mirror of
https://github.com/immich-app/immich.git
synced 2025-01-07 12:26:47 +01:00
2b1b43a7e4
* modularize model classes * various fixes * expose port * change response * round coordinates * simplify preload * update server * simplify interface simplify * update tests * composable endpoint * cleanup fixes remove unnecessary interface support text input, cleanup * ew camelcase * update server server fixes fix typing * ml fixes update locustfile fixes * cleaner response * better repo response * update tests formatting and typing rename * undo compose change * linting fix type actually fix typing * stricter typing fix detection-only response no need for defaultdict * update spec file update api linting * update e2e * unnecessary dimension * remove commented code * remove duplicate code * remove unused imports * add batch dim
48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
from pathlib import Path
|
|
from typing import Any
|
|
|
|
import numpy as np
|
|
from insightface.model_zoo import RetinaFace
|
|
from numpy.typing import NDArray
|
|
|
|
from app.models.base import InferenceModel
|
|
from app.models.transforms import decode_cv2
|
|
from app.schemas import FaceDetectionOutput, ModelSession, ModelTask, ModelType
|
|
|
|
|
|
class FaceDetector(InferenceModel):
|
|
depends = []
|
|
identity = (ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION)
|
|
|
|
def __init__(
|
|
self,
|
|
model_name: str,
|
|
min_score: float = 0.7,
|
|
cache_dir: Path | str | None = None,
|
|
**model_kwargs: Any,
|
|
) -> None:
|
|
self.min_score = model_kwargs.pop("minScore", min_score)
|
|
super().__init__(model_name, cache_dir, **model_kwargs)
|
|
|
|
def _load(self) -> ModelSession:
|
|
session = self._make_session(self.model_path)
|
|
self.model = RetinaFace(session=session)
|
|
self.model.prepare(ctx_id=0, det_thresh=self.min_score, input_size=(640, 640))
|
|
|
|
return session
|
|
|
|
def _predict(self, inputs: NDArray[np.uint8] | bytes, **kwargs: Any) -> FaceDetectionOutput:
|
|
inputs = decode_cv2(inputs)
|
|
|
|
bboxes, landmarks = self._detect(inputs)
|
|
return {
|
|
"boxes": bboxes[:, :4].round(),
|
|
"scores": bboxes[:, 4],
|
|
"landmarks": landmarks,
|
|
}
|
|
|
|
def _detect(self, inputs: NDArray[np.uint8] | bytes) -> tuple[NDArray[np.float32], NDArray[np.float32]]:
|
|
return self.model.detect(inputs) # type: ignore
|
|
|
|
def configure(self, **kwargs: Any) -> None:
|
|
self.model.det_thresh = kwargs.pop("minScore", self.model.det_thresh)
|