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