def compute_samples_stats(global_parameters, proceed=True):
'''
1. Compute the samples stats
'''
print " Polygon Classes Statistics"
main_dir = global_parameters["user_choices"]["main_dir"]
raw_img = op.join(main_dir, 'In_data', 'Image',
global_parameters["user_choices"]["raw_img"])
class_stats = op.join(main_dir, 'Statistics',
global_parameters["general"]["class_stats"])
training_shp = op.join(
main_dir, 'Intermediate',
global_parameters["general"]["training_shp_extended"])
no_data_shp = op.join(main_dir, 'In_data', 'Masks',
global_parameters["general"]["no_data_mask"])
no_data_mask = no_data_shp[0:-4] + '.tif'
PolygonClassStatistics = otbApplication.Registry_CreateApplication(
"PolygonClassStatistics")
PolygonClassStatistics.SetParameterString("in", str(raw_img))
PolygonClassStatistics.SetParameterString("vec", str(training_shp))
PolygonClassStatistics.SetParameterString("out", str(class_stats))
PolygonClassStatistics.SetParameterString("mask", str(no_data_mask))
PolygonClassStatistics.UpdateParameters()
PolygonClassStatistics.SetParameterStringList("field", ["class"])
PolygonClassStatistics.ExecuteAndWriteOutput()
print('Done')
return class_stats
def extract_samples(global_parameters, proceed=True):
'''
3. Extract the samples
'''
main_dir = global_parameters["user_choices"]["main_dir"]
raw_img = op.join(main_dir, 'In_data', 'Image',
global_parameters["user_choices"]["raw_img"])
training_samples_extracted = op.join(
main_dir, 'Samples',
global_parameters["general"]["training_samples_extracted"])
training_samples_location = op.join(
main_dir, 'Samples',
global_parameters["general"]["training_samples_location"])
print(" Training Samples Extraction")
SampleExtraction = otbApplication.Registry_CreateApplication(
"SampleExtraction")
SampleExtraction.SetParameterString("in", str(raw_img))
SampleExtraction.SetParameterString("vec", str(training_samples_location))
SampleExtraction.SetParameterString("outfield", "prefix")
SampleExtraction.SetParameterString("outfield.prefix.name", "band_")
SampleExtraction.SetParameterString("out", str(training_samples_extracted))
SampleExtraction.UpdateParameters()
SampleExtraction.SetParameterStringList("field", ["class"])
SampleExtraction.ExecuteAndWriteOutput()
print('Done')
return training_samples_extracted
def select_samples(global_parameters, strategy="smallest", proceed=True):
'''
2. Select the samples
'''
main_dir = global_parameters["user_choices"]["main_dir"]
raw_img = op.join(main_dir, 'In_data', 'Image',
global_parameters["user_choices"]["raw_img"])
training_samples_location = op.join(
main_dir, 'Samples',
global_parameters["general"]["training_samples_location"])
class_stats = op.join(main_dir, 'Statistics',
global_parameters["general"]["class_stats"])
training_shp = op.join(
main_dir, 'Intermediate',
global_parameters["general"]["training_shp_extended"])
rates = op.join(main_dir, 'Statistics', 'rates.csv')
no_data_shp = op.join(main_dir, 'In_data', 'Masks',
global_parameters["general"]["no_data_mask"])
no_data_mask = no_data_shp[0:-4] + '.tif'
print(" Training Samples Selection")
SampleSelection = otbApplication.Registry_CreateApplication(
"SampleSelection")
SampleSelection.SetParameterString("in", str(raw_img))
SampleSelection.SetParameterString("vec", str(training_shp))
SampleSelection.SetParameterString("mask", str(no_data_mask))
SampleSelection.SetParameterString("instats", str(class_stats))
SampleSelection.SetParameterString("out", str(training_samples_location))
SampleSelection.SetParameterString("outrates", str(rates))
SampleSelection.SetParameterString("sampler",
"random") # default is periodic
if strategy == "smallest":
SampleSelection.SetParameterString("strategy", "smallest")
elif strategy == "constant":
SampleSelection.SetParameterString("strategy", "constant")
minSamplesNb = min(get_samples_nb(class_stats))
SampleSelection.SetParameterString("strategy.constant.nb",
str(minSamplesNb))
elif strategy.split('_')[0] == "constant":
SampleSelection.SetParameterString("strategy", "constant")
SampleSelection.SetParameterString("strategy.constant.nb",
str(strategy.split('_')[1]))
SampleSelection.UpdateParameters()
SampleSelection.SetParameterStringList("field", ["class"])
SampleSelection.Execute()
print('Done')
return training_samples_location
# -*- coding: utf-8 -*-
#
# Example on the use of the outxml via BandMath
#
from sys import argv
import otbApplication as otb
imagelist = argv[1:-2]
exp = "cos(im1b1)+im2b1*im3b1-im3b2+ndvi(im3b3, im3b4)"
out = argv[-2]
outxml = argv[-1]
app = otb.Registry_CreateApplication('BandMath')
app.SetParameterStringList("il", imagelist, True)
app.SetParameterString("out", out, True)
app.SetParameterString("exp", exp, True)
app.SetParameterString("outxml", outxml, True)
app.UpdateParameters()
#for paramKey in app.GetParametersKeys():
#app.GetParameterValue(paramKey)
#print app.HasUserValue(paramKey)
app.ExecuteAndWriteOutput()
import otbApplication
app = otbApplication.Registry_CreateApplication('TrainImagesClassifier')
app.SetParameterFloat('classifier.libsvm.opt', True)
print app.GetParameterValue('classifier.libsvm.opt')