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Refactor face recognition (#17368)
* Refactor face recognition to allow for running lbph or embedding * Cleanup * Use weighted average for faces * Set correct url * Cleanup * Update docs * Update docs * Use scipy trimmed mean * Normalize * Handle color and gray landmark detection * Upgrade to new arcface model * Implement sigmoid function * Rename * Rename to arcface * Fix * Add face recognition model size to ui config * Update toast
This commit is contained in:
Nicolas Mowen
GitHub
308
frigate/data_processing/common/face/model.py
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308
frigate/data_processing/common/face/model.py
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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 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.facenet import ArcfaceEmbedding
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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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# landmark is run on grayscale images
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if image.ndim == 3:
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land_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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land_image = image
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_, lands = self.landmark_detector.fit(
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land_image, np.array([(0, 0, land_image.shape[1], land_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 ArcFaceRecognizer(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: ArcfaceEmbedding = ArcfaceEmbedding()
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def clear(self) -> None:
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self.mean_embs = {}
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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 = 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] = stats.trim_mean(embs, 0.15)
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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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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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# 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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# 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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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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confidence = self.similarity_to_confidence(cosine_similarity)
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if cosine_similarity > score:
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score = confidence
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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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