Update README.md

README.md

@@ -12,3 +12,79 @@ license: cc-by-4.0
 The increasing demand for high spatial resolution in remote sensing imagery has led to the necessity of super-resolution (SR) algorithms that convert low-resolution (LR) images into high-resolution (HR) ones. To address this need, we introduce SEN2NAIP, a large remote sensing dataset designed to support conventional and reference-based SR model training. SEN2NAIP is structured into two components to provide a broad spectrum of research and application needs. The first component comprises a cross-sensor dataset of 2,851 pairs of LR images from Sentinel-2 L2A and HR images from the National Agriculture Imagery Program (NAIP). Leveraging this dataset, we developed a degradation model capable of converting NAIP images to match the characteristics of Sentinel-2 imagery ($S2_{like}$). Subsequently, this degradation model was utilized to create the second component, a synthetic dataset comprising 17,657 NAIP and $S2_{like}$ image pairs. With the SEN2NAIP dataset, we aim to provide a valuable resource that facilitates the exploration of new techniques for enhancing the spatial resolution of Sentinel-2 satellite imagery.
 
 
+## Load cross-sensor dataset
+
+```{python}
+import rioxarray
+import torch
+
+DEMO_PATH = "https://huggingface.co/datasets/isp-uv-es/SEN2NAIP/resolve/main/demo/"
+
+cross_sensor_path = DEMO_PATH + "cross-sensor/ROI_0000/"
+hr_data = rioxarray.open_rasterio(cross_sensor_path + "hr.tif")
+lr_data = rioxarray.open_rasterio(cross_sensor_path + "lr.tif")
+hr_torch = torch.from_numpy(hr_data.to_numpy()) / 255
+lr_torch = torch.from_numpy(lr_data.to_numpy()) / 10000
+```
+
+
+## Load Synthetic dataset
+
+```{python}
+import opensr_degradation
+import rioxarray
+import datasets
+import requests
+import tempfile
+import torch
+import json
+
+
+def load_metadata(metadata_path: str) -> dict:
+    tmpfile = tempfile.NamedTemporaryFile(suffix=".json")
+    with requests.get(metadata_path) as response:
+        with open(tmpfile.name, "wb") as file:
+            file.write(response.content)
+        metadata_json = json.load(open(tmpfile.name, "r"))
+    return metadata_json
+
+DEMO_PATH = "https://huggingface.co/datasets/isp-uv-es/SEN2NAIP/resolve/main/demo/"
+
+# Synthetic LR and HR data ------------------------------
+synthetic_path = DEMO_PATH + "synthetic/ROI_0001/"
+
+hr_early_data = rioxarray.open_rasterio(synthetic_path + "early/01__m_4506807_nw_19_1_20110818.tif")
+hr_early_torch = torch.from_numpy(hr_early_data.to_numpy()) / 255
+hr_early_metadata = load_metadata(synthetic_path + "late/metadata.json")
+hr_early_torch_hat = opensr_degradation.main.predict_table(
+    hr_early_torch, hr_early_metadata["sim_histograms"], "gamma_multivariate_normal_50"
+)
+
+hr_late_data = rioxarray.open_rasterio(synthetic_path + "late/02__m_4506807_nw_19_060_20210920.tif")
+hr_late_torch = torch.from_numpy(hr_late_data.to_numpy()) / 255
+hr_late_metadata = load_metadata(synthetic_path + "late/metadata.json")
+hr_late_torch_hat = opensr_degradation.main.predict_table(
+    hr_late_torch, hr_late_metadata["sim_histograms"], "gamma_multivariate_normal_50"
+)
+
+import matplotlib.pyplot as plt
+fig, ax = plt.subplots(2, 2, figsize=(10, 5))
+ax = ax.flatten()
+ax[0].imshow(hr_early_torch[[3, 1, 2]].permute(1, 2, 0))
+ax[0].set_title("Original")
+ax[1].imshow(hr_early_torch_hat[[3, 1, 2]].permute(1, 2, 0)*3)
+ax[1].set_title("Degraded")
+ax[2].imshow(hr_late_torch[[3, 1, 2]].permute(1, 2, 0))
+ax[2].set_title("Original")
+ax[3].imshow(hr_late_torch_hat[[3, 1, 2]].permute(1, 2, 0)*3)
+ax[3].set_title("Degraded")
+# remove axis and space
+for a in ax:
+    a.axis("off")
+plt.tight_layout()
+plt.show()
+```
+
+<center>
+  <img src="demo/image_demo.png" width=70%>
+</center>