Blur Filters

import cv2
import numpy as np

image = cv2.imread("photo.jpg")

# Average Blur: simple mean of neighbourhood pixels
# Fast, but does not respect edges
avg = cv2.blur(image, (5, 5))

# Gaussian Blur: weighted average — centre pixels matter more
# Most commonly used. Good general-purpose pre-processing
gauss = cv2.GaussianBlur(image, (5, 5), sigmaX=0)

# Median Blur: replaces each pixel with the neighbourhood median
# Best for salt-and-pepper noise. Preserves edges well.
median = cv2.medianBlur(image, 5)

# Bilateral Filter: smooths while preserving sharp edges
# Use when you want smoothing without destroying contours
bilateral = cv2.bilateralFilter(image, d=9, sigmaColor=75, sigmaSpace=75)

# Box Filter: similar to average blur but more control
box = cv2.boxFilter(image, ddepth=-1, ksize=(5, 5))

# Print a comparison note
print("Blur comparison (all applied to same 5x5 neighbourhood):")
print("  Average:   Fast, blurs everything uniformly")
print("  Gaussian:  Weighted, more natural look")
print("  Median:    Best for noise, preserves edges")
print("  Bilateral: Slowest, best edge preservation")

# Save for visual comparison
for name, result in [("avg", avg), ("gauss", gauss),
                     ("median", median), ("bilateral", bilateral)]:
    cv2.imwrite(f"blur_{name}.jpg", result)