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import os
import cv2
from scenedetect import VideoManager, SceneManager
from scenedetect.detectors import ContentDetector
from moviepy.editor import VideoFileClip
from transformers import CLIPProcessor, CLIPModel
import torch
import yt_dlp
from PIL import Image
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
def download_video(url):
ydl_opts = {
'format': 'bestvideo[height<=1440]+bestaudio/best[height<=1440]',
'outtmpl': 'downloaded_video.%(ext)s',
'merge_output_format': 'mp4',
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
result = ydl.extract_info(url, download=True)
video_filename = ydl.prepare_filename(result)
safe_filename = sanitize_filename(video_filename)
if os.path.exists(video_filename) and video_filename != safe_filename:
os.rename(video_filename, safe_filename)
return safe_filename
def sanitize_filename(filename):
return "".join([c if c.isalnum() or c in " .-_()" else "_" for c in filename])
def find_scenes(video_path):
video_manager = VideoManager([video_path])
scene_manager = SceneManager()
scene_manager.add_detector(ContentDetector(threshold=30))
video_manager.set_downscale_factor()
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager)
scene_list = scene_manager.get_scene_list()
video_manager.release()
scenes = [(start.get_timecode(), end.get_timecode()) for start, end in scene_list]
return scenes
def convert_timestamp_to_seconds(timestamp):
h, m, s = map(float, timestamp.split(':'))
return int(h) * 3600 + int(m) * 60 + s
def extract_frames(video_path, start_time, end_time):
frames = []
start_seconds = convert_timestamp_to_seconds(start_time)
end_seconds = convert_timestamp_to_seconds(end_time)
video_clip = VideoFileClip(video_path).subclip(start_seconds, end_seconds)
for frame_time in range(0, int(video_clip.duration), 5):
frame = video_clip.get_frame(frame_time)
frames.append(frame)
return frames
def analyze_scenes(video_path, scenes, description):
highest_prob = 0.0
best_scene = None
for start_time, end_time in scenes:
frames = extract_frames(video_path, start_time, end_time)
for frame in frames:
image = Image.fromarray(frame[..., ::-1])
inputs = processor(text=description, images=image, return_tensors="pt", padding=True).to(device)
with torch.no_grad():
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)
max_prob = max(probs[0]).item()
if max_prob > highest_prob:
highest_prob = max_prob
best_scene = (start_time, end_time)
return best_scene
def extract_best_scene(video_path, scene):
if scene is None:
return None
start_time, end_time = scene
start_seconds = convert_timestamp_to_seconds(start_time)
end_seconds = convert_timestamp_to_seconds(end_time)
video_clip = VideoFileClip(video_path).subclip(start_seconds, end_seconds)
return video_clip
def process_video(video_url, description):
video_path = download_video(video_url)
scenes = find_scenes(video_path)
best_scene = analyze_scenes(video_path, scenes, description)
final_clip = extract_best_scene(video_path, best_scene)
if final_clip:
output_dir = "output"
os.makedirs(output_dir, exist_ok=True)
final_clip_path = os.path.join(output_dir, "final_clip.mp4")
final_clip.write_videofile(final_clip_path, codec='libx264', audio_codec='aac')
return final_clip_path
return None
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