Why image processing transforms visual data in SciPy - See It in Action

Why Image Processing Transforms Visual Data

📖 Scenario: Imagine you have a photo taken on a cloudy day. The colors look dull and details are hard to see. Image processing helps us change the photo to make it clearer and more colorful. This is useful in many areas like photography, medical imaging, and security cameras.

🎯 Goal: You will learn how to load an image, apply a simple transformation to change its brightness, and then see the result. This shows how image processing changes visual data to make it easier to understand or more useful.

📋 What You'll Learn

Use the scipy library to load and manipulate images

Create a variable to hold the image data

Create a variable to control brightness change

Apply the brightness change to the image data

Display the original and transformed images

💡 Why This Matters

🌍 Real World

Image processing helps improve photos and videos by adjusting brightness, contrast, and other features to make details clearer or to highlight important parts.

💼 Career

Understanding basic image transformations is useful for jobs in photography, medical imaging, computer vision, and security systems where visual data needs to be enhanced or analyzed.

1

Load the image data

Write code to load the image file named 'face.png' using scipy.ndimage.imread and store it in a variable called image.

SciPy

# Load the image 'face.png' into a variable called image
# Your code here

Need a hint?

Use imread('face.png') from the imageio library to read the image file into the variable image.

2

Set brightness adjustment value

Create a variable called brightness_change and set it to 50. This will control how much brighter the image becomes.

SciPy

from imageio import imread

image = imread('face.png')

# Set brightness_change to 50
# Your code here

Need a hint?

Just write brightness_change = 50 below the image loading code.

3

Apply brightness change to the image

Create a new variable called brighter_image by adding brightness_change to image. Use numpy.clip to keep values between 0 and 255.

SciPy

from imageio import imread
import numpy as np

image = imread('face.png')

brightness_change = 50

# Add brightness_change to image and clip values between 0 and 255
# Your code here

Need a hint?

Use np.clip(image + brightness_change, 0, 255) to keep pixel values valid.

4

Display the original and brighter images

Use matplotlib.pyplot to show the original image and the brighter_image side by side with titles 'Original' and 'Brighter'.

SciPy

from imageio import imread
import numpy as np
import matplotlib.pyplot as plt

image = imread('face.png')

brightness_change = 50

brighter_image = np.clip(image + brightness_change, 0, 255).astype(np.uint8)

# Display original and brighter images side by side
# Your code here

Need a hint?

Use plt.subplot to create two images side by side and plt.imshow to show them.