Hands-on Practical: Generate Simple Synthetic Images

Python is used to generate synthetic images, leveraging standard libraries. NumPy is utilized for numerical operations, which are essential for handling pixel data. Pillow, a fork of the Python Imaging Library (PIL), handles image manipulation tasks such as creating, saving, and modifying images.

Setting Up Your Environment

Before we start generating images, you need to make sure you have the necessary tools installed. If you have Python set up, you can typically install these libraries using pip, Python's package installer. Open your terminal or command prompt and run:

pip install numpy Pillow

This command downloads and installs NumPy and Pillow, making them available for use in your Python scripts.

Example 1: Creating an Image with a Simple Shape

Images, especially digital ones, are fundamentally grids of pixels. We can represent these grids using multi-dimensional arrays. For a color image, this is often a 3D array: (height, width, color_channels). The color channels typically represent Red, Green, and Blue (RGB) values, usually ranging from 0 to 255 for each channel.

Let's create a small, simple image: a 100x100 pixel black background with a 50x50 red square in the center.

import numpy as np
from PIL import Image

# Define image dimensions
height = 100
width = 100
channels = 3 # RGB

# Create a black background using a NumPy array filled with zeros
# Data type 'uint8' is standard for 8-bit pixel values (0-255)
image_array = np.zeros((height, width, channels), dtype=np.uint8)

# Define the coordinates for the red square
# Top-left corner (y1, x1) and bottom-right corner (y2, x2)
y1, x1 = 25, 25
y2, x2 = 75, 75

# Define the color red (R=255, G=0, B=0)
red_color = [255, 0, 0]

# Fill the square region with red
# Slicing format is [start_row:end_row, start_col:end_col]
image_array[y1:y2, x1:x2] = red_color

# Convert the NumPy array to a Pillow Image object
img = Image.fromarray(image_array, 'RGB')

# Save the image to a file
img.save('simple_square.png')

# Optionally, display the image (behavior might depend on your environment)
# img.show()

print("Generated image 'simple_square.png'")

After running this script, you'll find a file named simple_square.png in the same directory. It will contain the black image with the red square we defined. This demonstrates how programming allows direct control over pixel values to generate specific visual patterns.

Example 2: Adding Noise to an Image

Adding random noise is a common technique in image processing, often used to simulate sensor imperfections or as a basic form of data augmentation. Let's take the red square image we just created and add some simple "salt and pepper" noise. This type of noise randomly introduces white (salt) and black (pepper) pixels.

import numpy as np
from PIL import Image
import random

# Load the previously created image
try:
    img = Image.open('simple_square.png')
    image_array = np.array(img)
except FileNotFoundError:
    print("Error: 'simple_square.png' not found. Run Example 1 first.")
    exit()

# Define the amount of noise (proportion of pixels to affect)
noise_amount = 0.05 # Affect 5% of the pixels

# Get image dimensions
height, width, channels = image_array.shape

# Create a copy to modify
noisy_image_array = image_array.copy()

# Add salt noise (white pixels)
num_salt = int(noise_amount * height * width * 0.5)
for _ in range(num_salt):
    # Pick a random pixel location
    y = random.randint(0, height - 1)
    x = random.randint(0, width - 1)
    # Set pixel to white
    noisy_image_array[y, x] = [255, 255, 255]

# Add pepper noise (black pixels)
num_pepper = int(noise_amount * height * width * 0.5)
for _ in range(num_pepper):
    # Pick a random pixel location
    y = random.randint(0, height - 1)
    x = random.randint(0, width - 1)
    # Set pixel to black
    noisy_image_array[y, x] = [0, 0, 0]

# Convert the modified NumPy array back to a Pillow Image object
noisy_img = Image.fromarray(noisy_image_array, 'RGB')

# Save the noisy image
noisy_img.save('noisy_square.png')

# noisy_img.show()

print("Generated noisy image 'noisy_square.png'")

This script loads the simple_square.png, calculates the number of pixels to change based on noise_amount, and then randomly selects pixels, setting half to white and half to black. The result, saved as noisy_square.png, shows the original image speckled with random dots. This simulates a simple corruption process and generates a slightly different version of the original image.

Example 3: Basic Image Augmentation - Rotation

Image augmentation involves applying transformations to existing images to create slightly modified versions. This increases dataset variety. While sometimes used on real data, the same techniques can generate synthetic variations. Let's rotate our original square image.

import numpy as np
from PIL import Image

# Load the original image
try:
    img = Image.open('simple_square.png')
except FileNotFoundError:
    print("Error: 'simple_square.png' not found. Run Example 1 first.")
    exit()

# Define the rotation angle
angle = 45 # degrees

# Rotate the image using Pillow's rotate function
# 'expand=True' allows the image canvas to grow to fit the rotated image
rotated_img = img.rotate(angle, expand=True)

# Save the rotated image
rotated_img.save('rotated_square.png')

# rotated_img.show()

print("Generated rotated image 'rotated_square.png'")

Here, we used Pillow's built-in rotate method. The expand=True argument is useful because rotating a square image by 45 degrees makes it wider and taller; expand=True resizes the image boundaries to avoid cropping the corners. The output file rotated_square.png shows our red square tilted by 45 degrees.

Summary

In this practical session, you generated simple synthetic images using fundamental techniques:

1. Direct Pixel Manipulation: Creating an image from scratch by defining pixel values in a NumPy array.
2. Adding Noise: Modifying an existing image by introducing random pixel variations.
3. Basic Augmentation: Applying geometric transformations like rotation to generate variations.

These methods are foundational. Creating images that look truly realistic and diverse often requires much more sophisticated approaches, such as the rendering techniques mentioned earlier or advanced machine learning models like Generative Adversarial Networks (GANs) or Diffusion Models, which go further than this introductory course. However, understanding these basic building blocks provides a solid start for appreciating the process and challenges of synthetic image generation.