>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # Create sample images for demonstration
>>> # Source image: synthetic or simulated image (e.g., from a rendered game environment)
>>> source_img = np.zeros((100, 100, 3), dtype=np.uint8)
>>> # Create a pattern in the source image
>>> source_img[20:80, 20:80, 0] = 200  # Red square
>>> source_img[40:60, 40:60, 1] = 200  # Green inner square
>>>
>>> # Target domain image: real-world image with different texture/frequency characteristics
>>> # For this example, we'll create an image with different frequency patterns
>>> target_img = np.zeros((100, 100, 3), dtype=np.uint8)
>>> for i in range(100):
...     for j in range(100):
...         # Create a high-frequency pattern
...         target_img[i, j, 0] = ((i + j) % 8) * 30
...         target_img[i, j, 1] = ((i - j) % 8) * 30
...         target_img[i, j, 2] = ((i * j) % 8) * 30
>>>
>>> # Example 1: FDA with minimal adaptation (small beta value)
>>> # This will subtly adjust the frequency characteristics
>>> minimal_fda = A.Compose([
...     A.FDA(
...         beta_limit=(0.01, 0.05),  # Small beta range for subtle adaptation
...         metadata_key="target_domain",  # Custom metadata key
...         p=1.0
...     )
... ])
>>>
>>> # Apply the transform with minimal adaptation
>>> minimal_result = minimal_fda(
...     image=source_img,
...     target_domain=[target_img]  # Pass reference image via custom metadata key
... )
>>> minimal_adapted_img = minimal_result["image"]
>>>
>>> # Example 2: FDA with moderate adaptation (medium beta value)
>>> moderate_fda = A.Compose([
...     A.FDA(
...         beta_limit=(0.1, 0.2),  # Medium beta range
...         metadata_key="target_domain",
...         p=1.0
...     )
... ])
>>>
>>> moderate_result = moderate_fda(image=source_img, target_domain=[target_img])
>>> moderate_adapted_img = moderate_result["image"]
>>>
>>> # Example 3: FDA with strong adaptation (larger beta value)
>>> strong_fda = A.Compose([
...     A.FDA(
...         beta_limit=(0.3, 0.5),  # Larger beta range (upper limit is MAX_BETA_LIMIT)
...         metadata_key="target_domain",
...         p=1.0
...     )
... ])
>>>
>>> strong_result = strong_fda(image=source_img, target_domain=[target_img])
>>> strong_adapted_img = strong_result["image"]
>>>
>>> # Example 4: Using multiple target domain images
>>> # Creating a list of target domain images with different characteristics
>>> target_imgs = [target_img]
>>>
>>> # Add another target image with different pattern
>>> another_target = np.zeros((100, 100, 3), dtype=np.uint8)
>>> for i in range(100):
...     for j in range(100):
...         another_target[i, j, 0] = (i // 10) * 25
...         another_target[i, j, 1] = (j // 10) * 25
...         another_target[i, j, 2] = ((i + j) // 10) * 25
>>> target_imgs.append(another_target)
>>>
>>> # Using default FDA settings with multiple target images
>>> multi_target_fda = A.Compose([
...     A.FDA(p=1.0)  # Using default settings with default metadata_key="fda_metadata"
... ])
>>>
>>> # A random target image will be selected from the list for each application
>>> multi_target_result = multi_target_fda(image=source_img, fda_metadata=target_imgs)
>>> adapted_image = multi_target_result["image"]
Parameters

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