from bs4 import *import requests as rqimport osurl1 = 'https://www.gettyimages.in/photos/driving-mask?page='url2 = '&phrase=driving%20mask&sort=mostpopular'url_list=[]Links = []for i in range(1,56):


    
full_url = url1+str(i)+url2url_list.append(full_url)for lst in url_list:r2 = rq.get(lst)soup = BeautifulSoup(r2.text, 'html.parser')x=soup.select\('img\[src\^="https://media.gettyimages.com/photos/"\]'\)    for img in x:      Links.append\(img\['src'\]\)  print\(len\(Links\)\)for index, img_link in enumerate(Links):if i <= len(Links):img_data = rq.get(img_link).contentwith open("Masked_Drivers/" + str(index + 1) + '.jpg'


    
, 'wb+') as f:f.write(img_data)i += 1

import cv2,os
data_path='Dataset'categories=os.listdir(data_path)labels=[i for i in range(len(categories))]
label_dict=dict(zip(categories,labels)) #empty dictionary
print(label_dict)print(categories)print(labels)


img_size=100data=[]target=[]


    


img_size=100data=[]target=[]

for category in categories:    folder_path=os.path.join(data_path,category)    img_names=os.listdir(folder_path)            for img_name in img_names:        img_path=os.path.join(folder_path,img_name)        img=cv2.imread(img_path)
        try:            gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)                       #Coverting the image into gray scale            resized=cv2.resize(gray,(img_size,img_size))            #resizing the gray scale into 50x50, since we need a fixed common size for all the images in the dataset            data.append(resized)


    
            target.append(label_dict[category])            #appending the image and the label(categorized) into the list (dataset)
        except Exception as e:            print('Exception:',e)                          import numpy as np
data=np.array(data)/255.0data=np.reshape(data,(data.shape[0],img_size,img_size,1))target=np.array(target)
from keras.utils import np_utils
new_target=np_utils.to_categorical(target)
np.save('data',data)np.save('target',new_target)

from keras.models import Sequentialfrom keras.layers import Dense,Activation,Flatten,Dropoutfrom keras.layers import Conv2D,MaxPooling2Dfrom keras.callbacks import ModelCheckpointmodel=Sequential()model.add(Conv2D(200,(3,3),input_shape=data.shape[1:]))model.add(Activation('relu'))model.add(MaxPooling2D(pool_size=(2,2)))model.add(Conv2D(100,(3,3)))model.add(Activation('relu'))model.add


    
(MaxPooling2D(pool_size=(2,2)))model.add(Flatten())model.add(Dropout(0.5))model.add(Dense(50,activation='relu'))model.add(Dense(2,activation='softmax'))model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])

from sklearn.model_selection import train_test_splittrain_data,test_data,train_target,test_target=train_test_split(data,target,test_size=0.4)checkpoint = ModelCheckpoint('model-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')


    
history=model.fit(train_data,train_target,epochs=20,callbacks=[checkpoint],validation_split=0.1)

model.save('saved_model/my_model')INFO:tensorflow:Assets written to: saved_model/my_model\assetsfrom keras.models import load_modelnew_model = load_model('saved_model/my_model')import cv2,osimg_path = 'test/755.jpg'img_size=100data=[]img=cv2.imread(img_path)


    
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)resized=cv2.resize(gray,(img_size,img_size))data.append(resized)data=np.array(data)/255.0data=np.reshape(data,(data.shape[0],img_size,img_size,1))output = new_model.predict(data)print(output)[[0.00447709 0.995523  ]]

for f1 in files:img = cv2.imread(f1)img = cv2.resize(img, (IMAGE_SIZE,IMAGE_SIZE))X.append(np.array(img))from lxml import etreedef resizeannotation(f):tree = etree.parse(f)for


    
 dim in tree.xpath("size"):width = int(dim.xpath("width")[0].text)height = int(dim.xpath("height")[0].text)for dim in tree.xpath("object/bndbox"):xmin = int(dim.xpath("xmin")[0].text)/(width/IMAGE_SIZE)ymin = int(dim.xpath("ymin")[0].text)/(height/IMAGE_SIZE)xmax = int(dim.xpath("xmax")[0].text)/(width/IMAGE_SIZE)ymax = int(dim.xpath("ymax")[0].text)/(height/IMAGE_SIZE)return [int(xmax), int(ymax), int(xmin), int(ymin)]

import cv2import imutilsimport numpy as npimport pytesseractpytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'for i in lst_add[1562:1572]:print(i)img = cv2.imread(i,cv2.IMREAD_COLOR)img = cv2.resize(img, (600,400) )gray = cv2.cvtColor\(img, cv2.COLOR\_BGR2GRAY\)   


    
gray = cv2.bilateralFilter\(gray, 13, 15, 15\)     edged = cv2.Canny\(gray, 30, 200\)   contours = cv2.findContours\(edged.copy\(\), cv2.RETR\_TREE, cv2.CHAIN\_APPROX\_SIMPLE\)  contours = imutils.grab\_contours\(contours\)  contours = sorted\(contours, key = cv2.contourArea, reverse = True\)\[:10\]    screenCnt = None    for c in contours:      peri = cv2.arcLength\(c, True\)      approx = cv2.approxPolyDP\(c, 0.018 \* peri, True\)         if len\(approx\) == 4:          screenCnt = approx  


    
        break    if screenCnt is None:      detected = 0      print \("No contour detected"\)  else:       detected = 1    if detected == 1:      cv2.drawContours\(img, \[screenCnt\], \-1, \(0, 0, 255\), 3\)    mask = np.zeros\(gray.shape,np.uint8\)  new\_image = cv2.drawContours\(mask,\[screenCnt\],0,255,\-1,\)  


    
new\_image = cv2.bitwise\_and\(img,img,mask=mask\)    \(x, y\) = np.where\(mask == 255\)  \(topx, topy\) = \(np.min\(x\), np.min\(y\)\)  \(bottomx, bottomy\) = \(np.max\(x\), np.max\(y\)\)  Cropped = gray\[topx:bottomx+1, topy:bottomy+1\]    text = pytesseract.image\_to\_string\(Cropped, config='--psm 11'\)  print\("Detected license plate Number is:",text\)  img = cv2.resize\(img,\(500,300\)\)  Cropped = cv2.resize\(Cropped,\(400,200\)\)  cv2.imshow\('car',img\)  cv2.imshow\('Cropped',Cropped\)    cv2.waitKey\(1\)  


    
cv2.destroyAllWindows\(\)

from flask_pymongo import PyMongoDEFAULT_CONNECTION_URL = "mongodb://localhost:27017/"DB_NAME = "Charan"

client = pymongo.MongoClient(DEFAULT_CONNECTION_URL)client.list_database_names()dataBase = client[DB_NAME]COLLECTION_NAME = "License_Details"collection = dataBase[COLLECTION_NAME]

@app.route('/', methods=['POST'])def upload_file():img_size=100data=[]uploaded_file = request.files['file']result=''if uploaded_file.filename != '':    filename = uploaded\_file.filename      uploaded\_file.save\(os.path.join\(app.config\['UPLOAD\_PATH'\], filename\)\)      img\_path = os.path.join\(app.config\['UPLOAD\_PATH'\], filename\)      print\(img\_path\)        img=cv2.imread\(img\_path\)      gray=cv2.cvtColor\(img,cv2.COLOR\_BGR2GRAY\)  


    
    resized=cv2.resize\(gray,\(img\_size,img\_size\)\)      data.append\(resized\)      data=np.array\(data\)/255.0      data=np.reshape\(data,\(data.shape\[0\],img\_size,img\_size,1\)\)            new\_model = load\_model\('saved\_model/my\_model'\)      output = new\_model.predict\(data\)  


    if output\[0\]\[0\]>=0.5:          result = 'The person is Masked'      else:          result = 


    
'The Person is Non Masked'      print\(result\)    return render\_template\('Show.html',result=result\)

@app.route('/Vehicle', methods=['POST'])def table2():uploaded_file = request.files['file']result=''if uploaded_file.filename != '':


    
    path='static/car'      filename = uploaded\_file.filename      uploaded\_file.save\(os.path.join\(path, filename\)\)      img\_path = os.path.join\(path, filename\)      print\(img\_path\)        img = cv2.imread\(img\_path,cv2.IMREAD\_COLOR\)      img = cv2.resize\(img, \(600,400\) \)        gray = cv2.cvtColor\(img, cv2.COLOR\_BGR2GRAY\)       gray = cv2.bilateralFilter\(gray, 13, 15, 15\)        edged = cv2.Canny\(gray, 30, 200\)       contours = cv2.findContours\(edged.copy\(\), cv2.RETR\_TREE, cv2.CHAIN\_APPROX\_SIMPLE\)      contours = imutils.grab\_contours\(contours\)      contours = sorted\(contours, key = cv2.contourArea, reverse = True\)\[:10\]  


    





    screenCnt = None        for c in contours:          peri = cv2.arcLength\(c, True\)          approx = cv2.approxPolyDP\(c, 0.018 \* peri, True\)             if len\(approx\) == 4:              screenCnt = approx              break  




    if screenCnt is


    
 None:          detected = 0          print \("No contour detected"\)      else:          detected = 1     




    if detected == 1:          cv2.drawContours\(img, \[screenCnt\], \-1, \(0, 0, 255\), 3\)        mask = np.zeros\(gray.shape,np.uint8\)      new\_image = cv2.drawContours\(mask,\[screenCnt\],0,255,\-1,\)      new\_image = cv2.bitwise\_and\(img,img,mask=mask\)  


    
      \(x, y\) = np.where\(mask == 255\)      \(topx, topy\) = \(np.min\(x\), np.min\(y\)\)      \(bottomx, bottomy\) = \(np.max\(x\), np.max\(y\)\)      Cropped = gray\[topx:bottomx+1, topy:bottomy+1\]        text = pytesseract.image\_to\_string\(Cropped, config='--psm 11'\)      print\("Detected license plate Number is:",text\)           #text='GJW-1-15-A-1138'      print\('"\{\}"'.format\(text\)\)      re.sub\(r'\[\^\\x00-\\x7f\]',r'', text\)      text = text.replace\("\\n", " "\)       text = re.sub\('\[\\W\_\]+', '', text\)  


    
      print\(text\)        print\('"\{\}"'.format\(text\)\)      query1 = \{"Number Plate": text\}      print\("0"\)      for doc in collection.find\(query1\):         doc1 = doc        Name=doc1\['Name'\]      Address=doc1\['Address'\]      License=doc1\['License Number'\]    return render\_template\('Penalty.html',Name=Name,Address=Address,License=License\)