Patch Sampling Strategies

class PatchSampler:
    """
    Base class for patch sampling strategies.
    
    Parameters:
    - patch_size: Size of patches to extract (int or tuple of 3 ints)
    """
    def __init__(self, patch_size: TypeSpatialShape): ...
    
    def __call__(self, sample: Subject) -> dict:
        """
        Extract patch from subject.
        
        Parameters:
        - sample: Subject to sample from
        
        Returns:
        Dictionary with patch data and location information
        """
    
    @property
    def patch_size(self) -> tuple[int, int, int]:
        """Get patch size as tuple"""

class UniformSampler(PatchSampler):
    """
    Random uniform patch sampling.
    
    Extracts patches from random locations with equal probability.
    Useful for general training when no specific region needs emphasis.
    
    Parameters:
    - patch_size: Size of patches to extract
    """
    def __init__(self, patch_size: TypeSpatialShape): ...

import torchio as tio

# Create uniform sampler for 64x64x64 patches
sampler = tio.data.UniformSampler(patch_size=64)

# Create subject
subject = tio.Subject(
    t1=tio.ScalarImage('t1.nii.gz'),
    seg=tio.LabelMap('segmentation.nii.gz')
)

# Extract patch
patch = sampler(subject)

# Access patch data
t1_patch = patch['t1'][tio.DATA]  # Shape: (1, 64, 64, 64)
seg_patch = patch['seg'][tio.DATA]  # Shape: (1, 64, 64, 64)
location = patch[tio.LOCATION]  # Patch location in original image

class WeightedSampler(PatchSampler):
    """
    Weighted random patch sampling based on probability maps.
    
    Extracts patches with probability proportional to values in 
    probability map. Useful for focusing sampling on regions of interest.
    
    Parameters:
    - patch_size: Size of patches to extract
    - probability_map: Name of image to use as probability map
    """
    def __init__(
        self,
        patch_size: TypeSpatialShape,
        probability_map: str
    ): ...

# Create probability map (higher values = higher sampling probability)
probability_map = tio.ScalarImage('probability_map.nii.gz')

subject = tio.Subject(
    t1=tio.ScalarImage('t1.nii.gz'),
    probability_map=probability_map
)

# Create weighted sampler
sampler = tio.data.WeightedSampler(
    patch_size=64,
    probability_map='probability_map'
)

# Extract patch (more likely from high-probability regions)
patch = sampler(subject)

class LabelSampler(WeightedSampler):
    """
    Patch sampling focused on specific labels.
    
    Extracts patches that contain specified labels with configurable
    probabilities. Automatically creates probability map from label image.
    
    Parameters:
    - patch_size: Size of patches to extract
    - label_name: Name of label image in subject
    - label_probabilities: Dict mapping label values to sampling probabilities
    """
    def __init__(
        self,
        patch_size: TypeSpatialShape,
        label_name: str,
        label_probabilities: dict = None
    ): ...

# Define sampling probabilities for different labels
label_probs = {
    0: 0.1,   # Background: low probability
    1: 0.8,   # Tumor: high probability
    2: 0.4,   # Edema: medium probability
    3: 0.6,   # Necrosis: medium-high probability
}

subject = tio.Subject(
    t1=tio.ScalarImage('t1.nii.gz'),
    seg=tio.LabelMap('tumor_segmentation.nii.gz')
)

# Create label sampler
sampler = tio.data.LabelSampler(
    patch_size=64,
    label_name='seg',
    label_probabilities=label_probs
)

# Extract patch (more likely to contain tumor regions)
patch = sampler(subject)

class GridSampler(PatchSampler):
    """
    Regular grid-based patch sampling for inference.
    
    Extracts patches in a regular grid pattern to ensure complete
    volume coverage. Typically used for inference rather than training.
    
    Parameters:
    - patch_size: Size of patches to extract
    - patch_overlap: Overlap between adjacent patches (int or tuple)
    """
    def __init__(
        self,
        patch_size: TypeSpatialShape,
        patch_overlap: TypeSpatialShape = 0
    ): ...
    
    def __iter__(self):
        """Iterate through all patches in grid"""
    
    @property
    def num_patches(self) -> int:
        """Total number of patches in grid"""

subject = tio.Subject(
    t1=tio.ScalarImage('t1.nii.gz')
)

# Create grid sampler with 50% overlap
sampler = tio.data.GridSampler(
    patch_size=64,
    patch_overlap=32  # 50% overlap
)

# Extract all patches systematically
all_patches = []
for patch in sampler(subject):
    all_patches.append(patch)

print(f"Total patches: {len(all_patches)}")

class GridAggregator:
    """
    Aggregates predictions from grid-sampled patches.
    
    Reconstructs full-volume predictions from overlapping patches,
    handling various aggregation strategies for overlapping regions.
    
    Parameters:
    - sampler: GridSampler used to extract patches
    - overlap_mode: How to handle overlapping regions ('crop', 'average', 'hann')
    """
    def __init__(
        self,
        sampler: GridSampler,
        overlap_mode: str = 'crop'
    ): ...
    
    def add_batch(
        self,
        batch_tensor: torch.Tensor,
        batch_locations: torch.Tensor
    ):
        """Add batch of predictions to aggregator"""
    
    def get_output_tensor(self) -> torch.Tensor:
        """Get aggregated full-volume prediction"""

import torch
import torchio as tio

# Setup for inference
subject = tio.Subject(t1=tio.ScalarImage('t1.nii.gz'))
patch_size = 64
patch_overlap = 16

# Create grid sampler and aggregator
grid_sampler = tio.data.GridSampler(
    patch_size=patch_size,
    patch_overlap=patch_overlap
)
aggregator = tio.data.GridAggregator(
    sampler=grid_sampler,
    overlap_mode='average'
)

# Model inference on patches
model = load_your_model()
model.eval()

with torch.no_grad():
    for patch in grid_sampler(subject):
        # Get patch data and location
        patch_tensor = patch['t1'][tio.DATA].unsqueeze(0)  # Add batch dim
        location = patch[tio.LOCATION]
        
        # Run inference
        prediction = model(patch_tensor)
        
        # Add to aggregator
        aggregator.add_batch(prediction, location.unsqueeze(0))

# Get full-volume prediction
full_prediction = aggregator.get_output_tensor()
print(f"Full prediction shape: {full_prediction.shape}")

def get_batch_images_and_size(batch: dict) -> tuple[list[str], int]:
    """Extract image names and batch size from patch batch"""

def parse_spatial_shape(shape) -> tuple[int, int, int]:
    """Parse spatial shape specification into 3D tuple"""