def test_complicated_subdirs(self, ):
"""Check whether FileSet can find files in subdirectories that contain
text and placeholders.
"""
# The Pinocchio fileset from the cloud toolbox: a folder name contains
# normal text and a placeholder:
pinocchio = FileSet(
join(
self.refdir,
"pinocchio",
"t{year2}{month}{day}",
"tm{year2}{month}{day}{hour}{minute}{second}{millisecond}.jpg",
), )
# Find all files:
files = list(pinocchio)
check = [
FileInfo(
join(self.refdir, 'pinocchio', 't171102',
'tm171102132855573.jpg'), [
datetime.datetime(2017, 11, 2, 13, 28, 55, 573000),
datetime.datetime(2017, 11, 2, 13, 28, 55, 573000)
], {}),
]
assert files == check
def main():
args = get_cmd_line_parser().parse_args()
# TODO: Delete this line if you fixed this script
print("Please check the code before running this.")
#exit()
images_path = join(
args.root_dir,
"{temperature}/m{year2}{month}{day}{hour}{minute}{second}*.jpg")
images = FileSet(
path=images_path,
handler=cloud.pinocchio.ThermalCam(to_temperatures=False),
name="Calibration Images",
)
# Create the calibration mask. Only a small part of the image will show the
# the correct pixel values for the corresponding temperature. The rest will
# not see the calibration target.
calibration_mask = np.zeros((252, 336))
calibration_mask[115:130, 160:180] = 1
calibration_mask = calibration_mask.astype("bool")
# Get the temperatures (normally from a file, but this is not implemented
# yet):
temperature = get_temperatures()
create_calibration_file(
images,
temperature,
calibration_mask,
'pinocchio_calibration_%Y%m%d.csv',
)
def test_search(self):
"""Collocate fake MHS filesets"""
fake_mhs1 = FileSet(
path=join(
self.refdir, "{satname}_mhs_{year}", "{month}", "{day}",
"*NSS.MHSX.*.S{hour}{minute}.E{end_hour}{end_minute}.*.h5"),
handler=MHS_HDF(),
)
fake_mhs2 = fake_mhs1.copy()
with TemporaryDirectory() as outdir:
collocations = Collocations(path=join(
outdir, "{year}-{month}-{day}",
"{hour}{minute}{second}-{end_hour}{end_minute}{end_second}"), )
collocations.search([fake_mhs1, fake_mhs2],
start="2007",
end="2008",
max_interval="1h",
max_distance="10km")
def collect_spareice(version):
spareice_files = FileSet(
name="SPAREICE",
path=f"/work/um0878/user_data/jmrziglod/spareice/{version}/noaa18/"
"{year}/{month}/{day}/{year}{month}{day}_{hour}{minute}{second}-"
"{end_hour}{end_minute}{end_second}.nc",
max_processes=PROCESSES,
placeholder={"version": version})
print("Collect SPARE-ICE...")
data_list = spareice_files.map(
get_gridded_mean,
start=START,
end=END,
on_content=True,
pass_info=True,
)
data = xr.concat(data_list, dim="time")
#data.to_netcdf(f"data/{version}_SPARE-ICE_{START}.nc")
return data
def collect_cloudsat():
cloudsat_files = FileSet(
name="2C-ICE",
path="/work/um0878/data/cloudsat/2C-ICE.P1_R04/{year}/{doy}/"
"{year}{doy}{hour}{minute}{second}_*.hdf.zip",
handler=CloudSat(),
# Each file of CloudSat covers exactly 5933 seconds. Since we state it
# here, the indexing of files is much faster
time_coverage="5933 seconds",
# Load only the fields that we need:
read_args={
"fields": ["ice_water_path"],
},
max_threads=15,
)
print("Collect 2C-ICE...")
data = xr.concat(cloudsat_files[START:END], dim="scnline")
data.to_netcdf(f"data/2C-ICE_{START}.nc")
return data
not_null.lat, range=[[0, 90], [-90, 90]], bisectrix=False,
cmap="density", vmin=1,
)
scat.cmap.set_under("w")
plt.colorbar(scat)
plt.savefig(f"experiments/{experiment}/scnpos_lat_heatmap.png")
test_ratio = 0.3
train_data, test_data = train_test_split(
bdata, test_size=test_ratio, shuffle=True, random_state=5
)
print(f"Use {int(not_null.lat.size*(1-test_ratio))} points for training")
print(f"Use {int(not_null.lat.size*test_ratio)} points for testing")
experiments = FileSet("experiments/{experiment}/spareice.json")
if plot_all:
spareice = SPAREICE(
verbose=2, processes=processes,
sea_mask_file="data/land_water_mask_5min.png",
elevation_file="data/surface_elevation_1deg.nc",
)
for parameters in experiments:
try:
print(f"plot experiment {parameters.attr['experiment']}")
spareice.load(parameters)
spareice.report(
"experiments", parameters.attr["experiment"], test_data
)
except:
def __init__(
self,
images,
labels=None,
augmentator=None,
reader=None,
batch_size=None,
balance=False,
label_encoding='one-hot',
yield_mode='both',
shuffle=True,
random_seed=42,
max_workers=None,
classes=None,
preprocess_input=None,
target_size=None,
):
"""Create an ImageLoader
Args:
images: Must be either an iterable of image filenames, a path to a
directory (e.g. /path/to/images/*.tif) or a path containing the
placeholder *{label}* (e.g. /path/to/{label}/*.tif to match
/path/to/car/001.tif). In the latter case, you do not have to
set the parameter *labels*.
labels: This must be given or *images* must contain a placeholder
with *{label}* if you want to balance this dataset. Must be
an iterable of labels with the same length as *images*.
reader: Function to read the images. If None, images will be read
by opencv.imread function.
Default None.
shuffle: Shuffle the dataset once before yielding. Default: True.
random_seed: Number to initialize a random state. Default: 42.
augmentator: Use your favourite augmentator object. Actively
supported are keras, imgaug and Albumentations image
augmentators. Can be also set to a function that will be called
on each image before yielding it to the model. Default: None
classes: Classes which will be encoded in this dataset.
batch_size: Size of one batch. Default: 32.
balance: Can be either:
* *True*: the minority classes are going to be oversampled so
they have the same number as the majority class. If this is
used, *labels* must be given.
* *iterable*: An iterable with the weights for each sample. The
sum of all weights should be 1.
Default: False.
balance_batch: If *True*, all classes appear with equal numbers in
each batch. Works only if the number of classes is equal or
lower than the batch size. Defult: False.
label_encoding: Can be either:
* *False*: No encoding.
* *one-hot*: 1D numpy array of binary labels
* *binary*: Use when you have only two classes. One will be
labelled with 0, the other one with 1.
...
Default: *one-hot*.
yield_mode: Defines what the ImageLoader will yield for each batch:
* *both*: Yields inputs and labels (required for training models).
* *inputs*: Yields only the inputs.
* *labels*: Yields only the labels.
Default: *both*.
target_size: Set target size of images as a tuple of (height, width)
in pixels. Default: None
Examples:
from ai4eo.preprocessing import ImageLoader
from keras.preprocessing.image import ImageDataGenerator
keras_augmentator = ImageDataGenerator(
featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True
)
data = ImageLoader(
'/path/to/images/{label}/*.tif', augmentator=keras_augmentator,
)
# Create keras model
model = ...
model.fit_generator(data, ...)
"""
if isinstance(images, str):
# Let's try to find all images in the given path
files = FileSet(images).to_dataframe()
images = files.index.values
if "label" in files.columns:
labels = files['label'].values
if labels is not None and len(labels) != len(images):
raise ValueError("images and labels must have the same length!")
self.images = np.array(images)
self.labels = None if labels is None else np.array(labels)
self.classes = classes
if self.classes is None and self.labels is not None:
self.classes = np.unique(self.labels)
if self.classes is not None:
self.class_indices = {
index: label
for index, label in enumerate(self.classes)
}
else:
self.class_indices = None
if self.labels is not None:
if label_encoding == 'one-hot' or label_encoding == 'binary':
self.labels = label_binarize(self.labels, classes=self.classes)
if label_encoding == 'binary':
self.labels = np.squeeze(self.labels)
self.yield_mode = yield_mode
self.reader = reader
self.augmentator = augmentator
self.augmentator_type = None
if callable(getattr(self.augmentator, "random_transform", None)):
self.augmentator_type = 'keras'
elif callable(getattr(self.augmentator, "augment_batches", None)):
self.augmentator_type = 'imgaug'
# elif callable(getattr(self.augmentator, "augment_batches", None)):
# self.augmentator_type = 'imgaug'
self.batch_size = batch_size or 32
self.preprocess_input = preprocess_input
self.target_size = target_size
# To make the experiments reproducible:
self.random_state = np.random.RandomState(random_seed)
self.random_seed = random_seed
self.max_workers = max_workers
self._indices = list(range(len(self.images)))
if shuffle:
self.random_state.shuffle(self._indices)
if not balance:
self._weights = None
# Check explicitly for True because iterables could also return True in
# a boolean context
elif balance is True:
# We want to oversample the minority classes, i.e. the set the
# weights accordingly (the lower the amount of samples per class,
# the higher the weight for them).
if self.labels is None:
raise ValueError('Cannot balance samples by myself without'
'having any labels! Please set *labels*!')
unique_labels, counts = np.unique(labels, return_counts=True)
label_counts = pd.Series(counts, index=unique_labels)
self._weights = \
1 / label_counts.loc[labels].values / len(label_counts)
else:
self._weights = balance
self.reset()
def init_filesets(self):
if self.filesets is not None:
return self.filesets
self.filesets = FileSetManager()
self.filesets += FileSet(
join(
self.refdir, "tutorial", "{satellite}", "{year}-{month}-{day}",
"{hour}{minute}{second}-{end_hour}{end_minute}{end_second}.nc"
),
name="tutorial",
)
self.filesets += FileSet(
join(
self.refdir,
"single_file.nc",
),
name="single",
time_coverage=["2018-01-01", "2018-01-03"],
)
def sequence_get_info(file_info, **kwargs):
"""Small helper function for sequence fileset."""
with open(file_info) as f:
file_info.times[0] = datetime.datetime.strptime(
f.readline().rstrip(), "Start: %Y-%m-%d %H:%M:%S")
file_info.times[1] = datetime.datetime.strptime(
f.readline().rstrip(), "End: %Y-%m-%d %H:%M:%S")
return file_info
self.filesets += FileSet(
join(
self.refdir,
"sequence",
"{year}",
"{doy}",
"sequence*.txt",
),
name="sequence-wildcard",
handler=FileHandler(info=sequence_get_info, ),
info_via="handler",
)
self.filesets += FileSet(join(
self.refdir,
"sequence",
"{year}",
"{doy}",
"sequence{id}.txt",
),
handler=FileHandler(info=sequence_get_info, ),
name="sequence-placeholder",
info_via="both",
placeholder={"id": "\d{4}"})
self.filesets += FileSet(
join(
self.refdir,
# NSS.HIRX.NJ.D99127.S0632.E0820.B2241718.WI.gz
"regex",
"NSS.HIR[XS].{satcode}.D{year2}{doy}.S{hour}"
"{minute}.E{end_hour}{end_minute}.B{B}.{station}.gz"),
name="regex-HIRS",
)
self.filesets["regex-HIRS"].set_placeholders(
satcode=".{2}",
B="\d{7}",
station=".{2}",
)
return self.filesets
def test_glob(self):
files = FileSet(
join(self.refdir, "tutorial", "{satellite}", "*", "*.nc"),
placeholder={"satellite": 'SatelliteA'},
)
self._print_files(list(files))
# Sort this after paths rather than times (because the times are all
# equal)
check = list(
sorted([
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-02',
'000000-040000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-02',
'080000-120000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-02',
'200000-000000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-02',
'040000-080000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-02',
'120000-160000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-02',
'160000-200000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-01',
'000000-040000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-01',
'080000-120000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-01',
'200000-000000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-01',
'040000-080000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-01',
'120000-160000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
FileInfo(
join(self.refdir, 'tutorial', 'SatelliteA', '2018-01-01',
'160000-200000.nc'),
[
datetime.datetime(1, 1, 1, 0, 0),
datetime.datetime(9999, 12, 31, 23, 59, 59, 999999)
], {'satellite': 'SatelliteA'}),
],
key=lambda x: x.path))
assert list(sorted(files, key=lambda x: x.path)) == check
"""Create fold files for training and test images
"""
import numpy as np
import pandas as pd
from typhon.files import FileSet
df = pd.concat([
FileSet(
'/scratch-a/jmrziglod/sen2agri/data/malawi_summer/patches/original/{label}/*.png'
).to_dataframe(),
FileSet(
'/scratch-a/jmrziglod/sen2agri/data/malawi_summer/patches/augmented/{label}/*.png'
).to_dataframe()
])
unique_ids = np.unique(df.id.values)
shuffled_ids = np.random.choice(unique_ids,
size=len(unique_ids),
replace=False)
ratio = 0.1
test_ids = shuffled_ids[:int(shuffled_ids.size * ratio)]
train_ids = shuffled_ids[int(shuffled_ids.size * ratio):]
with open(
'/home/jmrziglod/projects/sen2agri/drone-crop-type/folds/malawi_summer/train_test_all_mosaics/train.txt',
'w') as txt_file:
txt_file.write("\n".join(df.index[df.id.isin(train_ids)].tolist()))
with open(
'/home/jmrziglod/projects/sen2agri/drone-crop-type/folds/malawi_summer/train_test_all_mosaics/test.txt',
'w') as txt_file:
START_TIME = "2007"
END_TIME = "March 2010"
PROCESSES = 12
TRAINING_FILE = "spareice_training_data.nc"
# Define a fileset with the files from MHS / NOAA18:
mhs = FileSet(
name="MHS",
path="/work/um0878/data/amsub_mhs_l1c_hdf/AAPP7_13/noaa18"
"_mhs_{year}/{month}/{day}/*NSS.MHSX.NN.*."
"S{hour}{minute}.E{end_hour}{end_minute}.*.h5",
handler=MHS_HDF(),
# Load only the fields that we need:
read_args={
"fields": [
"Data/btemps",
"Geolocation/Satellite_azimuth_angle",
"Geolocation/Satellite_zenith_angle",
"Geolocation/Solar_azimuth_angle",
"Geolocation/Solar_zenith_angle",
]
},
)
# Define a fileset with files from CloudSat / 2C-ICE:
cloudsat = FileSet(
name="2C-ICE",
path="/work/um0878/data/cloudsat/2C-ICE.P1_R04/{year}/{doy}/"
"{year}{doy}{hour}{minute}{second}_*.hdf.zip",
handler=CloudSat(),
END_TIME = "21 Jun 2013"
PROCESSES = 1
SAVE_COLLOCATIONS = False
version = "typhon"
# Define a fileset with the files from MHS / NOAA18:
mhs_files = FileSet(
name="MHS",
path="/work/um0878/data/amsub_mhs_l1c_hdf/AAPP7_13/noaa18"
"_mhs_{year}/{month}/{day}/*NSS.MHSX.NN.*."
"S{hour}{minute}.E{end_hour}{end_minute}.*.h5",
handler=MHS_HDF(),
# Load only the fields that we need:
read_args={
"fields": [
"Data/btemps",
"Geolocation/Satellite_azimuth_angle",
"Geolocation/Satellite_zenith_angle",
"Geolocation/Solar_azimuth_angle",
"Geolocation/Solar_zenith_angle",
]
},
)
# Define a fileset with the files from AVHRR / NOAA18:
avhrr_files = FileSet(
name="AVHRR",
path="/work/um0878/user_data/jmrziglod/avhrr_gac_hdf5/noaa18_gac_"
"{year}/{month}/{day}/NSS.*."
"S{hour}{minute}.E{end_hour}{end_minute}.*.h5",
def load_filesets(config):
"""Load all filesets into one FileSetManager object
Args:
config: Dictionary with configuration keys and values.
Returns:
A FileSetManager object.
"""
basedir = config["General"]["basedir"]
# This FileSetManager can handle all dataset objects:
filesets = FileSetManager()
###########################################################################
# Pinocchio - FileSets:
filesets += FileSet(
name="Pinocchio-netcdf",
path=os.path.join(
config["General"]["basedir"],
config["Pinocchio"]["nc_files"],
),
max_processes=int(config["General"]["processes"]),
)
filesets += FileSet(
name="Pinocchio-archive",
path=os.path.join(basedir, config["Pinocchio"]["archive_files"]),
max_processes=int(config["General"]["processes"]),
)
# Load logbook from Pinocchio. This logbook contains time intervals where
# the data is corrupted or bad.
logbook = None
if "logbook" in config["Pinocchio"]:
logbook = load_logbook(
os.path.join(basedir, config["Pinocchio"]["logbook"]))
pinocchio_calibration = os.path.join(
config["General"]["basedir"],
config["Pinocchio"]["calibration"],
)
filesets += FileSet(
name="Pinocchio-raw",
path=os.path.join(
config["General"]["basedir"],
os.path.splitext(config["Pinocchio"]["archive_files"])[0],
config["Pinocchio"]["files_in_archive"],
),
# Set the pinocchio file handler with the calibration file
handler=pinocchio.ThermalCam(calibration_file=pinocchio_calibration),
max_processes=int(config["General"]["processes"]),
# Exclude the time intervals from the logbook when searching for files:
exclude=logbook,
)
filesets += FileSet(
path=os.path.join(basedir, config["Pinocchio"]["stats"]),
name="Pinocchio-stats",
max_processes=int(config["General"]["processes"]),
)
###########################################################################
###########################################################################
# Dumbo - FileSets:
filesets += FileSet(
name="Dumbo-netcdf",
path=os.path.join(
config["General"]["basedir"],
config["Dumbo"]["nc_files"],
),
max_processes=int(config["General"]["processes"]),
)
# Load logbook from Dumbo:
logbook = None
if "logbook" in config["Dumbo"]:
logbook = load_logbook(
os.path.join(basedir, config["Dumbo"]["logbook"]))
filesets += FileSet(
name="Dumbo-raw",
path=os.path.join(
config["General"]["basedir"],
config["Dumbo"]["raw_files"],
),
handler=dumbo.ThermalCamASCII(),
# Since the raw files have no temporal information in their filename,
# we have to retrieve it from by their handler.
info_via="handler",
max_processes=int(config["General"]["processes"]),
# Exclude the time intervals from the logbook when searching for files:
exclude=logbook,
)
filesets += FileSet(
path=os.path.join(basedir, config["Dumbo"]["stats"]),
name="Dumbo-stats",
max_processes=int(config["General"]["processes"]),
)
###########################################################################
filesets += FileSet(
path=os.path.join(basedir, config["Ceilometer"]["files"]),
name="Ceilometer",
# Each file covers roughly 24 hours:
time_coverage="24 hours",
max_processes=int(config["General"]["processes"]),
)
filesets += FileSet(
path=os.path.join(basedir, config["DShip"]["files"]),
handler=metadata.ShipMSM(),
name="DShip",
max_processes=int(config["General"]["processes"]),
)
filesets += FileSet(
path=os.path.join(basedir, config["Plots"]["files"]),
name="plots",
handler=Plotter(),
max_processes=int(config["General"]["processes"]),
)
return filesets
