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More mypy cleanup (#22658)

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* Halfway point for fixing data processing

* Fix mixin types missing

* Cleanup LPR mypy

* Cleanup audio mypy

* Cleanup bird mypy

* Cleanup mypy for custom classification

* remove whisper

* Fix DB typing

* Cleanup events mypy

* Clenaup

* fix type evaluation

* Cleanup

* Fix broken imports
This commit is contained in:
Nicolas Mowen
2026-03-26 12:54:12 -06:00
committed by GitHub
parent 4772e6a2ab
commit 03d0139497
22 changed files with 398 additions and 274 deletions
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52
frigate/data_processing/common/face/model.py
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@@ -21,7 +21,7 @@ class FaceRecognizer(ABC):
def __init__(self, config: FrigateConfig) -> None:
self.config = config
self.landmark_detector: cv2.face.FacemarkLBF = None
self.landmark_detector: cv2.face.Facemark | None = None
self.init_landmark_detector()
@abstractmethod
@@ -38,13 +38,14 @@ class FaceRecognizer(ABC):
def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
pass
@redirect_output_to_logger(logger, logging.DEBUG)
@redirect_output_to_logger(logger, logging.DEBUG) # type: ignore[misc]
def init_landmark_detector(self) -> None:
landmark_model = os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
if os.path.exists(landmark_model):
self.landmark_detector = cv2.face.createFacemarkLBF()
self.landmark_detector.loadModel(landmark_model)
landmark_detector = cv2.face.createFacemarkLBF()
landmark_detector.loadModel(landmark_model)
self.landmark_detector = landmark_detector
def align_face(
self,
@@ -52,8 +53,10 @@ class FaceRecognizer(ABC):
output_width: int,
output_height: int,
) -> np.ndarray:
# landmark is run on grayscale images
if not self.landmark_detector:
raise ValueError("Landmark detector not initialized")
# landmark is run on grayscale images
if image.ndim == 3:
land_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
@@ -131,8 +134,11 @@ class FaceRecognizer(ABC):
def similarity_to_confidence(
cosine_similarity: float, median=0.3, range_width=0.6, slope_factor=12
):
cosine_similarity: float,
median: float = 0.3,
range_width: float = 0.6,
slope_factor: float = 12,
) -> float:
"""
Default sigmoid function to map cosine similarity to confidence.
@@ -151,14 +157,14 @@ def similarity_to_confidence(
bias = median
# Calculate confidence
confidence = 1 / (1 + np.exp(-slope * (cosine_similarity - bias)))
confidence: float = 1 / (1 + np.exp(-slope * (cosine_similarity - bias)))
return confidence
class FaceNetRecognizer(FaceRecognizer):
def __init__(self, config: FrigateConfig):
super().__init__(config)
self.mean_embs: dict[int, np.ndarray] = {}
self.mean_embs: dict[str, np.ndarray] = {}
self.face_embedder: FaceNetEmbedding = FaceNetEmbedding()
self.model_builder_queue: queue.Queue | None = None
@@ -168,7 +174,7 @@ class FaceNetRecognizer(FaceRecognizer):
def run_build_task(self) -> None:
self.model_builder_queue = queue.Queue()
def build_model():
def build_model() -> None:
face_embeddings_map: dict[str, list[np.ndarray]] = {}
idx = 0
@@ -187,7 +193,7 @@ class FaceNetRecognizer(FaceRecognizer):
img = cv2.imread(os.path.join(face_folder, image))
if img is None:
continue
continue # type: ignore[unreachable]
img = self.align_face(img, img.shape[1], img.shape[0])
emb = self.face_embedder([img])[0].squeeze()
@@ -195,12 +201,13 @@ class FaceNetRecognizer(FaceRecognizer):
idx += 1
assert self.model_builder_queue is not None
self.model_builder_queue.put(face_embeddings_map)
thread = threading.Thread(target=build_model, daemon=True)
thread.start()
def build(self):
def build(self) -> None:
if not self.landmark_detector:
self.init_landmark_detector()
return None
@@ -226,7 +233,7 @@ class FaceNetRecognizer(FaceRecognizer):
logger.debug("Finished building ArcFace model")
def classify(self, face_image):
def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
if not self.landmark_detector:
return None
@@ -245,7 +252,7 @@ class FaceNetRecognizer(FaceRecognizer):
img = self.align_face(face_image, face_image.shape[1], face_image.shape[0])
embedding = self.face_embedder([img])[0].squeeze()
score = 0
score: float = 0
label = ""
for name, mean_emb in self.mean_embs.items():
@@ -268,7 +275,7 @@ class FaceNetRecognizer(FaceRecognizer):
class ArcFaceRecognizer(FaceRecognizer):
def __init__(self, config: FrigateConfig):
super().__init__(config)
self.mean_embs: dict[int, np.ndarray] = {}
self.mean_embs: dict[str, np.ndarray] = {}
self.face_embedder: ArcfaceEmbedding = ArcfaceEmbedding(config.face_recognition)
self.model_builder_queue: queue.Queue | None = None
@@ -278,7 +285,7 @@ class ArcFaceRecognizer(FaceRecognizer):
def run_build_task(self) -> None:
self.model_builder_queue = queue.Queue()
def build_model():
def build_model() -> None:
face_embeddings_map: dict[str, list[np.ndarray]] = {}
idx = 0
@@ -297,20 +304,21 @@ class ArcFaceRecognizer(FaceRecognizer):
img = cv2.imread(os.path.join(face_folder, image))
if img is None:
continue
continue # type: ignore[unreachable]
img = self.align_face(img, img.shape[1], img.shape[0])
emb = self.face_embedder([img])[0].squeeze()
emb = self.face_embedder([img])[0].squeeze() # type: ignore[arg-type]
face_embeddings_map[name].append(emb)
idx += 1
assert self.model_builder_queue is not None
self.model_builder_queue.put(face_embeddings_map)
thread = threading.Thread(target=build_model, daemon=True)
thread.start()
def build(self):
def build(self) -> None:
if not self.landmark_detector:
self.init_landmark_detector()
return None
@@ -336,7 +344,7 @@ class ArcFaceRecognizer(FaceRecognizer):
logger.debug("Finished building ArcFace model")
def classify(self, face_image):
def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
if not self.landmark_detector:
return None
@@ -353,9 +361,9 @@ class ArcFaceRecognizer(FaceRecognizer):
# align face and run recognition
img = self.align_face(face_image, face_image.shape[1], face_image.shape[0])
embedding = self.face_embedder([img])[0].squeeze()
embedding = self.face_embedder([img])[0].squeeze() # type: ignore[arg-type]
score = 0
score: float = 0
label = ""
for name, mean_emb in self.mean_embs.items():