diff --git a/Vision/camera_coordinate_dete.py b/Vision/camera_coordinate_dete.py
index 7150977..f10bb2e 100644
--- a/Vision/camera_coordinate_dete.py
+++ b/Vision/camera_coordinate_dete.py
@@ -63,10 +63,11 @@ class Detection:
             self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
 
 
-    def get_position(self, Point_isVision=False, save_img_point=0, Height_reduce = 30, width_reduce = 30):
+    def get_position(self, Point_isVision=False, Box_isPoint=True, save_img_point=0, Height_reduce = 30, width_reduce = 30):
         """
             检测料袋相关信息
             :param Point_isVision:  点云可视化
+            :param Box_isPoint:     True 返回点云值；      False 返回box相机坐标
             :param save_img_point:  0不保存 ; 1保存原图  ;2保存处理后的图 ; 3保存点云和原图；4 保存点云和处理后的图
             :param Height_reduce:   检测框的高内缩像素
             :param width_reduce:    检测框的宽内缩像素
@@ -237,7 +238,13 @@ class Detection:
                         box[1][0][1], box[1][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[1][0][1], box[1][0][0])
                         box[2][0][1], box[2][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[2][0][1], box[2][0][0])
                         box[3][0][1], box[3][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[3][0][1], box[3][0][0])
-
+                        if Box_isPoint == True:
+                            box_point_x1, box_point_y1, box_point_z1 = remove_nan_mean_value(pm, box[0][0][1], box[0][0][0])
+                            box_point_x2, box_point_y2, box_point_z2 = remove_nan_mean_value(pm, box[1][0][1], box[1][0][0])
+                            box_point_x3, box_point_y3, box_point_z3 = remove_nan_mean_value(pm, box[2][0][1], box[2][0][0])
+                            box_point_x4, box_point_y4, box_point_z4 = remove_nan_mean_value(pm, box[3][0][1], box[3][0][0])
+                        else:
+                            pass
                         x_rotation_center = int((box[0][0][0] + box[1][0][0] + box[2][0][0] + box[3][0][0]) / 4)
                         y_rotation_center = int((box[0][0][1] + box[1][0][1] + box[2][0][1] + box[3][0][1]) / 4)
                         point_x, point_y, point_z = remove_nan_mean_value(pm, y_rotation_center, x_rotation_center)
@@ -245,7 +252,14 @@ class Detection:
                         if np.isnan(point_x):     # 点云值为无效值
                             continue
                         else:
-                            box_list.append(box)
+                            if Box_isPoint == True:
+                                box_list.append(
+                                    [[box_point_x1, box_point_y1, box_point_z1],
+                                     [box_point_x2, box_point_y2, box_point_z2],
+                                     [box_point_x3, box_point_y3, box_point_z3],
+                                     [box_point_x4, box_point_y4, box_point_z4]])
+                            else:
+                                box_list.append(box)
                             if self.cameraType=='RVC':
                                 xyz.append([point_x*1000, point_y*1000, point_z*1000])
                                 Depth_Z.append(point_z*1000)
diff --git a/Vision/camera_coordinate_dete_img.py b/Vision/camera_coordinate_dete_img.py
index 3a6c6d8..b43005d 100644
--- a/Vision/camera_coordinate_dete_img.py
+++ b/Vision/camera_coordinate_dete_img.py
@@ -45,10 +45,11 @@ class Detection:
         pass
 
 
-    def get_position(self, Point_isVision=False, save_img_point=0, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30):
+    def get_position(self, Point_isVision=False, Box_isPoint=True, save_img_point=0, seg_distance_threshold = 0.01, Height_reduce = 30, width_reduce = 30):
         """
             检测料袋相关信息
             :param Point_isVision:  点云可视化
+            :param Box_isPoint:     True 返回点云值；      False 返回box相机坐标
             :param save_img_point:  0不保存 ; 1保存原图  ;2保存处理后的图 ; 3保存点云和原图；4 保存点云和处理后的图
             :param Height_reduce:   检测框的高内缩像素
             :param width_reduce:    检测框的宽内缩像素
@@ -223,6 +224,13 @@ class Detection:
                     box[1][0][1], box[1][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[1][0][1], box[1][0][0])
                     box[2][0][1], box[2][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[2][0][1], box[2][0][0])
                     box[3][0][1], box[3][0][0] = out_bounds_dete(pm.shape[0], pm.shape[1], box[3][0][1], box[3][0][0])
+                    if Box_isPoint == True:
+                        box_point_x1, box_point_y1, box_point_z1 = remove_nan_mean_value(pm, box[0][0][1], box[0][0][0])
+                        box_point_x2, box_point_y2, box_point_z2 = remove_nan_mean_value(pm, box[1][0][1], box[1][0][0])
+                        box_point_x3, box_point_y3, box_point_z3 = remove_nan_mean_value(pm, box[2][0][1], box[2][0][0])
+                        box_point_x4, box_point_y4, box_point_z4 = remove_nan_mean_value(pm, box[3][0][1], box[3][0][0])
+                    else:
+                        pass
 
                     x_rotation_center = int((box[0][0][0] + box[1][0][0] + box[2][0][0] + box[3][0][0]) / 4)
                     y_rotation_center = int((box[0][0][1] + box[1][0][1] + box[2][0][1] + box[3][0][1]) / 4)
@@ -231,7 +239,14 @@ class Detection:
                     if np.isnan(point_x):  # 点云值为无效值
                         continue
                     else:
-                        box_list.append(box)
+                        if Box_isPoint == True:
+                            box_list.append(
+                                [[box_point_x1, box_point_y1, box_point_z1],
+                                 [box_point_x2, box_point_y2, box_point_z2],
+                                 [box_point_x3, box_point_y3, box_point_z3],
+                                 [box_point_x4, box_point_y4, box_point_z4]])
+                        else:
+                            box_list.append(box)
                         Depth_Z.append(point_z * 1000)
                         nx_ny_nz.append([a, b, c])
                         RegionalArea.append(cv2.contourArea(max_contour))