>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # Create a sample image for demonstration
>>> image = np.zeros((300, 300, 3), dtype=np.uint8)
>>> # Add some shapes to visualize blur effects
>>> cv2.rectangle(image, (100, 100), (200, 200), (255, 0, 0), -1)  # Red square
>>> cv2.circle(image, (150, 150), 30, (0, 255, 0), -1)  # Green circle
>>> cv2.putText(image, "Text Example", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
>>> cv2.line(image, (50, 250), (250, 250), (0, 0, 255), 3)  # Blue line
>>>
>>> # Example 1: Gaussian-like blur (beta = 1)
>>> gaussian_like = A.Compose([
...     A.AdvancedBlur(
...         blur_limit=5,
...         sigma_x_limit=(0.5, 0.5),
...         sigma_y_limit=(0.5, 0.5),
...         rotate_limit=0,
...         beta_limit=(1.0, 1.0),  # Standard Gaussian (beta = 1)
...         noise_limit=(1.0, 1.0),  # No noise
...         p=1.0
...     )
... ])
>>>
>>> gaussian_result = gaussian_like(image=image)
>>> gaussian_image = gaussian_result["image"]
>>> # The image will have a standard Gaussian blur applied
>>>
>>> # Example 2: Box-like blur (beta < 1)
>>> box_like = A.Compose([
...     A.AdvancedBlur(
...         blur_limit=(7, 9),
...         sigma_x_limit=(0.6, 0.8),
...         sigma_y_limit=(0.6, 0.8),
...         rotate_limit=0,
...         beta_limit=(0.5, 0.7),  # Box-like blur (beta < 1)
...         noise_limit=(0.9, 1.1),  # Slight noise
...         p=1.0
...     )
... ])
>>>
>>> box_result = box_like(image=image)
>>> box_image = box_result["image"]
>>> # The image will have a more box-like blur with heavier tails
>>>
>>> # Example 3: Peaked blur (beta > 1)
>>> peaked = A.Compose([
...     A.AdvancedBlur(
...         blur_limit=(7, 9),
...         sigma_x_limit=(0.6, 0.8),
...         sigma_y_limit=(0.6, 0.8),
...         rotate_limit=0,
...         beta_limit=(3.0, 6.0),  # Peaked blur (beta > 1)
...         noise_limit=(0.9, 1.1),  # Slight noise
...         p=1.0
...     )
... ])
>>>
>>> peaked_result = peaked(image=image)
>>> peaked_image = peaked_result["image"]
>>> # The image will have a more focused, peaked blur with lighter tails
>>>
>>> # Example 4: Anisotropic blur (directional)
>>> directional = A.Compose([
...     A.AdvancedBlur(
...         blur_limit=(9, 11),
...         sigma_x_limit=(0.8, 1.0),    # Stronger x blur
...         sigma_y_limit=(0.2, 0.3),    # Weaker y blur
...         rotate_limit=(0, 0),         # No rotation
...         beta_limit=(1.0, 2.0),
...         noise_limit=(0.9, 1.1),
...         p=1.0
...     )
... ])
>>>
>>> directional_result = directional(image=image)
>>> directional_image = directional_result["image"]
>>> # The image will have a horizontal directional blur
>>>
>>> # Example 5: Rotated directional blur
>>> rotated = A.Compose([
...     A.AdvancedBlur(
...         blur_limit=(9, 11),
...         sigma_x_limit=(0.8, 1.0),    # Stronger x blur
...         sigma_y_limit=(0.2, 0.3),    # Weaker y blur
...         rotate_limit=(45, 45),       # 45 degree rotation
...         beta_limit=(1.0, 2.0),
...         noise_limit=(0.9, 1.1),
...         p=1.0
...     )
... ])
>>>
>>> rotated_result = rotated(image=image)
>>> rotated_image = rotated_result["image"]
>>> # The image will have a diagonal directional blur
>>>
>>> # Example 6: Noisy blur
>>> noisy = A.Compose([
...     A.AdvancedBlur(
...         blur_limit=(5, 7),
...         sigma_x_limit=(0.4, 0.6),
...         sigma_y_limit=(0.4, 0.6),
...         rotate_limit=(-30, 30),
...         beta_limit=(0.8, 1.2),
...         noise_limit=(0.7, 1.3),      # Strong noise variation
...         p=1.0
...     )
... ])
>>>
>>> noisy_result = noisy(image=image)
>>> noisy_image = noisy_result["image"]
>>> # The image will have blur with significant noise in the kernel
>>>
>>> # Example 7: Random parameters (for general augmentation)
>>> random_blur = A.Compose([
...     A.AdvancedBlur(p=0.5)  # Using default parameter ranges
... ])
>>>
>>> random_result = random_blur(image=image)
>>> random_image = random_result["image"]
>>> # The image may have a random advanced blur applied with 50% probability
Parameters
