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import cv2
import numpy as np
from pathlib import Path
from typing import Generator, Tuple, List
import decord
from decord import VideoReader, cpu
# Fix decord seed to avoid warnings
decord.bridge.set_bridge('torch')
def extract_frames_decord(video_path: Path, fps: float = 1.0) -> Generator[Tuple[float, np.ndarray], None, None]:
"""Efficiently extracts frames from a video using Decord."""
if not video_path.exists():
raise FileNotFoundError(f"Video not found: {video_path}")
vr = VideoReader(str(video_path), ctx=cpu(0))
original_fps = vr.get_avg_fps()
# Calculate indices
step = int(original_fps / fps)
if step < 1: step = 1
indices = list(range(0, len(vr), step))
# Batch extraction
batch_size = 32
for i in range(0, len(indices), batch_size):
batch_indices = indices[i : i + batch_size]
frames = vr.get_batch(batch_indices).asnumpy()
for j, frame in enumerate(frames):
idx = batch_indices[j]
timestamp = idx / original_fps
yield timestamp, frame
def calculate_ssim_simplified(img1: np.ndarray, img2: np.ndarray) -> float:
"""Calculates a simple structural similarity score (MSE based)."""
if img1.shape != img2.shape:
img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))
g1 = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
g2 = cv2.cvtColor(img2, cv2.COLOR_RGB2GRAY)
mse = np.mean((g1 - g2) ** 2)
if mse == 0: return 1.0
return 1.0 / (1.0 + (mse / 1000.0))
def extract_key_scenes(video_path: Path, threshold: float = 0.85) -> List[Tuple[float, np.ndarray]]:
"""
Extracts ONLY significant scene changes (Keyframes).
Reduces 60 frames -> 5-10 keyframes.
"""
print("🎬 Detecting Scenes...")
keyframes = []
last_frame = None
# Scan at 1 FPS
for ts, frame in extract_frames_decord(video_path, fps=1.0):
if last_frame is None:
keyframes.append((ts, frame))
last_frame = frame
continue
score = calculate_ssim_simplified(last_frame, frame)
# If scene changed significantly (score < threshold)
if score < threshold:
keyframes.append((ts, frame))
last_frame = frame
print(f"🎬 Found {len(keyframes)} unique scenes.")
return keyframes |