>>> 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 defocus 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, "Sharp Text", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
>>>
>>> # Example 1: Subtle defocus effect (small aperture)
>>> subtle_transform = A.Compose([
...     A.Defocus(
...         radius=(2, 3),           # Small defocus radius
...         alias_blur=(0.1, 0.2),   # Minimal aliasing
...         p=1.0                    # Always apply
...     )
... ])
>>>
>>> subtle_result = subtle_transform(image=image)
>>> subtle_defocus = subtle_result["image"]
>>> # The image will have a subtle defocus effect, with just slight blurring
>>>
>>> # Example 2: Moderate defocus effect (medium aperture)
>>> moderate_transform = A.Compose([
...     A.Defocus(
...         radius=(4, 6),           # Medium defocus radius
...         alias_blur=(0.2, 0.3),   # Moderate aliasing
...         p=1.0
...     )
... ])
>>>
>>> moderate_result = moderate_transform(image=image)
>>> moderate_defocus = moderate_result["image"]
>>> # The image will have a noticeable defocus effect, similar to a poorly focused camera
>>>
>>> # Example 3: Strong defocus effect (large aperture)
>>> strong_transform = A.Compose([
...     A.Defocus(
...         radius=(8, 12),          # Large defocus radius
...         alias_blur=(0.4, 0.6),   # Strong aliasing
...         p=1.0
...     )
... ])
>>>
>>> strong_result = strong_transform(image=image)
>>> strong_defocus = strong_result["image"]
>>> # The image will have a strong defocus effect, heavily blurring the details
>>>
>>> # Example 4: Using in a pipeline with other transforms
>>> pipeline = A.Compose([
...     A.RandomBrightnessContrast(brightness_limit=0.1, contrast_limit=0.1, p=0.7),
...     A.Defocus(radius=(3, 8), alias_blur=0.3, p=0.5),  # 50% chance of applying defocus
...     A.GaussNoise(var_limit=(10, 30), p=0.3)           # Possible noise after defocus
... ])
>>>
>>> pipeline_result = pipeline(image=image)
>>> transformed_image = pipeline_result["image"]
>>> # The image may have defocus blur applied with 50% probability
Parameters

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier! Saves Data!
