diff --git a/Common/geotools.py b/Common/geotools.py
new file mode 100644
index 0000000000000000000000000000000000000000..646c2d2db27795f091f71e784baf0a9defcf8526
--- /dev/null
+++ b/Common/geotools.py
@@ -0,0 +1,23 @@
+from osgeo import gdal
+from osgeo import ogr
+from pyproj import Transformer as T
+
+def get_query_bbox(shp, query_srs=4326, return_all=False):
+ ds = ogr.Open(shp)
+ ly = ds.GetLayer(0)
+ xt = ly.GetExtent()
+ shp_srs = int(ly.GetSpatialRef().GetAuthorityCode(None))
+
+ i_bbox = [xt[0], xt[2], xt[1], xt[3]]
+ if shp_srs == query_srs:
+ bbox = i_bbox
+ else:
+ tr = T.from_crs(shp_srs, query_srs, always_xy=True)
+ lon_min, lat_min = tr.transform(xt[0], xt[2])
+ lon_max, lat_max = tr.transform(xt[1], xt[3])
+ bbox = [lon_min, lat_min, lon_max, lat_max]
+
+ if return_all:
+ return bbox, i_bbox, shp_srs
+ else:
+ return bbox
\ No newline at end of file
diff --git a/Learning/ObjectBased.py b/Learning/ObjectBased.py
index 893455d8c58557e65a5427ddfee5db1fc47d62eb..f1dd96c4a70a74ad43ebe74ef4af33bd124e0af3 100644
--- a/Learning/ObjectBased.py
+++ b/Learning/ObjectBased.py
@@ -9,8 +9,10 @@ from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, cohen_kappa_score, precision_recall_fscore_support
class ObjectBasedClassifier:
- def __init__(self, object_layer, reference_data, time_series_patterns, user_feature_list, ref_class_field='class'):
- self.obia_base = OBIABase(object_layer, ref_data = reference_data, ref_class_field=ref_class_field)
+ def __init__(self, object_layer, reference_data, time_series_patterns, user_feature_list, ref_class_field='class',
+ ref_id_field='id'):
+ self.obia_base = OBIABase(object_layer, ref_data=reference_data, ref_class_field=ref_class_field,
+ ref_id_field=ref_id_field)
for ptrn in time_series_patterns:
lst = sorted(glob.glob(ptrn))
self.obia_base.add_raster_time_series_for_stats(lst)
diff --git a/OBIA/OBIABase.py b/OBIA/OBIABase.py
index 48d8889d6514d4e2988c69d91741cb1088d13d79..90abe476d58620e9522c903483d6347bccd490c4 100644
--- a/OBIA/OBIABase.py
+++ b/OBIA/OBIABase.py
@@ -174,11 +174,14 @@ class OBIABase:
tile_obj = np.squeeze(clip_obj['array']).astype(np.uint32)
else:
assert (self.ref_obj_layer_pipe is not None)
- self.ref_obj_layer_pipe[-1].PropagateRequestedRegion('out', r)
- tile_obj = self.ref_obj_layer_pipe[-1].GetImageAsNumpyArray('out').astype(np.uint32)
-
- #tile_obj = self.ref_obj_layer[r['index'][1]:r['index'][1]+r['size'][1],
- # r['index'][0]:r['index'][0]+r['size'][0]]
+ tmp_er = otb.Registry.CreateApplication('ExtractROI')
+ tmp_er.SetParameterInputImage('in', self.ref_obj_layer_pipe[-1].GetParameterOutputImage('out'))
+ tmp_er.SetParameterInt('startx', r['index'][0])
+ tmp_er.SetParameterInt('starty', r['index'][1])
+ tmp_er.SetParameterInt('sizex', r['size'][0])
+ tmp_er.SetParameterInt('sizey', r['size'][1])
+ tmp_er.Execute()
+ tile_obj = tmp_er.GetImageAsNumpyArray('out').astype(np.uint32)
si = otb.Registry.CreateApplication('Superimpose')
si.SetParameterString('inm', input_image)
diff --git a/Postprocessing/Report.py b/Postprocessing/Report.py
index ad57dca9f7fa1c4170794fdac7fcb62ec7e2f391..d3b74bf0a319a1b0147f665c008d26479cc2be62 100644
--- a/Postprocessing/Report.py
+++ b/Postprocessing/Report.py
@@ -30,7 +30,7 @@ def parse_colormap_file(fn):
return labels, class_names, colors
-def generate_report_figures(map, palette_fn, results, summary, out_dir, map_name=None):
+def generate_report_figures(map, palette_fn, results, summary, out_dir, map_name=None, importance_perc=0.75):
labels, class_names, colors = parse_colormap_file(palette_fn)
colors_norm = [(x[0]/255,x[1]/255,x[2]/255,x[3]/255) for x in colors]
with plt.ioff():
@@ -81,7 +81,7 @@ def generate_report_figures(map, palette_fn, results, summary, out_dir, map_name
imp_s = [x for _, x in sorted(zip(imp_m, imp_s), reverse=True)]
imp_m = sorted(imp_m, reverse=True)
c_imp = np.cumsum(imp_m)
- idx = np.where(c_imp<0.75*c_imp[-1])[0][-1]
+ idx = np.where(c_imp<importance_perc * c_imp[-1])[0][-1]
imp_m = imp_m[:idx]
imp_s = imp_s[:idx]
imp_n = imp_n[:idx]
diff --git a/TimeSeries/planet_mosaics.py b/TimeSeries/planet_mosaics.py
index 8d0563f6685a7c23b58d53068485691af7c7f843..c7ec32b4de46fe3dd4cca59a89e6389494b04579 100644
--- a/TimeSeries/planet_mosaics.py
+++ b/TimeSeries/planet_mosaics.py
@@ -49,44 +49,46 @@ def fetch(shp, dt, out_fld, api_key):
}
res = session.get(API_URL, params=parameters)
mosaic = res.json()
- mosaic_id = mosaic['mosaics'][0]['id']
-
- search_parameters = {
- 'bbox': str_bbox,
- 'minimal': True
- }
- quads_url = "{}/{}/quads".format(API_URL, mosaic_id)
- res = session.get(quads_url, params=search_parameters, stream=True)
- quads = res.json()
-
- l_quads = []
- for q in tqdm(quads['items'], desc='Downloading quads for {}'.format(md)):
- link = q['_links']['download']
- l_quads.append(os.path.join(out_fld, q['id'] + '.tif'))
- if not os.path.exists(l_quads[-1]):
- urllib.request.urlretrieve(link, l_quads[-1])
-
- app_mos = otb.Registry.CreateApplication('Mosaic')
- app_mos.SetParameterStringList('il', l_quads)
- app_mos.Execute()
- app_roi = otb.Registry.CreateApplication('ExtractROI')
- app_roi.SetParameterInputImage('in', app_mos.GetParameterOutputImage('out'))
- fn = 'PlanetMosaic_{}_{}.tif'.format(os.path.splitext(os.path.basename(shp))[0], md)
- app_roi.SetParameterString('mode', 'fit')
- app_roi.SetParameterString('mode.fit.vect', shp)
- app_roi.SetParameterString('out', os.path.join(out_fld, fn))
- app_roi.SetParameterOutputImagePixelType('out', otb.ImagePixelType_uint16)
- app_roi.ExecuteAndWriteOutput()
-
- [os.remove(f) for f in l_quads]
-
- return
+ if len(mosaic['mosaics']) > 0:
+ mosaic_id = mosaic['mosaics'][0]['id']
+
+ search_parameters = {
+ 'bbox': str_bbox,
+ 'minimal': True
+ }
+ quads_url = "{}/{}/quads".format(API_URL, mosaic_id)
+ res = session.get(quads_url, params=search_parameters, stream=True)
+ quads = res.json()
+
+ l_quads = []
+ for q in tqdm(quads['items'], desc='Downloading quads for {}'.format(md)):
+ link = q['_links']['download']
+ l_quads.append(os.path.join(out_fld, q['id'] + '.tif'))
+ if not os.path.exists(l_quads[-1]):
+ urllib.request.urlretrieve(link, l_quads[-1])
+
+ app_mos = otb.Registry.CreateApplication('Mosaic')
+ app_mos.SetParameterStringList('il', l_quads)
+ app_mos.Execute()
+ app_roi = otb.Registry.CreateApplication('ExtractROI')
+ app_roi.SetParameterInputImage('in', app_mos.GetParameterOutputImage('out'))
+ fn = 'PlanetMosaic_{}_{}.tif'.format(os.path.splitext(os.path.basename(shp))[0], md)
+ app_roi.SetParameterString('mode', 'fit')
+ app_roi.SetParameterString('mode.fit.vect', shp)
+ app_roi.SetParameterString('out', os.path.join(out_fld, fn))
+ app_roi.SetParameterOutputImagePixelType('out', otb.ImagePixelType_uint16)
+ app_roi.ExecuteAndWriteOutput()
+
+ [os.remove(f) for f in l_quads]
+
+ return PlanetMosaicPipeline(out_fld)
class PlanetMosaicPipeline:
# --- BEGIN SENSOR PROTOTYPE ---
NAME = 'PLANET-MOSAICS'
REF_TYPE = otb.ImagePixelType_uint16
+ FEAT_TYPE = otb.ImagePixelType_float
PTRN_name = 'PlanetMosaic_*.tif'
FEAT_exp = {
'B': 'im1b1',
@@ -142,14 +144,23 @@ class PlanetMosaicPipeline:
return
def parse_folder(self, fld):
- img_list = [os.path.abspath(x) for x in glob.glob(os.path.join(fld, self.PTRN_dir))]
+ img_list = [os.path.abspath(x) for x in glob.glob(os.path.join(fld, self.PTRN_name))]
return sorted(img_list, key=lambda x: self._img_id(x))
+ def append(self, app, fname=None, ftype=None, outp=None, is_output=False):
+ if is_output:
+ self.out_idx.append(len(self.pipe))
+ self.pipe.append(app)
+ self.files.append(fname)
+ self.types.append(ftype)
+ self.out_p.append(outp)
+
def compute_features(self, feat_list=['B', 'G', 'R', 'NIR', 'NDVI']):
proc_idx = self.out_idx.copy()
self.out_idx = []
for k in range(len(proc_idx)):
bm = otb.Registry.CreateApplication('BandMathX')
+ bm.AddImageToParameterInputImageList('il', self.pipe[k].GetParameterOutputImage(self.out_p[k]))
expr = []
for f in feat_list:
expr.append(self.FEAT_exp[f])
@@ -157,7 +168,7 @@ class PlanetMosaicPipeline:
bm.SetParameterString('exp', expr)
bm.Execute()
fn = self.files[proc_idx[k]].replace('.tif', '_FEAT.tif')
- self.append(bm, fn, self.REF_TYPE, 'out', is_output=True)
+ self.append(bm, fn, self.FEAT_TYPE, 'out', is_output=True)
def write_outputs(self, fld, update_pipe=False, compress=False):
out = []
diff --git a/TimeSeries/s1base.py b/TimeSeries/s1base.py
index d0cbd0977f20ecc56b809f40338e67e3eecc813f..923b2f0d8eeb741340599eb3ba5d74ed2cd3b0af 100644
--- a/TimeSeries/s1base.py
+++ b/TimeSeries/s1base.py
@@ -7,13 +7,34 @@ import uuid
import xml.etree.ElementTree as ET
import shutil
from itertools import groupby
+from Common.geotools import get_query_bbox
+from eodag import EODataAccessGateway
+
+def fetch(shp, dt, output_fld, credentials):
+ bbox = get_query_bbox(shp)
+ dag = EODataAccessGateway(user_conf_file_path=credentials)
+ dag.set_preferred_provider("peps")
+ search_criteria = {
+ "productType": "S1_SAR_GRD",
+ "start": dt.split("/")[0],
+ "end": dt.split("/")[1],
+ "geom": {"lonmin": bbox[0], "latmin": bbox[1], "lonmax": bbox[2], "latmax": bbox[3]}
+ }
+ res = dag.search_all(**search_criteria)
+ res.filter_property(sensorMode='IW')
+ if len(res) > 0:
+ os.makedirs(output_fld, exist_ok=True)
+ dag.download_all(res, outputs_prefix=output_fld, extract=True)
+
+ return S1GRDPipeline(output_fld)
+
class S1GRDPipeline:
# --- BEGIN SENSOR PROTOTYPE ---
NAME = 'S1-IW-GRD'
VAL_TYPE = otb.ImagePixelType_int16
TMP_TYPE = otb.ImagePixelType_float
- PTRN_dir = 'S1*_IW_GRD*.SAFE'
+ PTRN_dir = 'S1*_IW_GRD*/S1*_IW_GRD*.SAFE'
PTRN_ref = '-iw-grd-'
VH_name = 'vh'
PTRN = ['measurement/s1*-iw-grd-vh-*.tiff', 'measurement/s1*-iw-grd-vv-*.tiff']
diff --git a/TimeSeries/s1planetary.py b/TimeSeries/s1planetary.py
index e104c9303c0fcb4567c183525a81c6e229b21ae8..28e04c2685f4fd36684e5ef2a3a85b013e3eb0eb 100644
--- a/TimeSeries/s1planetary.py
+++ b/TimeSeries/s1planetary.py
@@ -1,9 +1,7 @@
import os.path
-
from TimeSeries.s1base import *
import planetary_computer as PC
from pystac_client import Client
-from osgeo import ogr
from pyproj import Transformer as T
from shapely.geometry import Polygon
import rasterio
@@ -11,24 +9,12 @@ import rasterio.mask
import tqdm
import otbApplication as otb
import json
+from Common.geotools import get_query_bbox
def fetch(shp, dt, output_fld, api_key, direction=None):
os.environ['PC_SDK_SUBSCRIPTION_KEY'] = api_key
- ds = ogr.Open(shp)
- ly = ds.GetLayer(0)
- xt = ly.GetExtent()
- shp_srs = int(ly.GetSpatialRef().GetAuthorityCode(None))
- qry_srs = 4326
-
- i_bbox = [xt[0], xt[2], xt[1], xt[3]]
- if shp_srs == qry_srs:
- bbox = i_bbox
- else:
- tr = T.from_crs(shp_srs, qry_srs, always_xy=True)
- lon_min, lat_min = tr.transform(xt[0], xt[2])
- lon_max, lat_max = tr.transform(xt[1], xt[3])
- bbox = [lon_min, lat_min, lon_max, lat_max]
+ bbox, i_bbox, shp_srs = get_query_bbox(shp, return_all=True)
api = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1', modifier=PC.sign_inplace)
res = api.search(collections="sentinel-1-rtc", bbox=bbox, datetime=dt)
diff --git a/TimeSeries/s2planetary.py b/TimeSeries/s2planetary.py
index a0de40366d7655da645181a4762821b2376245fa..928ce5399ea04c0120d91aec304639f6924d6e33 100644
--- a/TimeSeries/s2planetary.py
+++ b/TimeSeries/s2planetary.py
@@ -1,31 +1,16 @@
-import warnings
-
from TimeSeries.s2sen2cor import *
import planetary_computer as PC
from pystac_client import Client
import rasterio
import rasterio.mask
-from osgeo import ogr
+from Common.geotools import get_query_bbox
from pyproj import Transformer as T
from shapely.geometry import Polygon
import tqdm
def fetch(shp, dt, output_fld, band_list=None):
- ds = ogr.Open(shp)
- ly = ds.GetLayer(0)
- xt = ly.GetExtent()
- shp_srs = int(ly.GetSpatialRef().GetAuthorityCode(None))
- qry_srs = 4326
-
- i_bbox = [xt[0], xt[2], xt[1], xt[3]]
- if shp_srs == qry_srs:
- bbox = i_bbox
- else:
- tr = T.from_crs(shp_srs,qry_srs,always_xy=True)
- lon_min, lat_min = tr.transform(xt[0], xt[2])
- lon_max, lat_max = tr.transform(xt[1], xt[3])
- bbox = [lon_min, lat_min, lon_max, lat_max]
+ bbox, i_bbox, shp_srs = get_query_bbox(shp, return_all=True)
api = Client.open('https://planetarycomputer.microsoft.com/api/stac/v1', modifier=PC.sign_inplace)
res = api.search(collections="sentinel-2-l2a", bbox=bbox, datetime=dt)
diff --git a/TimeSeries/s2theia.py b/TimeSeries/s2theia.py
index 39593e2c02b22f55eae467783853a3f5bbdeecc6..bd9ff9f0b2035064dbc3eb296f1fb2426d5ecfce 100644
--- a/TimeSeries/s2theia.py
+++ b/TimeSeries/s2theia.py
@@ -1,11 +1,7 @@
-import sys
import warnings
-
-from osgeo import gdal,ogr
+from osgeo import gdal
import otbApplication as otb
from theia_picker import TheiaCatalog
-from pyproj import Transformer as T
-import tqdm
from Common.otb_numpy_proc import to_otb_pipeline
import numpy as np
@@ -17,25 +13,11 @@ from osgeo import osr
import datetime
import uuid
import shutil
-
+from Common.geotools import get_query_bbox
from Common.geometry import get_displacements_to_ref, get_clearest_central_image
def fetch(shp, dt, output_fld, credentials):
-
- ds = ogr.Open(shp)
- ly = ds.GetLayer(0)
- xt = ly.GetExtent()
- shp_srs = int(ly.GetSpatialRef().GetAuthorityCode(None))
- qry_srs = 4326
-
- i_bbox = [xt[0], xt[2], xt[1], xt[3]]
- if shp_srs == qry_srs:
- bbox = i_bbox
- else:
- tr = T.from_crs(shp_srs,qry_srs,always_xy=True)
- lon_min, lat_min = tr.transform(xt[0], xt[2])
- lon_max, lat_max = tr.transform(xt[1], xt[3])
- bbox = [lon_min, lat_min, lon_max, lat_max]
+ bbox = get_query_bbox(shp)
theia = TheiaCatalog(credentials)
features = theia.search(
@@ -48,11 +30,8 @@ def fetch(shp, dt, output_fld, credentials):
lst = ['FRE_B2','FRE_B3','FRE_B4','FRE_B5','FRE_B6','FRE_B7','FRE_B8','FRE_B8A',
'FRE_B11','FRE_B12','EDG_R1','SAT_R1','CLM_R1']
- #prg = tqdm.tqdm(total=len(features) * 13, desc="Fetching from Theia")
for f in features:
f.download_files(matching=lst, download_dir=output_fld)
- #prg.update()
- #prg.close()
return S2TheiaPipeline(output_fld)
diff --git a/docker/dockerfile b/docker/dockerfile
index 38766075c4abfa6bab8bdef5beba6a8453ce4cad..47c533127a544f19c49781e5ee1ced33e1e492a4 100644
--- a/docker/dockerfile
+++ b/docker/dockerfile
@@ -115,7 +115,8 @@ RUN pip install gdal==3.4.2 \
planetary_computer \
theia_picker \
fpdf2 \
- planet
+ planet \
+ eodag
RUN mkdir moringav2
COPY --chown=ubuntu . /home/ubuntu/moringav2
\ No newline at end of file
diff --git a/moringa.py b/moringa.py
index 9b66d04ba7cdfd8d1d0af8516e0b8fd7a5844e1b..e57911a86b5cad493f4cb11fcac7b8e42ce041a7 100755
--- a/moringa.py
+++ b/moringa.py
@@ -3,15 +3,18 @@ import sys
import argparse
import OBIA.segmentation
import VHR.vhrbase
-from TimeSeries import s2theia, s2planetary
+from TimeSeries import s2theia, s2planetary, s1base, s1planetary
def run_segmentation(img, threshold, cw, sw , out_seg,
n_first_iter, margin, roi, n_proc, memory,
- remove_graph, force_parallel):
+ remove_graph, force_parallel, light):
if not os.path.exists(os.path.dirname(out_seg)):
os.makedirs(os.path.dirname(out_seg))
params = OBIA.segmentation.LSGRMParams(threshold, cw, sw, n_first_iter, margin)
- OBIA.segmentation.lsgrm(img, params, out_seg, n_proc, memory, roi, remove_graph, force_parallel)
+ if light:
+ OBIA.segmentation.lsgrm_light(img, params, out_seg, n_proc, memory, roi, force_parallel)
+ else:
+ OBIA.segmentation.lsgrm(img, params, out_seg, n_proc, memory, roi, remove_graph, force_parallel)
return
def preprocess_spot67(in_fld, out_fld, dem_fld, geoid_file, skip_ps, compress,
@@ -30,8 +33,7 @@ def preprocess_spot67(in_fld, out_fld, dem_fld, geoid_file, skip_ps, compress,
return
def preprocess_s2(in_fld, out_fld, output_dates_file=None, roi=None,
- align=False, align_to=None, align_to_band=3, align_using_band=3,
- provider='theia'):
+ align_to=None, align_to_band=3, align_using_band=3, provider='theia'):
S2Processor = None
if provider == 'theia':
S2Processor = s2theia.S2TheiaPipeline
@@ -48,26 +50,50 @@ def preprocess_s2(in_fld, out_fld, output_dates_file=None, roi=None,
else:
raise ValueError("Please provide path to a text file containing output dates.")
- align_flag = align is not None
- s2.extract_feature_set(out_fld, store_gapfill=True, mosaicking='vrt', align=align_flag, align_to=align_to,
- align_to_band=align_to_band, align_using_band=align_using_band, output_aligned=align)
+ align = align_to is not None
+ if (align_to == 'self'):
+ align_to = None
+
+ s2.extract_feature_set(out_fld, store_gapfill=True, mosaicking='vrt', align=align,
+ align_to=align_to, align_to_band=align_to_band, align_using_band=align_using_band)
+ return
+
+def preprocess_s1(in_fld, roi, out_fld, dem_fld=None, geoid=None, direction=None, satellite=None,
+ skip_despeckle=False, provider='native'):
+ S1processor = None
+ if provider == 'native':
+ S1Processor = s1base.S1GRDPipeline
+ elif provider == 'planetary':
+ S1Processor = s1planetary.S1RTCPipeline
+ else:
+ raise ValueError("Unsupported/non-valid provider")
+
+ s1 = S1Processor(in_fld, roi, out_fld, direction=direction, satellite=satellite)
+ if provider == 'native':
+ assert(os.path.exists(dem_fld) and os.path.exists(geoid))
+ s1.calibrate()
+ s1.orthorectify(dem_fld, geoid)
+ s1.stitch()
+ if not skip_despeckle:
+ s1.multitemp_speckle_filter()
+ s1.compute_features()
+ s1.write_outputs(out_fld)
return
def main(args):
parser = argparse.ArgumentParser(prog="moringa", add_help=False)
subpar = parser.add_subparsers(dest="cmd")
- prepr = subpar.add_parser("preprocess_s2", help="Performs Moringa preset time series preprocessing.",
+ prepr = subpar.add_parser("preprocess_s2", help="Performs Moringa preset time series preprocessing for Sentinel-2.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
prepr.add_argument("in_folder", type=str, help="Path to the folder containing (de-zipped) S2 images.")
prepr.add_argument("out_folder", type=str, help="Path to the folder in which preprocessed stacks will be stored.")
prepr.add_argument("--output_dates_file", type=str, default=None, help="Path to the text file containing output dates for temporal interpolation.")
prepr.add_argument("--roi", type=str, default=None, help="Path to the ROI vector file.")
- prepr.add_argument("--align", type=str, default=None, help="To perform within-series image alignment, set this as output path for the aligned series.")
- prepr.add_argument("--align_to", type=str, default=None, help="Path to a (optional)) reference image to which the stacks must be aligned.")
- prepr.add_argument("--align_to_band", type=int, default=1, help="Band of reference image used for alignment.")
- prepr.add_argument("--align_using_band", type=int, default=3, help="Band of current stack used for alignment.")
- prepr.add_argument("--provider", type=str, default='theia', help="S2 image provider. Supported: 'theia', 'theial3a', 'sen2cor', 'planetary'")
+ prepr.add_argument("--align_to", type=str, default=None, help="Optional strategy for S2 spatial alignment (self/<date>/path to reference image).")
+ prepr.add_argument("--align_to_band", type=int, default=3, help="Band of reference image used for co-registration.")
+ prepr.add_argument("--align_using_band", type=int, default=3, help="Band of current stack used for co-registration.")
+ prepr.add_argument("--provider", type=str, default='theia', help="S2 image provider. Currently supported: 'theia', 'planetary'")
segmt = subpar.add_parser("segment", help="Performs (large scale Baatz-Shape) segmentation of an input image.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -76,11 +102,12 @@ def main(args):
segmt.add_argument("outimg", type=str, help="Path to the output segmentation file (.tif, .shp, .gpkg, .gml).")
segmt.add_argument("--cw", type=float, default=0.5, help="Color weight in Baatz-Shape criterion.")
segmt.add_argument("--sw", type=float, default=0.5, help="Spatial weight in Baatz-Shape criterion.")
- segmt.add_argument("--n_first_iter", type=int, default=12, help="Number of iterations for parallel tile processing.")
+ segmt.add_argument("--n_first_iter", type=int, default=12, help="Number of iterations for parallel tile processing (no use in light mode).")
segmt.add_argument("--tile_margin", type=int, default=100, help="Margin for tile overlap.")
segmt.add_argument("--roi", type=str, default=None, help="Vector file containing an ROI.")
segmt.add_argument("--n_proc", type=int, help="Number of cores to use.")
segmt.add_argument("--mem_limit", type=int, help="Memory limit in MB.")
+ segmt.add_argument("--use_light_alg", help="Use the sub-obtimal version of the algorithm. Faster but may have artefacts.", action='store_true')
segmt.add_argument("--keep_graph", help="Keep the graph files (.bin) after segmentation.", action='store_true')
segmt.add_argument("--force_parallel", help="Force the spot6/7 preprocess one-liner parallelization of the process even if the full graph fits in memory.", action='store_true')
@@ -97,6 +124,19 @@ def main(args):
vhrprep.add_argument("--skip_ps", help="Skip pansharpening step.", action='store_true')
vhrprep.add_argument("--compress", help="Use lossless compression on outputs.", action='store_true')
+ s1prepr = subpar.add_parser("preprocess_s1", help="Performs Moringa preset time series preprocessing for Sentinel-1.",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ s1prepr.add_argument("in_folder", type=str, help="Path to the folder containing (de-zipped) S1 images.")
+ s1prepr.add_argument("roi", type=str, default=None, help="Path to an image whose geometry has to be fit (e.g. a S2 scene).")
+ s1prepr.add_argument("out_folder", type=str, help="Path to the folder in which preprocessed stacks will be stored.")
+ s1prepr.add_argument("--dem_fld", type=str, default=None, help="Path to the folder containing DEM covering the scene in WGS84 projection.")
+ s1prepr.add_argument("--geoid", type=str, default=None, help="Path to the geoid file.")
+ s1prepr.add_argument("--direction", type=str, default=None, help="Filter direction (ascending/descending)")
+ s1prepr.add_argument("--satellite", type=str, default=None, help="Filter satellite (s1a/s1b)")
+ s1prepr.add_argument("--skip_despeckle", help="Skip despeckling step", action='store_true')
+ s1prepr.add_argument("--provider", type=str, default='native',
+ help="S1 image provider. Currently supported: 'native' (e.g. esa/peps), 'planetary'")
+
if len(args) == 1:
parser.print_help()
sys.exit(0)
@@ -105,17 +145,21 @@ def main(args):
if arg.cmd == "preprocess_s2":
preprocess_s2(arg.in_folder, arg.out_folder, output_dates_file=arg.output_dates_file, roi=arg.roi,
- align=arg.align, align_to=arg.align_to, align_to_band=arg.align_to_band,
- align_using_band=arg.align_using_band, provider=arg.provider)
+ align_to=arg.align_to, align_to_band=arg.align_to_band, align_using_band=arg.align_using_band,
+ provider=arg.provider)
if arg.cmd == "segment":
run_segmentation(arg.img, arg.threshold, arg.cw, arg.sw, arg.outimg, arg.n_first_iter, arg.tile_margin,
- arg.roi, arg.n_proc, arg.mem_limit, not arg.keep_graph, arg.force_parallel)
+ arg.roi, arg.n_proc, arg.mem_limit, not arg.keep_graph, arg.force_parallel, arg.use_light_alg)
if arg.cmd == "preprocess_spot67":
preprocess_spot67(arg.fld, arg.out_fld, arg.dem_fld, arg.geoid, arg.skip_ps, arg.compress,
arg.clip, arg.align_to, arg.align_to_band, arg.align_using_band)
+ if arg.cmd == "preprocess_s1":
+ preprocess_s1(arg.in_folder, arg.roi, arg.out_folder, arg.dem_fld, arg.geoid, arg.direction, arg.satellite,
+ arg.skip_despeckle, arg.provider)
+
return 0
if __name__ == "__main__":