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import rasterio as rio
import pathlib
import opensr_test
import matplotlib.pyplot as plt
from typing import Callable
def create_geotiff(
model: Callable,
fn: Callable,
datasets: list,
output_path: str,
force: bool = False
) -> None:
"""Create all the GeoTIFFs for a specific dataset snippet
Args:
model (Callable): The model to use to run the fn function.
fn (Callable): A function that return a dictionary with the following keys:
- "lr": Low resolution image
- "sr": Super resolution image
- "hr": High resolution image
datasets (list): A list of dataset snippets to use to run the fn function.
output_path (str): The output path to save the GeoTIFFs.
force (bool, optional): If True, the dataset is redownloaded. Defaults
to False.
"""
for snippet in datasets:
create_geotiff_batch(
model=model,
fn=fn,
snippet=snippet,
output_path=output_path,
force=force
)
return None
def create_geotiff_batch(
model: Callable,
fn: Callable,
snippet: str,
output_path: str,
force: bool = False
) -> pathlib.Path:
"""Create all the GeoTIFFs for a specific dataset snippet
Args:
model (Callable): The model to use to run the fn function.
fn (Callable): A function that return a dictionary with the following keys:
- "lr": Low resolution image
- "sr": Super resolution image
- "hr": High resolution image
snippet (str): The dataset snippet to use to run the fn function.
output_path (str): The output path to save the GeoTIFFs.
force (bool, optional): If True, the dataset is redownloaded. Defaults
to False.
Returns:
pathlib.Path: The output path where the GeoTIFFs are saved.
"""
# Create folders to save results
output_path = pathlib.Path(output_path) / "results" / "SR"
output_path.mkdir(parents=True, exist_ok=True)
output_path_dataset_geotiff = output_path / snippet / "geotiff"
output_path_dataset_geotiff.mkdir(parents=True, exist_ok=True)
output_path_dataset_png = output_path / snippet / "png"
output_path_dataset_png.mkdir(parents=True, exist_ok=True)
# Load the dataset
dataset = opensr_test.load(snippet, force=False)
lr_dataset, hr_dataset, metadata = dataset["L2A"], dataset["HRharm"], dataset["metadata"]
for index in range(len(lr_dataset)):
print(f"Processing {index}/{len(lr_dataset)}")
# Run the model
results = fn(
model=model,
lr=lr_dataset[index],
hr=hr_dataset[index]
)
# Get the image name
image_name = metadata.iloc[index]["hr_file"]
# Get the CRS and transform
crs = metadata.iloc[index]["crs"]
transform_str = metadata.iloc[index]["affine"]
transform_list = [float(x) for x in transform_str.split(",")]
transform_rio = rio.transform.from_origin(
transform_list[2],
transform_list[5],
transform_list[0],
transform_list[4] * -1
)
# Create rio dict
meta_img = {
"driver": "GTiff",
"count": 3,
"dtype": "uint16",
"height": results["hr"].shape[1],
"width": results["hr"].shape[2],
"crs": crs,
"transform": transform_rio,
"compress": "deflate",
"predictor": 2,
"tiled": True
}
# Save the GeoTIFF
with rio.open(output_path_dataset_geotiff / (image_name + ".tif"), "w", **meta_img) as dst:
dst.write(results["sr"])
# Save the PNG
fig, ax = plt.subplots(1, 3, figsize=(15, 5))
ax[0].imshow(results["lr"].transpose(1, 2, 0) / 3000)
ax[0].set_title("LR")
ax[0].axis("off")
ax[1].imshow(results["sr"].transpose(1, 2, 0) / 3000)
ax[1].set_title("SR")
ax[1].axis("off")
ax[2].imshow(results["hr"].transpose(1, 2, 0) / 3000)
ax[2].set_title("HR")
# remove whitespace around the image
plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
plt.axis("off")
plt.savefig(output_path_dataset_png / (image_name + ".png"))
plt.close()
plt.clf()
return output_path_dataset_geotiff
def run(
model_path: str
) -> pathlib.Path:
"""Run the all metrics for a specific model.
Args:
model_path (str): The path to the model folder.
Returns:
pathlib.Path: The output path where the metrics are
saved as a pickle file.
"""
pass
def plot(
model_path: str
) -> pathlib.Path:
"""Generate the plots and tables for a specific model.
Args:
model_path (str): The path to the model folder.
Returns:
pathlib.Path: The output path where the plots and tables are
saved.
"""
pass |