<form method=”POST” action=”example.com”>
<input type=”file” name=”img”>
<input type=”submit” value=”送信”>
</form>

import json
import boto3
import base64
import urllib.parse

def lambda_handler(event, context):
    s3=boto3.resource('s3')
    bucket=s3.Bucket('example.com.img')
    data_base=event['body']
    data_base1=data_base.split("&",1)[1]
    data_base2=data_base1.split(",",1)[1]
    data_base3=data_base2.replace('"','')
    imageBody=base64.b64decode(data_base3)
    path=event['body'].split("&",1)[0].replace('"','')
    bucket.put_object(
            Body=imageBody,
            Key=path
        )

    postdata=json.dumps(event["body"]).split("&",1)
    collectionId='faceCollection'
    client=boto3.client('rekognition')
    response=client.index_faces
    response=client.index_faces(CollectionId=collectionId,Image={'S3Object':{'Bucket':"exsample.com.img",'Name':path}},ExternalImageId=postdata[0].replace('"',''),DetectionAttributes=["DEFAULT"])
    return {
        'statusCode': 200,
        'body': json.dumps(response)
    }

# OpenCV のインポート
#s3ストレージにアップロードする
#pyコマンドを先頭につけてanacondaから実行する必要アリ
import cv2
import os
import time
import math
import boto3
import json
from numpy.random import *
from threading import Thread
#AWS関連処理
bucket_name="example.com.mov"
bucket_serv="hogehoge"
s3=boto3.resource('s3')
post=[]
cond=True
flag=False

#処理抜けタイミング計測function
def f():
    global flag
    while flag ==False:
        global cond
        time.sleep(10)
        cond=False
#POST時に実行するやつ
def postf():
    global flag
    global post
    global s3
    global bucket_serv
    global bucket_name
    while flag == False:
        if len(post) > 0:
            #この場合はアップロード前のファイルあり
            path=str(post.pop(0))+".mp4"
            s3.Bucket(bucket_name).upload_file(path,path)
            s3.Bucket(bucket_serv).upload_file(path,path,ExtraArgs={'ACL':'public-read'})
#カメラ初期処理
cap = cv2.VideoCapture(0)
fourcc = cv2.VideoWriter_fourcc(*"H264")
#マルチスレッド開始
thread = Thread(target=f)
thread.start()
thread2 = Thread(target=postf)
thread2.start()
while True:
    name=str(math.floor(time.time()))
    out=cv2.VideoWriter(name+".mp4",fourcc,20.0,(640,480))
    print(name);
    while cond:
        ret, frame = cap.read()
        cv2.imshow("camera input",frame)
        out.write(frame)
        k=cv2.waitKey(1)

        if k==27:
            flag=True
            break
    cond=True
    out.release()
    post.append(name)
    if flag==True:
        cap.release()
        cv2.destroyAllWindows()
        break
#in lambda from matect

import json
import boto3
import sys
import os
import uuid
import urllib.request
import time
def lambda_handler(event, context):
    rek=boto3.client('rekognition')
    base_data=event["Records"][0]["s3"]["object"]
    base_name=base_data["key"].replace('"',"")
    rek_res = rek.start_face_search(Video={'S3Object':{'Bucket':"example.com.mov",'Name':base_name}},CollectionId='faceCollection'})


    index=list(range(0))
    dicts={}
    dictb={}
    dictp={}
    rek=boto3.client('rekognition')
    base_file=base_name
    base_data=rek_res["JobId"]
    time.sleep(20)
    base_name=json.dumps(base_data).replace('"',"")
    ress="IN_PROGRESS"    
    while "IN_PROGRESS" in ress:
        time.sleep(5)
        res=rek.get_face_search(JobId=base_name)
        ress=json.dumps(res)
        print(ress)
    data=json.dumps(res)
    data_sp=data.split('Timestamp')
    #personごとのあれ
    data_sp.pop(0)
    while len(data_sp)>0:
        data_arr=data_sp.pop(0)
        second=data_arr.split(":")[1].split(",")[0].replace(" ","")
        data_sub=data_arr.split('"Similarity":')
        data_sub.pop(0)
        while len(data_sub)>0:
            data_con=data_sub.pop(0)
            sec=second
            per=data_con.split(",")[0].replace(" ","")
            fid=data_con.split('"ExternalImageId":')[1].split(",")[0].replace('"','').replace(" ","")
            box=data_con.split('"BoundingBox": {')[1].split("}")[0]
            if fid in dictp:
                if float(dictp[fid])>float(per):
                    dicts[fid]=sec
                    dictb[fid]=box
                    dictp[fid]=per
            else:
                index.append(fid)
                dicts[fid]=sec
                dictb[fid]=box
                dictp[fid]=per
    txt="!"
    while len(index)>0:
        fid=index.pop(0)
        sec=dicts[fid]
        per=dictp[fid] 
        box=dictb[fid]
        txt=txt+"*"+fid+"#"+sec+"#"+per+"#"+box+"#"+base_file

        #ここで生成されたdataarrが処理用の内容
        #FaceIDがあるかで判定
    url_txt=urllib.parse.quote(txt)
    url_base="https://example.com/gete.php?n="
    url_all=url_base+url_txt
    with urllib.request.urlopen(url_all) as res:
        body = res.read()
        print(body)
    print(txt)
    return {
        'statusCode': 200,
        'body': data
    }