Refactor filters.py to add sketch effect and improve pixelization

- Added sketch effect filter
- Updated pixelize function formatting
- Improved dot effect filter spacing
- Ensured odd kernel size for blur
- Cleaned up code formatting and comments

filters.py

@@ -2,10 +2,12 @@ import cv2
 import numpy as np
 from registry import registry
 
+
 @registry.register("Original")
 def original(image):
     return image
 
+
 @registry.register("Dot Effect", defaults={
     "dot_size": 10,
     "dot_spacing": 2,
@@ -78,6 +80,7 @@ def dot_effect(image, dot_size: int = 10, dot_spacing: int = 2, invert: bool = F
 
     return canvas
 
+
 @registry.register("Pixelize", defaults={
     "pixel_size": 10,
 }, min_vals={
@@ -103,39 +106,54 @@ def pixelize(image, pixel_size: int = 10):
     # Resize the image to a smaller size
     small_height = height // pixel_size
     small_width = width // pixel_size
-    small_image = cv2.resize(image, (small_width, small_height), interpolation=cv2.INTER_LINEAR)
+    small_image = cv2.resize(
+        image, (small_width, small_height), interpolation=cv2.INTER_LINEAR)
 
     # Resize back to the original size with nearest neighbor interpolation
-    pixelized_image = cv2.resize(small_image, (width, height), interpolation=cv2.INTER_NEAREST)
+    pixelized_image = cv2.resize(
+        small_image, (width, height), interpolation=cv2.INTER_NEAREST)
 
     return pixelized_image
 
-@registry.register("Motion Blur", defaults={
-    "kernel_size": 10,
+
+@registry.register("Sketch Effect", defaults={
+    "blur_kernel_size": 21,
 }, min_vals={
-    "kernel_size": 1,
+    "blur_kernel_size": 1,
 }, max_vals={
-    "kernel_size": 50,
+    "blur_kernel_size": 51,
 }, step_vals={
-    "kernel_size": 1,
+    "blur_kernel_size": 2,
 })
-def motion_blur(image, kernel_size: int = 10):
+def sketch_effect(image, blur_kernel_size: int = 21):
     """
-    ## Apply a motion blur effect to the image.
+    ## Apply a sketch effect to the image.
 
     **Args:**
     * `image` (numpy.ndarray): Input image (BGR or grayscale)
-    * `kernel_size` (int): Size of the kernel
+    * `blur_kernel_size` (int): Size of the Gaussian blur kernel (must be odd)
 
     **Returns:**
-    * `numpy.ndarray`: Motion blurred image
+    * `numpy.ndarray`: Sketch effect applied image
     """
-    # Create a horizontal kernel
-    kernel = np.zeros((kernel_size, kernel_size))
-    kernel[kernel_size//2, :] = np.ones(kernel_size)
-    kernel = kernel / kernel_size
+    # Ensure the kernel size is odd
+    if blur_kernel_size % 2 == 0:
+        blur_kernel_size += 1
+
+    # Convert the image to grayscale
+    if len(image.shape) == 3:
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    else:
+        gray = image
+
+    # Invert the grayscale image
+    inverted_gray = cv2.bitwise_not(gray)
+
+    # Apply Gaussian blur to the inverted image
+    blurred = cv2.GaussianBlur(
+        inverted_gray, (blur_kernel_size, blur_kernel_size), 0)
 
-    # Apply the kernel to the image
-    motion_blurred_image = cv2.filter2D(image, -1, kernel)
+    # Blend the grayscale image with the blurred inverted image
+    sketch = cv2.divide(gray, 255 - blurred, scale=256)
 
-    return motion_blurred_image
+    return sketch