Update app.py

app.py

@@ -9,35 +9,31 @@ import shutil
 
 def process_image(img, strength, upscale=False):
     if upscale:
-        # Resize to 2x using LANCZOS for high-quality smoothing
         new_size = (img.width * 2, img.height * 2)
         img = img.resize(new_size, resample=Image.LANCZOS)
 
-    # Convert to grayscale and normalize
     img_cv = np.array(img.convert('L'))
     img_cv = img_cv.astype(np.float32) / 255.0
 
-    # Sobel filters for gradients
     sobelx = cv2.Sobel(img_cv, cv2.CV_64F, 1, 0, ksize=3)
     sobely = cv2.Sobel(img_cv, cv2.CV_64F, 0, 1, ksize=3)
 
-    # Normal vector components (-dz/dx, -dz/dy, 1)
+
     nx = -sobelx * strength
     ny = -sobely * strength
     nz = np.ones_like(img_cv)
 
-    # Normalize vectors
+
     length = np.sqrt(nx**2 + ny**2 + nz**2)
     nx /= length
     ny /= length
     nz /= length
 
-    # Convert [-1, 1] to [0, 255] for RGB
+
     nx = (nx + 1) / 2 * 255
     ny = (ny + 1) / 2 * 255
     nz = (nz + 1) / 2 * 255
 
-    # Merge to RGB (R=X, G=Y, B=Z)
     normal_map = np.stack((nx, ny, nz), axis=-1).astype(np.uint8)
     return Image.fromarray(normal_map)