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MediaPipe Team 2019-06-20 08:11:11 -07:00 committed by jqtang
parent 7ac795a621
commit 5687062d10
4 changed files with 4 additions and 5 deletions
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2
mediapipe/docs/framework_concepts.md
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@ -117,7 +117,7 @@ combination of tag name and index number. You can see some examples of input and
output identifiers in the example below. `SomeAudioVideoCalculator` identifies output identifiers in the example below. `SomeAudioVideoCalculator` identifies
its video output by tag and its audio outputs by the combination of tag and its video output by tag and its audio outputs by the combination of tag and
index. The input with tag `VIDEO` is connected to the stream named index. The input with tag `VIDEO` is connected to the stream named
`video_stream`. The inputs with tag `AUDIO` and indices `0` and `1` are `video_stream`. The outputs with tag `AUDIO` and indices `0` and `1` are
connected to the streams named `audio_left` and `audio_right`. connected to the streams named `audio_left` and `audio_right`.
`SomeAudioCalculator` identifies its audio inputs by index only (no tag needed). `SomeAudioCalculator` identifies its audio inputs by index only (no tag needed).

4
mediapipe/docs/media_sequence.md
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@ -20,7 +20,7 @@ process new data sets, in the [documentation](https://github.com/google/mediapip
1. Checkout mediapipe repository 1. Checkout mediapipe repository
```bash ```bash
git clone https://github.com/google/mediapipe/mediapipe git clone https://github.com/google/mediapipe.git
cd mediapipe cd mediapipe
``` ```
@ -115,7 +115,7 @@ python -m mediapipe.examples.desktop.media_sequence.demo_dataset \
### Preparing your own data set ### Preparing your own data set
The process for preparing your own data set is described in the [MediaSequence The process for preparing your own data set is described in the [MediaSequence
documentation](https://github.com/google/mediapipe/tree/master/mediapipe/util/sequence/README.md). documentation](https://github.com/google/mediapipe/blob/master/mediapipe/util/sequence/README.md).
The Python code for Charades can easily be modified to process most annotations, The Python code for Charades can easily be modified to process most annotations,
but the MediaPipe processing warrants further discussion. MediaSequence uses but the MediaPipe processing warrants further discussion. MediaSequence uses
MediaPipe graphs to extract features related to the metadata or previously MediaPipe graphs to extract features related to the metadata or previously

2
mediapipe/util/sequence/README.md
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@ -6,8 +6,6 @@ prototypes used in MediaSequence for storing multimedia data in
SequenceExamples. Finally, the documentation will describe the specific keys for SequenceExamples. Finally, the documentation will describe the specific keys for
storing specific types of data. storing specific types of data.
[TOC]
## Overview of MediaSequence for machine learning ## Overview of MediaSequence for machine learning
The goal of MediaSequence is to provide a tool for transforming annotations of The goal of MediaSequence is to provide a tool for transforming annotations of

1
mediapipe/util/tflite/operations/BUILD
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@ -54,5 +54,6 @@ cc_library(
"@org_tensorflow//tensorflow/lite/kernels:padding", "@org_tensorflow//tensorflow/lite/kernels:padding",
"@org_tensorflow//tensorflow/lite/kernels/internal:tensor", "@org_tensorflow//tensorflow/lite/kernels/internal:tensor",
"@org_tensorflow//tensorflow/lite/kernels/internal:tensor_utils", "@org_tensorflow//tensorflow/lite/kernels/internal:tensor_utils",
"@org_tensorflow//tensorflow/lite/kernels/internal:types",
], ],
) )