automated_local_coregistration_algorithm.py

# -*- coding: utf-8 -*-
"""
/***************************************************************************
 Coregistration
                          A QGIS plugin processing
 Image co-registration, projection and pixel alignment based on a target image
                              -------------------
        copyright            : (C) 2021-2024 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
import platform

from qgis.PyQt.QtGui import QIcon
from qgis.PyQt.QtCore import QCoreApplication
from qgis.core import (QgsProcessingAlgorithm, QgsProcessingParameterRasterDestination, QgsProcessingParameterRasterLayer,
                       QgsProcessingParameterNumber, QgsProcessingParameterDefinition, QgsProcessingParameterEnum,
                       QgsProcessingParameterBoolean, QgsProcessingUtils)

from Coregistration.utils.system_utils import get_raster_driver_name_by_extension


class AutomatedLocalCoregistrationAlgorithm(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'
    INPUT = 'INPUT'
    ALIGN_GRIDS = "ALIGN_GRIDS"
    MATCH_GSD = "MATCH_GSD"
    GRID_RES = 'GRID_RES'
    WINDOW_SIZE = 'WINDOW_SIZE'
    MAX_SHIFT = 'MAX_SHIFT'
    RESAMPLING = 'RESAMPLING'
    MASK = 'MASK'
    OUTPUT = 'OUTPUT'

    resampling_methods = (
        ('Nearest Neighbour', 'nearest'),
        ('Bilinear', 'bilinear'),
        ('Cubic', 'cubic'),
        ('Cubic Spline', 'cubic_spline'),
        ('Lanczos Windowed Sinc', 'lanczos'),
        ('Average', 'average'),
        ('Mode', 'mode'),
        ('Maximum', 'max'),
        ('Minimum', 'min'),
        ('Median', 'med'),
        ('First Quartile', 'q1'),
        ('Third Quartile', 'q3'))

    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>Detects and corrects local X/Y shifts misregistrations between two input images in the subpixel scale \
        using the content of the pixels in the matching window. Perform automatic subpixel co-registration of image \
        datasets based on an image matching approach working in the frequency domain, combined with a multistage \
        workflow for effective detection of false-positives [1].
        
        It is designed to robustly handle the typical \
        difficulties of multi-sensoral/multi-temporal images. Clouds are automatically handled by the implemented \
        outlier detection algorithms [1].

        This local algorithm is useful when the target image requires different pixel shifts in distances and \
        directions. The precision of this is based on mainly in two input parameters: tie point grid resolution and \
        matching window size.
        
        This is significantly more comprehensive and slower than global algorithm. 

        [1] This algorithm use Arosics software developed by Daniel Scheffler, for more info \
        <a href="https://danschef.git-pages.gfz-potsdam.de/arosics/doc/">url</a> and \
        <a href="https://doi.org/10.3390/rs9070676">paper</a> \
        </p>'''
        return html_help

    def createInstance(self):
        return AutomatedLocalCoregistrationAlgorithm()

    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 'Automated local Co-Registration'

    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/Coregistration/icons/coregistration.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 to use as based to co-register the target image')
            )
        )

        self.addParameter(
            QgsProcessingParameterRasterLayer(
                self.INPUT,
                self.tr('The TARGET image for co-register'),
            )
        )

        self.addParameter(
            QgsProcessingParameterBoolean(
                self.ALIGN_GRIDS,
                self.tr('Align the input coordinate grid to the reference'),
                defaultValue=True,
            )
        )

        self.addParameter(
            QgsProcessingParameterBoolean(
                self.MATCH_GSD,
                self.tr('Match the input pixel size to the reference pixel size'),
                defaultValue=True,
            )
        )

        self.addParameter(
            QgsProcessingParameterNumber(
                self.GRID_RES,
                self.tr('Tie point grid resolution (pixel units of the target image)'),
                type=QgsProcessingParameterNumber.Integer,
                defaultValue=200,
            )
        )

        self.addParameter(
            QgsProcessingParameterNumber(
                self.WINDOW_SIZE,
                self.tr('Custom matching window size (width and height in pixel units)'),
                type=QgsProcessingParameterNumber.Integer,
                defaultValue=256,
            )
        )

        parameter = \
            QgsProcessingParameterNumber(
                self.MAX_SHIFT,
                self.tr('Maximum shift distance in reference image pixel units'),
                type=QgsProcessingParameterNumber.Integer,
                defaultValue=5,
                optional=False
            )
        parameter.setFlags(parameter.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(parameter)

        parameter = \
            QgsProcessingParameterEnum(
                self.RESAMPLING,
                self.tr('The resampling algorithm to be used for shift correction (if necessary)'),
                options=[i[0] for i in self.resampling_methods],
                defaultValue=2,
                optional=False
            )
        parameter.setFlags(parameter.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
        self.addParameter(parameter)

        self.addParameter(
            QgsProcessingParameterRasterDestination(
                self.OUTPUT,
                self.tr('Output raster file co-registered')
            )
        )

    def processAlgorithm(self, parameters, context, feedback):
        """
        Here is where the processing itself takes place.
        """
        try:
            from arosics import COREG_LOCAL
        except:
            msg = "\nError loading Arosics, this plugin requires additional Python packages to work. " \
                  "Read the install instructions here:\n\n" \
                  "https://github.com/SMByC/Coregistration-Qgis-processing#installation\n\n"
            feedback.reportError(msg, fatalError=True)
            return {}

        def get_inputfilepath(layer):
            return os.path.realpath(layer.source().split("|layername")[0])

        img_ref = get_inputfilepath(self.parameterAsRasterLayer(parameters, self.IMG_REF, context))
        img_tgt = get_inputfilepath(self.parameterAsRasterLayer(parameters, self.INPUT, context))

        align_grids = self.parameterAsBoolean(parameters, self.ALIGN_GRIDS, context)
        match_gsd = self.parameterAsBoolean(parameters, self.MATCH_GSD, context)

        grid_res = self.parameterAsInt(parameters, self.GRID_RES, context)

        window_size = self.parameterAsInt(parameters, self.WINDOW_SIZE, context)

        max_shift = self.parameterAsInt(parameters, self.MAX_SHIFT, context)
        resampling_method = self.resampling_methods[self.parameterAsEnum(parameters, self.RESAMPLING, context)][1]

        output_file = self.parameterAsOutputLayer(parameters, self.OUTPUT, context)
        output_driver_name = get_raster_driver_name_by_extension(output_file)

        # fix save and load ENVI files
        if output_driver_name == "ENVI":
            output_file_envi = output_file.replace(".hdr", ".dat")
            if context.willLoadLayerOnCompletion(output_file):
                layer_detail = context.LayerDetails(os.path.basename(output_file_envi), context.project(),
                                                    os.path.basename(output_file_envi), QgsProcessingUtils.LayerHint.Raster)
                context.setLayersToLoadOnCompletion({output_file_envi: layer_detail})
            output_file = output_file_envi

        feedback.pushInfo("Image to image Co-Registration:")
        feedback.pushInfo("\nProcessing file: " + img_tgt)

        feedback.pushInfo("\nPerform automatic subpixel co-registration with Arosics...")
        feedback.pushInfo("\n(To check the complete log of the process, open the Python Console)...")
        if platform.system() == "Windows":
            feedback.reportError("\nWarning: in Windows due to restrictions to enable multiprocessing inside Qgis, "
                                 "the process could take longer. Continue with one core ...\n", fatalError=False)
        CRL = COREG_LOCAL(img_ref, img_tgt, path_out=output_file, align_grids=align_grids, match_gsd=match_gsd,
                          grid_res=grid_res, window_size=(window_size, window_size),
                          resamp_alg_deshift=resampling_method, max_shift=max_shift, max_iter=15,
                          fmt_out=output_driver_name, out_crea_options=["WRITE_METADATA=NO"],
                          CPUs=1)
        CRL.correct_shifts()

        feedback.pushInfo("DONE\n")

        return {self.OUTPUT: output_file}