add tab

lut_processor.py

@@ -21,14 +21,23 @@ def apply_lut(image, lut_name, gamma_correction=True, clip_values=True, strength
     # Get full path to LUT file
     lut_file = Path('cube_luts') / lut_name
     
-    # Read LUT file
+    # Read LUT file with error handling for different encodings
     try:
         lut = read_LUT_IridasCube(str(lut_file))
-        lut.name = lut_name
+    except UnicodeDecodeError:
+        # Try different encodings if utf-8 fails
+        try:
+            with open(str(lut_file), 'r', encoding='latin-1') as f:
+                lut = read_LUT_IridasCube(f)
+        except Exception as e:
+            print(f"Error reading LUT file with latin-1 encoding: {e}")
+            return image.numpy() * 255.0
     except Exception as e:
         print(f"Error reading LUT file: {e}")
         return image.numpy() * 255.0
 
+    lut.name = lut_name
+
     # Handle clipping
     if clip_values:
         if lut.domain[0].max() == lut.domain[0].min() and lut.domain[1].max() == lut.domain[1].min():
@@ -61,13 +70,19 @@ def apply_lut(image, lut_name, gamma_correction=True, clip_values=True, strength
     if is_non_default_domain:
         lut_img = (lut_img - lut.domain[0]) / dom_scale
 
+    # Ensure values are in valid range
+    lut_img = np.clip(lut_img, 0, 1)
+    
     lut_img = torch.from_numpy(lut_img).float()
     
     if strength < 1.0:
         lut_img = strength * lut_img + (1 - strength) * image
 
-    # Convert back to uint8 range
-    return (lut_img.numpy() * 255.0).astype(np.uint8)
+    # Convert back to uint8 range and ensure proper bounds
+    result = (lut_img.numpy() * 255.0)
+    result = np.clip(result, 0, 255).astype(np.uint8)
+    
+    return result
 
 def create_lut_tab():
     available_luts = get_available_luts()