From 689e95513d517b9dde46b2e7ca3d72a59c8d94ae Mon Sep 17 00:00:00 2001 From: ChengZi Date: Thu, 2 Feb 2023 14:51:00 +0800 Subject: [PATCH] update readme with dc2 --- README.md | 57 +++++++++++++++++-------------------------- result1.png | Bin 12265 -> 0 bytes result2.png | Bin 13254 -> 0 bytes result3.png | Bin 117745 -> 0 bytes result4.png | Bin 25039 -> 0 bytes text_emb_result.png | Bin 0 -> 14621 bytes video_emb_result.png | Bin 0 -> 30487 bytes 7 files changed, 22 insertions(+), 35 deletions(-) delete mode 100644 result1.png delete mode 100644 result2.png delete mode 100644 result3.png delete mode 100644 result4.png create mode 100644 text_emb_result.png create mode 100644 video_emb_result.png diff --git a/README.md b/README.md index 186a0cb..8ba3139 100644 --- a/README.md +++ b/README.md @@ -16,51 +16,38 @@ This operator extracts features for video or text with [Frozen In Time](https:// Load a video from path './demo_video.mp4' to generate a video embedding. -Read the text 'kids feeding and playing with the horse' to generate a text embedding. - - *Write the pipeline in simplified style*: - - Encode video (default): ```python -import towhee -towhee.dc(['./demo_video.mp4']) \ - .video_decode.ffmpeg(sample_type='uniform_temporal_subsample', args={'num_samples': 4}) \ - .runas_op(func=lambda x: [y for y in x]) \ - .video_text_embedding.frozen_in_time(model_name='frozen_in_time_base_16_244', modality='video', device='cpu') \ - .show() +from towhee.dc2 import pipe, ops, DataCollection -``` - +p = ( + pipe.input('video_path') \ + .map('video_path', 'flame_gen', ops.video_decode.ffmpeg(sample_type='uniform_temporal_subsample', args={'num_samples': 4})) \ + .map('flame_gen', 'flame_list', lambda x: [y for y in x]) \ + .map('flame_list', 'vec', ops.video_text_embedding.frozen_in_time(model_name='frozen_in_time_base_16_244', modality='video', device='cpu')) \ + .output('video_path', 'flame_list', 'vec') +) -- Encode text: -```python -import towhee +DataCollection(p('./demo_video.mp4')).show() -towhee.dc(['kids feeding and playing with the horse']) \ - .video_text_embedding.frozen_in_time(model_name='frozen_in_time_base_16_244', modality='text', device='cpu') \ - .show() ``` - + -*Write a same pipeline with explicit inputs/outputs name specifications:* +Read the text 'kids feeding and playing with the horse' to generate a text embedding. +- Encode text: ```python -import towhee - -towhee.dc['path'](['./demo_video.mp4']) \ - .video_decode.ffmpeg['path', 'frames'](sample_type='uniform_temporal_subsample', args={'num_samples': 4}) \ - .runas_op['frames', 'frames'](func=lambda x: [y for y in x]) \ - .video_text_embedding.frozen_in_time['frames', 'vec'](model_name='frozen_in_time_base_16_244', modality='video', device='cpu') \ - .select['path', 'vec']() \ - .show(formatter={'path': 'video_path'}) - -towhee.dc['text'](["kids feeding and playing with the horse"]) \ - .video_text_embedding.frozen_in_time['text','vec'](model_name='frozen_in_time_base_16_244', modality='text', device='cpu') \ - .select['text', 'vec']() \ - .show() +from towhee.dc2 import pipe, ops, DataCollection + +p = ( + pipe.input('text') \ + .map('text', 'vec', ops.video_text_embedding.frozen_in_time(model_name='frozen_in_time_base_16_244', modality='text', device='cpu')) \ + .output('text', 'vec') +) + +DataCollection(p('kids feeding and playing with the horse')).show() ``` - - +
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