Updated load_image to convert image to 3 channels if needed

app.py

@@ -10,19 +10,18 @@ import matplotlib.pyplot as plt
 from PIL import Image
 import io
 
-# Function to load bounding boxes from CSV
-def load_bounding_boxes(csv_file):
-    # Assuming CSV file has columns: 'filename', 'x_min', 'y_min', 'x_max', 'y_max'
-    df = pd.read_csv(csv_file)
-    return df
-
 def load_image(file_path):
     if file_path.endswith(".dcm"):
         ds = pydicom.dcmread(file_path)
         img = ds.pixel_array
     else:
-        img = np.array(Image.open(file_path).convert('L')) # Convert to grayscale
-    H, W = img.shape
+        img = np.array(Image.open(file_path))
+
+    # Convert grayscale to 3-channel RGB by replicating channels
+    if len(img.shape) == 2:  # Grayscale image (height, width)
+        img = np.stack((img,)*3, axis=-1)  # Replicate grayscale channel to get (height, width, 3)
+
+    H, W = img.shape[:2]
     return img, H, W
 
 # MedSAM inference function