Improve Face Library Management (#17213)

* Set maximum number of face images to be kept

* Fix vertical camera scaling

* adjust wording

* Add attributes to search data

* Add button to train face from event

* Handle event id saving in API
This commit is contained in:
Nicolas Mowen 2025-03-17 15:57:46 -06:00 committed by GitHub
parent ff8e145b90
commit bf22d89f67
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10 changed files with 167 additions and 26 deletions

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@ -6,6 +6,7 @@ import random
import shutil
import string
import cv2
from fastapi import APIRouter, Depends, Request, UploadFile
from fastapi.responses import JSONResponse
from pathvalidate import sanitize_filename
@ -14,9 +15,11 @@ from playhouse.shortcuts import model_to_dict
from frigate.api.auth import require_role
from frigate.api.defs.tags import Tags
from frigate.config.camera import DetectConfig
from frigate.const import FACE_DIR
from frigate.embeddings import EmbeddingsContext
from frigate.models import Event
from frigate.util.path import get_event_snapshot
logger = logging.getLogger(__name__)
@ -87,16 +90,27 @@ def train_face(request: Request, name: str, body: dict = None):
)
json: dict[str, any] = body or {}
training_file = os.path.join(
FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
)
training_file_name = sanitize_filename(json.get("training_file", ""))
training_file = os.path.join(FACE_DIR, f"train/{training_file_name}")
event_id = json.get("event_id")
if not training_file or not os.path.isfile(training_file):
if not training_file_name and not event_id:
return JSONResponse(
content=(
{
"success": False,
"message": f"Invalid filename or no file exists: {training_file}",
"message": "A training file or event_id must be passed.",
}
),
status_code=400,
)
if training_file_name and not os.path.isfile(training_file):
return JSONResponse(
content=(
{
"success": False,
"message": f"Invalid filename or no file exists: {training_file_name}",
}
),
status_code=404,
@ -106,7 +120,36 @@ def train_face(request: Request, name: str, body: dict = None):
rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
new_name = f"{sanitized_name}-{rand_id}.webp"
new_file = os.path.join(FACE_DIR, f"{sanitized_name}/{new_name}")
shutil.move(training_file, new_file)
if training_file_name:
shutil.move(training_file, new_file)
else:
try:
event: Event = Event.get(Event.id == event_id)
except DoesNotExist:
return JSONResponse(
content=(
{
"success": False,
"message": f"Invalid event_id or no event exists: {event_id}",
}
),
status_code=404,
)
snapshot = get_event_snapshot(event)
face_box = event.data["attributes"][0]["box"]
detect_config: DetectConfig = request.app.frigate_config.cameras[
event.camera
].detect
# crop onto the face box minus the bounding box itself
x1 = int(face_box[0] * detect_config.width) + 2
y1 = int(face_box[1] * detect_config.height) + 2
x2 = x1 + int(face_box[2] * detect_config.width) - 4
y2 = y1 + int(face_box[3] * detect_config.height) - 4
face = snapshot[y1:y2, x1:x2]
cv2.imwrite(new_file, face)
context: EmbeddingsContext = request.app.embeddings
context.clear_face_classifier()
@ -115,7 +158,7 @@ def train_face(request: Request, name: str, body: dict = None):
content=(
{
"success": True,
"message": f"Successfully saved {training_file} as {new_name}.",
"message": f"Successfully saved {training_file_name} as {new_name}.",
}
),
status_code=200,

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@ -701,6 +701,7 @@ def events_search(request: Request, params: EventsSearchQueryParams = Depends())
for k, v in event["data"].items()
if k
in [
"attributes",
"type",
"score",
"top_score",

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@ -28,6 +28,7 @@ logger = logging.getLogger(__name__)
MAX_DETECTION_HEIGHT = 1080
MAX_FACE_ATTEMPTS = 100
MIN_MATCHING_FACES = 2
@ -482,6 +483,16 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
)
shutil.move(current_file, new_file)
files = sorted(
os.listdir(folder),
key=lambda f: os.path.getctime(os.path.join(folder, f)),
reverse=True,
)
# delete oldest face image if maximum is reached
if len(files) > MAX_FACE_ATTEMPTS:
os.unlink(os.path.join(folder, files[-1]))
def expire_object(self, object_id: str):
if object_id in self.detected_faces:
self.detected_faces.pop(object_id)

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@ -4,6 +4,9 @@ import base64
import os
from pathlib import Path
import cv2
from numpy import ndarray
from frigate.const import CLIPS_DIR, THUMB_DIR
from frigate.models import Event
@ -21,6 +24,11 @@ def get_event_thumbnail_bytes(event: Event) -> bytes | None:
return None
def get_event_snapshot(event: Event) -> ndarray:
media_name = f"{event.camera}-{event.id}"
return cv2.imread(f"{os.path.join(CLIPS_DIR, media_name)}.jpg")
### Deletion

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@ -25,7 +25,7 @@
},
"readTheDocs": "Read the documentation to view more details on refining images for the Face Library",
"trainFaceAs": "Train Face as:",
"trainFaceAsPerson": "Train Face as Person",
"trainFace": "Train Face",
"toast": {
"success": {
"uploadedImage": "Successfully uploaded image.",

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@ -57,6 +57,7 @@ import {
DropdownMenu,
DropdownMenuContent,
DropdownMenuItem,
DropdownMenuLabel,
DropdownMenuTrigger,
} from "@/components/ui/dropdown-menu";
import { TransformComponent, TransformWrapper } from "react-zoom-pan-pinch";
@ -69,11 +70,12 @@ import {
PopoverContent,
PopoverTrigger,
} from "@/components/ui/popover";
import { LuInfo } from "react-icons/lu";
import { LuInfo, LuSearch } from "react-icons/lu";
import { TooltipPortal } from "@radix-ui/react-tooltip";
import { FaPencilAlt } from "react-icons/fa";
import TextEntryDialog from "@/components/overlay/dialog/TextEntryDialog";
import { useTranslation } from "react-i18next";
import { TbFaceId } from "react-icons/tb";
const SEARCH_TABS = [
"details",
@ -99,7 +101,7 @@ export default function SearchDetailDialog({
setSimilarity,
setInputFocused,
}: SearchDetailDialogProps) {
const { t } = useTranslation(["views/explore"]);
const { t } = useTranslation(["views/explore", "views/faceLibrary"]);
const { data: config } = useSWR<FrigateConfig>("config", {
revalidateOnFocus: false,
});
@ -555,6 +557,48 @@ function ObjectDetailsTab({
[search, apiHost, mutate, setSearch, t],
);
// face training
const hasFace = useMemo(() => {
if (!config?.face_recognition.enabled || !search) {
return false;
}
return search.data.attributes?.find((attr) => attr.label == "face");
}, [config, search]);
const { data: faceData } = useSWR(hasFace ? "faces" : null);
const faceNames = useMemo<string[]>(
() =>
faceData ? Object.keys(faceData).filter((face) => face != "train") : [],
[faceData],
);
const onTrainFace = useCallback(
(trainName: string) => {
axios
.post(`/faces/train/${trainName}/classify`, { event_id: search.id })
.then((resp) => {
if (resp.status == 200) {
toast.success(t("toast.success.trainedFace"), {
position: "top-center",
});
}
})
.catch((error) => {
const errorMessage =
error.response?.data?.message ||
error.response?.data?.detail ||
"Unknown error";
toast.error(t("toast.error.trainFailed", { errorMessage }), {
position: "top-center",
});
});
},
[search, t],
);
return (
<div className="flex flex-col gap-5">
<div className="flex w-full flex-row">
@ -673,20 +717,53 @@ function ObjectDetailsTab({
draggable={false}
src={`${apiHost}api/events/${search.id}/thumbnail.webp`}
/>
{config?.semantic_search.enabled && search.data.type == "object" && (
<Button
aria-label={t("itemMenu.findSimilar.aria")}
onClick={() => {
setSearch(undefined);
<div className="flex w-full flex-row gap-2">
{config?.semantic_search.enabled &&
search.data.type == "object" && (
<Button
className="w-full"
aria-label={t("itemMenu.findSimilar.aria")}
onClick={() => {
setSearch(undefined);
if (setSimilarity) {
setSimilarity();
}
}}
>
{t("itemMenu.findSimilar.label")}
</Button>
)}
if (setSimilarity) {
setSimilarity();
}
}}
>
<div className="flex gap-1">
<LuSearch />
{t("itemMenu.findSimilar.label")}
</div>
</Button>
)}
{hasFace && (
<DropdownMenu>
<DropdownMenuTrigger asChild>
<Button className="w-full">
<div className="flex gap-1">
<TbFaceId />
{t("trainFace", { ns: "views/faceLibrary" })}
</div>
</Button>
</DropdownMenuTrigger>
<DropdownMenuContent>
<DropdownMenuLabel>
{t("trainFaceAs", { ns: "views/faceLibrary" })}
</DropdownMenuLabel>
{faceNames.map((faceName) => (
<DropdownMenuItem
key={faceName}
className="cursor-pointer capitalize"
onClick={() => onTrainFace(faceName)}
>
{faceName}
</DropdownMenuItem>
))}
</DropdownMenuContent>
</DropdownMenu>
)}
</div>
</div>
</div>
<div className="flex flex-col gap-1.5">

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@ -472,7 +472,7 @@ function FaceAttempt({
))}
</DropdownMenuContent>
</DropdownMenu>
<TooltipContent>{t("trainFaceAsPerson")}</TooltipContent>
<TooltipContent>{t("trainFace")}</TooltipContent>
</Tooltip>
<Tooltip>
<TooltipTrigger>

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@ -50,6 +50,7 @@ export type SearchResult = {
score: number;
sub_label_score?: number;
region: number[];
attributes?: [{ box: number[]; label: string; score: number }];
box: number[];
area: number;
ratio: number;

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@ -323,7 +323,7 @@ export default function MotionTunerView({
</div>
{cameraConfig ? (
<div className="flex md:h-dvh md:max-h-full md:w-7/12 md:grow">
<div className="flex max-h-[70%] md:h-dvh md:max-h-full md:w-7/12 md:grow">
<div className="size-full min-h-10">
<AutoUpdatingCameraImage
camera={cameraConfig.name}

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@ -296,7 +296,7 @@ export default function ObjectSettingsView({
</div>
{cameraConfig ? (
<div className="flex md:h-dvh md:max-h-full md:w-7/12 md:grow">
<div className="flex max-h-[70%] md:h-dvh md:max-h-full md:w-7/12 md:grow">
<div ref={containerRef} className="relative size-full min-h-10">
<AutoUpdatingCameraImage
camera={cameraConfig.name}