def main():
Renascence.setStreamPath("./")
Renascence.setLibPath("./")
producer = Renascence.init(["func.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('TrBmp', "input.jpg")
x1 = producer.load('TrBmp', "input_sharp.jpg")
x2 = producer.load('TrBmp', "output.jpg")
filterMatrix = producer.build("FR(x0, x1)").run(producer.merge(x2, x1))
print filterMatrix.dump()
[Trainner, BestValue] = producer.train("S(ADF(Treator,x0,x1))",
producer.merge(x0, x1),
5,
cacheFile='temp.txt',
postFormula='FIT(x0, x1)',
postExtraInput=x2)
formula = Trainner.ADF("")
print 'Formula: ', formula
print 'Parameters: ', Trainner.parameters()
print BestValue
predictor = producer.build(formula, Trainner.parameters())
y_p = predictor.run(producer.merge(x0, x1))
y_p.save("output/output_trained.jpg")
def main():
Renascence.setStreamPath("./")
Renascence.setLibPath("./")
producer = Renascence.init(["func.xml"])
p_producer = Renascence.PieceFunctionProducer(producer, ['parallel.xml'], ['Map-Reduce.xml'])
print p_producer.listType()
sub_p_producer = Renascence.PieceFunctionProducerParallel(p_producer, 'server')
print "sub_p_producer created"
pfunction = sub_p_producer.createFunction('C(S(x0))', 'TrBmp')
inputPieces = sub_p_producer.createInput('deps/Renascence/res/pictures/', 'TrBmp', [5])
tempPiece = pfunction.run([inputPieces])
outputPieces = sub_p_producer.createOutput('output/pythonTestParallel')
sub_p_producer.copyPiece(tempPiece, outputPieces)
def main():
Renascence.setStreamPath("./")
Renascence.setLibPath("./")
producer = Renascence.init(["func.xml"])
p_producer = Renascence.PieceFunctionProducer(producer, ['mgpfunc.xml'], ['Map-Reduce.xml'])
print p_producer.listType()
sub_p_producer = Renascence.PieceFunctionProducerParallel(p_producer, 'thread')
pfunction = sub_p_producer.createFunction('C(S(x0))', 'TrBmp')
inputPieces = sub_p_producer.createInput('res/pictures/', 'TrBmp', [5])
outputPieces = sub_p_producer.createOutput('output/pythonTestParallel')
tempPiece = pfunction.run([inputPieces])
sub_p_producer.copyPiece(tempPiece, outputPieces)
input1 = inputPieces.read('0')
input1.save('output/pythonTestParallel/input.jpg')
tempPiece.write(input1, '0')
print input1
def main():
Renascence.setStreamPath("../../")
Renascence.setLibPath("../../")
producer = Renascence.init(["func.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('TrBmp', "input.jpg")
x1 = producer.load('TrBmp', "input_sharp.jpg")
x2 = producer.load('TrBmp', "output.jpg")
x0x1x2 = producer.merge(x0, x1, x2)
[Trainner, BestValue] = producer.train("FIT(ADF(Treator,x0,x1), x2)", x0x1x2, 500, cacheFile='temp.txt')
formula = Trainner.ADF("Treator")
print 'Formula: ', formula
print 'Parameters: ', Trainner.parameters()
print BestValue
predictor = producer.build(formula)
x0x1 = producer.merge(x0, x1)
y_p = predictor.run(x0x1)
u = producer.content(9.0)
y_p.save("output/output_trained.jpg")
def main():
Renascence.setStreamPath("./")
Renascence.setLibPath("./")
producer = Renascence.init(["func.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('TrBmp', "input.jpg")
x1 = producer.load('TrBmp', "input_sharp.jpg")
x2 = producer.load('TrBmp', "output.jpg")
filterMatrix = producer.build("FR(x0, x1)").run(producer.merge(x2, x1))
print filterMatrix.dump()
[Trainner, BestValue] = producer.train("S(ADF(Treator,x0,x1))", producer.merge(x0, x1), 5, cacheFile='temp.txt', postFormula='FIT(x0, x1)', postExtraInput=x2)
formula = Trainner.ADF("")
print 'Formula: ', formula
print 'Parameters: ', Trainner.parameters()
print BestValue
predictor = producer.build(formula, Trainner.parameters())
y_p = predictor.run(producer.merge(x0,x1))
y_p.save("output/output_trained.jpg")
def main():
Renascence.setStreamPath("../../")
Renascence.setLibPath("../../")
producer = Renascence.init(["func.xml"])
print producer.listAllFunctions()
print producer.listAllTypes()
x0 = producer.load('TrBmp', "input.jpg")
x1 = producer.load('TrBmp', "input_sharp.jpg")
x2 = producer.load('TrBmp', "output.jpg")
x0x1x2 = producer.merge(x0, x1, x2)
[Trainner, BestValue] = producer.train("FIT(ADF(Treator,x0,x1), x2)",
x0x1x2,
500,
cacheFile='temp.txt')
formula = Trainner.ADF("Treator")
print 'Formula: ', formula
print 'Parameters: ', Trainner.parameters()
print BestValue
predictor = producer.build(formula)
x0x1 = producer.merge(x0, x1)
y_p = predictor.run(x0x1)
u = producer.content(9.0)
y_p.save("output/output_trained.jpg")
