diff --git a/FaceRegWeb5.2/FaceRegWeb/flushredis.py b/FaceRegWeb5.2/FaceRegWeb/flushredis.py index 16faff9..c615ed8 100644 --- a/FaceRegWeb5.2/FaceRegWeb/flushredis.py +++ b/FaceRegWeb5.2/FaceRegWeb/flushredis.py @@ -2,7 +2,7 @@ import redis import os # Redis config -redis_host = str(os.getenv("REDIS_HOST", 'localhost')) +redis_host = str(os.getenv("REDIS_HOST", '192.168.0.14')) redis_port = int(os.getenv("REDIS_PORT", 2012)) redis_password = str(os.getenv("REDIS_PASSWORD", 'Xjsfzb@Redis123!')) num_workers = int(os.getenv("NUM_WORKERS", 10)) diff --git a/FaceRegWeb5.2/FaceRegWeb/process.py b/FaceRegWeb5.2/FaceRegWeb/process.py index f2085d8..f254d1e 100644 --- a/FaceRegWeb5.2/FaceRegWeb/process.py +++ b/FaceRegWeb5.2/FaceRegWeb/process.py @@ -2,52 +2,51 @@ # modified on 2024/6/12 # description: tool file (.py) import cv2 -import os +import os import hashlib import pickle import requests import base64 -import logging -import numpy as np +import logging +import numpy as np from datetime import datetime import redis from redisLock import RedisReadWriteLock -import onnxruntime -import time +import onnxruntime +import time import yaml from models import FaceRecoger, FaceBoxesV2, Landmark5er, FaceAlign, QualityOfClarity, QualityOfPose, QualityChecker so = onnxruntime.SessionOptions() so.log_severity_level = 3 # 0=VERBOSE, 1=INFO, 2=WARNING, 3=ERROR, 4=FATAL - -# 获取workers -if "NUM_WORKERS" not in os.environ: - raise RuntimeError("Environment variable NUM_WORKERS is required but not set.") -NUM_WORKERS = int(os.getenv("NUM_WORKERS", 10)) +# # 获取workers +# if "NUM_WORKERS" not in os.environ: +# raise RuntimeError("Environment variable NUM_WORKERS is required but not set.") +NUM_WORKERS = int(os.getenv("NUM_WORKERS", 10)) # max readers -max_readers = int(os.getenv("MAX_READERS",60)) +max_readers = int(os.getenv("MAX_READERS", 60)) # 连接到 Redis -redis_host = str(os.getenv("REDIS_HOST", 'localhost')) +redis_host = str(os.getenv("REDIS_HOST", '192.168.0.14')) redis_port = int(os.getenv("REDIS_PORT", 2012)) -redis_password = str(os.getenv("REDIS_PASSWORD", 'Xjsfzb@Redis123!')) +redis_password = str(os.getenv("REDIS_PASSWORD", 'Xjsfzb@Redis123!')) # connected redis_client = redis.Redis(host=redis_host, port=redis_port, password=redis_password, db=0) -PID_id = None +PID_id = None NUM_WORKERS = int(os.getenv("NUM_WORKERS", 10)) for i in range(NUM_WORKERS): - if redis_client.setnx(f"worker_{i}", 0): # 设置为dirty - PID_id = i + if redis_client.setnx(f"worker_{i}", 0): # 设置为dirty + PID_id = i break # create ReadWriteLock rw_lock = RedisReadWriteLock(redis_client, max_readers=max_readers) ErrorMsg = { - + "30003": "no face found in the database", "30004": "invalid file path", "30005": "invalid file suffix", @@ -69,39 +68,45 @@ ErrorMsg = { "30015": "face rotated downwards in the image", "30016": "low resolution of a face in the image", "30017": "poor clarity of a face in the image", - + "30019": "identity already exists; for database protection, operation rejected at this time" } + class FileError(Exception): - def __init__(self, arg:str): + def __init__(self, arg: str): self.code = arg self.args = [f"{str(self.__class__.__name__)} {str(arg)}: {ErrorMsg[arg]}"] + class NotImpltError(Exception): - def __init__(self, arg:str): + def __init__(self, arg: str): self.code = arg self.args = [f"{str(self.__class__.__name__)} {str(arg)}: {ErrorMsg[arg]}"] + class FaceError(Exception): - def __init__(self, arg:str): + def __init__(self, arg: str): self.code = arg self.args = [f"{str(self.__class__.__name__)} {str(arg)}: {ErrorMsg[arg]}"] + class UpdatedbError(Exception): - def __init__(self, arg:str): + def __init__(self, arg: str): self.code = arg self.args = [f"{str(self.__class__.__name__)} {str(arg)}: {ErrorMsg[arg]}"] -# setting Logger + +# setting Logger current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") log_dir = f"{os.path.dirname(os.path.abspath(__file__))}/log" os.makedirs(log_dir, exist_ok=True) -logging.basicConfig(filename=f'{log_dir}/{current_time}.log', level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logging.basicConfig(filename=f'{log_dir}/{current_time}.log', level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) # @@@@ print(log_dir) - + + def list_images(path: str): """ List images in a given path @@ -150,27 +155,28 @@ def find_image_hash(file_path: str) -> str: hasher.update(properties.encode("utf-8")) return hasher.hexdigest() + # 支持base64 local-path url 等多种检索图片的方式，返回 numpy -def load_img(img_path:str): - image = None - try: - if img_path.startswith(("http","www")): # url +def load_img(img_path: str): + image = None + try: + if img_path.startswith(("http", "www")): # url response = requests.get(url=img_path, stream=True, timeout=60, proxies={"http": None, "https": None}) response.raise_for_status() image_array = np.asarray(bytearray(response.raw.read()), dtype=np.uint8) image = cv2.imdecode(image_array, cv2.IMREAD_COLOR) - elif img_path.startswith(("./","/","C:","D:","E:",".\\")) or os.path.isfile(img_path): # local-path + elif img_path.startswith(("./", "/", "C:", "D:", "E:", ".\\")) or os.path.isfile(img_path): # local-path if not os.path.isfile(img_path): raise FileError("30004") # push: invalid file path - elif not img_path.lower().endswith((".jpg",'.jpeg','.png')): - raise FileError("30005") # push: invaild file suffix + elif not img_path.lower().endswith((".jpg", '.jpeg', '.png')): + raise FileError("30005") # push: invaild file suffix else: image = cv2.imread(img_path) - elif img_path.startswith("data:") and "base64" in img_path: # base64 + elif img_path.startswith("data:") and "base64" in img_path: # base64 encoded_data_parts = img_path.split(",") if len(encoded_data_parts) <= 0: raise FileError("104") # push: base64 is empty - print( "base64 is empty" ) + print("base64 is empty") encoded_data = encoded_data_parts[-1] nparr = np.fromstring(base64.b64decode(encoded_data), np.uint8) image = cv2.imdecode(nparr, cv2.IMREAD_COLOR) @@ -178,13 +184,13 @@ def load_img(img_path:str): raise NotImpltError("30006") # push: input file type is not support, only support: base64, url, local-path except Exception as e: logger.info(f"{e}") - raise e + raise e # return e - else: + else: return image - -def encoder_img2base64(img:np.ndarray): + +def encoder_img2base64(img: np.ndarray): success, encoded_img = cv2.imencode('.png', img) if success: img_base64 = base64.b64encode(encoded_img).decode("utf-8") @@ -195,41 +201,47 @@ def encoder_img2base64(img:np.ndarray): # from seetaface.api import * class FaceHelper: - def __init__(self, db_dir, config_path = './config.yaml'): + def __init__(self, db_dir, config_path='./config.yaml'): self.db_dir = os.path.abspath(db_dir) self.pid = PID_id - self.db_embeddings = None - self.db_identities = None + self.db_embeddings = None + self.db_identities = None # 根据config_path 读取ymal配置文件，然后进行初始化 - with open(config_path, 'r') as f: - config = yaml.safe_load(f) + with open(config_path, 'r') as f: + config = yaml.safe_load(f) - self.sim_threshold = config['faceReg']['sim_threshold'] # 0.7 - self.rotclsifer = onnxruntime.InferenceSession( config['ck_paths']['rotifer'], so) # "./checkpoints/model_gray_mobilenetv2_rotcls.onnx" - self.db_path = os.path.join( db_dir, "seetaface6.pkl" ).lower() + self.sim_threshold = config['faceReg']['sim_threshold'] # 0.7 + self.rotclsifer = onnxruntime.InferenceSession(config['ck_paths']['rotifer'], + so) # "./checkpoints/model_gray_mobilenetv2_rotcls.onnx" + self.db_path = os.path.join(db_dir, "seetaface6.pkl").lower() - self.fd = FaceBoxesV2(config['ck_paths']['FcBx'], config['ck_paths']['num_threads'] ) # r"./checkpoints/faceboxesv2-640x640.onnx" 4 - self.ld5er = Landmark5er( onnx_path1 = config['ck_paths']['landmk1'], # "./checkpoints/face_landmarker_pts5_net1.onnx", - onnx_path2 = config['ck_paths']['landmk2'], # "./checkpoints/face_landmarker_pts5_net2.onnx", - num_threads=config['ck_paths']['num_threads'] # 4 - ) + self.fd = FaceBoxesV2(config['ck_paths']['FcBx'], + config['ck_paths']['num_threads']) # r"./checkpoints/faceboxesv2-640x640.onnx" 4 + self.ld5er = Landmark5er(onnx_path1=config['ck_paths']['landmk1'], + # "./checkpoints/face_landmarker_pts5_net1.onnx", + onnx_path2=config['ck_paths']['landmk2'], + # "./checkpoints/face_landmarker_pts5_net2.onnx", + num_threads=config['ck_paths']['num_threads'] # 4 + ) self.fa = FaceAlign() - self.fr = FaceRecoger(onnx_path = config['ck_paths']['FcReg'], num_threads= config['ck_paths']['num_threads'] ) # "./checkpoints/face_recognizer.onnx" 4 - self.qc = QualityChecker(config['brightness']['v0'], config['brightness']['v1'], + self.fr = FaceRecoger(onnx_path=config['ck_paths']['FcReg'], + num_threads=config['ck_paths']['num_threads']) # "./checkpoints/face_recognizer.onnx" 4 + self.qc = QualityChecker(config['brightness']['v0'], config['brightness']['v1'], config['brightness']['v2'], config['brightness']['v3'], - hw = (config['resolution']['height'], config['resolution']['width']) - ) # v0=70.0, v1=100.0, v2=210.0, v3=230.0 - self.qpose = QualityOfPose(yaw_thrd=config['pose']['yaw_thrd'], pitch_thrd=config['pose']['pitch_thrd'], - var_onnx_path = config['pose']['var_onnx_path'], # './checkpoints/fsanet-var.onnx', - conv_onnx_path = config['pose']['conv_onnx_path'], # './checkpoints/fsanet-conv.onnx' + hw=(config['resolution']['height'], config['resolution']['width']) + ) # v0=70.0, v1=100.0, v2=210.0, v3=230.0 + self.qpose = QualityOfPose(yaw_thrd=config['pose']['yaw_thrd'], pitch_thrd=config['pose']['pitch_thrd'], + var_onnx_path=config['pose']['var_onnx_path'], # './checkpoints/fsanet-var.onnx', + conv_onnx_path=config['pose']['conv_onnx_path'], # './checkpoints/fsanet-conv.onnx' ) - self.qclarity = QualityOfClarity(low_thresh=config['clarity']['low_thrd'], high_thresh=config['clarity']['high_thrd']) + self.qclarity = QualityOfClarity(low_thresh=config['clarity']['low_thrd'], + high_thresh=config['clarity']['high_thrd']) # refresh the db - try: + try: self.updateDB(None, None, None, Onlyrefresh=True) - except Exception as e: + except Exception as e: # raise e pass @@ -237,49 +249,49 @@ class FaceHelper: logger.info(f"db_dir: {self.db_dir} ; PID: {self.pid}") # 读操作 - def faceRecognition(self, img_path:str): + def faceRecognition(self, img_path: str): rw_lock.acquire_read() 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_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 + redis_client.set(f"worker_{self.pid}", 1) # 同步完毕 - try: - if self.db_embeddings is None: + try: + if self.db_embeddings is None: raise FileError("30003") # push: no face in the database - - image = load_img(img_path) # get bgr numpy image + + image = load_img(img_path) # get bgr numpy image start = time.time() unknown_embeddings, cropped_images, names = [], [], [] - 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) cropped_images.append(cropped_face) unknown_embeddings.append(feature) - + unknown_embeddings = np.vstack(unknown_embeddings) results = np.dot(unknown_embeddings, self.db_embeddings.T) - max_values = np.max(results,axis=1) - max_idxs = np.argmax(results,axis=1) + max_values = np.max(results, axis=1) + max_idxs = np.argmax(results, axis=1) for i, (idx, value) in enumerate(zip(max_idxs, max_values)): name = "unknown" @@ -290,19 +302,19 @@ class FaceHelper: ret_data = [] for i, (facebox, name) in enumerate(zip(detect_result, names)): if name != 'unknown': - ret_data.append({'code':"30000", 'msg': ErrorMsg["30000"], 'data':[name,facebox]}) + ret_data.append({'code': "30000", 'msg': ErrorMsg["30000"], 'data': [name, facebox]}) else: code = self.check_face("None", image, facebox, prefix='facereg') if code == "30002": - ret_data.append({'code':"30001", 'msg': ErrorMsg["30001"], 'data':[name,facebox]}) + ret_data.append({'code': "30001", 'msg': ErrorMsg["30001"], 'data': [name, facebox]}) else: - ret_data.append({'code':code, 'msg': ErrorMsg[code], 'data':[name,facebox]}) + ret_data.append({'code': code, 'msg': ErrorMsg[code], 'data': [name, 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] - print("facereg runtime:", time.time() - start) + print("facereg runtime:", time.time() - start) except Exception as e: logger.info(f"{e}") @@ -314,25 +326,23 @@ class FaceHelper: # return names, [ encoder_img2base64(det) for det in cropped_images] # 只是提取人脸特征，不要对别的有任何影响 - def featureDetect(self, img_path:str): + def featureDetect(self, img_path: str): rw_lock.acquire_read() - - try: - image = load_img(img_path) # get bgr numpy image - + try: + image = load_img(img_path) # get bgr numpy image 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" \ No newline at end of file + 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 diff --git a/FaceRegWeb5.2/FaceRegWeb/setup.bat b/FaceRegWeb5.2/FaceRegWeb/setup.bat new file mode 100644 index 0000000..f296d71 --- /dev/null +++ b/FaceRegWeb5.2/FaceRegWeb/setup.bat @@ -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% diff --git a/FaceRegWeb5.2/FaceRegWeb/webmain.py b/FaceRegWeb5.2/FaceRegWeb/webmain.py index 7ea32ea..be23a5b 100644 --- a/FaceRegWeb5.2/FaceRegWeb/webmain.py +++ b/FaceRegWeb5.2/FaceRegWeb/webmain.py @@ -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)} + + diff --git a/FaceRegWeb5.2/人脸识别web端使用说明V5.2.docx b/FaceRegWeb5.2/人脸识别web端使用说明V5.2.docx index d45dc5a..1a505bf 100644 Binary files a/FaceRegWeb5.2/人脸识别web端使用说明V5.2.docx and b/FaceRegWeb5.2/人脸识别web端使用说明V5.2.docx differ