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Image Processing with OpenCV

This repository contains various image processing techniques implemented using OpenCV and Matplotlib in Python. The techniques include pixel value extraction, image rotation, resizing, cropping, blurring, edge detection, and more.

Requirements

• Python 3.x
• OpenCV
• NumPy
• Matplotlib

You can install the necessary libraries using pip:

pip install opencv-python-headless numpy matplotlib

1. Extracting Pixel Values and Color Space

   • Load and display an image.
   • Print pixel values and color space (BGR).

2. Rotating the Image

   • Rotate the image by 90 degrees.
   • Rotate the image with new dimensions while keeping the image center.

3. Resizing the Image

   • Resize the image to a specified size.

4. Cropping the Image

   • Crop a specific region of the image.

5. Blurring the Image

   • Apply a blur effect using a 5x5 kernel.

6. Converting the Image to Grayscale and Detecting Edges

   • Convert the image to grayscale.
   • Detect edges using the Canny edge detection algorithm.

7. Fourier Transform

   • Compute the Fourier transform of the image.
   • Display the magnitude spectrum.

8. Hough Transform for Line Detection

   • Perform edge detection and detect lines using the Hough transform.

9. Hough Transform for Circle Detection

   • Detect circles in the image using the Hough transform.

10. Morphological Transformations

    • Perform dilation and erosion on the image.

11. Thresholding

    • Apply binary thresholding to the image.

Usage

To run the scripts, use the following command:

python image_processing_with_opencv.py

License

This project is licensed under the MIT License - see the LICENSE file for details. **