From 266d889f3002ad445ae57961af436e3a47e8c53a Mon Sep 17 00:00:00 2001
From: LuisMelendez <luiguimelendez5@gmail.com>
Date: Thu, 2 Oct 2025 20:33:10 -0600
Subject: [PATCH 1/2] Create gaussian_blur.py

---
 computer_vision/gaussian_blur.py | 59 ++++++++++++++++++++++++++++++++
 1 file changed, 59 insertions(+)
 create mode 100644 computer_vision/gaussian_blur.py

diff --git a/computer_vision/gaussian_blur.py b/computer_vision/gaussian_blur.py
new file mode 100644
index 000000000000..81e2f5f9b12c
--- /dev/null
+++ b/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)

From ed18f851db3e8af02812688f4520adad0e081b91 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 3 Oct 2025 02:35:43 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 computer_vision/gaussian_blur.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/computer_vision/gaussian_blur.py b/computer_vision/gaussian_blur.py
index 81e2f5f9b12c..4d30c71b2e48 100644
--- a/computer_vision/gaussian_blur.py
+++ b/computer_vision/gaussian_blur.py
@@ -1,20 +1,22 @@
 import math
 import copy
-'''Mean thresholding algorithm for image processing  
+
+"""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]
+    [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)]
+    kernel = [[0] * size for _ in range(size)]
     center = size // 2
     s = 2 * sigma * sigma
     sum_val = 0
@@ -22,7 +24,7 @@ def gaussian_kernel(size, sigma=1):
     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)
+            kernel[i][j] = math.exp(-(x * x + y * y) / s)
             sum_val += kernel[i][j]
 
     # Normalizar
@@ -32,6 +34,7 @@ def gaussian_kernel(size, sigma=1):
 
     return kernel
 
+
 def apply_gaussian_blur(image, kernel):
     """Aplica el blur gaussiano a una imagen"""
     height = len(image)
@@ -52,6 +55,7 @@ def apply_gaussian_blur(image, kernel):
             new_image[i][j] = int(val)
     return new_image
 
+
 kernel = gaussian_kernel(3, sigma=1)
 blurred_image = apply_gaussian_blur(image, kernel)