def step_inputs(self):
"""
Return
------
the return could be and iterable or a callable
"""
from simplification import MergeTileRasters as mtr
from simplification import VectAndSimp as vas
from Common import FileUtils as fut
if os.path.exists(self.grid):
return mtr.getListVectToClip(self.outmos, self.clipfield,
self.outfilevect)
else:
params = []
if not self.clipvalue:
valsfield = vas.getFieldValues(self.clipfile, self.clipfield)
for val in valsfield:
params.append([
fut.FileSearch_AND(self.outmos, True, ".shp",
"hermite")[0], val
])
else:
params = [
fut.FileSearch_AND(self.outmos, True, ".shp", "hermite"),
self.clipvalue
]
print(params)
return params
def getAllDatasByDate(currentTile, rasterPatterns, masksPatterns, arboRaster,
arboMask, TileFolder, rasterInitValue, masksInitValues):
#get all raster into the current tile and store it with init value
buf = []
r = []
for currentPattern in rasterPatterns:
r += fu.fileSearchRegEx(TileFolder + currentTile + arboRaster +
currentPattern)
for currentR in r:
buf.append((currentR, rasterInitValue))
for currentPattern, currentMaskInit in zip(masksPatterns, masksInitValues):
m = []
m += fu.fileSearchRegEx(TileFolder + currentTile + arboMask +
currentPattern)
for currentM in m:
buf.append((currentM, currentMaskInit))
#sort it by date
buff = [(getDateFromRaster(currentRaster[0]), currentRaster)
for currentRaster in buf]
buff = fu.sortByFirstElem(buff)
allDates = []
allRasters = []
for date, rasters in buff:
allDates.append(date)
allRasters.append(rasters)
return allRasters, allDates
def extraction(vectorFill, vectorSource, field, field_val, driversFill,
driversSource):
ogrDriversSource = ogr.GetDriverByName(driversSource)
dataSourceSource = ogrDriversSource.Open(vectorSource, 0)
layerSource = dataSourceSource.GetLayer()
print("RECHERCHE DES FIDs")
All_FID = [(currentFeat.GetField(field), str(currentFeat.GetFID()))
for currentFeat in layerSource
if currentFeat.GetField(field) in field_val]
print("FIDs trouvée")
layerSource.ResetReading()
All_FID = fu.sortByFirstElem(All_FID)
for currentClass, FID in All_FID:
splits = fu.splitList(FID, len(vectorFill))
for currentSplit, currentVectorFill in zip(splits, vectorFill):
cmd = "ogr2ogr -append " + currentVectorFill + " " + vectorSource + " -where \" fid in (" + ",".join(
currentSplit) + ")\""
print(cmd)
print("Ajout de " + str(currentClass) + " dans " +
currentVectorFill.split("/")[-1])
os.system(cmd)
def DataAugmentationSynthetic(samples,
groundTruth,
dataField,
strategies,
workingDirectory=None):
"""Compute how many samples should be add in the sample set and launch data augmentation method
Parameters
----------
samples : string
path to a vector file to augment samples
groundTruth : string
path to the original ground truth vector file, in order to list interger / float fields
dataField : string
data field's name in samples
strategies : dict
dictionary
workingDirectory : string
path to a working directory
"""
if GetRegionFromSampleName(samples) in strategies[
"target_models"] or "all" in strategies["target_models"]:
from collections import Counter
class_count = Counter(
fut.getFieldElement(samples,
driverName="SQLite",
field=dataField,
mode="all",
elemType="int"))
class_augmentation = SamplesAugmentationCounter(
class_count,
mode=strategies["samples.strategy"],
minNumber=strategies.get("samples.strategy.minNumber", None),
byClass=strategies.get("samples.strategy.byClass", None))
fields_types = GetFieldsType(groundTruth)
excluded_fields_origin = [
field_name.lower()
for field_name, field_type in list(fields_types.items())
if "int" in field_type or "flaot" in field_type
]
samples_fields = fut.get_all_fields_in_shape(samples, driver='SQLite')
excluded_fields = list(
set(excluded_fields_origin).intersection(samples_fields))
excluded_fields.append("originfid")
DoAugmentation(samples,
class_augmentation,
strategy=strategies["strategy"],
field=dataField,
excluded_fields=excluded_fields,
Jstdfactor=strategies.get("strategy.jitter.stdfactor",
None),
Sneighbors=strategies.get("strategy.smote.neighbors",
None),
workingDirectory=workingDirectory)
def launchFit(sensorName, cmdPath, workingDirectory, outDirectory, outGridPath,
outputGrid_tileNameField, outPixRes, outputProjection,
interpolator, inputGrid_tileNameField, inputGRID, arboMask,
masksInitValues, masksPatterns, arboRaster, rasterInitValue,
rasterPatterns, TileFolder):
createOutGrid(TileFolder, rasterPatterns[0], outputProjection,
outputGrid_tileNameField, outGridPath)
intersections = getIntersections(inputGRID, outGridPath,
inputGrid_tileNameField,
outputGrid_tileNameField)
allCmd = []
for refTile, tiles in intersections:
outFolder_tile = outDirectory + "/" + refTile
if not os.path.exists(outFolder_tile):
os.mkdir(outFolder_tile)
minX_ref, maxX_ref, minY_ref, maxY_ref = getTileEnvelope(
inputGRID, inputGrid_tileNameField, refTile)
for currentTile in tiles:
datas, dates = getAllDatasByDate(currentTile, rasterPatterns,
masksPatterns, arboRaster,
arboMask, TileFolder,
rasterInitValue, masksInitValues)
for currentDatas, currentDate in zip(datas, dates):
outFolderDate = outFolder_tile + "/" + sensorName + "_" + refTile + "_" + currentDate + "_" + currentTile
outFolderDateMask = outFolderDate + "/MASKS"
if not os.path.exists(outFolderDate):
os.mkdir(outFolderDate)
os.mkdir(outFolderDateMask)
for currentRaster in currentDatas:
folder = outFolderDate
print currentRaster
if checkMaskFromRaster(currentRaster[0], masksPatterns):
folder = outFolderDateMask
outFolder = folder
if workingDirectory:
folder = workingDirectory + "/"
outName = currentRaster[0].split("/")[-1].replace(
currentTile,
refTile).replace(".tif", "_" + currentTile + ".tif")
out = folder + "/" + outName
cmd = "gdalwarp -t_srs EPSG:" + outputProjection + " -wo INIT_DEST=" + currentRaster[
1] + " -te " + str(minX_ref) + " " + str(
minY_ref
) + " " + str(maxX_ref) + " " + str(
maxY_ref
) + " -tr " + outPixRes + " -" + outPixRes + " -r " + interpolator + " " + currentRaster[
0] + " " + out
if not os.path.exists(outFolder + "/" + outName):
allCmd.append(cmd)
run(cmd)
if workingDirectory:
shutil.copy(out, outFolder + "/" + outName)
os.remove(out)
fu.writeCmds(cmdPath, allCmd)
def computeStats(pathConf, wD=None):
dataField = Config(file(pathConf)).chain.dataField
iota2Folder = Config(file(pathConf)).chain.outputPath
runs = Config(file(pathConf)).chain.runs
workingDirectory = iota2Folder + "/final/TMP"
if wD:
workingDirectory = wD
statsBySeed = []
for seed in range(runs):
#Get sqlites
dataBase = fut.FileSearch_AND(iota2Folder + "/final/TMP", True,
".sqlite", "extraction",
"learn") #stats only on learnt polygons
#dataBase = fut.FileSearch_AND("/work/OT/theia/oso/TMP/sampleExtraction", True, ".sqlite", "extraction")
finalDataBaseName = "statsDataBase_run_" + str(
seed) + ".sqlite" #will contain all data base
finalDataBasePath = workingDirectory + "/" + finalDataBaseName
if os.path.exists(finalDataBasePath):
os.remove(finalDataBasePath)
shutil.copy(dataBase[0], finalDataBasePath)
del dataBase[0]
fields = "GEOMETRY," + ",".join(
fut.getAllFieldsInShape(finalDataBasePath, driver='SQLite'))
conn = lite.connect(finalDataBasePath)
cursor = conn.cursor()
cursor.execute("select name from sqlite_master where type = 'table';")
tableName = str(cursor.fetchall()[-1][0])
print "Fill up statistics dataBase"
for currentDataBase in dataBase:
print("Add dataBase : {}".format(currentDataBase))
cursor.execute("ATTACH '%s' as db2;" % (currentDataBase))
cursor.execute("CREATE TABLE output2 AS SELECT * FROM db2.output;")
cursor.execute("INSERT INTO " + tableName + "(" + fields +
") SELECT " + fields + " FROM output2;")
conn.commit()
conn = cursor = None
conn = lite.connect(finalDataBasePath)
cursor = conn.cursor()
cleanSqliteDatabase(finalDataBasePath, "output2")
#plot relation
plotsSeed = plotRelation(finalDataBasePath, dataField, seed,
iota2Folder)
#Compute statistics
print "Compute statistics"
statsByClass = computeStatistics(finalDataBasePath, dataField)
statsBySeed.append(statsByClass)
return statsBySeed
def test_tdx_client_get_local_1min_bars(self):
df = self.tdx_client.get_local_stock_bars(
FileUtils.convert_file_path_based_on_system(
".\\LC1\\SZ\\sz000001.lc1"), StockDataType.ONE_MIN)
print(df)
df.to_csv(
FileUtils.convert_file_path_based_on_system(".\\CSV\\tdx001.csv"))
print(df.columns.values)
df['date_index'] = pd.to_datetime(df['date']).dt.strftime(
DatetimeUtils.DATE_FORMAT)
print(df)
def mergeSubVector(inpath, classes="", inbase="dept_", outbase="departement_"):
listout = fut.FileSearch_AND(inpath, True, inbase, ".shp", "chk")
listofchkofzones = fut.sortByFirstElem([
("_".join(x.split('_')[0:len(x.split('_')) - 1]), x) for x in listout
])
for zone in listofchkofzones:
zoneval = zone[0].split('_')[len(zone[0].split('_')) -
1:len(zone[0].split('_'))]
outfile = os.path.join(inpath, outbase + zoneval[0] + '.shp')
mf.mergeVectors(zone[1], outfile)
iota2Formatting(outfile, classes, outfile)
def check_errors_JA(log_dir, task_name):
"""
"""
from Common import FileUtils as fut
if os.path.isdir(log_dir):
all_logs = fut.FileSearch_AND(log_dir, True, ".ER")
else:
all_logs = fut.FileSearch_AND(os.path.split(log_dir)[0], True, task_name, ".log")
errors = []
for log in all_logs:
if check_errors(log):
errors.append(check_errors(log))
return errors
def getIntersections(outGridPath, inGridPath, tileField_first,
tileField_second):
"""
OUT
AllIntersections [list of tuple] : [(S2Tile, [L8Tile, L8Tile, L8Tile]), (...), ...]
"""
driver = ogr.GetDriverByName("ESRI Shapefile")
dataOut = driver.Open(outGridPath, 0)
dataIn = driver.Open(inGridPath, 0)
layerOut = dataOut.GetLayer()
layerIn = dataIn.GetLayer()
AllIntersections = [] #Ex : [[S2, [L8, L8, ..., L8]], [], ...]
outTiles = [(outTile.GetGeometryRef().Clone(),
outTile.GetField(tileField_first)) for outTile in layerOut]
inTiles = [(inTile.GetGeometryRef().Clone(),
inTile.GetField(tileField_second)) for inTile in layerIn]
for outTileGeom, outTile in outTiles:
for inTileGeom, inTile in inTiles:
intersection = outTileGeom.Intersection(inTileGeom)
if intersection.GetArea() != 0.0 and (
outTile, inTile) not in AllIntersections:
AllIntersections.append((outTile, inTile))
return fu.sortByFirstElem(AllIntersections)
def listTileEntities(raster, outpath, feature):
"""
entities ID list of tile
in :
raster : bi-band raster (classification - clump)
outpath : out directory
feature : feature of tile from shapefile
out :
tile_id : list with ID
"""
# Classification and Clump opening
datas_classif, xsize_classif, ysize_classif, projection_classif, transform_classif = fu.readRaster(raster, True, 1)
datas_clump, xsize_clump, ysize_clump, projection_clump, transform_clump = fu.readRaster(raster, True, 2)
# Generate pixel coordinates of square feature corresponding to raster transform
cols_xmin_decoup, cols_xmax_decoup, cols_ymin_decoup, cols_ymax_decoup = cellCoords(feature, transform_classif)
# subset raster data array based on feature coordinates
tile_classif = datas_classif[cols_ymin_decoup:cols_ymax_decoup, cols_xmin_decoup:cols_xmax_decoup]
tile_id_all = datas_clump[cols_ymin_decoup:cols_ymax_decoup, cols_xmin_decoup:cols_xmax_decoup]
del datas_classif, datas_clump
# entities ID list of tile (except nodata and sea)
tile_id = np.unique(np.where(((tile_classif > 1) & (tile_classif < 250)), tile_id_all, 0)).tolist()
# delete 0 value
tile_id = [int(x) for x in tile_id if x != 0]
return tile_id
def compute_fusion_options(iota2_dir_final, final_classifications, method,
undecidedlabel, dempstershafer_mob, pixType,
fusion_path):
""" use to determine fusion parameters
"""
if method == "majorityvoting":
options = {
"il": final_classifications,
"method": method,
"nodatalabel": "0",
"undecidedlabel": str(undecidedlabel),
"pixType": pixType,
"out": fusion_path
}
else:
confusionSeed = [
fut.FileSearch_AND(os.path.join(iota2_dir_final, "TMP"), True,
"Classif_Seed_{}.csv".format(run))[0]
for run in range(len(final_classifications))
]
confusionSeed.sort()
final_classifications.sort()
options = {
"il": final_classifications,
"method": "dempstershafer",
"nodatalabel": "0",
"undecidedlabel": str(undecidedlabel),
"method.dempstershafer.mob": dempstershafer_mob,
"method.dempstershafer.cmfl": confusionSeed,
"pixType": pixType,
"out": fusion_path
}
return options
def getNbSample(shape,
tile,
dataField,
valToFind,
resol,
region,
coeff,
current_seed,
region_field,
region_val="-1"):
driver = ogr.GetDriverByName("ESRI Shapefile")
buff = []
dataSource = driver.Open(shape, 0)
layer = dataSource.GetLayer()
for feature in layer:
if str(feature.GetField(dataField)) in valToFind and str(
feature.GetField(region_field)) == str(region_val) and str(
feature.GetField("seed_" + str(current_seed))) == "learn":
geom = feature.GetGeometryRef()
buff.append((feature.GetField(dataField), geom.GetArea()))
rep = fu.sortByFirstElem(buff)
repDict = {}
for currentClass, currentAreas in rep:
array = np.asarray(currentAreas)
totalArea = np.sum(array)
repDict[currentClass] = int(
(float(coeff) * totalArea) / (int(resol) * int(resol)))
return repDict
def GetDataAugmentationSyntheticParameters(IOTA2_dir):
""" read the */learningSample* directory
parse the directory */learningSamples* and return a list of all sqlite files
Parameters
----------
IOTA2_dir : string
absolute path to the IOTA2's directory
Example
-------
>>> os.listdir("/learningSamples")
["Samples_region_2_seed0_learn.sqlite",
"Samples_region_2_seed1_learn.sqlite",
"Samples_region_1_seed0_learn.sqlite",
"Samples_region_1_seed1_learn.sqlite"]
>>> GetAugmentationSamplesParameters("/IOTA2")
[Samples_region_1_seed0_learn.sqlite, Samples_region_2_seed0_learn.sqlite,
Samples_region_1_seed1_learn.sqlite, Samples_region_2_seed1_learn.sqlite]
Return
------
list
a list of sqlite files containing samples
"""
IOTA2_dir_learningSamples = os.path.join(IOTA2_dir, "learningSamples")
return fut.FileSearch_AND(IOTA2_dir_learningSamples, True, ".sqlite")
def test_Basic(self):
"""
this test verify if features labels generated are similar to a reference
produce thanks to a specific configuration file
"""
from Sampling.DataExtraction import VectorSampler
from Common import IOTA2Directory
#expected output
ref_path = os.path.join(self.iota2_directory, "data", "references",
"iota2tests_features_labels_test_Basic.txt")
#test inputs
vector_file = os.path.join(self.iota2_directory, "data", "references",
"sampler",
"D0005H0002_polygons_To_Sample.shp")
L8_rasters = os.path.join(self.iota2_directory, "data", "L8_50x50")
#generate IOTA output directory
IOTA2Directory.GenerateDirectories(self.test_working_directory)
#fill up configuration file
self.config.setParam('chain', 'outputPath',
self.test_working_directory)
self.config.setParam('chain', 'listTile', "D0005H0002")
self.config.setParam('chain', 'featuresPath',
self.test_working_directory_tmp)
self.config.setParam('chain', 'L8Path', L8_rasters)
self.config.setParam('chain', 'userFeatPath', 'None')
self.config.setParam('argTrain', 'samplesOptions',
'-sampler random -strategy all')
self.config.setParam('argTrain', 'cropMix', False)
self.config.setParam('argTrain', 'samplesClassifMix', False)
self.config.setParam('GlobChain', 'useAdditionalFeatures', False)
#Launch sampling
VectorSampler.generateSamples(vector_file, None, self.config)
test_vector = fut.fileSearchRegEx(self.test_working_directory +
"/learningSamples/*sqlite")[0]
test_field_list = fut.getAllFieldsInShape(test_vector, driver='SQLite')
with open(ref_path, 'r') as f:
ref_field_list = [line.rstrip() for line in f]
#check outputs
self.assertTrue(ref_field_list == test_field_list)
def getAll_regions(tileName, folder):
allRegion = []
allShape = fu.FileSearch_AND(folder, True, "learn", tileName, ".shp")
for currentShape in allShape:
currentRegion = currentShape.split("/")[-1].split("_")[2]
if currentRegion not in allRegion:
allRegion.append(currentRegion)
return allRegion
def __init__(self):
self.__parser = configparser.ConfigParser()
current_file = os.path.abspath(os.path.dirname(__file__))
config_file_path = os.path.join(
current_file,
FileUtils.convert_file_path_based_on_system('../../config.json'))
with open(config_file_path, 'r') as config_file:
self.__config = json.load(config_file)
def step_inputs(self):
"""
Return
------
the return could be and iterable or a callable
"""
from Common import FileUtils as fut
return fut.getCmd(os.path.join(self.output_path, "cmd", "confusion", "confusion.txt"))
def genRasterEnvelope(raster, outputShape):
"""
raster [string]
workingDir [string] workingDirectory
"""
rasterName = os.path.splitext(os.path.split(raster)[-1])[0]
minX, maxX, minY, maxY = fut.getRasterExtent(raster)
epsg = fut.getRasterProjectionEPSG(raster)
driver = ogr.GetDriverByName("ESRI Shapefile")
data_source = driver.CreateDataSource(outputShape)
srs = osr.SpatialReference()
srs.ImportFromEPSG(int(epsg))
out_lyr = data_source.CreateLayer(rasterName,
srs,
geom_type=ogr.wkbPolygon)
field_ext = ogr.FieldDefn("ext", ogr.OFTString)
field_ext.SetWidth(24)
out_lyr.CreateField(field_ext)
#geom
ul = (minX, maxY)
ur = (maxX, maxY)
lr = (maxX, minY)
ll = (minX, minY)
ring = ogr.Geometry(ogr.wkbLinearRing)
ring.AddPoint(ul[0], ul[1])
ring.AddPoint(ur[0], ur[1])
ring.AddPoint(lr[0], lr[1])
ring.AddPoint(ll[0], ll[1])
ring.AddPoint(ul[0], ul[1])
# Create polygon
poly = ogr.Geometry(ogr.wkbPolygon)
poly.AddGeometry(ring)
feature = ogr.Feature(out_lyr.GetLayerDefn())
feature.SetField("ext", "extent")
feature.SetGeometry(poly)
out_lyr.CreateFeature(feature)
feature = data_source = None
return outputShape
def createRegionsByTiles(shapeRegion,
field_Region,
pathToEnv,
pathOut,
pathWd,
logger_=logger):
"""
create a shapeFile into tile's envelope for each regions in shapeRegion and for each tiles
IN :
- shapeRegion : the shape which contains all regions
- field_Region : the field into the region's shape which describes each tile belong to which model
- pathToEnv : path to the tile's envelope with priority
- pathOut : path to store all resulting shapeFile
- pathWd : path to working directory (not mandatory, due to cluster's architecture default = None)
"""
pathName = pathWd
if pathWd == None:
#sequential case
pathName = pathOut
#getAllTiles
AllTiles = fu.FileSearch_AND(pathToEnv, True, ".shp")
regionList = fu.getFieldElement(shapeRegion, "ESRI Shapefile",
field_Region, "unique")
shpRegionList = splitVectorLayer(shapeRegion, field_Region, "int",
regionList, pathName)
AllClip = []
for shp in shpRegionList:
for tile in AllTiles:
logger_.info("Extract %s in %s", shp, tile)
pathToClip = fu.ClipVectorData(shp, tile, pathName)
AllClip.append(pathToClip)
if pathWd:
for clip in AllClip:
cmd = "cp " + clip.replace(".shp", "*") + " " + pathOut
run(cmd)
else:
for shp in shpRegionList:
path = shp.replace(".shp", "")
os.remove(path + ".shp")
os.remove(path + ".shx")
os.remove(path + ".dbf")
os.remove(path + ".prj")
return AllClip
def getTileSameDate(folder):
buf = []
content = os.listdir(folder)
for currentContent in content:
date = currentContent.split("_")[2].split("-")[0]
buf.append((date, folder+"/"+currentContent))
buf = fu.sortByFirstElem(buf)
out = [currentList for date, currentList in buf if len(currentList) > 1]
return out
def test_config_provider(self):
current_file = os.path.abspath(os.path.dirname(__file__))
parent_of_parent_dir = os.path.join(
current_file, FileUtils.convert_file_path_based_on_system('../'))
print(parent_of_parent_dir)
print(
self.cfg_provider.get_tdx_stock_directory_path(
StockConfig.StockDataType.DAILY, 'sz'))
def generateTif(vectorFile, pixSize):
minX, minY, maxX, maxY = fu.getShapeExtent(vectorFile)
cmd = "gdal_rasterize -te " + str(minX) + " " + str(minY) + " " + str(
maxX) + " " + str(maxY) + " -a Tile -tr " + str(pixSize) + " " + str(
pixSize) + " " + vectorFile + " " + vectorFile.replace(
".shp", ".tif")
print(cmd)
os.system(cmd)
def extractPixelValue(rasters,
bands,
paramstats,
xpt,
ypt,
dataframe,
idval=0):
"""Extract pixel value and store it on a Pandas dataframe
Parameters
----------
rasters : list
list of rasters to analyse
bands : ndarray
raster bands or raster files store in numpy array
paramstats : dict
list of statistics to compute (e.g. {1:'stats', 2:'rate'})
xpt : float
Point x coordinates
ypt : float
Point y coordinates
dataframe : Pandas Dataframe
Pandas Dataframe
idval : integer
index value to store in dataframe
Return
----------
GeoPandas or Pandas DataFrame
"""
for param in paramstats:
band = bands[:, :, int(param) - 1]
nbband = int(param)
### Pixel value extraction ###
if band.size != 0:
methodstat = paramstats[param]
if "val" in methodstat:
colpt, rowpt = fut.geoToPix(rasters[0], xpt, ypt)
cols = "valb%s" % (param)
dataframe.update(pad.DataFrame(data=[rasterStats(band, nbband, None, (colpt, rowpt))], \
index=[idval], \
columns=[cols]))
band = None
return dataframe
def getPaths(TileFolder, pattern):
Tiles = os.listdir(TileFolder)
paths = []
for currentS2Tile in Tiles:
if os.path.isdir(TileFolder + "/" + currentS2Tile):
stack = fu.FileSearch_AND(TileFolder + "/" + currentS2Tile, True,
pattern)
if stack:
paths.append(stack[0])
return paths
def step_clean(self):
"""
"""
from Common import FileUtils as fut
for filetoremove in fut.FileSearch_AND(self.tmpdir, True, "mask", ".tif"):
os.remove(filetoremove)
if os.path.exists(os.path.join(self.outputPath, 'final', 'simplification', 'classif_regul.tif')):
os.remove(os.path.join(self.tmpdir, 'regul1.tif'))
def createOutGrid(TileFolder, pattern, outputProjection, tileNameField,
outGridPath):
paths = getPaths(TileFolder, pattern)
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driver = ogr.GetDriverByName("ESRI Shapefile")
try:
if os.path.exists(outGridPath):
driver.DeleteDataSource(outGridPath)
output = driver.CreateDataSource(outGridPath)
except ValueError:
raise Exception("Could not create output datasource " + outGridPath)
srs = osr.SpatialReference()
srs.ImportFromEPSG(int(outputProjection))
layer = output.CreateLayer(outGridPath.split("/")[-1].replace(".shp", ""),
geom_type=ogr.wkbPolygon,
srs=srs)
layer.CreateField(ogr.FieldDefn(tileNameField, ogr.OFTString))
layerDef = layer.GetLayerDefn()
if layer is None:
raise Exception("Could not create output layer")
for currentTile in paths:
currentProjection = fu.getRasterProjectionEPSG(currentTile)
minX, maxX, minY, maxY = fu.getRasterExtent(currentTile)
upperL = (minX, maxY)
upperR = (maxX, maxY)
lowerL = (minX, minY)
lowerR = (maxX, minY)
upperL_out = converCoord(upperL, int(currentProjection),
int(outputProjection))
upperR_out = converCoord(upperR, int(currentProjection),
int(outputProjection))
lowerL_out = converCoord(lowerL, int(currentProjection),
int(outputProjection))
lowerR_out = converCoord(lowerR, int(currentProjection),
int(outputProjection))
TileName = getTileNameFromRaster(currentTile)
addTileToGrid(upperL_out, upperR_out, lowerR_out, lowerL_out, layer,
layerDef, tileNameField, TileName)
output.Destroy()
def storeRasterInArray(rasters):
"""Store list of raster or multi-band raster in a ndarray
Parameters
----------
rasters : list
list of rasters to analyse
Return
----------
ndarray
"""
# get raster size with first raster file
data = fut.readRaster(rasters[0], False)
# get rasters or bands number
if len(rasters) == 1:
nbbands = fut.getRasterNbands(rasters[0])
elif len(rasters) > 1:
nbbands = len(rasters)
# Set a empty ndarrays with same dimension as input rasters
outdata = np.zeros([data[1], data[0], nbbands])
# Populate output ndarrays
if len(rasters) == 1:
for nbband in range(nbbands):
outdata[:, :, nbband] = fut.readRaster(rasters[0], True,
nbband + 1)[0]
elif len(rasters) > 1:
for idx, raster in enumerate(rasters):
outdata[:, :, idx] = fut.readRaster(raster, True)[0]
else:
raise Exception("No input raster provided to store in Numpy array")
return outdata
def step_inputs(self):
"""
Return
------
the return could be and iterable or a callable
"""
from Common import FileUtils as fut
listout = fut.FileSearch_AND(self.tmpdir, True, "mask", ".tif")
return [listout]
def GetDataAugmentationByCopyParameters(iota2_dir_samples):
""" read the */learningSample* directory
parse the IOTA2's directory */learningSamples* and return a list by seed
in order to feed DataAugmentationByCopy function
Parameters
----------
iota2_dir_samples : string
absolute path to the /learningSamples IOTA2's directory
Example
-------
>>> os.listdir("/learningSamples")
["Samples_region_2_seed0_learn.sqlite",
"Samples_region_2_seed1_learn.sqlite",
"Samples_region_1_seed0_learn.sqlite",
"Samples_region_1_seed1_learn.sqlite"]
>>> GetSamplesSet("/learningSamples")
[[Samples_region_1_seed0_learn.sqlite, Samples_region_2_seed0_learn.sqlite],
[Samples_region_1_seed1_learn.sqlite, Samples_region_2_seed1_learn.sqlite]]
Return
------
list
a list of list where each inner list contains all samples for a given run
"""
seed_pos = 3
samples_set = [(os.path.basename(samples).split("_")[seed_pos], samples)
for samples in fut.FileSearch_AND(
iota2_dir_samples, True, "Samples_region", "sqlite")]
samples_set = [
samplesSeed for seed, samplesSeed in fut.sortByFirstElem(samples_set)
]
return samples_set
