new!

em_code/em.py

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+import pandas as pd
+import numpy as np
+from pandas import Series, DataFrame
+import os
+# 데이터 프레임 맞춰준다
+# 6
+# 964
+# test = pd.DataFrame(np.arange(0, 5784).reshape(964, 6))
+# test = test.drop([0])
+# test = test.drop([0], axis=1)
+outp = []
+path_dir = '/media/sf_.VirtualBox/em_Data/'
+file_list = os.listdir(path_dir)
+
+for k in file_list:
+    os.mkdir('/media/sf_.VirtualBox/em/{}'.format(k))
+    for i in range(1, 6):
+        for j in range(1, len(file_list)):
+            area_df = pd.read_table('/media/sf_.VirtualBox/em_Data/{}/area_s_{}_{}'.format(
+                k, i, j), sep='\n', engine="python", header=None)
+            da = area_df.drop([0], axis=0)
+            x = np.mean(da)
+            test[i][j] = x
+    area = DataFrame(test)
+    area.to_table('/media/sf_VirtualBox/em/{}'.format(k))

em_code/emconsumer.py

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+from kafka import KafkaConsumer
+from json import loads
+import pymysql
+
+mydb = pymysql.connect(
+    host="13.124.240.211",
+    user="user",
+    passwd="12345",
+    database="SmartInside",
+    port=51642,
+)
+sensor_list = []
+consumer1 = KafkaConsumer(
+    'em',
+    bootstrap_servers=['smartinside1:9092'],
+    auto_offset_reset='earliest',
+    enable_auto_commit=True,
+    #      group_id='my-group',
+    value_deserializer=lambda x: loads(x.decode('utf-8')))
+for message in consumer1:
+    a = message.offset
+    b = message.value
+    mycursor = mydb.cursor()
+    sql = "INSERT INTO SmartInside.ke (id,sensor1,sensor2,sensor3,sensor4,sensor5) VALUES (%s,%s,%s,%s,%s,%s)"
+    val = (a, b["sensor1"], b["sensor2"],
+           b["sensor3"], b["sensor4"], b["sensor5"])
+    mycursor.execute(sql, val)
+    mydb.commit()

em_code/emproducer.py

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+import json
+from kafka import KafkaProducer
+from kafka import KafkaConsumer
+from kafka import KafkaClient
+from kafka import TopicPartition
+
+import msgpack
+zookeeper_servers = ["172.20.40.21:2181"]
+bootstrap_servers = ["172.20.40.21:9092"]
+client = KafkaClient(bootstrap_servers=bootstrap_servers)
+producer = KafkaProducer(bootstrap_servers=bootstrap_servers)
+producer = KafkaProducer(bootstrap_servers=bootstrap_servers,
+                         value_serializer=lambda v: json.dumps(v).encode('utf-8'))
+path_dir = '/media/sf_.VirtualBox/em/'
+file_list = os.listdir(path_dir)
+for j in file_list:
+    with open("/media/sf_.VirtualBox/em/{}/em.json".format(j)) as em:
+        em = json.load(em)
+    for i in range(0, len(em)):
+        producer.send("em", em["{}".format(i)])

em_code/emtojson.py

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+# aftojson em 데이터 json 으로 변환
+# 각 날짜별 폴더의 em 데이터를 json 으로 변환
+
+import json
+import os
+import pandas as pd
+
+path_dir = '/media/sf_.VirtualBox/em/'
+file_list = os.listdir(path_dir)
+
+for i in file_list:
+    af = pd.read_table(path_dir+"{}/area_mean".format(i),sep=',',engine="python")
+    bf=af.drop(['Unnamed: 0'],axis=1)
+    bf.columns=['sensor1', 'sensor2', 'sensor3', 'sensor4', 'sensor5']
+
+    result = bf.to_json(orient="index")
+    json.dumps(result)
+    em = json.loads(result)
+
+    with open(path_dir+"{}/".format(i)+"em.json", 'w', encoding='utf-8') as make_file:
+        json.dump(em, make_file)

mfl_code/mfl_consumer.py

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+from kafka import KafkaConsumer
+from json import loads
+import pymysql
+
+mydb = pymysql.connect(
+    host="13.124.240.211",
+    user="user",
+    passwd="12345",
+    database="SmartInside",
+    port=51642,
+)
+consumer1 = KafkaConsumer(
+    'mfl',
+    bootstrap_servers=['smartinside1:9092'],
+    auto_offset_reset='earliest',
+    enable_auto_commit=True,
+    #      group_id='my-group',
+    value_deserializer=lambda x: loads(x.decode('utf-8')))
+
+for message in consumer1:
+    a = message.offset
+    b = message.value
+    mycursor = mydb.cursor()
+    sql = "INSERT INTO SmartInside.km (id,sensor1,sensor2,sensor3,sensor4,sensor5,sensor6) VALUES (%s,%s,%s,%s,%s,%s,%s)"
+
+    val = (a, b["sensor1"], b["sensor2"], b["sensor3"],
+           b["sensor4"], b["sensor5"], b["sensor6"])
+    mycursor.execute(sql, val)
+    mydb.commit()

mfl_code/mfl_producer.py

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+import json
+from kafka import KafkaProducer
+from kafka import KafkaConsumer
+from kafka import KafkaClient
+from kafka import TopicPartition
+import msgpack
+
+zookeeper_servers = ["172.20.40.21:2181"]
+bootstrap_servers = ["172.20.40.21:9092"]
+client = KafkaClient(bootstrap_servers=bootstrap_servers)
+producer = KafkaProducer(bootstrap_servers=bootstrap_servers,
+                         value_serializer=lambda v: json.dumps(v).encode('utf-8'))
+
+with open("/media/sf_.VirtualBox/mfl/20210507/mfl.json") as mfl:
+    mfl = json.load(mfl)
+for i in range(1, len(mfl)):
+    producer.send("mfl", mfl["{}".format(i)])

mfl_code/mfl_reshape.py

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+import pandas as pd
+
+origin_df = pd.read_table('td50.lvm', sep ='\t',   engine="python" ,header=None)
+new_col = ["space","sensor1","sensor2","sensor3","sensor4","sensor5","sensor6","sensor7","sensor8","sensor9","sensor10"]
+origin_df.columns = new_col
+reshape_df=origin_df.drop(columns=["space"])
+reshape_df.to_csv('td50_reshape.lvm', index=False,sep="\t")

streamlit/app.py

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+from matplotlib import colors as mcolors  ## 첫번째 프로토타입
+import matplotlib.pyplot as plt           ## 그래프, 동영상 업로딩 용이 
+                                          ## 
+from matplotlib.collections import PolyCollection
+from mpl_toolkits.mplot3d import Axes3D
+# import comedata1 as cd1
+import altair as alt
+import streamlit as st         
+import numpy as np
+import pandas as pd
+import comedata as cd
+
+
+MFL_df = cd.MFLdf
+AREA_df = cd.AREAdf
+MFL_df1 = MFL_df.drop('id', axis=1)
+AREA_df1 = AREA_df.drop('id', axis=1)
+
+
+def main():
+    st.title("PROJECT")
+    st.write("\n")
+    st.write("센서데이터 활용 건설안전을 위한 신호분석 및 모델링 프로젝트입니다")
+    st.write("\t\a멘토 -신주호")
+    st.write("\n")
+    st.write("\t\a조원 - 김용준,배형근,변영준")
+    st.write("\n")
+    st.write("\t\a프로젝트 기간 - 2021년 3월 8일 ~ 2021년 5월 18일")
+    st.image('C:\\Users\\kyjoo\\Desktop\\산학자료\\code\\streamlit\\cone.jpg')
+
+
+def MFL():
+    st.title("MFL Data")
+    st.write("\n")
+    sensor_list = MFL_df1.columns.unique()
+    sensors = st.multiselect(
+        "센서 선택", list(sensor_list)), [
+            sensor_list[0], sensor_list[1]]
+
+    st.line_chart(MFL_df1)
+    if st.checkbox('Show dataframe'):
+        st.dataframe(MFL_df1)
+    st.image("C:\\Users\\kyjoo\\Desktop\\산학자료\\image\\\m1.jpg")
+
+
+def AREA():
+    st.title("AREA Data")
+    st.write("\n")
+
+    st.line_chart(AREA_df1)
+    if st.checkbox('Show dataframe'):
+        st.dataframe(AREA_df1)
+
+
+def DETECT():
+    st.video('C:\\Users\\kyjoo\\Desktop\\산학자료\\code\\streamlit\\detect.mp4')
+
+
+options = st.sidebar.selectbox('센서데이터', ['main', 'MFL', 'AREA', 'DETECT'])
+
+if options == 'main':
+    main()
+elif options == 'MFL':
+    MFL()
+elif options == 'AREA':
+    AREA()
+elif options == 'DETECT':
+    DETECT()

streamlit/app1.py

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+from matplotlib import colors as mcolors  # 두번째 프로토타입
+import matplotlib.pyplot as plt  # 대시보드
+from matplotlib.collections import PolyCollection
+from mpl_toolkits.mplot3d import Axes3D
+import streamlit.components.v1 as components
+import altair as alt
+import streamlit as st
+import numpy as np
+import pandas as pd
+import comedata as cd
+import matplotlib.animation as animation
+import cv2
+# from streamlit_webrtc import webrtc_streamer
+
+
+MFL_df = cd.MFLdf
+AREA_df = cd.AREAdf
+MFL_df1 = MFL_df.drop('id', axis=1)
+AREA_df1 = AREA_df.drop('id', axis=1)
+video_file = open(
+    'C:\\Users\\kyjoo\\Desktop\\smartinside\\code\\streamlit\\demo_output.mp4', 'rb')
+video_bytes2 = video_file.read()
+video_file = open(
+    "C:\\Users\\kyjoo\\Desktop\\smartinside\\code\\gitadd\\streamlit\\workman.mp4", 'rb')
+video_bytes = video_file.read()
+
+
+def main():
+    st.title("건설안전-작업자현황 및 안전감시 시스템")
+    st.write("\n")
+    cols = st.beta_columns(4)
+    cols[0].write("현재 작업인원: {}".format(20))
+    cols[1].write("경고(2명이상): {}".format("삐용"))
+
+    st.sidebar.markdown(f'''
+        <div class="card text-white bg-info mb-3" style="width: 18rem">
+            <div class="card-body">
+                <h5 class="card-title">Total Worker : {20}\n</h5>
+                <h5 class="card-text">Warning : {1}</h5>
+            </div>
+        </div>''', unsafe_allow_html=True)
+
+    if 3 >= 2:
+        st.warning("작업환경이 위험합니다.")
+
+    fig = plt.figure(figsize=(22, 3))
+
+    times_work = [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
+    tn_work = [15, 16, 15, 14, 15, 16, 17, 18, 17, 17, 14, 6]
+    tn_warn = [1, 1, 1, 2, 3, 4, 3, 2, 0, 1, 1, 1]
+    ax = fig.add_subplot(1, 2, 1)
+    bx = fig.add_subplot(1, 2, 2)
+    ax.bar(
+        times_work,
+        tn_work
+    )
+    bx.bar(
+        times_work,
+        tn_warn
+    )
+    bx.set_xlabel("time")
+    bx.set_ylabel("warning")
+    ax.set_xlabel("time")
+    ax.set_ylabel("workers")
+
+    st.pyplot(fig)
+
+
+def MFL():
+    st.title("MFL Data")
+    st.write("\n")
+    # sensor_list = MFL_df1.columns.unique()
+    # sensors = st.multiselect(
+    #     "센서 선택", list(sensor_list)), [
+    #         sensor_list[0], sensor_list[1]]
+
+    st.line_chart(MFL_df1)
+    if st.checkbox('Show dataframe'):
+        st.dataframe(MFL_df1)
+
+
+def AREA():
+    st.title("AREA Data")
+    st.write("\n")
+
+    st.line_chart(AREA_df1)
+    if st.checkbox('Show dataframe'):
+        st.dataframe(AREA_df1)
+
+
+def DETECT():
+    st.title("DETECTING")
+
+    st.video(video_bytes)
+
+
+options = st.sidebar.selectbox('센서데이터', ['PROJECT', 'MFL', 'AREA', 'DETECT'])
+
+if options == 'PROJECT':
+    main()
+elif options == 'MFL':
+    MFL()
+elif options == 'AREA':
+    AREA()
+elif options == 'DETECT':
+    DETECT()
+
+
+if st.sidebar.button("작업장"):
+    st.video(video_bytes2)
+
+# if st.sidebar.button("커멘드"):
+#     run = st.checkbox('Run')
+#     FRAME_WINDOW = st.image([])
+#     camera = cv2.VideoCapture(0)
+#     while run:
+#         _, frame = camera.read()
+#         frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+#         FRAME_WINDOW.image(frame)
+#     else:
+#         webrtc_streamer(key="example")

streamlit/comedata.py

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+import pymysql
+import pandas as pd
+import matplotlib.pyplot as plt
+sensor_db = pymysql.connect(
+    user="user",
+    passwd="12345",
+    host="13.124.240.211",
+    db="SmartInside",
+    port=51642,
+    charset='utf8'
+)
+cursor = sensor_db.cursor(pymysql.cursors.DictCursor)
+
+mflsql = "select * from `MFL`;"
+areasql = "select * from `AREA`;"
+# sql = "load data local infile 'td50_reshape.lvm' into table sensor columns terminated by '\t' ignore 1 lines;"
+# cursor.execute(selectsql)
+cursor.execute(mflsql)
+mflresult = cursor.fetchall()
+
+cursor.execute(areasql)
+arearesult = cursor.fetchall()
+
+
+MFLaf = pd.DataFrame(mflresult)
+MFLdf = MFLaf.astype(float)
+AREAdf = pd.DataFrame(arearesult)