Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Adding plotting to ChesapeakeCVPR dataset #820

Merged
merged 2 commits into from
Oct 6, 2022
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 10 additions & 0 deletions tests/datasets/test_chesapeake.py
Original file line number Diff line number Diff line change
Expand Up @@ -211,3 +211,13 @@ def test_multiple_hits_query(self, dataset: ChesapeakeCVPR) -> None:
IndexError, match="query: .* spans multiple tiles which is not valid"
):
ds[dataset.bounds]

def test_plot(self, dataset: ChesapeakeCVPR) -> None:
x = dataset[dataset.bounds].copy()
dataset.plot(x, suptitle="Test")
plt.close()
dataset.plot(x, show_titles=False)
plt.close()
x["prediction"] = x["mask"][:, :, 0].clone().unsqueeze(2)
dataset.plot(x)
plt.close()
136 changes: 136 additions & 0 deletions torchgeo/datasets/chesapeake.py
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
import torch
from matplotlib.colors import ListedColormap
from rasterio.crs import CRS
from torch import Tensor

from .geo import GeoDataset, RasterDataset
from .utils import BoundingBox, download_url, extract_archive
Expand Down Expand Up @@ -438,6 +439,46 @@ class ChesapeakeCVPR(GeoDataset):
crs = CRS.from_epsg(3857)
res = 1

lc_cmap = {
0: (0, 0, 0, 0),
1: (0, 197, 255, 255),
2: (38, 115, 0, 255),
3: (163, 255, 115, 255),
4: (255, 170, 0, 255),
5: (156, 156, 156, 255),
6: (0, 0, 0, 255),
15: (0, 0, 0, 0),
}

nlcd_cmap = {
0: (0, 0, 0, 0),
11: (70, 107, 159, 255),
12: (209, 222, 248, 255),
21: (222, 197, 197, 255),
22: (217, 146, 130, 255),
23: (235, 0, 0, 255),
24: (171, 0, 0, 255),
31: (179, 172, 159, 255),
41: (104, 171, 95, 255),
42: (28, 95, 44, 255),
43: (181, 197, 143, 255),
52: (204, 184, 121, 255),
71: (223, 223, 194, 255),
81: (220, 217, 57, 255),
82: (171, 108, 40, 255),
90: (184, 217, 235, 255),
95: (108, 159, 184, 255),
}

prior_color_matrix = np.array(
[
[0.0, 0.77254902, 1.0, 1.0],
[0.14901961, 0.45098039, 0.0, 1.0],
[0.63921569, 1.0, 0.45098039, 1.0],
[0.61176471, 0.61176471, 0.61176471, 1.0],
]
)

valid_layers = [
"naip-new",
"naip-old",
Expand Down Expand Up @@ -540,6 +581,16 @@ def __init__(

super().__init__(transforms)

lc_colors = np.zeros((max(self.lc_cmap.keys()) + 1, 4))
lc_colors[list(self.lc_cmap.keys())] = list(self.lc_cmap.values())
lc_colors = lc_colors[:, :3] / 255
self._lc_cmap = ListedColormap(lc_colors)

nlcd_colors = np.zeros((max(self.nlcd_cmap.keys()) + 1, 4))
nlcd_colors[list(self.nlcd_cmap.keys())] = list(self.nlcd_cmap.values())
nlcd_colors = nlcd_colors[:, :3] / 255
self._nlcd_cmap = ListedColormap(nlcd_colors)

# Add all tiles into the index in epsg:3857 based on the included geojson
mint: float = 0
maxt: float = sys.maxsize
Expand Down Expand Up @@ -694,3 +745,88 @@ def _extract(self) -> None:
"""Extract the dataset."""
for subdataset in self.subdatasets:
extract_archive(os.path.join(self.root, self.filenames[subdataset]))

def plot(
self,
sample: Dict[str, Tensor],
show_titles: bool = True,
suptitle: Optional[str] = None,
) -> plt.Figure:
"""Plot a sample from the dataset.

Args:
sample: a sample returned by :meth:`__getitem__`
show_titles: flag indicating whether to show titles above each panel
suptitle: optional string to use as a suptitle

Returns:
a matplotlib Figure with the rendered sample

.. versionadded:: 0.4
"""
image = np.rollaxis(sample["image"].numpy(), 0, 3)
mask = np.rollaxis(sample["mask"].numpy(), 0, 3)

num_panels = len(self.layers)
showing_predictions = "prediction" in sample
if showing_predictions:
predictions = sample["prediction"].numpy()
num_panels += 1

fig, axs = plt.subplots(1, num_panels, figsize=(num_panels * 4, 5))

i = 0
for layer in self.layers:
if layer == "naip-new" or layer == "naip-old":
img = image[:, :, :3] / 255
image = image[:, :, 4:]
axs[i].axis("off")
axs[i].imshow(img)
elif layer == "landsat-leaf-on" or layer == "landsat-leaf-off":
img = image[:, :, [3, 2, 1]] / 3000
image = image[:, :, 9:]
axs[i].axis("off")
axs[i].imshow(img)
elif layer == "nlcd":
img = mask[:, :, 0]
mask = mask[:, :, 1:]
axs[i].imshow(
img, vmin=0, vmax=95, cmap=self._nlcd_cmap, interpolation="none"
)
axs[i].axis("off")
elif layer == "lc":
img = mask[:, :, 0]
mask = mask[:, :, 1:]
axs[i].imshow(
img, vmin=0, vmax=15, cmap=self._lc_cmap, interpolation="none"
)
axs[i].axis("off")
elif layer == "buildings":
img = mask[:, :, 0]
mask = mask[:, :, 1:]
axs[i].imshow(img, vmin=0, vmax=1, cmap="gray", interpolation="none")
axs[i].axis("off")
elif layer == "prior_from_cooccurrences_101_31_no_osm_no_buildings":
img = (mask[:, :, :4] @ self.prior_color_matrix) / 255
mask = mask[:, :, 4:]
axs[i].imshow(img)
axs[i].axis("off")

if show_titles:
if layer == "prior_from_cooccurrences_101_31_no_osm_no_buildings":
axs[i].set_title("prior")
else:
axs[i].set_title(layer)
i += 1

if showing_predictions:
axs[i].imshow(
predictions, vmin=0, vmax=15, cmap=self._lc_cmap, interpolation="none"
)
axs[i].axis("off")
if show_titles:
axs[i].set_title("Predictions")

if suptitle is not None:
plt.suptitle(suptitle)
return fig