179 lines
7.4 KiB
Python
179 lines
7.4 KiB
Python
#!/usr/bin/env python3
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"""
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Simulate a full pipeline run — pushes all event types in sequence.
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Usage:
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python tests/detect/manual/push_pipeline.py [--job JOB_ID] [--port PORT] [--delay SECS]
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Opens: http://mpr.local.ar/detection/?job=<JOB_ID>
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"""
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import argparse
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import json
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import time
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from datetime import datetime, timezone
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import redis
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def ts():
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return datetime.now(timezone.utc).isoformat()
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def push(r, key, event):
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event["ts"] = event.get("ts", ts())
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r.rpush(key, json.dumps(event))
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etype = event["event"]
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detail = event.get("msg", event.get("stage", ""))
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print(f" [{etype:14s}] {detail}")
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return event
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--job", default="pipeline-test")
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parser.add_argument("--port", type=int, default=6382)
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parser.add_argument("--delay", type=float, default=0.5)
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args = parser.parse_args()
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r = redis.Redis(port=args.port, decode_responses=True)
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key = f"detect_events:{args.job}"
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# Clear previous events for this job
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r.delete(key)
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print(f"Simulating pipeline run → {key}")
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print(f"Open: http://mpr.local.ar/detection/?job={args.job}")
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print()
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delay = args.delay
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# Stage 1: Frame extraction
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push(r, key, {"event": "log", "level": "INFO", "stage": "FrameExtractor",
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"msg": "Starting extraction: soccer_clip.mp4 (60.0s, 1920x1080, fps=2)"})
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time.sleep(delay)
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push(r, key, {"event": "stats_update",
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"frames_extracted": 120, "frames_after_scene_filter": 0,
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"regions_detected": 0, "regions_resolved_by_ocr": 0,
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"regions_escalated_to_local_vlm": 0, "regions_escalated_to_cloud_llm": 0,
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"cloud_llm_calls": 0, "processing_time_seconds": 3.2, "estimated_cloud_cost_usd": 0})
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time.sleep(delay)
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push(r, key, {"event": "log", "level": "INFO", "stage": "FrameExtractor",
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"msg": "Extracted 120 frames"})
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time.sleep(delay)
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# Stage 2: Scene filter
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push(r, key, {"event": "log", "level": "INFO", "stage": "SceneFilter",
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"msg": "Filtering duplicates (hamming_threshold=8)"})
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time.sleep(delay)
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push(r, key, {"event": "stats_update",
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"frames_extracted": 120, "frames_after_scene_filter": 45,
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"regions_detected": 0, "regions_resolved_by_ocr": 0,
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"regions_escalated_to_local_vlm": 0, "regions_escalated_to_cloud_llm": 0,
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"cloud_llm_calls": 0, "processing_time_seconds": 5.1, "estimated_cloud_cost_usd": 0})
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time.sleep(delay)
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push(r, key, {"event": "log", "level": "INFO", "stage": "SceneFilter",
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"msg": "Kept 45 frames (62.5% reduction)"})
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time.sleep(delay)
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# Stage 3: YOLO detection
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push(r, key, {"event": "log", "level": "INFO", "stage": "YOLODetector",
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"msg": "Loading yolov8n.pt (fp16, 1.2GB VRAM)"})
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time.sleep(delay)
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for batch in range(1, 4):
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push(r, key, {"event": "log", "level": "DEBUG", "stage": "YOLODetector",
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"msg": f"Processing batch {batch}/3 (15 frames)"})
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time.sleep(delay * 0.5)
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push(r, key, {"event": "stats_update",
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"frames_extracted": 120, "frames_after_scene_filter": 45,
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"regions_detected": 32, "regions_resolved_by_ocr": 0,
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"regions_escalated_to_local_vlm": 0, "regions_escalated_to_cloud_llm": 0,
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"cloud_llm_calls": 0, "processing_time_seconds": 12.4, "estimated_cloud_cost_usd": 0})
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time.sleep(delay)
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# Stage 4: OCR
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push(r, key, {"event": "log", "level": "INFO", "stage": "OCRStage",
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"msg": "Running PaddleOCR on 32 regions"})
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time.sleep(delay)
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push(r, key, {"event": "stats_update",
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"frames_extracted": 120, "frames_after_scene_filter": 45,
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"regions_detected": 32, "regions_resolved_by_ocr": 24,
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"regions_escalated_to_local_vlm": 0, "regions_escalated_to_cloud_llm": 0,
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"cloud_llm_calls": 0, "processing_time_seconds": 18.7, "estimated_cloud_cost_usd": 0})
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time.sleep(delay)
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# Stage 5: Brand resolver
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push(r, key, {"event": "log", "level": "INFO", "stage": "BrandResolver",
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"msg": "Matched 20 exact, 4 fuzzy. 8 unresolved → VLM"})
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time.sleep(delay)
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# Emit some detections
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for brand, conf in [("Nike", 0.95), ("Emirates", 0.91), ("Adidas", 0.88), ("Coca-Cola", 0.82)]:
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push(r, key, {"event": "detection",
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"brand": brand, "confidence": conf, "source": "ocr",
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"timestamp": 12.5, "duration": 0.5, "content_type": "soccer_broadcast",
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"frame_ref": 25})
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time.sleep(delay * 0.3)
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# Stage 6: VLM escalation
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push(r, key, {"event": "log", "level": "INFO", "stage": "VLMLocal",
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"msg": "Processing 8 unresolved crops with moondream2"})
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time.sleep(delay)
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push(r, key, {"event": "log", "level": "WARNING", "stage": "VLMLocal",
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"msg": "Low confidence on 2 crops, escalating to cloud LLM"})
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time.sleep(delay)
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push(r, key, {"event": "stats_update",
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"frames_extracted": 120, "frames_after_scene_filter": 45,
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"regions_detected": 32, "regions_resolved_by_ocr": 24,
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"regions_escalated_to_local_vlm": 8, "regions_escalated_to_cloud_llm": 2,
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"cloud_llm_calls": 2, "processing_time_seconds": 28.3, "estimated_cloud_cost_usd": 0.0042})
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time.sleep(delay)
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# More detections from VLM
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push(r, key, {"event": "detection",
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"brand": "Mastercard", "confidence": 0.76, "source": "local_vlm",
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"timestamp": 34.0, "duration": 1.0, "content_type": "soccer_broadcast",
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"frame_ref": 68})
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time.sleep(delay * 0.3)
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push(r, key, {"event": "detection",
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"brand": "Heineken", "confidence": 0.71, "source": "cloud_llm",
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"timestamp": 45.5, "duration": 0.5, "content_type": "soccer_broadcast",
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"frame_ref": 91})
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time.sleep(delay * 0.3)
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# Final
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push(r, key, {"event": "log", "level": "INFO", "stage": "Aggregator",
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"msg": "Report complete: 6 brands, 26 total appearances"})
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time.sleep(delay)
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push(r, key, {"event": "job_complete", "job_id": args.job,
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"report": {
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"video_source": "soccer_clip.mp4",
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"content_type": "soccer_broadcast",
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"duration_seconds": 60.0,
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"brands": {
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"Nike": {"total_appearances": 8, "total_screen_time": 4.0, "avg_confidence": 0.93},
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"Emirates": {"total_appearances": 6, "total_screen_time": 3.0, "avg_confidence": 0.89},
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"Adidas": {"total_appearances": 5, "total_screen_time": 2.5, "avg_confidence": 0.85},
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"Coca-Cola": {"total_appearances": 4, "total_screen_time": 2.0, "avg_confidence": 0.80},
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"Mastercard": {"total_appearances": 2, "total_screen_time": 1.0, "avg_confidence": 0.76},
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"Heineken": {"total_appearances": 1, "total_screen_time": 0.5, "avg_confidence": 0.71},
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},
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}})
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print(f"\nPipeline simulation complete.")
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if __name__ == "__main__":
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main()
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