cast to uint8

app.py

@@ -1,13 +1,13 @@
 import gradio as gr
 import cv2
 from transforms import *
+import numpy as np
 
 # Wrapper function to call the correct transform
 def apply_transform(image, domain, transform):
     img, img_type = load_image(image)
 
-    # Default value for result to avoid UnboundLocalError
-    result = None  
+    result = None  # Initialize result to avoid UnboundLocalError
 
     if domain == 'Spatial':
         if transform == 'Histogram Equalization':
@@ -22,21 +22,29 @@ def apply_transform(image, domain, transform):
             result = median_filter(img)
         else:
             return "Invalid spatial transform selection."
-
+    
     elif domain == 'Frequency':
         if transform == 'Fourier Transform':
             result = fourier_transform(img)
+            # Normalize the Fourier transform result to fit within 0-255 for visualization
+            result = np.abs(result)
+            result = 255 * result / np.max(result)
+            result = result.astype(np.uint8)
         elif transform == 'Discrete Cosine Transform':
             result = discrete_cosine_transform(img)
+            result = 255 * result / np.max(result)
+            result = result.astype(np.uint8)
         elif transform == 'High-Pass Filter':
             result = high_pass_filter(img)
         elif transform == 'Low-Pass Filter':
             result = low_pass_filter(img)
         elif transform == 'Wavelet Transform':
             result = wavelet_transform(img)
+            result = 255 * result / np.max(result)  # Normalize wavelet transform output
+            result = result.astype(np.uint8)
         else:
             return "Invalid frequency transform selection."
-
+    
     else:
         return "Invalid domain selection."