Update app.py

app.py

@@ -3,7 +3,8 @@ import numpy as np
 
 def process_rooftop_image(image_path, geographic_bounds):
     """
-    Process the rooftop image to detect rooftops, exclude shadows, and calculate real-world area.
+    Process the rooftop image to detect rooftops, exclude shadows, 
+    and calculate usable area for solar panels.
 
     Parameters:
         image_path (str): Path to the input image (PNG or JPG).
@@ -12,7 +13,7 @@ def process_rooftop_image(image_path, geographic_bounds):
 
     Returns:
         final_image (numpy array): Image with predicted boundaries and excluded shadows.
-        real_world_area (float): Computed rooftop area in square meters.
+        usable_area (float): Computed rooftop area usable for solar panels in square meters.
     """
     # Load the image
     image = cv2.imread(image_path)
@@ -69,30 +70,46 @@ def process_rooftop_image(image_path, geographic_bounds):
 
     # Conversion factor: real-world area per pixel
     conversion_factor = (lat_conversion * lon_conversion) / (image.shape[0] * image.shape[1])
-    real_world_area = pixel_area * conversion_factor  # in square meters
+    usable_area = pixel_area * conversion_factor  # in square meters
 
     # Step 9: Overlay contours on the original image
     final_image = image.copy()
     cv2.drawContours(final_image, contours, -1, (0, 255, 0), 2)
 
-    return final_image, real_world_area
+    return final_image, usable_area
 
-# Example Usage
-geographic_bounds = {
-    'lat_min': 12.9716,
-    'lat_max': 12.9756,
-    'lon_min': 77.5946,
-    'lon_max': 77.5986,
-}
-input_image_path = "behindpmi.png"
 
-final_image, calculated_area = process_rooftop_image(input_image_path, geographic_bounds)
-print(f"Calculated Rooftop Area: {calculated_area:.2f} square meters")
-
-# Save the final image with contours
-cv2.imwrite("processed_image_with_boundaries.png", final_image)
-
-# Display the final image
-cv2.imshow("Rooftop Detection", final_image)
-cv2.waitKey(0)
-cv2.destroyAllWindows()
+if __name__ == "__main__":
+    # Prompt user for inputs
+    image_path = input("Enter the path to the rooftop image (PNG or JPG): ")
+    print("Enter the geographic bounds of the image:")
+    lat_min = float(input("Latitude Min: "))
+    lat_max = float(input("Latitude Max: "))
+    lon_min = float(input("Longitude Min: "))
+    lon_max = float(input("Longitude Max: "))
+    
+    geographic_bounds = {
+        'lat_min': lat_min,
+        'lat_max': lat_max,
+        'lon_min': lon_min,
+        'lon_max': lon_max,
+    }
+
+    try:
+        # Process the image and calculate the usable area
+        final_image, calculated_area = process_rooftop_image(image_path, geographic_bounds)
+        
+        # Save and display results
+        output_path = "output_rooftop_with_boundaries.jpg"
+        cv2.imwrite(output_path, final_image)
+        
+        print(f"Usable Rooftop Area for Solar Panels: {calculated_area:.2f} square meters")
+        print(f"Processed image saved to: {output_path}")
+
+        # Display the final image with detected boundaries
+        cv2.imshow("Processed Rooftop", final_image)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+
+    except Exception as e:
+        print(f"Error: {e}")