Image Segmentation with Deep Learning

Hello everyone, I am thrilled to guide you through an exciting journey where we will learn about image segmentation using deep learning. Before we dive in, I need to make sure you have all the necessary materials. Let’s start by downloading the dataset we will be working on. Here’s how you can do it:

wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
tar -xf images.tar.gz
tar -xf annotations.tar.gz

Once you’ve run these commands, you should have all the images and annotations we need for our experiment. Now, let’s get into the real fun part: the code.

To begin, I make sure to import all the necessary modules. This includes os, numpy, random, keras, layers from keras, keras’ image processing utilities, and matplotlib for visualization.

import os
import numpy as np
import random
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import matplotlib.pyplot as plt

Next, I define the directories where our images and annotations are stored. I call these image_dir and annotation_dir. With these defined, I can now obtain a list of paths for the images and annotations.

image_dir = "images/"…