Image augmentation is a technique for artificially adding more images to your image data to expand the data set. It is mostly used to add variety to the data set so that models don’t over-fit.
Some of the most common augmentation methods are flipping, rotating, and tweaking image properties like contrast, brightness, and color.
We shall apply all these techniques to a sample of image leaves from the Plant-Village Data Set. The data set is available here. Open the link while you are signed in to your Google account so that it’s available in the “Shared with me” folder of your Google Drive.
To make the setup easy and fast, we’ll be hosting our notebook on Google Colab. For those new to Colab, it is a free development environment offered by Google which lets you use GPUs or TPUs for your modeling needs. It also comes with the Python Imaging Library (PIL) and OpenCV preinstalled, so it saves us the trouble of installing them as they are not shipped with the Anaconda distribution of Python. Using Colab also lets you use Google Drive to host your data, so you don’t have to download 4000+ photos of leaves to your local machine.
Once you open Colab, choose “New Notebook” to open a blank Jupyter Notebook. Then click on “Connect” at the top-right. Once you’ve connected, you’ll be able to see a green tick with the RAM and the disk utilization where “Connect” was earlier. Once this is done, go to “Runtime” from the menu bar, select “Change runtime type”, and then choose “GPU” from the Hardware accelerator drop-down. You can also uncheck the “Omit code cell output when saving this notebook” option if you want to save your notebook with outputs. Click on “Save”, and your GPU-hosted Jupyter Notebook is now ready!
We start by importing the libraries. We will use:
import numpy as np import pandas as pd import matplotlib.pyplot as plt import cv2 import glob import datetime import random from tqdm.notebook import tqdm from PIL import Image from PIL import ImageEnhance np.random.seed(1) #to have reproducible results pd.set_option('display.max_colwidth', None)
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu" >>> LastName = "Jordan" >>> FirstName, LastName = LastName, FirstName >>> print(FirstName, LastName) ('Jordan', 'kalebu')
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Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
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Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…
You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).
Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.
class AnyClass: def __init__(): print("Init called on its own") obj = AnyClass()
The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.
The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.
Init called on its own
Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,
class AnyClass: def __init__(self, var): self.some_var = var def __add__(self, other_obj): print("Calling the add method") return self.some_var + other_obj.some_var obj1 = AnyClass(5) obj2 = AnyClass(6) obj1 + obj2
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