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import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader, Subset
from pathlib import Path
import os
import rasterio
import cv2
import pdb
from pyproj import Transformer
EXTENSIONS = (".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif", ".tiff", ".webp")
ALL_BANDS = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B10', 'B11', 'B12', 'B8A']
S2A_BANDS = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B09', 'B11', 'B12', 'B8A']
RGB_BANDS = ['B04', 'B03', 'B02']
S1_BANDS = ['VV', 'VH']
### SSL4EO stats
BAND_STATS = {
'mean': {
'B01': 1353.72696296,
'B02': 1117.20222222,
'B03': 1041.8842963,
'B04': 946.554,
'B05': 1199.18896296,
'B06': 2003.00696296,
'B07': 2374.00874074,
'B08': 2301.22014815,
'B8A': 2599.78311111,
'B09': 732.18207407,
'B10': 12.09952894,
'B11': 1820.69659259,
'B12': 1118.20259259,
'VV': -12.54847273,
'VH': -20.19237134
},
'std': {
'B01': 897.27143653,
'B02': 736.01759721,
'B03': 684.77615743,
'B04': 620.02902871,
'B05': 791.86263829,
'B06': 1341.28018273,
'B07': 1595.39989386,
'B08': 1545.52915718,
'B8A': 1750.12066835,
'B09': 475.11595216,
'B10': 98.26600935,
'B11': 1216.48651476,
'B12': 736.6981037,
'VV': 5.25697717,
'VH': 5.91150917
}
}
# BAND_STATS_S1 = {
# 'mean': {
# 'VV': -12.54847273,
# 'VH': -20.19237134
# },
# 'std': {
# 'VV': 5.25697717,
# 'VH': 5.91150917
# }
# }
def is_valid_file(filename):
return filename.lower().endswith(EXTENSIONS)
def normalize(img, mean, std):
min_value = mean - 2 * std
max_value = mean + 2 * std
img = (img - min_value) / (max_value - min_value) * 255.0
img = np.clip(img, 0, 255).astype(np.uint8)
#img = (img - min_value) / (max_value - min_value)
#img = np.clip(img, 0, 1).astype(np.float32)
return img
class EurosatDataset(Dataset):
def __init__(self, root, bands='B2', split='train', transform=None, normalize=False, meta=False):
self.root = Path(root,split)
self.transform = transform
if bands=='B13':
self.bands = ALL_BANDS
elif bands=='B12':
self.bands = S2A_BANDS
elif bands=='RGB':
self.bands = RGB_BANDS
elif bands=='B2':
self.bands = S1_BANDS
self.normalize = normalize
self.classes = sorted([d.name for d in self.root.iterdir() if d.is_dir()])
self.class_to_idx = {cls_name: i for i, cls_name in enumerate(self.classes)}
self.samples = []
self.targets = []
#pdb.set_trace()
for froot, _, fnames in sorted(os.walk(self.root, followlinks=True)):
for fname in sorted(fnames):
if is_valid_file(fname):
path = os.path.join(froot, fname)
self.samples.append(path)
target = self.class_to_idx[Path(path).parts[-2]]
self.targets.append(target)
#print(self.root)
#print(f"Found {len(self.samples)} images belonging to {len(self.classes)} classes")
self.meta = meta
def __getitem__(self, index):
path = self.samples[index]
target = self.targets[index]
with rasterio.open(path) as f:
if self.bands == ALL_BANDS:
array = f.read().astype(np.int16)
elif self.bands == S2A_BANDS:
array = f.read((1,2,3,4,5,6,7,8,9,11,12,13)).astype(np.int16)
elif self.bands == RGB_BANDS:
array = f.read((4,3,2)).astype(np.int16)
elif self.bands == S1_BANDS:
array = f.read().astype(np.float32)
img = array.transpose(1, 2, 0)
if self.meta:
# get lon, lat, time
cx,cy = f.xy(f.height // 2, f.width // 2)
# convert to lon, lat
crs_transformer = Transformer.from_crs(f.crs, 'epsg:4326')
lon, lat = crs_transformer.transform(cx,cy)
# no time
meta_info = np.array([lon, lat, 0, 0]).astype(np.float32)
#meta_info = np.array([0, 0, 0, 0]).astype(np.float32)
#meta_info = np.array([np.nan, np.nan, np.nan, np.nan]).astype(np.float32)
channels = []
for i,b in enumerate(self.bands):
ch = img[:,:,i]
if self.normalize:
ch = normalize(ch, mean=BAND_STATS['mean'][b], std=BAND_STATS['std'][b])
elif self.bands == S2A_BANDS:
ch = (ch / 10000.0 * 255.0).astype('uint8')
if b=='B8A': # EuSAT band order is different than SSL4EO
channels.insert(8,ch)
else:
channels.append(ch)
#img = np.dstack(channels)
img = np.stack(channels, axis=0).astype('float32') / 255.0
if self.transform is not None:
img = self.transform(img)
if self.meta:
return img, target, meta_info
else:
return img, target
def __len__(self):
return len(self.samples)
class Subset(Dataset):
r"""
Subset of a dataset at specified indices.
Arguments:
dataset (Dataset): The whole Dataset
indices (sequence): Indices in the whole set selected for subset
"""
def __init__(self, dataset, indices, transform=None):
self.dataset = dataset
self.indices = indices
self.transform = transform
def __getitem__(self, idx):
im, target = self.dataset[self.indices[idx]]
if self.transform:
im = self.transform(im)
return im, target
def __len__(self):
return len(self.indices)
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