Update app.py

app.py

@@ -11,6 +11,8 @@ import rasterio
 import cv2
 import tensorflow as tf
 import tempfile
+from rasterio import features
+from shapely.geometry import shape
 
 # Configuration
 HEIGHT = WIDTH = 256
@@ -145,11 +147,12 @@ st.title("Satellite Mining Segmentation: SAR + Optic Image Inference")
 sar_file = st.file_uploader("Upload SAR Image", type=["tiff"])
 optic_file = st.file_uploader("Upload Optical Image", type=["tiff"])
 mask_file = st.file_uploader("Upload Mask Image", type=["tiff"])
+wiup_file = st.file_uploader("Upload WIUP Boundary (Shapefile ZIP)", type=["zip"])
 
 num_samples = 1
 if st.button("Run Inference"):
     with st.spinner("Loading data and model..."):
-        if sar_file is not None and optic_file is not None and mask_file is not None:
+        if sar_file is not None and optic_file is not None and mask_file is not None and wiup_file is not None:
             st.success("All files uploaded successfully!")
             
             # Save uploaded files
@@ -157,6 +160,14 @@ if st.button("Run Inference"):
             optic_path = save_uploaded_file(optic_file, suffix=".tif")
             mask_path = save_uploaded_file(mask_file, suffix=".tif")
 
+            wiup_zip_path = save_uploaded_file(wiup_file, suffix=".zip")
+            extract_folder = wiup_zip_path.replace(".zip", "")
+            with zipfile.ZipFile(wiup_zip_path, "r") as zip_ref:
+                zip_ref.extractall(extract_folder)
+
+            # Load WIUP shapefile
+            wiup_gdf = gpd.read_file(extract_folder)
+            
             # Create image lists
             sarImages = [sar_path]
             opticImages = [optic_path]
@@ -274,6 +285,29 @@ if st.button("Run Inference"):
 
             plt.tight_layout()
             st.pyplot(fig)
+
+            wiup_mask = features.rasterize(
+                [(geom, 1) for geom in wiup_gdf.geometry],
+                out_shape=(HEIGHT, WIDTH),
+                transform=transform,
+                fill=0,
+                dtype=np.uint8
+            )
+            # Binary mask of predicted illegal mining
+            pred_illegal_mask = illegal_mask.astype(np.uint8)
+
+            # Mining outside WIUP
+            outside_mask = (pred_illegal_mask == 1) & (wiup_mask == 0)
+            outside_percentage = 100 * np.sum(outside_mask) / np.sum(pred_illegal_mask)
+
+            st.markdown(f"### 🚨 Illegal Mining Outside WIUP: `{outside_percentage:.2f}%`")
+
+            fig2, ax2 = plt.subplots(figsize=(10, 6))
+            ax2.imshow(outside_mask, cmap='Reds')
+            ax2.set_title("Illegal Mining Outside WIUP Boundary")
+            ax2.axis('off')
+            st.pyplot(fig2)
+            
         else:
             st.warning("Please upload all three .tiff files to proceed.")