OliviaCandClaude Opus 5.5
9aa63e4e02
One log line per phase every PHASE_LOG_EVERY samples (default 100, 0 = off): mean and max of wait_frames, align, convert, each JPEG/PNG encoding, colormap, depth_stats, base64, json and ws_send. Meant for the measurement on PC2 via `docker logs` (optimize-streams R01): where a capture spends its ~130 ms of CPU is not known yet. capture() moves its numpy conversion into _frame_from() unchanged, only so the block can be timed; _depth_colormap() likewise wraps _jet_colormap(). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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# Server
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api_host: str = "0.0.0.0"
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api_port: int = 8878
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# NATS
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nats_url: str = "nats://nats:4222"
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nats_token: str = ""
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# Service identity
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service_name: str = "realsense-d435"
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service_domain: str = "vision"
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# Security
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api_key: str = ""
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# Storage
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images_dir: str = "/app/images"
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# RealSense stream config
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camera_width: int = 640
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camera_height: int = 480
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camera_fps: int = 30
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camera_auto_open: bool = True
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# Frame-path timings: one log line per phase every N samples (0 = off).
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# Read from `docker logs` to see where a capture spends its time on PC2.
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phase_log_every: int = 100
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# YOLO detector (optional — disabled if model file or onnxruntime missing)
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yolo_onnx_path: str = "/app/models/best.onnx"
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yolo_classes: str = ""
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yolo_conf_default: float = 0.5
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@property
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def yolo_class_list(self) -> list[str]:
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return [s.strip() for s in self.yolo_classes.split(",") if s.strip()]
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# CORS (comma-separated string, parsed into list)
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cors_origins: str = "http://localhost:5173,http://localhost,http://localhost:80"
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@property
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def cors_origins_list(self) -> list[str]:
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return [s.strip() for s in self.cors_origins.split(",")]
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model_config = {
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"env_file": ".env",
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"env_file_encoding": "utf-8",
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}
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settings = Settings()
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