enhance: add original image handling and return types in BizyAirSegme…

…ntAnythingPointBox

segment_anything.py

@@ -136,7 +136,8 @@ def INPUT_TYPES(s):
             },
         }
 
-    RETURN_TYPES = ("IMAGE", "MASK")
+    RETURN_TYPES = ("IMAGE", "MASK", "IMAGE")
+    RETURN_NAMES = ("processed_image", "mask", "original_image")
     FUNCTION = "apply"
 
     CATEGORY = "☁️BizyAir/segment-anything"
@@ -145,10 +146,13 @@ def apply(self, image, is_point):
         API_KEY = get_api_key()
         SIZE_LIMIT = 1536
 
-        image, _ = LoadImage().load_image(image)
+        # 加载原始图像
+        original_image, _ = LoadImage().load_image(image)
+        # 直接克隆原始图像用于处理和透传
+        image_to_process = original_image.clone()
 
-        device = image.device
-        _, w, h, c = image.shape
+        device = image_to_process.device
+        _, w, h, c = image_to_process.shape
         assert (
             w <= SIZE_LIMIT and h <= SIZE_LIMIT
         ), f"width and height must be less than {SIZE_LIMIT}x{SIZE_LIMIT}, but got {w} and {h}"
@@ -167,7 +171,7 @@ def apply(self, image, is_point):
             input_label = [coord["pointType"] for coord in coordinates]
             payload = {
                 "image": None,
-                "mode": INFER_MODE.points_box.value,  # Point/Box分割模式
+                "mode": INFER_MODE.points_box.value,
                 "params": {
                     "input_points": json.dumps(input_points),
                     "input_label": json.dumps(input_label),
@@ -191,7 +195,7 @@ def apply(self, image, is_point):
 
             payload = {
                 "image": None,
-                "mode": INFER_MODE.batched_boxes.value,  # Point/Box分割模式
+                "mode": INFER_MODE.batched_boxes.value,
                 "params": {
                     "input_points": None,
                     "input_label": None,
@@ -205,8 +209,9 @@ def apply(self, image, is_point):
             "content-type": "application/json",
             "authorization": auth,
         }
-        image = image.squeeze(0).numpy()
-        image_pil = Image.fromarray((image * 255).astype(np.uint8))
+        # 处理用于API的图像
+        api_image = image_to_process.squeeze(0).numpy()
+        image_pil = Image.fromarray((api_image * 255).astype(np.uint8))
         input_image = encode_image_to_base64(image_pil, format="webp")
         payload["image"] = input_image
 
@@ -232,7 +237,7 @@ def apply(self, image, is_point):
         img = msg["image"]
         mask_image = msg["mask_image"]
 
-        img = (
+        processed_img = (
             (torch.from_numpy(decode_base64_to_np(img)).float() / 255.0)
             .unsqueeze(0)
             .to(device)
@@ -243,7 +248,8 @@ def apply(self, image, is_point):
         img_mask = img_mask.mean(dim=-1)
         img_mask = img_mask.unsqueeze(0)
 
-        return (img, img_mask)
+        # 直接返回克隆的原始图像,无需转换
+        return (processed_img, img_mask, image_to_process)
 
     @classmethod
     def IS_CHANGED(s, image, is_point):