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import os
import sys
import uuid
from src.utils import makedirs, sanitize_filename, get_gradio_tmp
def extract_unique_frames(urls=None, file=None, download_dir=None, export_dir=None, extract_frames=10):
temp_workaround = False
if temp_workaround:
download_dir = './'
else:
download_dir = download_dir or os.getenv('VID_DOWNLOADS', "viddownloads")
download_dir = os.path.join(download_dir, str(uuid.uuid4()))
makedirs(download_dir, exist_ok=True)
# os.environ['FIFTYONE_DISABLE_SERVICES'] = 'True'
if urls:
if 'openai_server' not in sys.path:
sys.path.append('openai_server')
from openai_server.agent_tools.download_web_video import download_web_video
for url in urls:
download_web_video(video_url=url, base_url="https://www.youtube.com", output_dir=download_dir)
#import fiftyone.utils.youtube as fouy
#fouy.download_youtube_videos(urls, download_dir=download_dir)
# Create a FiftyOne Dataset
import fiftyone as fo
if file:
dataset = fo.Dataset.from_videos([file])
else:
dataset = fo.Dataset.from_videos_dir(download_dir)
# Convert videos to images, sample 1 frame per second
frame_view = dataset.to_frames(sample_frames=True, fps=1)
import fiftyone.brain as fob
# Index images by similarity
results = fob.compute_similarity(frame_view, brain_key="frame_sim")
# Find maximally unique frames
num_unique = min(extract_frames, frame_view.count()) # Scale this to whatever you want
results.find_unique(num_unique)
unique_view = frame_view.select(results.unique_ids)
# Visualize in the App
# session = fo.launch_app(frame_view)
# session = fo.launch_app(unique_view)
san_file = sanitize_filename(os.path.basename(file)) if file else None
gradio_tmp = get_gradio_tmp()
if san_file:
export_dir = export_dir or os.path.join(gradio_tmp, "extraction_%s" % san_file)
if os.path.isdir(export_dir):
export_dir += "_%s" % str(uuid.uuid4())
else:
export_dir = export_dir or os.path.join(gradio_tmp, "extraction_%s" % str(uuid.uuid4()))
makedirs(export_dir, exist_ok=True)
unique_view.export(export_dir, dataset_type=fo.types.VideoDirectory)
return export_dir
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