Add histogram normalization on foreground pixels after alpha premultiply

app.py

@@ -376,8 +376,25 @@ def preprocess_image(input: Image.Image) -> Image.Image:
     bbox = center[0] - size // 2, center[1] - size // 2, center[0] + size // 2, center[1] + size // 2
     output = output.crop(bbox)  # type: ignore
     output = np.array(output).astype(np.float32) / 255
-    output = output[:, :, :3] * output[:, :, 3:4]
-    output = Image.fromarray((output * 255).astype(np.uint8))
+    rgb = output[:, :, :3]
+    alpha = output[:, :, 3:4]
+    rgb = rgb * alpha  # premultiply alpha
+
+    # Histogram normalization on foreground pixels only
+    fg_mask = (alpha[:, :, 0] > 0.05)
+    if fg_mask.any():
+        for c in range(3):
+            ch = rgb[:, :, c]
+            fg_vals = ch[fg_mask]
+            if fg_vals.max() > fg_vals.min():
+                # Stretch foreground channel to use full [0, 1] range
+                lo, hi = np.percentile(fg_vals, [1, 99])
+                if hi > lo:
+                    ch_norm = np.clip((ch - lo) / (hi - lo), 0, 1)
+                    # Only apply to foreground pixels
+                    rgb[:, :, c] = np.where(fg_mask, ch_norm, 0)
+
+    output = Image.fromarray((rgb * 255).astype(np.uint8))
     return output