def add_Training(clumpsFile, input_training, config):
print('input_training', input_training)
classesDict = dict()
classesDict['landslide'] = [1, input_training]
classesDict['no_landslide'] = [
0, config['k_means_segmentation']['input']['training_labels']
]
tmpPath = config['k_means_segmentation']['temps']['shapes']
trainCol = 'class'
trainColName = 'class_name'
ratutils.populateClumpsWithClassTraining(clumpsFile, classesDict, tmpPath,
trainCol, trainColName)
return (clumpsFile)
# imageutils.popImageStats(outputMeanImg,True,0.,True)
# populate clumps with training data
print('Populating clumps with stats...')
classesDict = dict()
classesDict['Water'] = [1, waterMask]
classesDict['Other'] = [2, otherMask]
classesDict['VegWater'] = [3, vegwaterMask]
tmpPath = './temp'
classesIntCol = 'ClassInt'
classesNameCol = 'ClassStr'
ratutils.populateClumpsWithClassTraining(outputClumps, classesDict,
tmpPath, classesIntCol,
classesNameCol)
# balance the training data
rsgislib.classification.classratutils.balanceSampleTrainingRandom(
outputClumps, classesIntCol, 'classesIntColBal', 50, 5000)
classesIntCol = 'classesIntColBal'
# classify the image ---------------------------
# define the classifier
classifier = ExtraTreesClassifier(n_estimators=500, n_jobs=-1)
# classifier = RandomForestClassifier(n_estimators=100, max_features=3, oob_score=True, n_jobs=-1, verbose=0)
# classifier = svm.SVC(kernel='rbf')
# define the output colours
