修改代码与文档
This commit is contained in:
jiang
parent
7c63e11132
commit
3a1c18c29d
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@ -2,7 +2,7 @@ import redis
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import os
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# Redis config
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redis_host = str(os.getenv("REDIS_HOST", 'localhost'))
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redis_host = str(os.getenv("REDIS_HOST", '192.168.0.14'))
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redis_port = int(os.getenv("REDIS_PORT", 2012))
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redis_password = str(os.getenv("REDIS_PASSWORD", 'Xjsfzb@Redis123!'))
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num_workers = int(os.getenv("NUM_WORKERS", 10))
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@ -2,52 +2,51 @@
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# modified on 2024/6/12
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# description: tool file (.py)
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import cv2
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import os
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import os
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import hashlib
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import pickle
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import requests
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import base64
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import logging
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import numpy as np
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import logging
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import numpy as np
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from datetime import datetime
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import redis
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from redisLock import RedisReadWriteLock
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import onnxruntime
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import time
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import onnxruntime
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import time
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import yaml
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from models import FaceRecoger, FaceBoxesV2, Landmark5er, FaceAlign, QualityOfClarity, QualityOfPose, QualityChecker
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so = onnxruntime.SessionOptions()
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so.log_severity_level = 3 # 0=VERBOSE, 1=INFO, 2=WARNING, 3=ERROR, 4=FATAL
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# 获取workers
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if "NUM_WORKERS" not in os.environ:
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raise RuntimeError("Environment variable NUM_WORKERS is required but not set.")
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NUM_WORKERS = int(os.getenv("NUM_WORKERS", 10))
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# # 获取workers
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# if "NUM_WORKERS" not in os.environ:
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# raise RuntimeError("Environment variable NUM_WORKERS is required but not set.")
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NUM_WORKERS = int(os.getenv("NUM_WORKERS", 10))
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# max readers
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max_readers = int(os.getenv("MAX_READERS",60))
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max_readers = int(os.getenv("MAX_READERS", 60))
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# 连接到 Redis
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redis_host = str(os.getenv("REDIS_HOST", 'localhost'))
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redis_host = str(os.getenv("REDIS_HOST", '192.168.0.14'))
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redis_port = int(os.getenv("REDIS_PORT", 2012))
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redis_password = str(os.getenv("REDIS_PASSWORD", 'Xjsfzb@Redis123!'))
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redis_password = str(os.getenv("REDIS_PASSWORD", 'Xjsfzb@Redis123!'))
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# connected
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redis_client = redis.Redis(host=redis_host, port=redis_port, password=redis_password, db=0)
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PID_id = None
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PID_id = None
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NUM_WORKERS = int(os.getenv("NUM_WORKERS", 10))
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for i in range(NUM_WORKERS):
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if redis_client.setnx(f"worker_{i}", 0): # 设置为dirty
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PID_id = i
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if redis_client.setnx(f"worker_{i}", 0): # 设置为dirty
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PID_id = i
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break
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# create ReadWriteLock
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rw_lock = RedisReadWriteLock(redis_client, max_readers=max_readers)
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ErrorMsg = {
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"30003": "no face found in the database",
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"30004": "invalid file path",
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"30005": "invalid file suffix",
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@ -69,39 +68,45 @@ ErrorMsg = {
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"30015": "face rotated downwards in the image",
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"30016": "low resolution of a face in the image",
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"30017": "poor clarity of a face in the image",
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"30019": "identity already exists; for database protection, operation rejected at this time"
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}
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class FileError(Exception):
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def __init__(self, arg:str):
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def __init__(self, arg: str):
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self.code = arg
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self.args = [f"{str(self.__class__.__name__)} {str(arg)}: {ErrorMsg[arg]}"]
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class NotImpltError(Exception):
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def __init__(self, arg:str):
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def __init__(self, arg: str):
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self.code = arg
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self.args = [f"{str(self.__class__.__name__)} {str(arg)}: {ErrorMsg[arg]}"]
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class FaceError(Exception):
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def __init__(self, arg:str):
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def __init__(self, arg: str):
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self.code = arg
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self.args = [f"{str(self.__class__.__name__)} {str(arg)}: {ErrorMsg[arg]}"]
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class UpdatedbError(Exception):
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def __init__(self, arg:str):
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def __init__(self, arg: str):
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self.code = arg
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self.args = [f"{str(self.__class__.__name__)} {str(arg)}: {ErrorMsg[arg]}"]
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# setting Logger
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# setting Logger
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current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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log_dir = f"{os.path.dirname(os.path.abspath(__file__))}/log"
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os.makedirs(log_dir, exist_ok=True)
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logging.basicConfig(filename=f'{log_dir}/{current_time}.log', level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logging.basicConfig(filename=f'{log_dir}/{current_time}.log', level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__) # @@@@
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print(log_dir)
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def list_images(path: str):
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"""
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List images in a given path
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@ -150,27 +155,28 @@ def find_image_hash(file_path: str) -> str:
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hasher.update(properties.encode("utf-8"))
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return hasher.hexdigest()
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# 支持base64 local-path url 等多种检索图片的方式，返回 numpy
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def load_img(img_path:str):
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image = None
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try:
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if img_path.startswith(("http","www")): # url
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def load_img(img_path: str):
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image = None
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try:
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if img_path.startswith(("http", "www")): # url
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response = requests.get(url=img_path, stream=True, timeout=60, proxies={"http": None, "https": None})
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response.raise_for_status()
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image_array = np.asarray(bytearray(response.raw.read()), dtype=np.uint8)
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image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
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elif img_path.startswith(("./","/","C:","D:","E:",".\\")) or os.path.isfile(img_path): # local-path
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elif img_path.startswith(("./", "/", "C:", "D:", "E:", ".\\")) or os.path.isfile(img_path): # local-path
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if not os.path.isfile(img_path):
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raise FileError("30004") # push: invalid file path
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elif not img_path.lower().endswith((".jpg",'.jpeg','.png')):
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raise FileError("30005") # push: invaild file suffix
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elif not img_path.lower().endswith((".jpg", '.jpeg', '.png')):
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raise FileError("30005") # push: invaild file suffix
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else:
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image = cv2.imread(img_path)
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elif img_path.startswith("data:") and "base64" in img_path: # base64
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elif img_path.startswith("data:") and "base64" in img_path: # base64
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encoded_data_parts = img_path.split(",")
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if len(encoded_data_parts) <= 0:
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raise FileError("104") # push: base64 is empty
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print( "base64 is empty" )
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print("base64 is empty")
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encoded_data = encoded_data_parts[-1]
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nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8)
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image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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@ -178,13 +184,13 @@ def load_img(img_path:str):
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raise NotImpltError("30006") # push: input file type is not support, only support: base64, url, local-path
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except Exception as e:
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logger.info(f"{e}")
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raise e
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raise e
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# return e
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else:
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else:
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return image
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def encoder_img2base64(img:np.ndarray):
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def encoder_img2base64(img: np.ndarray):
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success, encoded_img = cv2.imencode('.png', img)
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if success:
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img_base64 = base64.b64encode(encoded_img).decode("utf-8")
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@ -195,41 +201,47 @@ def encoder_img2base64(img:np.ndarray):
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# from seetaface.api import *
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class FaceHelper:
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def __init__(self, db_dir, config_path = './config.yaml'):
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def __init__(self, db_dir, config_path='./config.yaml'):
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self.db_dir = os.path.abspath(db_dir)
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self.pid = PID_id
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self.db_embeddings = None
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self.db_identities = None
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self.db_embeddings = None
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self.db_identities = None
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# 根据config_path 读取ymal配置文件，然后进行初始化
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with open(config_path, 'r') as f:
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config = yaml.safe_load(f)
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with open(config_path, 'r') as f:
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config = yaml.safe_load(f)
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self.sim_threshold = config['faceReg']['sim_threshold'] # 0.7
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self.rotclsifer = onnxruntime.InferenceSession( config['ck_paths']['rotifer'], so) # "./checkpoints/model_gray_mobilenetv2_rotcls.onnx"
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self.db_path = os.path.join( db_dir, "seetaface6.pkl" ).lower()
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self.sim_threshold = config['faceReg']['sim_threshold'] # 0.7
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self.rotclsifer = onnxruntime.InferenceSession(config['ck_paths']['rotifer'],
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so) # "./checkpoints/model_gray_mobilenetv2_rotcls.onnx"
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self.db_path = os.path.join(db_dir, "seetaface6.pkl").lower()
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self.fd = FaceBoxesV2(config['ck_paths']['FcBx'], config['ck_paths']['num_threads'] ) # r"./checkpoints/faceboxesv2-640x640.onnx" 4
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self.ld5er = Landmark5er( onnx_path1 = config['ck_paths']['landmk1'], # "./checkpoints/face_landmarker_pts5_net1.onnx",
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onnx_path2 = config['ck_paths']['landmk2'], # "./checkpoints/face_landmarker_pts5_net2.onnx",
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num_threads=config['ck_paths']['num_threads'] # 4
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)
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self.fd = FaceBoxesV2(config['ck_paths']['FcBx'],
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config['ck_paths']['num_threads']) # r"./checkpoints/faceboxesv2-640x640.onnx" 4
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self.ld5er = Landmark5er(onnx_path1=config['ck_paths']['landmk1'],
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# "./checkpoints/face_landmarker_pts5_net1.onnx",
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onnx_path2=config['ck_paths']['landmk2'],
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# "./checkpoints/face_landmarker_pts5_net2.onnx",
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num_threads=config['ck_paths']['num_threads'] # 4
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)
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self.fa = FaceAlign()
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self.fr = FaceRecoger(onnx_path = config['ck_paths']['FcReg'], num_threads= config['ck_paths']['num_threads'] ) # "./checkpoints/face_recognizer.onnx" 4
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self.qc = QualityChecker(config['brightness']['v0'], config['brightness']['v1'],
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self.fr = FaceRecoger(onnx_path=config['ck_paths']['FcReg'],
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num_threads=config['ck_paths']['num_threads']) # "./checkpoints/face_recognizer.onnx" 4
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self.qc = QualityChecker(config['brightness']['v0'], config['brightness']['v1'],
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config['brightness']['v2'], config['brightness']['v3'],
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hw = (config['resolution']['height'], config['resolution']['width'])
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) # v0=70.0, v1=100.0, v2=210.0, v3=230.0
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self.qpose = QualityOfPose(yaw_thrd=config['pose']['yaw_thrd'], pitch_thrd=config['pose']['pitch_thrd'],
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var_onnx_path = config['pose']['var_onnx_path'], # './checkpoints/fsanet-var.onnx',
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conv_onnx_path = config['pose']['conv_onnx_path'], # './checkpoints/fsanet-conv.onnx'
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hw=(config['resolution']['height'], config['resolution']['width'])
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) # v0=70.0, v1=100.0, v2=210.0, v3=230.0
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self.qpose = QualityOfPose(yaw_thrd=config['pose']['yaw_thrd'], pitch_thrd=config['pose']['pitch_thrd'],
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var_onnx_path=config['pose']['var_onnx_path'], # './checkpoints/fsanet-var.onnx',
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conv_onnx_path=config['pose']['conv_onnx_path'], # './checkpoints/fsanet-conv.onnx'
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)
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self.qclarity = QualityOfClarity(low_thresh=config['clarity']['low_thrd'], high_thresh=config['clarity']['high_thrd'])
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self.qclarity = QualityOfClarity(low_thresh=config['clarity']['low_thrd'],
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high_thresh=config['clarity']['high_thrd'])
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# refresh the db
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try:
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try:
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self.updateDB(None, None, None, Onlyrefresh=True)
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except Exception as e:
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except Exception as e:
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# raise e
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pass
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@ -237,49 +249,49 @@ class FaceHelper:
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logger.info(f"db_dir: {self.db_dir} ; PID: {self.pid}")
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# 读操作
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def faceRecognition(self, img_path:str):
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def faceRecognition(self, img_path: str):
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rw_lock.acquire_read()
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if int(redis_client.get(f"worker_{self.pid}")) == 0: # 说明self中的db和磁盘中的db不同步
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with open(self.db_path, "rb") as f:
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representations = pickle.load(f)
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if representations == []:
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self.db_embeddings, self.db_identities = None, None
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if representations == []:
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self.db_embeddings, self.db_identities = None, None
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else:
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self.db_embeddings = np.array([rep["embedding"] for rep in representations], dtype=np.float32)
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self.db_embeddings = np.array([rep["embedding"] for rep in representations], dtype=np.float32)
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self.db_identities = [os.path.splitext(os.path.basename(rep["identity"]))[0] for rep in representations]
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redis_client.set(f"worker_{self.pid}", 1) # 同步完毕
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redis_client.set(f"worker_{self.pid}", 1) # 同步完毕
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try:
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if self.db_embeddings is None:
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try:
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if self.db_embeddings is None:
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raise FileError("30003") # push: no face in the database
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image = load_img(img_path) # get bgr numpy image
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image = load_img(img_path) # get bgr numpy image
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start = time.time()
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unknown_embeddings, cropped_images, names = [], [], []
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image = self.rotadjust(image) # 调整角度
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image = self.rotadjust(image) # 调整角度
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detect_result = self.fd.detect(image)
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detect_result = [(box.x1, box.y1, box.x2, box.y2) for box in detect_result]
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if len(detect_result) == 0:
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logger.info(f"{img_path[:200]}: no face in the image")
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print(f"{img_path[:200]}: no face in the image")
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raise FaceError("30018") # push: no face in the image
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raise FaceError("30018") # push: no face in the image
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for facebox in detect_result:
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landmarks5 = self.ld5er.inference(image, facebox) # return: [(),(),(),(),()] 左眼 右眼 鼻子 左嘴角 右嘴角
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landmarks5 = self.ld5er.inference(image, facebox) # return: [(),(),(),(),()] 左眼 右眼 鼻子 左嘴角 右嘴角
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# print("5点关键点：",landmarks5)
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# 输入image 和5点特征点位置(基于原图image的位置) , return all cropped aligned face （裁剪后的对齐后的人脸部分图像, 简写为aligned_faces）
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landmarks5 = [ [ld5[0],ld5[1]] for ld5 in landmarks5]
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landmarks5 = [[ld5[0], ld5[1]] for ld5 in landmarks5]
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cropped_face = self.fa.align(image, landmarks5=landmarks5)
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# 输入aligned_faces ，return all features of aligned_faces
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feature = self.fr.inference(cropped_face)
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cropped_images.append(cropped_face)
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unknown_embeddings.append(feature)
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unknown_embeddings = np.vstack(unknown_embeddings)
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results = np.dot(unknown_embeddings, self.db_embeddings.T)
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max_values = np.max(results,axis=1)
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max_idxs = np.argmax(results,axis=1)
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max_values = np.max(results, axis=1)
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max_idxs = np.argmax(results, axis=1)
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for i, (idx, value) in enumerate(zip(max_idxs, max_values)):
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name = "unknown"
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@ -290,19 +302,19 @@ class FaceHelper:
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ret_data = []
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for i, (facebox, name) in enumerate(zip(detect_result, names)):
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if name != 'unknown':
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ret_data.append({'code':"30000", 'msg': ErrorMsg["30000"], 'data':[name,facebox]})
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ret_data.append({'code': "30000", 'msg': ErrorMsg["30000"], 'data': [name, facebox]})
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else:
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code = self.check_face("None", image, facebox, prefix='facereg')
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if code == "30002":
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ret_data.append({'code':"30001", 'msg': ErrorMsg["30001"], 'data':[name,facebox]})
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ret_data.append({'code': "30001", 'msg': ErrorMsg["30001"], 'data': [name, facebox]})
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else:
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ret_data.append({'code':code, 'msg': ErrorMsg[code], 'data':[name,facebox]})
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ret_data.append({'code': code, 'msg': ErrorMsg[code], 'data': [name, facebox]})
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if len(ret_data) != 1:
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ret_data = {'code':"30008", 'msg': ErrorMsg["30008"], 'data': ret_data}
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ret_data = {'code': "30008", 'msg': ErrorMsg["30008"], 'data': ret_data}
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else:
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ret_data = ret_data[0]
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print("facereg runtime:", time.time() - start)
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print("facereg runtime:", time.time() - start)
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except Exception as e:
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logger.info(f"{e}")
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@ -314,25 +326,23 @@ class FaceHelper:
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# return names, [ encoder_img2base64(det) for det in cropped_images]
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# 只是提取人脸特征，不要对别的有任何影响
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def featureDetect(self, img_path:str):
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def featureDetect(self, img_path: str):
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rw_lock.acquire_read()
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try:
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image = load_img(img_path) # get bgr numpy image
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try:
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image = load_img(img_path) # get bgr numpy image
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unknown_embeddings = []
|
||||
image = self.rotadjust(image) # 调整角度
|
||||
image = self.rotadjust(image) # 调整角度
|
||||
detect_result = self.fd.detect(image)
|
||||
detect_result = [(box.x1, box.y1, box.x2, box.y2) for box in detect_result]
|
||||
if len(detect_result) == 0:
|
||||
logger.info(f"{img_path[:200]}: no face in the image")
|
||||
print(f"{img_path[:200]}: no face in the image")
|
||||
raise FaceError("30018") # push: no face in the image
|
||||
raise FaceError("30018") # push: no face in the image
|
||||
for facebox in detect_result:
|
||||
landmarks5 = self.ld5er.inference(image, facebox) # return: [(),(),(),(),()] 左眼 右眼 鼻子 左嘴角 右嘴角
|
||||
landmarks5 = self.ld5er.inference(image, facebox) # return: [(),(),(),(),()] 左眼 右眼 鼻子 左嘴角 右嘴角
|
||||
# print("5点关键点：",landmarks5)
|
||||
# 输入image 和5点特征点位置(基于原图image的位置) , return all cropped aligned face （裁剪后的对齐后的人脸部分图像, 简写为aligned_faces）
|
||||
landmarks5 = [ [ld5[0],ld5[1]] for ld5 in landmarks5]
|
||||
landmarks5 = [[ld5[0], ld5[1]] for ld5 in landmarks5]
|
||||
cropped_face = self.fa.align(image, landmarks5=landmarks5)
|
||||
# 输入aligned_faces ，return all features of aligned_faces
|
||||
feature = self.fr.inference(cropped_face)
|
||||
|
|
@ -341,10 +351,10 @@ class FaceHelper:
|
|||
ret_data = []
|
||||
for _, (facebox, feature) in enumerate(zip(detect_result, unknown_embeddings)):
|
||||
code = self.check_face("None", image, facebox, prefix='featuredetect')
|
||||
ret_data.append({'code':code, 'msg': ErrorMsg[code], 'data': [f"{feature.tobytes()},{str(feature.dtype)}", facebox]})
|
||||
|
||||
ret_data.append({'code': code, 'msg': ErrorMsg[code], 'data': [{str(num) for num in feature}, facebox]})
|
||||
|
||||
if len(ret_data) != 1:
|
||||
ret_data = {'code':"30008", 'msg': ErrorMsg["30008"], 'data': ret_data}
|
||||
ret_data = {'code': "30008", 'msg': ErrorMsg["30008"], 'data': ret_data}
|
||||
else:
|
||||
ret_data = ret_data[0]
|
||||
|
||||
|
|
@ -358,73 +368,77 @@ class FaceHelper:
|
|||
|
||||
# opt in ['add','delete','replace'] identity作为检索的标识符，img_path只是提供文件路径
|
||||
# 写操作
|
||||
def updateDB(self, img_path :str, opt :str, identity :str, Onlyrefresh=False):
|
||||
def updateDB(self, img_path: str, opt: str, identity: str, Onlyrefresh=False):
|
||||
global rw_lock
|
||||
rw_lock.acquire_write() # 写锁定
|
||||
rw_lock.acquire_write() # 写锁定
|
||||
print("come in the updatedb")
|
||||
try:
|
||||
try:
|
||||
if not Onlyrefresh:
|
||||
if int(redis_client.get(f"worker_{self.pid}")) == 0: # 说明self中的db和磁盘中的db不同步
|
||||
with open(self.db_path, "rb") as f:
|
||||
representations = pickle.load(f)
|
||||
if representations == []:
|
||||
self.db_embeddings, self.db_identities = None, None
|
||||
if representations == []:
|
||||
self.db_embeddings, self.db_identities = None, None
|
||||
else:
|
||||
self.db_embeddings = np.array([rep["embedding"] for rep in representations], dtype=np.float32)
|
||||
self.db_identities = [os.path.splitext(os.path.basename(rep["identity"]))[0] for rep in representations]
|
||||
redis_client.set(f"worker_{self.pid}", 1) # 同步完毕
|
||||
self.db_embeddings = np.array([rep["embedding"] for rep in representations], dtype=np.float32)
|
||||
self.db_identities = [os.path.splitext(os.path.basename(rep["identity"]))[0] for rep in
|
||||
representations]
|
||||
redis_client.set(f"worker_{self.pid}", 1) # 同步完毕
|
||||
|
||||
img = load_img(img_path)
|
||||
img = self.rotadjust(img) # 调整角度
|
||||
if opt in ["add","replace"]:
|
||||
if opt == "add" and self.db_identities is not None and identity in self.db_identities:
|
||||
raise UpdatedbError("30019") # push: identity has exist. to pretect the db, reject opt of this time
|
||||
img = self.rotadjust(img) # 调整角度
|
||||
if opt in ["add", "replace"]:
|
||||
if opt == "add" and self.db_identities is not None and identity in self.db_identities:
|
||||
raise UpdatedbError(
|
||||
"30019") # push: identity has exist. to pretect the db, reject opt of this time
|
||||
else:
|
||||
|
||||
|
||||
detect_result = self.fd.detect(img)
|
||||
if len(detect_result) == 0: # no face
|
||||
if len(detect_result) == 0: # no face
|
||||
logger.info(f"{img_path[:200]}: when update, no face in the image")
|
||||
print(f"{img_path[:200]}: when update, no face in the image")
|
||||
raise FaceError("30018") # push: no face in the image
|
||||
else: # 获取最大的face,然后进行check
|
||||
raise FaceError("30018") # push: no face in the image
|
||||
else: # 获取最大的face,然后进行check
|
||||
# H, W = img.shape[:2]
|
||||
areas = [ box.area() for box in detect_result]
|
||||
areas = [box.area() for box in detect_result]
|
||||
max_idx = areas.index(max(areas))
|
||||
facebox = detect_result[max_idx]
|
||||
facebox = (facebox.x1, facebox.y1, facebox.x2, facebox.y2) # top_left point, bottom_right point
|
||||
FaceError_number = self.check_face(img_path=img_path[:200], img=img, facebox=facebox, prefix='update')
|
||||
facebox = (
|
||||
facebox.x1, facebox.y1, facebox.x2, facebox.y2) # top_left point, bottom_right point
|
||||
FaceError_number = self.check_face(img_path=img_path[:200], img=img, facebox=facebox,
|
||||
prefix='update')
|
||||
if FaceError_number != "30002":
|
||||
raise FaceError(FaceError_number)
|
||||
|
||||
cv2.imwrite(os.path.join(self.db_dir, identity+'.jpg'),img,[cv2.IMWRITE_JPEG_QUALITY, 100]) # 如果file已经存在，则会替换它
|
||||
|
||||
cv2.imwrite(os.path.join(self.db_dir, identity + '.jpg'), img,
|
||||
[cv2.IMWRITE_JPEG_QUALITY, 100]) # 如果file已经存在，则会替换它
|
||||
|
||||
elif opt == "delete":
|
||||
try:
|
||||
os.remove(os.path.join(self.db_dir, identity+'.jpg'))
|
||||
try:
|
||||
os.remove(os.path.join(self.db_dir, identity + '.jpg'))
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
pass
|
||||
else:
|
||||
raise NotImpltError("30007") # push: this updateDB type is not support
|
||||
|
||||
print("end the updateDB")
|
||||
logger.info(f"end the updateDB")
|
||||
|
||||
self.refresh_database(check = Onlyrefresh) # 结束时刷新下db, 并通知别的进程，dirty
|
||||
self.refresh_database(check=Onlyrefresh) # 结束时刷新下db, 并通知别的进程，dirty
|
||||
except Exception as e:
|
||||
logger.info(f"{e}")
|
||||
rw_lock.release_write()
|
||||
raise e
|
||||
else:
|
||||
raise e
|
||||
else:
|
||||
rw_lock.release_write()
|
||||
return 0
|
||||
|
||||
def refresh_database(self, check = True):
|
||||
def refresh_database(self, check=True):
|
||||
# ensure db exist
|
||||
os.makedirs(self.db_dir, exist_ok=True)
|
||||
if not os.path.exists(self.db_path):
|
||||
with open(self.db_path, "wb") as f:
|
||||
pickle.dump([], f)
|
||||
representations = [] # representations 最后要储存在db中
|
||||
representations = [] # representations 最后要储存在db中
|
||||
# Load the representations from the pickle file
|
||||
with open(self.db_path, "rb") as f:
|
||||
representations = pickle.load(f)
|
||||
|
|
@ -436,22 +450,24 @@ class FaceHelper:
|
|||
storage_images = list_images(self.db_dir)
|
||||
|
||||
# transform all images in storage_images to `.jpg`
|
||||
for idx in range(len(storage_images)):
|
||||
for idx in range(len(storage_images)):
|
||||
img_path = storage_images[idx]
|
||||
base_path, ext = os.path.splitext(img_path)
|
||||
if ext == '.jpg':
|
||||
continue
|
||||
continue
|
||||
iimg = cv2.imread(img_path)
|
||||
cv2.imwrite(base_path+'.jpg', iimg, [cv2.IMWRITE_JPEG_QUALITY, 100])
|
||||
storage_images[idx] = base_path+'.jpg'
|
||||
cv2.imwrite(base_path + '.jpg', iimg, [cv2.IMWRITE_JPEG_QUALITY, 100])
|
||||
storage_images[idx] = base_path + '.jpg'
|
||||
|
||||
must_save_pickle = False
|
||||
new_images = []; old_images = []; replaced_images = []
|
||||
new_images = [];
|
||||
old_images = [];
|
||||
replaced_images = []
|
||||
|
||||
new_images = list(set(storage_images) - set(pickle_images))
|
||||
new_images = list(set(storage_images) - set(pickle_images))
|
||||
old_images = list(set(pickle_images) - set(storage_images))
|
||||
|
||||
for current_representation in representations: # 找到被替换的images
|
||||
for current_representation in representations: # 找到被替换的images
|
||||
identity = current_representation["identity"]
|
||||
if identity in old_images:
|
||||
continue
|
||||
|
|
@ -459,8 +475,8 @@ class FaceHelper:
|
|||
beta_hash = find_image_hash(identity)
|
||||
if alpha_hash != beta_hash:
|
||||
# logger.debug(f"Even though {identity} represented before, it's replaced later.")
|
||||
replaced_images.append(identity)
|
||||
|
||||
replaced_images.append(identity)
|
||||
|
||||
new_images = new_images + replaced_images
|
||||
old_images = old_images + replaced_images
|
||||
|
||||
|
|
@ -481,37 +497,38 @@ class FaceHelper:
|
|||
logger.info(f"{new_image}: when refresh, no face in the image, delete")
|
||||
print(f"{new_image}: when refresh, no face in the image, delete")
|
||||
else:
|
||||
if len(detect_result) > 1:
|
||||
if len(detect_result) > 1:
|
||||
logger.info(f"{new_image}: find too many face, get and extract the biggest face in them")
|
||||
else:
|
||||
else:
|
||||
logger.info(f"{new_image}: find one face, perfect!")
|
||||
|
||||
areas = [ box.area() for box in detect_result]
|
||||
areas = [box.area() for box in detect_result]
|
||||
max_idx = areas.index(max(areas))
|
||||
facebox = detect_result[max_idx]
|
||||
facebox = (facebox.x1, facebox.y1, facebox.x2, facebox.y2) # top_left point, bottom_right point
|
||||
|
||||
if check:
|
||||
FaceError_number = self.check_face(img_path=new_image[:200], img=image, facebox=facebox, prefix='refreshdb')
|
||||
if FaceError_number != "30002":
|
||||
continue
|
||||
facebox = (facebox.x1, facebox.y1, facebox.x2, facebox.y2) # top_left point, bottom_right point
|
||||
|
||||
landmarks5 = self.ld5er.inference(image, facebox) # return: [(),(),(),(),()] 左眼 右眼 鼻子 左嘴角 右嘴角
|
||||
landmarks5 = [ [ld5[0],ld5[1]] for ld5 in landmarks5]
|
||||
if check:
|
||||
FaceError_number = self.check_face(img_path=new_image[:200], img=image, facebox=facebox,
|
||||
prefix='refreshdb')
|
||||
if FaceError_number != "30002":
|
||||
continue
|
||||
|
||||
landmarks5 = self.ld5er.inference(image, facebox) # return: [(),(),(),(),()] 左眼 右眼 鼻子 左嘴角 右嘴角
|
||||
landmarks5 = [[ld5[0], ld5[1]] for ld5 in landmarks5]
|
||||
cropped_face = self.fa.align(image, landmarks5=landmarks5)
|
||||
feature = self.fr.inference(cropped_face)
|
||||
|
||||
new_representations.append({
|
||||
"identity": new_image,
|
||||
"identity": new_image,
|
||||
"hash": find_image_hash(new_image),
|
||||
"embedding": feature,
|
||||
"detected_face_base64": encoder_img2base64(cropped_face),
|
||||
})
|
||||
|
||||
|
||||
representations += new_representations
|
||||
must_save_pickle = True
|
||||
|
||||
if must_save_pickle:
|
||||
must_save_pickle = True
|
||||
|
||||
if must_save_pickle:
|
||||
print("must save the pickle")
|
||||
with open(self.db_path, "wb") as f:
|
||||
pickle.dump(representations, f)
|
||||
|
|
@ -521,14 +538,14 @@ class FaceHelper:
|
|||
|
||||
# 保证db_dir 中的图片和self.db["identity"] 一致
|
||||
remove_images = list(set(storage_images) - set([rep["identity"] for rep in representations]))
|
||||
for remove_img in remove_images:
|
||||
try:
|
||||
for remove_img in remove_images:
|
||||
try:
|
||||
# os.remove(remove_img)
|
||||
fname = os.path.basename(remove_img)
|
||||
fname = os.path.basename(remove_img)
|
||||
# os.rename( remove_img, os.path.join(self.db_dir, "..","images","remove",fname) )
|
||||
except FileNotFoundError:
|
||||
pass
|
||||
|
||||
pass
|
||||
|
||||
if int(redis_client.get(f"worker_{self.pid}")) == 0:
|
||||
empty = False
|
||||
if len(representations) <= 0:
|
||||
|
|
@ -539,22 +556,22 @@ class FaceHelper:
|
|||
self.db_embeddings = np.array([rep["embedding"] for rep in representations], dtype=np.float32)
|
||||
self.db_identities = [os.path.splitext(os.path.basename(rep["identity"]))[0] for rep in representations]
|
||||
redis_client.set(f"worker_{self.pid}", 1) # 当前进程已更新
|
||||
if empty:
|
||||
if empty:
|
||||
logger.info("no face in the database")
|
||||
raise FileError("30003") # push: no face in db
|
||||
|
||||
def rotadjust(self, img: np.ndarray):
|
||||
image = img.copy()
|
||||
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 转换为灰度图像
|
||||
image = cv2.resize(image, (256, 256)) # resize (256,256)
|
||||
|
||||
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 转换为灰度图像
|
||||
image = cv2.resize(image, (256, 256)) # resize (256,256)
|
||||
|
||||
# 中心裁剪到 224x224
|
||||
start = (256 - 224) // 2
|
||||
image = image[start:start+224, start:start+224]
|
||||
|
||||
image = image[start:start + 224, start:start + 224]
|
||||
|
||||
# 将单通道灰度图像转换为三通道
|
||||
image = np.stack((image,)*3, axis=-1)
|
||||
|
||||
image = np.stack((image,) * 3, axis=-1)
|
||||
|
||||
# 转换为符合 ONNX 需要的格式
|
||||
image = image.astype(np.float32) / 255.0 # 归一化
|
||||
image = image.transpose(2, 0, 1) # 将图像从 HWC 格式转换为 CHW 格式
|
||||
|
|
@ -562,7 +579,7 @@ class FaceHelper:
|
|||
|
||||
inputs = {self.rotclsifer.get_inputs()[0].name: image}
|
||||
probs = self.rotclsifer.run(None, inputs)
|
||||
|
||||
|
||||
label = np.argmax(probs[0][0]) # 推理得到的逆时针旋转角度 [0,90,180,270]
|
||||
if label == 1:
|
||||
img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
|
||||
|
|
@ -578,15 +595,15 @@ class FaceHelper:
|
|||
print("img turn right, use `cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)` to repair")
|
||||
|
||||
return img
|
||||
|
||||
|
||||
def get_feature(self, img: np.ndarray):
|
||||
time.sleep(0.08)
|
||||
# assert img.shape[0] == img.shape[1] and img.shape[0] == 256 and img.shape[2] == 3
|
||||
img = cv2.resize( img, (256,256) )
|
||||
img = cv2.resize(img, (256, 256))
|
||||
input_feed = {}
|
||||
# crop_img = cv2.resize(img,(248,248))
|
||||
# crop_img = crop_img[...,::-1]
|
||||
crop_img = img[4:252, 4:252, :][...,::-1] # 注意要考虑 长或宽 < 248的情况
|
||||
crop_img = img[4:252, 4:252, :][..., ::-1] # 注意要考虑 长或宽 < 248的情况
|
||||
input_data = crop_img.transpose((2, 0, 1))
|
||||
# resize_img = cv2.resize(img, (248, 248))
|
||||
# input_data = resize_img.transpose((2, 0, 1))
|
||||
|
|
@ -599,7 +616,7 @@ class FaceHelper:
|
|||
# 2 normalization feature
|
||||
norm = temp_result / np.linalg.norm(temp_result, axis=1)
|
||||
return norm.flatten()
|
||||
|
||||
|
||||
def check_face(self, img_path, img, facebox, prefix="update"):
|
||||
H, W = img.shape[:2]
|
||||
if facebox[0] < 0 or facebox[2] >= W:
|
||||
|
|
@ -609,25 +626,92 @@ class FaceHelper:
|
|||
if facebox[1] < 0 or facebox[3] >= H:
|
||||
logger.info(f"{img_path}: when {prefix}, face shifted top/bottom")
|
||||
print(f"{img_path}: when {prefix}, face shifted top/bottom")
|
||||
return "30011" # face shifted top/bottom, partially not captured.
|
||||
face_img = img[ max(0,int(facebox[1])):int(facebox[3]), max(0,int(facebox[0])):int(facebox[2]) ]
|
||||
return "30011" # face shifted top/bottom, partially not captured.
|
||||
face_img = img[max(0, int(facebox[1])):int(facebox[3]), max(0, int(facebox[0])):int(facebox[2])]
|
||||
if not self.qc.check_bright(face_img):
|
||||
logger.info(f"{img_path}: when {prefix}, bad bright face in the image")
|
||||
print(f"{img_path}: when {prefix}, bad bright face in the image")
|
||||
return "30009" # bad bright face in the image
|
||||
return "30009" # bad bright face in the image
|
||||
if not self.qc.check_resolution(face_img):
|
||||
logger.info(f"{img_path}: when {prefix}, too small resolution of face in the image")
|
||||
print(f"{img_path}: when {prefix}, too small resolution of face in the image")
|
||||
return "30016" # small face in the image
|
||||
return "30016" # small face in the image
|
||||
pose = self.qpose.check(face_img)
|
||||
if pose != "frontFace":
|
||||
logger.info(f"{img_path}: when {prefix}, {pose} in the image")
|
||||
print(f"{img_path}: when {prefix}, {pose} in the image")
|
||||
dictt = {"rightFace": "30012", "leftFace": "30013", "upFace": "30014", "downFace": "30015"}
|
||||
return dictt[pose] # pose of face in the image
|
||||
return dictt[pose] # pose of face in the image
|
||||
if not self.qclarity.check(face_img):
|
||||
logger.info(f"{img_path}: when {prefix}, bad clarity of face in the image")
|
||||
print(f"{img_path}: when {prefix}, bad clarity of face in the image")
|
||||
return "30017" # poor clarity of face in the image
|
||||
return "30017" # poor clarity of face in the image
|
||||
|
||||
return "30002"
|
||||
return "30002"
|
||||
|
||||
def imageComparison(self, firstImage, secondImage):
|
||||
rw_lock.acquire_read()
|
||||
try:
|
||||
# Load and adjust images
|
||||
imageFirst = self.rotadjust(load_img(firstImage))
|
||||
imageSecond = self.rotadjust(load_img(secondImage))
|
||||
print("1")
|
||||
# Detect faces
|
||||
detect_result_first = self.fd.detect(imageFirst)
|
||||
detect_result_second = self.fd.detect(imageSecond)
|
||||
detect_result_first = [(box.x1, box.y1, box.x2, box.y2) for box in detect_result_first]
|
||||
detect_result_second = [(box.x1, box.y1, box.x2, box.y2) for box in detect_result_second]
|
||||
if len(detect_result_first) < 1:
|
||||
raise FaceError("30018")
|
||||
if len(detect_result_second) < 1:
|
||||
raise FaceError("30018")
|
||||
if len(detect_result_first) > 1:
|
||||
raise FaceError("30008")
|
||||
if len(detect_result_second) > 1:
|
||||
raise FaceError("30008")
|
||||
|
||||
# Validate detected faces
|
||||
self._validate_face_detection(detect_result_first, "firstImage")
|
||||
self._validate_face_detection(detect_result_second, "secondImage")
|
||||
# 提取人脸特征
|
||||
features_first = self._extract_single_feature(imageFirst, detect_result_first)
|
||||
features_second = self._extract_single_feature(imageSecond, detect_result_second)
|
||||
# Calculate the Euclidean distance between features
|
||||
distance = calculate_euclidean_distance(features_first, features_second)
|
||||
# Compare distance with threshold
|
||||
return distance >= 0.75
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error occurred: {e}")
|
||||
raise e
|
||||
finally:
|
||||
rw_lock.release_read()
|
||||
|
||||
def _validate_face_detection(self, detect_result, image_name):
|
||||
if not detect_result:
|
||||
logger.warning(f"No face detected in the image: {image_name}")
|
||||
raise FaceError("30018") # no face in the image
|
||||
|
||||
def extract_features(self, image, detect_result):
|
||||
return [
|
||||
self._extract_single_feature(image, facebox)
|
||||
for facebox in detect_result
|
||||
]
|
||||
|
||||
def _extract_single_feature(self, image, detect_result):
|
||||
for facebox in detect_result:
|
||||
landmarks5 = self.ld5er.inference(image, facebox)
|
||||
landmarks5 = np.array([[ld5[0], ld5[1]] for ld5 in landmarks5])
|
||||
cropped_face = self.fa.align(image, landmarks5=landmarks5)
|
||||
return self.fr.inference(cropped_face)
|
||||
|
||||
|
||||
def calculate_euclidean_distance(features1, features2):
|
||||
if len(features1) != len(features2):
|
||||
raise ValueError("Feature arrays must have the same length")
|
||||
# Calculate the Euclidean distance
|
||||
total = 0
|
||||
for f1, f2 in zip(features1, features2):
|
||||
total += f1 * f2
|
||||
|
||||
return total
|
||||
|
|
|
|||
|
|
@ -0,0 +1,36 @@
|
|||
@echo off
|
||||
setlocal enabledelayedexpansion
|
||||
|
||||
rem 从 redis.yaml 文件中读取 Redis 相关配置信息
|
||||
set "REDIS_CONFIG="
|
||||
for /f "delims=" %%i in (redis.yaml) do (
|
||||
set "REDIS_CONFIG=!REDIS_CONFIG! %%i"
|
||||
)
|
||||
|
||||
rem 工作进程数 workers
|
||||
set "NUM_WORKERS=%3"
|
||||
|
||||
rem 提取 Redis 配置信息
|
||||
for %%i in (!REDIS_CONFIG!) do (
|
||||
for /f "tokens=1,* delims==" %%j in (%%i) do (
|
||||
if "%%j"=="REDIS_HOST" set "REDIS_HOST=%%k"
|
||||
if "%%j"=="REDIS_PORT" set "REDIS_PORT=%%k"
|
||||
if "%%j"=="REDIS_PASSWORD" set "REDIS_PASSWORD=%%k"
|
||||
)
|
||||
)
|
||||
|
||||
echo num_workers: %NUM_WORKERS%
|
||||
echo redis_host: %REDIS_HOST%; redis_port: %REDIS_PORT%
|
||||
|
||||
rem 运行 Python 脚本
|
||||
set REDIS_HOST=%REDIS_HOST%
|
||||
set REDIS_PORT=%REDIS_PORT%
|
||||
set REDIS_PASSWORD=%REDIS_PASSWORD%
|
||||
set NUM_WORKERS=%NUM_WORKERS%
|
||||
|
||||
rem 确保 Python 脚本能够正确读取环境变量
|
||||
python flushredis.py
|
||||
|
||||
rem 启动 uvicorn
|
||||
set MAX_READERS=%4%
|
||||
uvicorn webmain:app --host %1 --port %2 --workers %NUM_WORKERS%
|
||||
|
|
@ -1,83 +1,154 @@
|
|||
from fastapi import FastAPI
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel
|
||||
from process import FaceHelper, FileError, ErrorMsg
|
||||
from process import FaceHelper, FileError, ErrorMsg
|
||||
import threading
|
||||
import os
|
||||
import time
|
||||
import time
|
||||
|
||||
# 创建 FastAPI 实例
|
||||
app = FastAPI()
|
||||
|
||||
# 创建线程锁，确保在多线程环境中对共享资源的访问是线程安全的
|
||||
lock = threading.Lock()
|
||||
|
||||
# 全局变量 facehelper，稍后将用于管理人脸识别相关操作
|
||||
facehelper = None
|
||||
|
||||
facehelper = FaceHelper(
|
||||
db_dir="./dbface",
|
||||
)
|
||||
|
||||
class face(BaseModel):
|
||||
img:str
|
||||
def get_facehelper():
|
||||
"""
|
||||
懒加载模式初始化 facehelper 对象。
|
||||
只有在第一次调用时才会创建 FaceHelper 实例。
|
||||
使用线程锁以确保 facehelper 实例在多线程环境下的安全初始化。
|
||||
"""
|
||||
global facehelper
|
||||
if facehelper is None:
|
||||
with lock:
|
||||
if facehelper is None:
|
||||
facehelper = FaceHelper(db_dir="./dbface")
|
||||
return facehelper
|
||||
|
||||
|
||||
# 定义请求模型，用于接收前端传递的图像数据
|
||||
class FaceRequest(BaseModel):
|
||||
img: str
|
||||
|
||||
|
||||
# 定义请求模型，用于接收前端传递的数据库操作数据
|
||||
class DBFaceRequest(BaseModel):
|
||||
img: str
|
||||
optMode: str
|
||||
uniqueKey: str
|
||||
|
||||
|
||||
# 接两图片对比的参数
|
||||
class ImageComparison(BaseModel):
|
||||
firstImage: str
|
||||
secondImage: str
|
||||
|
||||
class dbface(BaseModel):
|
||||
img:str
|
||||
optMode:str
|
||||
uniqueKey:str
|
||||
|
||||
@app.post("/refreshdb")
|
||||
@app.post("/refreshdb/")
|
||||
def refresh():
|
||||
global facehelper
|
||||
try:
|
||||
"""
|
||||
刷新人脸数据库的接口。
|
||||
该接口将调用 facehelper 的 updateDB 方法，刷新数据库内容。
|
||||
"""
|
||||
facehelper = get_facehelper()
|
||||
try:
|
||||
# 加锁确保数据库更新操作的线程安全性
|
||||
with lock:
|
||||
facehelper.updateDB(None, None, None, Onlyrefresh=True)
|
||||
except FileError as e:
|
||||
# return {"status":e.code, "detail":f"{e}"}
|
||||
return {'code': e.code, 'msg': f"{e}", 'data': 'None'}
|
||||
else:
|
||||
return {'code': "30002", 'msg': ErrorMsg['30002'], 'data': 'None'}
|
||||
except FileError as e:
|
||||
# 处理文件错误，返回相应的错误代码和信息
|
||||
return {'code': e.code, 'msg': str(e), 'data': None}
|
||||
else:
|
||||
# 成功刷新数据库后返回相应的状态码和消息
|
||||
return {'code': "30002", 'msg': ErrorMsg['30002'], 'data': None}
|
||||
|
||||
|
||||
@app.post("/facerecognition")
|
||||
@app.post("/facerecognition/")
|
||||
def faceRecognition(input:face):
|
||||
start = time.time()
|
||||
global facehelper
|
||||
try:
|
||||
def faceRecognition(input: FaceRequest):
|
||||
"""
|
||||
进行人脸识别的接口。
|
||||
接收前端传递的图像数据，并使用 facehelper 进行人脸识别。
|
||||
返回识别结果和运行时间。
|
||||
"""
|
||||
facehelper = get_facehelper()
|
||||
start = time.time() # 记录开始时间
|
||||
try:
|
||||
# 调用 facehelper 的 faceRecognition 方法进行人脸识别
|
||||
ret_data = facehelper.faceRecognition(input.img)
|
||||
print("finished recognition...")
|
||||
end = time.time()
|
||||
print("runtime: ", end-start)
|
||||
except Exception as e:
|
||||
return {'code': e.code, 'msg': f"{e}", 'data': 'None'}
|
||||
# return {"status":f"{e.code}", "detail":f"{e}"}
|
||||
else:
|
||||
return ret_data
|
||||
return {"status":1, "name":identity, "resImg":res_img_base64}
|
||||
# 处理异常，返回相应的错误代码和信息
|
||||
raise HTTPException(status_code=400, detail={'code': e.code, 'msg': str(e), 'data': None})
|
||||
finally:
|
||||
end = time.time() # 记录结束时间
|
||||
# 打印运行时间
|
||||
print(f"Recognition finished. Runtime: {end - start:.2f} seconds")
|
||||
|
||||
# 返回识别结果
|
||||
return ret_data
|
||||
|
||||
|
||||
@app.post("/featuredetect")
|
||||
@app.post("/featuredetect/")
|
||||
def featureDetect(input:face):
|
||||
start = time.time()
|
||||
global facehelper
|
||||
try:
|
||||
def featureDetect(input: FaceRequest):
|
||||
"""
|
||||
特征检测接口。
|
||||
接收前端传递的图像数据，并使用 facehelper 进行特征检测。
|
||||
返回检测结果和运行时间。
|
||||
"""
|
||||
facehelper = get_facehelper()
|
||||
start = time.time() # 记录开始时间
|
||||
try:
|
||||
# 调用 facehelper 的 featureDetect 方法进行特征检测
|
||||
ret_data = facehelper.featureDetect(input.img)
|
||||
print("finished featuredetect...")
|
||||
end = time.time()
|
||||
print("runtime: ", end-start)
|
||||
except Exception as e:
|
||||
return {'code': e.code, 'msg': f"{e}", 'data': 'None'}
|
||||
# return {"status":f"{e.code}", "detail":f"{e}"}
|
||||
else:
|
||||
return ret_data
|
||||
# 处理异常，返回相应的错误代码和信息
|
||||
raise HTTPException(status_code=400, detail={'code': e.code, 'msg': str(e), 'data': None})
|
||||
finally:
|
||||
end = time.time() # 记录结束时间
|
||||
# 打印运行时间
|
||||
print(f"Feature detection finished. Runtime: {end - start:.2f} seconds")
|
||||
|
||||
# 返回检测结果
|
||||
return ret_data
|
||||
|
||||
|
||||
@app.post("/updatedb")
|
||||
@app.post("/updatedb/")
|
||||
def updateDB(input:dbface):
|
||||
global facehelper
|
||||
# input.uniqueKey = os.path.splitext(os.path.basename(input.uniqueKey))[0]
|
||||
|
||||
try:
|
||||
def updateDB(input: DBFaceRequest):
|
||||
"""
|
||||
更新人脸数据库的接口。
|
||||
接收前端传递的数据库操作数据，并使用 facehelper 更新数据库。
|
||||
"""
|
||||
facehelper = get_facehelper()
|
||||
try:
|
||||
# 加锁确保数据库更新操作的线程安全性
|
||||
with lock:
|
||||
facehelper.updateDB(input.img, input.optMode, input.uniqueKey)
|
||||
except Exception as e:
|
||||
return {'code': e.code, 'msg': f"{e}", 'data': 'None'}
|
||||
# return {"status":f"{e.code}", "detail":f"{e}"}
|
||||
else:
|
||||
return {'code': "30002", 'msg': ErrorMsg['30002'], 'data': 'None'}
|
||||
|
||||
# 处理异常，返回相应的错误代码和信息
|
||||
raise HTTPException(status_code=400, detail={'code': e.code, 'msg': str(e), 'data': None})
|
||||
else:
|
||||
# 成功更新数据库后返回相应的状态码和消息
|
||||
return {'code': "30002", 'msg': ErrorMsg['30002'], 'data': None}
|
||||
|
||||
|
||||
@app.post("/imageComparison")
|
||||
def imageComparison(input: ImageComparison):
|
||||
"""
|
||||
更新人脸数据库的接口。
|
||||
接收前端传递的数据库操作数据，并使用 facehelper 更新数据库。
|
||||
"""
|
||||
facehelper = get_facehelper()
|
||||
|
||||
try:
|
||||
# 加锁确保数据库更新操作的线程安全性
|
||||
with lock:
|
||||
state = facehelper.imageComparison(input.firstImage, input.secondImage)
|
||||
except Exception as e:
|
||||
# 处理异常，返回相应的错误代码和信息
|
||||
raise HTTPException(status_code=400, detail={'code': e.code, 'msg': str(e), 'data': None})
|
||||
else:
|
||||
# 成功更新数据库后返回相应的状态码和消息
|
||||
return {'code': "30002", 'msg': ErrorMsg['30002'], 'data': bool(state)}
|
||||
|
||||
|
||||
|
|
|
|||
Binary file not shown.
Loading…
Reference in New Issue