|
|
|
@ -18,38 +18,23 @@ and maps vectors with labels provided by datasets used for pre-training. |
|
|
|
Use the pretrained Movinet model to classify and generate a vector for the given video path './jumpingjack.gif' |
|
|
|
([download](https://github.com/tensorflow/models/raw/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/jumpingjack.gif)). |
|
|
|
|
|
|
|
*Write the pipeline in simplified style*: |
|
|
|
*Write a pipeline with explicit inputs/outputs name specifications*: |
|
|
|
|
|
|
|
- Predict labels (default): |
|
|
|
```python |
|
|
|
import towhee |
|
|
|
|
|
|
|
( |
|
|
|
towhee.glob('./jumpingjack.gif') |
|
|
|
.video_decode.ffmpeg() |
|
|
|
.action_classification.movinet( |
|
|
|
model_name='movineta0', topk=5) |
|
|
|
.show() |
|
|
|
from towhee.dc2 import pipe, ops, DataCollection |
|
|
|
|
|
|
|
p = ( |
|
|
|
pipe.input('path') |
|
|
|
.map('path', 'frames', ops.video_decode.ffmpeg()) |
|
|
|
.map('frames', ('labels', 'scores', 'features'), |
|
|
|
ops.action_classification.movinet(model_name='movineta0')) |
|
|
|
.output('path', 'labels', 'scores', 'features') |
|
|
|
) |
|
|
|
``` |
|
|
|
<img src="./result1.png" height="px"/> |
|
|
|
|
|
|
|
*Write a same pipeline with explicit inputs/outputs name specifications*: |
|
|
|
|
|
|
|
```python |
|
|
|
import towhee |
|
|
|
|
|
|
|
( |
|
|
|
towhee.glob['path']('./jumpingjack.gif') |
|
|
|
.video_decode.ffmpeg['path', 'frames']() |
|
|
|
.action_classification.movinet['frames', ('labels', 'scores', 'features')]( |
|
|
|
model_name='movineta0') |
|
|
|
.select['path', 'labels', 'scores', 'features']() |
|
|
|
.show(formatter={'path': 'video_path'}) |
|
|
|
) |
|
|
|
DataCollection(p('./jumpingjack.gif')).show() |
|
|
|
``` |
|
|
|
|
|
|
|
<img src="./result2.png" height="px"/> |
|
|
|
<img src="./result.png" height="px"/> |
|
|
|
|
|
|
|
<br /> |
|
|
|
|
|
|
|
|
