├── models └── __init__.py ├── utils ├── __init__.py ├── image_processing.py ├── face_encoding.py ├── face_detection.py ├── face_storage.py └── face_quality.py ├── services ├── __init__.py ├── authentication.py ├── registration.py └── recognition.py ├── business └── xunfei │ ├── __init__.py │ ├── config.py │ ├── utils.py │ ├── face_feature_client.py │ ├── face_compare_client.py │ └── spark.py ├── .idea ├── vcs.xml ├── inspectionProfiles │ └── profiles_settings.xml ├── modules.xml ├── poetry.xml ├── misc.xml ├── iflyface.iml └── workspace.xml ├── pyproject.toml ├── .gitignore ├── app.py └── poetry.lock /models/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /services/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /business/xunfei/__init__.py: -------------------------------------------------------------------------------- 1 | from .face_compare_client import FaceCompareClient 2 | from .face_feature_client import FaceFeatureClient 3 | from .spark import SparkAPI 4 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/poetry.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 9 | 10 | -------------------------------------------------------------------------------- /business/xunfei/config.py: -------------------------------------------------------------------------------- 1 | # 请填写控制台获取的APPID、APISecret、APIKey 2 | APP_ID = '142b649c' 3 | API_KEY_FACE_COMPARE = "3790438c24750aa9ed3b02ab6321c360" 4 | API_SECRET_FACE_COMPARE = 'ZGE2OWRlNjVkNzM0MzM5MzYwYWNkZTYy' 5 | SERVER_ID_FACE_COMPARE = 's67c9c78c' 6 | API_KEY_FACE_FEATURE = "002c130bd3c09614c4173a250b1ebd1b" 7 | API_KEY_SPARK = '3790438c24750aa9ed3b02ab6321c360' 8 | API_SECRET_SPARK = 'ZGE2OWRlNjVkNzM0MzM5MzYwYWNkZTYy' 9 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | 8 | 10 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "iflyface" 3 | version = "0.1.0" 4 | description = "" 5 | authors = ["p0ise "] 6 | readme = "README.md" 7 | 8 | [tool.poetry.dependencies] 9 | python = "^3.10" 10 | flask = "^3.0.2" 11 | requests = "^2.31.0" 12 | flask-cors = "^4.0.0" 13 | flask-socketio = "^5.3.6" 14 | opencv-python = "^4.9.0.80" 15 | facenet-pytorch = "^2.5.3" 16 | websocket-client = "^1.7.0" 17 | 18 | 19 | [build-system] 20 | requires = ["poetry-core"] 21 | build-backend = "poetry.core.masonry.api" 22 | -------------------------------------------------------------------------------- /utils/image_processing.py: -------------------------------------------------------------------------------- 1 | import base64 2 | 3 | import cv2 4 | import numpy as np 5 | from PIL import Image 6 | 7 | 8 | def cv2_to_pil(image): 9 | """Convert a CV2 image to PIL format.""" 10 | image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) 11 | return Image.fromarray(image) 12 | 13 | 14 | def parse_frame_data(frame_data): 15 | """Parse frame data from base64 encoding.""" 16 | frame_data = base64.b64decode(frame_data.split(',')[1]) 17 | frame = np.frombuffer(frame_data, dtype=np.uint8) 18 | frame = cv2.imdecode(frame, flags=1) 19 | return cv2_to_pil(frame) 20 | -------------------------------------------------------------------------------- /.idea/iflyface.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 18 | 19 | -------------------------------------------------------------------------------- /utils/face_encoding.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from facenet_pytorch import InceptionResnetV1 3 | from torchvision import transforms 4 | 5 | device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') 6 | resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device) 7 | 8 | 9 | def encode_face(face_image): 10 | """Encode a face image into a high-dimensional vector.""" 11 | preprocess = transforms.Compose([ 12 | transforms.Resize((160, 160)), 13 | transforms.ToTensor(), 14 | ]) 15 | face_tensor = preprocess(face_image).unsqueeze(0) 16 | with torch.no_grad(): 17 | face_encoding = resnet(face_tensor) 18 | return face_encoding.detach().cpu().numpy()[0] 19 | -------------------------------------------------------------------------------- /services/authentication.py: -------------------------------------------------------------------------------- 1 | from utils.face_detection import find_primary_face, align_face 2 | from utils.face_encoding import encode_face 3 | from utils.face_quality import evaluate_face_quality 4 | from utils.face_storage import FaceStorage 5 | 6 | face_storage = FaceStorage() 7 | 8 | 9 | def authenticate_face(image): 10 | """ 11 | 对给定的图像进行人脸认证。 12 | 13 | 参数: 14 | - image: 包含用户人脸的图像（PIL.Image格式）。 15 | 16 | 返回: 17 | - 认证成功与否的布尔值以及相应的消息。 18 | """ 19 | primary_face = find_primary_face(image) 20 | if primary_face: 21 | box, prob, landmark = primary_face 22 | if evaluate_face_quality(box, prob, image, landmark): 23 | aligned_face = align_face(image, box, landmark) 24 | face_encoding = encode_face(aligned_face) 25 | matched_face = face_storage.match_face(face_encoding) 26 | if matched_face: 27 | return True, matched_face['name'] 28 | else: 29 | return False, "认证失败，未知人脸，请先注册" 30 | else: 31 | return False, "人脸质量不符合要求" 32 | else: 33 | return False, "未检测到人脸" 34 | -------------------------------------------------------------------------------- /services/registration.py: -------------------------------------------------------------------------------- 1 | from utils.face_detection import find_primary_face, align_face 2 | from utils.face_encoding import encode_face 3 | from utils.face_quality import is_face_forward, evaluate_face_quality 4 | from utils.face_storage import FaceStorage 5 | 6 | face_storage = FaceStorage() 7 | 8 | 9 | def register_face(image, username): 10 | """ 11 | 注册新的用户人脸。 12 | 13 | 参数: 14 | - image: 包含用户人脸的图像（PIL.Image格式）。 15 | - username: 用户的名称。 16 | 17 | 返回: 18 | - 成功或失败的消息。 19 | """ 20 | primary_face = find_primary_face(image) 21 | if primary_face: 22 | box, prob, landmark = primary_face 23 | if is_face_forward(landmark) and evaluate_face_quality(box, prob, image, landmark): 24 | aligned_face = align_face(image, box, landmark) 25 | face_encoding = encode_face(aligned_face) 26 | matched_face = face_storage.match_face(face_encoding) 27 | if matched_face is None: 28 | face = face_storage.add_known_face(face_encoding, username) 29 | face_id = face['id'] 30 | return True, username 31 | else: 32 | return False, "人脸已注册" 33 | else: 34 | return False, "人脸质量不符合要求或非正脸" 35 | else: 36 | return False, "未检测到人脸" 37 | -------------------------------------------------------------------------------- /business/xunfei/utils.py: -------------------------------------------------------------------------------- 1 | import base64 2 | import hashlib 3 | import hmac 4 | import mimetypes 5 | 6 | 7 | class AssembleHeaderException(Exception): 8 | pass 9 | 10 | 11 | def parse_url(request_url): 12 | stidx = request_url.index("://") 13 | host = request_url[stidx + 3:] 14 | schema = request_url[:stidx + 3] 15 | edidx = host.index("/") 16 | if edidx <= 0: 17 | raise AssembleHeaderException("Invalid request URL: " + request_url) 18 | path = host[edidx:] 19 | host = host[:edidx] 20 | return schema, host, path 21 | 22 | 23 | def generate_signature(api_secret, host, date, method, path): 24 | signature_origin = f"host: {host}\ndate: {date}\n{method} {path} HTTP/1.1" 25 | signature_sha = hmac.new(api_secret.encode('utf-8'), signature_origin.encode('utf-8'), 26 | digestmod=hashlib.sha256).digest() 27 | return base64.b64encode(signature_sha).decode('utf-8') 28 | 29 | 30 | def assemble_authorization_header(api_key, signature_sha): 31 | authorization_origin = (f'api_key="{api_key}", algorithm="hmac-sha256", ' 32 | f'headers="host date request-line", signature="{signature_sha}"') 33 | return base64.b64encode(authorization_origin.encode('utf-8')).decode('utf-8') 34 | 35 | 36 | def encode_image(image_path): 37 | """ 38 | 对图片文件进行Base64编码，并自动检测图片类型。 39 | 返回编码后的图片字符串和图片类型。 40 | """ 41 | # 检测文件类型 42 | mime_type, _ = mimetypes.guess_type(image_path) 43 | if mime_type is None: 44 | raise ValueError("Cannot determine the file type of the image.") 45 | # 获取文件的扩展名，例如 "image/jpeg" -> "jpeg" 46 | file_type = mime_type.split('/')[-1] 47 | 48 | with open(image_path, 'rb') as image_file: 49 | encoded_string = base64.b64encode(image_file.read()).decode('utf-8') 50 | return encoded_string, file_type 51 | -------------------------------------------------------------------------------- /utils/face_detection.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import torch 4 | from PIL import Image 5 | from facenet_pytorch import MTCNN 6 | 7 | device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') 8 | mtcnn = MTCNN(keep_all=True, device=device) 9 | 10 | 11 | def find_faces(image): 12 | # 使用MTCNN检测人脸，同时返回框、置信度和关键点 13 | boxes, probs, landmarks = mtcnn.detect(image, landmarks=True) 14 | if boxes is not None: 15 | return boxes, probs, landmarks 16 | else: 17 | return [], [], [] 18 | 19 | 20 | def find_primary_face(image): 21 | """ 22 | 查找图像中最显著的人脸。 23 | 24 | 参数: 25 | - image: PIL.Image对象，包含待检测的图像。 26 | 27 | 返回: 28 | - primary_face: 最显著的人脸信息，包括框、置信度和关键点。 29 | 如果没有检测到人脸，则返回None。 30 | """ 31 | boxes, probs, landmarks = mtcnn.detect(image, landmarks=True) 32 | if boxes is not None: 33 | return boxes[0], probs[0], landmarks[0] 34 | else: 35 | return None 36 | 37 | 38 | def align_face(image, box, landmark): 39 | # 计算两眼之间的角度 40 | eye_left = np.array(landmark[0]) 41 | eye_right = np.array(landmark[1]) 42 | d_y = eye_right[1] - eye_left[1] 43 | d_x = eye_right[0] - eye_left[0] 44 | angle = np.degrees(np.arctan2(d_y, d_x)) 45 | 46 | # 计算box中心作为旋转中心 47 | box_center = ((box[0] + box[2]) / 2, (box[1] + box[3]) / 2) 48 | 49 | # 将PIL.Image转换为numpy数组以便使用cv2函数处理 50 | image_np = np.array(image) 51 | 52 | # 应用仿射变换对齐整个画面 53 | scale = 1 54 | M = cv2.getRotationMatrix2D(box_center, angle, scale) 55 | rotated_image_np = cv2.warpAffine(image_np, M, (image_np.shape[1], image_np.shape[0])) 56 | 57 | # 将旋转后的numpy数组图像转换回PIL.Image 58 | rotated_image = Image.fromarray(rotated_image_np) 59 | 60 | # 从旋转后的图像中截取人脸区域 61 | face_image = rotated_image.crop(box) 62 | 63 | # 缩放图像到指定的尺寸 64 | aligned_face = face_image.resize((160, 160)) 65 | 66 | return aligned_face 67 | -------------------------------------------------------------------------------- /utils/face_storage.py: -------------------------------------------------------------------------------- 1 | import uuid 2 | 3 | import numpy as np 4 | 5 | 6 | class SingletonMeta(type): 7 | _instances = {} 8 | 9 | def __call__(cls, *args, **kwargs): 10 | if cls not in cls._instances: 11 | instance = super().__call__(*args, **kwargs) 12 | cls._instances[cls] = instance 13 | return cls._instances[cls] 14 | 15 | 16 | class FaceStorage(metaclass=SingletonMeta): 17 | def __init__(self): 18 | self.known_faces = [] # 存储已知人脸的信息，每个条目是一个字典 19 | 20 | def add_known_face(self, face_encoding, name): 21 | """将新的人脸编码和名称添加到存储中""" 22 | face_id = str(uuid.uuid4()) # 生成唯一标识符 23 | face = { 24 | "id": face_id, 25 | "name": name, 26 | "encoding": face_encoding 27 | } 28 | self.known_faces.append(face) 29 | return face 30 | 31 | def match_face(self, face_encoding, tolerance=0.6): 32 | """在已知的人脸编码中寻找匹配项""" 33 | if not self.known_faces: 34 | return None 35 | distances = np.linalg.norm([face["encoding"] for face in self.known_faces] - face_encoding, axis=1) 36 | best_match_index = np.argmin(distances) 37 | if distances[best_match_index] < tolerance: 38 | return self.known_faces[best_match_index] 39 | return None 40 | 41 | def update_face_encoding(self, face_id, new_encoding): 42 | """根据人脸ID更新人脸编码""" 43 | for face in self.known_faces: 44 | if face["id"] == face_id: 45 | face["encoding"] = new_encoding 46 | return True 47 | return False 48 | 49 | def rename_face(self, face_id, new_name): 50 | """根据人脸ID更新人脸名称""" 51 | for face in self.known_faces: 52 | if face["id"] == face_id: 53 | face["name"] = new_name 54 | return True 55 | return False 56 | 57 | def get_face_info(self, face_id): 58 | """根据人脸ID获取人脸信息""" 59 | for face in self.known_faces: 60 | if face["id"] == face_id: 61 | return face 62 | return None 63 | -------------------------------------------------------------------------------- /business/xunfei/face_feature_client.py: -------------------------------------------------------------------------------- 1 | import base64 2 | import hashlib 3 | import os.path 4 | import time 5 | from urllib.parse import urljoin 6 | 7 | import requests 8 | 9 | from .config import APP_ID, API_KEY_FACE_FEATURE 10 | 11 | 12 | class FaceFeatureClient: 13 | def __init__(self): 14 | self.base_url = "http://tupapi.xfyun.cn/v1/" 15 | self.types = ['age', 'sex', 'expression', 'face_score'] 16 | 17 | def get_header(self, image_name): 18 | cur_time = str(int(time.time())) 19 | # param = {"image_name": image_name, "image_url": ''} 20 | param = "{\"image_name\":\"" + image_name + "\",\"image_url\":\"\"}" 21 | param_base64 = base64.b64encode(param.encode("utf-8")) 22 | 23 | m2 = hashlib.md5() 24 | m2.update((API_KEY_FACE_FEATURE + cur_time + str(param_base64, 'utf-8')).encode('utf-8')) 25 | check_sum = m2.hexdigest() 26 | 27 | header = { 28 | 'X-CurTime': cur_time, 29 | 'X-Param': param_base64, 30 | 'X-Appid': APP_ID, 31 | 'X-CheckSum': check_sum, 32 | } 33 | return header 34 | 35 | def analyze(self, type, image_path): 36 | url = urljoin(self.base_url, type) 37 | image_name = os.path.basename(image_path) 38 | headers = self.get_header(image_name) 39 | with open(image_path, 'rb') as f: 40 | data = f.read() 41 | response = requests.post(url, data, headers=headers) 42 | if response.status_code == 200: 43 | result = response.json() 44 | code = result['code'] 45 | if code == 0: 46 | value = result['data']['fileList'][0]['label'] 47 | else: 48 | value = result['desc'] 49 | else: 50 | code = -1 51 | value = '请求错误' 52 | 53 | return code, value 54 | 55 | def analyze_all(self, image_path): 56 | results = {} 57 | for type in self.types: 58 | code, value = self.analyze(type, image_path) 59 | if code == 0: 60 | results[type] = value 61 | return {"results": results} 62 | -------------------------------------------------------------------------------- /utils/face_quality.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def is_face_forward(landmarks): 5 | """ 6 | 判断人脸是否正对相机,并考虑人脸的俯仰角。 7 | 8 | 参数: 9 | - landmarks: 人脸的关键点坐标,假设是一个包含五个关键点(两只眼睛、鼻尖、两只嘴角)的列表。 10 | 11 | 返回: 12 | - is_forward: 布尔值,表示人脸是否正对相机,没有显著的俯仰、偏航或滚动角。 13 | """ 14 | # 提取两只眼睛和鼻尖的坐标 15 | eye_left = np.array(landmarks[0]) 16 | eye_right = np.array(landmarks[1]) 17 | nose = np.array(landmarks[2]) 18 | mouth_left = np.array(landmarks[3]) 19 | mouth_right = np.array(landmarks[4]) 20 | 21 | # 计算中点 22 | eye_center = (eye_left + eye_right) / 2 23 | 24 | # 计算方向向量 25 | direction_vector = eye_right - eye_left 26 | 27 | # 计算单位向量 28 | unit_vector = direction_vector / np.linalg.norm(direction_vector) 29 | 30 | # 计算鼻子到左右眼连线的向量 31 | nose_to_eye_line_vector = nose - eye_left 32 | 33 | # 计算投影长度 34 | projection_length = np.dot(nose_to_eye_line_vector, unit_vector) 35 | 36 | # 计算垂足坐标 37 | foot_of_perpendicular = eye_left + projection_length * unit_vector 38 | 39 | # 计算垂足到中点的距离 40 | foot_offset = np.linalg.norm(foot_of_perpendicular - eye_center) 41 | eye_dist = np.linalg.norm(eye_right - eye_left) 42 | 43 | # 设定偏移量阈值,判断是否为正面 44 | threshold = 0.1 * eye_dist 45 | if foot_offset < threshold: 46 | return True 47 | else: 48 | return False 49 | 50 | 51 | def evaluate_face_quality(box, prob, image, landmarks, quality_threshold=0.8): 52 | """ 53 | 根据人脸的大小、置信度和其他因素来评估人脸的质量。 54 | 返回是否通过质量检查。 55 | 56 | 参数: 57 | - box: 人脸框（x1, y1, x2, y2） 58 | - prob: 人脸检测的置信度 59 | - image: 包含人脸的图像（PIL.Image格式） 60 | - landmarks: 人脸的关键点坐标 61 | - quality_threshold: 质量评分的阈值，高于此阈值表示质量足够 62 | 63 | 返回: 64 | - boolean: 表示人脸是否通过质量检查 65 | """ 66 | # 基于人脸大小和置信度的简单评分：0-1 67 | face_area = (box[2] - box[0]) * (box[3] - box[1]) 68 | size_score = np.sqrt(face_area) / 160 if np.sqrt(face_area) / 160 <= 1 else 1 69 | 70 | # 置信度分数，prob在0到1之间 71 | confidence_score = prob 72 | 73 | # 综合评分 74 | quality_score = size_score * confidence_score 75 | 76 | if quality_score > quality_threshold: 77 | return True 78 | else: 79 | return False 80 | -------------------------------------------------------------------------------- /business/xunfei/face_compare_client.py: -------------------------------------------------------------------------------- 1 | import base64 2 | import json 3 | from datetime import datetime 4 | from urllib.parse import urlencode 5 | 6 | import requests 7 | 8 | from .config import APP_ID, API_KEY_FACE_COMPARE, API_SECRET_FACE_COMPARE, SERVER_ID_FACE_COMPARE 9 | from .utils import parse_url, generate_signature, assemble_authorization_header, encode_image 10 | 11 | 12 | class FaceCompareClient: 13 | def __init__(self): 14 | self.base_url = f'http://api.xf-yun.com/v1/private/{SERVER_ID_FACE_COMPARE}' 15 | 16 | def generate_request_url(self): 17 | schema, host, path = parse_url(self.base_url) 18 | date = format(datetime.utcnow(), '%a, %d %b %Y %H:%M:%S GMT') 19 | signature_sha = generate_signature(API_SECRET_FACE_COMPARE, host, date, "POST", path) 20 | authorization = assemble_authorization_header(API_KEY_FACE_COMPARE, signature_sha) 21 | values = {"host": host, "date": date, "authorization": authorization} 22 | return f"{self.base_url}?{urlencode(values)}" 23 | 24 | def compare_faces(self, img1_path, img2_path): 25 | code = 0 26 | data = None 27 | 28 | request_url = self.generate_request_url() 29 | headers = {'content-type': "application/json", 'host': 'api.xf-yun.com', 'app_id': APP_ID} 30 | 31 | img1_encoded, img1_type = encode_image(img1_path) 32 | img2_encoded, img2_type = encode_image(img2_path) 33 | 34 | body = json.dumps({ 35 | "header": {"app_id": APP_ID, "status": 3}, 36 | "parameter": {SERVER_ID_FACE_COMPARE: {"service_kind": "face_compare", 37 | "face_compare_result": {"encoding": "utf8", "compress": "raw", 38 | "format": "json"}}}, 39 | "payload": { 40 | "input1": {"encoding": img1_type, "status": 3, "image": img1_encoded}, 41 | "input2": {"encoding": img2_type, "status": 3, "image": img2_encoded} 42 | } 43 | }) 44 | 45 | response = requests.post(request_url, data=body, headers=headers) 46 | if response.status_code == 200: 47 | result = json.loads(response.text) 48 | code = result['header']['code'] 49 | if code == 0: 50 | compare_result = json.loads(base64.b64decode(result['payload']['face_compare_result']['text'])) 51 | code = compare_result['ret'] 52 | if code == 0: 53 | data = compare_result['score'] 54 | else: 55 | data = '非人像图片' 56 | else: 57 | data = result['header']['message'] 58 | else: 59 | code = -1 60 | data = '请求错误' 61 | 62 | return code, data 63 | -------------------------------------------------------------------------------- /services/recognition.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from utils.face_detection import find_faces, align_face 4 | from utils.face_encoding import encode_face 5 | from utils.face_quality import is_face_forward, evaluate_face_quality 6 | from utils.face_storage import FaceStorage 7 | 8 | face_storage = FaceStorage() 9 | last_faces = [] 10 | last_face_boxes = [] 11 | 12 | 13 | def calculate_distance(box1, box2): 14 | center1 = ((box1[0] + box1[2]) / 2, (box1[1] + box1[3]) / 2) 15 | center2 = ((box2[0] + box2[2]) / 2, (box2[1] + box2[3]) / 2) 16 | distance = np.sqrt((center1[0] - center2[0]) ** 2 + (center1[1] - center2[1]) ** 2) 17 | return distance 18 | 19 | 20 | def has_new_face(face_boxes, threshold=50): 21 | global last_face_boxes, last_faces 22 | if len(face_boxes) == 0: 23 | return False 24 | # 如果当前帧和上一帧检测到的人脸数量不同，则认为有新的人脸出现 25 | if len(face_boxes) != len(last_face_boxes): 26 | return True 27 | else: 28 | # 计算当前帧与上一帧中人脸位置的最小距离 29 | for box in face_boxes: 30 | is_far = True 31 | for last_box in last_face_boxes: 32 | distance = calculate_distance(box, last_box) 33 | # 如果当前人脸与上一帧中所有人脸的最小距离超过阈值，则认为是新的人脸 34 | if distance <= threshold: 35 | is_far = False 36 | break 37 | if is_far: 38 | return True 39 | 40 | return False 41 | 42 | 43 | def recognize_faces(image): 44 | global last_face_boxes, last_faces 45 | boxes, probs, landmarks = find_faces(image) 46 | 47 | faces = [] 48 | if has_new_face(boxes) or any(face['name'] == "忽略" for face in last_faces): 49 | for box, prob, landmark in zip(boxes, probs, landmarks): 50 | is_forward = is_face_forward(landmark) 51 | face_quality_ok = evaluate_face_quality(box, prob, image, landmark) 52 | 53 | if is_forward and face_quality_ok: 54 | aligned_face = align_face(image, box, landmark) 55 | face_encoding = encode_face(aligned_face) 56 | face = face_storage.match_face(face_encoding) 57 | if face is None: 58 | face = face_storage.add_known_face(face_encoding, "未知") 59 | else: 60 | face = {'id': None, 'name': "忽略"} 61 | 62 | faces.append(face) 63 | else: 64 | faces = last_faces 65 | 66 | last_face_boxes = boxes 67 | last_faces = faces 68 | 69 | # 构建识别结果，包括未识别和被忽略的人脸 70 | recognized_faces = [] 71 | for face, (left, top, right, bottom), prob, landmark in zip(faces, boxes, probs, landmarks): 72 | face_id = face['id'] 73 | name = face['name'] 74 | location = (top, right, bottom, left) 75 | recognized_faces.append({ 76 | 'id': face_id, 77 | 'location': location, 78 | 'name': name, 79 | 'prob': prob, 80 | 'landmark': landmark.tolist() 81 | }) 82 | 83 | return recognized_faces 84 | 85 | 86 | def rename_face(face_id, name): 87 | face_storage.rename_face(face_id, name) 88 | -------------------------------------------------------------------------------- /business/xunfei/spark.py: -------------------------------------------------------------------------------- 1 | import base64 2 | import hashlib 3 | import hmac 4 | import json 5 | import ssl 6 | from datetime import datetime 7 | from time import mktime 8 | from urllib.parse import urlencode, urlparse 9 | from wsgiref.handlers import format_date_time 10 | 11 | import websocket 12 | 13 | from .config import APP_ID, API_KEY_SPARK, API_SECRET_SPARK 14 | 15 | 16 | class SparkAPI: 17 | def __init__(self, app_id: str = APP_ID, api_key: str = API_KEY_SPARK, api_secret: str = API_SECRET_SPARK, 18 | service_url: str = "wss://spark-api.xf-yun.com/v3.5/chat"): 19 | self.app_id = app_id 20 | self.api_key = api_key 21 | self.api_secret = api_secret 22 | self.host = urlparse(service_url).netloc 23 | self.path = urlparse(service_url).path 24 | self.service_url = service_url 25 | self.domain = self._get_model_domain(self.path) 26 | 27 | def _get_model_domain(self, path: str) -> str: 28 | domain_map = { 29 | "/v1.1/chat": "general", 30 | "/v2.1/chat": "generalv2", 31 | "/v3.1/chat": "generalv3", 32 | "/v3.5/chat": "generalv3.5", 33 | } 34 | return domain_map.get(path, "unknown") 35 | 36 | def generate_auth_url(self) -> str: 37 | timestamp = datetime.now() 38 | formatted_date = format_date_time(mktime(timestamp.timetuple())) 39 | signature = self._create_signature(formatted_date) 40 | authorization_origin = f'api_key="{self.api_key}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature}"' 41 | authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode('utf-8') 42 | url_params = {"authorization": authorization, "date": formatted_date, "host": self.host} 43 | return f"{self.service_url}?{urlencode(url_params)}" 44 | 45 | def _create_signature(self, date: str) -> str: 46 | signature_origin = f"host: {self.host}\ndate: {date}\nGET {self.path} HTTP/1.1" 47 | signature_sha = hmac.new(self.api_secret.encode('utf-8'), signature_origin.encode('utf-8'), 48 | hashlib.sha256).digest() 49 | return base64.b64encode(signature_sha).decode('utf-8') 50 | 51 | def connect_and_query(self, on_message, messages: list): 52 | url = self.generate_auth_url() 53 | websocket.enableTrace(False) 54 | ws = websocket.WebSocketApp(url, 55 | on_message=lambda ws, msg: self.on_message(on_message, msg), 56 | on_error=self.on_error, 57 | on_close=self.on_close, 58 | on_open=lambda ws: self.on_open(ws, messages)) 59 | ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE}) 60 | 61 | def on_message(self, on_message, message: str): 62 | data = json.loads(message) 63 | on_message(data) 64 | 65 | def on_error(self, ws, error): 66 | print(f"WebSocket error: {error}") 67 | 68 | def on_close(self, ws, close_status_code, close_msg): 69 | print("WebSocket closed") 70 | 71 | def on_open(self, ws, messages): 72 | data = { 73 | "header": { 74 | "app_id": self.app_id, 75 | "uid": "1234" 76 | }, 77 | "parameter": { 78 | "chat": { 79 | "domain": self.domain, 80 | "temperature": 0.5, 81 | "max_tokens": 4096, 82 | "top_k": 5, 83 | "auditing": "default" 84 | } 85 | }, 86 | "payload": { 87 | "message": { 88 | "text": messages 89 | } 90 | } 91 | } 92 | ws.send(json.dumps(data)) 93 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Created by .ignore support plugin (hsz.mobi) 2 | ### Python template 3 | # Byte-compiled / optimized / DLL files 4 | __pycache__/ 5 | *.py[cod] 6 | *$py.class 7 | 8 | # C extensions 9 | *.so 10 | 11 | # Distribution / packaging 12 | .Python 13 | env/ 14 | build/ 15 | develop-eggs/ 16 | dist/ 17 | downloads/ 18 | eggs/ 19 | .eggs/ 20 | lib/ 21 | lib64/ 22 | parts/ 23 | sdist/ 24 | var/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .coverage 43 | .coverage.* 44 | .cache 45 | nosetests.xml 46 | coverage.xml 47 | *,cover 48 | .hypothesis/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | 58 | # Flask stuff: 59 | instance/ 60 | .webassets-cache 61 | 62 | # Scrapy stuff: 63 | .scrapy 64 | 65 | # Sphinx documentation 66 | docs/_build/ 67 | 68 | # PyBuilder 69 | target/ 70 | 71 | # IPython Notebook 72 | .ipynb_checkpoints 73 | 74 | # pyenv 75 | .python-version 76 | 77 | # celery beat schedule file 78 | celerybeat-schedule 79 | 80 | # dotenv 81 | .env 82 | 83 | # virtualenv 84 | venv/ 85 | ENV/ 86 | 87 | # Spyder project settings 88 | .spyderproject 89 | 90 | # Rope project settings 91 | .ropeproject 92 | ### VirtualEnv template 93 | # Virtualenv 94 | # http://iamzed.com/2009/05/07/a-primer-on-virtualenv/ 95 | [Bb]in 96 | [Ii]nclude 97 | [Ll]ib 98 | [Ll]ib64 99 | [Ll]ocal 100 | [Ss]cripts 101 | pyvenv.cfg 102 | .venv 103 | pip-selfcheck.json 104 | 105 | ### JetBrains template 106 | # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider 107 | # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 108 | 109 | # User-specific stuff 110 | .idea/**/workspace.xml 111 | .idea/**/tasks.xml 112 | .idea/**/usage.statistics.xml 113 | .idea/**/dictionaries 114 | .idea/**/shelf 115 | 116 | # AWS User-specific 117 | .idea/**/aws.xml 118 | 119 | # Generated files 120 | .idea/**/contentModel.xml 121 | 122 | # Sensitive or high-churn files 123 | .idea/**/dataSources/ 124 | .idea/**/dataSources.ids 125 | .idea/**/dataSources.local.xml 126 | .idea/**/sqlDataSources.xml 127 | .idea/**/dynamic.xml 128 | .idea/**/uiDesigner.xml 129 | .idea/**/dbnavigator.xml 130 | 131 | # Gradle 132 | .idea/**/gradle.xml 133 | .idea/**/libraries 134 | 135 | # Gradle and Maven with auto-import 136 | # When using Gradle or Maven with auto-import, you should exclude module files, 137 | # since they will be recreated, and may cause churn. Uncomment if using 138 | # auto-import. 139 | # .idea/artifacts 140 | # .idea/compiler.xml 141 | # .idea/jarRepositories.xml 142 | # .idea/modules.xml 143 | # .idea/*.iml 144 | # .idea/modules 145 | # *.iml 146 | # *.ipr 147 | 148 | # CMake 149 | cmake-build-*/ 150 | 151 | # Mongo Explorer plugin 152 | .idea/**/mongoSettings.xml 153 | 154 | # File-based project format 155 | *.iws 156 | 157 | # IntelliJ 158 | out/ 159 | 160 | # mpeltonen/sbt-idea plugin 161 | .idea_modules/ 162 | 163 | # JIRA plugin 164 | atlassian-ide-plugin.xml 165 | 166 | # Cursive Clojure plugin 167 | .idea/replstate.xml 168 | 169 | # SonarLint plugin 170 | .idea/sonarlint/ 171 | 172 | # Crashlytics plugin (for Android Studio and IntelliJ) 173 | com_crashlytics_export_strings.xml 174 | crashlytics.properties 175 | crashlytics-build.properties 176 | fabric.properties 177 | 178 | # Editor-based Rest Client 179 | .idea/httpRequests 180 | 181 | # Android studio 3.1+ serialized cache file 182 | .idea/caches/build_file_checksums.ser 183 | 184 | # idea folder, uncomment if you don't need it 185 | # .idea -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tempfile 3 | import uuid 4 | 5 | from flask import Flask, request, session 6 | from flask import jsonify 7 | from flask_cors import CORS 8 | from flask_socketio import SocketIO, Namespace, emit, disconnect 9 | 10 | from business.xunfei import FaceCompareClient, FaceFeatureClient, SparkAPI 11 | from services.authentication import authenticate_face 12 | from services.recognition import recognize_faces, rename_face 13 | from services.registration import register_face 14 | from utils.face_detection import find_primary_face 15 | from utils.face_quality import is_face_forward 16 | from utils.image_processing import parse_frame_data 17 | 18 | # Set this variable to "threading", "eventlet" or "gevent" to test the 19 | # different async modes, or leave it set to None for the application to choose 20 | # the best option based on installed packages. 21 | async_mode = None 22 | 23 | app = Flask(__name__) 24 | CORS(app) 25 | socketio = SocketIO(app, cors_allowed_origins="*") 26 | 27 | # 配置上传文件夹和允许的扩展 28 | UPLOAD_FOLDER = 'static/uploads' 29 | ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'bmp'} 30 | app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER 31 | 32 | 33 | # Utility function to check allowed file 34 | def allowed_file(filename): 35 | return '.' in filename and \ 36 | filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS 37 | 38 | 39 | # Utility function to generate a secure filename 40 | def secure_filename(filename): 41 | # 使用UUID或其他方法生成唯一的文件名 42 | unique_filename = str(uuid.uuid4()) 43 | ext = filename.rsplit('.', 1)[1].lower() if '.' in filename else '' 44 | safe_filename = f"{unique_filename}.{ext}" if ext else unique_filename 45 | return safe_filename 46 | 47 | 48 | # 讯飞API - 人脸比对 49 | @app.route('/api/xunfei/face-compare', methods=['POST']) 50 | def face_compare(): 51 | # 检查是否上传了两个文件 52 | if 'image1' not in request.files or 'image2' not in request.files: 53 | return jsonify({'error': '缺少文件参数'}), 400 54 | file1 = request.files['image1'] 55 | file2 = request.files['image2'] 56 | if file1.filename == '' or file2.filename == '': 57 | return jsonify({'error': '未选择文件'}), 400 58 | elif not allowed_file(file1.filename) or not allowed_file(file2.filename): 59 | return jsonify({'error': '文件类型不正确'}), 400 60 | 61 | # 使用临时文件 62 | with tempfile.TemporaryDirectory() as tmpdirname: 63 | filepath1 = os.path.join(tmpdirname, secure_filename(file1.filename)) 64 | filepath2 = os.path.join(tmpdirname, secure_filename(file2.filename)) 65 | 66 | file1.save(filepath1) 67 | file2.save(filepath2) 68 | 69 | # 调用人脸比对服务 70 | client = FaceCompareClient() 71 | code, data = client.compare_faces(filepath1, filepath2) 72 | 73 | if code == 0: 74 | return jsonify({'message': '比对成功', 'data': data}), 200 75 | else: 76 | return jsonify({'error': data}), 400 77 | 78 | 79 | # 讯飞API - 人脸特征分析 80 | @app.route('/api/xunfei/face-features', methods=['POST']) 81 | def face_features(): 82 | if 'image' not in request.files: 83 | return jsonify({'error': '缺少文件参数'}), 400 84 | file = request.files['image'] 85 | if file.filename == '': 86 | return jsonify({'error': '未选择文件或文件类型不正确'}), 400 87 | elif not allowed_file(file.filename): 88 | return jsonify({'error': '文件类型不正确'}), 400 89 | 90 | # 使用临时文件 91 | with tempfile.NamedTemporaryFile(delete=False) as tmpfile: 92 | file.save(tmpfile.name) 93 | # 分析人脸特征 94 | client = FaceFeatureClient() 95 | result = client.analyze_all(tmpfile.name) 96 | 97 | # 清理临时文件 98 | os.remove(tmpfile.name) 99 | return jsonify(result) 100 | 101 | 102 | # 人脸认证的命名空间 103 | class FaceAuthNamespace(Namespace): 104 | def on_connect(self): 105 | print("Client connected to Face Auth") 106 | 107 | def on_disconnect(self): 108 | print("Client disconnected from Face Auth") 109 | 110 | def on_frame(self, data): 111 | image_data = data['image'] 112 | timestamp = data['timestamp'] 113 | if image_data.endswith('data:,'): 114 | print('empty frame data') 115 | return 116 | image = parse_frame_data(image_data) 117 | 118 | primary_face = find_primary_face(image) 119 | if primary_face: 120 | box, prob, landmark = primary_face 121 | 122 | if is_face_forward(landmark): 123 | left, top, right, bottom = box 124 | location = (top, right, bottom, left) 125 | result = {'success': True, 'face': {'location': location, 'landmark': landmark.tolist()}, 126 | 'timestamp': timestamp} 127 | else: 128 | result = {'success': False, 'message': "人脸质量不符合要求或非正脸", 'timestamp': timestamp} 129 | else: 130 | result = {'success': False, 'message': "未检测到人脸", 'timestamp': timestamp} 131 | 132 | emit('detection_result', result) 133 | 134 | def on_register(self, data): 135 | """ 136 | 处理用户注册事件。 137 | """ 138 | image_data = data['image'] 139 | username = data['username'] 140 | if image_data.endswith('data:,'): 141 | result = {'success': False, 'message': "帧数据为空"} 142 | else: 143 | image = parse_frame_data(image_data) 144 | success, message = register_face(image, username) 145 | result = {'success': success, 'message': message} 146 | 147 | emit('register_response', result) 148 | 149 | def on_login(self, data): 150 | """ 151 | 处理用户登录事件。 152 | """ 153 | image_data = data['image'] 154 | if image_data.endswith('data:,'): 155 | print('Empty frame data') 156 | result = {'success': False, 'message': "帧数据为空"} 157 | else: 158 | image = parse_frame_data(image_data) 159 | success, message = authenticate_face(image) 160 | result = {'success': success, 'message': message} 161 | 162 | emit('login_response', result) 163 | 164 | 165 | # 实时人脸识别的命名空间 166 | class FaceRecognitionNamespace(Namespace): 167 | 168 | def on_disconnect_request(self): 169 | emit('my_response', 170 | {'data': 'Disconnected!', 'count': session['receive_count']}) 171 | disconnect() 172 | 173 | # WebSocket事件处理，接收视频帧 174 | def on_frame(self, data): 175 | image_data = data['image'] 176 | timestamp = data['timestamp'] 177 | if image_data.endswith('data:,'): 178 | print('empty frame data') 179 | return 180 | image = parse_frame_data(image_data) 181 | 182 | # 在这里调用你的人脸检测逻辑 183 | faces = recognize_faces(image) 184 | emit('detection_results', {'faces': faces, 'timestamp': timestamp}) 185 | 186 | def on_rename_face(self, data): 187 | # TODO: 增加对重命名结果的处理 188 | face_id = data['id'] 189 | name = data['name'] 190 | rename_face(face_id, name) 191 | 192 | def on_connect(self): 193 | print("Client connected to Face Recognition") 194 | 195 | def on_disconnect(self): 196 | print('Client disconnected from Face Recognition', request.sid) 197 | 198 | 199 | class ChatNamespace(Namespace): 200 | 201 | def on_connect(self): 202 | print("Client connected to Chat") 203 | 204 | def on_disconnect(self): 205 | print('Client disconnected from Chat', request.sid) 206 | 207 | def on_send_message(self, data): 208 | messages = data['messages'] 209 | spark_api = SparkAPI() 210 | spark_api.connect_and_query(self.on_response, messages) 211 | return jsonify({"status": "connecting"}) 212 | 213 | def on_response(self, data): 214 | if data['header']['code'] != 0: 215 | print(data['header']['code']) 216 | emit('error', {'error': f"Error from API: {data['header']['code']}"}) 217 | else: 218 | choices = data["payload"]['choices'] 219 | status = choices['status'] 220 | seq = choices['seq'] 221 | text = choices['text'][0] 222 | emit('message_response', { 223 | 'status': status, 224 | 'seq': seq, 225 | 'text': { 226 | 'role': text['role'], 227 | 'content': text['content'] 228 | } 229 | }) 230 | 231 | 232 | # 注册命名空间 233 | socketio.on_namespace(FaceAuthNamespace('/api/face-auth')) 234 | socketio.on_namespace(FaceRecognitionNamespace('/api/face-recognition')) 235 | 236 | socketio.on_namespace(ChatNamespace('/api/chat')) 237 | 238 | if __name__ == '__main__': 239 | socketio.run(app, debug=True, allow_unsafe_werkzeug=True) 240 | -------------------------------------------------------------------------------- /.idea/workspace.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 6 | 7 | 8 | 9 | 10 | 11 | 16 | 17 | 24 | 25 | 26 | 27 | 28 | 29 | 35 | 36 | 37 | 39 | { 40 | "lastFilter": { 41 | "state": "OPEN", 42 | "assignee": "p0ise" 43 | } 44 | } 45 | { 46 | "selectedUrlAndAccountId": { 47 | "url": "https://github.com/p0ise/iflyface-backend.git", 48 | "accountId": "1410cdc2-b2ba-469b-ae38-28d431d2598e" 49 | } 50 | } 51 | { 52 | "associatedIndex": 7 53 | } 54 | 55 | 56 | 57 | 58 | 59 | 62 | { 63 | "keyToString": { 64 | "DefaultHtmlFileTemplate": "HTML File", 65 | "Flask 服务器.Flask (hello.py).executor": "Run", 66 | "Flask 服务器.iflyface.executor": "Run", 67 | "Python.app.executor": "Run", 68 | "Python.face_recognition.executor": "Run", 69 | "Python.spark.executor": "Run", 70 | "Python.sparkdemo.executor": "Run", 71 | "RunOnceActivity.OpenProjectViewOnStart": "true", 72 | "RunOnceActivity.ShowReadmeOnStart": "true", 73 | "SHARE_PROJECT_CONFIGURATION_FILES": "true", 74 | "git-widget-placeholder": "master", 75 | "ignore.virus.scanning.warn.message": "true", 76 | "last_opened_file_path": "C:/Users/ADMIN/PycharmProjects/iflyface-backend", 77 | "node.js.detected.package.eslint": "true", 78 | "node.js.detected.package.tslint": "true", 79 | "node.js.selected.package.eslint": "(autodetect)", 80 | "node.js.selected.package.tslint": "(autodetect)", 81 | "nodejs_package_manager_path": "npm", 82 | "settings.editor.selected.configurable": "com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable", 83 | "vue.rearranger.settings.migration": "true" 84 | } 85 | } 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 120 | 121 | 122 | 143 | 144 | 145 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 172 | 173 | 174 | 175 | 176 | 177 | 178 | 1711421904128 179 | 198 | 199 | 206 | 207 | 214 | 215 | 222 | 223 | 230 | 231 | 238 | 239 | 246 | 247 | 254 | 255 | 262 | 263 | 270 | 271 | 278 | 279 | 286 | 287 | 294 | 295 | 302 | 303 | 310 | 311 | 318 | 319 | 326 | 327 | 334 | 335 | 342 | 343 | 350 | 351 | 358 | 359 | 366 | 367 | 374 | 375 | 382 | 383 | 390 | 391 | 398 | 399 | 406 | 407 | 414 | 415 | 422 | 425 | 426 | 428 | 429 | 450 | 451 | 452 | 453 | 454 | 455 | 456 | 457 | 458 | 459 | 460 | 461 | 462 | 463 | 464 | 465 | 466 | 467 | 468 | 469 | 470 | 471 | 472 | 473 | 474 | 475 | 476 | 478 | 479 | 480 | 481 | 482 | 483 | 484 | 485 | 486 | -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | [[package]] 2 | name = "bidict" 3 | version = "0.23.1" 4 | description = "The bidirectional mapping library for Python." 5 | category = "main" 6 | optional = false 7 | python-versions = ">=3.8" 8 | 9 | [[package]] 10 | name = "blinker" 11 | version = "1.7.0" 12 | description = "Fast, simple object-to-object and broadcast signaling" 13 | category = "main" 14 | optional = false 15 | python-versions = ">=3.8" 16 | 17 | [[package]] 18 | name = "certifi" 19 | version = "2024.2.2" 20 | description = "Python package for providing Mozilla's CA Bundle." 21 | category = "main" 22 | optional = false 23 | python-versions = ">=3.6" 24 | 25 | [[package]] 26 | name = "charset-normalizer" 27 | version = "3.3.2" 28 | description = "The Real First Universal Charset Detector. 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95 | async = ["asgiref (>=3.2)"] 96 | dotenv = ["python-dotenv"] 97 | 98 | [[package]] 99 | name = "flask-cors" 100 | version = "4.0.0" 101 | description = "A Flask extension adding a decorator for CORS support" 102 | category = "main" 103 | optional = false 104 | python-versions = "*" 105 | 106 | [package.dependencies] 107 | Flask = ">=0.9" 108 | 109 | [[package]] 110 | name = "flask-socketio" 111 | version = "5.3.6" 112 | description = "Socket.IO integration for Flask applications" 113 | category = "main" 114 | optional = false 115 | python-versions = ">=3.6" 116 | 117 | [package.dependencies] 118 | Flask = ">=0.9" 119 | python-socketio = ">=5.0.2" 120 | 121 | [package.extras] 122 | docs = ["sphinx"] 123 | 124 | [[package]] 125 | name = "fsspec" 126 | version = "2024.3.1" 127 | description = "File-system specification" 128 | category = "main" 129 | optional = false 130 | python-versions = ">=3.8" 131 | 132 | [package.extras] 133 | abfs = ["adlfs"] 134 | adl = ["adlfs"] 135 | arrow = 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