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GitOrigin-RevId: ac03a471f5b9df34de46dd684202e4365c5ceac3
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MediaPipe Team
2019-10-29 15:59:27 -07:00
committed by jqtang
parent c6fea4c9d9
commit fce372d153
10 changed files with 197 additions and 15 deletions
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mediapipe/docs/android_archive_library.md
+1
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@@ -117,6 +117,7 @@ project.
implementation 'com.google.code.findbugs:jsr305:3.0.2'
implementation 'com.google.guava:guava:27.0.1-android'
implementation 'com.google.guava:guava:27.0.1-android'
implementation 'com.google.protobuf:protobuf-lite:3.0.0'
// CameraX core library
def camerax_version = "1.0.0-alpha06"
implementation "androidx.camera:camera-core:$camerax_version"
mediapipe/docs/install.md
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@@ -579,6 +579,11 @@ export ANDROID_HOME=<path to the Android SDK>
export ANDROID_NDK_HOME=<path to the Android NDK>
```
In order to use MediaPipe on earlier Android versions, MediaPipe needs to switch
to a lower Android API level. You can achieve this by specifying `api_level =
<api level integer>` in android_ndk_repository() and/or android_sdk_repository()
in the [`WORKSPACE`] file.
Please verify all the necessary packages are installed.
* Android SDK Platform API Level 28 or 29
mediapipe/docs/youtube_8m.md
+14 -5
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@@ -64,7 +64,7 @@ videos.
4. Generate a MediaSequence metadata from the input video.
Note: the output file is /tmp/mediapipe/metadata.tfrecord
Note: the output file is /tmp/mediapipe/metadata.pb
```bash
# change clip_end_time_sec to match the length of your video.
@@ -82,8 +82,17 @@ videos.
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features \
--calculator_graph_config_file=mediapipe/graphs/youtube8m/feature_extraction.pbtxt \
--input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.tfrecord \
--output_side_packets=output_sequence_example=/tmp/mediapipe/output.tfrecord
--input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.pb \
--output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb
```
6. [Optional] Read the features.pb in Python.
```
import tensorflow as tf
sequence_example = open('/tmp/mediapipe/features.pb', 'rb').read()
print(tf.train.SequenceExample.FromString(sequence_example))
```
## Model Inference for YouTube-8M Challenge
@@ -136,7 +145,7 @@ the inference for both local videos and the dataset
### Steps to run the YouTube-8M model inference graph with a local video
1. Make sure you have the output tfrecord from the feature extraction pipeline.
1. Make sure you have the features.pb from the feature extraction pipeline.
2. Copy the baseline model
[(model card)](https://drive.google.com/file/d/1xTCi9-Nm9dt2KIk8WR0dDFrIssWawyXy/view)
@@ -158,7 +167,7 @@ the inference for both local videos and the dataset
# overlap is the number of seconds adjacent segments share.
GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/model_inference \
--calculator_graph_config_file=mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt \
--input_side_packets=input_sequence_example_path=/tmp/mediapipe/output.tfrecord,input_video_path=/absolute/path/to/the/local/video/file,output_video_path=/tmp/mediapipe/annotated_video.mp4,segment_size=5,overlap=4
--input_side_packets=input_sequence_example_path=/tmp/mediapipe/features.pb,input_video_path=/absolute/path/to/the/local/video/file,output_video_path=/tmp/mediapipe/annotated_video.mp4,segment_size=5,overlap=4
```
4. View the annotated video.