Sistema completo de detección de highlights con VLM y análisis de gameplay
- Implementación de detector híbrido (Whisper + Chat + Audio + VLM) - Sistema de detección de gameplay real vs hablando - Scene detection con FFmpeg - Soporte para RTX 3050 y RX 6800 XT - Guía completa en 6800xt.md para próxima IA - Scripts de filtrado visual y análisis de contexto - Pipeline automatizado de generación de videos
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renato97
173
rage_in_gameplay.py
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173
rage_in_gameplay.py
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#!/usr/bin/env python3
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"""
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RAGE IN GAMEPLAY - Solo momentos de rage durante gameplay activo
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"""
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import json
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import re
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def find_rage_in_gameplay():
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"""Busca rage solo durante regiones de gameplay activo."""
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print("=" * 60)
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print("RAGE IN GAMEPLAY DETECTOR")
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print("=" * 60)
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# Cargar transcripción
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with open("transcripcion_rage.json", "r") as f:
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trans = json.load(f)
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# Cargar regiones de gameplay
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with open("gameplay_regions.json", "r") as f:
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gameplay_regions = json.load(f)
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print(f"Regiones de gameplay: {len(gameplay_regions)}")
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# Convertir regiones a set para búsqueda rápida
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gameplay_seconds = set()
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for start, end in gameplay_regions:
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for i in range(start, end):
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gameplay_seconds.add(i)
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print(f"Total segundos de gameplay: {len(gameplay_seconds)}")
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# Patrones de rage
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rage_patterns = [
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(r"\bputa\w*", 10, "EXTREME"),
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(r"\bmierda\b", 8, "RAGE"),
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(r"\bjoder\b", 8, "RAGE"),
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(r"\bhostia\b", 7, "RAGE"),
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(r"\bme mataron\b", 12, "DEATH"),
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(r"\bme mori\b", 12, "DEATH"),
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(r"\bme matan\b", 10, "DEATH"),
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(r"\bmatenme\b", 10, "DEATH"),
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(r"\bla cague\b", 8, "FAIL"),
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(r"\bfall[eé]\b", 6, "FAIL"),
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(r"\bretrasad\w*", 9, "INSULT"),
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(r"\bimbecil\b", 9, "INSULT"),
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(r"\bestupid\w*", 8, "INSULT"),
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(r"\bnooo+\b", 6, "FRUSTRATION"),
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(r"\bno puede ser\b", 7, "FRUSTRATION"),
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]
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# Buscar momentos de rage durante gameplay
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rage_moments = []
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for seg in trans["segments"]:
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start = int(seg["start"])
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end = int(seg["end"])
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# Verificar si hay gameplay durante este segmento
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overlap = sum(1 for i in range(start, end) if i in gameplay_seconds)
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if overlap < (end - start) * 0.3: # Menos del 30% en gameplay
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continue
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text = seg["text"].lower()
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score = 0
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reasons = []
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for pattern, points, reason in rage_patterns:
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if re.search(pattern, text, re.IGNORECASE):
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score += points
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if reason not in reasons:
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reasons.append(reason)
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if score >= 6: # Mínimo significativo
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rage_moments.append(
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{
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"start": start,
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"end": end,
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"score": score,
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"text": seg["text"][:70],
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"reasons": reasons,
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"gameplay_pct": overlap / (end - start),
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}
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)
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print(f"\nMomentos de rage durante gameplay: {len(rage_moments)}")
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# Ordenar por score
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rage_moments.sort(key=lambda x: -x["score"])
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# Mostrar top 15
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print("\nTop momentos:")
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for i, m in enumerate(rage_moments[:15], 1):
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mins = int(m["start"]) // 60
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secs = int(m["start"]) % 60
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print(
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f"{i:2d}. {mins:02d}:{secs:02d} [Score: {m['score']:2d}] "
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f"{'/'.join(m['reasons'])} - {m['text'][:50]}..."
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)
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# Crear clips extendidos
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clips = []
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for m in rage_moments[:20]:
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# Extender solo dentro del gameplay activo
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clip_start = max(455, int(m["start"]) - 8)
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clip_end = min(8237, int(m["end"]) + 15)
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# Verificar que esté dentro de gameplay
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valid_start = None
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valid_end = None
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for g_start, g_end in gameplay_regions:
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if clip_start < g_end and clip_end > g_start:
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# Hay superposición
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valid_start = max(clip_start, g_start)
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valid_end = min(clip_end, g_end)
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break
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if valid_start and valid_end and valid_end - valid_start >= 15:
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clips.append(
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{
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"start": int(valid_start),
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"end": int(valid_end),
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"score": m["score"],
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"reasons": m["reasons"],
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}
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)
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# Eliminar solapamientos
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clips.sort(key=lambda x: x["start"])
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filtered = []
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for clip in clips:
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if not filtered:
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filtered.append(clip)
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else:
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last = filtered[-1]
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if clip["start"] <= last["end"] + 5:
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# Fusionar
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last["end"] = max(last["end"], clip["end"])
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last["score"] = max(last["score"], clip["score"])
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last["reasons"] = list(set(last["reasons"] + clip["reasons"]))
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else:
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filtered.append(clip)
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# Tomar top 10
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filtered.sort(key=lambda x: -x["score"])
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final = filtered[:10]
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final.sort(key=lambda x: x["start"])
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print(f"\nClips finales: {len(final)}")
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total_dur = sum(c["end"] - c["start"] for c in final)
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print(f"Duración total: {total_dur}s ({total_dur // 60}m {total_dur % 60}s)")
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print("\nTimeline:")
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for i, c in enumerate(final, 1):
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mins, secs = divmod(c["start"], 60)
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dur = c["end"] - c["start"]
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print(f"{i:2d}. {mins:02d}:{secs:02d} - {dur}s [{'/'.join(c['reasons'])}]")
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# Guardar
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highlights = [[c["start"], c["end"]] for c in final]
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with open("highlights_gameplay_rage.json", "w") as f:
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json.dump(highlights, f)
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print(f"\nGuardado en highlights_gameplay_rage.json")
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return highlights
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if __name__ == "__main__":
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find_rage_in_gameplay()
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