Digital_Image_Fundamentals_Cat_Image

This code utilizes various image processing techniques using libraries such as PIL, Matplotlib, NumPy, and scikit-image. Let's break down the functionalities provided by each section:

1. Image Display and Histogram:

   • The code loads an image (Cat.jpeg), displays it using Matplotlib, and then plots the color histogram of the image.

2. Edge Detection:

   • The code performs edge detection on the loaded image using the Sobel filter from scikit-image.

3. Image Rotation:

   • The code rotates the image by 180 and 90 degrees and displays the results.

4. Image Metadata:

   • Extracts metadata from the image using the _getexif() method.

5. 3D Surface Plot:

   • Converts the grayscale image to a 3D surface plot.

6. Visual Intensity Array:

   • Displays the image as a visual intensity array.

7. Normalization:

   • Normalizes the pixel values of the grayscale image to be between 0 and 1.

8. Image Saturation and Noise:

   • Creates an image with saturation and noise.

9. Storage Calculation:

   • Calculates the storage required for images with different dimensions and bit depths.

10. Conversion of Coordinates:

    • Converts 2D coordinates to a linear index for column-major order.

11. Image Resizing:

    • Resizes the image to half its original dimensions.

12. Reducing Intensity Resolution:

    • Reduces the intensity resolution of the image.

13. Reducing Intensity Levels:

    • Reduces the number of intensity levels in the image.

14. Isopreference Curves:

    • Plots isopreference curves for different image types based on spatial and intensity resolutions.

Each section of the code demonstrates a specific aspect of image processing, including visualization, manipulation, and analysis.