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Support downloading face models

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This commit is contained in:
Nicolas Mowen 2025-01-03 09:23:30 -07:00
parent 160664836f
commit 2c182d59f0
3 changed files with 59 additions and 40 deletions
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13
frigate/embeddings/embeddings.py
View File

@ -123,19 +123,6 @@ class Embeddings:
device="GPU" if config.semantic_search.model_size == "large" else "CPU",
)
if self.config.face_recognition.enabled:
self.face_embedding = GenericONNXEmbedding(
model_name="facedet",
model_file="facedet.onnx",
download_urls={
"facedet.onnx": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facedet.onnx",
"landmarkdet.yaml": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/landmarkdet.yaml",
},
model_size="small",
model_type=ModelTypeEnum.face,
requestor=self.requestor,
)
self.lpr_detection_model = None
self.lpr_classification_model = None
self.lpr_recognition_model = None

18
frigate/embeddings/maintainer.py
View File

@ -100,19 +100,6 @@ class EmbeddingMaintainer(threading.Thread):
self.lpr_config, self.requestor, self.embeddings
)
@property
def face_detector(self) -> cv2.FaceDetectorYN:
# Lazily create the classifier.
if "face_detector" not in self.__dict__:
self.__dict__["face_detector"] = cv2.FaceDetectorYN.create(
"/config/model_cache/facedet/facedet.onnx",
config="",
input_size=(320, 320),
score_threshold=0.8,
nms_threshold=0.3,
)
return self.__dict__["face_detector"]
def run(self) -> None:
"""Maintain a SQLite-vec database for semantic search."""
while not self.stop_event.is_set():
@ -395,10 +382,9 @@ class EmbeddingMaintainer(threading.Thread):
def _detect_face(self, input: np.ndarray) -> tuple[int, int, int, int]:
"""Detect faces in input image."""
self.face_detector.setInputSize((input.shape[1], input.shape[0]))
faces = self.face_detector.detect(input)
faces = self.face_classifier.detect_faces(input)
if faces[1] is None:
if faces is None or faces[1] is None:
return None
face = None

68
frigate/util/model.py
View File

@ -2,7 +2,7 @@
import logging
import os
from typing import Any, Optional
from typing import Any
import cv2
import numpy as np
@ -10,6 +10,8 @@ import onnxruntime as ort
from playhouse.sqliteq import SqliteQueueDatabase
from frigate.config.semantic_search import FaceRecognitionConfig
from frigate.const import MODEL_CACHE_DIR
from frigate.util.downloader import ModelDownloader
try:
import openvino as ov
@ -162,21 +164,50 @@ class FaceClassificationModel:
def __init__(self, config: FaceRecognitionConfig, db: SqliteQueueDatabase):
self.config = config
self.db = db
self.landmark_detector = cv2.face.createFacemarkLBF()
self.face_detector: cv2.FaceDetectorYN = None
self.landmark_detector: cv2.face.FacemarkLBF = None
self.face_recognizer: cv2.face.LBPHFaceRecognizer = None
if os.path.isfile("/config/model_cache/facedet/landmarkdet.yaml"):
self.landmark_detector.loadModel(
"/config/model_cache/facedet/landmarkdet.yaml"
)
download_path = os.path.join(MODEL_CACHE_DIR, "facedet")
model_files = ["facedet.onnx", "landmarkdet.yaml"]
self.model_urls = [
"https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facedet.onnx",
"https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/landmarkdet.yaml",
]
self.recognizer: cv2.face.LBPHFaceRecognizer = (
cv2.face.LBPHFaceRecognizer_create(
radius=2, threshold=(1 - config.min_score) * 1000
if not all(os.path.exists(os.path.join(download_path, n)) for n in model_files):
logger.debug(f"starting model download for {self.model_name}")
self.downloader = ModelDownloader(
model_name="facedet.onnx",
download_path=download_path,
file_names=model_files,
download_func=self.__download_models,
)
)
self.downloader.ensure_model_files()
else:
self.__build_detector()
self.label_map: dict[int, str] = {}
self.__build_classifier()
def __download_models(self, path: str) -> None:
try:
file_name = os.path.basename(path)
ModelDownloader.download_from_url(self.model_urls[file_name], path)
except Exception:
pass
def __build_detector(self) -> None:
self.face_detector = cv2.FaceDetectorYN.create(
"/config/model_cache/facedet/facedet.onnx",
config="",
input_size=(320, 320),
score_threshold=0.8,
nms_threshold=0.3,
)
self.landmark_detector = cv2.face.createFacemarkLBF()
self.landmark_detector.loadModel("/config/model_cache/facedet/landmarkdet.yaml")
def __build_classifier(self) -> None:
labels = []
faces = []
@ -203,6 +234,11 @@ class FaceClassificationModel:
faces.append(img)
labels.append(idx)
self.recognizer: cv2.face.LBPHFaceRecognizer = (
cv2.face.LBPHFaceRecognizer_create(
radius=2, threshold=(1 - self.config.min_score) * 1000
)
)
self.recognizer.train(faces, np.array(labels))
def __align_face(
@ -267,7 +303,17 @@ class FaceClassificationModel:
self.labeler = None
self.label_map = {}
def classify_face(self, face_image: np.ndarray) -> Optional[tuple[str, float]]:
def detect_faces(self, input: np.ndarray) -> tuple[int, cv2.typing.MatLike] | None:
if not self.face_detector:
return None
self.face_detector.setInputSize((input.shape[1], input.shape[0]))
return self.face_detector.detect(input)
def classify_face(self, face_image: np.ndarray) -> tuple[str, float] | None:
if not self.landmark_detector:
return None
if not self.label_map:
self.__build_classifier()