Implementing Guided Filter in Python with OpenCV and NumPy

python opencv Guided Filter

The Guided Filter is a popular edge-preserving smoothing filter used in image processing. It can be used for tasks such as noise reduction, detail enhancement, and image matting. OpenCV does not have a built-in function for the Guided Filter, but you can implement it using NumPy and OpenCV.

Here's a step-by-step guide to implementing the Guided Filter in Python using OpenCV and NumPy:

1. Install OpenCV and NumPy: If you haven't already, you can install these libraries using pip:

   pip install opencv-python-headless numpy
   

2. Implement the Guided Filter: Below is a Python implementation of the Guided Filter.

import cv2
import numpy as np

def guided_filter(I, p, r, eps):
    """
    Perform guided filtering.

    Parameters:
    I -- guidance image (should be a grayscale/single channel image)
    p -- input image to be filtered (should be a grayscale/single channel image)
    r -- radius of the window
    eps -- regularization parameter

    Returns:
    q -- filtered image
    """
    I = I.astype(np.float32)
    p = p.astype(np.float32)

    # Step 1: Compute the mean of I, p, I*p, and I*I
    mean_I = cv2.boxFilter(I, cv2.CV_32F, (r, r))
    mean_p = cv2.boxFilter(p, cv2.CV_32F, (r, r))
    mean_Ip = cv2.boxFilter(I * p, cv2.CV_32F, (r, r))
    mean_II = cv2.boxFilter(I * I, cv2.CV_32F, (r, r))

    # Step 2: Compute the covariance of (I, p) in each local patch
    cov_Ip = mean_Ip - mean_I * mean_p

    # Step 3: Compute the variance of I in each local patch
    var_I = mean_II - mean_I * mean_I

    # Step 4: Compute the linear coefficients a and b
    a = cov_Ip / (var_I + eps)
    b = mean_p - a * mean_I

    # Step 5: Compute the mean of a and b
    mean_a = cv2.boxFilter(a, cv2.CV_32F, (r, r))
    mean_b = cv2.boxFilter(b, cv2.CV_32F, (r, r))

    # Step 6: Compute the output image
    q = mean_a * I + mean_b

    return q

# Example usage
if __name__ == "__main__":
    # Load the input image
    input_image = cv2.imread('input.jpg', cv2.IMREAD_GRAYSCALE)
    guidance_image = input_image  # In this case, we use the input image as the guidance image

    # Parameters
    radius = 8
    epsilon = 0.01 ** 2

    # Apply the guided filter
    output_image = guided_filter(guidance_image, input_image, radius, epsilon)

    # Save the result
    cv2.imwrite('output.jpg', output_image)

Explanation:

1. Guidance Image (I): This is the image that guides the filtering process. It can be the same as the input image or a different image.
2. Input Image (p): This is the image to be filtered.
3. Radius (r): This defines the size of the local window used for filtering.
4. Epsilon (eps): This is a regularization parameter that prevents division by zero and controls the degree of smoothing.

Steps:

1. Compute the mean of the guidance image, input image, and their products over a local window.
2. Compute the covariance and variance within the local window.
3. Calculate the linear coefficients a and b.
4. Compute the mean of these coefficients.
5. Use these means to compute the output image.

This implementation should work for grayscale images. For color images, you would need to apply the filter to each channel separately.