238 lines
7.4 KiB
Python
238 lines
7.4 KiB
Python
from compreface_utils import FaceUtils, DataUtils
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from compreface import CompreFace
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from compreface.service import RecognitionService
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from compreface.collections import FaceCollection
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from compreface.collections.face_collections import Subjects
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import yaml
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import tempfile
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import json
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from flask_cors import CORS
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import os
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from flask import Flask, request, jsonify
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app = Flask(__name__)
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CORS(app)
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clock_in_dict = {}
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# 初始化 CompreFace 客户端
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with open('config.yaml', 'r') as file:
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configData = yaml.safe_load(file)
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DOMAIN: str = configData['compreface_service']['DOMAIN']
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PORT: str = configData['compreface_service']['PORT']
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API_KEY: str = configData['compreface_service']['API_KEY']
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compre_face: CompreFace = CompreFace(DOMAIN, PORT)
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# recognition 是服务实例
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recognition: RecognitionService = compre_face.init_face_recognition(API_KEY)
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# 获取人员列表
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@app.route('/getSubjects')
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def getSubjects():
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subjects: Subjects = recognition.get_subjects()
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subjects_list = subjects.list()
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return jsonify({"data": subjects_list})
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# 添加人员
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@app.route('/addSubject', methods=['POST'])
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def addSubject():
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subjects: Subjects = recognition.get_subjects()
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data = request.json
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subject_name = data.get('subject_name')
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result = subjects.add(subject_name)
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return jsonify({"data": result})
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# 删除人员
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@app.route('/deleteSubject',methods=['POST'])
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def deleteSubject():
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subjects: Subjects = recognition.get_subjects()
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# face_collection是人脸集合
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face_collection: FaceCollection = recognition.get_face_collection()
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data = request.json
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subject_name = data.get('subject_name')
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face_collection.delete_all(subject_name)
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result = subjects.delete(subject_name)
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return jsonify({"data": result})
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# 更改人员名称
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@app.route('/renameSubject', methods=['POST'])
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def renameSubject():
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subjects: Subjects = recognition.get_subjects()
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data = request.json
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old_subject_name = data.get('old_subject_name')
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new_subject_name = data.get('new_subject_name')
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result = subjects.update(old_subject_name, new_subject_name)
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return jsonify({"data": result})
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# 按名字查询
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@app.route('/selectSubject', methods=['POST'])
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def selectSubject():
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subjects: Subjects = recognition.get_subjects()
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data = request.json
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Query = data.get('query', [])
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subject_list = subjects.list()
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matching_subjects = [
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name for name in subject_list['subjects']
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if all(query in name for query in Query)
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]
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return jsonify({"data": matching_subjects}),200
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# 获取图片id集合
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@app.route('/getFaceImgs', methods=['POST'])
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def getFaceImgs():
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# face_collection是人脸集合
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face_collection: FaceCollection = recognition.get_face_collection()
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data = request.json
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subject_name = data.get('subject_name')
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face_list = face_collection.list()
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print(face_list)
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filtered_ids = DataUtils.filter_images_by_subject(face_list, subject_name)
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return jsonify({"data": filtered_ids})
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# 根据人名添加图片
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@app.route('/addFaceImg', methods=['POST'])
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def addFaceImg():
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# face_collection是人脸集合
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face_collection: FaceCollection = recognition.get_face_collection()
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# 获取图片文件和主题名称
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imgFile = request.files['img']
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subject_name = request.form.get('subject_name')
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# 检查是否成功获取了图片文件和主题名称
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if not imgFile or not subject_name:
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return jsonify({'data': 'error'}), 400
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try:
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# 创建一个临时文件
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temp_dir = tempfile.mkdtemp()
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temp_path = os.path.join(temp_dir, imgFile.filename)
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imgFile.save(temp_path)
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# 这里可以继续执行你的人脸识别集合添加逻辑
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result = face_collection.add(image_path=temp_path, subject=subject_name)
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print(result)
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return jsonify({'data': result})
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finally:
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# 删除临时文件和目录
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if os.path.exists(temp_path):
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os.remove(temp_path)
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os.rmdir(temp_dir)
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# 根据image_id删除图片
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@app.route('/deleteFaceImg', methods=['POST'])
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def deleteFaceImg():
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# face_collection是人脸集合
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face_collection: FaceCollection = recognition.get_face_collection()
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data = request.json
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image_id = data.get('image_id')
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print(image_id)
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result = face_collection.delete(image_id)
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return jsonify({"data": result})
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# 修改识别配置
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@app.route('/config', methods=['POST'])
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def config():
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data = request.json
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start_time_config = data.get('start_time')
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end_time_config = data.get('end_time')
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frequency_face_config = data.get('frequency_face')
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# @app.route('/login', methods=['POST'])
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# def login():
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# data = request.json
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# username = data.get('username')
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# password = data.get('password')
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# if username == 'admin' and password == '12345678Aa':
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# return 'success', 200
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# TODO
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# 1. 先接收半个小时内的告警图片保存到本地
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# 2. 定时任务方法，将半个小时内的图片依次调用compreface接口，返回result 整理数据，删除本地图片
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# 2.1 整理数据 三种情况，工人，陌生人 ，脏数据
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# 置信值 > 0.8 ==>工人 0.5 < 置信值 < 0.8 ==>陌生人 置信值 < 0.5 ==>抛弃
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@app.route('/compreface', methods=['POST'])
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def receiveFrameToComprefaceOnly():
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if 'file' not in request.files:
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return jsonify({"error": "No file part"}), 400
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file = request.files['file']
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if file.filename == '':
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return jsonify({"error": "No selected file"}), 400
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if file:
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# 保存文件到临时位置
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file_path = os.path.join("/tmp", file.filename)
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file.save(file_path)
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# 使用 CompreFace SDK 进行面部识别
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result = recognition.recognize(file_path)
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# 删除临时文件
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os.remove(file_path)
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return jsonify(result)
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if __name__ == '__main__':
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app.run(port=12500 ,debug=False, host='0.0.0.0')
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# {
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# "result" : [ {
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# "age" : {
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# "probability": 0.9308982491493225,
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# "high": 32,
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# "low": 25
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# },
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# "gender" : {
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# "probability": 0.9898611307144165,
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# "value": "female"
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# },
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# "mask" : {
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# "probability": 0.9999470710754395,
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# "value": "without_mask"
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# },
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# "embedding" : [ 9.424854069948196E-4, "...", -0.011415496468544006 ],
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# "box" : {
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# "probability" : 1.0,表示检测到的面部位置的置信度 TODO 可以通过这部分检测是否有人脸
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# "x_max" : 1420,表示检测到的面部在图像中的边界框坐标 TODO 画框
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# "y_max" : 1368,
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# "x_min" : 548,
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# "y_min" : 295
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# },
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# "landmarks" : [ [ 814, 713 ], [ 1104, 829 ], [ 832, 937 ], [ 704, 1030 ], [ 1017, 1133 ] ],
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# "subjects" : [ {
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# "similarity" : 0.97858, 百分之0.9的可能性是subject1
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# "subject" : "subject1"
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# } ],
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# "execution_time" : {
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# "age" : 28.0,
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# "gender" : 26.0,
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# "detector" : 117.0,
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# "calculator" : 45.0,
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# "mask": 36.0
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# }
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# } ],
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# "plugins_versions" : {
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# "age" : "agegender.AgeDetector",
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# "gender" : "agegender.GenderDetector",
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# "detector" : "facenet.FaceDetector",
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# "calculator" : "facenet.Calculator",
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# "mask": "facemask.MaskDetector"
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# }
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# }
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