get unique filenames and layer names for all algorithms

clustering.py

@@ -3,6 +3,8 @@ import numpy as np
 from pathlib import Path
 from typing import Dict, Any
 import joblib
+import tempfile
+import json
 
 import rasterio
 from rasterio import windows
@@ -26,8 +28,7 @@ from qgis.core import (Qgis,
 
 from sklearn.cluster import KMeans
 
-import json
-
+from .utils.misc import get_unique_filename
 
 
 class ClusterAlgorithm(QgsProcessingAlgorithm):
@@ -53,6 +54,7 @@ class ClusterAlgorithm(QgsProcessingAlgorithm):
         with some other properties.
         """
         cwd = Path(__file__).parent.absolute()
+        tmp_wd = os.path.join(tempfile.gettempdir(), "iamap_clustering")
 
         self.addParameter(
             QgsProcessingParameterRasterLayer(
@@ -145,7 +147,8 @@ class ClusterAlgorithm(QgsProcessingAlgorithm):
                 self.OUTPUT,
                 self.tr(
                     "Output directory (choose the location that the image features will be saved)"),
-            defaultValue=os.path.join(cwd,'models'),
+            # defaultValue=os.path.join(cwd,'models'),
+            defaultValue=tmp_wd,
             )
         )
 
@@ -221,15 +224,16 @@ class ClusterAlgorithm(QgsProcessingAlgorithm):
             params_file = os.path.join(self.output_dir, 'cluster_parameters.json')
             
             
-            if os.path.exists(dst_path):
-                    i = 1
-                    while True:
-                        modified_output_file = os.path.join(self.output_dir, f"cluster_{i}.tif")
-                        if not os.path.exists(modified_output_file):
-                            dst_path = modified_output_file
-                            break
-                        i += 1
+            # if os.path.exists(dst_path):
+            #         i = 1
+            #         while True:
+            #             modified_output_file = os.path.join(self.output_dir, f"cluster_{i}.tif")
+            #             if not os.path.exists(modified_output_file):
+            #                 dst_path = modified_output_file
+            #                 break
+            #             i += 1
                         
+            dst_path, layer_name = get_unique_filename(self.output_dir, 'cluster.tif', 'clustered features')
             if os.path.exists(params_file):
                     i = 1
                     while True:
@@ -251,7 +255,7 @@ class ClusterAlgorithm(QgsProcessingAlgorithm):
 
             parameters['OUTPUT_RASTER']=dst_path
 
-        return {'OUTPUT_RASTER':dst_path}
+        return {'OUTPUT_RASTER':dst_path, 'OUTPUT_LAYER_NAME':layer_name}
 
     def process_options(self,parameters, context, feedback):
         self.iPatch = 0
@@ -299,10 +303,12 @@ class ClusterAlgorithm(QgsProcessingAlgorithm):
             parameters, self.CRS, context)
         extent = self.parameterAsExtent(
             parameters, self.EXTENT, context)
-        self.output_dir = self.parameterAsString(
+        output_dir = self.parameterAsString(
             parameters, self.OUTPUT, context)
         self.save_model = self.parameterAsBoolean(
             parameters, self.SAVE_MODEL, context)
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
 
         rlayer_data_provider = rlayer.dataProvider()
 

iamap.py

@@ -9,7 +9,12 @@ 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
+from .icons import (QIcon_EncoderTool, 
+                    QIcon_ReductionTool, 
+                    QIcon_ClusterTool, 
+                    QIcon_SimilarityTool, 
+                    QIcon_RandomforestTool,
+                    )
 
 
 class IAMap(QObject):
@@ -135,9 +140,10 @@ class IAMap(QObject):
             # Retrieve output parameters from the result dictionary
             if 'OUTPUT_RASTER' in result:
                 output_raster_path = result['OUTPUT_RASTER']
+                output_layer_name = result['OUTPUT_LAYER_NAME']
 
                 # Add the output raster layer to the map canvas
-                self.iface.addRasterLayer(str(output_raster_path), 'reduced features')
+                self.iface.addRasterLayer(str(output_raster_path), output_layer_name)
             else:
                 # Handle missing or unexpected output
                 print('Output raster not found in algorithm result.')
@@ -157,9 +163,10 @@ class IAMap(QObject):
             # Retrieve output parameters from the result dictionary
             if 'OUTPUT_RASTER' in result:
                 output_raster_path = result['OUTPUT_RASTER']
+                output_layer_name = result['OUTPUT_LAYER_NAME']
 
                 # Add the output raster layer to the map canvas
-                self.iface.addRasterLayer(str(output_raster_path), 'clustering')
+                self.iface.addRasterLayer(str(output_raster_path), output_layer_name)
             else:
                 # Handle missing or unexpected output
                 print('Output raster not found in algorithm result.')
@@ -179,9 +186,10 @@ class IAMap(QObject):
             # Retrieve output parameters from the result dictionary
             if 'OUTPUT_RASTER' in result:
                 output_raster_path = result['OUTPUT_RASTER']
+                output_layer_name = result['OUTPUT_LAYER_NAME']
 
                 # Add the output raster layer to the map canvas
-                self.iface.addRasterLayer(str(output_raster_path), 'similarity map')
+                self.iface.addRasterLayer(str(output_raster_path), output_layer_name)
             else:
                 # Handle missing or unexpected output
                 print('Output raster not found in algorithm result.')
@@ -200,9 +208,10 @@ class IAMap(QObject):
             # Retrieve output parameters from the result dictionary
             if 'OUTPUT_RASTER' in result:
                 output_raster_path = result['OUTPUT_RASTER']
+                output_layer_name = result['OUTPUT_LAYER_NAME']
 
                 # Add the output raster layer to the map canvas
-                self.iface.addRasterLayer(str(output_raster_path), 'random forest map')
+                self.iface.addRasterLayer(str(output_raster_path), output_layer_name)
             else:
                 # Handle missing or unexpected output
                 print('Output raster not found in algorithm result.')

random_forest.py

@@ -3,6 +3,8 @@ import numpy as np
 from pathlib import Path
 from typing import Dict, Any
 import joblib
+import json
+import tempfile
 
 import rasterio
 from rasterio import windows
@@ -34,7 +36,8 @@ from sklearn.metrics import accuracy_score
 from sklearn.preprocessing import LabelEncoder
 from sklearn.model_selection import train_test_split
 
-import json
+from .utils.misc import get_unique_filename
+
 
 class RFAlgorithm(QgsProcessingAlgorithm):
     """
@@ -57,6 +60,7 @@ class RFAlgorithm(QgsProcessingAlgorithm):
         with some other properties.
         """
         cwd = Path(__file__).parent.absolute()
+        tmp_wd = os.path.join(tempfile.gettempdir(), "iamap_rf")
 
         self.addParameter(
             QgsProcessingParameterRasterLayer(
@@ -109,7 +113,8 @@ class RFAlgorithm(QgsProcessingAlgorithm):
                 name=self.TEMPLATE,
                 description=self.tr(
                     'Input shapefile path for training data set for random forest (if no test data_set, will be devised in train and test)'),
-            defaultValue=os.path.join(cwd,'assets','rf.gpkg'),
+            # defaultValue=os.path.join(cwd,'assets','rf.gpkg'),
+            defaultValue=os.path.join(cwd,'assets','rf.shp'),
             ),
         )
         
@@ -128,7 +133,7 @@ class RFAlgorithm(QgsProcessingAlgorithm):
                 self.OUTPUT,
                 self.tr(
                     "Output directory (choose the location that the image features will be saved)"),
-            defaultValue=os.path.join(cwd,'models'),
+            defaultValue=tmp_wd,
             )
         )
         
@@ -324,14 +329,15 @@ class RFAlgorithm(QgsProcessingAlgorithm):
             
 
             
-            if os.path.exists(dst_path):
-                    i = 1
-                    while True:
-                        modified_output_file = os.path.join(self.output_dir, f"random_forest_{i}.tif")
-                        if not os.path.exists(modified_output_file):
-                            dst_path = modified_output_file
-                            break
-                        i += 1
+            dst_path, layer_name = get_unique_filename(self.output_dir, 'random_forest.tif', 'random forest')
+            # if os.path.exists(dst_path):
+            #         i = 1
+            #         while True:
+            #             modified_output_file = os.path.join(self.output_dir, f"random_forest_{i}.tif")
+            #             if not os.path.exists(modified_output_file):
+            #                 dst_path = modified_output_file
+            #                 break
+            #             i += 1
                         
             if os.path.exists(params_file):
                     i = 1
@@ -353,7 +359,7 @@ class RFAlgorithm(QgsProcessingAlgorithm):
 
             parameters['OUTPUT_RASTER']=dst_path
 
-        return {'OUTPUT_RASTER':dst_path}
+        return {'OUTPUT_RASTER':dst_path, 'OUTPUT_LAYER_NAME':layer_name}
 
     def process_options(self,parameters, context, feedback):
         self.iPatch = 0
@@ -402,8 +408,11 @@ class RFAlgorithm(QgsProcessingAlgorithm):
             parameters, self.CRS, context)
         extent = self.parameterAsExtent(
             parameters, self.EXTENT, context)
-        self.output_dir = self.parameterAsString(
+        output_dir = self.parameterAsString(
             parameters, self.OUTPUT, context)
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+
 
         rlayer_data_provider = rlayer.dataProvider()
 

reduction.py

 import os
+import tempfile
 import numpy as np
 from pathlib import Path
 from typing import Dict, Any
@@ -31,6 +32,8 @@ from qgis.core import (Qgis,
 from sklearn.preprocessing import StandardScaler
 from sklearn.decomposition import PCA, IncrementalPCA
 from sklearn.cluster import KMeans
+
+from .utils.misc import get_unique_filename
 #from umap.umap_ import UMAP
 
 
@@ -76,6 +79,7 @@ class ReductionAlgorithm(QgsProcessingAlgorithm):
         with some other properties.
         """
         cwd = Path(__file__).parent.absolute()
+        tmp_wd = os.path.join(tempfile.gettempdir(), "iamap_reduction")
 
         self.addParameter(
             QgsProcessingParameterRasterLayer(
@@ -181,7 +185,8 @@ class ReductionAlgorithm(QgsProcessingAlgorithm):
                 self.OUTPUT,
                 self.tr(
                     "Output directory (choose the location that the image features will be saved)"),
-            defaultValue=os.path.join(cwd,'models'),
+            # defaultValue=os.path.join(cwd,'models'),
+            defaultValue=tmp_wd,
             )
         )
 
@@ -320,14 +325,15 @@ class ReductionAlgorithm(QgsProcessingAlgorithm):
             
 
             feedback.pushInfo(f'Export to geotif\n')
-            if os.path.exists(dst_path):
-                    i = 1
-                    while True:
-                        modified_output_file = os.path.join(self.output_dir, f"proj_{i}.tif")
-                        if not os.path.exists(modified_output_file):
-                            dst_path = modified_output_file
-                            break
-                        i += 1
+            dst_path, layer_name = get_unique_filename(self.output_dir, 'proj.tif', 'reduced features')
+            # if os.path.exists(dst_path):
+            #         i = 1
+            #         while True:
+            #             modified_output_file = os.path.join(self.output_dir, f"proj_{i}.tif")
+            #             if not os.path.exists(modified_output_file):
+            #                 dst_path = modified_output_file
+            #                 break
+            #             i += 1
 
             with rasterio.open(dst_path, 'w', driver='GTiff',
                                height=height, width=width, count=channels, dtype='float32',
@@ -340,7 +346,7 @@ class ReductionAlgorithm(QgsProcessingAlgorithm):
 
             parameters['OUTPUT_RASTER']=dst_path
 
-        return {'OUTPUT_RASTER':dst_path}
+        return {'OUTPUT_RASTER':dst_path, 'OUTPUT_LAYER_NAME':layer_name}
 
 
     def process_options(self,parameters, context, feedback):
@@ -393,10 +399,12 @@ class ReductionAlgorithm(QgsProcessingAlgorithm):
             parameters, self.CRS, context)
         extent = self.parameterAsExtent(
             parameters, self.EXTENT, context)
-        self.output_dir = self.parameterAsString(
+        output_dir = self.parameterAsString(
             parameters, self.OUTPUT, context)
         self.save_model = self.parameterAsBoolean(
             parameters, self.SAVE_MODEL, context)
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
 
         rlayer_data_provider = rlayer.dataProvider()
 

similarity.py

@@ -3,6 +3,8 @@ import numpy as np
 from pathlib import Path
 from typing import Dict, Any
 import joblib
+import json
+import tempfile
 
 import rasterio
 from rasterio import windows
@@ -11,9 +13,7 @@ from shapely.geometry import box
 from qgis.PyQt.QtCore import QCoreApplication
 from qgis.core import (Qgis,
                        QgsGeometry,
-                       QgsProcessingParameterBoolean,
                        QgsProcessingParameterFile,
-                       QgsProcessingParameterEnum,
                        QgsCoordinateTransform,
                        QgsProcessingException,
                        QgsProcessingAlgorithm,
@@ -28,7 +28,8 @@ from qgis.core import (Qgis,
 import torch
 import torch.nn as nn
 
-import json
+from .utils.misc import get_unique_filename
+
 
 
 class SimilarityAlgorithm(QgsProcessingAlgorithm):
@@ -51,6 +52,7 @@ class SimilarityAlgorithm(QgsProcessingAlgorithm):
         with some other properties.
         """
         cwd = Path(__file__).parent.absolute()
+        tmp_wd = os.path.join(tempfile.gettempdir(), "iamap_sim")
 
         self.addParameter(
             QgsProcessingParameterRasterLayer(
@@ -113,7 +115,7 @@ class SimilarityAlgorithm(QgsProcessingAlgorithm):
                 self.OUTPUT,
                 self.tr(
                     "Output directory (choose the location that the image features will be saved)"),
-            defaultValue=os.path.join(cwd,'features'),
+            defaultValue=tmp_wd,
             )
         )
 
@@ -207,17 +209,17 @@ class SimilarityAlgorithm(QgsProcessingAlgorithm):
             sim = sim.numpy()
             height, width, channels = sim.shape
 
-            dst_path = os.path.join(self.output_dir,'similarity.tif')
+            dst_path, layer_name = get_unique_filename(self.output_dir, 'similarity.tif', 'similarity')
             params_file = os.path.join(self.output_dir,'cosine.json')
             
-            if os.path.exists(dst_path):
-                    i = 1
-                    while True:
-                        modified_output_file = os.path.join(self.output_dir, f"similarity_{i}.tif")
-                        if not os.path.exists(modified_output_file):
-                            dst_path = modified_output_file
-                            break
-                        i += 1
+            # if os.path.exists(dst_path):
+            #         i = 1
+            #         while True:
+            #             modified_output_file = os.path.join(self.output_dir, f"similarity_{i}.tif")
+            #             if not os.path.exists(modified_output_file):
+            #                 dst_path = modified_output_file
+            #                 break
+            #             i += 1
             if os.path.exists(params_file):
                     i = 1
                     while True:
@@ -238,7 +240,7 @@ class SimilarityAlgorithm(QgsProcessingAlgorithm):
 
             parameters['OUTPUT_RASTER']=dst_path
 
-        return {'OUTPUT_RASTER':dst_path}
+        return {'OUTPUT_RASTER':dst_path, 'OUTPUT_LAYER_NAME':layer_name}
 
     def process_options(self,parameters, context, feedback):
         self.iPatch = 0
@@ -281,8 +283,10 @@ class SimilarityAlgorithm(QgsProcessingAlgorithm):
             parameters, self.CRS, context)
         extent = self.parameterAsExtent(
             parameters, self.EXTENT, context)
-        self.output_dir = self.parameterAsString(
+        output_dir = self.parameterAsString(
             parameters, self.OUTPUT, context)
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
 
         rlayer_data_provider = rlayer.dataProvider()