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import os
import glob
import warnings
import numpy as np
from natsort import natsorted
from datetime import datetime
to_date = lambda string: datetime.strptime(string, "%Y-%m-%d")
S1_LAUNCH = to_date("2014-04-03")
# s2cloudless: see https://github.com/sentinel-hub/sentinel2-cloud-detector
from s2cloudless import S2PixelCloudDetector
import rasterio
from rasterio.merge import merge
from scipy.ndimage import gaussian_filter
from torch.utils.data import Dataset
# import sys
# sys.path.append(".")
from util.detect_cloudshadow import get_cloud_mask, get_shadow_mask
# utility functions used in the dataloaders of SEN12MS-CR and SEN12MS-CR-TS
def read_tif(path_IMG):
tif = rasterio.open(path_IMG)
return tif
def read_img(tif):
return tif.read().astype(np.float32)
def rescale(img, oldMin, oldMax):
oldRange = oldMax - oldMin
img = (img - oldMin) / oldRange
return img
def process_MS(img, method):
if method == "default":
intensity_min, intensity_max = (
0,
10000,
) # define a reasonable range of MS intensities
img = np.clip(
img, intensity_min, intensity_max
) # intensity clipping to a global unified MS intensity range
img = rescale(
img, intensity_min, intensity_max
) # project to [0,1], preserve global intensities (across patches), gets mapped to [-1,+1] in wrapper
if method == "resnet":
intensity_min, intensity_max = (
0,
10000,
) # define a reasonable range of MS intensities
img = np.clip(
img, intensity_min, intensity_max
) # intensity clipping to a global unified MS intensity range
img /= 2000 # project to [0,5], preserve global intensities (across patches)
img = np.nan_to_num(img)
return img
def process_SAR(img, method):
if method == "default":
dB_min, dB_max = -25, 0 # define a reasonable range of SAR dB
img = np.clip(
img, dB_min, dB_max
) # intensity clipping to a global unified SAR dB range
img = rescale(
img, dB_min, dB_max
) # project to [0,1], preserve global intensities (across patches), gets mapped to [-1,+1] in wrapper
if method == "resnet":
# project SAR to [0, 2] range
dB_min, dB_max = [-25.0, -32.5], [0, 0]
img = np.concatenate(
[
(
2
* (np.clip(img[0], dB_min[0], dB_max[0]) - dB_min[0])
/ (dB_max[0] - dB_min[0])
)[None, ...],
(
2
* (np.clip(img[1], dB_min[1], dB_max[1]) - dB_min[1])
/ (dB_max[1] - dB_min[1])
)[None, ...],
],
axis=0,
)
img = np.nan_to_num(img)
return img
def get_cloud_cloudshadow_mask(img, cloud_threshold=0.2):
cloud_mask = get_cloud_mask(img, cloud_threshold, binarize=True)
shadow_mask = get_shadow_mask(img)
# encode clouds and shadows as segmentation masks
cloud_cloudshadow_mask = np.zeros_like(cloud_mask)
cloud_cloudshadow_mask[shadow_mask < 0] = -1
cloud_cloudshadow_mask[cloud_mask > 0] = 1
# label clouds and shadows
cloud_cloudshadow_mask[cloud_cloudshadow_mask != 0] = 1
return cloud_cloudshadow_mask
# recursively apply function to nested dictionary
def iterdict(dictionary, fct):
for k, v in dictionary.items():
if isinstance(v, dict):
dictionary[k] = iterdict(v, fct)
else:
dictionary[k] = fct(v)
return dictionary
def get_cloud_map(img, detector, instance=None):
# get cloud masks
img = np.clip(img, 0, 10000)
mask = np.ones((img.shape[-1], img.shape[-1]))
# note: if your model may suffer from dark pixel artifacts,
# you may consider adjusting these filtering parameters
if not (img.mean() < 1e-5 and img.std() < 1e-5):
if detector == "cloud_cloudshadow_mask":
threshold = 0.2 # set to e.g. 0.2 or 0.4
mask = get_cloud_cloudshadow_mask(img, threshold)
elif detector == "s2cloudless_map":
threshold = 0.5
mask = instance.get_cloud_probability_maps(
np.moveaxis(img / 10000, 0, -1)[None, ...]
)[0, ...]
mask[mask < threshold] = 0
mask = gaussian_filter(mask, sigma=2)
elif detector == "s2cloudless_mask":
mask = instance.get_cloud_masks(np.moveaxis(img / 10000, 0, -1)[None, ...])[
0, ...
]
else:
mask = np.ones((img.shape[-1], img.shape[-1]))
warnings.warn(f"Method {detector} not yet implemented!")
else:
warnings.warn(f"Encountered a blank sample, defaulting to cloudy mask.")
return mask.astype(np.float32)
# function to fetch paired data, which may differ in modalities or dates
def get_pairedS1(patch_list, root_dir, mod=None, time=None):
paired_list = []
for patch in patch_list:
seed, roi, modality, time_number, fname = patch.split("/")
time = time_number if time is None else time # unless overwriting, ...
mod = (
modality if mod is None else mod
) # keep the patch list's original time and modality
n_patch = fname.split("patch_")[-1].split(".tif")[0]
paired_dir = os.path.join(seed, roi, mod.upper(), str(time))
candidates = os.path.join(
root_dir,
paired_dir,
f"{mod}_{seed}_{roi}_ImgNo_{time}_*_patch_{n_patch}.tif",
)
paired_list.append(
os.path.join(paired_dir, os.path.basename(glob.glob(candidates)[0]))
)
return paired_list
""" SEN12MSCR data loader class, inherits from torch.utils.data.Dataset
IN:
root: str, path to your copy of the SEN12MS-CR-TS data set
split: str, in [all | train | val | test]
region: str, [all | africa | america | asiaEast | asiaWest | europa]
cloud_masks: str, type of cloud mask detector to run on optical data, in []
sample_type: str, [generic | cloudy_cloudfree]
n_input_samples: int, number of input samples in time series
rescale_method: str, [default | resnet]
OUT:
data_loader: SEN12MSCRTS instance, implements an iterator that can be traversed via __getitem__(pdx),
which returns the pdx-th dictionary of patch-samples (whose structure depends on sample_type)
"""
class SEN12MSCR(Dataset):
def __init__(
self,
root,
split="all",
region="all",
cloud_masks="s2cloudless_mask",
sample_type="pretrain",
rescale_method="default",
):
self.root_dir = root # set root directory which contains all ROI
self.region = region # region according to which the ROI are selected
if self.region != "all":
raise NotImplementedError # TODO: currently only supporting 'all'
self.ROI = {
"ROIs1158": ["106"],
"ROIs1868": [
"17",
"36",
"56",
"73",
"85",
"100",
"114",
"119",
"121",
"126",
"127",
"139",
"142",
"143",
],
"ROIs1970": [
"20",
"21",
"35",
"40",
"57",
"65",
"71",
"82",
"83",
"91",
"112",
"116",
"119",
"128",
"132",
"133",
"135",
"139",
"142",
"144",
"149",
],
"ROIs2017": [
"8",
"22",
"25",
"32",
"49",
"61",
"63",
"69",
"75",
"103",
"108",
"115",
"116",
"117",
"130",
"140",
"146",
],
}
# define splits conform with SEN12MS-CR-TS
self.splits = {}
self.splits["train"] = [
"ROIs1970_fall_s1/s1_3",
"ROIs1970_fall_s1/s1_22",
"ROIs1970_fall_s1/s1_148",
"ROIs1970_fall_s1/s1_107",
"ROIs1970_fall_s1/s1_1",
"ROIs1970_fall_s1/s1_114",
"ROIs1970_fall_s1/s1_135",
"ROIs1970_fall_s1/s1_40",
"ROIs1970_fall_s1/s1_42",
"ROIs1970_fall_s1/s1_31",
"ROIs1970_fall_s1/s1_149",
"ROIs1970_fall_s1/s1_64",
"ROIs1970_fall_s1/s1_28",
"ROIs1970_fall_s1/s1_144",
"ROIs1970_fall_s1/s1_57",
"ROIs1970_fall_s1/s1_35",
"ROIs1970_fall_s1/s1_133",
"ROIs1970_fall_s1/s1_30",
"ROIs1970_fall_s1/s1_134",
"ROIs1970_fall_s1/s1_141",
"ROIs1970_fall_s1/s1_112",
"ROIs1970_fall_s1/s1_116",
"ROIs1970_fall_s1/s1_37",
"ROIs1970_fall_s1/s1_26",
"ROIs1970_fall_s1/s1_77",
"ROIs1970_fall_s1/s1_100",
"ROIs1970_fall_s1/s1_83",
"ROIs1970_fall_s1/s1_71",
"ROIs1970_fall_s1/s1_93",
"ROIs1970_fall_s1/s1_119",
"ROIs1970_fall_s1/s1_104",
"ROIs1970_fall_s1/s1_136",
"ROIs1970_fall_s1/s1_6",
"ROIs1970_fall_s1/s1_41",
"ROIs1970_fall_s1/s1_125",
"ROIs1970_fall_s1/s1_91",
"ROIs1970_fall_s1/s1_131",
"ROIs1970_fall_s1/s1_120",
"ROIs1970_fall_s1/s1_110",
"ROIs1970_fall_s1/s1_19",
"ROIs1970_fall_s1/s1_14",
"ROIs1970_fall_s1/s1_81",
"ROIs1970_fall_s1/s1_39",
"ROIs1970_fall_s1/s1_109",
"ROIs1970_fall_s1/s1_33",
"ROIs1970_fall_s1/s1_88",
"ROIs1970_fall_s1/s1_11",
"ROIs1970_fall_s1/s1_128",
"ROIs1970_fall_s1/s1_142",
"ROIs1970_fall_s1/s1_122",
"ROIs1970_fall_s1/s1_4",
"ROIs1970_fall_s1/s1_27",
"ROIs1970_fall_s1/s1_147",
"ROIs1970_fall_s1/s1_85",
"ROIs1970_fall_s1/s1_82",
"ROIs1970_fall_s1/s1_105",
"ROIs1158_spring_s1/s1_9",
"ROIs1158_spring_s1/s1_1",
"ROIs1158_spring_s1/s1_124",
"ROIs1158_spring_s1/s1_40",
"ROIs1158_spring_s1/s1_101",
"ROIs1158_spring_s1/s1_21",
"ROIs1158_spring_s1/s1_134",
"ROIs1158_spring_s1/s1_145",
"ROIs1158_spring_s1/s1_141",
"ROIs1158_spring_s1/s1_66",
"ROIs1158_spring_s1/s1_8",
"ROIs1158_spring_s1/s1_26",
"ROIs1158_spring_s1/s1_77",
"ROIs1158_spring_s1/s1_113",
"ROIs1158_spring_s1/s1_100",
"ROIs1158_spring_s1/s1_117",
"ROIs1158_spring_s1/s1_119",
"ROIs1158_spring_s1/s1_6",
"ROIs1158_spring_s1/s1_58",
"ROIs1158_spring_s1/s1_120",
"ROIs1158_spring_s1/s1_110",
"ROIs1158_spring_s1/s1_126",
"ROIs1158_spring_s1/s1_115",
"ROIs1158_spring_s1/s1_121",
"ROIs1158_spring_s1/s1_39",
"ROIs1158_spring_s1/s1_109",
"ROIs1158_spring_s1/s1_63",
"ROIs1158_spring_s1/s1_75",
"ROIs1158_spring_s1/s1_132",
"ROIs1158_spring_s1/s1_128",
"ROIs1158_spring_s1/s1_142",
"ROIs1158_spring_s1/s1_15",
"ROIs1158_spring_s1/s1_45",
"ROIs1158_spring_s1/s1_97",
"ROIs1158_spring_s1/s1_147",
"ROIs1868_summer_s1/s1_90",
"ROIs1868_summer_s1/s1_87",
"ROIs1868_summer_s1/s1_25",
"ROIs1868_summer_s1/s1_124",
"ROIs1868_summer_s1/s1_114",
"ROIs1868_summer_s1/s1_135",
"ROIs1868_summer_s1/s1_40",
"ROIs1868_summer_s1/s1_101",
"ROIs1868_summer_s1/s1_42",
"ROIs1868_summer_s1/s1_31",
"ROIs1868_summer_s1/s1_36",
"ROIs1868_summer_s1/s1_139",
"ROIs1868_summer_s1/s1_56",
"ROIs1868_summer_s1/s1_133",
"ROIs1868_summer_s1/s1_55",
"ROIs1868_summer_s1/s1_43",
"ROIs1868_summer_s1/s1_113",
"ROIs1868_summer_s1/s1_76",
"ROIs1868_summer_s1/s1_123",
"ROIs1868_summer_s1/s1_143",
"ROIs1868_summer_s1/s1_93",
"ROIs1868_summer_s1/s1_125",
"ROIs1868_summer_s1/s1_89",
"ROIs1868_summer_s1/s1_120",
"ROIs1868_summer_s1/s1_126",
"ROIs1868_summer_s1/s1_72",
"ROIs1868_summer_s1/s1_115",
"ROIs1868_summer_s1/s1_121",
"ROIs1868_summer_s1/s1_146",
"ROIs1868_summer_s1/s1_140",
"ROIs1868_summer_s1/s1_95",
"ROIs1868_summer_s1/s1_102",
"ROIs1868_summer_s1/s1_7",
"ROIs1868_summer_s1/s1_11",
"ROIs1868_summer_s1/s1_132",
"ROIs1868_summer_s1/s1_15",
"ROIs1868_summer_s1/s1_137",
"ROIs1868_summer_s1/s1_4",
"ROIs1868_summer_s1/s1_27",
"ROIs1868_summer_s1/s1_147",
"ROIs1868_summer_s1/s1_86",
"ROIs1868_summer_s1/s1_47",
"ROIs2017_winter_s1/s1_68",
"ROIs2017_winter_s1/s1_25",
"ROIs2017_winter_s1/s1_62",
"ROIs2017_winter_s1/s1_135",
"ROIs2017_winter_s1/s1_42",
"ROIs2017_winter_s1/s1_64",
"ROIs2017_winter_s1/s1_21",
"ROIs2017_winter_s1/s1_55",
"ROIs2017_winter_s1/s1_112",
"ROIs2017_winter_s1/s1_116",
"ROIs2017_winter_s1/s1_8",
"ROIs2017_winter_s1/s1_59",
"ROIs2017_winter_s1/s1_49",
"ROIs2017_winter_s1/s1_104",
"ROIs2017_winter_s1/s1_81",
"ROIs2017_winter_s1/s1_146",
"ROIs2017_winter_s1/s1_75",
"ROIs2017_winter_s1/s1_94",
"ROIs2017_winter_s1/s1_102",
"ROIs2017_winter_s1/s1_61",
"ROIs2017_winter_s1/s1_47",
"ROIs1868_summer_s1/s1_100", # note: this ROI is also used for testing in SEN12MS-CR-TS. If you wish to combine both datasets, please comment out this line
]
self.splits["val"] = [
"ROIs2017_winter_s1/s1_22",
"ROIs1868_summer_s1/s1_19",
"ROIs1970_fall_s1/s1_65",
"ROIs1158_spring_s1/s1_17",
"ROIs2017_winter_s1/s1_107",
"ROIs1868_summer_s1/s1_80",
"ROIs1868_summer_s1/s1_127",
"ROIs2017_winter_s1/s1_130",
"ROIs1868_summer_s1/s1_17",
"ROIs2017_winter_s1/s1_84",
]
self.splits["test"] = [
"ROIs1158_spring_s1/s1_106",
"ROIs1158_spring_s1/s1_123",
"ROIs1158_spring_s1/s1_140",
"ROIs1158_spring_s1/s1_31",
"ROIs1158_spring_s1/s1_44",
"ROIs1868_summer_s1/s1_119",
"ROIs1868_summer_s1/s1_73",
"ROIs1970_fall_s1/s1_139",
"ROIs2017_winter_s1/s1_108",
"ROIs2017_winter_s1/s1_63",
]
self.splits["all"] = (
self.splits["train"] + self.splits["test"] + self.splits["val"]
)
self.split = split
assert split in [
"all",
"train",
"val",
"test",
], "Input dataset must be either assigned as all, train, test, or val!"
assert sample_type in ["pretrain"], "Input data must be pretrain!"
assert cloud_masks in [
None,
"cloud_cloudshadow_mask",
"s2cloudless_map",
"s2cloudless_mask",
], "Unknown cloud mask type!"
self.modalities = ["S1", "S2"]
self.cloud_masks = cloud_masks # e.g. 'cloud_cloudshadow_mask', 's2cloudless_map', 's2cloudless_mask'
self.sample_type = sample_type # e.g. 'pretrain'
self.time_points = range(1)
self.n_input_t = 1 # specifies the number of samples, if only part of the time series is used as an input
if self.cloud_masks in ["s2cloudless_map", "s2cloudless_mask"]:
self.cloud_detector = S2PixelCloudDetector(
threshold=0.4, all_bands=True, average_over=4, dilation_size=2
)
else:
self.cloud_detector = None
self.paths = self.get_paths()
self.n_samples = len(self.paths)
# raise a warning if no data has been found
if not self.n_samples:
self.throw_warn()
self.method = rescale_method
# indexes all patches contained in the current data split
def get_paths(
self,
): # assuming for the same ROI+num, the patch numbers are the same
print(f"\nProcessing paths for {self.split} split of region {self.region}")
paths = []
seeds_S1 = natsorted(
[s1dir for s1dir in os.listdir(self.root_dir) if "_s1" in s1dir]
)
for seed in seeds_S1:
rois_S1 = natsorted(os.listdir(os.path.join(self.root_dir, seed)))
for roi in rois_S1:
roi_dir = os.path.join(self.root_dir, seed, roi)
paths_S1 = natsorted(
[os.path.join(roi_dir, s1patch) for s1patch in os.listdir(roi_dir)]
)
paths_S2 = [
patch.replace("/s1", "/s2").replace("_s1", "_s2")
for patch in paths_S1
]
paths_S2_cloudy = [
patch.replace("/s1", "/s2_cloudy").replace("_s1", "_s2_cloudy")
for patch in paths_S1
]
for pdx, _ in enumerate(paths_S1):
# omit patches that are potentially unpaired
if not all(
[
os.path.isfile(paths_S1[pdx]),
os.path.isfile(paths_S2[pdx]),
os.path.isfile(paths_S2_cloudy[pdx]),
]
):
continue
# don't add patch if not belonging to the selected split
if not any(
[
split_roi in paths_S1[pdx]
for split_roi in self.splits[self.split]
]
):
continue
sample = {
"S1": paths_S1[pdx],
"S2": paths_S2[pdx],
"S2_cloudy": paths_S2_cloudy[pdx],
}
paths.append(sample)
return paths
def __getitem__(self, pdx): # get the triplet of patch with ID pdx
s1_tif = read_tif(self.paths[pdx]["S1"])
s2_tif = read_tif(self.paths[pdx]["S2"])
s2_cloudy_tif = read_tif(self.paths[pdx]["S2_cloudy"])
coord = list(s2_tif.bounds)
s1 = process_SAR(read_img(s1_tif), self.method)
s2 = read_img(s2_tif) # note: pre-processing happens after cloud detection
s2_cloudy = read_img(
s2_cloudy_tif
) # note: pre-processing happens after cloud detection
mask = (
None
if not self.cloud_masks
else get_cloud_map(s2_cloudy, self.cloud_masks, self.cloud_detector)
)
sample = {
"input": {
"S1": s1,
"S2": process_MS(s2_cloudy, self.method),
"masks": mask,
"coverage": np.mean(mask),
"S1 path": os.path.join(self.root_dir, self.paths[pdx]["S1"]),
"S2 path": os.path.join(self.root_dir, self.paths[pdx]["S2_cloudy"]),
"coord": coord,
},
"target": {
"S2": process_MS(s2, self.method),
"S2 path": os.path.join(self.root_dir, self.paths[pdx]["S2"]),
"coord": coord,
},
}
return sample
def throw_warn(self):
warnings.warn(
"""No data samples found! Please use the following directory structure:
path/to/your/SEN12MSCR/directory:
├───ROIs1158_spring_s1
| ├─s1_1
| | |...
| | ├─ROIs1158_spring_s1_1_p407.tif
| | |...
| ...
├───ROIs1158_spring_s2
| ├─s2_1
| | |...
| | ├─ROIs1158_spring_s2_1_p407.tif
| | |...
| ...
├───ROIs1158_spring_s2_cloudy
| ├─s2_cloudy_1
| | |...
| | ├─ROIs1158_spring_s2_cloudy_1_p407.tif
| | |...
| ...
...
Note: Please arrange the dataset in a format as e.g. provided by the script dl_data.sh.
"""
)
def __len__(self):
# length of generated list
return self.n_samples
if __name__ == "__main__":
dataset = SEN12MSCR(
root="data2/SEN12MSCR",
split="all",
region="all",
cloud_masks="s2cloudless_mask",
sample_type="pretrain",
rescale_method="default",
)
for each in dataset:
print(f"{each['input']['S1'].shape}")
print(f"{each['input']['S2'].shape}")
print(f"{each['input']['masks'].shape}")
print(f"{each['target']['S2'].shape}")
# (2, 256, 256)
# (13, 256, 256)
# (256, 256)
# (13, 256, 256)
break
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