mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-02-05 00:15:51 +01:00
128 lines
3.7 KiB
Python
128 lines
3.7 KiB
Python
"""Object classification APIs."""
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import logging
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import os
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import random
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import shutil
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import string
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from fastapi import APIRouter, Request, UploadFile
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from fastapi.responses import JSONResponse
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from pathvalidate import sanitize_filename
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from frigate.api.defs.tags import Tags
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from frigate.const import FACE_DIR
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from frigate.embeddings import EmbeddingsContext
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logger = logging.getLogger(__name__)
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router = APIRouter(tags=[Tags.events])
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@router.get("/faces")
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def get_faces():
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face_dict: dict[str, list[str]] = {}
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for name in os.listdir(FACE_DIR):
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face_dir = os.path.join(FACE_DIR, name)
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if not os.path.isdir(face_dir):
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continue
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face_dict[name] = []
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for file in sorted(
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os.listdir(face_dir),
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key=lambda f: os.path.getctime(os.path.join(face_dir, f)),
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reverse=True,
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):
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face_dict[name].append(file)
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return JSONResponse(status_code=200, content=face_dict)
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@router.post("/faces/{name}")
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async def register_face(request: Request, name: str, file: UploadFile):
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if not request.app.frigate_config.face_recognition.enabled:
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return JSONResponse(
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status_code=400,
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content={"message": "Face recognition is not enabled.", "success": False},
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)
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context: EmbeddingsContext = request.app.embeddings
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result = context.register_face(name, await file.read())
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return JSONResponse(
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status_code=200 if result.get("success", True) else 400,
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content=result,
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)
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@router.post("/faces/train/{name}/classify")
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def train_face(request: Request, name: str, body: dict = None):
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if not request.app.frigate_config.face_recognition.enabled:
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return JSONResponse(
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status_code=400,
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content={"message": "Face recognition is not enabled.", "success": False},
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)
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json: dict[str, any] = body or {}
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training_file = os.path.join(
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FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
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)
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if not training_file or not os.path.isfile(training_file):
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return JSONResponse(
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content=(
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{
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"success": False,
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"message": f"Invalid filename or no file exists: {training_file}",
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}
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),
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status_code=404,
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)
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rand_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
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new_name = f"{name}-{rand_id}.webp"
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new_file = os.path.join(FACE_DIR, f"{name}/{new_name}")
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shutil.move(training_file, new_file)
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context: EmbeddingsContext = request.app.embeddings
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context.clear_face_classifier()
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return JSONResponse(
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content=(
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{
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"success": True,
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"message": f"Successfully saved {training_file} as {new_name}.",
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}
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),
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status_code=200,
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)
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@router.post("/faces/{name}/delete")
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def deregister_faces(request: Request, name: str, body: dict = None):
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if not request.app.frigate_config.face_recognition.enabled:
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return JSONResponse(
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status_code=400,
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content={"message": "Face recognition is not enabled.", "success": False},
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)
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json: dict[str, any] = body or {}
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list_of_ids = json.get("ids", "")
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if not list_of_ids or len(list_of_ids) == 0:
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return JSONResponse(
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content=({"success": False, "message": "Not a valid list of ids"}),
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status_code=404,
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)
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context: EmbeddingsContext = request.app.embeddings
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context.delete_face_ids(
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name, map(lambda file: sanitize_filename(file), list_of_ids)
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)
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return JSONResponse(
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content=({"success": True, "message": "Successfully deleted faces."}),
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status_code=200,
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)
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