def qrcode(token):
url = f'https://qrcode.iqiyipic.com/login/?data=https%3A%2F%2Fpassport.iqiyi.com%2Fapis%2Fqrcode%2Ftoken_login.action%3Ftoken%3D{token}&property=0&salt={md5Encode(f"35f4223bb8f6c8638dc91d94e9b16f5https%3A%2F%2Fpassport.iqiyi.com%2Fapis%2Fqrcode%2Ftoken_login.action%3Ftoken%3D{token}")}&width=162&_={random()}'
if qrShowType == 'tg推送':
tgpush(url)
else:
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36'
}
req = get(url, headers=headers)
with open('登录二维码.png', 'wb') as f:
f.write(req.content)
image.open('登录二维码.png').show()
def upload(request):
reqfile = request.FILES['photo']
image = image.open(reqfile)
image.thumbnail((128,128), Image.ANTIALIAS)
image.save("/home/bing/1.jpeg", "jpeg")
return HttpResponse("success.")
def login(user_name,
password,
source='index_nav',
redir='https://www.douban.com/',
login='******'):
caprcha_id, caprcha_link = get_captcha(
url) #把get_captcha函数返回的值,获取验证码的id和链接
if caprcha_id:
img_html = session.get(caprcha_link)
with open('caprcha.jpg', 'w') as f:
f.write(img_html.content)
try:
im = image.open(caprcha.jpg)
im.show()
im.close()
except:
print("打开图片出错")
else:
caprcha = input('请输入验证码：') #把看到的验证码图片输入进去
data = { #需要传去的数据
'source':source,
'redir':redir,
'form_email':username,
'form_password':password,
'login':login,
}
if caprcha_id: #如果需要验证码就把下面的两个数据加入到data里面
data['captcha-id'] = caprcha_id
data['captcha-solution'] = caprcha
html = html = session.post(url, data=data, headers=headers) #获取登入后的页面
print(session.cookies.items()) #打印此时的cookies
def upload(request):
reqfile = request.FILES['photo']
image = image.open(reqfile)
image.thumbnail((128, 128), Image.ANTIALIAS)
image.save("/home/bing/1.jpeg", "jpeg")
return HttpResponse("success.")
def __getitem__(self, item):
image = io.open(self.image[item]).convert('RGB')
image = self.transform(image)
text = self.text[item]
input_ids, attention_mask = self._preprocess(text)
if not self.infer:
return image, input_ids.flatten(), attention_mask.flatten(
), self.label[item]
else:
return image, input_ids.flatten(), attention_mask.flatten()
def get_string(img_path): img=cv2.imread(img_path) img=cv2.cvtColor(img, cv2,COLOR_BGR2GRAY) kernel=np.ones((1,1), np.uint8) img= cv2.dilate(img, kernel, iterations=1) img= cv2.erode(img, kernel, iterations=1) cv2.imwrite(src_path + "1.jpg", img) img= cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2) cv2.imwrite(src_path+ "thres.png", img) result=pytesseract.image to string(image.open(src_path +"thres.png")) return result
def get_string(img_path):
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2,
COLOR_BGR2GRAY) # for convering Color image to gray
kernel = np.ones((1, 1), np.uint8)
img = cv2.dilate(img, kernel, iterations=1)
# for removal of noise
img = cv2.erode(img, kernel, iterations=1)
# cv2.imwrite(src_path + "removed_noise.png", img)
img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2)
cv2.imwrite(src_path + "thres.png", img)
result = pytesseract.image_to_string(image.open(src_path + "thres.png"))
return
from PIL import image
img = image.open("prajwal.jpg")
img.show()
from PIL import image
im = image.open("corgi.jpg")
im.rotate(45).show()
new_image = image.new(im.mode, im.size)
new_image.save("output.jpg")
im.getdata() => sequence
print(im.getdata())
im.putdata(data)
im.putdata(data, scale, offset)
from PIL import image
img = image.open("image.jpg")
area = (100, 100, 300, 375)
cropped_img = img.crop(area)
img.show()
cropped_img.show()
#%%
## 기타 참조 - konlp.pdf 참조
## new_ko.concordance('영화') # 주변부 단어 확인
## new_ko.collocations() # 어떤 단어들이 언어로 사용되었을까?
new_ko.collocations()
#%%
# 워드 클라우드 적용
from wordcloud import WordCloud, STOPWORDS
import numpy as np
from PIL import image
#%%
stopwords = set(STOPWORDS) # 불용어 처리
alice_mask = np.array(image.open('img/Draw_ca1.png'))
#%%
data = new_ko.vocab().most_common(150)
#%%
path = 'C:/Windows/Fonts/malgun.ttf'
wc = WordCloud(font_path=path,
relative_scaling=0.2,
background_color='while',
mask=alice_mask).generate_from_frequencies(dict(data))
plt.figure(gifsize=(12, 8))
plt.imshow(wc)
plt.axis('off')
plt.show()
###############################################
# Py method to calculate RGB value of an image
###############################################
from PIL import image
import random
im = image.open("map-o1.png")
rgb_im = im.convert("RGB")
count_India = 0
count_Punjab = 0
count = 0
areaIndia = 3287263
while (count <= 1000000):
x = random.randint(0, 2480)
y = random.randint(0, 2735)
z = 0
r, g, b = rgb_im.getpixel((x, y))
if (r == 60):
count_India = count_India + 1
count = count + 1
else:
if (r == 80):
count_Punjab = count_Punjab + 1
count = count + 1
areaPunjab = (count_Punjab / count_India) * areaIndia
print("Estimated Area of Punjab in sq km is ", areaPunjab)
data=np.array([[age, bmi,children,region,gender,smoker]])
data_s=scalar.transform(data)
prediction=regression.pred(data_s)[0,:]
return(prediction)
def main()
st.sidebar.header('About')
st.sidebar.info('This app is created to predict Medical Insurance')
st.sidebar.info('The dataset consist of 1338 people divided into 4 regions such as Southeast, Southwest, Nrtheast,Northwest')
from PIL import image
image=image.open()
st.sidebar.image(image,width=300)
st.title("Medical Insurance Prediction App")
html_temp = """
<div style="background-color:#ADD8E6;padding:5px">
<h2 style="color:white;text-align:center;">Web app Build using Streamlit, Deployed on Heroku </h2>
</div>
"""
st.markdown(html_temp, unsafe_allow_html=True)
age=st.number_input("Age", min_value=0, max_value=150, value=0)
bmi=st.number_input('BMI', min_value=0, max_value=100, value=0)
Children=st.number_input('Number of Children', min_value=0, max_value=100, value=0)
# -*- coding: utf-8 -*-
from PIL import image
a = image.open("filename path")
a.size()
a.format()
a.filename()
a.save("save as other format")
from PIL import image
import os
for filename in os.listdir(os.getcwd()):
img = image.open(filename)
img2 = img.rotate(-90)
img2.save("rot_" + filename)