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feat: add pipeline to predict
Browse files- app.py +67 -15
- requirements.txt +2 -1
- run_on_video/data_utils.py +1 -1
- run_on_video/run.py +2 -2
app.py
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@@ -1,20 +1,30 @@
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import gradio as gr
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with gr.Blocks() as demo:
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gr.
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with gr.Row():
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with gr.Blocks():
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with gr.Column():
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gr.
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input_video = gr.PlayableVideo()
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retrieval_text = gr.Textbox(
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placeholder="What should be highlighted?",
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visible=True
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@@ -22,17 +32,59 @@ with gr.Blocks() as demo:
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submit =gr.Button("Submit")
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with gr.Blocks():
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with gr.Column():
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gr.
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submit.click(
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demo.launch()
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import gradio as gr
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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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ckpt_path = "run_on_video/moment_detr_ckpt/model_best.ckpt"
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clip_model_name_or_path = "ViT-B/32"
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device = 'cpu'
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moment_detr_predictor = MomentDETRPredictor(
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ckpt_path=ckpt_path,
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clip_model_name_or_path=clip_model_name_or_path,
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device=device
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)
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def trim_video(video_path, start, end, output_file='result.mp4'):
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ffmpeg_extract_subclip(video_path, start, end, targetname=output_file)
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return output_file
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with gr.Blocks() as demo:
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output_videos = gr.State([])
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moment_scores = gr.State([])
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gr.HTML("""<h2 align="center"> ✍️ Highlight Detection with MomentDETR </h2>""")
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with gr.Row():
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with gr.Blocks():
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with gr.Column():
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gr.HTML("""<h3 align="center"> Input Video </h3>""")
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input_video = gr.PlayableVideo()
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retrieval_text = gr.Textbox(
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placeholder="What should be highlighted?",
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visible=True
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submit =gr.Button("Submit")
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with gr.Blocks():
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with gr.Column():
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gr.HTML("""<h3 align="center"> Highlight Videos </h3>""")
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display_score = gr.Markdown("### Moment Score: ")
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radio_button = gr.Radio(
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choices=[i for i in range(10)],
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label="Moments",
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value=0
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)
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playable_video = gr.PlayableVideo(visible=True)
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def update_video_player(radio_value, output_videos, moment_scores):
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return {
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playable_video: output_videos[radio_value],
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display_score: f'### Moment Score: {moment_scores[radio_value]}'
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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 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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video_path=input_video,
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query_list=[retrieval_text]
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)
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pred_windows = [[pred[0], pred[1]]for pred in predictions[0]['pred_relevant_windows']]
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scores = [pred[-1] for pred in predictions[0]['pred_relevant_windows']]
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print(f'== predict start end time: {pred_windows}')
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print(f'== prediction scores: {scores}')
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output_files = [ trim_video(
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video_path=input_video,
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start=pred_windows[i][0],
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end=pred_windows[i][1],
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output_file=f'{i}.mp4'
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) for i in range(10)]
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print(f'== output_files: {output_files}')
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return {
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output_videos: output_files,
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moment_scores: scores,
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playable_video: output_files[0],
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display_score: f'### Moment Score: {scores[0]}'
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}
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radio_button.change(
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fn=update_video_player,
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inputs=[radio_button, output_videos, moment_scores],
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outputs=[playable_video, display_score]
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)
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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, moment_scores, playable_video, display_score]
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)
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demo.launch()
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requirements.txt
CHANGED
@@ -11,4 +11,5 @@ pandas
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ffmpeg-python
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ftfy
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regex
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Pillow
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ffmpeg-python
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ftfy
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regex
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Pillow
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moviepy
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run_on_video/data_utils.py
CHANGED
@@ -3,7 +3,7 @@ import os
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import numpy as np
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import ffmpeg
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import math
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import clip
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class ClipFeatureExtractor:
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import numpy as np
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import ffmpeg
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import math
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import run_on_video.clip as clip
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class ClipFeatureExtractor:
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run_on_video/run.py
CHANGED
@@ -1,7 +1,7 @@
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import torch
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from data_utils import ClipFeatureExtractor
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from model_utils import build_inference_model
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from utils.tensor_utils import pad_sequences_1d
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from moment_detr.span_utils import span_cxw_to_xx
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from utils.basic_utils import l2_normalize_np_array
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import torch
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from run_on_video.data_utils import ClipFeatureExtractor
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from run_on_video.model_utils import build_inference_model
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from utils.tensor_utils import pad_sequences_1d
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from moment_detr.span_utils import span_cxw_to_xx
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from utils.basic_utils import l2_normalize_np_array
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