人脸识别，使用 deepface + api + flask, 改写 + 调试

1. 起因， 目的， 感受:

• github deepface 这个项目写的很好， 继续研究
• 使用这个项目，改写 api。
• 增加一个前端 flask app

2. 先看效果

3. 过程:

大力改写原始项目中 api 这部分的代码，
原始项目的文件结构太繁杂了:

我把这部分的内容，合为一个文件，即 api.py， 能删尽删。

代码 1, api

from flask import Flask
from flask_cors import CORS
import argparse
from typing import Union
from flask import Blueprint, request
import numpy as np
import os
import tempfile
import logging
from deepface import DeepFace
from deepface.api.src.modules.core import service
from deepface.commons import image_utils
from deepface.commons.logger import Logger# 配置日志
logging.basicConfig(level=logging.INFO)
logger = Logger()
blueprint = Blueprint("routes", __name__)# 辅助函数：将 NumPy 类型转换为 JSON 可序列化格式
def convert_numpy(obj):if isinstance(obj, np.floating):return float(obj)elif isinstance(obj, np.integer):return int(obj)elif isinstance(obj, np.ndarray):return obj.tolist()elif isinstance(obj, dict):return {k: convert_numpy(v) for k, v in obj.items()}elif isinstance(obj, list):return [convert_numpy(i) for i in obj]return objdef extract_image_from_request(img_key: str) -> Union[str, np.ndarray]:"""Extracts an image from the request either from json or a multipart/form-data file.Args:img_key (str): The key used to retrieve the image datafrom the request (e.g., 'img').Returns:img (str or np.ndarray): Given image detail (base64 encoded string, image path or url)or the decoded image as a numpy array."""if request.files:logging.info(f"request: {request}")logging.info(f"request.files: {request.files}")file = request.files.get(img_key)logging.info(f"img_key: {img_key}")logging.info(f"file: {file}")if file is None:raise ValueError(f"Request form data doesn't have {img_key}")if file.filename == "":raise ValueError(f"No file uploaded for '{img_key}'")# 获取文件扩展名_, ext = os.path.splitext(file.filename)if not ext:ext = '.jpg'# 保存到临时文件with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as temp_file:file.save(temp_file.name)temp_file_path = temp_file.namelogging.info(f"Saved temp file: {temp_file_path}, size: {os.path.getsize(temp_file_path)} bytes")try:if not os.path.exists(temp_file_path):raise ValueError(f"Temporary file not found: {temp_file_path}")img, _ = image_utils.load_image(temp_file_path)if img is None:raise ValueError(f"Failed to load image from {temp_file_path}")logging.info(f"Loaded image shape: {img.shape if isinstance(img, np.ndarray) else 'not a numpy array'}")return imgfinally:if os.path.exists(temp_file_path):os.unlink(temp_file_path)elif request.is_json or request.form:logging.info(f"request.json: {request.json}")logging.info(f"request.form: {request.form}")input_args = request.get_json() or request.form.to_dict()if input_args is None:raise ValueError("empty input set passed")img = input_args.get(img_key)if not img:raise ValueError(f"'{img_key}' not found in either json or form data request")return imgraise ValueError(f"'{img_key}' not found in request in either json or form data")@blueprint.route("/")
def home():return f"<h1>Welcome to DeepFace API v{DeepFace.__version__}!</h1>"@blueprint.route("/represent", methods=["POST"])
def represent():input_args = (request.is_json and request.get_json()) or (request.form and request.form.to_dict())try:img = extract_image_from_request("img")except Exception as err:return {"exception": str(err)}, 400obj = service.represent(img_path=img,model_name=input_args.get("model_name", "VGG-Face"),detector_backend=input_args.get("detector_backend", "opencv"),enforce_detection=input_args.get("enforce_detection", True),align=input_args.get("align", True),anti_spoofing=input_args.get("anti_spoofing", False),max_faces=input_args.get("max_faces"),)logger.debug(obj)return convert_numpy(obj)  # 转换 NumPy 类型@blueprint.route("/verify", methods=["POST"])
def verify():input_args = (request.is_json and request.get_json()) or (request.form and request.form.to_dict())try:img1 = extract_image_from_request("img1")except Exception as err:return {"exception": str(err)}, 400try:img2 = extract_image_from_request("img2")except Exception as err:return {"exception": str(err)}, 400verification = service.verify(img1_path=img1,img2_path=img2,model_name=input_args.get("model_name", "VGG-Face"),detector_backend=input_args.get("detector_backend", "opencv"),distance_metric=input_args.get("distance_metric", "cosine"),align=input_args.get("align", True),enforce_detection=input_args.get("enforce_detection", True),anti_spoofing=input_args.get("anti_spoofing", False),)logger.debug(verification)return convert_numpy(verification)  # 转换 NumPy 类型@blueprint.route("/analyze", methods=["POST"])
def analyze():input_args = (request.is_json and request.get_json()) or (request.form and request.form.to_dict())try:img = extract_image_from_request("img")logging.info(f"api 里面收到的 img 是: {type(img)}")except Exception as err:return {"exception": str(err)}, 400actions = input_args.get("actions", ["age", "gender", "emotion", "race"])if isinstance(actions, str):actions = (actions.replace("[", "").replace("]", "").replace("(", "").replace(")", "").replace('"', "").replace("'", "").replace(" ", "").split(","))try:demographies = service.analyze(img_path=img,actions=actions,detector_backend=input_args.get("detector_backend", "opencv"),enforce_detection=input_args.get("enforce_detection", True),align=input_args.get("align", True),anti_spoofing=input_args.get("anti_spoofing", False),)except Exception as e:return {"error": f"Exception while analyzing: {str(e)}"}, 400logger.debug(demographies)return convert_numpy(demographies)  # 转换 NumPy 类型def create_app():app = Flask(__name__)CORS(app)app.register_blueprint(blueprint)logger.info(f"Welcome to DeepFace API v{DeepFace.__version__}!")return appif __name__ == "__main__":deepface_app = create_app()parser = argparse.ArgumentParser()parser.add_argument("-p", "--port", type=int, default=5005, help="Port of serving api")args = parser.parse_args()deepface_app.run(host="0.0.0.0", port=args.port, debug=True)

代码 2, flask app.py

• 此项目，后端 api 是用 flask 写的， 前端我也用 flask 来写。

from flask import Flask, render_template, request, redirect, url_for, flash
from werkzeug.utils import secure_filename
import os
import uuid
import requests
import json
import numpy as npapp = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'static/uploads'
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 限制上传文件大小为16MB
app.secret_key = 'your_secret_key'  # 用于 flash 消息# DeepFace API 的地址
DEEPFACE_API_URL = 'http://127.0.0.1:5005/analyze'# 允许的图片扩展名
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}# 检查文件扩展名是否允许
def allowed_file(filename):return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS# 确保上传文件夹存在
if not os.path.exists(app.config['UPLOAD_FOLDER']):os.makedirs(app.config['UPLOAD_FOLDER'])# 辅助函数：将 NumPy 数据转换为 JSON 可序列化格式
def convert_numpy(obj):if isinstance(obj, np.floating):return float(obj)elif isinstance(obj, np.integer):return int(obj)elif isinstance(obj, np.ndarray):return obj.tolist()elif isinstance(obj, dict):return {k: convert_numpy(v) for k, v in obj.items()}elif isinstance(obj, list):return [convert_numpy(i) for i in obj]return [e-mail]('/')
def index():# return render_template('index.html')return render_template('home.html')@app.route('/analyze', methods=['POST'])
def analyze():# 处理文件上传if 'file' in request.files and request.files['file'].filename:file = request.files['file']if not allowed_file(file.filename):flash('不支持的文件类型，仅支持 PNG、JPG、JPEG')return redirect(url_for('index'))# 保存文件（用于前端显示）filename = str(uuid.uuid4()) + '.' + file.filename.rsplit('.', 1)[1].lower()file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)file.save(file_path)# 重置文件流指针file.stream.seek(0)# 发送到 DeepFace APIfiles = {'img': (filename, file.stream, file.content_type)}data = {'actions': json.dumps(['age', 'gender', 'emotion', 'race']),'detector_backend': 'opencv','enforce_detection': 'true','align': 'true','anti_spoofing': 'false'}response = requests.post(DEEPFACE_API_URL, files=files, data=data)# 处理 Base64 输入（保留以兼容现有前端）elif request.form.get('base64'):base64_string = request.form['base64']if 'base64,' in base64_string:base64_string = base64_string.split('base64,')[1]payload = {'img': f'data:image/jpeg;base64,{base64_string}','actions': ['age', 'gender', 'emotion', 'race'],'detector_backend': 'opencv','enforce_detection': True,'align': True,'anti_spoofing': False}headers = {'Content-Type': 'application/json'}response = requests.post(DEEPFACE_API_URL, json=payload, headers=headers)else:flash('请上传图片文件或提供 Base64 字符串')return render_template('home.html')# 检查响应if response.status_code == 200:results = response.json()results = convert_numpy(results)flash('分析成功！')print(f"results: {results}")return render_template('home.html',   results=results, image_url=file_path if 'file' in request.files else None)else:print("API 响应：", response.text)error_msg = response.json()flash(f'API 调用失败：{error_msg}')return  render_template('home.html')if __name__ == '__main__':app.run(debug=True, host='0.0.0.0', port=8989)

4. 结论 ，todo， 感受

• 有些地方我觉得能自己写，但是却不行。 步子太大了。 即便是有AI， 很多地方我还是不理解。
• 这个项目只能说是，不尽完善。 所以我做起来，麻烦重重。
• 一个球投不进，也不能全怪我，有可能是队友球传的不好，传的太偏了，太低了。

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希望对大家有帮助。