From 74284be8cbb6f248caa67cd48ccf57ea03123a35 Mon Sep 17 00:00:00 2001
From: ptresson <paul.tresson@ird.fr>
Date: Mon, 2 Sep 2024 13:22:47 +0200
Subject: [PATCH] reorganize plugin structure
---
src/iamap/iamap.py | 234 -----------------------------------------
src/iamap/provider.py | 46 --------
src/iamap/utils/geo.py | 118 ---------------------
3 files changed, 398 deletions(-)
delete mode 100644 src/iamap/iamap.py
delete mode 100644 src/iamap/provider.py
delete mode 100644 src/iamap/utils/geo.py
diff --git a/src/iamap/iamap.py b/src/iamap/iamap.py
deleted file mode 100644
index a0a36a0..0000000
--- a/src/iamap/iamap.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import processing
-from PyQt5.QtWidgets import (
- QAction,
- QToolBar,
- QApplication,
- QDialog
-)
-from PyQt5.QtCore import pyqtSignal, QObject
-from qgis.core import QgsApplication
-from qgis.gui import QgisInterface
-from .provider import IAMapProvider
-from .icons import QIcon_EncoderTool, QIcon_ReductionTool, QIcon_ClusterTool, QIcon_SimilarityTool, QIcon_RandomforestTool
-
-
-class IAMap(QObject):
- execute_iamap = pyqtSignal()
-
- def __init__(self, iface: QgisInterface, cwd: str):
- super().__init__()
- self.iface = iface
- self.cwd = cwd
-
- def initProcessing(self):
- self.provider = IAMapProvider()
- QgsApplication.processingRegistry().addProvider(self.provider)
-
- def initGui(self):
- self.initProcessing()
-
- self.toolbar: QToolBar = self.iface.addToolBar('IAMap Toolbar')
- self.toolbar.setObjectName('IAMapToolbar')
- self.toolbar.setToolTip('IAMap Toolbar')
-
- self.actionEncoder = QAction(
- QIcon_EncoderTool,
- "Deep Learning Image Encoder",
- self.iface.mainWindow()
- )
- self.actionReducer = QAction(
- QIcon_ReductionTool,
- "Reduce dimensions",
- self.iface.mainWindow()
- )
- self.actionCluster = QAction(
- QIcon_ClusterTool,
- "Cluster raster",
- self.iface.mainWindow()
- )
- self.actionSimilarity = QAction(
- QIcon_SimilarityTool,
- "Compute similarity",
- self.iface.mainWindow()
- )
- self.actionRF = QAction(
- QIcon_RandomforestTool,
- "Use Random Forest algorithm",
- self.iface.mainWindow()
- )
- self.actionEncoder.setObjectName("mActionEncoder")
- self.actionReducer.setObjectName("mActionReducer")
- self.actionCluster.setObjectName("mActionCluster")
- self.actionSimilarity.setObjectName("mactionSimilarity")
- self.actionRF.setObjectName("mactionRF")
-
- self.actionEncoder.setToolTip(
- "Encode a raster with a deep learning backbone")
- self.actionReducer.setToolTip(
- "Reduce raster dimensions")
- self.actionCluster.setToolTip(
- "Cluster raster")
- self.actionSimilarity.setToolTip(
- "Compute similarity")
- self.actionRF.setToolTip(
- "Use Random Forest ")
-
- self.actionEncoder.triggered.connect(self.encodeImage)
- self.actionReducer.triggered.connect(self.reduceImage)
- self.actionCluster.triggered.connect(self.clusterImage)
- self.actionSimilarity.triggered.connect(self.similarityImage)
- self.actionRF.triggered.connect(self.rfImage)
-
- self.toolbar.addAction(self.actionEncoder)
- self.toolbar.addAction(self.actionReducer)
- self.toolbar.addAction(self.actionCluster)
- self.toolbar.addAction(self.actionSimilarity)
- self.toolbar.addAction(self.actionRF)
-
- def unload(self):
- # self.wdg_select.setVisible(False)
- self.iface.removeToolBarIcon(self.actionEncoder)
- self.iface.removeToolBarIcon(self.actionReducer)
- self.iface.removeToolBarIcon(self.actionCluster)
- self.iface.removeToolBarIcon(self.actionSimilarity)
- self.iface.removeToolBarIcon(self.actionRF)
-
- del self.actionEncoder
- del self.actionReducer
- del self.actionCluster
- del self.actionSimilarity
- del self.actionRF
- del self.toolbar
- QgsApplication.processingRegistry().removeProvider(self.provider)
-
- def encodeImage(self):
- '''
- '''
- result = processing.execAlgorithmDialog('iamap:encoder', {})
- print(result)
- # Check if algorithm execution was successful
- if result:
- # Retrieve output parameters from the result dictionary
- if 'OUTPUT_RASTER' in result:
- output_raster_path = result['OUTPUT_RASTER']
-
- # Add the output raster layer to the map canvas
- self.iface.addRasterLayer(str(output_raster_path), 'merged features')
- else:
- # Handle missing or unexpected output
- print('Output raster not found in algorithm result.')
- else:
- # Handle algorithm execution failure or cancellation
- print('Algorithm execution was not successful.')
- # processing.execAlgorithmDialog('', {})
- # self.close_all_dialogs()
-
-
- def reduceImage(self):
- '''
- '''
- result = processing.execAlgorithmDialog('iamap:reduction', {})
- print(result)
- # Check if algorithm execution was successful
- if result:
- # Retrieve output parameters from the result dictionary
- if 'OUTPUT_RASTER' in result:
- output_raster_path = result['OUTPUT_RASTER']
-
- # Add the output raster layer to the map canvas
- self.iface.addRasterLayer(str(output_raster_path), 'reduced features')
- else:
- # Handle missing or unexpected output
- print('Output raster not found in algorithm result.')
- else:
- # Handle algorithm execution failure or cancellation
- print('Algorithm execution was not successful.')
- # processing.execAlgorithmDialog('', {})
-
-
- def clusterImage(self):
- '''
- '''
- result = processing.execAlgorithmDialog('iamap:cluster', {})
- print(result)
- # Check if algorithm execution was successful
- if result:
- # Retrieve output parameters from the result dictionary
- if 'OUTPUT_RASTER' in result:
- output_raster_path = result['OUTPUT_RASTER']
-
- # Add the output raster layer to the map canvas
- self.iface.addRasterLayer(str(output_raster_path), 'clustering')
- else:
- # Handle missing or unexpected output
- print('Output raster not found in algorithm result.')
- else:
- # Handle algorithm execution failure or cancellation
- print('Algorithm execution was not successful.')
- # processing.execAlgorithmDialog('', {})
-
-
- def similarityImage(self):
- '''
- '''
- result = processing.execAlgorithmDialog('iamap:similarity', {})
- print(result)
- # Check if algorithm execution was successful
- if result:
- # Retrieve output parameters from the result dictionary
- if 'OUTPUT_RASTER' in result:
- output_raster_path = result['OUTPUT_RASTER']
-
- # Add the output raster layer to the map canvas
- self.iface.addRasterLayer(str(output_raster_path), 'similarity map')
- else:
- # Handle missing or unexpected output
- print('Output raster not found in algorithm result.')
- else:
- # Handle algorithm execution failure or cancellation
- print('Algorithm execution was not successful.')
- # processing.execAlgorithmDialog('', {})
-
- def rfImage(self):
- '''
- '''
- result = processing.execAlgorithmDialog('iamap:Random_forest', {})
- print(result)
- # Check if algorithm execution was successful
- if result:
- # Retrieve output parameters from the result dictionary
- if 'OUTPUT_RASTER' in result:
- output_raster_path = result['OUTPUT_RASTER']
-
- # Add the output raster layer to the map canvas
- self.iface.addRasterLayer(str(output_raster_path), 'random forest map')
- else:
- # Handle missing or unexpected output
- print('Output raster not found in algorithm result.')
- else:
- # Handle algorithm execution failure or cancellation
- print('Algorithm execution was not successful.')
- # processing.execAlgorithmDialog('', {})
-
-
-
- def close_all_dialogs(self):
- # Get the main QGIS window (QgisInterface)
- qgis_main_window = self.iface.mainWindow()
-
- # Get all open dialogs associated with the main window
- open_dialogs = qgis_main_window.findChildren(QDialog)
-
- # Iterate through the open dialogs and close them
- for dialog in open_dialogs:
- # Check if the dialog is visible (to avoid closing hidden dialogs)
- if dialog.isVisible():
- # Close the dialog
- dialog.close()
-
-
-
-
-
-
-
diff --git a/src/iamap/provider.py b/src/iamap/provider.py
deleted file mode 100644
index 5d7797b..0000000
--- a/src/iamap/provider.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from qgis.core import QgsProcessingProvider
-
-from .encoder import EncoderAlgorithm
-from .reduction import ReductionAlgorithm
-from .clustering import ClusterAlgorithm
-from .similarity import SimilarityAlgorithm
-from .random_forest import RFAlgorithm
-from .icons import QIcon_EncoderTool
-
-
-class IAMapProvider(QgsProcessingProvider):
-
- def loadAlgorithms(self, *args, **kwargs):
- self.addAlgorithm(EncoderAlgorithm())
- self.addAlgorithm(ReductionAlgorithm())
- self.addAlgorithm(ClusterAlgorithm())
- self.addAlgorithm(SimilarityAlgorithm())
- self.addAlgorithm(RFAlgorithm())
- # add additional algorithms here
- # self.addAlgorithm(MyOtherAlgorithm())
-
- def id(self, *args, **kwargs):
- """The ID of your plugin, used for identifying the provider.
-
- This string should be a unique, short, character only string,
- eg "qgis" or "gdal". This string should not be localised.
- """
- return 'iamap'
-
- def name(self, *args, **kwargs):
- """The human friendly name of your plugin in Processing.
-
- This string should be as short as possible (e.g. "Lastools", not
- "Lastools version 1.0.1 64-bit") and localised.
- """
- return self.tr('IAMap')
-
- def icon(self):
- """Should return a QIcon which is used for your provider inside
- the Processing toolbox.
- """
- return QIcon_EncoderTool
-
- def longName(self) -> str:
- return self.name()
-
diff --git a/src/iamap/utils/geo.py b/src/iamap/utils/geo.py
deleted file mode 100644
index 4d5d6f8..0000000
--- a/src/iamap/utils/geo.py
+++ /dev/null
@@ -1,118 +0,0 @@
-from typing import Callable, Union
-import rasterio
-import geopandas as gpd
-import numpy as np
-import warnings
-from rasterio.io import MemoryFile
-from rasterio.merge import merge
-
-def replace_nan_with_zero(array):
- array[array != array] = 0 # Replace NaN values with zero
- return array
-
-def custom_method_avg(merged_data, new_data, merged_mask, new_mask, **kwargs):
- """Returns the average value pixel.
- cf. https://amanbagrecha.github.io/posts/2022-07-31-merge-rasters-the-modern-way-using-python/index.html
- """
- mask = np.empty_like(merged_mask, dtype="bool")
- np.logical_or(merged_mask, new_mask, out=mask)
- np.logical_not(mask, out=mask)
- np.nanmean([merged_data, new_data], axis=0, out=merged_data, where=mask)
- np.logical_not(new_mask, out=mask)
- np.logical_and(merged_mask, mask, out=mask)
- np.copyto(merged_data, new_data, where=mask, casting="unsafe")
-
-def merge_tiles(
- tiles:list,
- dst_path,
- dtype:str = 'float32',
- nodata=None,
- #method:str | Callable ='first',
- method: Union[str, Callable] = 'first',
- ):
- """
- cf. https://amanbagrecha.github.io/posts/2022-07-31-merge-rasters-the-modern-way-using-python/index.html
- """
-
- file_handler = [rasterio.open(ds) for ds in tiles]
- extents = [ds.bounds for ds in file_handler]
- # Extract individual bounds
- lefts, bottoms, rights, tops = zip(*extents)
- union_extent = (
- min(lefts), # Left
- min(bottoms), # Bottom
- max(rights), # Right
- max(tops) # Top
- )
-
- if method == 'average':
- method = custom_method_avg
-
- # memfile = MemoryFile()
- merge(datasets=file_handler, # list of dataset objects opened in 'r' mode
- bounds=union_extent, # tuple
- nodata=nodata, # float
- dtype=dtype, # dtype
- # resampling=Resampling.nearest,
- method=method, # strategy to combine overlapping rasters
- # dst_path=memfile.name, # str or PathLike to save raster
- dst_path=dst_path,
- # dst_kwds={'blockysize':512, 'blockxsize':512} # Dictionary
- )
-
-def get_mean_sd_by_band(path, force_compute=True, ignore_zeros=True):
- '''
- Reads metadata or computes mean and sd of each band of a geotiff.
- If the metadata is not available, mean and standard deviation can be computed via numpy.
-
- Parameters
- ----------
- path : str
- path to a geotiff file
- ignore_zeros : boolean
- ignore zeros when computing mean and sd via numpy
-
- Returns
- -------
- means : list
- list of mean values per band
- sds : list
- list of standard deviation values per band
- '''
-
- src = rasterio.open(path)
- means = []
- sds = []
- if force_compute:
- for band in range(1, src.count+1):
- arr = src.read(band)
- arr = replace_nan_with_zero(arr)
- if ignore_zeros:
- mean = np.ma.masked_equal(arr, 0).mean()
- sd = np.ma.masked_equal(arr, 0).std()
- else:
- mean = np.mean(arr)
- sd = np.std(arr)
- means.append(float(mean))
- sds.append(float(sd))
-
- else:
- for band in range(1, src.count+1):
- try:
- tags = src.tags(band)
- if 'STATISTICS_MEAN' in tags and 'STATISTICS_STDDEV' in tags:
- mean = float(tags['STATISTICS_MEAN'])
- sd = float(tags['STATISTICS_STDDEV'])
- means.append(mean)
- sds.append(sd)
- else:
- raise KeyError("Statistics metadata not found.")
-
- except KeyError:
- warnings.warn("Statistics metadata not found and computation not enabled.", UserWarning)
- except Exception as e:
- print(f"Error processing band {band}: {e}")
-
- src.close()
- return means, sds
-
--
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