mirror of
https://github.com/immich-app/immich.git
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258b98c262
* load models in thread * set clip mode logs to debug level * updated tests * made fixtures slightly less ugly * moved responses to json file * formatting
108 lines
3.7 KiB
Python
108 lines
3.7 KiB
Python
import zipfile
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from pathlib import Path
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from typing import Any
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import cv2
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import numpy as np
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import onnxruntime as ort
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from insightface.model_zoo import ArcFaceONNX, RetinaFace
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from insightface.utils.face_align import norm_crop
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from insightface.utils.storage import BASE_REPO_URL, download_file
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from ..schemas import ModelType
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from .base import InferenceModel
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class FaceRecognizer(InferenceModel):
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_model_type = ModelType.FACIAL_RECOGNITION
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def __init__(
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self,
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model_name: str,
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min_score: float = 0.7,
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cache_dir: Path | str | None = None,
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**model_kwargs: Any,
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) -> None:
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self.min_score = model_kwargs.pop("minScore", min_score)
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super().__init__(model_name, cache_dir, **model_kwargs)
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def _download(self) -> None:
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zip_file = self.cache_dir / f"{self.model_name}.zip"
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download_file(f"{BASE_REPO_URL}/{self.model_name}.zip", zip_file)
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with zipfile.ZipFile(zip_file, "r") as zip:
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members = zip.namelist()
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det_file = next(model for model in members if model.startswith("det_"))
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rec_file = next(model for model in members if model.startswith("w600k_"))
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zip.extractall(self.cache_dir, members=[det_file, rec_file])
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zip_file.unlink()
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def _load(self) -> None:
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try:
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det_file = next(self.cache_dir.glob("det_*.onnx"))
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rec_file = next(self.cache_dir.glob("w600k_*.onnx"))
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except StopIteration:
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raise FileNotFoundError("Facial recognition models not found in cache directory")
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self.det_model = RetinaFace(
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session=ort.InferenceSession(
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det_file.as_posix(),
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sess_options=self.sess_options,
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providers=self.providers,
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provider_options=self.provider_options,
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),
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)
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self.rec_model = ArcFaceONNX(
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rec_file.as_posix(),
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session=ort.InferenceSession(
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rec_file.as_posix(),
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sess_options=self.sess_options,
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providers=self.providers,
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provider_options=self.provider_options,
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),
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)
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self.det_model.prepare(
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ctx_id=0,
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det_thresh=self.min_score,
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input_size=(640, 640),
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)
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self.rec_model.prepare(ctx_id=0)
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def _predict(self, image: np.ndarray[int, np.dtype[Any]] | bytes) -> list[dict[str, Any]]:
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if isinstance(image, bytes):
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image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR)
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bboxes, kpss = self.det_model.detect(image)
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if bboxes.size == 0:
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return []
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assert isinstance(image, np.ndarray) and isinstance(kpss, np.ndarray)
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scores = bboxes[:, 4].tolist()
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bboxes = bboxes[:, :4].round().tolist()
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results = []
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height, width, _ = image.shape
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for (x1, y1, x2, y2), score, kps in zip(bboxes, scores, kpss):
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cropped_img = norm_crop(image, kps)
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embedding = self.rec_model.get_feat(cropped_img)[0].tolist()
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results.append(
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{
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"imageWidth": width,
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"imageHeight": height,
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"boundingBox": {
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"x1": x1,
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"y1": y1,
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"x2": x2,
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"y2": y2,
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},
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"score": score,
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"embedding": embedding,
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}
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)
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return results
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@property
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def cached(self) -> bool:
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return self.cache_dir.is_dir() and any(self.cache_dir.glob("*.onnx"))
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def configure(self, **model_kwargs: Any) -> None:
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self.det_model.det_thresh = model_kwargs.pop("minScore", self.det_model.det_thresh)
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