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import sys, cv2, random
import numpy as np

def extract():
  J=cv2.imread('image1.png')
  f=open('output_payload.txt', 'w+', errors="ignore")
  
  idx=0
  bitidx=0
  bitval=0

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# Import required computer vision and array-based math libraries
import cv2, numpy as np

# Function to convert any type of data into binary
def to_bin(data):
  """Used to convert payload and pixel values to binary in payload hiding and extracting phase"""
  if isinstance(data, str):
    return ''.join([ format(ord(i), "08b") for i in data ])
  elif isinstance(data, bytes) or isinstance(data, np.ndarray):
    return [ format(ord(i), "08b") for i in data ]

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import requests
import json
# Spam 10 negative feedback forms 
for x in range(0,10):
	# GET captcha id and answer
	r = requests.get("http://127.0.0.1:3000/rest/captcha/")
	data = r.json()
	captcha_id = data['captchaId']
	captcha_answer = data['answer'] 
	# Create form parameters

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import pandas as pd
import numpy as np
import string
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, classification_report

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# 1. Data Loading
# 1.1 Load from CSV file
df = spark.read.csv('filename.csv', header=True, inferSchema=True)
# 1.2 Load from JDBC (Java Database)
df = spark.read.format("jdbc").options(url="jdbc_url", dbtable="tablename").load()
# 1.3 Load from text file
df = spark.read.text('filename.txt')

# 2. Data Inspection
# 2.1 Display top rows

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from sklearn.preprocessing import PolynomialFeatures, StandardScaler
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression

pipe_lgrg = make_pipeline(
    PolynomialFeatures(include_bias=False),
    StandardScaler(),
    SelectPercentile(score_func=f_classif),
    LogisticRegression(max_iter=2000, random_state=22)

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import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# Sample ratings matrix for 5 movies by 5 users
ratings = np.array([
    # [Movie 1, Movie 2, Movie 3, Movie 4, Movie 5]
    [5, 3, 4, 4, 0],  # User 1
    [3, 1, 2, 3, 3],  # User 2
    [4, 3, 4, 3, 5],  # User 3
    [3, 3, 1, 5, 4],  # User 4

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# 1. Extreme Gradient Boosting (comparable to LightGBM)
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
xgb = XGBClassifier(random_state=22)
param_grid_xgb = {
    'n_estimators': [300],
    'max_depth': [4],
    'learning_rate': [0.1],
    'subsample': [1.0],
    'colsample_bytree': [0.8]

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# 1. Algorithm to create depiction of Sigmoid class activation function
class Sigmoid:
    def __init__(self):
        return
    def forward(self, x):
        sigmoid_x = 1 / (1 + np.exp(-x))
        return sigmoid_x
    def derivative(self, z):
        sigmoid_z = self.forward(z)
        sigmoid_derivative_z = sigmoid_z * (1 - sigmoid_z)

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# 1. Algorithm to normalize the training and testing data
def normalize_data(X_train, X_test):
    mean_X_train = np.mean(X_train, axis=0)
    std_X_train = np.std(X_train, axis=0)
    X_train = np.divide((X_train - mean_X_train), std_X_train)
    X_test = np.divide((X_test - mean_X_train), std_X_train)
    return X_train, X_test 
X_train, X_test = normalize_data(X_train, X_test)
print("First three lines of X_train:", X_train[:5])
print("\nFirst three lines of X_test:", X_test[:5])