现在能运行. 主体文件为faceApp.py和config.yaml

compreface_utils/DataUtils.py

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+import requests
+import json
+import datetime
+
+def group_get_subjects(faces_data):
+    grouped_faces = {}
+    for face in faces_data['faces']:
+        subject = face['subject']
+        image_id = face['image_id']
+        if subject in grouped_faces:
+            grouped_faces[subject].append(image_id)
+        else:
+            grouped_faces[subject] = [image_id]
+    return grouped_faces
+
+
+def filter_images_by_subject(data, subject):
+    # 初始化一个空列表来存储匹配的image_id
+    image_ids = []
+
+    # 遍历data中的faces列表
+    for face in data['faces']:
+        # 检查face字典中的'subject'键是否与传入的subject相匹配
+        if face['subject'] == subject:
+            # 如果匹配，将'image_id'添加到列表中
+            image_ids.append(face['image_id'])
+    # 返回包含所有匹配image_id的列表
+    return image_ids
+
+
+# 该方法最后的返回结果是 打卡的人或者是陌生人
+def getMaxSimilaritySubject(recognition_face_data):
+    first_result = recognition_face_data['result'][0]
+    # 从first_result中获取Box的probability
+    box_probability = first_result['box']['probability']
+    # 先判断是否存在人脸
+    if box_probability > 0.9:
+        recognition_subject_list = first_result['subjects']
+        # TODO 此处判断最为相似的subject，如果recognition_subject 低于阈值 直接返回陌生人
+        recognition_subject = recognition_subject_list[0]
+
+
+# TODO postMsg 发送请求
+# 1. 判断当前姓名,是否已经打卡过,若打卡过则不处理
+# 2. minio保存,发送请求
+def postMsg(recognition_name,temp_path,clock_in_dict) :
+    # 判断当前 recognition_name 之前是否已经在打卡了
+    # 若打卡则直接返回
+    if recognition_name in clock_in_dict :
+        return
+
+    # TODO
+    # 1. 保存minio
+    # 2. 发送请求
+    send_post_request()
+    return
+
+def send_post_request(url, token, msg, picUrl, videoUrl):
+    payload = {
+        "channelNo": 'ip',
+        "alarmContent": msg,
+        "alarmTime": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "picInfo": [
+            {"url": picUrl}
+        ],
+        "videoInfo": [
+            {"url": videoUrl}
+        ]
+    }
+    headers = {
+        'X-Access-Token': token,
+        'Content-Type': 'application/json'
+    }
+    response = requests.post(url, headers=headers, data=json.dumps(payload))
+    print(response)
+
+
+def getFacelist(data):
+    # 解析JSON数据
+    parsed_data = json.loads(json.dumps(data))
+
+    # 提取faces中的数据
+    faces = parsed_data['faces']
+
+    # 用于存储唯一的subject
+    unique_subjects = set()
+
+    # 用于存储过滤后的face
+    filtered_faces = []
+
+    # 遍历faces
+    for face in faces:
+        subject = face['subject']
+
+        # 如果subject是唯一的，则将其添加到unique_subjects集合和filtered_faces列表中
+        if subject not in unique_subjects:
+            unique_subjects.add(subject)
+            filtered_faces.append(face)
+    return filtered_faces
+

compreface_utils/FaceUtils.py

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+#
+def add_face_by_name(image_path,subject,face_collection):
+    face_collection.add(image_path=image_path, subject=subject)
+
+
+#  分组

compreface_utils/config.yaml

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+compreface_service:
+  DOMAIN: 'http://10.0.0.18'
+  PORT: '8000'
+  API_KEY: 'db1c448e-2076-4543-ae9f-11d8d7e7eb51'
+
+recognize_ip:
+  v1: '192.168.50.5:8088/config'
+

compreface_utils/test.py

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+import FaceUtils, DataUtils
+from compreface import CompreFace
+import yaml
+from compreface.service import RecognitionService
+from compreface.collections import FaceCollection
+from compreface.collections.face_collections import Subjects
+import cv2
+import tempfile
+import json
+import os
+from flask import Flask, request, jsonify
+
+# with open('config.yaml', 'r') as file:
+#     configData = yaml.safe_load(file)
+# DOMAIN: str = configData['compreface_service']['DOMAIN']
+# PORT: str = configData['compreface_service']['PORT']
+# API_KEY: str = configData['compreface_service']['API_KEY']
+# compre_face: CompreFace = CompreFace(DOMAIN, PORT)
+# # recognition 是服务实例
+# recognition: RecognitionService = compre_face.init_face_recognition(API_KEY)
+# # face_collection是人脸集合
+# face_collection: FaceCollection = recognition.get_face_collection()
+# subjects: Subjects = recognition.get_subjects()
+#
+# recognition_face_data = recognition.recognize(image_path=r'C:\Users\admin\Desktop\项目\jingzhu\face_project\compreface_utils\5.jpg')
+# print(recognition_face_data)
+import cv2
+
+
+def draw_faces(image_path, recognition_data):
+    # 读取图像
+    image = cv2.imread(r'C:\Users\admin\Desktop\imgs\jingzhuImgs\5.jpg')
+
+    # 检查图像是否成功加载
+    if image is None:
+        print("Failed to load image.")
+        return
+
+    # 遍历识别结果中的每一个结果
+    for face_data in recognition_data['result']:
+        # 获取边界框坐标
+        box = face_data['box']
+        x_min = box['x_min']
+        y_min = box['y_min']
+        x_max = box['x_max']
+        y_max = box['y_max']
+
+        # 画出边界框
+        cv2.rectangle(image, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
+
+        # 获取名称（如果有）
+        if face_data['subjects']:
+            subject_name = face_data['subjects'][0]['subject']
+            # 在框下方显示名字
+            cv2.putText(image, subject_name, (x_min, y_max + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+
+    # 显示图像
+    cv2.imwrite('out.jpg', image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+# 测试数据和函数
+recognition_face_data = {
+    'result': [
+        {'box': {'probability': 0.99981, 'x_max': 1207, 'y_max': 1348, 'x_min': 1092, 'y_min': 1234},
+         'subjects': [{'subject': 'liuxing', 'similarity': 0.45167}]},
+        {'box': {'probability': 0.99575, 'x_max': 853, 'y_max': 1308, 'x_min': 759, 'y_min': 1220},
+         'subjects': [{'subject': 'liudehua', 'similarity': 0.9288}]},
+        {'box': {'probability': 0.87615, 'x_max': 255, 'y_max': 1484, 'x_min': 175, 'y_min': 1388},
+         'subjects': [{'subject': 'tangzong', 'similarity': 0.48748}]}
+    ]
+}
+
+# 调用函数
+draw_faces('0', recognition_face_data)

config.yaml

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+compreface_service:
+  DOMAIN: 'http://10.0.0.202'
+  PORT: '8000'
+  API_KEY: 'ce04b456-88df-4df8-a7a2-69849111916f'
+
+recognize_ip:
+  v1: '192.168.50.5:8088/config'
+

faceApp.py

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

jingzhutest.py

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+from compreface_utils import FaceUtils, DataUtils
+from compreface import CompreFace
+from compreface.service import RecognitionService
+from compreface.collections import FaceCollection
+from compreface.collections.face_collections import Subjects
+import yaml
+import tempfile
+import json
+from flask_cors import CORS
+import os
+from flask import Flask, request, jsonify
+
+
+# 初始化 CompreFace 客户端
+with open('config.yaml', 'r') as file:
+    configData = yaml.safe_load(file)
+DOMAIN: str = configData['compreface_service']['DOMAIN']
+PORT: str = configData['compreface_service']['PORT']
+API_KEY: str = configData['compreface_service']['API_KEY']
+compre_face: CompreFace = CompreFace(DOMAIN, PORT)
+recognition: RecognitionService = compre_face.init_face_recognition(API_KEY)
+face_collection: FaceCollection = recognition.get_face_collection()
+face_list = face_collection.list()
+print(face_list)
+print(1)