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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> |
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| compat | ||
| doc | ||
| ffbuild | ||
| fftools | ||
| libavcodec | ||
| libavdevice | ||
| libavfilter | ||
| libavformat | ||
| libavutil | ||
| libpostproc | ||
| libswresample | ||
| libswscale | ||
| presets | ||
| tests | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .mailmap | ||
| .travis.yml | ||
| Changelog | ||
| configure | ||
| CONTRIBUTING.md | ||
| COPYING.GPLv2 | ||
| COPYING.GPLv3 | ||
| COPYING.LGPLv2.1 | ||
| COPYING.LGPLv3 | ||
| CREDITS | ||
| INSTALL.md | ||
| LICENSE.md | ||
| MAINTAINERS | ||
| Makefile | ||
| README.md | ||
| RELEASE | ||
FFmpeg README
FFmpeg is a collection of libraries and tools to process multimedia content such as audio, video, subtitles and related metadata.
Libraries
libavcodecprovides implementation of a wider range of codecs.libavformatimplements streaming protocols, container formats and basic I/O access.libavutilincludes hashers, decompressors and miscellaneous utility functions.libavfilterprovides a mean to alter decoded Audio and Video through chain of filters.libavdeviceprovides an abstraction to access capture and playback devices.libswresampleimplements audio mixing and resampling routines.libswscaleimplements color conversion and scaling routines.
Tools
- ffmpeg is a command line toolbox to manipulate, convert and stream multimedia content.
- ffplay is a minimalistic multimedia player.
- ffprobe is a simple analysis tool to inspect multimedia content.
- Additional small tools such as
aviocat,ismindexandqt-faststart.
Documentation
The offline documentation is available in the doc/ directory.
The online documentation is available in the main website and in the wiki.
Examples
Coding examples are available in the doc/examples directory.
License
FFmpeg codebase is mainly LGPL-licensed with optional components licensed under GPL. Please refer to the LICENSE file for detailed information.
Contributing
Patches should be submitted to the ffmpeg-devel mailing list using
git format-patch or git send-email. Github pull requests should be
avoided because they are not part of our review process and will be ignored.