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Handling Image Data

Handling Image Data

Explore questions about handling image data as they can be crucial for interview preparation in data science and machine learning roles.

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Image data is everywhere—whether it's medical scans, social media photos, or product images in e-commerce. Knowing how to process, filter, and augment this kind of data is a key skill for data scientists and machine learning engineers. In this lesson, we’ll build a Gaussian blur filter from scratch and apply common data augmentations in Python. Let’s get started.

Implement a simple Gaussian filter

You’re working on an image preprocessing pipeline for a computer vision application. One of the tasks involves blurring images using a Gaussian filter to reduce noise and improve feature extraction downstream.

In this challenge, you’re asked to implement a 2D Gaussian kernel from scratch using NumPy—no high-level libraries like OpenCV or SciPy are allowed.

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Python 3.10.4
import numpy as np

import matplotlib.pyplot as plt



def create_gaussian_kernel(size, sigma):

    """

    Create a 2D Gaussian kernel.

    

    Args:

        size: Kernel size (should be odd)

        sigma: Standard deviation of Gaussian distribution

    

    Returns:

        2D numpy array containing the Gaussian kernel

    """

    return kernel



def apply_gaussian_filter(image, kernel_size=5, sigma=1.0):

    """

    Apply Gaussian filter to an image.

    

    Args:

        image: Input image (2D numpy array)

        kernel_size: Size of the Gaussian kernel

        sigma: Standard deviation of Gaussian distribution

    

    Returns:

        Filtered image

    """

    # TODO - Create kernel

    # TODO - Pad image

    # TODO - Create output image

    # TODO - Apply convolution

    

    return filtered_image



# Example usage

if __name__ == "__main__":

    # Create a sample image (100x100 with a white square in the middle)

    image = np.zeros((100, 100))

    image[40:60, 40:60] = 1.0

    # Add some noise

    noisy_image = image + np.random.normal(0, 0.1, image.shape)

    # Apply Gaussian filter

    filtered_image = apply_gaussian_filter(noisy_image, kernel_size=5, sigma=1.0)

    

# Plot results if matplotlib is available

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

ax1.imshow(noisy_image, cmap='gray')

ax1.set_title('Noisy Image')

ax2.imshow(filtered_image, cmap='gray')

ax2.set_title('Filtered Image')

plt.savefig('output/graph.png')

Sample answer

Here’s how we can proceed with this:

  1. Define the grid: Start by creating a square grid centered around 0. For a k x k kernel, use np.linspace or np.arange to form the x and y axes.

  2. Apply the Gaussian formula: Use the 2D Gaussian function and vectorize this using NumPy to apply it across the meshgrid.

  3. Normalize the kernel: Ensure that the sum of all weights in the kernel is ...