Step by step tutorial on how to recognize cats in a list of images using Machine Learning and AI in Python
I’ll guide you through the process of recognizing cats in a list of images using machine learning and AI in Python. We’ll be using a popular deep learning framework called TensorFlow and its high-level API called Keras. Let’s get started!
Step 1: Set up the environment Before we begin, make sure you have Python installed on your system. You’ll also need to install the following libraries:
pip install tensorflow
pip install keras
pip install matplotlib
pip install numpy
Step 2: Collect cat images To recognize cats, you’ll need a dataset of cat images for training the model. You can either collect the images manually or download a pre-existing dataset. Popular cat image datasets include the Cat vs Dog dataset and the Oxford-IIIT Pet Dataset. For this tutorial, let’s assume you have a folder called ‘cat_images’ containing cat images.
Step 3: Preprocess the data Before training the model, we need to preprocess the images. This involves resizing the images, normalizing pixel values, and splitting the data into training and validation sets.
import numpy as np
image_dir = 'cat_images'
image_size = (150, 150) # Resize images to 150x150 pixels
images = 
labels = 
# Iterate over each image in the directory
for filename in os.listdir(image_dir):
if filename.endswith('.jpg') or filename.endswith('.png'):
image_path = os.path.join(image_dir, filename)
label = 1 # 1 represents cat
# Read and resize the image
image = cv2.imread(image_path)
image = cv2.resize(image, image_size)
image = image.astype('float32') / 255.0 # Normalize pixel values
images = np.array(images)
labels = np.array(labels)
return images, labels
# Preprocess the images
images, labels = preprocess_images(image_dir)
# Split the data into training and validation sets
from sklearn.model_selection import train_test_split