Merge pull request #136 from PUTvision/feat/0.6.0

Feat/0.6.0

docs/source/creators/creators_add_metadata_to_model.rst

@@ -8,34 +8,37 @@ The plugin allows you to load the meta parameters of the onnx model automaticall
 List of parameters parsed by plugin
 ===================================
 
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| Parameter          |  Type |            Example                    | Description                                                 |
-+====================+=======+=======================================+=============================================================+
-| model_type         |  str  |   :code:`'Segmentor'`                 | Types of models available: Segmentor, Regressor, Detector.  |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| class_names        |  dict | :code:`{0: 'background', 1: 'field'}` | A dictionary that maps a class id to its name.              |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| resolution         | float |        :code:`100`                    | Real-world resolution of images (centimeters per pixel).    |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| tiles_size         |  int  |        :code:`512`                    | What size (in pixels) is the tile to crop.                  |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| tiles_overlap      |  int  |         :code:`40`                    | How many percent of the image size overlap.                 |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| seg_thresh         | float |       :code:`0.5`                     | Segmentor: class confidence threshold.                      |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| seg_small_segment  |  int  |       :code:`7`                       | Segmentor: remove small occurrences of the class.           |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| reg_output_scaling | float |       :code:`1.0`                     | Regressor: scaling factor for the model output.             |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| det_conf           | float |       :code:`0.6`                     | Detector: object confidence threshold.                      |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| det_iou_thresh     | float |       :code:`0.4`                     | Detector: IOU threshold for NMS.                            |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-| 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   |
-+--------------------+-------+---------------------------------------+-------------------------------------------------------------+
-
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| Parameter            |  Type |            Example                    | Description                                                 |
++======================+=======+=======================================+=============================================================+
+| model_type           |  str  |   :code:`'Segmentor'`                 | Types of models available: Segmentor, Regressor, Detector.  |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| class_names          |  dict | :code:`{0: 'background', 1: 'field'}` | A dictionary that maps a class id to its name.              |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| resolution           | float |        :code:`100`                    | Real-world resolution of images (centimeters per pixel).    |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| tiles_size           |  int  |        :code:`512`                    | What size (in pixels) is the tile to crop.                  |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| tiles_overlap        |  int  |         :code:`40`                    | How many percent of the image size overlap.                 |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| standardization_mean |  list |         :code:`[0.0, 0.0, 0.0]`       | Mean - if you want to standarize input after normalisation  |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| standardization_std  |  list |         :code:`[1.0, 1.0, 1.0]`       | Std - if you want to standarize input after normalisation   |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| seg_thresh           | float |       :code:`0.5`                     | Segmentor: class confidence threshold.                      |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| seg_small_segment    |  int  |       :code:`7`                       | Segmentor: remove small occurrences of the class.           |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| reg_output_scaling   | float |       :code:`1.0`                     | Regressor: scaling factor for the model output.             |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| det_conf             | float |       :code:`0.6`                     | Detector: object confidence threshold.                      |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| det_iou_thresh       | float |       :code:`0.4`                     | Detector: IOU threshold for NMS.                            |
++----------------------+-------+---------------------------------------+-------------------------------------------------------------+
+| 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
@@ -70,6 +73,15 @@ The example below shows how to add string, float, and dictionary metadata into a
     m3.key = 'resolution'
     m3.value = json.dumps(50)
 
+    # optional, if you want to standarize input after normalisation
+    m4 = model.metadata_props.add()
+    m4.key = 'standardization_mean'
+    m4.value = json.dumps([0.0, 0.0, 0.0])
+
+    m5 = model.metadata_props.add()
+    m5.key = 'standardization_std'
+    m5.value = json.dumps([1.0, 1.0, 1.0])
+
     onnx.save(model, 'deeplabv3_landcover_4c.onnx')
 
 

docs/source/creators/creators_example_onnx_model.rst

@@ -82,4 +82,4 @@ Steps based on the `tensorflow-onnx <https://github.com/onnx/tensorflow-onnx>`_
 Update ONNX model to support dynamic batch size
 ===============================================
 
-To convert model to support dynamic batch size, you need to update :code:`model.onnx` file. You can do it manually using `this <https://github.com/onnx/onnx/issues/2182#issuecomment-881752539>` script. Please note that the script is not perfect and may not work for all models.
+To convert model to support dynamic batch size, you need to update :code:`model.onnx` file. You can do it manually using `this <https://github.com/onnx/onnx/issues/2182#issuecomment-881752539>`_ script. Please note that the script is not perfect and may not work for all models.

docs/source/main/model_zoo/MODEL_ZOO.md

@@ -2,30 +2,33 @@
 
 The [Model ZOO](https://chmura.put.poznan.pl/s/2pJk4izRurzQwu3) is a collection of pre-trained, deep learning models in the ONNX format. It allows for an easy-to-use start with the plugin.
 
- NOTE: the provided models are not universal tools and will perform well only on similar data as in the training datasets. If you notice the model is not perfroming well on your data, consider re-training (or fine-tuning) it on your data.
+ > NOTE: the provided models are not universal tools and will perform well only on similar data as in the training datasets. If you notice the model is not perfroming well on your data, consider re-training (or fine-tuning) it on your data.
 
- If you do not have machine learning expertise, feel free to contact the plugin authors for help or advice.
+> If you do not have machine learning expertise, feel free to contact the plugin authors for help or advice.
 
 ## Segmentation models
 
 | Model                                                                            | Input size | CM/PX | Description                                                                                                                                                                                                                                                                                                                                                                         | Example image |
 |----------------------------------------------------------------------------------|------------|-------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------|
 | [Corn Field Damage Segmentation](https://chmura.put.poznan.pl/s/abWFTVYSDIcncWs) | 512        | 3     | [PUT Vision](https://putvision.github.io/) model for Corn Field Damage Segmentation created on own dataset labeled by experts. We used the classical UNet++ model. It generates 3 outputs: healthy crop, damaged crop, and out-of-field area.                                                                                                                                       | [Image](https://chmura.put.poznan.pl/s/i5WVmcfqPNdBTAQ) |
 | [Land Cover Segmentation](https://chmura.put.poznan.pl/s/PnAFJw27uneROkV)        | 512        | 40    | The model is trained on the [LandCover.ai dataset](https://landcover.ai.linuxpolska.com/). It provides satellite images with 25 cm/px and 50 cm/px resolution. Annotation masks for the following classes are provided for the images: building (1), woodland (2), water(3), road(4). We use `DeepLabV3+` model with `tu-semnasnet_100` backend and `FocalDice` as a loss function. NOTE: the dataset covers only the area of Poland, therefore the performance may be inferior in other parts of the world. | [Image](https://chmura.put.poznan.pl/s/Xa29vnieNQTvSt5) |
+| [Buildings Segmentation](https://chmura.put.poznan.pl/s/MwhgQNhyQF3fuBs)         | 256        | 40    | Trained on the [RampDataset dataset](https://cmr.earthdata.nasa.gov/search/concepts/C2781412367-MLHUB.html). Annotation masks for buildings and background. Xunet network. Val F1-score 81.0 | [Image](https://chmura.put.poznan.pl/s/XCjuDKDS3FFovDl) |
+| [Land Cover Segmentation Sentinel-2](https://chmura.put.poznan.pl/s/UbljXBr1XSc9hCL) | 64         | 1000  | Trained on the [Eurosat dataset](https://www.tensorflow.org/datasets/catalog/eurosat). Uses 13 spectral bands from Sentinel-2, with 10 classes. Model ConvNeXt.  | [Image](https://chmura.put.poznan.pl/s/pGR5VX6AV3hYKVl) |
+| [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) |
 
 ## Regression models
 
 | Model   | Input size | CM/PX | Description | Example image |
 |---------|---|---|---|---|
 |         |  |  |  |  |
-|         |  |  |  |  |
 
 ## Recognition models
 
 | Model   | Input size | CM/PX | Description | Example image |
 |---------|---|---|---|---|
-|  NAIP Place recognition | 224 | 100 | ConvNeXt nano trained using SimSiam onn NAIP imagery |  |
+|  [NAIP Place recognition](https://chmura.put.poznan.pl/s/k7EvbNGc2udHvck) | 224 | 100 | ConvNeXt nano trained using SimSiam onn [NAIP imagery](https://earth.esa.int/eogateway/catalog/pleiades-esa-archive). Rank1-accuracy 75.0. | [Image](https://chmura.put.poznan.pl/s/UzAvz8w5ceCui9y) |
 |         |  |  |  |  |
 
 ## Object detection models
@@ -44,8 +47,6 @@ The [Model ZOO](https://chmura.put.poznan.pl/s/2pJk4izRurzQwu3) is a collection
 |[Residual Dense Network (RDN X4)](https://chmura.put.poznan.pl/s/AaKySmOoOhxW6qZ)      |64      |Trained on 10 cm/px images set it same as input data   | X4 |   Model originally trained by H Zhang et. al. in "[A Comparative Study on CNN-Based Single-Image Super-Resolution Techniques for Satellite Images](https://github.com/farahmand-m/satellite-image-super-resolution)" converted to onnx format   | [Image](https://chmura.put.poznan.pl/s/Ruz24ZpMNg97joV) from Massachusetts Roads Dataset [Dataset in kaggle](https://www.kaggle.com/datasets/balraj98/massachusetts-roads-dataset) |
 
 
-
-
 ## Contributing
 
 PRs with models are welcome!

src/deepness/common/processing_parameters/standardization_parameters.py

@@ -0,0 +1,11 @@
+import numpy as np
+
+
+class StandardizationParameters:
+    def __init__(self, channels_number: int):
+        self.mean = np.array([0.0 for _ in range(channels_number)], dtype=np.float32)
+        self.std = np.array([1.0 for _ in range(channels_number)], dtype=np.float32)
+
+    def set_mean_std(self, mean: np.array, std: np.array):
+        self.mean = np.array(mean, dtype=np.float32)
+        self.std = np.array(std, dtype=np.float32)

src/deepness/deepness.py

@@ -8,24 +8,22 @@
 import logging
 import traceback
 
+from qgis.core import Qgis, QgsApplication, QgsProject, QgsVectorLayer
+from qgis.gui import QgisInterface
 from qgis.PyQt.QtCore import QCoreApplication, Qt
 from qgis.PyQt.QtGui import QIcon
-from qgis.PyQt.QtWidgets import QAction
-from qgis.PyQt.QtWidgets import QMessageBox
-from qgis.core import Qgis
-from qgis.core import QgsApplication
-from qgis.core import QgsProject
-from qgis.core import QgsVectorLayer
-from qgis.gui import QgisInterface
+from qgis.PyQt.QtWidgets import QAction, QMessageBox
 
-from deepness.common.defines import PLUGIN_NAME, IS_DEBUG
+from deepness.common.defines import IS_DEBUG, PLUGIN_NAME
 from deepness.common.lazy_package_loader import LazyPackageLoader
 from deepness.common.processing_parameters.map_processing_parameters import MapProcessingParameters, ProcessedAreaType
 from deepness.common.processing_parameters.training_data_export_parameters import TrainingDataExportParameters
 from deepness.deepness_dockwidget import DeepnessDockWidget
+from deepness.dialogs.resizable_message_box import ResizableMessageBox
 from deepness.images.get_image_path import get_icon_path
-from deepness.processing.map_processor.map_processing_result import MapProcessingResult, MapProcessingResultFailed, \
-    MapProcessingResultCanceled, MapProcessingResultSuccess
+from deepness.processing.map_processor.map_processing_result import (MapProcessingResult, MapProcessingResultCanceled,
+                                                                     MapProcessingResultFailed,
+                                                                     MapProcessingResultSuccess)
 from deepness.processing.map_processor.map_processor_training_data_export import MapProcessorTrainingDataExport
 from deepness.processing.models.model_types import ModelDefinition
 
@@ -308,5 +306,11 @@ def _map_processor_finished(self, result: MapProcessingResult):
             msg = 'Processing finished!'
             self.iface.messageBar().pushMessage(PLUGIN_NAME, msg, level=Qgis.Success, duration=3)
             message_to_show = result.message
-            QMessageBox.information(self.dockwidget, "Processing Result", message_to_show)
+
+            msgBox = ResizableMessageBox(self.dockwidget)
+            msgBox.setWindowTitle("Processing Result")
+            msgBox.setText(message_to_show)
+            msgBox.setStyleSheet("QLabel{min-width:800 px; font-size: 24px;} QPushButton{ width:250px; font-size: 18px; }")
+            msgBox.exec()
+
         self._map_processor = None

src/deepness/dialogs/resizable_message_box.py

@@ -0,0 +1,20 @@
+from qgis.PyQt.QtWidgets import QMessageBox, QTextEdit
+
+
+class ResizableMessageBox(QMessageBox):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.setSizeGripEnabled(True)
+
+    def event(self, event):
+        if event.type() in (event.LayoutRequest, event.Resize):
+            if event.type() == event.Resize:
+                res = super().event(event)
+            else:
+                res = False
+            details = self.findChild(QTextEdit)
+            if details:
+                details.setMaximumSize(16777215, 16777215)
+            self.setMaximumSize(16777215, 16777215)
+            return res
+        return super().event(event)

src/deepness/metadata.txt

@@ -6,7 +6,7 @@
 name=Deepness: Deep Neural Remote Sensing
 qgisMinimumVersion=3.22
 description=Inference of deep neural network models (ONNX) for segmentation, detection and regression
-version=0.5.4
+version=0.6.0
 author=PUT Vision
 [email protected]