blakeblackshear.frigate/frigate/api/classification.py
Josh Hawkins 60b34bcfca
Refactor processors and add LPR postprocessing (#16722)
* recordings data pub/sub

* function to process recording stream frames

* model runner

* lpr model runner

* refactor to mixin class and use model runner

* separate out realtime and post processors

* move model and mixin folders

* basic postprocessor

* clean up

* docs

* postprocessing logic

* clean up

* return none if recordings are disabled

* run postprocessor handle_requests too

* tweak expansion

* add put endpoint

* postprocessor tweaks with endpoint
2025-02-21 06:51:37 -07:00

215 lines
6.3 KiB
Python

"""Object classification APIs."""
import logging
import os
import random
import shutil
import string
from fastapi import APIRouter, Request, UploadFile
from fastapi.responses import JSONResponse
from pathvalidate import sanitize_filename
from peewee import DoesNotExist
from playhouse.shortcuts import model_to_dict
from frigate.api.defs.tags import Tags
from frigate.const import FACE_DIR
from frigate.embeddings import EmbeddingsContext
from frigate.models import Event
logger = logging.getLogger(__name__)
router = APIRouter(tags=[Tags.events])
@router.get("/faces")
def get_faces():
face_dict: dict[str, list[str]] = {}
for name in os.listdir(FACE_DIR):
face_dir = os.path.join(FACE_DIR, name)
if not os.path.isdir(face_dir):
continue
face_dict[name] = []
for file in sorted(
os.listdir(face_dir),
key=lambda f: os.path.getctime(os.path.join(face_dir, f)),
reverse=True,
):
face_dict[name].append(file)
return JSONResponse(status_code=200, content=face_dict)
@router.post("/faces/reprocess")
def reclassify_face(request: Request, body: dict = None):
if not request.app.frigate_config.face_recognition.enabled:
return JSONResponse(
status_code=400,
content={"message": "Face recognition is not enabled.", "success": False},
)
json: dict[str, any] = body or {}
training_file = os.path.join(
FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
)
if not training_file or not os.path.isfile(training_file):
return JSONResponse(
content=(
{
"success": False,
"message": f"Invalid filename or no file exists: {training_file}",
}
),
status_code=404,
)
context: EmbeddingsContext = request.app.embeddings
response = context.reprocess_face(training_file)
return JSONResponse(
content=response,
status_code=200,
)
@router.post("/faces/train/{name}/classify")
def train_face(request: Request, name: str, body: dict = None):
if not request.app.frigate_config.face_recognition.enabled:
return JSONResponse(
status_code=400,
content={"message": "Face recognition is not enabled.", "success": False},
)
json: dict[str, any] = body or {}
training_file = os.path.join(
FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
)
if not training_file or not os.path.isfile(training_file):
return JSONResponse(
content=(
{
"success": False,
"message": f"Invalid filename or no file exists: {training_file}",
}
),
status_code=404,
)
sanitized_name = sanitize_filename(name)
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)
context: EmbeddingsContext = request.app.embeddings
context.clear_face_classifier()
return JSONResponse(
content=(
{
"success": True,
"message": f"Successfully saved {training_file} as {new_name}.",
}
),
status_code=200,
)
@router.post("/faces/{name}/create")
async def create_face(request: Request, name: str):
if not request.app.frigate_config.face_recognition.enabled:
return JSONResponse(
status_code=400,
content={"message": "Face recognition is not enabled.", "success": False},
)
os.makedirs(
os.path.join(FACE_DIR, sanitize_filename(name.replace(" ", "_"))), exist_ok=True
)
return JSONResponse(
status_code=200,
content={"success": False, "message": "Successfully created face folder."},
)
@router.post("/faces/{name}/register")
async def register_face(request: Request, name: str, file: UploadFile):
if not request.app.frigate_config.face_recognition.enabled:
return JSONResponse(
status_code=400,
content={"message": "Face recognition is not enabled.", "success": False},
)
context: EmbeddingsContext = request.app.embeddings
result = context.register_face(name, await file.read())
return JSONResponse(
status_code=200 if result.get("success", True) else 400,
content=result,
)
@router.post("/faces/{name}/delete")
def deregister_faces(request: Request, name: str, body: dict = None):
if not request.app.frigate_config.face_recognition.enabled:
return JSONResponse(
status_code=400,
content={"message": "Face recognition is not enabled.", "success": False},
)
json: dict[str, any] = body or {}
list_of_ids = json.get("ids", "")
if not list_of_ids or len(list_of_ids) == 0:
return JSONResponse(
content=({"success": False, "message": "Not a valid list of ids"}),
status_code=404,
)
context: EmbeddingsContext = request.app.embeddings
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,
)
@router.put("/lpr/reprocess")
def reprocess_license_plate(request: Request, event_id: str):
if not request.app.frigate_config.lpr.enabled:
message = "License plate recognition is not enabled."
logger.error(message)
return JSONResponse(
content=(
{
"success": False,
"message": message,
}
),
status_code=400,
)
try:
event = Event.get(Event.id == event_id)
except DoesNotExist:
message = f"Event {event_id} not found"
logger.error(message)
return JSONResponse(
content=({"success": False, "message": message}), status_code=404
)
context: EmbeddingsContext = request.app.embeddings
response = context.reprocess_plate(model_to_dict(event))
return JSONResponse(
content=response,
status_code=200,
)