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remove insightface dependency

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mertalev 2026-08-06 22:43:53 -04:00
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commit a6ef4b61ff
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13 changed files with 206 additions and 702 deletions
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2
machine-learning/immich_ml/models/base.py
View file

@ -57,7 +57,7 @@ class InferenceModel(ABC):
self.load()
if model_kwargs:
self.configure(**model_kwargs)
return self._predict(*inputs)
return self._predict(*inputs, **model_kwargs)
@abstractmethod
def _predict(self, *inputs: Any, **model_kwargs: Any) -> Any: ...

74
machine-learning/immich_ml/models/facial_recognition/_ops.py Normal file
View file

@ -0,0 +1,74 @@
"""
Host-side geometry for the fused face models.
The decode, NMS and Umeyama similarity are ports of insightface (Apache-2.0, https://github.com/deepinsight/insightface)
"""
from functools import lru_cache
import cv2
import numpy as np
from numpy.typing import NDArray
from immich_ml.models.transforms import ensure_dims
DET_SIZE = 640
ALIGNED_SIZE = 112
# the FPN levels for which the fused detector emits a (scores, boxes, kps) triple,
# and the anchors each feature-map cell carries, laid out anchor-major
DET_STRIDES = (8, 16, 32)
ANCHORS_PER_CELL = 2
# canonical ArcFace 5-point template for a 112x112 crop
ARCFACE_DST = np.array(
[[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], [41.5493, 92.3655], [70.7299, 92.2041]],
dtype=np.float32,
)
@lru_cache(maxsize=len(DET_STRIDES))
def _anchor_centers(size: int, stride: int) -> NDArray[np.float32]:
ys, xs = np.mgrid[: size // stride, : size // stride]
centers = np.stack([xs, ys], axis=-1, dtype=np.float32).reshape(-1, 2) * stride
return np.repeat(centers, ANCHORS_PER_CELL, axis=0)
def decode_scrfd(
heads: list[NDArray[np.float32]], size: int = DET_SIZE
) -> tuple[NDArray[np.float32], NDArray[np.float32], NDArray[np.float32]]:
heads = [ensure_dims(head, 4) for head in heads]
scores, boxes, kps = [], [], []
for level, stride in enumerate(DET_STRIDES):
centers = _anchor_centers(size, stride)
distance = heads[level + len(DET_STRIDES)][0] * stride
offsets = heads[level + 2 * len(DET_STRIDES)][0] * stride
scores.append(heads[level][0].squeeze(-1))
boxes.append(np.concatenate([centers[None] - distance[:, :, :2], centers[None] + distance[:, :, 2:]], axis=2))
kps.append(np.tile(centers, offsets.shape[2] // 2) + offsets)
return np.concatenate(scores, axis=1), np.concatenate(boxes, axis=1), np.concatenate(kps, axis=1)
def nms(boxes: NDArray[np.float32], scores: NDArray[np.float32], threshold: float = 0.4) -> NDArray[np.intp]:
wh = np.column_stack([boxes[:, 0], boxes[:, 1], boxes[:, 2] - boxes[:, 0], boxes[:, 3] - boxes[:, 1]])
keep = cv2.dnn.NMSBoxes(wh.tolist(), scores.tolist(), 0.0, threshold) # NMSBoxes treats the inputs as Sequences
return np.asarray(keep, dtype=np.intp).reshape(-1)
def umeyama(src: NDArray[np.float32], dst: NDArray[np.float32]) -> NDArray[np.float32]:
src_mean, dst_mean = src.mean(0), dst.mean(0)
src_c, dst_c = src - src_mean, dst - dst_mean
cov = dst_c.T @ src_c / len(src)
u, s, vt = np.linalg.svd(cov)
d = np.sign(np.linalg.det(u @ vt))
diag = np.diag([1.0, d])
rotation = u @ diag @ vt
scale = np.trace(np.diag(s) @ diag) / (src_c**2).sum() * len(src)
translation = dst_mean - scale * rotation @ src_mean
return np.hstack([scale * rotation, translation[:, None]], dtype=np.float32)
def align_face(image: NDArray[np.uint8], kps: NDArray[np.float32]) -> NDArray[np.float32]:
matrix = umeyama(kps, ARCFACE_DST)
return cv2.warpAffine(image, matrix, (ALIGNED_SIZE, ALIGNED_SIZE)).astype(np.float32)

42
machine-learning/immich_ml/models/facial_recognition/detection.py
View file

@ -1,41 +1,31 @@
from typing import Any
import numpy as np
from insightface.model_zoo import RetinaFace
from numpy.typing import NDArray
from immich_ml.models.base import InferenceModel
from immich_ml.models.transforms import decode_cv2
from immich_ml.schemas import FaceDetectionOutput, ModelSession, ModelTask, ModelType
from immich_ml.models.transforms import decode_pil, letterbox, normalize
from immich_ml.schemas import FaceDetectionOutput, ModelTask, ModelType
from ._ops import DET_SIZE, decode_scrfd, nms
class FaceDetector(InferenceModel):
depends = []
identity = (ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION)
def __init__(self, model_name: str, min_score: float = 0.7, **model_kwargs: Any) -> None:
self.min_score = model_kwargs.pop("minScore", min_score)
super().__init__(model_name, **model_kwargs)
def _predict(self, inputs: NDArray[np.uint8] | bytes, minScore: float) -> FaceDetectionOutput:
canvas, scale = letterbox(decode_pil(inputs), DET_SIZE)
blob = normalize(canvas.astype(np.float32), mean=127.5, std=128).transpose(2, 0, 1)[None]
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))
input_name = self.session.get_inputs()[0].name
heads = self.session.run(None, {input_name: blob})
scores, boxes, kps = decode_scrfd(heads, DET_SIZE)
return session
candidates = scores >= minScore
scores, boxes, kps = scores[candidates], boxes[candidates] / scale, kps[candidates] / scale
keep = nms(boxes, scores)
def _predict(self, inputs: NDArray[np.uint8] | bytes) -> FaceDetectionOutput:
inputs = decode_cv2(inputs)
bboxes, landmarks = self._detect(inputs)
return {
"boxes": bboxes[:, :4].round(),
"scores": bboxes[:, 4],
"landmarks": landmarks,
"boxes": boxes[keep].round(),
"scores": scores[keep],
"landmarks": kps[keep].reshape(-1, 5, 2),
}
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)

36
machine-learning/immich_ml/models/facial_recognition/recognition.py
View file

@ -4,15 +4,13 @@ from typing import Any
import numpy as np
import onnx
import onnxruntime as ort
from insightface.model_zoo import ArcFaceONNX
from insightface.utils.face_align import norm_crop
from numpy.typing import NDArray
from onnx.tools.update_model_dims import update_inputs_outputs_dims
from PIL import Image
from immich_ml.config import log, settings
from immich_ml.models.base import InferenceModel
from immich_ml.models.transforms import decode_cv2, serialize_np_array
from immich_ml.models.transforms import decode_pil, normalize, serialize_np_array
from immich_ml.schemas import (
FaceDetectionOutput,
FacialRecognitionOutput,
@ -22,6 +20,8 @@ from immich_ml.schemas import (
ModelType,
)
from ._ops import align_face
class FaceRecognizer(InferenceModel):
depends = [(ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION)]
@ -37,10 +37,6 @@ class FaceRecognizer(InferenceModel):
if (not self.batch_size or self.batch_size > 1) and str(session.get_inputs()[0].shape[0]) != "batch":
self._add_batch_axis(self.model_path)
session = self._make_session(self.model_path)
self.model = ArcFaceONNX(
self.model_path_for_format(ModelFormat.ONNX).as_posix(),
session=session,
)
return session
def _predict(
@ -48,20 +44,21 @@ class FaceRecognizer(InferenceModel):
) -> FacialRecognitionOutput:
if faces["boxes"].shape[0] == 0:
return []
inputs = decode_cv2(inputs)
cropped_faces = self._crop(inputs, faces)
embeddings = self._predict_batch(cropped_faces)
image = np.asarray(decode_pil(inputs), dtype=np.uint8)
crops = np.stack([align_face(image, kps).transpose(2, 0, 1) for kps in faces["landmarks"]])
embeddings = self._predict_batch(normalize(crops, mean=127.5, std=127.5))
return self.postprocess(faces, embeddings)
def _predict_batch(self, cropped_faces: list[NDArray[np.uint8]]) -> NDArray[np.float32]:
if not self.batch_size or len(cropped_faces) <= self.batch_size:
embeddings: NDArray[np.float32] = self.model.get_feat(cropped_faces)
return embeddings
def _predict_batch(self, crops: NDArray[np.float32]) -> NDArray[np.float32]:
input_name = self.session.get_inputs()[0].name
if not self.batch_size or crops.shape[0] <= self.batch_size:
return self.session.run(None, {input_name: crops})[0]
batch_embeddings: list[NDArray[np.float32]] = []
for i in range(0, len(cropped_faces), self.batch_size):
batch_embeddings.append(self.model.get_feat(cropped_faces[i : i + self.batch_size]))
return np.concatenate(batch_embeddings, axis=0)
batches = [
self.session.run(None, {input_name: crops[i : i + self.batch_size]})[0]
for i in range(0, crops.shape[0], self.batch_size)
]
return np.concatenate(batches, axis=0)
def postprocess(self, faces: FaceDetectionOutput, embeddings: NDArray[np.float32]) -> FacialRecognitionOutput:
return [
@ -73,9 +70,6 @@ class FaceRecognizer(InferenceModel):
for (x1, y1, x2, y2), embedding, score in zip(faces["boxes"], embeddings, faces["scores"])
]
def _crop(self, image: NDArray[np.uint8], faces: FaceDetectionOutput) -> list[NDArray[np.uint8]]:
return [norm_crop(image, landmark) for landmark in faces["landmarks"]]
def _add_batch_axis(self, model_path: Path) -> None:
log.debug(f"Adding batch axis to model {model_path}")
proto = onnx.load(model_path)

3
machine-learning/immich_ml/models/ocr/detection.py
View file

@ -63,7 +63,8 @@ class TextDetector(InferenceModel):
return OrtSession(self.model_path)
# partly adapted from RapidOCR
def _predict(self, inputs: Image.Image) -> TextDetectionOutput:
# TODO: take the options as parameters instead of applying them through `configure`
def _predict(self, inputs: Image.Image, **kwargs: Any) -> TextDetectionOutput:
w, h = inputs.size
if w < 32 or h < 32:
return self._empty

3
machine-learning/immich_ml/models/ocr/recognition.py
View file

@ -69,7 +69,8 @@ class TextRecognizer(InferenceModel):
)
return session
def _predict(self, img: Image.Image, texts: TextDetectionOutput) -> TextRecognitionOutput:
# TODO: take the options as parameters instead of applying them through `configure`
def _predict(self, img: Image.Image, texts: TextDetectionOutput, **kwargs: Any) -> TextRecognitionOutput:
boxes, box_scores = texts["boxes"], texts["scores"]
if boxes.shape[0] == 0:
return self._empty

53
machine-learning/immich_ml/models/transforms.py
View file

@ -1,6 +1,5 @@
import string
from io import BytesIO
from typing import IO
import cv2
import numpy as np
@ -12,11 +11,10 @@ _PIL_RESAMPLING_METHODS = {resampling.name.lower(): resampling for resampling in
_PUNCTUATION_TRANS = str.maketrans("", "", string.punctuation)
def resize_pil(img: Image.Image, size: int) -> Image.Image:
def resize_pil(img: Image.Image, size: int, resample: Image.Resampling = Image.Resampling.BICUBIC) -> Image.Image:
if img.width < img.height:
return img.resize((size, int((img.height / img.width) * size)), resample=Image.Resampling.BICUBIC)
else:
return img.resize((int((img.width / img.height) * size), size), resample=Image.Resampling.BICUBIC)
return img.resize((size, int((img.height / img.width) * size)), resample=resample)
return img.resize((int((img.width / img.height) * size), size), resample=resample)
# https://stackoverflow.com/a/60883103
@ -36,7 +34,9 @@ def to_numpy(img: Image.Image) -> NDArray[np.float32]:
def normalize(
img: NDArray[np.float32], mean: float | NDArray[np.float32], std: float | NDArray[np.float32]
) -> NDArray[np.float32]:
return (img - mean) / std
img *= 1.0 / std
img -= mean / std
return img
def get_pil_resampling(resample: str) -> Image.Resampling:
@ -47,26 +47,21 @@ def pil_to_cv2(image: Image.Image) -> NDArray[np.uint8]:
return cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) # type: ignore
def decode_pil(image_bytes: bytes | IO[bytes] | Image.Image) -> Image.Image:
if isinstance(image_bytes, Image.Image):
return image_bytes
image: Image.Image = Image.open(BytesIO(image_bytes) if isinstance(image_bytes, bytes) else image_bytes)
def decode_pil(image_bytes: bytes | Image.Image | NDArray[np.uint8]) -> Image.Image:
image: Image.Image
match image_bytes:
case Image.Image():
image = image_bytes
case np.ndarray():
image = Image.fromarray(image_bytes)
case bytes():
image = Image.open(BytesIO(image_bytes))
image.load()
if not image.mode == "RGB":
image = image.convert("RGB")
return image
def decode_cv2(image_bytes: NDArray[np.uint8] | bytes | Image.Image) -> NDArray[np.uint8]:
match image_bytes:
case bytes() | memoryview() | bytearray():
return pil_to_cv2(decode_pil(image_bytes)) # pillow is much faster than cv2
case Image.Image():
return pil_to_cv2(image_bytes)
case _:
return image_bytes
def clean_text(text: str, canonicalize: bool = False) -> str:
text = " ".join(text.split())
if canonicalize:
@ -78,3 +73,21 @@ def clean_text(text: str, canonicalize: bool = False) -> str:
# TODO: use this in a less invasive way
def serialize_np_array(arr: NDArray[np.float32]) -> str:
return orjson.dumps(arr, option=orjson.OPT_SERIALIZE_NUMPY).decode()
def letterbox(image: NDArray[np.uint8] | Image.Image, size: int) -> tuple[NDArray[np.uint8], float]:
if isinstance(image, Image.Image):
image = np.asarray(image)
height, width = image.shape[:2]
if height > width:
new_height, new_width = size, int(size * width / height)
else:
new_width, new_height = size, int(size * height / width)
canvas = np.zeros((size, size, 3), dtype=np.uint8)
cv2.resize(image, (new_width, new_height), dst=canvas[:new_height, :new_width])
return canvas, new_height / height
def ensure_dims(array: NDArray[np.float32], ndim: int) -> NDArray[np.float32]:
return array if array.ndim >= ndim else np.expand_dims(array, axis=tuple(range(ndim - array.ndim)))

7
machine-learning/immich_ml/schemas.py
View file

@ -14,10 +14,7 @@ class ORJSONResponse(JSONResponse):
class StrEnum(str, Enum):
value: str
def __str__(self) -> str:
return self.value
__str__ = str.__str__
class BoundingBox(TypedDict):
@ -63,7 +60,7 @@ ModelIdentity = tuple[ModelType, ModelTask]
class SessionNode(Protocol):
@property
def name(self) -> str | None: ...
def name(self) -> str: ...
@property
def shape(self) -> tuple[int, ...]: ...

6
machine-learning/immich_ml/sessions/ann/__init__.py
View file

@ -37,11 +37,11 @@ class AnnSession:
def get_inputs(self) -> list[SessionNode]:
shapes = self.ann.input_shapes[self.model]
return [AnnNode(None, s) for s in shapes]
return [AnnNode(f"input.{i + 1}", s) for i, s in enumerate(shapes)]
def get_outputs(self) -> list[SessionNode]:
shapes = self.ann.output_shapes[self.model]
return [AnnNode(None, s) for s in shapes]
return [AnnNode(f"output.{i + 1}", s) for i, s in enumerate(shapes)]
def run(
self,
@ -54,5 +54,5 @@ class AnnSession:
class AnnNode(NamedTuple):
name: str | None
name: str
shape: tuple[int, ...]

2
machine-learning/immich_ml/sessions/rknn/__init__.py
View file

@ -69,7 +69,7 @@ class RknnSession:
class RknnNode(NamedTuple):
name: str | None
name: str
shape: tuple[int, ...]

2
machine-learning/pyproject.toml
View file

@ -10,8 +10,8 @@ dependencies = [
"fastapi>=0.95.2,<1.0",
"gunicorn>=21.1.0",
"huggingface-hub>=1.0,<2.0",
"insightface>=0.7.3,<2.0",
"numpy>=2.4.0,<3.0",
"onnx>=1.22.0",
"opencv-python-headless>=4.7.0.72,<5.0",
"orjson>=3.9.5",
"pillow>=12.2,<13",

4
machine-learning/test_main.py
View file

@ -516,7 +516,7 @@ class TestAnnSession:
inputs = session.get_inputs()
assert len(inputs) == 1
assert inputs[0].name is None
assert inputs[0].name == "input.1"
assert inputs[0].shape == (1, 3, 224, 224)
def test_get_outputs(self, ann_session: mock.Mock) -> None:
@ -527,7 +527,7 @@ class TestAnnSession:
outputs = session.get_outputs()
assert len(outputs) == 1
assert outputs[0].name is None
assert outputs[0].name == "output.1"
assert outputs[0].shape == (1, 3, 224, 224)
def test_run(self, ann_session: mock.Mock, mocker: MockerFixture) -> None:

674
machine-learning/uv.lock generated
View file

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]
[[package]]
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{ name = "pyyaml" },
{ name = "qudida" },
{ name = "scikit-image" },
{ name = "scipy" },
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{ name = "numpy" },
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