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Runtime error
Runtime error
chore: handle mov to mp4 conversion
Browse files
app.py
CHANGED
@@ -3,6 +3,8 @@ from run_on_video.run import MomentDETRPredictor
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from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip
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import torch
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from lbhd.infer import lbhd_predict
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DESCRIPTION = """
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_This Space demonstrates model [QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries](https://arxiv.org/abs/2107.09609), NeurIPS 2021, by [Jie Lei](http://www.cs.unc.edu/~jielei/), [Tamara L. Berg](http://tamaraberg.com/), [Mohit Bansal](http://www.cs.unc.edu/~mbansal/)_
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@@ -78,10 +80,15 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
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}
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def submit_video(input_video, retrieval_text):
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print(f'== video path: {input_video}')
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print(f'== retrieval_text: {retrieval_text}')
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if input_video is None:
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return [None, None, None, None, None, None, None, None, 1]
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if retrieval_text is None:
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retrieval_text = ''
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predictions, video_frames = moment_detr_predictor.localize_moment(
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@@ -90,7 +97,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
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)
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predictions = predictions[0]['pred_relevant_windows']
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output_files = [ trim_video(
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video_path=input_video,
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start=predictions[i][0],
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end=predictions[i][1],
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output_file=f'{i}.mp4'
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@@ -99,23 +106,37 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
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lbhd_predictions = lbhd_predict(input_video)
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print(f'== lbhd_predictions: {lbhd_predictions}')
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output_files_lbhd = [ trim_video(
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video_path=input_video,
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start=lbhd_predictions[i][0],
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end=lbhd_predictions[i][1],
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output_file=f'{i}_lbhd.mp4'
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) for i in range(min(10, len(lbhd_predictions)))]
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return
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radio_button.change(
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fn=update_video_player,
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@@ -126,7 +147,7 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
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submit.click(
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fn=submit_video,
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inputs=[input_video, retrieval_text],
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outputs=[output_videos, output_lbhd_videos, moment_prediction, our_prediction, playable_video, our_result_video, display_score, display_clip_score, radio_button]
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)
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demo.launch()
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from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip
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import torch
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from lbhd.infer import lbhd_predict
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import os
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import subprocess
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DESCRIPTION = """
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_This Space demonstrates model [QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries](https://arxiv.org/abs/2107.09609), NeurIPS 2021, by [Jie Lei](http://www.cs.unc.edu/~jielei/), [Tamara L. Berg](http://tamaraberg.com/), [Mohit Bansal](http://www.cs.unc.edu/~mbansal/)_
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}
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def submit_video(input_video, retrieval_text):
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ext = os.path.splitext(input_video)[-1].lower()
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if ext == ".mov":
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output_file = os.path.join(input_video.replace(".mov", ".mp4"))
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subprocess.call(['ffmpeg', '-i', input_video, output_file])
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print(f'== video path: {input_video}')
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print(f'== retrieval_text: {retrieval_text}')
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if input_video is None:
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return [None, None, None, None, None, None, None, None, None, 1]
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if retrieval_text is None:
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retrieval_text = ''
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predictions, video_frames = moment_detr_predictor.localize_moment(
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)
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predictions = predictions[0]['pred_relevant_windows']
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output_files = [ trim_video(
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video_path= output_file if ext == ".mov" else input_video,
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start=predictions[i][0],
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end=predictions[i][1],
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output_file=f'{i}.mp4'
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lbhd_predictions = lbhd_predict(input_video)
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print(f'== lbhd_predictions: {lbhd_predictions}')
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output_files_lbhd = [ trim_video(
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video_path= output_file if ext == ".mov" else input_video,
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start=lbhd_predictions[i][0],
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end=lbhd_predictions[i][1],
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output_file=f'{i}_lbhd.mp4'
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) for i in range(min(10, len(lbhd_predictions)))]
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return [
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output_file if ext == ".mov" else input_video,
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output_files,
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output_files_lbhd,
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predictions,
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lbhd_predictions,
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output_files[0],
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output_files_lbhd[0],
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display_prediction(predictions[0]),
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display_prediction(lbhd_predictions[0]),
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1
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]
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# return {
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# input_video: output_file if ext == ".mov" else input_video,
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# output_videos: output_files,
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# output_lbhd_videos: output_files_lbhd,
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# moment_prediction: predictions,
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# our_prediction: lbhd_predictions,
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# playable_video: output_files[0],
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# our_result_video: output_files_lbhd[0],
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# display_score: display_prediction(predictions[0]),
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# display_clip_score: display_prediction(lbhd_predictions[0]),
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# radio_button: 1
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# }
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radio_button.change(
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fn=update_video_player,
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submit.click(
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fn=submit_video,
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inputs=[input_video, retrieval_text],
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outputs=[input_video, output_videos, output_lbhd_videos, moment_prediction, our_prediction, playable_video, our_result_video, display_score, display_clip_score, radio_button]
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)
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demo.launch()
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