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c2b1b26 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 | """Dataset utilities for seaweed binary segmentation."""
from __future__ import annotations
from pathlib import Path
import albumentations as A
import numpy as np
import rasterio
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
IMAGE_EXTS = {".tif", ".tiff", ".png", ".jpg", ".jpeg"}
MASK_EXTS = (".png", ".tif", ".tiff", ".jpg", ".jpeg")
class SeaweedSegmentationDataset(Dataset):
def __init__(self, image_dir, mask_dir, transform=None, target_size=256, use_4channel=True):
self.image_dir = Path(image_dir)
self.mask_dir = Path(mask_dir)
self.transform = transform
self.target_size = int(target_size)
self.use_4channel = bool(use_4channel)
if not self.image_dir.exists():
raise FileNotFoundError(f"Image directory not found: {self.image_dir}")
if not self.mask_dir.exists():
raise FileNotFoundError(f"Mask directory not found: {self.mask_dir}")
self.images = sorted(p.name for p in self.image_dir.iterdir() if p.is_file() and p.suffix.lower() in IMAGE_EXTS)
self.normalize_3ch = transforms.Normalize(mean=(0.430, 0.411, 0.296), std=(0.213, 0.156, 0.143))
self.normalize_4ch = transforms.Normalize(mean=(0.430, 0.411, 0.296, 0.350), std=(0.213, 0.156, 0.143, 0.180))
def __len__(self):
return len(self.images)
def find_mask_path(self, image_name: str) -> Path:
stem = Path(image_name).stem
for ext in MASK_EXTS:
for suffix in (ext, ext.upper()):
candidate = self.mask_dir / f"{stem}{suffix}"
if candidate.exists():
return candidate
return self.mask_dir / f"{stem}.png"
@staticmethod
def read_raster(path: Path) -> np.ndarray:
if path.suffix.lower() in {".tif", ".tiff"}:
with rasterio.open(path) as src:
return np.transpose(src.read(), (1, 2, 0))
image = Image.open(path)
return np.asarray(image)
@staticmethod
def extract_432_bands(image: np.ndarray) -> np.ndarray:
if image.ndim == 2:
image = image[:, :, None]
if image.shape[2] >= 4:
return image[:, :, [3, 2, 1]]
output = image[:, :, : min(3, image.shape[2])]
while output.shape[2] < 3:
output = np.concatenate([output, output[:, :, -1:]], axis=2)
return output
@staticmethod
def read_mask(path: Path, fallback_shape: tuple[int, int]) -> np.ndarray:
if not path.exists():
return np.zeros(fallback_shape, dtype=np.uint8)
if path.suffix.lower() in {".tif", ".tiff"}:
with rasterio.open(path) as src:
mask = src.read(1)
else:
mask = np.asarray(Image.open(path).convert("L"))
return (mask > 127).astype(np.uint8)
def __getitem__(self, idx):
img_name = self.images[idx]
img_path = self.image_dir / img_name
mask_path = self.find_mask_path(img_name)
try:
image = self.read_raster(img_path)
if image.ndim == 2:
image = image[:, :, None]
mask = self.read_mask(mask_path, image.shape[:2])
if self.use_4channel and image.shape[2] >= 4:
processed = image[:, :, :4]
processed = torch.from_numpy(processed.astype(np.float32))
if processed.max() > 1.0:
processed = processed / 65535.0
processed = self.normalize_4ch(processed.permute(2, 0, 1))
else:
processed = self.extract_432_bands(image)
processed = torch.from_numpy(processed.astype(np.float32))
if processed.max() > 1.0:
processed = processed / 65535.0
processed = self.normalize_3ch(processed.permute(2, 0, 1))
mask_tensor = torch.from_numpy(mask).long()
if self.target_size != processed.shape[1] or self.target_size != processed.shape[2]:
processed = transforms.Resize((self.target_size, self.target_size), antialias=True)(processed)
mask_tensor = transforms.Resize(
(self.target_size, self.target_size),
interpolation=transforms.InterpolationMode.NEAREST,
)(mask_tensor.unsqueeze(0)).squeeze(0)
if self.transform:
augmented = self.transform(image=processed.permute(1, 2, 0).numpy(), mask=mask_tensor.numpy())
processed = torch.from_numpy(augmented["image"]).permute(2, 0, 1).float()
mask_tensor = torch.from_numpy(augmented["mask"]).long()
return {"image": processed, "mask": mask_tensor, "filename": img_name}
except Exception as exc:
print(f"Error loading {img_name}: {exc}")
return None
def get_train_transforms(target_size=256, use_4channel=True):
return A.Compose(
[
A.Resize(target_size, target_size),
A.HorizontalFlip(p=0.5),
A.VerticalFlip(p=0.3),
A.RandomRotate90(p=0.3),
A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=15, p=0.5),
A.RandomBrightnessContrast(p=0.3),
A.GaussNoise(p=0.2),
]
)
def get_val_transforms(target_size=256, use_4channel=True):
return None
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