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@ -18,38 +18,23 @@ and maps vectors with labels provided by datasets used for pre-training. |
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Use the pretrained Omnivore model to classify and generate a vector for the given video path './archery.mp4' |
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([download](https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4)). |
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*Write the pipeline in simplified style*: |
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*Write a pipeline with explicit inputs/outputs name specifications*: |
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- Predict labels (default): |
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```python |
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import towhee |
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( |
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towhee.glob('./archery.mp4') |
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.video_decode.ffmpeg() |
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.action_classification.omnivore( |
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model_name='omnivore_swinT', topk=5) |
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.show() |
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from towhee.dc2 import pipe, ops, DataCollection |
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p = ( |
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pipe.input('path') |
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.map('path', 'frames', ops.video_decode.ffmpeg()) |
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.map('frames', ('labels', 'scores', 'features'), |
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ops.action_classification.omnivore(model_name='omnivore_swinT')) |
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.output('path', 'labels', 'scores', 'features') |
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) |
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``` |
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<img src="./result1.png" height="px"/> |
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*Write a same pipeline with explicit inputs/outputs name specifications*: |
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```python |
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import towhee |
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( |
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towhee.glob['path']('./archery.mp4') |
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.video_decode.ffmpeg['path', 'frames']() |
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.action_classification.omnivore['frames', ('labels', 'scores', 'features')]( |
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model_name='omnivore_swinT') |
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.select['path', 'labels', 'scores', 'features']() |
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.show(formatter={'path': 'video_path'}) |
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) |
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DataCollection(p('./archery.mp4')).show() |
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``` |
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<img src="./result2.png" height="px"/> |
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<img src="./result.png" height="px"/> |
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<br /> |
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