mirror of
https://github.com/KevinMidboe/immich.git
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a2f5674bbb
* basic refactor and styling * removed batching * module entrypoint * removed unused imports * model superclass, model cache now in app state * fixed cache dir and enforced abstract method --------- Co-authored-by: Alex Tran <alex.tran1502@gmail.com>
60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
from pathlib import Path
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from typing import Any
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import cv2
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from insightface.app import FaceAnalysis
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from ..config import settings
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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 = settings.min_face_score,
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cache_dir: Path | None = None,
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**model_kwargs,
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):
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super().__init__(model_name, cache_dir)
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self.min_score = min_score
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model = FaceAnalysis(
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name=self.model_name,
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root=self.cache_dir.as_posix(),
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allowed_modules=["detection", "recognition"],
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**model_kwargs,
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)
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model.prepare(
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ctx_id=0,
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det_thresh=self.min_score,
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det_size=(640, 640),
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)
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self.model = model
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def predict(self, image: cv2.Mat) -> list[dict[str, Any]]:
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height, width, _ = image.shape
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results = []
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faces = self.model.get(image)
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for face in faces:
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x1, y1, x2, y2 = face.bbox
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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": round(x1),
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"y1": round(y1),
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"x2": round(x2),
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"y2": round(y2),
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},
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"score": face.det_score.item(),
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"embedding": face.normed_embedding.tolist(),
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}
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)
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return results
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