phase cv 0
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@@ -17,6 +17,7 @@ from detect.profiles import SoccerBroadcastProfile
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from detect.state import DetectState
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from detect.stages.frame_extractor import extract_frames
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from detect.stages.scene_filter import scene_filter
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from detect.stages.edge_detector import detect_edge_regions
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from detect.stages.yolo_detector import detect_objects
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from detect.stages.preprocess import preprocess_regions
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from detect.stages.ocr_stage import run_ocr
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@@ -31,6 +32,7 @@ INFERENCE_URL = os.environ.get("INFERENCE_URL") # None = local mode
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NODES = [
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"extract_frames",
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"filter_scenes",
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"detect_edges",
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"detect_objects",
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"preprocess",
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"run_ocr",
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@@ -119,6 +121,28 @@ def node_filter_scenes(state: DetectState) -> dict:
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return {"filtered_frames": kept, "stats": stats}
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def node_detect_edges(state: DetectState) -> dict:
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_emit_transition(state, "detect_edges", "running")
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with trace_node(state, "detect_edges") as span:
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profile = _get_profile(state)
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config = profile.region_analysis_config()
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frames = state.get("filtered_frames", [])
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job_id = state.get("job_id")
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regions = detect_edge_regions(
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frames, config, inference_url=INFERENCE_URL, job_id=job_id,
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)
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total = sum(len(r) for r in regions.values())
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span.set_output({"frames": len(frames), "edge_regions": total})
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stats = state.get("stats", PipelineStats())
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stats.cv_regions_detected = total
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_emit_transition(state, "detect_edges", "done")
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return {"edge_regions_by_frame": regions, "stats": stats}
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def node_detect_objects(state: DetectState) -> dict:
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_emit_transition(state, "detect_objects", "running")
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@@ -359,6 +383,7 @@ def _checkpointing_node(node_name: str, node_fn):
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NODE_FUNCTIONS = [
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("extract_frames", node_extract_frames),
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("filter_scenes", node_filter_scenes),
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("detect_edges", node_detect_edges),
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("detect_objects", node_detect_objects),
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("preprocess", node_preprocess),
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("run_ocr", node_run_ocr),
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