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"""
Modules for roof segmentation.
"""
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple
import albumentations as A
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
from albumentations.pytorch import ToTensorV2
from torch.utils.data import Dataset
class DoubleConv(nn.Module):
"""Double convolution block: (conv => BN => ReLU) * 2"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.double_conv(x)
class Down(nn.Module):
"""Downscaling with maxpool then double conv"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.maxpool_conv = nn.Sequential(
nn.MaxPool2d(2),
DoubleConv(in_channels, out_channels)
)
def forward(self, x):
return self.maxpool_conv(x)
class Up(nn.Module):
"""Upscaling then double conv"""
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
# Use bilinear upsampling or transpose convolution
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels)
else:
self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv = DoubleConv(in_channels, out_channels)
def forward(self, x1, x2):
x1 = self.up(x1)
# Input is CHW
diffY = x2.size()[2] - x1.size()[2]
diffX = x2.size()[3] - x1.size()[3]
x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
diffY // 2, diffY - diffY // 2])
# Concatenate along channel dimension
x = torch.cat([x2, x1], dim=1)
return self.conv(x)
class OutConv(nn.Module):
"""Output convolution"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def forward(self, x):
return self.conv(x)
class UNet(nn.Module):
"""Simple U-Net implementation with configurable base channels"""
def __init__(self, n_channels=3, n_classes=1, base_channels=32, bilinear=True):
super().__init__()
self.n_channels = n_channels
self.n_classes = n_classes
self.bilinear = bilinear
# Use configurable base channels (default 32 instead of 64)
c = base_channels
# Encoder (downsampling path)
self.inc = DoubleConv(n_channels, c)
self.down1 = Down(c, c*2)
self.down2 = Down(c*2, c*4)
self.down3 = Down(c*4, c*8)
factor = 2 if bilinear else 1
self.down4 = Down(c*8, c*16 // factor)
# Decoder (upsampling path)
self.up1 = Up(c*16, c*8 // factor, bilinear)
self.up2 = Up(c*8, c*4 // factor, bilinear)
self.up3 = Up(c*4, c*2 // factor, bilinear)
self.up4 = Up(c*2, c, bilinear)
self.outc = OutConv(c, n_classes)
def forward(self, x):
# Encoder
x1 = self.inc(x)
x2 = self.down1(x1)
x3 = self.down2(x2)
x4 = self.down3(x3)
x5 = self.down4(x4)
# Decoder with skip connections
x = self.up1(x5, x4)
x = self.up2(x, x3)
x = self.up3(x, x2)
x = self.up4(x, x1)
logits = self.outc(x)
return logits
def get_unet_model(n_channels=3, n_classes=1, base_channels=32, bilinear=True):
"""
Create a U-Net model.
Args:
n_channels: Number of input channels
n_classes: Number of output classes
base_channels: Base number of channels (32 = lighter, 64 = standard)
bilinear: Use bilinear upsampling
"""
return UNet(n_channels=n_channels, n_classes=n_classes, base_channels=base_channels, bilinear=bilinear)
class SegmentationLightningModule(pl.LightningModule):
def __init__(self, config: Dict[str, Any]):
super().__init__()
self.model = get_unet_model(
n_channels=config["in_channels"],
n_classes=config["classes"],
base_channels=config.get("base_channels", 32),
bilinear=config.get("bilinear", True)
)
def forward(self, x):
return self.model(x)
class RoofSegmentationDataset(Dataset):
"""Dataset for roof segmentation with images and masks."""
def __init__(
self,
images_dir: Path,
masks_dir: Path,
transform: Optional[Callable] = None,
image_size: Tuple[int, int] = (512, 512)
):
"""
Args:
images_dir: Directory containing input images
masks_dir: Directory containing segmentation masks
transform: Albumentations transforms to apply
image_size: Target size for images (height, width)
"""
self.images_dir = Path(images_dir)
self.masks_dir = Path(masks_dir)
self.image_size = image_size
self.transform = transform
# Get all image files
self.image_files = []
for ext in ['.jpg', '.jpeg', '.png', '.tiff', '.tif']:
self.image_files.extend(self.images_dir.glob(f'*{ext}'))
self.image_files.extend(self.images_dir.glob(f'*{ext.upper()}'))
self.image_files = sorted(self.image_files)
# Verify that corresponding masks exist
self.valid_pairs = []
for image_path in self.image_files:
mask_candidates = []
for ext in ['.jpg', '.jpeg', '.png', '.tiff', '.tif']:
mask_path = self.masks_dir / f"{image_path.stem}{ext}"
if mask_path.exists():
mask_candidates.append(mask_path)
if mask_candidates:
self.valid_pairs.append((image_path, mask_candidates[0]))
print(f"Dataset initialized with {len(self.valid_pairs)} image-mask pairs")
def __len__(self) -> int:
return len(self.valid_pairs)
def __getitem__(self, idx: int) -> dict:
image_path, mask_path = self.valid_pairs[idx]
# Load image
image = cv2.imread(str(image_path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Load mask
mask = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE)
# Resize to target size
image = cv2.resize(image, self.image_size)
mask = cv2.resize(mask, self.image_size, interpolation=cv2.INTER_NEAREST)
# Normalize mask to 0-1 (assuming binary segmentation)
mask = (mask > 127).astype(np.uint8)
# Apply transforms
if self.transform:
transformed = self.transform(image=image, mask=mask)
image = transformed['image']
mask = transformed['mask']
# Ensure mask is float for loss calculations
if isinstance(mask, torch.Tensor):
mask = mask.float()
else:
# Convert to tensors manually if no transforms
image = torch.from_numpy(image.transpose(2, 0, 1)).float()
mask = torch.from_numpy(mask).float()
return {
'image': image,
'mask': mask,
'image_path': str(image_path),
'mask_path': str(mask_path)
}
def get_training_transforms(image_size: Tuple[int, int] = (512, 512)) -> A.Compose:
"""Get augmentation transforms for training."""
return A.Compose([
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.5),
A.RandomRotate90(p=0.5),
A.ShiftScaleRotate(
shift_limit=0.1,
scale_limit=0.2,
rotate_limit=45,
border_mode=cv2.BORDER_CONSTANT,
value=0,
p=0.5
),
A.OneOf([
A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=1.0),
A.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=1.0),
], p=0.5),
A.OneOf([
A.GaussianBlur(blur_limit=(3, 7), p=1.0),
A.MedianBlur(blur_limit=5, p=1.0),
], p=0.3),
A.Resize(image_size[0], image_size[1]),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
),
ToTensorV2()
])
def get_validation_transforms(image_size: Tuple[int, int] = (512, 512)) -> A.Compose:
"""Get transforms for validation (no augmentation)."""
return A.Compose([
A.Resize(image_size[0], image_size[1]),
A.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
),
ToTensorV2()
])
def create_dataloaders(
train_images_dir: Path,
train_masks_dir: Path,
val_images_dir: Path,
val_masks_dir: Path,
batch_size: int = 8,
num_workers: int = 4,
image_size: Tuple[int, int] = (512, 512)
) -> Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader]:
"""Create training and validation dataloaders."""
# Create datasets
train_dataset = RoofSegmentationDataset(
images_dir=train_images_dir,
masks_dir=train_masks_dir,
transform=get_training_transforms(image_size),
image_size=image_size
)
val_dataset = RoofSegmentationDataset(
images_dir=val_images_dir,
masks_dir=val_masks_dir,
transform=get_validation_transforms(image_size),
image_size=image_size
)
# Create dataloaders
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
drop_last=True
)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True
)
return train_loader, val_loader
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