from parinya import LINE
import time
import cv2
import numpy as np
import os
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/classifier_face.xml')
cascadePath = "Cascade/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
line = LINE('cdAJfHOi8LLFdXY9RtzEnLTO33cOid62cpvKr69PNDc')
font = cv2.FONT_HERSHEY_SIMPLEX
id = 0
names = ['None', 'Precha', 'Steve Job', 'Toey']
# Initialize and start realtime video capture
camera = cv2.VideoCapture(0)
camera.set(3, 640) # set video widht
camera.set(4, 480) # set video height
# Define min window size to be recognized as a face
minW = 0.1 * camera.get(3)
minH = 0.1 * camera.get(4)
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID') # กำหนด FourCC code
out = cv2.VideoWriter('output.avi', fourcc, 5.0, (640, 480))
while (True):
ret, img = camera.read()
# token id group myloan= "d3dVYwoTtmtGtLrDGXz491ZZlzogquSTAgVAa0bF7bI"
from parinya import LINE
line = LINE("d3dVYwoTtmtGtLrDGXz491ZZlzogquSTAgVAa0bF7bI")
#line.sendtext("hello you")
line.sendsticker(1, 1)
import requests
import time
from bs4 import BeautifulSoup
from parinya import LINE
line = LINE("d3dVYwoTtmtGtLrDGXz491ZZlzogquSTAgVAa0bF7bI")
web = "https://www.huay.com/login"
def send():
page = requests.get(web)
page.encoding = 'utf-8'
soup = BeautifulSoup(page.content, 'lxml')
soup1 = soup.find('div', class_='card-body p-2')
header = soup1.find(class_="text-danger").text
header1 = header[:25] # จับยี่กี่รอบที่ ....
soup2 = soup1.find_all('p', class_="number text-center m-0")
soup3 = [] # สามตัวบนกับสองตัวล่าง
for i in soup2:
soup3.append(i.text)
post = header1 + 'สามตัวบน ' + str(soup3[0]) + ' สองตัวล่าง ' + str(
soup3[1])
print(post)
line.sendtext(post)
def check():
page = requests.get(web)
page.encoding = 'utf-8'
soup = BeautifulSoup(page.content, 'lxml')
soup1 = soup.find('div', class_='card-body p-2')
header = soup1.find(class_="text-danger").text
# Parameters
df = df[df['Tag'].isin(tags)]
df = df.reset_index(drop=True)
return df
# Hyperparameters
url = 'https://xrpscan.com/account/rPFXvVo2fYXVPdV9gCHQouHsMgMhQ2aUwM'
tags = ['2197236416', '102086525']
line = LINE('')
# Algorithm
driver = webdriver.Chrome()
while True:
try:
df = get_data(driver, url, tags)
if len(df) > 0:
df_alert = pd.read_csv(r'.\log\alert_log.csv')
df_alert['Date'] = pd.to_datetime(df_alert['Date'])
from parinya import LINE
import time
import cv2
import numpy as np
import os
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/classifier_face.xml')
cascadePath = "Cascade/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);
line = LINE('cdAJfHOi8LLFdXY9RtzEnLTO33cOid62cpvKr69PNDc')
font = cv2.FONT_HERSHEY_SIMPLEX
id = 0
names = ['None', 'Precha', 'Steve Job','Toey']
# Initialize and start realtime video capture
camera = cv2.VideoCapture(0)
camera.set(3, 640) # set video widht
camera.set(4, 480) # set video height
# Define min window size to be recognized as a face
minW = 0.1*camera.get(3)
minH = 0.1*camera.get(4)
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID') # กำหนด FourCC code
out = cv2.VideoWriter('output.avi',fourcc, 5.0, (640,480))
while (True):
from parinya import LINE
import datetime
import csv
import time as Time
def make_csv():
with open('homeWork.csv', mode='w',encoding='utf-8',newline="") as cvs_file:
csv_writer = csv.writer(cvs_file, delimiter=',')
for i in range(len(data)):
csv_writer.writerow(data[i])
line = LINE("4vV3B81Wt7BLoVytgmE2U4H7o8ZaYSFCx0LL1X9gQzJ")
data = []
with open("homeWork.csv",mode="r",encoding='utf-8')as cvs_file:
csv_reader = csv.reader(cvs_file, delimiter=',')
for row in csv_reader:
data.append(row)
print(data)
while True:
now = datetime.datetime.now()
day = str(now.day)+"/"+str(now.month)+"/"+str(now.year)
time = str(now.now().hour)+":"+str(now.minute)+":"+str(now.second)
if time != "6:0:0":
for i in range(len(data)):
subject =str("วิชา :"+data[i][0])
sent_time = str("วันที่ส่ง :"+data[i][1])
discrip =str("รายละเอียด :"+data[i][2])
text = day+"\n"+subject+"\n"+sent_time+"\n"+discrip
line.sendtext(text)
if str(data[i][1]) == str(day):
del data[i]
import datetime
import imutils
import time
import cv2
import os
import datetime
from pathlib import Path
import arrow
from flask_cors import CORS
import base64
# filesPath = r"/var/www/html/image/"
from parinya import YOLOv3
from parinya import LINE
# criticalTime = arrow.now().shift(hours=+7).shift(days=-1)
yolo = YOLOv3('coco.names','yolov3-tiny.cfg','yolov3-tiny.weights')
line = LINE('IlxhXDiH1r1avlZkK8n5vMGpwsKZLkHee5gL3Im9yUm')
base64_message = None
# for item in Path(filesPath).glob('*'):
# if item.is_file():
# print (str(item.absolute()))
# itemTime = arrow.get(item.stat().st_mtime)
# # if itemTime < criticalTime:
# # #remove it
# # pass
# for filename in os.listdir(path):
# print(filen ame)
# # if os.stat(os.path.join(path, filename)).st_mtime < now - 7 * 86400:
from parinya import LINE
import time
import datetime
import psycopg2
conn = psycopg2.connect(
host="localhost",
database="postgres",
user="******",
password="******")
token_kmitl = 'SuWZzmcKMMH5RvFaglsCT8jWlIDEBltqjEwhNR7aepd'
x = datetime.datetime.now()
print(x.strftime("%x")) #ex. 12/31/18
print(x.strftime("%A")) #Weekday, full version Wednesday
line = LINE(token_kmitl)
msg ='''Morning report 3/19/2021
Name Arrival Status
Tachrat 9.00 on time
Kree 9.45 late
'''
line.sendtext(msg)
def line_guest(data): #มีรูปมีข้อความรับ 3 ค่า
return
def Morning_report(): #รับค่าดาต้าbaseรายวัน
txt = "Morning report"+ x.strftime("%x") +"\n" +"Name Arrival Status" +"\n"
# if Arrival > 9.30:
# txt += " late"
# elif Arrival < 9.30:
# txt += " on time"
# elif Arrival == null:
import tensorflow as tf
import cv2
import multiprocessing as _mp
from src.utils import load_graph, mario, detect_hands, predict
from src.config import BLUE, RED, GREEN
from parinya import LINE
line = LINE('m9urUawCA7xtS43PmP6fKCmcmtFOPdiRpa7u9vSnxwb')
tf.flags.DEFINE_integer("width", 640, "Screen width")
tf.flags.DEFINE_integer("height", 480, "Screen height")
tf.flags.DEFINE_float("threshold", 0.6, "Threshold for score")
tf.flags.DEFINE_float("alpha", 0.3, "Transparent level")
tf.flags.DEFINE_string("pre_trained_model_path", "src/pretrained_model.pb",
"Path to pre-trained model")
FLAGS = tf.flags.FLAGS
def main():
graph, sess = load_graph(FLAGS.pre_trained_model_path)
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, FLAGS.width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, FLAGS.height)
mp = _mp.get_context("spawn")
v = mp.Value('i', 0)
lock = mp.Lock()
process = mp.Process(target=mario, args=(v, lock))
process.start()
line.sendtext('Start Game')
while True:
key = cv2.waitKey(1)
if key == ord("q"):