Update app.py

app.py

@@ -5,6 +5,9 @@ import streamlit as st
 from skimage import measure
 from io import BytesIO
 
+# Define the scale factor: 1 pixel = X meters (for example, 1 pixel = 0.5 meter)
+SCALE_FACTOR = 0.5  # In meters, adjust as per your image's scale
+
 def preprocess_image(image):
     img = cv2.imdecode(np.frombuffer(image.read(), np.uint8), cv2.IMREAD_COLOR)
     
@@ -34,10 +37,12 @@ def draw_contours(image, contours):
     cv2.drawContours(output_image, contours, -1, (0, 255, 0), 3)
     return output_image
 
-def calculate_area(contours):
-    areas = [cv2.contourArea(contour) for contour in contours]
-    total_area = sum(areas)
-    return total_area
+def calculate_area_in_meters(contours):
+    total_area_pixels = sum(cv2.contourArea(contour) for contour in contours)
+    
+    # Convert pixel area to real-world area (in square meters)
+    total_area_meters = total_area_pixels * (SCALE_FACTOR ** 2)
+    return total_area_meters
 
 def remove_shadows(image):
     hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
@@ -75,12 +80,12 @@ def main():
         # Step 6: Draw the predicted rooftop boundaries
         output_img = draw_contours(image_no_shadows, filtered_contours)
 
-        # Step 7: Calculate the area of the rooftop
-        total_area = calculate_area(filtered_contours)
+        # Step 7: Calculate the actual area of the rooftop (in square meters)
+        total_area = calculate_area_in_meters(filtered_contours)
 
         # Step 8: Display the result
         st.image(output_img, channels="BGR", caption="Detected Rooftop with Boundaries")
-        st.write(f"Total Rooftop Area: {total_area} pixels")
+        st.write(f"Total Rooftop Area: {total_area:.2f} square meters")
 
 if __name__ == "__main__":
     main()