Update app.py

app.py

@@ -45,28 +45,46 @@ transform = transforms.Compose([
 def looks_like_fundus(image):
     """
     Heuristic to check if an image is likely a retinal fundus scan.
-    Fundus images typically have a brighter central region (retina)
-    and a darker outer border (background).
+
+    Assumptions:
+    - Fundus images usually have a bright circular central region (retina)
+      and a darker outer border (background).
+    - Non-fundus images (documents, labels, screens) tend to have
+      similar brightness across the whole frame.
     """
+    # Grayscale + resize for consistency
     img = np.array(image.convert("L").resize((224, 224)))
 
-    # Central crop
+    # Central square (potential retina)
     center = img[40:184, 40:184]
-    center_mean = center.mean()
 
-    # Border = everything outside the central crop
-    border = img.copy()
-    border[40:184, 40:184] = 0
-    border_pixels = border[border > 0]
+    # Border = everything outside the center
+    border_mask = np.ones_like(img, dtype=bool)
+    border_mask[40:184, 40:184] = False
+    border_pixels = img[border_mask]
 
-    # If no real border pixels, don't block it
+    # Safety: if something weird happens, don't block it
     if border_pixels.size == 0:
         return True
 
+    center_mean = center.mean()
     border_mean = border_pixels.mean()
 
-    # Fundus: center clearly brighter than border
-    return center_mean > border_mean + 8
+    # Ratios of dark/bright pixels
+    border_dark_ratio = np.mean(border_pixels < 40)     # dark background
+    center_bright_ratio = np.mean(center > 80)          # bright retina
+
+    # Conditions (tunable):
+    # 1) Center clearly brighter than border
+    cond_contrast = center_mean - border_mean > 25
+
+    # 2) Most of the border is dark
+    cond_border_dark = border_dark_ratio > 0.5
+
+    # 3) A good portion of the center is bright
+    cond_center_bright = center_bright_ratio > 0.4
+
+    return cond_contrast and cond_border_dark and cond_center_bright
 
 
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