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ArrNorm_algorithm.py
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ArrNorm_algorithm.py
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# -*- coding: utf-8 -*-
"""
/***************************************************************************
ArrNorm
A QGIS plugin processing
Automatic relative radiometric normalization
-------------------
copyright : (C) 2021-2022 by Xavier Corredor Llano, SMByC
email : xavier.corredor.llano@gmail.com
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
"""
import os
from qgis.PyQt.QtGui import QIcon
from qgis.PyQt.QtCore import QCoreApplication
from qgis.core import (QgsProcessingAlgorithm, QgsProcessingParameterDefinition,
QgsProcessingParameterRasterDestination, QgsProcessingParameterNumber,
QgsProcessingParameterRasterLayer, QgsProcessingParameterBoolean)
from ArrNorm.core.arrnorm import Normalization
class ArrNormAlgorithm(QgsProcessingAlgorithm):
"""
This algorithm compute a specific statistic using the time
series of all pixels across (the time) all raster in the specific band
"""
# Constants used to refer to parameters and outputs. They will be
# used when calling the algorithm from another algorithm, or when
# calling from the QGIS console.
IMG_REF = 'IMG_REF'
MAX_ITERS = 'MAX_ITERS'
PROB_THRES = 'PROB_THRES'
NEG_TO_NODATA = 'NEG_TO_NODATA'
NODATA_MASK = 'NODATA_MASK'
IMG_TARGET = 'IMG_TARGET'
OUTPUT = 'OUTPUT'
def __init__(self):
super().__init__()
def tr(self, string, context=''):
if context == '':
context = self.__class__.__name__
return QCoreApplication.translate(context, string)
def shortHelpString(self):
"""
Returns a localised short helper string for the algorithm. This string
should provide a basic description about what the algorithm does and the
parameters and outputs associated with it.
"""
html_help = '''
<p>ArrNorm is a Qgis processing plugin for apply the radiometric normalization to the target image \
based on reference image using the IR-MAD algorithm to locate invariant/variant pixels for a relative \
radiometric normalization.</p>
<p>Stop condition is set by max iteration or with a minimum no-change probability threshold. With more \
iterations the algorithm try to find a better match to the reference image, decreasing the delta, the plugin \
select the best delta for the final result. However, after several iterations the changes in the delta are \
imperceptible.</p>
<p>Note: For now, this plugin only takes values of zeros as nodata, so adjust the data accordingly.</p>
'''
return html_help
def createInstance(self):
return ArrNormAlgorithm()
def name(self):
"""
Returns the algorithm name, used for identifying the algorithm. This
string should be fixed for the algorithm, and must not be localised.
The name should be unique within each provider. Names should contain
lowercase alphanumeric characters only and no spaces or other
formatting characters.
"""
return 'Automatic relative radiometric normalization'
def displayName(self):
"""
Returns the translated algorithm name, which should be used for any
user-visible display of the algorithm name.
"""
return self.tr(self.name())
def group(self):
"""
Returns the name of the group this algorithm belongs to. This string
should be localised.
"""
return None
def groupId(self):
"""
Returns the unique ID of the group this algorithm belongs to. This
string should be fixed for the algorithm, and must not be localised.
The group id should be unique within each provider. Group id should
contain lowercase alphanumeric characters only and no spaces or other
formatting characters.
"""
return None
def icon(self):
return QIcon(":/plugins/ArrNorm/icons/arrnorm.svg")
def initAlgorithm(self, config=None):
"""
Here we define the inputs and output of the algorithm, along
with some other properties.
"""
self.addParameter(
QgsProcessingParameterRasterLayer(
self.IMG_REF,
self.tr('The REFERENCE image:'),
optional=False
)
)
self.addParameter(
QgsProcessingParameterRasterLayer(
self.IMG_TARGET,
self.tr('The TARGET image to normalize:')
)
)
parameter = \
QgsProcessingParameterNumber(
self.MAX_ITERS,
self.tr('Maximum number of iterations'),
type=QgsProcessingParameterNumber.Integer,
defaultValue=25,
optional=True
)
parameter.setFlags(parameter.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
self.addParameter(parameter)
parameter = \
QgsProcessingParameterNumber(
self.PROB_THRES,
self.tr('No-change probability threshold'),
type=QgsProcessingParameterNumber.Double,
defaultValue=0.95,
optional=True
)
parameter.setFlags(parameter.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
self.addParameter(parameter)
parameter = \
QgsProcessingParameterBoolean(
self.NEG_TO_NODATA,
self.tr('Convert negative values to nodata (0) to result'),
defaultValue=False,
optional=True
)
parameter.setFlags(parameter.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
self.addParameter(parameter)
parameter = \
QgsProcessingParameterBoolean(
self.NODATA_MASK,
self.tr('Mask nodata values (0) to result'),
defaultValue=True,
optional=True
)
parameter.setFlags(parameter.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
self.addParameter(parameter)
self.addParameter(
QgsProcessingParameterRasterDestination(
self.OUTPUT,
self.tr('Output raster file normalized')
)
)
def processAlgorithm(self, parameters, context, feedback):
"""
Here is where the processing itself takes place.
"""
def get_inputfilepath(layer):
return os.path.realpath(layer.source().split("|layername")[0])
output_file = self.parameterAsOutputLayer(parameters, self.OUTPUT, context)
arrnorm = Normalization(
img_ref=get_inputfilepath(self.parameterAsRasterLayer(parameters, self.IMG_REF, context)),
img_target=get_inputfilepath(self.parameterAsRasterLayer(parameters, self.IMG_TARGET, context)),
max_iters=self.parameterAsInt(parameters, self.MAX_ITERS, context),
prob_thres= self.parameterAsDouble(parameters, self.PROB_THRES, context),
neg_to_nodata=self.parameterAsBoolean(parameters, self.NEG_TO_NODATA, context),
nodata_mask=self.parameterAsBoolean(parameters, self.NODATA_MASK, context),
output_file=output_file,
feedback=feedback)
arrnorm.run()
return {self.OUTPUT: output_file}