def prepare_tile_low_par(self, tile):
if self.args.refr is not None:
tile_reference_eroded = self.get_output_path(
"reference-eroded-{}.tif", tile.id)
tile_spectral_features = self.get_output_path(
"spectral-features-{}.tif", tile.id)
tile_reference_trimmed_full = self.get_output_path(
"reference-trimmed-full-{}.tif", tile.id)
tile_reference_trimmed = self.get_output_path(
"reference-trimmed-{}.tif", tile.id)
step_args = [
"otbcli", "Trimming", self.args.buildfolder, "-progress",
"false", "-alpha", self.args.alpha, "-nbsamples", 0, "-seed",
self.args.rseed, "-feat", tile_spectral_features, "-ref",
tile_reference_eroded, "-out", tile_reference_trimmed_full
]
run_step(Step("Trimming_" + tile.id, step_args))
bm_exp = "im1b1 == -10000 ? -10000 : 0"
for cl in self.args.crop_classes:
bm_exp += " || im1b1 == " + str(cl)
bm_exp += " ? 1 : 0"
step_args = [
"otbcli_BandMath", "-progress", "false", "-il",
tile_reference_trimmed_full, "-out", tile_reference_trimmed,
"-exp", bm_exp
]
run_step(Step("ClassifyReference_" + tile.id, step_args))
if not self.args.keepfiles:
os.remove(tile_reference_trimmed_full)
def train_stratum(self, stratum):
features_shapefile = self.get_output_path("features-{}.shp",
stratum.id)
split_features(stratum, self.args.refp, self.args.outdir)
area_training_polygons = self.get_output_path(
"training_polygons-{}.shp", stratum.id)
area_validation_polygons = self.get_output_path(
"validation_polygons-{}.shp", stratum.id)
area_statistics = self.get_output_path("statistics-{}.xml", stratum.id)
area_model = self.get_output_path("model-{}.txt", stratum.id)
area_confmatout = self.get_output_path(
"confusion-matrix-training-{}.csv", stratum.id)
area_days = self.get_output_path("days-{}.txt", stratum.id)
area_descriptors = []
area_prodpertile = []
for tile in stratum.tiles:
area_descriptors += tile.get_descriptor_paths()
area_prodpertile.append(len(tile.descriptors))
run_step(
Step("SampleSelection", [
"otbcli", "SampleSelection", self.args.buildfolder, "-ref",
features_shapefile, "-ratio", self.args.ratio, "-seed",
self.args.rseed, "-tp", area_training_polygons, "-vp",
area_validation_polygons
]))
step_args = [
"otbcli", "CropTypeTrainImagesClassifier", self.args.buildfolder,
"-mission", self.args.mission.name, "-nodatalabel", -10000,
"-pixsize", self.args.pixsize, "-outdays", area_days, "-mode",
self.args.trm, "-io.vd", area_training_polygons, "-rand",
self.args.rseed, "-sample.bm", 0, "-io.confmatout",
area_confmatout, "-io.out", area_model, "-sample.mt",
self.args.nbtrsample, "-sample.mv", 10, "-sample.vfn", "CODE",
"-sample.vtr", 0.01, "-classifier", self.args.classifier
]
if self.args.red_edge:
step_args += ["-rededge", "true"]
step_args += ["-sp"] + self.args.sp
step_args += ["-prodpertile"] + area_prodpertile
step_args += ["-il"] + area_descriptors
if self.args.classifier == "rf":
step_args += [
"-classifier.rf.nbtrees", self.args.rfnbtrees,
"-classifier.rf.min", self.args.rfmin, "-classifier.rf.max",
self.args.rfmax
]
else:
step_args += [
"-classifier.svm.k", "rbf", "-classifier.svm.opt", 1,
"-imstat", area_statistics
]
run_step(Step("TrainImagesClassifier", step_args))
def prepare_tile_low_par(self, tile):
if self.args.refr is not None:
tile_reference_eroded = self.get_output_path(
"reference-eroded-{}.tif", tile.id)
tile_spectral_features = self.get_output_path(
"spectral-features-{}.tif", tile.id)
tile_reference_trimmed = self.get_output_path(
"reference-trimmed-{}.tif", tile.id)
step_args = [
"otbcli", "Trimming", self.args.buildfolder, "-progress",
"false", "-alpha", self.args.alpha, "-nbsamples", 0, "-seed",
self.args.rseed, "-feat", tile_spectral_features, "-ref",
tile_reference_eroded, "-out", tile_reference_trimmed
]
run_step(Step("Trimming_" + tile.id, step_args))
def postprocess_tile(self, tile):
if tile.crop_mask is not None:
tile_crop_map = self.get_output_path("crop_type_map_{}.tif",
tile.id)
tile_crop_map_masked = self.get_output_path(
"crop_type_map_masked_{}.tif", tile.id)
step_args = [
"otbcli_BandMath", "-progress", "false", "-exp",
"im2b1 == 0 ? 0 : im1b1", "-il", tile_crop_map, tile.crop_mask,
"-out",
format_otb_filename(tile_crop_map_masked,
compression='DEFLATE'), "int16"
]
run_step(Step("Mask by crop mask " + tile.id, step_args))
run_step(
Step("Nodata_" + tile.id, [
"gdal_edit.py", "-a_nodata", -10000, tile_crop_map_masked
]))
def prepare_tile_high_par(self, tile):
if self.args.refr is not None:
tile_reference = self.get_output_path("reference-{}.tif", tile.id)
tile_reference_eroded = self.get_output_path(
"reference-eroded-{}.tif", tile.id)
tile_spectral_features = self.get_output_path(
"spectral-features-{}.tif", tile.id)
ring = tile.footprint.GetGeometryRef(0)
ll, ur = ring.GetPoint(3), ring.GetPoint(1)
run_step(
Step("PrepareReference_" + tile.id, [
"gdalwarp", "-q", "-dstnodata", 0, "-t_srs",
tile.projection, "-te", ll[0], ll[1], ur[0], ur[1], "-tr",
self.args.pixsize, self.args.pixsize, "-overwrite",
self.args.refr, tile_reference
]))
run_step(
Step("Erosion_" + tile.id, [
"otbcli", "Erosion", self.args.buildfolder, "-progress",
"false", "-radius", self.args.eroderad, "-in",
tile_reference, "-out", tile_reference_eroded
]))
step_args = [
"otbcli", "SpectralFeaturesExtraction", self.args.buildfolder,
"-progress", "false", "-mission", self.args.mission.name,
"-pixsize", self.args.pixsize, "-lambda", self.args.lmbd,
"-out", tile_spectral_features
]
if self.args.red_edge:
step_args += ["-rededge", "true"]
step_args += ["-il"] + tile.get_descriptor_paths()
run_step(Step("SpectralFeatures_" + tile.id, step_args))
def validate(self, context):
want_validation = self.args.refp is not None or self.args.force_validation
if want_validation:
for stratum in self.strata:
area_statistics = self.get_output_path(
"confusion-matrix-validation-{}.csv", stratum.id)
area_quality_metrics = self.get_output_path(
"quality-metrics-{}.txt", stratum.id)
area_validation_metrics_xml = self.get_output_path(
"validation-metrics-{}.xml", stratum.id)
step_args = [
"otbcli", "ComputeConfusionMatrixMulti",
self.args.buildfolder, "-out", area_statistics,
"-nodatalabel", -10000, "-il"
]
for tile in stratum.tiles:
step_args.append(self.get_tile_crop_mask(tile))
if self.args.refp is not None:
area_validation_polygons = self.get_output_path(
"validation_polygons-{}.shp", stratum.id)
step_args += [
"-ref", "vector", "-ref.vector.in",
area_validation_polygons, "-ref.vector.field", "CROP"
]
else:
step_args += ["-ref", "raster", "-ref.raster.in"]
for tile in stratum.tiles:
tile_stratum_reference_trimmed = self.get_output_path(
"reference-trimmed-{}-{}.tif", stratum.id, tile.id)
step_args.append(tile_stratum_reference_trimmed)
run_step(
Step("ComputeConfusionMatrix_" + str(stratum.id),
step_args,
out_file=area_quality_metrics))
step_args = [
"otbcli", "XMLStatistics", self.args.buildfolder, "-root",
"CropMask", "-confmat", area_statistics, "-quality",
area_quality_metrics, "-out", area_validation_metrics_xml
]
run_step(Step("XMLStatistics_" + str(stratum.id), step_args))
if not self.single_stratum:
global_validation_metrics_xml = self.get_output_path(
"validation-metrics-global.xml")
if len(self.strata) > 1:
global_statistics = self.get_output_path(
"confusion-matrix-validation-global.csv")
global_quality_metrics = self.get_output_path(
"quality-metrics-global.txt")
step_args = [
"otbcli", "ComputeConfusionMatrixMulti",
self.args.buildfolder, "-out", global_statistics,
"-nodatalabel", -10000, "-il"
]
for tile in self.tiles:
step_args.append(self.get_tile_crop_mask(tile))
if self.args.refp is not None:
global_validation_polygons = self.get_output_path(
"validation_polygons_global.shp")
global_prj_file = self.get_output_path(
"validation_polygons_global.prj")
files = []
for stratum in self.strata:
area_validation_polygons = self.get_output_path(
"validation_polygons-{}.shp", stratum.id)
files.append(area_validation_polygons)
run_step(
Step("ConcatenateVectorData", [
"otbcli_ConcatenateVectorData", "-out",
global_validation_polygons, "-vd"
] + files))
first_prj_file = self.get_output_path(
"validation_polygons-{}.prj", self.strata[0].id)
shutil.copyfile(first_prj_file, global_prj_file)
step_args += [
"-ref", "vector", "-ref.vector.in",
global_validation_polygons, "-ref.vector.field",
"CROP"
]
else:
step_args += ["-ref", "raster", "-ref.raster.in"]
for tile in self.tiles:
tile_reference_trimmed = self.get_output_path(
"reference-trimmed-{}.tif", tile.id)
step_args.append(tile_reference_trimmed)
run_step(
Step("ComputeConfusionMatrix_Global",
step_args,
out_file=global_quality_metrics))
step_args = [
"otbcli", "XMLStatistics", self.args.buildfolder,
"-root", "CropMask", "-confmat", global_statistics,
"-quality", global_quality_metrics, "-out",
global_validation_metrics_xml
]
run_step(Step("XMLStatistics_Global", step_args))
else:
area_validation_metrics_xml = self.get_output_path(
"validation-metrics-{}.xml", self.strata[0].id)
shutil.copyfile(area_validation_metrics_xml,
global_validation_metrics_xml)
step_args = [
"otbcli", "ProductFormatter", self.args.buildfolder, "-destroot",
self.args.targetfolder, "-fileclass", "SVT1", "-level", "L4A",
"-baseline", "01.00", "-siteid", self.args.siteid, "-gipp",
self.get_metadata_file(), "-processor", "cropmask"
]
if self.args.refp is not None:
step_args += ["-isd", self.get_in_situ_data_file()]
if self.args.lut is not None:
qgis_lut = self.get_output_path("qgis-color-map.txt")
step_args += ["-lut", self.args.lut, "-lutqgis", qgis_lut]
if self.args.outprops is not None:
step_args += ["-outprops", self.args.outprops]
step_args.append("-processor.cropmask.file")
for tile in self.tiles:
tile_crop_mask = self.get_tile_crop_mask(tile)
step_args.append("TILE_" + tile.id)
step_args.append(tile_crop_mask)
if not self.args.skip_segmentation:
step_args.append("-processor.cropmask.rawfile")
for tile in self.tiles:
tile_crop_mask = self.get_tile_classification_output(tile)
step_args.append("TILE_" + tile.id)
step_args.append(tile_crop_mask)
step_args.append("-processor.cropmask.flags")
for tile in self.tiles:
tile_quality_flags = self.get_output_path("status_flags_{}.tif",
tile.id)
step_args.append("TILE_" + tile.id)
step_args.append(tile_quality_flags)
if want_validation:
step_args.append("-processor.cropmask.quality")
if not self.single_stratum:
global_validation_metrics_xml = self.get_output_path(
"validation-metrics-global.xml")
step_args.append(global_validation_metrics_xml)
for stratum in self.strata:
area_validation_metrics_xml = self.get_output_path(
"validation-metrics-{}.xml", stratum.id)
if not self.single_stratum:
step_args.append("REGION_" + str(stratum.id))
step_args.append(area_validation_metrics_xml)
step_args.append("-il")
step_args += self.args.input
run_step(Step("ProductFormatter", step_args))
def postprocess_tile(self, tile):
if self.args.skip_segmentation:
return
tile_crop_mask = self.get_tile_classification_output(tile)
if not os.path.exists(tile_crop_mask):
print(
"Skipping post-processing for tile {} due to missing raw mask".
format(tile.id))
return
tile_ndvi = self.get_output_path("ndvi-{}.tif", tile.id)
tile_pca = self.get_output_path("pca-{}.tif", tile.id)
tile_smoothed = self.get_output_path("smoothed-{}.tif", tile.id)
tile_smoothed_spatial = self.get_output_path("smoothed-spatial-{}.tif",
tile.id)
tile_segmentation = self.get_output_path("segmentation-{}.tif",
tile.id)
tile_segmentation_merged = self.get_output_path(
"segmentation-merged-{}.tif", tile.id)
tile_segmented = self.get_output_path("crop-mask-segmented-{}.tif",
tile.id)
if not self.args.reuse_segmentation:
needs_segmentation_merged = True
needs_segmentation = True
needs_tile_smoothed = True
needs_tile_smoothed_spatial = True
needs_pca = True
needs_ndvi = True
else:
if os.path.exists(tile_segmented):
return
needs_segmentation_merged = not os.path.exists(
tile_segmentation_merged)
needs_segmentation = needs_segmentation_merged and not os.path.exists(
tile_segmentation)
needs_tile_smoothed = needs_segmentation and not os.path.exists(
tile_smoothed)
needs_tile_smoothed_spatial = needs_segmentation and not os.path.exists(
tile_smoothed_spatial)
needs_pca = (needs_tile_smoothed or needs_tile_smoothed_spatial
) and not os.path.exists(tile_pca)
needs_ndvi = needs_pca and not os.path.exists(tile_ndvi)
if self.args.main_mission_segmentation:
tile_descriptors = tile.get_mission_descriptor_paths(
self.args.mission)
else:
tile_descriptors = tile.get_descriptor_paths()
step_args = [
"otbcli", "NDVISeries", self.args.buildfolder, "-progress",
"false", "-mission", self.args.mission.name, "-pixsize",
self.args.pixsize, "-mode", "gapfill", "-out", tile_ndvi
]
step_args += ["-il"] + tile_descriptors
step_args += ["-sp"] + self.args.sp
# if self.args.main_mission_segmentation:
# step_args += ["-mode", "gapfillmain"]
# else:
# step_args += ["-mode", "gapfill"]
if not needs_ndvi:
print("Skipping NDVI extraction for tile {}".format(tile.id))
else:
run_step(Step("NDVI Series " + tile.id, step_args))
step_args = [
"otbcli", "PrincipalComponentAnalysis", self.args.buildfolder,
"-progress", "false", "-nbcomp", self.args.nbcomp, "-bv", -10000,
"-in", tile_ndvi, "-out", tile_pca
]
if not needs_pca:
print("Skipping PCA for tile {}".format(tile.id))
else:
run_step(Step("NDVI PCA " + tile.id, step_args))
if not self.args.keepfiles:
os.remove(tile_ndvi)
step_args = [
"otbcli_MeanShiftSmoothing", "-progress", "false", "-in", tile_pca,
"-modesearch", 0, "-spatialr", self.args.spatialr, "-ranger",
self.args.ranger, "-maxiter", 20, "-fout", tile_smoothed,
"-foutpos", tile_smoothed_spatial
]
if not needs_tile_smoothed and not needs_tile_smoothed_spatial:
print("Skipping mean-shift smoothing for tile {}".format(tile.id))
else:
run_step(Step("Mean-Shift Smoothing " + tile.id, step_args))
if not self.args.keepfiles:
os.remove(tile_pca)
step_args = [
"otbcli_LSMSSegmentation", "-progress", "false", "-in",
tile_smoothed, "-inpos", tile_smoothed_spatial, "-spatialr",
self.args.spatialr, "-ranger", self.args.ranger, "-minsize", 0,
"-tilesizex", 1024, "-tilesizey", 1024, "-tmpdir",
self.args.tmpfolder, "-out",
format_otb_filename(tile_segmentation,
compression='DEFLATE'), "uint32"
]
if not needs_segmentation:
print("Skipping segmentation for tile {}".format(tile.id))
else:
run_step(Step("Segmentation " + tile.id, step_args))
step_args = [
"otbcli_LSMSSmallRegionsMerging", "-progress", "false", "-in",
tile_smoothed, "-inseg", tile_segmentation, "-minsize",
self.args.minsize, "-tilesizex", 1024, "-tilesizey", 1024, "-out",
format_otb_filename(tile_segmentation_merged,
compression='DEFLATE'), "uint32"
]
if not needs_segmentation_merged:
print("Skipping small regions merging for tile {}".format(tile.id))
else:
run_step(Step("Small regions merging " + tile.id, step_args))
if not self.args.keepfiles:
os.remove(tile_smoothed)
os.remove(tile_smoothed_spatial)
os.remove(tile_segmentation)
step_args = [
"otbcli", "MajorityVoting", self.args.buildfolder, "-progress",
"false", "-nodatasegvalue", 0, "-nodataclassifvalue", -10000,
"-minarea", self.args.minarea, "-inclass", tile_crop_mask,
"-inseg", tile_segmentation_merged, "-rout",
format_otb_filename(tile_segmented, compression='DEFLATE'), "int16"
]
run_step(Step("Majority voting " + tile.id, step_args))
if not self.args.keepfiles and not self.args.reuse_segmentation:
os.remove(tile_segmentation_merged)
def classify_tile(self, tile):
models = []
model_ids = []
days = []
statistics = []
for stratum in tile.strata:
area_model = self.get_output_path("model-{}.txt", stratum.id)
area_days = self.get_output_path("days-{}.txt", stratum.id)
area_statistics = self.get_output_path("statistics-{}.xml",
stratum.id)
models.append(area_model)
model_ids.append(stratum.id)
days.append(area_days)
statistics.append(area_statistics)
if len(models) == 0:
print("Skipping classification for tile {} due to stratum filter".
format(tile.id))
return
if not self.single_stratum:
tile_model_mask = self.get_output_path("model-mask-{}.tif",
tile.id)
run_step(
Step("Rasterize model mask", [
"otbcli_Rasterization", "-progress", "false", "-mode",
"attribute", "-mode.attribute.field", "ID", "-in",
self.args.filtered_strata, "-im", tile.reference_raster,
"-out",
format_otb_filename(tile_model_mask,
compression='DEFLATE'), "uint8"
]))
tile_crop_mask_uncompressed = self.get_output_path(
"crop_mask_map_{}_uncompressed.tif", tile.id)
if self.args.refp is not None:
step_args = [
"otbcli", "CropMaskImageClassifier", self.args.buildfolder,
"-progress", "false", "-mission", self.args.mission.name,
"-pixsize", self.args.pixsize, "-bv", -10000, "-nodatalabel",
-10000, "-bm", "true" if self.args.bm else "false", "-out",
tile_crop_mask_uncompressed, "-indays"
] + days
if self.args.red_edge:
step_args += ["-rededge", "true"]
step_args += ["-model"] + models
step_args += ["-il"] + tile.get_descriptor_paths()
if self.args.classifier == "svm":
step_args += ["-imstat"] + statistics
if not self.single_stratum:
step_args += ["-mask", tile_model_mask]
step_args += ["-modelid"] + model_ids
else:
tile_spectral_features = self.get_output_path(
"spectral-features-{}.tif", tile.id)
step_args = [
"otbcli", "MultiModelImageClassifier", self.args.buildfolder,
"-progress", "false", "-in", tile_spectral_features, "-out",
tile_crop_mask_uncompressed
]
step_args += ["-model"] + models
if not self.single_stratum:
step_args += ["-mask", tile_model_mask]
step_args += ["-modelid"] + model_ids
run_step(
Step("ImageClassifier_{}".format(tile.id), step_args, retry=True))
if not self.args.keepfiles:
if not self.single_stratum:
os.remove(tile_model_mask)
if self.args.refp is None:
tile_spectral_features = self.get_output_path(
"spectral-features-{}.tif", tile.id)
os.remove(tile_spectral_features)
tile_crop_mask_map = self.get_tile_classification_output(tile)
step_args = [
"otbcli_Convert", "-progress", "false", "-in",
tile_crop_mask_uncompressed, "-out",
format_otb_filename(tile_crop_mask_map,
compression='DEFLATE'), "int16"
]
run_step(Step("Compression_{}".format(tile.id), step_args))
if not self.args.keepfiles:
os.remove(tile_crop_mask_uncompressed)
def train_stratum(self, stratum):
area_model = self.get_output_path("model-{}.txt", stratum.id)
area_confmatout = self.get_output_path(
"confusion-matrix-training-{}.csv", stratum.id)
if self.args.refp is not None:
features_shapefile = self.get_output_path("features-{}.shp",
stratum.id)
split_features(stratum, self.args.refp, self.args.outdir)
area_training_polygons = self.get_output_path(
"training_polygons-{}.shp", stratum.id)
area_validation_polygons = self.get_output_path(
"validation_polygons-{}.shp", stratum.id)
area_statistics = self.get_output_path("statistics-{}.xml",
stratum.id)
area_days = self.get_output_path("days-{}.txt", stratum.id)
area_descriptors = []
area_prodpertile = []
for tile in stratum.tiles:
area_descriptors += tile.get_descriptor_paths()
area_prodpertile.append(len(tile.descriptors))
run_step(
Step("SampleSelection", [
"otbcli", "SampleSelection", self.args.buildfolder, "-ref",
features_shapefile, "-ratio", self.args.ratio, "-seed",
self.args.rseed, "-nofilter", "true", "-tp",
area_training_polygons, "-vp", area_validation_polygons
]))
step_args = [
"otbcli", "CropMaskTrainImagesClassifier",
self.args.buildfolder, "-progress", "false", "-mission",
self.args.mission.name, "-nodatalabel", -10000, "-pixsize",
self.args.pixsize, "-outdays", area_days, "-mode",
self.args.trm, "-io.vd", area_training_polygons, "-rand",
self.args.rseed, "-sample.bm", 0, "-io.confmatout",
area_confmatout, "-io.out", area_model, "-sample.mt",
self.args.nbtrsample, "-sample.mv", 10, "-sample.vfn", "CROP",
"-sample.vtr", 0.01, "-window", self.args.window, "-bm",
"true" if self.args.bm else "false", "-classifier",
self.args.classifier
]
if self.args.red_edge:
step_args += ["-rededge", "true"]
step_args += ["-sp"] + self.args.sp
step_args += ["-prodpertile"] + area_prodpertile
step_args += ["-il"] + area_descriptors
if self.args.classifier == "rf":
step_args += [
"-classifier.rf.nbtrees", self.args.rfnbtrees,
"-classifier.rf.min", self.args.rfmin,
"-classifier.rf.max", self.args.rfmax
]
else:
step_args += [
"-classifier.svm.k", "rbf", "-classifier.svm.opt", 1,
"-imstat", area_statistics
]
run_step(Step("TrainImagesClassifier", step_args))
else:
for tile in stratum.tiles:
tile_reference_trimmed = self.get_output_path(
"reference-trimmed-{}.tif", tile.id)
tile_stratum_reference_trimmed = self.get_output_path(
"reference-trimmed-{}-{}.tif", stratum.id, tile.id)
self.rasterize_tile_mask(stratum, tile)
stratum_tile_mask = self.get_stratum_tile_mask(stratum, tile)
step_args = [
"otbcli_BandMath", "-progress", "false", "-exp",
"im1b1 > 0 ? im2b1 : -10000", "-il", stratum_tile_mask,
tile_reference_trimmed, "-out",
format_otb_filename(tile_stratum_reference_trimmed,
compression='DEFLATE'), "int16"
]
run_step(Step("BandMath_" + str(tile.id), step_args))
area_model = self.get_output_path("model-{}.txt", stratum.id)
area_confmatout = self.get_output_path(
"confusion-matrix-training-{}.csv", stratum.id)
if self.args.classifier == "svm":
files = []
for tile in stratum.tiles:
tile_spectral_features = self.get_output_path(
"spectral-features-{}.tif", tile.id)
tile_stratum_spectral_features = self.get_output_path(
"spectral-features-{}-{}.tif", stratum.id, tile.id)
stratum_tile_mask = self.get_stratum_tile_mask(
stratum, tile)
step_args = [
"otbcli_BandMath", "-progress", "false", "-exp",
"im1b1 > 0 ? im2b1 : -10000", "-il", stratum_tile_mask,
tile_spectral_features, "-out",
format_otb_filename(tile_stratum_spectral_features,
compression='DEFLATE'), "int16"
]
run_step(Step("BandMath_" + str(tile.id), step_args))
files.append(tile_stratum_spectral_features)
step_args = [
"otbcli_ComputeImagesStatistics", "-bv", -10000, "-out",
area_statistics, "-il"
] + files
step_args = [
"otbcli", "TrainImagesClassifierNew", self.args.buildfolder,
"-nodatalabel", -10000, "-rand", self.args.rseed, "-sample.bm",
0, "-io.confmatout", area_confmatout, "-io.out", area_model,
"-sample.mt", self.args.nbtrsample, "-sample.mv", 1000,
"-sample.vfn", "CROP", "-sample.vtr", 0.01, "-classifier",
self.args.classifier
]
if self.args.classifier == "rf":
step_args += [
"-classifier.rf.nbtrees", self.args.rfnbtrees,
"-classifier.rf.min", self.args.rfmin,
"-classifier.rf.max", self.args.rfmax
]
else:
step_args += [
"-classifier.svm.k", "rbf", "-classifier.svm.opt", 1,
"-imstat", area_statistics
]
step_args.append("-io.rs")
for tile in stratum.tiles:
tile_stratum_reference_trimmed = self.get_output_path(
"reference-trimmed-{}-{}.tif", stratum.id, tile.id)
step_args.append(tile_stratum_reference_trimmed)
step_args.append("-io.il")
for tile in stratum.tiles:
tile_spectral_features = self.get_output_path(
"spectral-features-{}.tif", tile.id)
step_args.append(tile_spectral_features)
run_step(Step("TrainImagesClassifier", step_args))
def validate(self, context):
for stratum in self.strata:
area_validation_polygons = self.get_output_path(
"validation_polygons-{}.shp", stratum.id)
area_statistics = self.get_output_path(
"confusion-matrix-validation-{}.csv", stratum.id)
area_quality_metrics = self.get_output_path(
"quality-metrics-{}.txt", stratum.id)
area_validation_metrics_xml = self.get_output_path(
"validation-metrics-{}.xml", stratum.id)
step_args = [
"otbcli", "ComputeConfusionMatrixMulti", self.args.buildfolder,
"-ref", "vector", "-ref.vector.in", area_validation_polygons,
"-ref.vector.field", "CODE", "-out", area_statistics,
"-nodatalabel", -10000, "-il"
]
for tile in stratum.tiles:
step_args.append(self.get_tile_crop_map(tile))
run_step(
Step("ComputeConfusionMatrix_" + str(stratum.id),
step_args,
out_file=area_quality_metrics))
step_args = [
"otbcli", "XMLStatistics", self.args.buildfolder, "-root",
"CropType", "-confmat", area_statistics, "-quality",
area_quality_metrics, "-out", area_validation_metrics_xml
]
run_step(Step("XMLStatistics_" + str(stratum.id), step_args))
if not self.single_stratum:
global_validation_metrics_xml = self.get_output_path(
"validation-metrics-global.xml")
if len(self.strata) > 1:
global_validation_polygons = self.get_output_path(
"validation_polygons_global.shp")
global_prj_file = self.get_output_path(
"validation_polygons_global.prj")
global_statistics = self.get_output_path(
"confusion-matrix-validation-global.csv")
global_quality_metrics = self.get_output_path(
"quality-metrics-global.txt")
files = []
for stratum in self.strata:
area_validation_polygons = self.get_output_path(
"validation_polygons-{}.shp", stratum.id)
files.append(area_validation_polygons)
step_args = [
"otbcli_ConcatenateVectorData", "-out",
global_validation_polygons, "-vd"
] + files
run_step(Step("ConcatenateVectorData", step_args))
first_prj_file = self.get_output_path(
"validation_polygons-{}.prj", self.strata[0].id)
shutil.copyfile(first_prj_file, global_prj_file)
step_args = [
"otbcli", "ComputeConfusionMatrixMulti",
self.args.buildfolder, "-ref", "vector", "-ref.vector.in",
global_validation_polygons, "-ref.vector.field", "CODE",
"-out", global_statistics, "-nodatalabel", -10000, "-il"
]
for tile in self.tiles:
step_args.append(self.get_tile_crop_map(tile))
run_step(
Step("ComputeConfusionMatrix_Global",
step_args,
out_file=global_quality_metrics))
step_args = [
"otbcli", "XMLStatistics", self.args.buildfolder, "-root",
"CropType", "-confmat", global_statistics, "-quality",
global_quality_metrics, "-out",
global_validation_metrics_xml
]
run_step(Step("XMLStatistics_Global", step_args))
else:
area_validation_metrics_xml = self.get_output_path(
"validation-metrics-{}.xml", self.strata[0].id)
shutil.copyfile(area_validation_metrics_xml,
global_validation_metrics_xml)
step_args = [
"otbcli", "ProductFormatter", self.args.buildfolder, "-destroot",
self.args.targetfolder, "-fileclass", "SVT1", "-level", "L4B",
"-baseline", "01.00", "-siteid", self.args.siteid, "-gipp",
self.get_metadata_file(), "-isd",
self.get_in_situ_data_file(), "-processor", "croptype"
]
if self.args.lut is not None:
qgis_lut = self.get_output_path("qgis-color-map.txt")
step_args += ["-lut", self.args.lut, "-lutqgis", qgis_lut]
if self.args.outprops is not None:
step_args += ["-outprops", self.args.outprops]
has_mask = False
for tile in self.tiles:
if tile.crop_mask is not None:
has_mask = True
break
step_args.append("-processor.croptype.file")
for tile in self.tiles:
tile_crop_map = self.get_tile_crop_map(tile)
step_args.append("TILE_" + tile.id)
step_args.append(tile_crop_map)
if has_mask and self.args.include_raw_map:
step_args.append("-processor.croptype.rawfile")
for tile in self.tiles:
if tile.crop_mask is not None:
tile_crop_map = self.get_output_path(
"crop_type_map_{}.tif", tile.id)
step_args.append("TILE_" + tile.id)
step_args.append(tile_crop_map)
step_args.append("-processor.croptype.flags")
for tile in self.tiles:
tile_quality_flags = self.get_output_path("status_flags_{}.tif",
tile.id)
step_args.append("TILE_" + tile.id)
step_args.append(tile_quality_flags)
step_args.append("-processor.croptype.quality")
if not self.single_stratum:
global_validation_metrics_xml = self.get_output_path(
"validation-metrics-global.xml")
step_args.append(global_validation_metrics_xml)
for stratum in self.strata:
area_validation_metrics_xml = self.get_output_path(
"validation-metrics-{}.xml", stratum.id)
if not self.single_stratum:
step_args.append("REGION_" + str(stratum.id))
step_args.append(area_validation_metrics_xml)
step_args.append("-il")
step_args += self.args.input
run_step(Step("ProductFormatter", step_args))
def run_step_list(steps):
for step in steps:
run_step(step)
