gameplay-clip-extractorlisted

# Gameplay Clip Extractor Reduce a 1-hour gameplay capture to a list of 10-second clips worth posting. ## When to use - User has raw `.mp4`/`.mov` gameplay footage (their own, no piracy). - User wants timestamps for highlights without scrubbing manually. - User wants the actual clipped files written to disk. ## Detection signals (combine, don't pick one) ### 1. Audio peak detection (cheapest, works everywhere) Gunfire, goal whistles, killstreak voice lines, crowd reactions — all show up as audio amplitude spikes. ```bash ffmpeg -i raw.mp4 -af "silencedetect=noise=-30dB:d=0.5" -f null - 2>&1 \ | grep silence_end ``` Use the *inverse* — long non-silent stretches with sudden RMS jumps are candidates. Python (cleaner): ```python import librosa, numpy as np y, sr = librosa.load("raw.mp4", sr=22050, mono=True) rms = librosa.feature.rms(y=y, frame_length=2048, hop_length=512)[0] peaks = np.where(rms > rms.mean() + 2 * rms.std())[0] peak_times = librosa.frames_to_time(peaks, sr=sr, hop_length=512) ``` ### 2. Scoreboard / kill-feed OCR (game-specific, higher accuracy) - BGMI / COD: top-right kill feed → run Tesseract on that ROI every 1s. - FIFA: scoreboard score-change → diff frames at fixed coords. - Cache last value; emit event only on change. ### 3. Pacing heuristic Cluster nearby peaks (within 3s) into single moments. A real highlight is usually 1 spike, not a noise burst. ## Output ```json [ {"start": 142.3, "end": 152.8, "label": "kill burst (3 audio peaks)", "sc