Update app.py

app.py

@@ -6,7 +6,7 @@ import numpy as np
 # Load the YOLOv5 model (adjust the path to your model if needed)
 model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.pt')  # Adjust if needed
 
-# Example function to calculate materials based on detected areas
+# Define the function to calculate materials based on detected areas
 def calculate_materials(detected_objects, image_width, image_height):
     materials = {
         "cement": 0,
@@ -16,16 +16,20 @@ def calculate_materials(detected_objects, image_width, image_height):
 
     # Proportionality factors (simplified for this example, adjust based on real-world data)
     for obj in detected_objects:
-        # Calculate bounding box area in real-world units (cm or meters, as per the blueprint size)
-        x1, y1, x2, y2 = obj['bbox']  # Coordinates of the bounding box
-        width = (x2 - x1) * image_width  # Convert to real-world width
-        height = (y2 - y1) * image_height  # Convert to real-world height
-        
-        # Calculate the area (length × width)
-        area = width * height  # Simplified area calculation
-        
-        print(f"Detected {obj['name']} with area {area} cm²")  # Debugging output
+        # Get the bounding box coordinates
+        x1, y1, x2, y2 = obj['bbox']
+
+        # Convert the bounding box coordinates to real-world units based on image size and blueprint size
+        width = (x2 - x1) * image_width  # Convert to real-world width (in cm or meters)
+        height = (y2 - y1) * image_height  # Convert to real-world height (in cm or meters)
         
+        # Calculate the area (length × width) in real-world units
+        area = width * height  # cm² or m² based on the scale
+
+        # Debugging output to verify bounding box size
+        print(f"Detected {obj['name']} with area {area} cm²")  # Adjust units based on the scale
+
+        # Example material estimation based on detected object type
         if obj['name'] == 'wall':  # Example: For 'wall' objects
             materials['cement'] += area * 0.1  # Cement estimation (in kg)
             materials['bricks'] += area * 10  # Bricks estimation
@@ -46,7 +50,7 @@ def predict_image(image):
     # Get the detected objects as pandas dataframe (xywh format)
     detected_objects = results.pandas().xywh[0]  # First image in batch
     
-    # Print out the detection results for debugging purposes
+    # Debugging output: Print out the detected objects for inspection
     print(f"Detected objects: {detected_objects}")
     
     # Set the confidence threshold (e.g., 0.5 means 50% confidence)
@@ -54,8 +58,8 @@ def predict_image(image):
     detected_objects = detected_objects[detected_objects['confidence'] > confidence_threshold]
     
     # Assume blueprint image size (in cm, adjust based on real-world image size)
-    image_width = 91  # Example width in cm (adjust this)
-    image_height = 61  # Example height in cm (adjust this)
+    image_width = 91  # Example width in cm (adjust this to the real-world blueprint size)
+    image_height = 61  # Example height in cm (adjust this to the real-world blueprint size)
     
     # Process the detected objects and calculate materials
     detected_objects_list = []