diff --git a/docker/main/install_deps.sh b/docker/main/install_deps.sh index 6c32ae168..af349fd84 100755 --- a/docker/main/install_deps.sh +++ b/docker/main/install_deps.sh @@ -16,7 +16,9 @@ apt-get -qq install --no-install-recommends -y \ curl \ lsof \ jq \ - nethogs + nethogs \ + libgl1 \ + libglib2.0-0 # ensure python3 defaults to python3.9 update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1 diff --git a/docker/main/requirements-wheels.txt b/docker/main/requirements-wheels.txt index ce20b28de..b163e8627 100644 --- a/docker/main/requirements-wheels.txt +++ b/docker/main/requirements-wheels.txt @@ -34,8 +34,8 @@ unidecode == 1.3.* # Image Manipulation numpy == 1.26.* opencv-python-headless == 4.9.0.* +opencv-contrib-python == 4.9.0.* scipy == 1.13.* -scikit-learn == 1.5.* # OpenVino & ONNX openvino == 2024.3.* onnxruntime-openvino == 1.19.* ; platform_machine == 'x86_64' diff --git a/docs/docs/configuration/face_recognition.md b/docs/docs/configuration/face_recognition.md index af079a226..3e0cfd30c 100644 --- a/docs/docs/configuration/face_recognition.md +++ b/docs/docs/configuration/face_recognition.md @@ -18,4 +18,18 @@ Face recognition is disabled by default and requires semantic search to be enabl ```yaml face_recognition: enabled: true -``` \ No newline at end of file +``` + +## Dataset + +The number of images needed for a sufficient training set for face recognition varies depending on several factors: + +- Complexity of the task: A simple task like recognizing faces of known individuals may require fewer images than a complex task like identifying unknown individuals in a large crowd. +- Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset. +- Desired accuracy: The higher the desired accuracy, the more images are typically needed. + +However, here are some general guidelines: + +- Minimum: For basic face recognition tasks, a minimum of 10-20 images per person is often recommended. +- Recommended: For more robust and accurate systems, 30-50 images per person is a good starting point. +- Ideal: For optimal performance, especially in challenging conditions, 100 or more images per person can be beneficial. \ No newline at end of file diff --git a/frigate/api/classification.py b/frigate/api/classification.py index d862008c8..fe54bebe9 100644 --- a/frigate/api/classification.py +++ b/frigate/api/classification.py @@ -1,11 +1,14 @@ """Object classification APIs.""" import logging +import os from fastapi import APIRouter, Request, UploadFile from fastapi.responses import JSONResponse +from pathvalidate import sanitize_filename from frigate.api.defs.tags import Tags +from frigate.const import FACE_DIR from frigate.embeddings import EmbeddingsContext logger = logging.getLogger(__name__) @@ -15,20 +18,18 @@ router = APIRouter(tags=[Tags.events]) @router.get("/faces") def get_faces(): - return JSONResponse(content={"message": "there are faces"}) + face_dict: dict[str, list[str]] = {} + + for name in os.listdir(FACE_DIR): + face_dict[name] = [] + for file in os.listdir(os.path.join(FACE_DIR, name)): + face_dict[name].append(file) + + return JSONResponse(status_code=200, content=face_dict) @router.post("/faces/{name}") async def register_face(request: Request, name: str, file: UploadFile): - # if not file.content_type.startswith("image"): - # return JSONResponse( - # status_code=400, - # content={ - # "success": False, - # "message": "Only an image can be used to register a face.", - # }, - # ) - context: EmbeddingsContext = request.app.embeddings context.register_face(name, await file.read()) return JSONResponse( @@ -37,8 +38,8 @@ async def register_face(request: Request, name: str, file: UploadFile): ) -@router.delete("/faces") -def deregister_faces(request: Request, body: dict = None): +@router.post("/faces/{name}/delete") +def deregister_faces(request: Request, name: str, body: dict = None): json: dict[str, any] = body or {} list_of_ids = json.get("ids", "") @@ -49,7 +50,9 @@ def deregister_faces(request: Request, body: dict = None): ) context: EmbeddingsContext = request.app.embeddings - context.delete_face_ids(list_of_ids) + context.delete_face_ids( + name, map(lambda file: sanitize_filename(file), list_of_ids) + ) return JSONResponse( content=({"success": True, "message": "Successfully deleted faces."}), status_code=200, diff --git a/frigate/config/semantic_search.py b/frigate/config/semantic_search.py index f5e881e4e..f0eb5d98c 100644 --- a/frigate/config/semantic_search.py +++ b/frigate/config/semantic_search.py @@ -24,7 +24,10 @@ class SemanticSearchConfig(FrigateBaseModel): class FaceRecognitionConfig(FrigateBaseModel): enabled: bool = Field(default=False, title="Enable face recognition.") threshold: float = Field( - default=0.9, title="Face similarity score required to be considered a match." + default=170, + title="minimum face distance score required to be considered a match.", + gt=0.0, + le=1.0, ) min_area: int = Field( default=500, title="Min area of face box to consider running face recognition." diff --git a/frigate/db/sqlitevecq.py b/frigate/db/sqlitevecq.py index d630e1ddf..ccb75ae54 100644 --- a/frigate/db/sqlitevecq.py +++ b/frigate/db/sqlitevecq.py @@ -29,10 +29,6 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase): ids = ",".join(["?" for _ in event_ids]) self.execute_sql(f"DELETE FROM vec_descriptions WHERE id IN ({ids})", event_ids) - def delete_embeddings_face(self, face_ids: list[str]) -> None: - ids = ",".join(["?" for _ in face_ids]) - self.execute_sql(f"DELETE FROM vec_faces WHERE id IN ({ids})", face_ids) - def drop_embeddings_tables(self) -> None: self.execute_sql(""" DROP TABLE vec_descriptions; @@ -40,11 +36,8 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase): self.execute_sql(""" DROP TABLE vec_thumbnails; """) - self.execute_sql(""" - DROP TABLE vec_faces; - """) - def create_embeddings_tables(self, face_recognition: bool) -> None: + def create_embeddings_tables(self) -> None: """Create vec0 virtual table for embeddings""" self.execute_sql(""" CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0( @@ -58,11 +51,3 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase): description_embedding FLOAT[768] distance_metric=cosine ); """) - - if face_recognition: - self.execute_sql(""" - CREATE VIRTUAL TABLE IF NOT EXISTS vec_faces USING vec0( - id TEXT PRIMARY KEY, - face_embedding FLOAT[512] distance_metric=cosine - ); - """) diff --git a/frigate/embeddings/__init__.py b/frigate/embeddings/__init__.py index 235b15df3..9836ae28e 100644 --- a/frigate/embeddings/__init__.py +++ b/frigate/embeddings/__init__.py @@ -14,7 +14,7 @@ from setproctitle import setproctitle from frigate.comms.embeddings_updater import EmbeddingsRequestEnum, EmbeddingsRequestor from frigate.config import FrigateConfig -from frigate.const import CONFIG_DIR +from frigate.const import CONFIG_DIR, FACE_DIR from frigate.db.sqlitevecq import SqliteVecQueueDatabase from frigate.models import Event from frigate.util.builtin import serialize @@ -209,8 +209,13 @@ class EmbeddingsContext: return self.db.execute_sql(sql_query).fetchall() - def delete_face_ids(self, ids: list[str]) -> None: - self.db.delete_embeddings_face(ids) + def delete_face_ids(self, face: str, ids: list[str]) -> None: + folder = os.path.join(FACE_DIR, face) + for id in ids: + file_path = os.path.join(folder, id) + + if os.path.isfile(file_path): + os.unlink(file_path) def update_description(self, event_id: str, description: str) -> None: self.requestor.send_data( diff --git a/frigate/embeddings/embeddings.py b/frigate/embeddings/embeddings.py index 255b72cb2..63597e49e 100644 --- a/frigate/embeddings/embeddings.py +++ b/frigate/embeddings/embeddings.py @@ -3,8 +3,6 @@ import base64 import logging import os -import random -import string import time from numpy import ndarray @@ -14,7 +12,6 @@ from frigate.comms.inter_process import InterProcessRequestor from frigate.config import FrigateConfig from frigate.const import ( CONFIG_DIR, - FACE_DIR, UPDATE_EMBEDDINGS_REINDEX_PROGRESS, UPDATE_MODEL_STATE, ) @@ -68,7 +65,7 @@ class Embeddings: self.requestor = InterProcessRequestor() # Create tables if they don't exist - self.db.create_embeddings_tables(self.config.face_recognition.enabled) + self.db.create_embeddings_tables() models = [ "jinaai/jina-clip-v1-text_model_fp16.onnx", @@ -126,22 +123,6 @@ class Embeddings: device="GPU" if config.semantic_search.model_size == "large" else "CPU", ) - self.face_embedding = None - - if self.config.face_recognition.enabled: - self.face_embedding = GenericONNXEmbedding( - model_name="facenet", - model_file="facenet.onnx", - download_urls={ - "facenet.onnx": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facenet.onnx", - "facedet.onnx": "https://github.com/opencv/opencv_zoo/raw/refs/heads/main/models/face_detection_yunet/face_detection_yunet_2023mar_int8.onnx", - }, - model_size="large", - model_type=ModelTypeEnum.face, - requestor=self.requestor, - device="GPU", - ) - self.lpr_detection_model = None self.lpr_classification_model = None self.lpr_recognition_model = None @@ -277,40 +258,12 @@ class Embeddings: return embeddings - def embed_face(self, label: str, thumbnail: bytes, upsert: bool = False) -> ndarray: - embedding = self.face_embedding(thumbnail)[0] - - if upsert: - rand_id = "".join( - random.choices(string.ascii_lowercase + string.digits, k=6) - ) - id = f"{label}-{rand_id}" - - # write face to library - folder = os.path.join(FACE_DIR, label) - file = os.path.join(folder, f"{id}.webp") - os.makedirs(folder, exist_ok=True) - - # save face image - with open(file, "wb") as output: - output.write(thumbnail) - - self.db.execute_sql( - """ - INSERT OR REPLACE INTO vec_faces(id, face_embedding) - VALUES(?, ?) - """, - (id, serialize(embedding)), - ) - - return embedding - def reindex(self) -> None: logger.info("Indexing tracked object embeddings...") self.db.drop_embeddings_tables() logger.debug("Dropped embeddings tables.") - self.db.create_embeddings_tables(self.config.face_recognition.enabled) + self.db.create_embeddings_tables() logger.debug("Created embeddings tables.") # Delete the saved stats file diff --git a/frigate/embeddings/maintainer.py b/frigate/embeddings/maintainer.py index ce1a344fa..13c5a26c5 100644 --- a/frigate/embeddings/maintainer.py +++ b/frigate/embeddings/maintainer.py @@ -3,7 +3,9 @@ import base64 import logging import os +import random import re +import string import threading from multiprocessing.synchronize import Event as MpEvent from pathlib import Path @@ -23,7 +25,12 @@ from frigate.comms.event_metadata_updater import ( from frigate.comms.events_updater import EventEndSubscriber, EventUpdateSubscriber from frigate.comms.inter_process import InterProcessRequestor from frigate.config import FrigateConfig -from frigate.const import CLIPS_DIR, FRIGATE_LOCALHOST, UPDATE_EVENT_DESCRIPTION +from frigate.const import ( + CLIPS_DIR, + FACE_DIR, + FRIGATE_LOCALHOST, + UPDATE_EVENT_DESCRIPTION, +) from frigate.embeddings.lpr.lpr import LicensePlateRecognition from frigate.events.types import EventTypeEnum from frigate.genai import get_genai_client @@ -70,7 +77,9 @@ class EmbeddingMaintainer(threading.Thread): self.requires_face_detection = "face" not in self.config.objects.all_objects self.detected_faces: dict[str, float] = {} self.face_classifier = ( - FaceClassificationModel(db) if self.face_recognition_enabled else None + FaceClassificationModel(self.config.face_recognition, db) + if self.face_recognition_enabled + else None ) # create communication for updating event descriptions @@ -145,12 +154,14 @@ class EmbeddingMaintainer(threading.Thread): if not self.face_recognition_enabled: return False + rand_id = "".join( + random.choices(string.ascii_lowercase + string.digits, k=6) + ) + label = data["face_name"] + id = f"{label}-{rand_id}" + if data.get("cropped"): - self.embeddings.embed_face( - data["face_name"], - base64.b64decode(data["image"]), - upsert=True, - ) + pass else: img = cv2.imdecode( np.frombuffer( @@ -164,12 +175,18 @@ class EmbeddingMaintainer(threading.Thread): return False face = img[face_box[1] : face_box[3], face_box[0] : face_box[2]] - ret, webp = cv2.imencode( + ret, thumbnail = cv2.imencode( ".webp", face, [int(cv2.IMWRITE_WEBP_QUALITY), 100] ) - self.embeddings.embed_face( - data["face_name"], webp.tobytes(), upsert=True - ) + + # write face to library + folder = os.path.join(FACE_DIR, label) + file = os.path.join(folder, f"{id}.webp") + os.makedirs(folder, exist_ok=True) + + # save face image + with open(file, "wb") as output: + output.write(thumbnail.tobytes()) self.face_classifier.clear_classifier() return True @@ -202,7 +219,9 @@ class EmbeddingMaintainer(threading.Thread): # Create our own thumbnail based on the bounding box and the frame time try: - yuv_frame = self.frame_manager.get(frame_name, camera_config.frame_shape_yuv) + yuv_frame = self.frame_manager.get( + frame_name, camera_config.frame_shape_yuv + ) except FileNotFoundError: pass @@ -479,16 +498,7 @@ class EmbeddingMaintainer(threading.Thread): ), ] - ret, webp = cv2.imencode( - ".webp", face_frame, [int(cv2.IMWRITE_WEBP_QUALITY), 100] - ) - - if not ret: - logger.debug("Not processing face due to error creating cropped image.") - return - - embedding = self.embeddings.embed_face("unknown", webp.tobytes(), upsert=False) - res = self.face_classifier.classify_face(embedding) + res = self.face_classifier.classify_face(face_frame) if not res: return @@ -499,11 +509,9 @@ class EmbeddingMaintainer(threading.Thread): f"Detected best face for person as: {sub_label} with score {score}" ) - if score < self.config.face_recognition.threshold or ( - id in self.detected_faces and score <= self.detected_faces[id] - ): + if id in self.detected_faces and score <= self.detected_faces[id]: logger.debug( - f"Recognized face score {score} is less than threshold ({self.config.face_recognition.threshold}) / previous face score ({self.detected_faces.get(id)})." + f"Recognized face distance {score} is less than previous face distance ({self.detected_faces.get(id)})." ) return diff --git a/frigate/util/model.py b/frigate/util/model.py index d43320006..2b7cf1848 100644 --- a/frigate/util/model.py +++ b/frigate/util/model.py @@ -4,13 +4,12 @@ import logging import os from typing import Any, Optional +import cv2 import numpy as np import onnxruntime as ort from playhouse.sqliteq import SqliteQueueDatabase -from sklearn.preprocessing import LabelEncoder, Normalizer -from sklearn.svm import SVC -from frigate.util.builtin import deserialize +from frigate.config.semantic_search import FaceRecognitionConfig try: import openvino as ov @@ -21,6 +20,9 @@ except ImportError: logger = logging.getLogger(__name__) +MIN_MATCHING_FACES = 2 + + def get_ort_providers( force_cpu: bool = False, device: str = "AUTO", requires_fp16: bool = False ) -> tuple[list[str], list[dict[str, any]]]: @@ -157,38 +159,42 @@ class ONNXModelRunner: class FaceClassificationModel: - def __init__(self, db: SqliteQueueDatabase): + def __init__(self, config: FaceRecognitionConfig, db: SqliteQueueDatabase): + self.config = config self.db = db - self.labeler: Optional[LabelEncoder] = None - self.classifier: Optional[SVC] = None + self.recognizer = cv2.face.LBPHFaceRecognizer_create(radius=4, threshold=(1 - config.threshold) * 1000) + self.label_map: dict[int, str] = {} def __build_classifier(self) -> None: - faces: list[tuple[str, bytes]] = self.db.execute_sql( - "SELECT id, face_embedding FROM vec_faces" - ).fetchall() - embeddings = np.array([deserialize(f[1]) for f in faces]) - self.labeler = LabelEncoder() - norms = Normalizer(norm="l2").transform(embeddings) - labels = self.labeler.fit_transform([f[0].split("-")[0] for f in faces]) - self.classifier = SVC(kernel="linear", probability=True) - self.classifier.fit(norms, labels) + labels = [] + faces = [] + + dir = "/media/frigate/clips/faces" + for idx, name in enumerate(os.listdir(dir)): + self.label_map[idx] = name + face_folder = os.path.join(dir, name) + for image in os.listdir(face_folder): + img = cv2.imread(os.path.join(face_folder, image)) + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + equ = cv2.equalizeHist(gray) + faces.append(equ) + labels.append(idx) + + self.recognizer.train(faces, np.array(labels)) def clear_classifier(self) -> None: self.classifier = None self.labeler = None - def classify_face(self, embedding: np.ndarray) -> Optional[tuple[str, float]]: - if not self.classifier: + def classify_face(self, face_image: np.ndarray) -> Optional[tuple[str, float]]: + if not self.label_map: self.__build_classifier() - res = self.classifier.predict([embedding]) + index, distance = self.recognizer.predict(cv2.equalizeHist(cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY))) - if res is None: + if index == -1: return None - label = res[0] - probabilities = self.classifier.predict_proba([embedding])[0] - return ( - self.labeler.inverse_transform([label])[0], - round(probabilities[label], 2), - ) + score = 1.0 - (distance / 1000) + return self.label_map[index], round(score, 2) + diff --git a/web/src/App.tsx b/web/src/App.tsx index 3bc2e7836..ef0a9497e 100644 --- a/web/src/App.tsx +++ b/web/src/App.tsx @@ -19,6 +19,7 @@ const ConfigEditor = lazy(() => import("@/pages/ConfigEditor")); const System = lazy(() => import("@/pages/System")); const Settings = lazy(() => import("@/pages/Settings")); const UIPlayground = lazy(() => import("@/pages/UIPlayground")); +const FaceLibrary = lazy(() => import("@/pages/FaceLibrary")); const Logs = lazy(() => import("@/pages/Logs")); function App() { @@ -51,6 +52,7 @@ function App() { } /> } /> } /> + } /> } /> diff --git a/web/src/components/overlay/dialog/UploadImageDialog.tsx b/web/src/components/overlay/dialog/UploadImageDialog.tsx new file mode 100644 index 000000000..b4fbd5065 --- /dev/null +++ b/web/src/components/overlay/dialog/UploadImageDialog.tsx @@ -0,0 +1,88 @@ +import { Button } from "@/components/ui/button"; +import { + Dialog, + DialogContent, + DialogDescription, + DialogFooter, + DialogHeader, + DialogTitle, +} from "@/components/ui/dialog"; +import { Form, FormControl, FormField, FormItem } from "@/components/ui/form"; +import { Input } from "@/components/ui/input"; +import { zodResolver } from "@hookform/resolvers/zod"; +import { useCallback } from "react"; +import { useForm } from "react-hook-form"; +import { z } from "zod"; + +type UploadImageDialogProps = { + open: boolean; + title: string; + description?: string; + setOpen: (open: boolean) => void; + onSave: (file: File) => void; +}; +export default function UploadImageDialog({ + open, + title, + description, + setOpen, + onSave, +}: UploadImageDialogProps) { + const formSchema = z.object({ + file: z.instanceof(FileList, { message: "Please select an image file." }), + }); + + const form = useForm>({ + resolver: zodResolver(formSchema), + }); + const fileRef = form.register("file"); + + // upload handler + + const onSubmit = useCallback( + (data: z.infer) => { + if (!data["file"]) { + return; + } + + onSave(data["file"]["0"]); + }, + [onSave], + ); + + return ( + + + + {title} + {description && {description}} + +
+ + ( + + + + + + )} + /> + + + + + + +
+ + ); +} diff --git a/web/src/hooks/use-navigation.ts b/web/src/hooks/use-navigation.ts index 06ebd6c1d..daed383d3 100644 --- a/web/src/hooks/use-navigation.ts +++ b/web/src/hooks/use-navigation.ts @@ -1,20 +1,29 @@ import { ENV } from "@/env"; +import { FrigateConfig } from "@/types/frigateConfig"; import { NavData } from "@/types/navigation"; import { useMemo } from "react"; +import { isDesktop } from "react-device-detect"; import { FaCompactDisc, FaVideo } from "react-icons/fa"; import { IoSearch } from "react-icons/io5"; import { LuConstruction } from "react-icons/lu"; import { MdVideoLibrary } from "react-icons/md"; +import { TbFaceId } from "react-icons/tb"; +import useSWR from "swr"; export const ID_LIVE = 1; export const ID_REVIEW = 2; export const ID_EXPLORE = 3; export const ID_EXPORT = 4; export const ID_PLAYGROUND = 5; +export const ID_FACE_LIBRARY = 6; export default function useNavigation( variant: "primary" | "secondary" = "primary", ) { + const { data: config } = useSWR("config", { + revalidateOnFocus: false, + }); + return useMemo( () => [ @@ -54,7 +63,15 @@ export default function useNavigation( url: "/playground", enabled: ENV !== "production", }, + { + id: ID_FACE_LIBRARY, + variant, + icon: TbFaceId, + title: "Face Library", + url: "/faces", + enabled: isDesktop && config?.face_recognition.enabled, + }, ] as NavData[], - [variant], + [config?.face_recognition.enabled, variant], ); } diff --git a/web/src/pages/FaceLibrary.tsx b/web/src/pages/FaceLibrary.tsx new file mode 100644 index 000000000..e955a17de --- /dev/null +++ b/web/src/pages/FaceLibrary.tsx @@ -0,0 +1,170 @@ +import { baseUrl } from "@/api/baseUrl"; +import Chip from "@/components/indicators/Chip"; +import UploadImageDialog from "@/components/overlay/dialog/UploadImageDialog"; +import { Button } from "@/components/ui/button"; +import { ScrollArea, ScrollBar } from "@/components/ui/scroll-area"; +import { Toaster } from "@/components/ui/sonner"; +import { ToggleGroup, ToggleGroupItem } from "@/components/ui/toggle-group"; +import useOptimisticState from "@/hooks/use-optimistic-state"; +import axios from "axios"; +import { useCallback, useEffect, useMemo, useRef, useState } from "react"; +import { isDesktop } from "react-device-detect"; +import { LuImagePlus, LuTrash } from "react-icons/lu"; +import { toast } from "sonner"; +import useSWR from "swr"; + +export default function FaceLibrary() { + const [page, setPage] = useState(); + const [pageToggle, setPageToggle] = useOptimisticState(page, setPage, 100); + const tabsRef = useRef(null); + + // face data + + const { data: faceData } = useSWR("faces"); + + const faces = useMemo( + () => (faceData ? Object.keys(faceData) : []), + [faceData], + ); + const faceImages = useMemo( + () => (pageToggle && faceData ? faceData[pageToggle] : []), + [pageToggle, faceData], + ); + + useEffect(() => { + if (!pageToggle && faces) { + setPageToggle(faces[0]); + } + // we need to listen on the value of the faces list + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [faces]); + + // upload + + const [upload, setUpload] = useState(false); + + const onUploadImage = useCallback( + (file: File) => { + const formData = new FormData(); + formData.append("file", file); + axios.post(`faces/${pageToggle}`, formData, { + headers: { + "Content-Type": "multipart/form-data", + }, + }); + }, + [pageToggle], + ); + + return ( +
+ + + + +
+ +
+ { + if (value) { + setPageToggle(value); + } + }} + > + {Object.values(faces).map((item) => ( + +
{item}
+ + ))} + + +
+ +
+ {pageToggle && ( +
+ {faceImages.map((image: string) => ( + + ))} + +
+ )} +
+ ); +} + +type FaceImageProps = { + name: string; + image: string; +}; +function FaceImage({ name, image }: FaceImageProps) { + const [hovered, setHovered] = useState(false); + + const onDelete = useCallback(() => { + axios + .post(`/faces/${name}/delete`, { ids: [image] }) + .then((resp) => { + if (resp.status == 200) { + toast.error(`Successfully deleted face.`, { position: "top-center" }); + } + }) + .catch((error) => { + if (error.response?.data?.message) { + toast.error(`Failed to delete: ${error.response.data.message}`, { + position: "top-center", + }); + } else { + toast.error(`Failed to delete: ${error.message}`, { + position: "top-center", + }); + } + }); + }, [name, image]); + + return ( +
setHovered(true) : undefined} + onMouseLeave={isDesktop ? () => setHovered(false) : undefined} + onClick={isDesktop ? undefined : () => setHovered(!hovered)} + > + {hovered && ( +
+ onDelete()} + > + + +
+ )} + +
+ ); +} diff --git a/web/src/types/frigateConfig.ts b/web/src/types/frigateConfig.ts index 8ed3119dc..6c29ca427 100644 --- a/web/src/types/frigateConfig.ts +++ b/web/src/types/frigateConfig.ts @@ -288,6 +288,10 @@ export interface FrigateConfig { environment_vars: Record; + face_recognition: { + enabled: boolean; + }; + ffmpeg: { global_args: string[]; hwaccel_args: string;