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