dtoro
bbb8e4850c
Centralize execution provider selection in providers.py with auto-detection and graceful fallback. All ONNX sessions (embedder, detector, face processor, recognizer) now use the configured providers. - New VISION_EXECUTION_PROVIDERS env var: "auto" for GPU auto-detect, or explicit "CUDAExecutionProvider,CPUExecutionProvider" - Provider priority: CUDA > ROCm > OpenVINO > CPU (when set to "auto") - docker-compose.yml includes commented-out NVIDIA GPU deploy section - Supports onnxruntime-gpu as a drop-in replacement for onnxruntime Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
"""
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Face detection + recognition using InsightFace (RetinaFace + ArcFace).
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Uses the buffalo_l model pack which auto-downloads on first use (~300MB).
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Produces 512-d ArcFace embeddings. Non-commercial research license —
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fine for homelab self-hosting.
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"""
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import logging
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from pathlib import Path
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import numpy as np
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from app.config import VisionSettings
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from app.services.vision.base import FaceProcessor, FaceDetection
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logger = logging.getLogger(__name__)
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class InsightFaceProcessor(FaceProcessor):
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def __init__(self, settings: VisionSettings):
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from insightface.app import FaceAnalysis
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model_root = str(Path(settings.models_dir) / "face" / "insightface")
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logger.info("Loading InsightFace buffalo_l from %s", model_root)
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from app.services.vision.providers import get_providers
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providers = get_providers(settings.execution_providers)
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self._app = FaceAnalysis(
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name="buffalo_l",
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root=model_root,
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providers=providers,
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)
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self._app.prepare(ctx_id=-1, det_size=(640, 640))
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self._min_det_score = settings.faces.recognition_threshold
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def process(self, image: np.ndarray) -> list[FaceDetection]:
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orig_h, orig_w = image.shape[:2]
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# InsightFace expects BGR
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bgr = image[:, :, ::-1].copy()
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faces = self._app.get(bgr)
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if not faces:
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return []
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results = []
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for face in faces:
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if face.det_score < self._min_det_score:
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continue
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# face.bbox is [x1, y1, x2, y2] in pixel coords
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x1, y1, x2, y2 = face.bbox
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bbox = [
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max(0, float(x1) / orig_w),
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max(0, float(y1) / orig_h),
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min(1, float(x2) / orig_w),
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min(1, float(y2) / orig_h),
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]
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embedding = face.normed_embedding # already L2-normalized, 512-d
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results.append(FaceDetection(
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bbox=bbox,
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embedding=embedding.astype(np.float32),
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quality=float(face.det_score),
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))
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return results
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@property
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def embedding_dim(self) -> int:
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return 512
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