing, Windows will not let you access the card settings if there is no monitor attached. The Nvidia control center also will not allow you to access any of the card's settings, and you can't set processor affinity.

  


  So I connected a second monitor, and set up the Nvidia card as the primary.

  


  Now ffmpeg -hwaccel cuda works the first time. The command I was using before :

  


  ffmpeg -hide_banner -hwaccel cuda -i "input.avi" -c:a copy -ac 1 -c:v h264_nvenc -preset hq -movflags faststart -qp 30 "output.mp4"


  


  Was failing because it couldn't find the Nvidia adapter. This command now works correctly the first time and uses hardware acceleration for both decode and encode. (The audio portion is irrelevant, if I also re-encode the audio the results are the same.)

  


  With scaling, the command was like this :

  


  ffmpeg -hide_banner -hwaccel cuda -i "input.avi" -c:a copy -ac 1 -c:v h264_nvenc -preset hq -vf "scale=640:480" -movflags faststart -qp 30 "output.mp4"


  


  This works. However :

  


  ffmpeg -hide_banner -hwaccel cuda -i "input.avi" -c:a copy -ac 1 -c:v h264_nvenc -preset hq -vf "scale_cuda=640:480" -movflags faststart -qp 30 "output.mp4


  


  Fails with

  


  Impossible to convert between the formats supported by the filter 'graph 0 input from stream 0:0' and the filter 'auto_scaler_0'
Error reinitializing filters!
Failed to inject frame into filter network: Function not implemented
Error while processing the decoded data for stream #0:0


  


  I was able to get around this, by rearranging things in what seems to be to be an unnecessarily convoluted syntax.

  


  ffmpeg -hide_banner -hwaccel cuvid -hwaccel_output_format cuda -i "input.avi" -c:a aac -b:a 192k -ar 48000 -vf "scale_cuda=856:480" -c:v h264_nvenc -preset hq -movflags faststart -qp 26 "output.mp4"


  


  Having to specify the output format twice seems weird, but Task Manager shows near 100% Video Decode activity, and the time it takes to do this indicates to me that the scale_cuda filter is being used.

  


  I don't particularly like having to use a second monitor (If VirtualDub worked properly I probably wouldn't have to), but I'm willing to live with it. It appears that if you have two different video cards and you want to use hardware acceleration on one of them it has to be the primary.

  


  I haven't tested if Intel QSV is still accessible, nor have I tried switching the order of the graphics adapters back to completely verify the source of the problem, and I'm not really planning to do so (unless some of you think that would be useful). I get the definite impression that few people, if any, have tried to get both an Nvidia and an Intel adapter to provide hardware video acceleration on the same system. I will try to access QSV to see if using both accelerators is an improvement.

  


  I can live with the weird command line to get the cuda filters to work, but if anyone knows a better way to do it I think it would be helpful to post it here for future reference if anyone else runs into a similar problem. None of the examples of using cuda accelerated filters that I've found on any of the many web sites I've read worked exactly as given.

  


  ==================

  


  The good news :

  


  It's possible to use both Nvidia and QSV hardware in at least some cases.

  


  This command works :

  


  ffmpeg -hide_banner -hwaccel dxva2 -i "input.avi" -c:a copy ^
  -c:v h264_qsv -vf "crop=1920:1044:0:0" -preset veryfast -profile:v high -level 4.1 -qp 22 "output.mp4"


  


  Task Manager says Nvidia is decoding the input, and GPU-Z says Intel is also active, so it must be doing the encoding.

  


  The bad news : I can't figure out a way to use both a CUDA filter and a standard filter in the same process.

  


  This does not work :

  


  ffmpeg -hide_banner -hwaccel cuvid -hwaccel_output_format cuda -i "input.avi" -c:a aac -b:a 192k -ar 48000 -vf "scale_cuda=856:480,crop=1280:696:0:24" -c:v h264_nvenc -preset hq -movflags faststart -qp 30 "output.mp4"


  


  Reversing the order of scale_cuda and crop (with appropriate adjustments to the numbers) also does not work. There are errors about not being able to transfer the processing stream.

  


  I will try the changes in the latest comment, but I think I may have tried it before and something didn't work. But I will check again.

  


  In my web searches I have not found an example of 'mixed' filters.

  


  I did see "-crop" and "-resize" on the Nvidia ffmpeg trancode web page similar to this :

  


  –crop 0x36x0x0 –resize 1280x696


  


  Once again, I did a cut and paste from the Nvidia web page to my command window and it didn't work. If there is a way to invoke the Nvidia command for these options that has been tested and found to actually work I would really like to see it.