diff --git a/clustering.py b/clustering.py
index 27706ac5505ea8c44526b5b10cc14ff4a495eed0..2f3f776cc8e932ab51e7443da2b148b6554b148d 100644
--- a/clustering.py
+++ b/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()
diff --git a/iamap.py b/iamap.py
index f66f95b781000424ce68d2c784759b730b7e534b..fbd320cdad0fe9a8e3c68745b3fc3bc01673a5b6 100644
--- a/iamap.py
+++ b/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.')
diff --git a/random_forest.py b/random_forest.py
index 690b3eda381e92788a19dc32b34eeb4e671c8212..fa5e1a2ec9a055aa61ffff56c2d2824d16082b45 100644
--- a/random_forest.py
+++ b/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()
diff --git a/reduction.py b/reduction.py
index 35e0b2846df7a595e32501ffe07b0b3f19a775a4..d9339c5628c2687971639b1dbe45d623b65bb2be 100644
--- a/reduction.py
+++ b/reduction.py
@@ -1,4 +1,5 @@
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()
diff --git a/similarity.py b/similarity.py
index fe6d750507446c95acec8d4907dd02b1a6b343bb..c7cc89d537f598f510623bdab5158d9744ac6599 100644
--- a/similarity.py
+++ b/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()