def classify(self, feature):
tile = feature.GetFieldAsString(self.tileAttribute);
print( "{0} Tile {1}".format(time.strftime("%d/%m/%Y"), tile) );
filterTile="{0} = '{1}'".format(self.tileAttribute, tile);
gridFile = self.basepath('INPUT', 'GRID', 'CLASSIFICATION_GRID.shp');
mvcFile = self.getMvcFile(tile);
trainningDataset = "DATASET_{0}".format(self.trainningDataset);
trainningFilename = "{0}_TRAINNING.shp".format(tile);
trainningFile = self.basepath('INPUT', 'TRAINNING', trainningDataset, trainningFilename);
tempOutputFilename = "{0}_G{1}_{2}_MVC.tif".format(tile, self.gapfill.zfill(2), self.trainningDataset);
tempOutputFile = self.temppath(tempOutputFilename);
outputFilename = "{0}_CLASSIFICATION.tif".format(tile);
outputFile = self.basepath('OUTPUT', self.runtimeDataset, 'CLASSIFICATION', outputFilename);
outputStatsFilename = "{0}_STATS.csv".format(tile);
outputStatsFile = self.basepath('OUTPUT', self.runtimeDataset, 'VALIDATION', outputStatsFilename);
if not self.noClassify:
print( " {0} Classify".format( time.strftime("%H:%M:%S") ) );
resultClassification = garsect2.run2(mvcFile, trainningFile, self.trainningAttribute, self.mvcNullValue, tempOutputFile, outputStatsFile, 1, self.nTreeRandomForest, [1]);
command = 'gdalwarp -co COMPRESS=lzw -co INTERLEAVE=BAND -co TILED=YES -ot Byte -q -dstnodata "{0}" -cwhere "{1}" -crop_to_cutline -overwrite -multi -cutline {2} {3} {4}'.format(self.mvcNullValue, filterTile, gridFile, tempOutputFile, outputFile);
os.system(command);
os.remove(tempOutputFile);
return outputFile;
def classifyWithRandTrainning(self, feature):
tile = feature.GetFieldAsString(self.tileAttribute);
mvcFile = self.getMvcFile(tile)
boundsGeometry = feature.geometry();
nRandomPoints = self.nTreeRandomPoints;
execNumber = int(self.trainningDataset[1:]);
classificationResults = [ ];
print( "{0} Tile {1}".format(time.strftime("%d/%m/%Y"), tile) );
for i in range(execNumber):
tmpFiles = [];
rSuffix = "_R" + str(i) + '-' + str(execNumber);
fileBaseName = tile + rSuffix;
pctRandom = float(feature.GetFieldAsString(self.pctClassAttribute));
print( " Execution {0}".format( str(i + 1) ) );
filterTile="{0} = '{1}'".format(self.tileAttribute, tile);
gridFile = self.basepath('INPUT', 'GRID', 'CLASSIFICATION_GRID.shp');
validationDataset = "DATASET_{0}".format(self.validationDataset);
validationFile = self.basepath('INPUT', 'VALIDATION', validationDataset, 'POLYGON_VALIDATION.shp');
trainningFile = self.temppath(fileBaseName + ".shp");
tmpFiles.append(self.temppath(fileBaseName + ".shp"));
tmpFiles.append(self.temppath(fileBaseName + ".dbf"));
tmpFiles.append(self.temppath(fileBaseName + ".shx"));
tmpFiles.append(self.temppath(fileBaseName + ".prj"));
try:
os.remove(trainningFile);
except:
pass
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326)
trainningDs = self.geoDB['drivers']['shp'].CreateDataSource(trainningFile)
trainningLayer = trainningDs.CreateLayer("point_out", srs, ogr.wkbPoint )
trainningLayer.CreateField( ogr.FieldDefn(self.trainningAttribute, ogr.OFTString) )
fixedTotalPoints = int(math.ceil(nRandomPoints * 0.25));
pctTotalPoints = int(math.trunc(nRandomPoints * 0.75));
nRandPointIn = int(math.trunc(pctTotalPoints * pctRandom)) + (fixedTotalPoints / 2)
print( " {0} {1} randomPoints".format( time.strftime("%H:%M:%S"), nRandPointIn ) );
inTrainningPoints = randompoints.randomPointsIn(validationFile, nRandPointIn, boundsGeometry);
nRandPointOut = int(math.ceil(pctTotalPoints * (1 - pctRandom))) + (fixedTotalPoints / 2)
print( " {0} {1} randomPointsOut".format( time.strftime("%H:%M:%S"), nRandPointOut ) );
notInTrainningPoints = randompoints.randomPointsOut(validationFile, nRandPointOut, boundsGeometry);
for point in notInTrainningPoints:
feat = ogr.Feature(trainningLayer.GetLayerDefn())
feat.SetGeometry(point['geometry']);
feat.SetField(self.trainningAttribute, 1)
trainningLayer.CreateFeature(feat)
for point in inTrainningPoints:
feat = ogr.Feature(trainningLayer.GetLayerDefn())
feat.SetGeometry(point['geometry']);
feat.SetField(self.trainningAttribute, 2)
trainningLayer.CreateFeature(feat)
trainningLayer.SyncToDisk();
tempOutputFile = self.temppath(fileBaseName + "_noclip.tif");
tmpFiles.append(tempOutputFile);
classificationResult = self.temppath(fileBaseName + '.tif');
classificationResults.append(classificationResult);
print( " {0} Classify".format( time.strftime("%H:%M:%S") ) );
resultClassification = garsect2.run2(mvcFile, trainningFile, self.trainningAttribute, self.mvcNullValue, tempOutputFile, None, 1, self.nTreeRandomForest, [1]);
command = 'gdalwarp -co COMPRESS=lzw -co INTERLEAVE=BAND -co TILED=YES -ot Int16 -q -dstnodata "{0}" -cwhere "{1}" -crop_to_cutline -overwrite -multi -cutline {2} {3} {4}'.format(self.mvcNullValue, filterTile, gridFile, tempOutputFile, classificationResult);
os.system(command);
print( " {0} Remove temp files".format( time.strftime("%H:%M:%S") ) );
for tmpFile in tmpFiles:
os.remove(tmpFile);
letters = [];
params = '';
for i,classificationResult in enumerate(classificationResults):
letter = ascii_uppercase[i];
params += " -{0} {1} ".format(letter, classificationResult);
letters.append(letter);
expr = "({0})/{1}".format( '+'.join(letters), len(letters) );
outputFilename = "{0}_CLASSIFICATION.tif".format(tile);
outputFile = self.basepath('OUTPUT', self.runtimeDataset, 'CLASSIFICATION', outputFilename);
print( " {0} AVG classification calcultion".format( time.strftime("%H:%M:%S") ) );
command = 'gdal_calc.py --co="COMPRESS=deflate" --co="INTERLEAVE=BAND" --co="TILED=YES" --NoDataValue=0 {0} --outfile={1} --calc="{2}"'.format(params, outputFile, expr)
os.system(command);
for classificationResult in classificationResults:
print( " {0} Remove temp files".format( time.strftime("%H:%M:%S") ) );
os.remove(classificationResult);
return '';