cassdlcm/mediapipe
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Project import generated by Copybara.

Browse Source 
PiperOrigin-RevId: 253518716
This commit is contained in:
MediaPipe Team 2019-06-16 23:11:32 -07:00 committed by jqtang
parent d68f5e4169
commit e647b361e7
3 changed files with 6 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
README.md
View File

@ -5,7 +5,7 @@
[MediaPipe](http://g.co/mediapipe) is a framework for building multimodal (eg. video, audio, any time series data) applied ML pipelines. With MediaPipe, a perception pipeline can be built as a graph of modular components, including, for instance, inference models (e.g., TensorFlow, TFLite) and media processing functions.
![Real-time Face Detection](mediapipe/docs/images/mobile/face_detection_android_gpu_small.gif)
![Real-time Face Detection](mediapipe/docs/images/realtime_face_detection.gif)
## Installation
Follow these [instructions](mediapipe/docs/install.md).
@ -14,14 +14,14 @@ Follow these [instructions](mediapipe/docs/install.md).
See mobile and desktop [examples](mediapipe/docs/examples.md).
## Documentation
On [MediaPipe Read-the-Docs](https://mediapipe.readthedocs.io/).
[MediaPipe Read-the-Docs](https://mediapipe.readthedocs.io/).
## Visualizing MediaPipe graphs
A web-based visualizer is hosted on [MediaPipe Visualizer](https://mediapipe-viz.appspot.com/). Please also see instructions [here](mediapipe/docs/visualizer.md).
## Publications
* [MediaPipe: A Framework for Building Perception Pipelines](https://arxiv.org/) on [arXiv](https://arxiv.org/).
* [MediaPipe: A Framework for Perceiving and Augmenting Reality](http://mixedreality.cs.cornell.edu/s/22_crv2_MediaPipe_CVPR_CV4ARVR_Workshop_2019_v2.pdf), extended abstract for [Third Workshop on Computer Vision for AR/VR](http://mixedreality.cs.cornell.edu/workshop/program).
* [MediaPipe: A Framework for Perceiving and Augmenting Reality](http://mixedreality.cs.cornell.edu/s/22_crv2_MediaPipe_CVPR_CV4ARVR_Workshop_2019_v2.pdf), extended abstract for [Third Workshop on Computer Vision for AR/VR](https://sites.google.com/corp/view/perception-cv4arvr/mediapipe).
* Full-length draft: [MediaPipe: A Framework for Building Perception Pipelines](https://tiny.cc/mediapipe_paper)
## Contributing
We welcome contributions. Please follow these [guidelines](./CONTRIBUTING.md).

BIN
mediapipe/docs/images/realtime_face_detection.gif Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 1.6 MiB

4
mediapipe/docs/index.rst
View File

@ -8,9 +8,9 @@ machine learning pipeline can be built as a graph of modular components,
including, for instance, inference models and media processing functions. Sensory
data such as audio and video streams enter the graph, and perceived descriptions
such as object-localization and face-landmark streams exit the graph. An example
graph that performs real-time face detection on mobile GPU is shown below.
graph that performs real-time hair segmentation on mobile GPU is shown below.
.. image:: images/mobile/face_detection_android_gpu.png
.. image:: images/mobile/hair_segmentation_android_gpu.png
:width: 400
:alt: Example MediaPipe graph