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https://github.com/blakeblackshear/frigate.git
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Implement facenet tflite for small face recognition model (#17402)
This commit is contained in:
Nicolas Mowen
GitHub
@@ -10,7 +10,7 @@ from scipy import stats
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from frigate.config import FrigateConfig
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from frigate.const import MODEL_CACHE_DIR
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from frigate.embeddings.onnx.face_embedding import ArcfaceEmbedding
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from frigate.embeddings.onnx.face_embedding import ArcfaceEmbedding, FaceNetEmbedding
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logger = logging.getLogger(__name__)
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@@ -124,83 +124,140 @@ class FaceRecognizer(ABC):
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return 1.0
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class LBPHRecognizer(FaceRecognizer):
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def similarity_to_confidence(
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cosine_similarity: float, median=0.3, range_width=0.6, slope_factor=12
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):
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"""
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Default sigmoid function to map cosine similarity to confidence.
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Args:
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cosine_similarity (float): The input cosine similarity.
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median (float): Assumed median of cosine similarity distribution.
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range_width (float): Assumed range of cosine similarity distribution (90th percentile - 10th percentile).
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slope_factor (float): Adjusts the steepness of the curve.
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Returns:
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float: The confidence score.
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"""
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# Calculate slope and bias
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slope = slope_factor / range_width
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bias = median
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# Calculate confidence
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confidence = 1 / (1 + np.exp(-slope * (cosine_similarity - bias)))
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return confidence
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class FaceNetRecognizer(FaceRecognizer):
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def __init__(self, config: FrigateConfig):
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super().__init__(config)
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self.label_map: dict[int, str] = {}
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self.recognizer: cv2.face.LBPHFaceRecognizer | None = None
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self.mean_embs: dict[int, np.ndarray] = {}
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self.face_embedder: FaceNetEmbedding = FaceNetEmbedding()
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self.model_builder_queue: queue.Queue | None = None
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def clear(self) -> None:
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self.face_recognizer = None
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self.label_map = {}
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self.mean_embs = {}
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def run_build_task(self) -> None:
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self.model_builder_queue = queue.Queue()
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def build_model():
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face_embeddings_map: dict[str, list[np.ndarray]] = {}
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idx = 0
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dir = "/media/frigate/clips/faces"
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for name in os.listdir(dir):
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if name == "train":
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continue
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face_folder = os.path.join(dir, name)
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if not os.path.isdir(face_folder):
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continue
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face_embeddings_map[name] = []
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for image in os.listdir(face_folder):
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img = cv2.imread(os.path.join(face_folder, image))
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if img is None:
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continue
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img = self.align_face(img, img.shape[1], img.shape[0])
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emb = self.face_embedder([img])[0].squeeze()
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face_embeddings_map[name].append(emb)
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idx += 1
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self.model_builder_queue.put(face_embeddings_map)
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thread = threading.Thread(target=build_model, daemon=True)
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thread.start()
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def build(self):
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if not self.landmark_detector:
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self.init_landmark_detector()
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return None
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labels = []
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faces = []
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idx = 0
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dir = "/media/frigate/clips/faces"
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for name in os.listdir(dir):
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if name == "train":
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continue
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face_folder = os.path.join(dir, name)
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if not os.path.isdir(face_folder):
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continue
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self.label_map[idx] = name
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for image in os.listdir(face_folder):
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img = cv2.imread(os.path.join(face_folder, image))
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if img is None:
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continue
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img = self.align_face(img, img.shape[1], img.shape[0])
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faces.append(img)
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labels.append(idx)
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idx += 1
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if not faces:
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if self.model_builder_queue is not None:
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try:
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face_embeddings_map: dict[str, list[np.ndarray]] = (
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self.model_builder_queue.get(timeout=0.1)
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)
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self.model_builder_queue = None
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except queue.Empty:
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return
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else:
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self.run_build_task()
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return
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self.recognizer: cv2.face.LBPHFaceRecognizer = (
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cv2.face.LBPHFaceRecognizer_create(radius=2, threshold=400)
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)
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self.recognizer.train(faces, np.array(labels))
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if not face_embeddings_map:
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return
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def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
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for name, embs in face_embeddings_map.items():
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if embs:
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self.mean_embs[name] = stats.trim_mean(embs, 0.15)
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logger.debug("Finished building ArcFace model")
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def classify(self, face_image):
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if not self.landmark_detector:
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return None
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if not self.label_map or not self.recognizer:
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if not self.mean_embs:
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self.build()
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if not self.recognizer:
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if not self.mean_embs:
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return None
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# face recognition is best run on grayscale images
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img = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
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# get blur factor before aligning face
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blur_factor = self.get_blur_factor(img)
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logger.debug(f"face detected with bluriness {blur_factor}")
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blur_factor = self.get_blur_factor(face_image)
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logger.debug(f"face detected with blurriness {blur_factor}")
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# align face and run recognition
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img = self.align_face(img, img.shape[1], img.shape[0])
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index, distance = self.recognizer.predict(img)
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img = self.align_face(face_image, face_image.shape[1], face_image.shape[0])
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embedding = self.face_embedder([img])[0].squeeze()
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if index == -1:
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return None
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score = 0
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label = ""
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score = (1.0 - (distance / 1000)) * blur_factor
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return self.label_map[index], round(score, 2)
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for name, mean_emb in self.mean_embs.items():
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dot_product = np.dot(embedding, mean_emb)
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magnitude_A = np.linalg.norm(embedding)
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magnitude_B = np.linalg.norm(mean_emb)
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cosine_similarity = dot_product / (magnitude_A * magnitude_B)
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confidence = similarity_to_confidence(
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cosine_similarity, median=0.5, range_width=0.6
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)
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if confidence > score:
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score = confidence
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label = name
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return label, round(score * blur_factor, 2)
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class ArcFaceRecognizer(FaceRecognizer):
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@@ -274,30 +331,6 @@ class ArcFaceRecognizer(FaceRecognizer):
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logger.debug("Finished building ArcFace model")
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def similarity_to_confidence(
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self, cosine_similarity: float, median=0.3, range_width=0.6, slope_factor=12
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):
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"""
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Default sigmoid function to map cosine similarity to confidence.
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Args:
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cosine_similarity (float): The input cosine similarity.
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median (float): Assumed median of cosine similarity distribution.
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range_width (float): Assumed range of cosine similarity distribution (90th percentile - 10th percentile).
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slope_factor (float): Adjusts the steepness of the curve.
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Returns:
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float: The confidence score.
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"""
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# Calculate slope and bias
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slope = slope_factor / range_width
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bias = median
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# Calculate confidence
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confidence = 1 / (1 + np.exp(-slope * (cosine_similarity - bias)))
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return confidence
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def classify(self, face_image):
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if not self.landmark_detector:
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return None
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@@ -312,7 +345,7 @@ class ArcFaceRecognizer(FaceRecognizer):
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# get blur factor before aligning face
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blur_factor = self.get_blur_factor(face_image)
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logger.debug(f"face detected with bluriness {blur_factor}")
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logger.debug(f"face detected with blurriness {blur_factor}")
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# align face and run recognition
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img = self.align_face(face_image, face_image.shape[1], face_image.shape[0])
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@@ -327,7 +360,7 @@ class ArcFaceRecognizer(FaceRecognizer):
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magnitude_B = np.linalg.norm(mean_emb)
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cosine_similarity = dot_product / (magnitude_A * magnitude_B)
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confidence = self.similarity_to_confidence(cosine_similarity)
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confidence = similarity_to_confidence(cosine_similarity)
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if confidence > score:
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score = confidence
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