1

main.py

@@ -1,13 +1,15 @@
 import io
 import os
 import urllib.request  # 必须这样写，不能只 import urllib
+from typing import Optional, List
+
 import cv2
 import litserve as ls
 import numpy as np
 import torch
 from PIL import Image, ImageDraw
 from minio import Minio
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from fastapi import Response  # 导入 FastAPI 的 Response
 
 from config import settings
@@ -18,9 +20,11 @@ minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secre
 
 
 class SAMRequest(BaseModel):
-    image_path: str
-    points: list[list[float]]
-    labels: list[int]
+    image_path: str = Field(..., description="图片路径，必填字段")
+    type: str = Field(..., description="推理类型，必填字段")
+    points: Optional[List[List[float]]] = None
+    labels: Optional[List[int]] = None
+    box: Optional[List[int]] = None
 
 
 class SimpleLitAPI(ls.LitAPI):
@@ -64,11 +68,23 @@ class SimpleLitAPI(ls.LitAPI):
         input_points = np.array(request.points)
         input_labels = np.array(request.labels)
 
-        masks, scores, logits = self.predictor.predict(
-            point_coords=input_points,
-            point_labels=input_labels,
-            multimask_output=False
-        )
+        if request.type == "point":
+            masks, scores, logits = self.predictor.predict(
+                point_coords=input_points,
+                point_labels=input_labels,
+                multimask_output=False
+            )
+        elif request.type == "box":
+            box = np.array(request.box)
+            masks, scores, logits = self.predictor.predict(
+                box=box,
+                multimask_output=False
+            )
+        else:
+            raise ValueError(
+                f"request.type 参数错误！合法值为 'point' 或 'box'，实际传入：{request.type}"
+            )
+
         mask = masks[0]  # 获取第一个掩码
 
         image = Image.fromarray(image)

scripts/amg_box.py

@@ -170,7 +170,7 @@ class SAMBoxSegmenter:
 
         # 准备框坐标
         box = np.array(self.current_box)
-
+        print(self.current_box)
         # 调用SAM生成掩码
         masks, _, _ = self.predictor.predict(
             box=box,