>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # Create sample images for demonstration
>>> # Source image: simulated image from domain A (e.g., medical scan from one scanner)
>>> source_image = np.random.normal(100, 20, (100, 100, 3)).clip(0, 255).astype(np.uint8)
>>>
>>> # Reference image: image from domain B with different statistical properties
>>> # (e.g., scan from a different scanner with different intensity distribution)
>>> reference_image = np.random.normal(150, 30, (100, 100, 3)).clip(0, 255).astype(np.uint8)
>>>
>>> # Example 1: Using PCA transformation (default)
>>> pca_transform = A.Compose([
...     A.PixelDistributionAdaptation(
...         transform_type="pca",
...         blend_ratio=(0.8, 1.0),  # Strong adaptation
...         metadata_key="reference_images",
...         p=1.0
...     )
... ])
>>>
>>> # Apply the transform with the reference image
>>> pca_result = pca_transform(
...     image=source_image,
...     reference_images=[reference_image]
... )
>>>
>>> # Get the adapted image
>>> pca_adapted_image = pca_result["image"]
>>>
>>> # Example 2: Using StandardScaler transformation
>>> standard_transform = A.Compose([
...     A.PixelDistributionAdaptation(
...         transform_type="standard",
...         blend_ratio=(0.5, 0.7),  # Moderate adaptation
...         metadata_key="reference_images",
...         p=1.0
...     )
... ])
>>>
>>> standard_result = standard_transform(
...     image=source_image,
...     reference_images=[reference_image]
... )
>>> standard_adapted_image = standard_result["image"]
>>>
>>> # Example 3: Using MinMaxScaler transformation
>>> minmax_transform = A.Compose([
...     A.PixelDistributionAdaptation(
...         transform_type="minmax",
...         blend_ratio=(0.3, 0.5),  # Subtle adaptation
...         metadata_key="reference_images",
...         p=1.0
...     )
... ])
>>>
>>> minmax_result = minmax_transform(
...     image=source_image,
...     reference_images=[reference_image]
... )
>>> minmax_adapted_image = minmax_result["image"]
>>>
>>> # Example 4: Using multiple reference images
>>> # When multiple reference images are provided, one is randomly selected for each transformation
>>> multiple_references = [
...     reference_image,
...     np.random.normal(180, 25, (100, 100, 3)).clip(0, 255).astype(np.uint8),
...     np.random.normal(120, 40, (100, 100, 3)).clip(0, 255).astype(np.uint8)
... ]
>>>
>>> multi_ref_transform = A.Compose([
...     A.PixelDistributionAdaptation(p=1.0)  # Using default settings
... ])
>>>
>>> # Each time the transform is applied, it randomly selects one of the reference images
>>> multi_ref_result = multi_ref_transform(
...     image=source_image,
...     pda_metadata=multiple_references  # Using the default metadata key
... )
>>> adapted_image = multi_ref_result["image"]
Parameters
