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###############################################################################
# pytest_classifyOgr.py: classify vector dataset
# Author(s): Pieter.Kempeneers@ec.europa.eu
# Copyright (c) 2016-2019 European Union (Joint Research Centre)
# License EUPLv1.2
#
# This file is part of jiplib
###############################################################################
# History
# 2018/03/07 - Created by Pieter Kempeneers (pieter.kempeneers@ec.europa.eu)
# Change log
import argparse
import os
import math
import jiplib as jl
parser=argparse.ArgumentParser()
parser.add_argument("-input","--input",help="Path of the input raster dataset",dest="input",required=True,type=str)
parser.add_argument("-vector","--vector",help="Path of the sample vector dataset with labels",dest="vector",required=True,type=str)
parser.add_argument("-model","--model",help="Path of the model output filename used for training",dest="model",required=True,type=str)
parser.add_argument("-output","--output",help="Path of the classification output raster dataset",dest="output",required=True,type=str)
parser.add_argument("-classifier","--classifier",help="classifier (svm, ann)",dest="classifier",required=False,type=str,default="svm")
args = parser.parse_args()
try:
print("createJim")
jim=jl.createJim(args.input)
print("createVector")
sample=jl.createVector();
print("open vector",args.vector)
sample.open(args.vector)
print("extractOgr")
training=jim.extractOgr(sample,{'output':'training','oformat':'Memory','copy':'label'})
test=jim.extractOgr(sample,{'output':'test','oformat':'Memory','copy':'None'})
if args.classifier == 'svm':
#SVM classification
print("training")
training.train({'method':args.classifier,'label':'label','model':args.model})
print("classification")
vclass=test.classify({'output':args.output,'co':'OVERWRITE=YES','model':args.model,'method':args.classifier})
print("write")
vclass.write()
vclass.close()
else:
#ANN classification
print("training")
training.train({'method':args.classifier,'label':'label','model':args.model})
print("classification")
vclass=test.classify({'output':args.output,'co':'OVERWRITE=YES','model':args.model,'method':args.classifier})
print("write")
vclass.write()
vclass.close()
sample.close()
test.close()
training.close()
jim.close()
print("Success: classify")
except:
print("Failed: classify")
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