Create app.py

app.py

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+import os
+import cv2
+from PIL import Image
+import numpy as np
+from keras import backend as K
+from matplotlib import pyplot as plt
+from tensorflow.keras.utils import to_categorical
+import geopandas as gpd
+import matplotlib.pyplot as plt
+from keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from google.colab.patches import cv2_imshow
+import geopandas as gpd
+from skimage.measure import regionprops, label
+from shapely.geometry import Polygon
+import shutil
+import gradio as gr
+
+def predict(img):
+    # model = load_model('drive/My Drive/building_footprint_extraction_model.h5')
+    model = load_model('/content/drive/MyDrive/Colab Notebooks/building_footprint_extraction_model.h5')
+    img_array = img_to_array(img)
+    img_array = img_array.reshape((1, 256, 256, 3))
+    img_array = img_array / 255.0
+    predictions = model.predict(img_array)
+    predicted_image = np.argmax(predictions, axis=3)
+    predicted_image = predicted_image[0,:,:]
+    predicted_image = predicted_image * 255
+    return predictions,predicted_image
+
+def get_shape_files(img):
+  # predictions,_ = predict(img)
+  model = load_model('/content/drive/MyDrive/Colab Notebooks/building_footprint_extraction_model.h5')
+  img_array = img_to_array(img)
+  img_array = img_array.reshape((1, 256, 256, 3))
+  img_array = img_array / 255.0
+  predictions = model.predict(img_array)
+  threshold = 0.5
+  binary_mask = (predictions > threshold).astype(np.uint8)[:, :, 1]
+  if np.sum(binary_mask) == 0:
+      print("No building pixels detected. Saving an empty shapefile.")
+  else:
+      labeled_mask = label(binary_mask)
+      building_polygons = []
+      props = regionprops(labeled_mask)
+      for prop in props:
+          polygon = Polygon([(point[1], point[0]) for point in prop.coords])
+          building_polygons.append(polygon)
+      gdf = gpd.GeoDataFrame(geometry=building_polygons, crs="EPSG:4326")
+      output_shapefile = "shapefiles/building_footprints.shp"
+      if os.path.exists('shapefiles'):
+        pass
+      else:
+        os.mkdir('shapefiles')
+      gdf.to_file(output_shapefile)
+
+      # To get Masked Image
+      predicted_image = np.argmax(predictions, axis=3)
+      predicted_image = predicted_image[0,:,:]
+      predicted_image = predicted_image * 255
+      cv2.imwrite('shapefiles/mask.jpg',predicted_image)
+      shutil.make_archive('shapefile', 'zip', 'shapefiles')
+      return 'shapefile.zip',predicted_image
+
+my_app = gr.Blocks()
+with my_app:
+  gr.Markdown("<center><h1>Building Footprint Extraction</h1></center>")
+  with gr.Tabs():
+    with gr.TabItem("Get Mask Image"):
+      with gr.Row():
+        with gr.Column():
+            img_source = gr.Image(label="Please select source Image", shape=(256, 256))
+            source_image_loader = gr.Button("Load above Image")
+        with gr.Column():
+            img_output = gr.Image(label="Image Output")
+    source_image_loader.click(predict,img_source,img_output)
+    with gr.TabItem("Get Shapefiles"):
+      with gr.Row():
+        with gr.Column():
+            img_source = gr.Image(label="Please select source Image", shape=(256, 256))
+            get_shape_loader = gr.Button("Get Shape File")
+        
+        with gr.Column():
+          with gr.Row():
+              mask_img=gr.Image(label="Image Output")
+          with gr.Row():
+            output_zip = gr.outputs.File()
+            
+    get_shape_loader.click(get_shape_files,img_source,[output_zip,mask_img])
+my_app.launch(debug = True)