Upload app.py

app.py

@@ -0,0 +1,145 @@
+import os
+import zipfile
+import tempfile
+import uuid
+import rasterio
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from rasterio.enums import Resampling
+
+def generate_temp_path(suffix=".tif"):
+    return os.path.join(tempfile.gettempdir(), f"{uuid.uuid4().hex}{suffix}")
+
+def read_band(path):
+    with rasterio.open(path) as src:
+        return src.read(1).astype(np.float32), src.profile
+
+def resample_to_10m(band_path, ref_shape, ref_transform):
+    with rasterio.open(band_path) as src:
+        data = src.read(
+            out_shape=(1, ref_shape[0], ref_shape[1]),
+            resampling=Resampling.bilinear
+        )
+        return data[0].astype(np.float32)
+
+def normalize(array):
+    array /= 10000.0
+    return np.clip(array, 0, 1)
+
+def array_to_plot(img, title, cmap=None):
+    fig = plt.figure(figsize=(6, 6))
+    if cmap:
+        plt.imshow(img, cmap=cmap)
+        plt.colorbar()
+    else:
+        plt.imshow(img)
+    plt.title(title)
+    plt.axis('off')
+    return fig
+
+def save_tif(path, array, profile, count=3):
+    profile = profile.copy()
+    profile.update({
+        'driver': 'GTiff',
+        'count': count,
+        'dtype': rasterio.float32,
+        'compress': 'deflate',
+        'predictor': 2,
+        'tiled': True,
+        'blockxsize': 512,
+        'blockysize': 512
+    })
+    with rasterio.open(path, 'w', **profile) as dst:
+        if count == 1:
+            dst.write(array, 1)
+        else:
+            for i in range(count):
+                dst.write(array[:, :, i], i + 1)
+
+def process_visualization(zip_file_path, vis_type):
+    with tempfile.TemporaryDirectory() as temp_dir:
+        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
+            zip_ref.extractall(temp_dir)
+
+        dirs = [d for d in os.listdir(temp_dir) if d.endswith(".SAFE")]
+        if not dirs:
+            raise Exception(".SAFE folder not found inside the zip file.")
+        extract_dir = os.path.join(temp_dir, dirs[0])
+
+        granule_dir = os.path.join(extract_dir, "GRANULE")
+        granule_path = [os.path.join(granule_dir, d) for d in os.listdir(granule_dir) if d.startswith("L2A_")][0]
+        img_data_dir = os.path.join(granule_path, "IMG_DATA")
+
+        res_paths = {
+            "R10m": os.path.join(img_data_dir, "R10m"),
+            "R20m": os.path.join(img_data_dir, "R20m")
+        }
+
+        # Geometry reference
+        b4, profile = read_band(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B04" in f][0]))
+
+        if vis_type == "Natural Color (B4, B3, B2)":
+            b2, _ = read_band(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B02" in f][0]))
+            b3, _ = read_band(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B03" in f][0]))
+            rgb = np.stack([b4, b3, b2], axis=-1)
+            rgb_plot = array_to_plot(normalize(rgb), vis_type)
+            tif_path = generate_temp_path(".tif")
+            save_tif(tif_path, rgb, profile, count=3)
+            return rgb_plot, tif_path
+
+        elif vis_type == "False Color Vegetation (B8, B4, B3)":
+            b3, _ = read_band(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B03" in f][0]))
+            b8, _ = read_band(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B08" in f][0]))
+            fcv = np.stack([b8, b4, b3], axis=-1)
+            fcv_plot = array_to_plot(normalize(fcv), vis_type)
+            tif_path = generate_temp_path(".tif")
+            save_tif(tif_path, fcv, profile, count=3)
+            return fcv_plot, tif_path
+
+        elif vis_type == "False Color SWIR (B12, B8, B4)":
+            b8, _ = read_band(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B08" in f][0]))
+            b12_path = os.path.join(res_paths["R20m"], [f for f in os.listdir(res_paths["R20m"]) if "_B12" in f][0])
+            b12 = resample_to_10m(b12_path, b4.shape, profile["transform"])
+            fcswir = np.stack([b12, b8, b4], axis=-1)
+            swir_plot = array_to_plot(normalize(fcswir), vis_type)
+            tif_path = generate_temp_path(".tif")
+            save_tif(tif_path, fcswir, profile, count=3)
+            return swir_plot, tif_path
+
+        elif vis_type == "NDVI":
+            b8, _ = read_band(os.path.join(res_paths["R10m"], [f for f in os.listdir(res_paths["R10m"]) if "_B08" in f][0]))
+            ndvi = (b8 - b4) / (b8 + b4 + 1e-6)
+            ndvi_plot = array_to_plot(ndvi, "NDVI", cmap='RdYlGn')
+            tif_path = generate_temp_path(".tif")
+            save_tif(tif_path, ndvi, profile, count=1)
+            return ndvi_plot, tif_path
+
+        else:
+            raise ValueError("Invalid visualization type.")
+
+# === Gradio Interface ===
+demo = gr.Interface(
+    fn=process_visualization,
+    inputs=[
+        gr.File(label="Sentinel-2 Archive (.zip)", type="filepath"),
+        gr.Dropdown(
+            choices=[
+                "Natural Color (B4, B3, B2)",
+                "False Color Vegetation (B8, B4, B3)",
+                "False Color SWIR (B12, B8, B4)",
+                "NDVI"
+            ],
+            label="Visualization Type"
+        )
+    ],
+    outputs=[
+        gr.Plot(label="Preview"),
+        gr.File(label="Download GeoTIFF")
+    ],
+    title="Sentinel-2 Viewer + GeoTIFF Export",
+    description="Upload a .SAFE.zip file, choose a visualization type, and download the corresponding GeoTIFF file."
+)
+
+if __name__ == "__main__":
+    demo.launch()