InputBlackBoxOutput

from PIL import Image

def resize_image(img_path, desired_size = 300, background_color=None):
  img = Image.open(img_path)

  old_size = img.size

  if background_color == None:
    background_color = img.load()[0,0]

InputBlackBoxOutput

import cv2
import numpy as np
from PIL import Image

def process_pixels(img):    
    pixel_data = list(img.getdata())
    pixels = np.array(pixel_data).flatten()
    width = img.size[0]
    height = img.size[1]

InputBlackBoxOutput

import os
import hashlib
import glob

# Get the SHA-256 hash of a file
def sha256(fname, size=4096):
 
  sha256_hash = hashlib.sha256()
  with open(fname, 'rb') as f:
    for byte_block in iter(lambda: f.read(4096), b""):

InputBlackBoxOutput

import cv2
import numpy as np

def calculate_threshold(img):
	hist, bin_edges = np.histogram(img, bins=256)
	bin_mids = (bin_edges[:-1] + bin_edges[1:]) / 2.

	weight1 = np.cumsum(hist)
	weight2 = np.cumsum(hist[::-1])[::-1]
	mean1 = np.cumsum(hist * bin_mids) / weight1

InputBlackBoxOutput

import re, sys

input_file ="input.csv"
output_file ="output.csv"
input_file_delimiter = ';'
output_file_delimiter = ','

output = []
with open(input_file) as f_input:
    lines = f_input.read().splitlines()

InputBlackBoxOutput

from wordcloud import WordCloud
import matplotlib.pyplot as plt

def plot_wordcloud(text, mask=None, max_words=200, max_font_size=100, figure_size=(12.0,12.0), 
                   title = None, title_size=18, image_color=False):

    wordcloud = WordCloud(background_color='white',
                    max_words = max_words,
                    max_font_size = max_font_size, 
                    random_state = 42,

InputBlackBoxOutput

import json

seperator = ','

with open("text.txt", encoding='utf-8') as file:
	data = file.read().splitlines()
	print(len(data))

	json_object = {}
	for i, each in enumerate(data):

InputBlackBoxOutput

with open("text.txt", 'r') as infile:
	data = infile.read().splitlines()
	# print(data)

with open("text.txt", 'w') as outfile:
	for each in list(set(data)):
		outfile.write(each + '\n')

InputBlackBoxOutput

# Image Data augmentation
# Use this program to perform image data augmentation in case you do not want to use a generator for some reason
# Check out ImageDataGenerator if you are looking for a generator in TensorFlow: https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator

import cv2
import numpy as np
import glob
import hashlib

search_pattern = "images/*.jpg"

InputBlackBoxOutput

import sys
try:
  import matplotlib.pyplot as plt
except ModuleNotFoundError:
  print("Error: Module was not found")
  sys.exit()
except:
  print("Something went wrong while importing module/s \n")
  sys.exit()