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# Import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
import cv2

# Load image and convert it from BGR to RGB
image = cv2.cvtColor(cv2.imread("folder/imageName.jpg"), cv2.COLOR_BGR2RGB)

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def resize(img, height=800):
    """ Resize image to given height """
    rat = height / img.shape[0]
    return cv2.resize(img, (int(rat * img.shape[1]), height))

# Resize and convert to grayscale
img = cv2.cvtColor(resize(image), cv2.COLOR_BGR2GRAY)

# Bilateral filter preserv edges
img = cv2.bilateralFilter(img, 9, 75, 75)

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# Getting contours  
im2, contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Finding contour of biggest rectangle
# Otherwise return corners of original image
# Don't forget on our 5px border!
height = edges.shape[0]
width = edges.shape[1]
MAX_COUNTOUR_AREA = (width - 10) * (height - 10)

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def fourCornersSort(pts):
    """ Sort corners: top-left, bot-left, bot-right, top-right """
    # Difference and sum of x and y value
    # Inspired by http://www.pyimagesearch.com
    diff = np.diff(pts, axis=1)
    summ = pts.sum(axis=1)
    
    # Top-left point has smallest sum...
    # np.argmin() returns INDEX of min
    return np.array([pts[np.argmin(summ)],

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import ipywidgets as widgets
# Import display function
from IPython.display import display

# Create costume widget
w = widgets.IntProgress(value = 50, description='Loading:')

# Display widget
display(w)

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from IPython.display import display, clear_output

# Creating and Displaying button
button = widgets.Button(description="Button Text!")
display(button)

# Callback function with button parameter
def clicked(b):
    clear_output()
    print("Result of button click: ")

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#!/bin/bash
# Update
apt-get update && apt-get upgrade -y
# Install python+packages and curl
apt-get install -y python3 python3-pip python3-numpy python3-scipy python3-matplotlib ipython3 ipython3-notebook python3-pandas python3-nose curl wget
# Update pip
python3 -m easy_install -U pip

# Correctinng python file 
# which causes stuck during package installation

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import tensorflow as tf
### Linear Regression ###
# Input placeholders
x = tf.placeholder(tf.float32, name='x')
y = tf.placeholder(tf.float32, name='y')
# Model parameters
W1 = tf.Variable([0.1], tf.float32)
W2 = tf.Variable([0.1], tf.float32)
W3 = tf.Variable([0.1], tf.float32)
b = tf.Variable([0.1], tf.float32)

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sess = tf.Session()

# Import graph from the path and recover session
saver = tf.train.import_meta_graph('models/model_name.meta', clear_devices=True)
saver.restore(sess, 'models/model_name')

# There are TWO options how to access the operation (choose one)
  # FROM SAVED COLLECTION:
activation = tf.get_collection('activation')[0]
  # BY NAME: