Merge branch 'main' of github.com:geo-engine/geoengine-python into re…

…sultdescriptor-with-time

.github/workflows/ci.yml

@@ -14,7 +14,7 @@ jobs:
       fail-fast: false
       matrix:
         # use all supported versions from https://devguide.python.org/versions/
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
 
     steps:
       - uses: actions/checkout@v3
@@ -61,7 +61,7 @@ jobs:
 
     env:
       # use minimum supported versions from https://devguide.python.org/versions/
-      python-version: "3.8"
+      python-version: "3.9"
       # lowest compatible versions for all direct dependencies
       # cf., https://github.com/astral-sh/uv#resolution-strategy
       resolution: "lowest-direct"

examples/ml_water_bodies/labeling.py

@@ -0,0 +1,201 @@
+'''
+Labeling tools for smaller ML use cases.
+'''
+
+import os
+from typing import Tuple, Callable, Optional, Mapping, TypedDict, List
+import matplotlib.pyplot as plt
+from matplotlib.backend_bases import MouseButton, MouseEvent
+from matplotlib.backend_tools import Cursors
+from matplotlib.patches import Circle
+import ipywidgets as widgets
+from IPython.display import display
+import geopandas as gpd
+from shapely.geometry import Point
+import pandas as pd
+
+
+class ClassValue(TypedDict):
+    value: int
+    color: str
+
+
+class PointLabelingTool(widgets.VBox):
+    '''
+    Create points labels by overlaying them over a background.
+
+    Select a class and click to add one.
+    '''
+
+    points: gpd.GeoDataFrame
+    crs: str
+    filename: str
+    class_column: str
+    selected_class_value: int
+    classes: Mapping[str, ClassValue]
+    color_map: Mapping[int, str]
+
+    fig: plt.Figure
+    ax: plt.Axes
+    plt_fg: Optional[pd.plotting.PlotAccessor]
+    legend_handles: List[Circle]
+
+    def __init__(self,
+                 *,
+                 filename: str,
+                 class_column: str,
+                 classes: Mapping[str, ClassValue],
+                 crs: str,
+                 background: Callable[[plt.Axes], None],
+                 figsize: Optional[Tuple[int, int]] = None) -> None:
+        super().__init__()
+
+        self.filename = filename
+        self.crs = crs
+
+        self.class_column = class_column
+        self.classes = classes
+
+        self.color_map = {c['value']: c['color'] for c in self.classes.values()}
+        self.selected_class_value = next(iter(self.classes.values()))['value']
+
+        self.points = self.__make_gdf(None)
+
+        if os.path.exists(filename):
+            self.points = pd.concat(
+                [
+                    self.points,
+                    gpd.read_file(filename).set_crs(self.crs, allow_override=True),
+                ],
+                ignore_index=True,
+            )
+
+        self.children = [
+            self.__create_selection(background),
+            self.__create_plot(background, figsize),
+        ]
+
+        self.__plot_and_save(background)
+
+    def __make_gdf(self, entry: Optional[Tuple[Point, int]]) -> gpd.GeoDataFrame:
+        '''
+        Create a GeoDataFrame from a given entry.
+        If `entry` is None, an empty GeoDataFrame is created.
+        '''
+
+        data: dict[str, list] = {'geometry': [], self.class_column: []}
+
+        if entry:
+            geom, class_value = entry
+            data['geometry'].append(geom)
+            data[self.class_column].append(class_value)
+
+        return gpd.GeoDataFrame(
+            data=data,
+            crs=self.crs,
+            geometry='geometry',
+        )
+
+    def __create_plot(self,
+                      background: Callable[[plt.Axes], None],
+                      figsize: Optional[Tuple[int, int]]) -> widgets.Output:
+        """
+        Creates a plot with a specified background and figure size,
+        and sets up an interactive widget for labeling points.
+        """
+
+        output = widgets.Output()
+
+        with output, plt.ioff():
+            self.fig, self.ax = plt.subplots(figsize=figsize, constrained_layout=True)
+
+            self.fig.canvas.toolbar_visible = False
+            self.fig.canvas.header_visible = False
+            self.fig.canvas.footer_visible = False
+
+            display(self.fig.canvas)
+
+        background(self.ax)
+
+        def on_click(event: MouseEvent):
+            if self.fig.canvas.widgetlock.locked():
+                return  # Don't do anything if the zoom/pan tools have been enabled.
+            if event.button is not MouseButton.LEFT:
+                return
+            if not event.inaxes:
+                return
+
+            self.points.loc[len(self.points)] = [
+                Point(event.xdata, event.ydata),
+                self.selected_class_value,
+            ]
+            self.points.set_geometry(col='geometry', inplace=True)
+
+            self.__plot_and_save(background)
+
+        self.fig.canvas.mpl_connect('button_press_event', on_click)
+
+        self.legend_handles = [
+            Circle((0.5, 0.5), 1, label=name, facecolor=v['color']) for (name, v) in self.classes.items()
+        ]
+
+        return output
+
+    def __create_selection(self, background: Callable[[plt.Axes], None]) -> widgets.HBox:
+        """
+        Creates a selection interface for labeling water bodies.
+        This method generates a horizontal box layout containing toggle buttons for class selection
+        and an undo button. The class buttons allow the user to select a class from the available
+        options, while the undo button removes the last point added to the selection.
+        """
+
+        class_buttons = widgets.ToggleButtons(
+            options=[(c, v['value']) for (c, v) in self.classes.items()],
+            description='Class:',
+            button_style='info',
+        )
+
+        undo_button = widgets.Button(
+            icon='undo',
+            button_style="warning",
+        )
+
+        def on_undo(_event):
+            self.points.drop(self.points.tail(1).index, inplace=True)
+
+            self.__plot_and_save(background)
+
+        undo_button.on_click(on_undo)
+
+        def set_selected_class_value(change) -> None:
+            self.selected_class_value = change['new']
+
+        class_buttons.observe(set_selected_class_value, names=["value"])
+
+        return widgets.HBox([class_buttons, undo_button])
+
+    def __plot_and_save(self,
+                        background: Callable[[plt.Axes], None]) -> None:
+        """
+        Plots the points on the given background and saves the plot to a file.
+        """
+
+        self.ax.clear()
+
+        background(self.ax)
+
+        if len(self.points) == 0:
+            return
+
+        colors = self.points[self.class_column].map(self.color_map)
+        self.plt_fg = self.points.plot(ax=self.ax, c=colors)
+
+        self.points.to_file(self.filename)
+
+        self.ax.cursor_to_use = Cursors.POINTER
+
+        self.ax.legend(
+            handles=self.legend_handles,
+            loc='center left',
+            bbox_to_anchor=(1, 0.5),
+        )

examples/ml_water_bodies/training_data.geojson

@@ -0,0 +1,62 @@
+{
+"type": "FeatureCollection",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::32632" } },
+"features": [
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 359492.047223033325281, 5649059.958522516302764 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 359436.58185506798327, 5648255.710687017999589 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 361045.077526063658297, 5639935.90549221355468 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 359381.116487102583051, 5640102.301596108824015 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 357966.749603985692374, 5641599.866531174629927 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 357328.89787238399731, 5643374.758306065574288 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 357134.769084505212959, 5644983.253977061249316 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 357134.769084505212959, 5645676.571076628752053 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 357606.224712210823782, 5646453.086228143423796 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 358382.739863725961186, 5647118.670643728226423 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 358992.858911345014349, 5647395.997483555227518 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 357467.561292297381442, 5642903.30267836060375 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 357245.699820435955189, 5643763.015881823375821 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 359076.056963293056469, 5649531.414150222204626 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 358770.997439483529888, 5649753.275622082874179 ] } },
+{ "type": "Feature", "properties": { "water": 1.0 }, "geometry": { "type": "Point", "coordinates": [ 360213.097006583120674, 5639963.63817619625479 ] } },
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+]
+}