Create gaussian_blur.py

Author: LuisOfL

computer_vision/gaussian_blur.py

@@ -0,0 +1,59 @@
+import math
+import copy
+'''Mean thresholding algorithm for image processing  
+[More info on Wikipedia](https://en.wikipedia.org/wiki/Thresholding_(image_processing))
+'''
+# Imagen de ejemplo: matriz 5x5
+image = [
+    [10, 20, 30, 40, 50],
+    [20, 30, 40, 50, 60],
+    [30, 40, 50, 60, 70],
+    [40, 50, 60, 70, 80],
+    [50, 60, 70, 80, 90]
+]
+
+def gaussian_kernel(size, sigma=1):
+    """Genera un kernel gaussiano de tamaño 'size' y desviación 'sigma'"""
+    kernel = [[0]*size for _ in range(size)]
+    center = size // 2
+    s = 2 * sigma * sigma
+    sum_val = 0
+
+    for i in range(size):
+        for j in range(size):
+            x, y = i - center, j - center
+            kernel[i][j] = math.exp(-(x*x + y*y)/s)
+            sum_val += kernel[i][j]
+
+    # Normalizar
+    for i in range(size):
+        for j in range(size):
+            kernel[i][j] /= sum_val
+
+    return kernel
+
+def apply_gaussian_blur(image, kernel):
+    """Aplica el blur gaussiano a una imagen"""
+    height = len(image)
+    width = len(image[0])
+    k_size = len(kernel)
+    k_center = k_size // 2
+    new_image = copy.deepcopy(image)
+
+    for i in range(height):
+        for j in range(width):
+            val = 0
+            for ki in range(k_size):
+                for kj in range(k_size):
+                    ni = i + ki - k_center
+                    nj = j + kj - k_center
+                    if 0 <= ni < height and 0 <= nj < width:
+                        val += image[ni][nj] * kernel[ki][kj]
+            new_image[i][j] = int(val)
+    return new_image
+
+kernel = gaussian_kernel(3, sigma=1)
+blurred_image = apply_gaussian_blur(image, kernel)
+
+for row in blurred_image:
+    print(row)