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https://github.com/blakeblackshear/frigate.git
synced 2024-11-26 19:06:11 +01:00
Use opencv instead of face embedding
This commit is contained in:
Nicolas Mowen
parent
8b8df6d978
commit
51a28d3027
@ -24,7 +24,7 @@ class SemanticSearchConfig(FrigateBaseModel):
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class FaceRecognitionConfig(FrigateBaseModel):
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enabled: bool = Field(default=False, title="Enable face recognition.")
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threshold: float = Field(
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default=0.9, title="Face similarity score required to be considered a match."
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default=170, title="minimum face distance score required to be considered a match."
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)
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min_area: int = Field(
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default=500, title="Min area of face box to consider running face recognition."
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@ -451,29 +451,20 @@ class EmbeddingMaintainer(threading.Thread):
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),
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]
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ret, webp = cv2.imencode(
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".webp", face_frame, [int(cv2.IMWRITE_WEBP_QUALITY), 100]
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)
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if not ret:
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logger.debug("Not processing face due to error creating cropped image.")
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return
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embedding = self.embeddings.embed_face("unknown", webp.tobytes(), upsert=False)
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res = self.face_classifier.classify_face(embedding)
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res = self.face_classifier.classify_face(face_frame)
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if not res:
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return
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sub_label, score = res
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sub_label, distance = res
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logger.debug(
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f"Detected best face for person as: {sub_label} with score {score}"
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f"Detected best face for person as: {sub_label} with distance {distance}"
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)
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if id in self.detected_faces and score <= self.detected_faces[id]:
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if id in self.detected_faces and distance >= self.detected_faces[id]:
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logger.debug(
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f"Recognized face score {score} is less than previous face score ({self.detected_faces.get(id)})."
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f"Recognized face distance {distance} is greater than previous face distance ({self.detected_faces.get(id)})."
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)
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return
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@ -482,12 +473,11 @@ class EmbeddingMaintainer(threading.Thread):
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json={
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"camera": obj_data.get("camera"),
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"subLabel": sub_label,
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"subLabelScore": score,
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},
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)
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if resp.status_code == 200:
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self.detected_faces[id] = score
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self.detected_faces[id] = distance
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def _detect_license_plate(self, input: np.ndarray) -> tuple[int, int, int, int]:
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"""Return the dimensions of the input image as [x, y, width, height]."""
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@ -4,6 +4,7 @@ import logging
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import os
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from typing import Any, Optional
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import cv2
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import numpy as np
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import onnxruntime as ort
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from playhouse.sqliteq import SqliteQueueDatabase
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@ -164,81 +165,38 @@ class FaceClassificationModel:
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def __init__(self, config: FaceRecognitionConfig, db: SqliteQueueDatabase):
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self.config = config
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self.db = db
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self.labeler: Optional[LabelEncoder] = None
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self.classifier: Optional[SVC] = None
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self.embedding_query = f"""
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SELECT
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id,
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distance
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FROM vec_faces
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WHERE face_embedding MATCH ?
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AND k = {MIN_MATCHING_FACES} ORDER BY distance
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"""
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self.recognizer = cv2.face.LBPHFaceRecognizer_create(radius=4, threshold=config.threshold)
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self.label_map: dict[int, str] = {}
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def __build_classifier(self) -> None:
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faces: list[tuple[str, bytes]] = self.db.execute_sql(
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"SELECT id, face_embedding FROM vec_faces"
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).fetchall()
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embeddings = np.array([deserialize(f[1]) for f in faces])
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self.labeler = LabelEncoder()
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norms = Normalizer(norm="l2").transform(embeddings)
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labels = self.labeler.fit_transform([f[0].split("-")[0] for f in faces])
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self.classifier = SVC(
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kernel="linear", probability=True, decision_function_shape="ovo"
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)
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self.classifier.fit(norms, labels)
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labels = []
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faces = []
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dir = "/media/frigate/clips/faces"
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for idx, name in enumerate(os.listdir(dir)):
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self.label_map[idx] = name
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face_folder = os.path.join(dir, 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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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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equ = cv2.equalizeHist(gray)
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faces.append(equ)
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labels.append(idx)
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self.recognizer.train(faces, np.array(labels))
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def clear_classifier(self) -> None:
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self.classifier = None
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self.labeler = None
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def classify_face(self, embedding: np.ndarray) -> Optional[tuple[str, float]]:
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best_faces = self.db.execute_sql(
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self.embedding_query, [serialize(embedding)]
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).fetchall()
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logger.debug(f"Face embedding match: {best_faces}")
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if not best_faces or len(best_faces) < MIN_MATCHING_FACES:
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logger.debug(
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f"{len(best_faces)} < {MIN_MATCHING_FACES} min required faces."
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)
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return None
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sub_label = str(best_faces[0][0]).split("-")[0]
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avg_score = 0
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# check that the cosine similarity is close enough to match the face
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for face in best_faces:
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score = 1.0 - face[1]
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if face[0].split("-")[0] != sub_label:
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logger.debug("Detected multiple faces, result is not valid.")
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return None
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avg_score += score
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avg_score = round(avg_score / MIN_MATCHING_FACES, 2)
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if avg_score < self.config.threshold:
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logger.debug(
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f"Recognized face score {avg_score} is less than threshold ({self.config.threshold}))."
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)
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return None
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if not self.classifier:
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def classify_face(self, face_image: np.ndarray) -> Optional[tuple[str, float]]:
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if not self.label_map:
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self.__build_classifier()
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cosine_index = self.labeler.transform([sub_label])[0]
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probabilities: np.ndarray = self.classifier.predict_proba([embedding])[0]
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svc_probability = max(probabilities)
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logger.debug(
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f"SVC face classification probability: {svc_probability} and index match: {cosine_index} / {np.where(probabilities == svc_probability)[0]}"
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)
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if cosine_index == np.where(probabilities == svc_probability)[0]:
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return (
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sub_label,
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min(avg_score, svc_probability),
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)
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index, distance = self.recognizer.predict(cv2.equalizeHist(cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)))
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if index == -1:
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return None
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return self.label_map[index], distance
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