Merge pull request #6 from sebi06/work-in-progress

Work in progress

README.md

@@ -11,15 +11,6 @@ Semantic Segmentation using DeepLearning ONNX models packaged as *.czann files.
 
 This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.
 
-<!--
-Don't miss the full getting started guide to set up your new package:
-https://github.com/napari/cookiecutter-napari-plugin#getting-started
-
-and review the napari docs for plugin developers:
-https://napari.org/plugins/index.html
--->
-
-
 ![Train on APEER and use model in Napari](https://github.com/sebi06/napari-czann-segment/raw/main/readme_images/Train_APEER_run_Napari_CZANN_no_highlights_small.gif)
 
 ## Installation
@@ -47,7 +38,6 @@ Therer two main ways hwo such a model can be created:
 - Train the segmentation model fully automated on [APEER] and download the *.czann file
 - Train your model in a Jupyter notebook etc. and package it using the [czmodel] python package as an *.czann
 
-
 ## Using this plugin
 
 ### Sample Data
@@ -61,10 +51,21 @@ In order to use this plugin the user has to do the following things:
 - Open the image using "File - Open Files(s)" (requires [napari-aicsimageio] plugin).
 - Click **napari-czann-segment: Segment with CZANN model** in the "Plugins" menu.
 - **Select a *.czann file** to use the model for segmentation.
+- metadata of the model will be shown (see example below)
+
+| Parameter    | Value                                        | Explanation                                             |
+| :----------- | :------------------------------------------- | ------------------------------------------------------- |
+| model_type   | ModelType.SINGLE_CLASS_SEMANTIC_SEGMENTATION | see: [czmodel] for details
+| input_shape  | [1024, 1024, 1]                              | tile dimensions required for the input                  |
+| output_shape | [1024, 1024, 3]                              | tile dimensionsof the output                            |
+| model_id     | ba32bc6d-6bc9-4774-8b47-20646c7cb838         | unique GUID for that model                              |
+| min_overlap  | [128, 128]                                   | tile overlap used during training                       |
+| classes      | ['background', 'grains', 'inclusions']       | availbale classes                                       |
+| model_name   | APEER-trained model                          | name of the model                                       |
 
 ![Napari - Image loaded and czann selected](https://github.com/sebi06/napari-czann-segment/raw/main/readme_images/napari_czann1.png)
 
-- Adjust the **minimum overlap** used to the tiling (optional).
+- Adjust the **minimum overlap** for the tiling (optional, see [cztile] for details).
 - Select the **layer** to be segmented.
 - Press **Segment Selected Image Layer** to run the segmentation.
 
@@ -104,7 +105,6 @@ To install latest development version:
 
     pip install git+https://github.com/sebi06/napari_czann_segment.git
 
-
 ## Contributing
 
 Contributions and Feedback are very welcome.

src/napari_czann_segment/dock_widget.py

@@ -22,13 +22,77 @@
 from czmodel.convert import DefaultConverter
 from typing import Dict, List, Tuple, Union
 from qtpy.QtWidgets import (QComboBox, QHBoxLayout, QLabel, QPushButton, QLineEdit, QListWidgetItem,
-                            QVBoxLayout, QWidget, QFileDialog, QDialogButtonBox, QSlider)
+                            QVBoxLayout, QWidget, QFileDialog, QDialogButtonBox, QSlider,
+                            QTableWidget, QTableWidgetItem, QSizePolicy)
 
-from qtpy.QtCore import Qt, Signal, QObject, QEvent
+from qtpy.QtCore import Qt, Signal, QObject, QEvent, QSize
+from qtpy.QtGui import QFont
 from magicgui.widgets import FileEdit, PushButton, Slider, Container, Label, CheckBox
 from magicgui.types import FileDialogMode
 
 
+class TableWidget(QWidget):
+
+    def __init__(self) -> None:
+
+        super(QWidget, self).__init__()
+
+        self.layout = QVBoxLayout(self)
+        self.model_table = QTableWidget()
+
+        self.model_table.setShowGrid(True)
+        self.model_table.setHorizontalHeaderLabels(["Parameter", "Value"])
+        #self.model_table.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)
+        header = self.model_table.horizontalHeader()
+        header.setDefaultAlignment(Qt.AlignLeft)
+        self.layout.addWidget(self.model_table)
+
+    def update_model_metadata(self, md_dict: Dict) -> None:
+
+        # number of rows is set to number of metadata entries
+        row_count = len(md_dict)
+        col_count = 2
+        self.model_table.setColumnCount(col_count)
+        self.model_table.setRowCount(row_count)
+
+        row = 0
+
+        # update the table with the entries from metadata dictionary
+        for key, value in md_dict.items():
+            newkey = QTableWidgetItem(key)
+            self.model_table.setItem(row, 0, newkey)
+            newvalue = QTableWidgetItem(str(value))
+            self.model_table.setItem(row, 1, newvalue)
+            row += 1
+
+        # fit columns and rows to content
+        self.model_table.resizeColumnsToContents()
+        self.model_table.resizeRowsToContents()
+        self.model_table.adjustSize()
+
+    def update_style(self) -> None:
+
+        # define font size and type
+        fnt = QFont()
+        fnt.setPointSize(8)
+        fnt.setBold(True)
+        fnt.setFamily("Arial")
+
+        # update both header items
+        fc = (25, 25, 25)
+        #item1 = QtWidgets.QTableWidgetItem("Parameter")
+        item1 = QTableWidgetItem("Parameter")
+        #item1.setForeground(QtGui.QColor(25, 25, 25))
+        item1.setFont(fnt)
+        self.model_table.setHorizontalHeaderItem(0, item1)
+
+        #item2 = QtWidgets.QTableWidgetItem("Value")
+        item2 = QTableWidgetItem("Value")
+        #item2.setForeground(QtGui.QColor(25, 25, 25))
+        item2.setFont(fnt)
+        self.model_table.setHorizontalHeaderItem(1, item2)
+
+
 # our manifest widget command points to this class
 class segment_with_czann(QWidget):
     """Widget allows selection of an Image layer, a model file and the desired border width
@@ -52,8 +116,9 @@ def __init__(self, napari_viewer):
         self.min_overlap: int = 128
         self.model_metadata = None
         self.czann_file: str = ""
-        self.dnn_tile_width: int = 1024
-        self.dnn_tile_height: int = 1024
+        self.dnn_tile_width = 1024
+        self.dnn_tile_height = 1024
+        self.dnn_channel_number = 1
 
         # TODO : Enable GPU support and make it work
         self.use_gpu: bool = False
@@ -64,7 +129,7 @@ def __init__(self, napari_viewer):
         # add a label
         self.layout().addWidget(QLabel("Model File Selection"))
 
-        # define filter to allowed file extensions
+        # define filter based on file extension
         model_extension = "*.czann"
 
         # create the FileEdit widget and add to the layout and connect it
@@ -75,13 +140,25 @@ def __init__(self, napari_viewer):
         self.layout().addWidget(self.filename_edit.native)
         self.filename_edit.line_edit.changed.connect(self._file_changed)
 
+        # add table for model metadata
+        self.model_metadata_label = QLabel("Model Metadata")
+        self.model_metadata_label.setFont(QFont('Arial', 9, QFont.Normal))
+        # setting up background color and border
+        #self.model_metadata_label.setStyleSheet("background-color: yellow;border: 1px solid black;")
+        self.layout().addWidget(self.model_metadata_label)
+
+        self.model_metadata_table = TableWidget()
+        #self.model_metadata_table.setSizePolicy(QSizePolicy.MinimumExpanding, QSizePolicy.MinimumExpanding)
+        #self.model_metadata_table.setFixedSize(QSize(275, 250))
+        self.layout().addWidget(self.model_metadata_table)
+
         # add button for reading the model metadata
-        self.read_modeldata_btn = QPushButton("Reload minimum overlap from Metadata")
-        self.read_modeldata_btn.clicked.connect(self._read_min_overlap)
+        self.read_modeldata_btn = QPushButton("Reload minimum overlap")
+        self.read_modeldata_btn.clicked.connect(self._read_model_metadata)
         self.layout().addWidget(self.read_modeldata_btn)
 
         # add label and slider for adjusting the minimum overlap
-        min_overlap_label = QLabel("Adjust minimum overlap for Segmentation")
+        self.min_overlap_label = QLabel("Adjust minimum overlap for Segmentation")
         self.min_overlap_slider = Slider(orientation="horizontal",
                                          label="Minimum Overlap",
                                          value=128,
@@ -92,7 +169,7 @@ def __init__(self, napari_viewer):
                                          tooltip="Adjust the desired minimum overlap",
                                          tracking=False)
 
-        self.layout().addWidget(min_overlap_label)
+        self.layout().addWidget(self.min_overlap_label)
         self.layout().addWidget(self.min_overlap_slider.native)
         self.min_overlap_slider.changed.connect(self._update_min_overlap)
 
@@ -104,7 +181,7 @@ def __init__(self, napari_viewer):
         self.viewer.layers.events.inserted.connect(self._reset_layer_options)
         self.viewer.layers.events.removed.connect(self._reset_layer_options)
 
-        # TODO : This checkbox is currentyl hidden
+        # TODO : This checkbox is currently hidden
         # add the checkbox the use the GPU for the inference
         self.use_gpu_checkbox = CheckBox(name="Use GPU for inference",
                                          visible=False,
@@ -154,18 +231,26 @@ def add_image_combo_box(self, label_text):
         # returning the combo box so we know which is which when we click run
         return new_layer_combo
 
-    def _read_min_overlap(self):
-        """Get model minimum overlap and store them"""
+    def _read_model_metadata(self):
+        """Get model metadata and store them"""
 
         # extract the model information and path
         with tempfile.TemporaryDirectory() as temp_path:
             self.model_metadata, self.model_path = DefaultConverter().unpack_model(model_file=self.czann_file,
                                                                                    target_dir=Path(temp_path))
 
-        # update the value for the minimum overlap and the respective slider
+        # get model metadata as dictionary
+        self.model_metadata_dict = self.model_metadata._asdict()
+        self.model_metadata_table.update_model_metadata(self.model_metadata_dict)
+        self.model_metadata_table.update_style()
+        print(self.model_metadata_table.sizeHint())
+
+        # get the values for the minimum overlap and the required input tile size
         self.min_overlap = self.model_metadata.min_overlap[0]
         self.min_overlap_slider.value = self.min_overlap
-        print("Updated value for minimum overlap and Slider from ModelMetaData: ", self.min_overlap)
+        self.dnn_tile_width = self.model_metadata.input_shape[0]
+        self.dnn_tile_height = self.model_metadata.input_shape[1]
+        self.dnn_channel_number = self.model_metadata.input_shape[2]
 
     def _segment(self):
 
@@ -222,8 +307,8 @@ def _file_changed(self):
                               ).replace("\\", "/").replace("//", "/")
         print("Model Path: ", self.czann_file)
 
-        # update the minimum overlap
-        self._read_min_overlap()
+        # update the model metadata
+        self._read_model_metadata()
 
     def _reset_layer_options(self, event):
         """Clear existing combo boxes and repopulate
@@ -245,7 +330,7 @@ def _reset_layer_options(self, event):
     def _check_min_overlap(self):
 
         # Minimum border width must be less than half the tile size.
-        min_tilesize = min(self.dnn_tile_width, self.dnn_tile_width)
+        min_tilesize = min(self.dnn_tile_width, self.dnn_tile_height)
 
         # check
         if self.min_overlap >= np.round(min_tilesize / 2, 0):