mirror of
https://github.com/blakeblackshear/frigate.git
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Refactor face recognition to allow for running lbph or embedding
This commit is contained in:
Nicolas Mowen
parent
2c3ea5b74e
commit
a13494c8df
@ -51,6 +51,9 @@ 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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model_size: str = Field(
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default="small", title="The size of the embeddings model used."
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)
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min_score: float = Field(
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title="Minimum face distance score required to save the attempt.",
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default=0.8,
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277
frigate/data_processing/common/face/model.py
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277
frigate/data_processing/common/face/model.py
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@ -0,0 +1,277 @@
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import logging
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import os
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from abc import ABC, abstractmethod
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import cv2
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import numpy as np
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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.facenet import FaceNetEmbedding
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logger = logging.getLogger(__name__)
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class FaceRecognizer(ABC):
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"""Face recognition runner."""
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def __init__(self, config: FrigateConfig) -> None:
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self.config = config
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self.landmark_detector = cv2.face.createFacemarkLBF()
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self.landmark_detector.loadModel(
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os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
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)
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@abstractmethod
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def build(self) -> None:
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"""Build face recognition model."""
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pass
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@abstractmethod
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def clear(self) -> None:
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"""Clear current built model."""
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pass
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@abstractmethod
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def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
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pass
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def align_face(
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self,
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image: np.ndarray,
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output_width: int,
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output_height: int,
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) -> np.ndarray:
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_, lands = self.landmark_detector.fit(
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image, np.array([(0, 0, image.shape[1], image.shape[0])])
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)
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landmarks: np.ndarray = lands[0][0]
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# get landmarks for eyes
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leftEyePts = landmarks[42:48]
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rightEyePts = landmarks[36:42]
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# compute the center of mass for each eye
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leftEyeCenter = leftEyePts.mean(axis=0).astype("int")
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rightEyeCenter = rightEyePts.mean(axis=0).astype("int")
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# compute the angle between the eye centroids
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dY = rightEyeCenter[1] - leftEyeCenter[1]
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dX = rightEyeCenter[0] - leftEyeCenter[0]
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angle = np.degrees(np.arctan2(dY, dX)) - 180
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# compute the desired right eye x-coordinate based on the
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# desired x-coordinate of the left eye
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desiredRightEyeX = 1.0 - 0.35
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# determine the scale of the new resulting image by taking
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# the ratio of the distance between eyes in the *current*
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# image to the ratio of distance between eyes in the
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# *desired* image
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dist = np.sqrt((dX**2) + (dY**2))
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desiredDist = desiredRightEyeX - 0.35
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desiredDist *= output_width
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scale = desiredDist / dist
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# compute center (x, y)-coordinates (i.e., the median point)
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# between the two eyes in the input image
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# grab the rotation matrix for rotating and scaling the face
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eyesCenter = (
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int((leftEyeCenter[0] + rightEyeCenter[0]) // 2),
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int((leftEyeCenter[1] + rightEyeCenter[1]) // 2),
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)
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M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
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# update the translation component of the matrix
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tX = output_width * 0.5
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tY = output_height * 0.35
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M[0, 2] += tX - eyesCenter[0]
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M[1, 2] += tY - eyesCenter[1]
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# apply the affine transformation
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return cv2.warpAffine(
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image, M, (output_width, output_height), flags=cv2.INTER_CUBIC
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)
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def get_blur_factor(self, input: np.ndarray) -> float:
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"""Calculates the factor for the confidence based on the blur of the image."""
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if not self.config.face_recognition.blur_confidence_filter:
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return 1.0
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variance = cv2.Laplacian(input, cv2.CV_64F).var()
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if variance < 60: # image is very blurry
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return 0.96
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elif variance < 70: # image moderately blurry
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return 0.98
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elif variance < 80: # image is slightly blurry
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return 0.99
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else:
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return 1.0
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class LBPHRecognizer(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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def clear(self) -> None:
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self.face_recognizer = None
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self.label_map = {}
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def build(self):
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if not self.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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return
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self.recognizer: cv2.face.LBPHFaceRecognizer = (
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cv2.face.LBPHFaceRecognizer_create(
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radius=2, threshold=(1 - self.config.face_recognition.min_score) * 1000
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)
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)
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self.recognizer.train(faces, np.array(labels))
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def classify(self, face_image: np.ndarray) -> tuple[str, float] | None:
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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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self.build()
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if not self.recognizer:
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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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# 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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if index == -1:
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return None
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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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class FaceNetRecognizer(FaceRecognizer):
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def __init__(self, config: FrigateConfig):
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super().__init__(config)
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self.mean_embs: dict[int, np.ndarray] = {}
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self.face_embedder: FaceNetEmbedding = FaceNetEmbedding()
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def clear(self) -> None:
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self.mean_embs = None
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def build(self):
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if not self.landmark_detector:
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return None
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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 = 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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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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if not face_embeddings_map:
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return
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for name, embs in face_embeddings_map.items():
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self.mean_embs[name] = np.mean(embs, axis=0)
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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.mean_embs:
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self.build()
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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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# align face and run recognition
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img = self.align_face(img, img.shape[1], img.shape[0])
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embedding = self.face_embedder([img])[0].squeeze()
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score = 0
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label = ""
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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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if cosine_similarity > score:
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score = cosine_similarity
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label = name
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if score < self.config.face_recognition.min_score:
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return None
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return label, round(score * blur_factor, 2)
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@ -19,6 +19,11 @@ from frigate.comms.event_metadata_updater import (
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)
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from frigate.config import FrigateConfig
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from frigate.const import FACE_DIR, MODEL_CACHE_DIR
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from frigate.data_processing.common.face.model import (
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FaceNetRecognizer,
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FaceRecognizer,
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LBPHRecognizer,
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)
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from frigate.util.image import area
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from ..types import DataProcessorMetrics
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@ -42,10 +47,9 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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self.face_config = config.face_recognition
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self.sub_label_publisher = sub_label_publisher
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self.face_detector: cv2.FaceDetectorYN = None
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self.landmark_detector: cv2.face.FacemarkLBF = None
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self.recognizer: cv2.face.LBPHFaceRecognizer = None
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self.requires_face_detection = "face" not in self.config.objects.all_objects
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self.detected_faces: dict[str, float] = {}
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self.recognizer: FaceRecognizer | None = None
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download_path = os.path.join(MODEL_CACHE_DIR, "facedet")
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self.model_files = {
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@ -72,7 +76,13 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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self.__build_detector()
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self.label_map: dict[int, str] = {}
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self.__build_classifier()
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if self.face_config.model_size == "smal":
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self.recognizer = LBPHRecognizer(self.config)
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else:
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self.recognizer = FaceNetRecognizer(self.config)
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self.recognizer.build()
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def __download_models(self, path: str) -> None:
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try:
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@ -92,126 +102,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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score_threshold=0.5,
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nms_threshold=0.3,
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)
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self.landmark_detector = cv2.face.createFacemarkLBF()
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self.landmark_detector.loadModel(
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os.path.join(MODEL_CACHE_DIR, "facedet/landmarkdet.yaml")
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)
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def __build_classifier(self) -> None:
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if not self.landmark_detector:
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return None
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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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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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if not faces:
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return
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self.recognizer: cv2.face.LBPHFaceRecognizer = (
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cv2.face.LBPHFaceRecognizer_create(
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radius=2, threshold=(1 - self.face_config.min_score) * 1000
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)
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)
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self.recognizer.train(faces, np.array(labels))
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def __align_face(
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self,
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image: np.ndarray,
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output_width: int,
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output_height: int,
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) -> np.ndarray:
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_, lands = self.landmark_detector.fit(
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image, np.array([(0, 0, image.shape[1], image.shape[0])])
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)
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landmarks: np.ndarray = lands[0][0]
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# get landmarks for eyes
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leftEyePts = landmarks[42:48]
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rightEyePts = landmarks[36:42]
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# compute the center of mass for each eye
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leftEyeCenter = leftEyePts.mean(axis=0).astype("int")
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rightEyeCenter = rightEyePts.mean(axis=0).astype("int")
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# compute the angle between the eye centroids
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dY = rightEyeCenter[1] - leftEyeCenter[1]
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dX = rightEyeCenter[0] - leftEyeCenter[0]
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angle = np.degrees(np.arctan2(dY, dX)) - 180
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# compute the desired right eye x-coordinate based on the
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# desired x-coordinate of the left eye
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desiredRightEyeX = 1.0 - 0.35
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# determine the scale of the new resulting image by taking
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# the ratio of the distance between eyes in the *current*
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# image to the ratio of distance between eyes in the
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# *desired* image
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dist = np.sqrt((dX**2) + (dY**2))
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desiredDist = desiredRightEyeX - 0.35
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desiredDist *= output_width
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scale = desiredDist / dist
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# compute center (x, y)-coordinates (i.e., the median point)
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# between the two eyes in the input image
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# grab the rotation matrix for rotating and scaling the face
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eyesCenter = (
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int((leftEyeCenter[0] + rightEyeCenter[0]) // 2),
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int((leftEyeCenter[1] + rightEyeCenter[1]) // 2),
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)
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M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
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# update the translation component of the matrix
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tX = output_width * 0.5
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tY = output_height * 0.35
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M[0, 2] += tX - eyesCenter[0]
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M[1, 2] += tY - eyesCenter[1]
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# apply the affine transformation
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return cv2.warpAffine(
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image, M, (output_width, output_height), flags=cv2.INTER_CUBIC
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)
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def __get_blur_factor(self, input: np.ndarray) -> float:
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"""Calculates the factor for the confidence based on the blur of the image."""
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if not self.face_config.blur_confidence_filter:
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return 1.0
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variance = cv2.Laplacian(input, cv2.CV_64F).var()
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if variance < 60: # image is very blurry
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return 0.96
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elif variance < 70: # image moderately blurry
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return 0.98
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elif variance < 80: # image is slightly blurry
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return 0.99
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else:
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return 1.0
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def __clear_classifier(self) -> None:
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self.face_recognizer = None
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self.label_map = {}
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def __detect_face(
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self, input: np.ndarray, threshold: float
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@ -254,33 +144,6 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
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return face
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def __classify_face(self, face_image: np.ndarray) -> tuple[str, float] | None:
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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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self.__build_classifier()
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if not self.recognizer:
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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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# 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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if index == -1:
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return None
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score = (1.0 - (distance / 1000)) * blur_factor
|
||||
return self.label_map[index], round(score, 2)
|
||||
|
||||
def __update_metrics(self, duration: float) -> None:
|
||||
self.metrics.face_rec_fps.value = (
|
||||
self.metrics.face_rec_fps.value * 9 + duration
|
||||
@ -370,7 +233,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
max(0, face_box[0]) : min(frame.shape[1], face_box[2]),
|
||||
]
|
||||
|
||||
res = self.__classify_face(face_frame)
|
||||
res = self.recognizer.classify(face_frame)
|
||||
|
||||
if not res:
|
||||
return
|
||||
@ -431,7 +294,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
return {"message": "No face was detected.", "success": False}
|
||||
|
||||
face = img[face_box[1] : face_box[3], face_box[0] : face_box[2]]
|
||||
res = self.__classify_face(face)
|
||||
res = self.recognizer.classify(face)
|
||||
|
||||
if not res:
|
||||
return {"success": False, "message": "No face was recognized."}
|
||||
@ -500,7 +363,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
|
||||
"success": False,
|
||||
}
|
||||
|
||||
res = self.__classify_face(img)
|
||||
res = self.recognizer.classify(img)
|
||||
|
||||
if not res:
|
||||
return
|
||||
|
||||
@ -69,6 +69,8 @@ class BaseEmbedding(ABC):
|
||||
image = Image.open(BytesIO(response.content)).convert(output)
|
||||
elif isinstance(image, bytes):
|
||||
image = Image.open(BytesIO(image)).convert(output)
|
||||
elif isinstance(image, np.ndarray):
|
||||
image = Image.fromarray(image)
|
||||
|
||||
return image
|
||||
|
||||
|
||||
94
frigate/embeddings/onnx/facenet.py
Normal file
94
frigate/embeddings/onnx/facenet.py
Normal file
@ -0,0 +1,94 @@
|
||||
"""Facenet Embeddings."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
|
||||
from frigate.const import MODEL_CACHE_DIR
|
||||
from frigate.util.downloader import ModelDownloader
|
||||
|
||||
from .base_embedding import BaseEmbedding
|
||||
from .runner import ONNXModelRunner
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
FACE_EMBEDDING_SIZE = 160
|
||||
|
||||
|
||||
class FaceNetEmbedding(BaseEmbedding):
|
||||
def __init__(
|
||||
self,
|
||||
device: str = "AUTO",
|
||||
):
|
||||
super().__init__(
|
||||
model_name="facedet",
|
||||
model_file="facenet.onnx",
|
||||
download_urls={
|
||||
"facenet.onnx": "https://huggingface.co/jinaai/jina-clip-v1/resolve/main/onnx/text_model_fp16.onnx",
|
||||
},
|
||||
)
|
||||
self.device = device
|
||||
self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
|
||||
self.tokenizer = None
|
||||
self.feature_extractor = None
|
||||
self.runner = None
|
||||
files_names = list(self.download_urls.keys())
|
||||
|
||||
if not all(
|
||||
os.path.exists(os.path.join(self.download_path, n)) for n in files_names
|
||||
):
|
||||
logger.debug(f"starting model download for {self.model_name}")
|
||||
self.downloader = ModelDownloader(
|
||||
model_name=self.model_name,
|
||||
download_path=self.download_path,
|
||||
file_names=files_names,
|
||||
download_func=self._download_model,
|
||||
)
|
||||
self.downloader.ensure_model_files()
|
||||
else:
|
||||
self.downloader = None
|
||||
self._load_model_and_utils()
|
||||
logger.debug(f"models are already downloaded for {self.model_name}")
|
||||
|
||||
def _load_model_and_utils(self):
|
||||
if self.runner is None:
|
||||
if self.downloader:
|
||||
self.downloader.wait_for_download()
|
||||
|
||||
self.runner = ONNXModelRunner(
|
||||
os.path.join(self.download_path, self.model_file),
|
||||
self.device,
|
||||
)
|
||||
|
||||
def _preprocess_inputs(self, raw_inputs):
|
||||
pil = self._process_image(raw_inputs[0])
|
||||
|
||||
# handle images larger than input size
|
||||
width, height = pil.size
|
||||
if width != FACE_EMBEDDING_SIZE or height != FACE_EMBEDDING_SIZE:
|
||||
if width > height:
|
||||
new_height = int(((height / width) * FACE_EMBEDDING_SIZE) // 4 * 4)
|
||||
pil = pil.resize((FACE_EMBEDDING_SIZE, new_height))
|
||||
else:
|
||||
new_width = int(((width / height) * FACE_EMBEDDING_SIZE) // 4 * 4)
|
||||
pil = pil.resize((new_width, FACE_EMBEDDING_SIZE))
|
||||
|
||||
og = np.array(pil).astype(np.float32)
|
||||
|
||||
# Image must be FACE_EMBEDDING_SIZExFACE_EMBEDDING_SIZE
|
||||
og_h, og_w = og.shape
|
||||
frame = np.zeros(
|
||||
(FACE_EMBEDDING_SIZE, FACE_EMBEDDING_SIZE, 3), dtype=np.float32
|
||||
)
|
||||
|
||||
# compute center offset
|
||||
x_center = (FACE_EMBEDDING_SIZE - og_w) // 2
|
||||
y_center = (FACE_EMBEDDING_SIZE - og_h) // 2
|
||||
|
||||
# copy img image into center of result image
|
||||
frame[y_center : y_center + og_h, x_center : x_center + og_w, 0] = og
|
||||
frame[y_center : y_center + og_h, x_center : x_center + og_w, 1] = og
|
||||
frame[y_center : y_center + og_h, x_center : x_center + og_w, 2] = og
|
||||
frame = np.expand_dims(frame, axis=0)
|
||||
return [{"input_2": frame}]
|
||||
Loading…
x
Reference in New Issue
Block a user