diff --git a/test.ipynb b/test.ipynb
index 530c9412fb0..86276ecaaea 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -95,21 +95,1079 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import plotly.express as px\n",
+ "\n",
+ "def plot(\n",
+ " sample: dict,\n",
+ " indices_to_plot,\n",
+ " show = False,\n",
+ " **kwargs,\n",
+ "):\n",
+ " \"\"\"Plots the image data from the given sample.\n",
+ "\n",
+ " Args:\n",
+ " sample (dict): A dictionary containing the image data returned by self.__get_item__. Should contain the key \"image\".\n",
+ " indices_to_plot (list, optional): A list of indices to plot. If not provided, the method will use the RGB bands defined in `self.rgb_bands`.\n",
+ " show (bool, optional): Whether to display the plot. Defaults to False.\n",
+ " **kwargs (dict): Additional keyword arguments to be passed to `px.imshow`.\n",
+ "\n",
+ " Returns:\n",
+ " fig: The plotly figure object.\n",
+ " \"\"\"\n",
+ " image = sample[\"image\"]\n",
+ "\n",
+ " # Reorder and rescale the image\n",
+ " if (sample[\"image\"].ndim == 4) and (sample[\"image\"].shape[0] > 1):\n",
+ " # Shape of image = [d, c, h, w]\n",
+ " image = image[:, indices_to_plot, :, :].permute(0, 2, 3, 1)\n",
+ " if image.shape[-1] == 1:\n",
+ " image = image.squeeze(-1)\n",
+ " image = torch.clamp(image / 10000, min=0, max=1).numpy()\n",
+ "\n",
+ " fig = px.imshow(\n",
+ " image, animation_frame=0, labels={\"animation_frame\": \"Date\"}, **kwargs\n",
+ " )\n",
+ " # Todo, currently taking the first date, need to handle multiple dates\n",
+ " date_labels = [\n",
+ " dates[0].strftime(\"%m/%d/%Y, %H:%M:%S\") for dates in sample[\"dates\"]\n",
+ " ]\n",
+ " for i, label in enumerate(date_labels):\n",
+ " fig.layout.sliders[0].steps[i].label = label\n",
+ "\n",
+ " else:\n",
+ " image = image[indices_to_plot].permute(1, 2, 0)\n",
+ " image = torch.clamp(image / 10000, min=0, max=1).numpy()\n",
+ "\n",
+ " # Plot the image\n",
+ " fig = px.imshow(image, **kwargs)\n",
+ "\n",
+ " fig.update_xaxes(showticklabels=False).update_yaxes(showticklabels=False)\n",
+ " if show:\n",
+ " fig.show()\n",
+ " return fig"
+ ]
+ },
+ {
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+ "easing": "linear"
+ }
+ }
+ ],
+ "label": "▶",
+ "method": "animate"
+ },
+ {
+ "args": [
+ [
+ null
+ ],
+ {
+ "frame": {
+ "duration": 0,
+ "redraw": true
+ },
+ "fromcurrent": true,
+ "mode": "immediate",
+ "transition": {
+ "duration": 0,
+ "easing": "linear"
+ }
+ }
+ ],
+ "label": "◼",
+ "method": "animate"
+ }
+ ],
+ "direction": "left",
+ "pad": {
+ "r": 10,
+ "t": 70
+ },
+ "showactive": false,
+ "type": "buttons",
+ "x": 0.1,
+ "xanchor": "right",
+ "y": 0,
+ "yanchor": "top"
+ }
+ ],
+ "xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 1
+ ],
+ "showticklabels": false
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ],
+ "showticklabels": false
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
- "import rasterio\n",
- "with rasterio.open(r\"C:\\Users\\Sieger.Falkena\\Documents\\torchgeo\\tests\\data\\sentinel2\\S2A_MSIL2A_20220414T110751_N0400_R108_T26EMU_20220414T165533.SAFE\\GRANULE\\L2A_T26EMU_A035569_20220414T110747\\IMG_DATA\\R10m\\T26EMU_20190414T110751_B02_10m.jp2\") as src:\n",
- " print(src.nodata)"
+ "plot(sample, show=False, indices_to_plot=[2, 1, 0])"
]
}
],
diff --git a/torchgeo/datasets/geo.py b/torchgeo/datasets/geo.py
index 98cdef2d4d4..9ea6de07746 100644
--- a/torchgeo/datasets/geo.py
+++ b/torchgeo/datasets/geo.py
@@ -649,7 +649,9 @@ def _get_regex_groups_as_df(self, filepaths: list[str]) -> pd.DataFrame:
return pd.DataFrame(file_metadata)
- def __merge_single_bbox(self, query: BoundingBox) -> tuple[torch.Tensor | None, list[str]]:
+ def __merge_single_bbox(
+ self, query: BoundingBox
+ ) -> tuple[torch.Tensor | None, list[str]]:
"""Merge all files that intersect with a single bounding box.
Args:
@@ -687,7 +689,9 @@ def __merge_single_bbox(self, query: BoundingBox) -> tuple[torch.Tensor | None,
if res_single_bbox is not None:
# Check if res_single_date contains nodata values and only append if it doesn't
- if not self.drop_nodata or not torch.any(res_single_bbox == self.nodata_value):
+ if not self.drop_nodata or not torch.any(
+ res_single_bbox == self.nodata_value
+ ):
return res_single_bbox, dates
return None, []