Merge pull request #153 from PUTvision/devel

devel -> master

.github/workflows/python-app-ubuntu.yml

@@ -39,8 +39,8 @@ jobs:
     - name: Install dependencies
       run: |
         pip install --upgrade pip
-        pip install --upgrade -r requirements.txt
-        pip install --upgrade -r requirements_development.txt
+        pip install --upgrade -r ./src/deepness/python_requirements/requirements.txt
+        pip install --upgrade -r ./src/deepness/python_requirements/requirements_development.txt
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -56,7 +56,7 @@ jobs:
         # apparently there is some issue with opencv-python-headless, we need to reinstall it 
         pip uninstall opencv-python-headless --yes
         
-        pip install opencv-python-headless==4.6.0.66
+        pip install --upgrade -r ./src/deepness/python_requirements/requirements.txt
         # run one this without pytest, because pytest creates obfuscated error messages
         # we need 'xvfb-run' to simulate UI - otherwise qgis crushes
         xvfb-run python3 test/test_map_processor_segmentation.py

.gitignore

@@ -333,7 +333,7 @@ compile_commands.json
 
 # End of https://www.toptal.com/developers/gitignore/api/python,pycharm,qt
 
-
+grid_data
 .idea/
-
+src/deepness/python3.10/
 _autosummary/

README.md

@@ -49,7 +49,7 @@ ln -s $PROJECT_DIR/src/deepness ~/.local/share/QGIS/QGIS3/profiles/default/pytho
 
 ```bash
 . venv/bin/activate
-pip install -r requirements.txt
+pip install -r ./src/deepness/python_requirements/requirements.txt
 ```
 
 - Run QGis in the virtual environment:

docs/source/creators/creators_add_metadata_to_model.rst

@@ -37,8 +37,6 @@ List of parameters parsed by plugin
 +----------------------+-------+---------------------------------------+-------------------------------------------------------------+
 | det_type             | str   |       :code:`YOLO_v5_or_v7_default`   | Detector: type of the detector model format                 |
 +----------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| det_remove_overlap   | bool  |       :code:`True`                    | Detector: Whether overlapping detection should be removed   |
-+----------------------+-------+---------------------------------------+-------------------------------------------------------------+
 
 =======
 Example

docs/source/main/main_installation.rst

@@ -2,37 +2,6 @@ Installation
 ============
 
 
-============
-Requirements
-============
-
-* Ubuntu
-
-  * (option 1) Install requirements using system Python interpreter:
-
-  .. code-block:: 
-
-    python3 -m pip install opencv-python-headless onnxruntime-gpu
-
-  * (option 2) Run QGIS and Python Console. Then call command:
-
-  .. code-block:: 
-
-    import pip; pip.main(['install', 'opencv-python-headless', 'onnxruntime-gpu'])
-
-
-* Windows
-
-  * Go to QGIS installation path (for example :code:`C:\Program Files\QGIS 3.26.3\`)
-  
-  * Run :code:`OSGeo4W.bat` and type installation command:
-
-  .. code-block:: 
-
-    python3 -m pip install opencv-python-headless onnxruntime-gpu
-
-* MacOS - SOON
-
 ===================
 Plugin installation
 ===================
@@ -69,3 +38,40 @@ Plugin installation
   * Select the ZIP file using system prompt
 
   * Click the Install Plugin button
+
+============
+Requirements
+============
+
+The plugin should install all required dependencies automatically during the first run. However, if you want to install them manually, you can use the following commands:
+
+.. note:: 
+
+     The plugin requirements and versions are listed in the `requirements.txt <https://github.com/PUTvision/qgis-plugin-deepness/blob/master/src/deepness/python_requirements/requirements.txt>`_ file.
+
+* Ubuntu
+
+  * (option 1) Install requirements using system Python interpreter:
+
+  .. code-block:: 
+
+    python3 -m pip install opencv-python-headless onnxruntime-gpu
+
+  * (option 2) Run QGIS and Python Console. Then call command:
+
+  .. code-block:: 
+
+    import pip; pip.main(['install', 'opencv-python-headless', 'onnxruntime-gpu'])
+
+
+* Windows
+
+  * Go to QGIS installation path (for example :code:`C:\Program Files\QGIS 3.26.3\`)
+  
+  * Run :code:`OSGeo4W.bat` and type installation command:
+
+  .. code-block:: 
+
+    python3 -m pip install opencv-python-headless onnxruntime-gpu
+
+* MacOS - SOON

docs/source/main/model_zoo/MODEL_ZOO.md

@@ -17,6 +17,7 @@ The [Model ZOO](https://chmura.put.poznan.pl/s/2pJk4izRurzQwu3) is a collection
 | [Agriculture segmentation RGB+NIR](https://chmura.put.poznan.pl/s/wf5Ml1ZDyiVdNiy)   | 256        | 30    | Trained on the [Agriculture Vision 2021 dataset](https://www.agriculture-vision.com/agriculture-vision-2021/dataset-2021). 4 channels input (RGB + NIR). 9 output classes within agricultural field (weed_cluster, waterway, ...). Uses X-UNet.                                                                                                                                                                                                                                                              | [Image](https://chmura.put.poznan.pl/s/35A5ISUxLxcK7kL) |
 | [Fire risk assesment](https://chmura.put.poznan.pl/s/NxKLdfdr9s9jsVA)                | 384        | 100   | Trained on the FireRisk dataset (RGB data). Classifies risk of fires (ver_high, high, low, ...). Uses ConvNeXt XXL. Val F1-score 	65.5.                                                                                                                                                                                                                                                                                                                                                                      | [Image](https://chmura.put.poznan.pl/s/Ijn3VgG76NvYtDY) |
 | [Roads Segmentation](https://chmura.put.poznan.pl/s/y6S3CmodPy1fYYz)                 | 512        | 21    | The model segments the Google Earth satellite images into 'road' and 'not-road' classes. Model works best on wide car roads, crossroads and roundabouts.                                                                                                                                                                                                                                                                                                                                                     | [Image](https://chmura.put.poznan.pl/s/rln6mpbjpsXWpKg) |
+| [Solar PV Segmentation](https://owncloud.fraunhofer.de/index.php/s/Ph9TC6BTxPi5oZZ)                 | 512        | 3    | Model trained by M Kleebauer et al. in "[Multi-resolution segmentation of solar photovoltaic systems using deep learning](https://www.mdpi.com/2596164) on a diverse range of image data, spanning UAV, aerial, and satellite imagery at both native and aggregated resolutions of 0.1 m, 0.2 m, 0.3 m, 0.8 m, 1.6 m, and 3.2 m.                                                                                                                                                                                                                                                                                                                                                      | [Image](https://github.com/Kleebaue/multi-resolution-pv-system-segmentation/blob/main/figures/prediction_multi_res.png) |
 
 ## Regression models
 

src/deepness/common/config_entry_key.py

@@ -33,7 +33,6 @@ class ConfigEntryKey(enum.Enum):
 
     DETECTION_CONFIDENCE = enum.auto(), 0.5
     DETECTION_IOU = enum.auto(), 0.5
-    DETECTION_REMOVE_OVERLAPPING = enum.auto(), True
     DETECTOR_TYPE = enum.auto(), 'YOLO_v5_v7_DEFAULT'
 
     DATA_EXPORT_DIR = enum.auto(), ''

src/deepness/common/processing_parameters/detection_parameters.py

@@ -59,6 +59,5 @@ class DetectionParameters(MapProcessingParameters):
 
     confidence: float
     iou_threshold: float
-    remove_overlapping_detections: bool  # whether overlapping detections can be deleted
 
     detector_type: DetectorType = DetectorType.YOLO_v5_v7_DEFAULT  # parameters specific for each model type

src/deepness/deepness.py

@@ -25,7 +25,6 @@
                                                                      MapProcessingResultFailed,
                                                                      MapProcessingResultSuccess)
 from deepness.processing.map_processor.map_processor_training_data_export import MapProcessorTrainingDataExport
-from deepness.processing.models.model_types import ModelDefinition
 
 cv2 = LazyPackageLoader('cv2')
 
@@ -252,6 +251,8 @@ def _run_training_data_export(self, training_data_export_parameters: TrainingDat
         self._display_processing_started_info()
 
     def _run_model_inference(self, params: MapProcessingParameters):
+        from deepness.processing.models.model_types import ModelDefinition  # import here to avoid pulling external dependencies to early
+
         if not self._are_map_processing_parameters_are_correct(params):
             return
 

src/deepness/deepness_dockwidget.py

@@ -14,6 +14,7 @@
 from deepness.common.config_entry_key import ConfigEntryKey
 from deepness.common.defines import IS_DEBUG, PLUGIN_NAME
 from deepness.common.errors import OperationFailedException
+from deepness.common.lazy_package_loader import LazyPackageLoader
 from deepness.common.processing_overlap import ProcessingOverlap, ProcessingOverlapOptions
 from deepness.common.processing_parameters.detection_parameters import DetectionParameters, DetectorType
 from deepness.common.processing_parameters.map_processing_parameters import (MapProcessingParameters, ModelOutputFormat,
@@ -23,12 +24,11 @@
 from deepness.common.processing_parameters.segmentation_parameters import SegmentationParameters
 from deepness.common.processing_parameters.superresolution_parameters import SuperresolutionParameters
 from deepness.common.processing_parameters.training_data_export_parameters import TrainingDataExportParameters
-from deepness.processing.models.detector import Detector
 from deepness.processing.models.model_base import ModelBase
-from deepness.processing.models.model_types import ModelDefinition, ModelType
 from deepness.widgets.input_channels_mapping.input_channels_mapping_widget import InputChannelsMappingWidget
 from deepness.widgets.training_data_export_widget.training_data_export_widget import TrainingDataExportWidget
 
+
 FORM_CLASS, _ = uic.loadUiType(os.path.join(
     os.path.dirname(__file__), 'deepness_dockwidget.ui'))
 
@@ -113,7 +113,6 @@ def _load_ui_from_config(self):
 
             self.doubleSpinBox_confidence.setValue(ConfigEntryKey.DETECTION_CONFIDENCE.get())
             self.doubleSpinBox_iouScore.setValue(ConfigEntryKey.DETECTION_IOU.get())
-            self.checkBox_removeOverlappingDetections.setChecked(ConfigEntryKey.DETECTION_REMOVE_OVERLAPPING.get())
             self.comboBox_detectorType.setCurrentText(ConfigEntryKey.DETECTOR_TYPE.get())
         except Exception:
             logging.exception("Failed to load the ui state from config!")
@@ -146,7 +145,6 @@ def _save_ui_to_config(self):
 
         ConfigEntryKey.DETECTION_CONFIDENCE.set(self.doubleSpinBox_confidence.value())
         ConfigEntryKey.DETECTION_IOU.set(self.doubleSpinBox_iouScore.value())
-        ConfigEntryKey.DETECTION_REMOVE_OVERLAPPING.set(self.checkBox_removeOverlappingDetections.isChecked())
         ConfigEntryKey.DETECTOR_TYPE.set(self.comboBox_detectorType.currentText())
 
         self._input_channels_mapping_widget.save_ui_to_config()
@@ -158,6 +156,8 @@ def _rlayer_updated(self):
     def _setup_misc_ui(self):
         """ Setup some misceleounous ui forms
         """
+        from deepness.processing.models.model_types import ModelDefinition  # import here to avoid pulling external dependencies to early
+
         self._show_debug_warning()
         combobox = self.comboBox_processedAreaSelection
         for name in ProcessedAreaType.get_all_names():
@@ -168,6 +168,8 @@ def _setup_misc_ui(self):
 
         self.mMapLayerComboBox_inputLayer.setFilters(QgsMapLayerProxyModel.RasterLayer)
         self.mMapLayerComboBox_areaMaskLayer.setFilters(QgsMapLayerProxyModel.VectorLayer)
+
+        self.mGroupBox_8.setCollapsed(True)  # collapse the group by default
         self._set_processed_area_mask_options()
         self._set_processing_overlap_enabled()
 
@@ -212,6 +214,8 @@ def _create_connections(self):
         self.radioButton_processingTileOverlapPixels.toggled.connect(self._set_processing_overlap_enabled)
 
     def _model_type_changed(self):
+        from deepness.processing.models.model_types import ModelType  # import here to avoid pulling external dependencies to early
+
         model_type = ModelType(self.comboBox_modelType.currentText())
 
         segmentation_enabled = False
@@ -251,11 +255,11 @@ def _model_output_format_changed(self):
         model_output_format = ModelOutputFormat(txt)
         class_number_selection_enabled = bool(model_output_format == ModelOutputFormat.ONLY_SINGLE_CLASS_AS_LAYER)
         self.comboBox_outputFormatClassNumber.setEnabled(class_number_selection_enabled)
-        
+
     def _set_processing_overlap_enabled(self):
         overlap_percentage_enabled = self.radioButton_processingTileOverlapPercentage.isChecked()
         self.spinBox_processingTileOverlapPercentage.setEnabled(overlap_percentage_enabled)
-        
+
         overlap_pixels_enabled = self.radioButton_processingTileOverlapPixels.isChecked()
         self.spinBox_processingTileOverlapPixels.setEnabled(overlap_pixels_enabled)
 
@@ -284,15 +288,15 @@ def _browse_query_image_path(self):
         )
         if file_path:
             self.lineEdit_recognitionPath.setText(file_path)
-    
+
     def _load_default_model_parameters(self):
         """
         Load the default parameters from model metadata
         """
         value = self._model.get_metadata_resolution()
         if value is not None:
             self.doubleSpinBox_resolution_cm_px.setValue(value)
-            
+
         value = self._model.get_model_batch_size()
         if value is not None:
             self.spinBox_batchSize.setValue(value)
@@ -339,15 +343,13 @@ def _load_default_model_parameters(self):
         if value is not None:
             self.doubleSpinBox_iouScore.setValue(value)
 
-        value = self._model.get_metadata_detection_remove_overlapping()
-        if value is not None:
-            self.checkBox_removeOverlappingDetections.setChecked(value)
-
     def _load_model_with_type_from_metadata(self, model_class_from_ui, file_path):
         """
         If model has model_type in metadata - use this type to create proper model class.
         Otherwise model_class_from_ui will be used
         """
+        from deepness.processing.models.model_types import ModelDefinition, ModelType  # import here to avoid pulling external dependencies to early
+
         model_class = model_class_from_ui
 
         model_type_str_from_metadata = ModelBase.get_model_type_from_metadata(file_path)
@@ -365,6 +367,9 @@ def _load_model_and_display_info(self, abort_if_no_file_path: bool = False):
         """
         Tries to load the model and display its message.
         """
+        from deepness.processing.models.model_types import ModelType  # import here to avoid pulling external dependencies to early
+        import deepness.processing.models.detector as detector_module  # import here to avoid pulling external dependencies to early
+
         file_path = self.lineEdit_modelPath.text()
 
         if not file_path and abort_if_no_file_path:
@@ -387,13 +392,13 @@ def _load_model_and_display_info(self, abort_if_no_file_path: bool = False):
             # TODO idk how variable input will be handled
             self.spinBox_tileSize_px.setValue(input_size_px)
             self.spinBox_tileSize_px.setEnabled(False)
-            
+
             if batch_size is not None:
                 self.spinBox_batchSize.setValue(batch_size)
                 self.spinBox_batchSize.setEnabled(False)
             else:
                 self.spinBox_batchSize.setEnabled(True)
-            
+
             self._input_channels_mapping_widget.set_model(self._model)
 
             # super resolution
@@ -418,7 +423,7 @@ def _load_model_and_display_info(self, abort_if_no_file_path: bool = False):
 
         self.label_modelInfo.setText(txt)
 
-        if isinstance(self._model, Detector):
+        if isinstance(self._model, detector_module.Detector):
             detector_type = DetectorType(self.comboBox_detectorType.currentText())
             self._model.set_model_type_param(detector_type)
 
@@ -470,10 +475,12 @@ def _get_pixel_classification_threshold(self):
             return 0
         return self.doubleSpinBox_probabilityThreshold.value()
 
-    def get_selected_model_class_definition(self) -> ModelDefinition:
+    def get_selected_model_class_definition(self):   # -> ModelDefinition: # we cannot import it here yet
         """
         Get the currently selected model class (in UI)
         """
+        from deepness.processing.models.model_types import ModelDefinition, ModelType  # import here to avoid pulling external dependencies to early
+
         model_type_txt = self.comboBox_modelType.currentText()
         model_type = ModelType(model_type_txt)
         model_definition = ModelDefinition.get_definition_for_type(model_type)
@@ -483,6 +490,8 @@ def get_inference_parameters(self) -> MapProcessingParameters:
         """ Get the parameters for the model interface.
         The returned type is derived from `MapProcessingParameters` class, depending on the selected model type.
         """
+        from deepness.processing.models.model_types import ModelType  # import here to avoid pulling external dependencies to early
+
         map_processing_parameters = self._get_map_processing_parameters()
 
         if self._model is None:
@@ -541,14 +550,13 @@ def get_recognition_parameters(self, map_processing_parameters: MapProcessingPar
             query_image_path=self.lineEdit_recognitionPath.text(),
         )
         return params
-    
+
     def get_detection_parameters(self, map_processing_parameters: MapProcessingParameters) -> DetectionParameters:
 
         params = DetectionParameters(
             **map_processing_parameters.__dict__,
             confidence=self.doubleSpinBox_confidence.value(),
             iou_threshold=self.doubleSpinBox_iouScore.value(),
-            remove_overlapping_detections=self.checkBox_removeOverlappingDetections.isChecked(),
             model=self._model,
             detector_type=DetectorType(self.comboBox_detectorType.currentText()),
         )