diff --git a/image_fusion/Image_Registration_test.py b/image_fusion/Image_Registration_test.py
index 9de54ac..d909b9a 100644
--- a/image_fusion/Image_Registration_test.py
+++ b/image_fusion/Image_Registration_test.py
@@ -11,7 +11,7 @@ from ultralytics import YOLO
 from skimage.metrics import structural_similarity as ssim
 
 # 添加YOLOv8模型初始化
-yolo_model = YOLO("yolov8n.pt")  # 可替换为yolov8s/m/l等
+yolo_model = YOLO("best.pt")  # 可替换为yolov8s/m/l等
 yolo_model.to('cuda')  # 启用GPU加速
 
 
@@ -177,21 +177,21 @@ def main(matchimg_vi, matchimg_in):
         # (3, 3)//获取对应的配准坐标点
         flag, H, dot = Images_matching(matchimg_vi, matchimg_in)
         if flag == 0:
-            return 0, None, 0
+            return 0, None, 0, 0.0, 0.0, 0.0, 0.0
         else:
             # 配准处理
             matched_ni = cv2.warpPerspective(orimg_in, H, (w, h))
             matched_ni, left, right, top, bottom = removeBlackBorder(matched_ni)
             
             # 裁剪可见光图像
-            cropped_vi = orimg_vi[left:right, top:bottom]
-            
             # fusion = fusions(orimg_vi[left:right, top:bottom], matched_ni)
-            fusion = fusions(cropped_vi, matched_ni)
+            
+            # 不裁剪可见光图像
+            fusion = fusions(orimg_vi, matched_ni)
             
             # 转换为灰度计算指标
             fusion_gray = cv2.cvtColor(fusion, cv2.COLOR_RGB2GRAY)
-            cropped_vi_gray = cv2.cvtColor(cropped_vi, cv2.COLOR_BGR2GRAY)
+            cropped_vi_gray = cv2.cvtColor(orimg_vi, cv2.COLOR_BGR2GRAY)
             matched_ni_gray = matched_ni  # 红外图已经是灰度
             
             # 计算指标
@@ -200,9 +200,15 @@ def main(matchimg_vi, matchimg_in):
             mi_visible = calculate_mi(fusion_gray, cropped_vi_gray)
             mi_infrared = calculate_mi(fusion_gray, matched_ni_gray)
             mi_total = mi_visible + mi_infrared
-            ssim_visible = calculate_ssim(fusion_gray, cropped_vi_gray)
-            ssim_infrared = calculate_ssim(fusion_gray, matched_ni_gray)
-            ssim_avg = (ssim_visible + ssim_infrared) / 2
+            
+            # 添加SSIM容错处理
+            try:
+                ssim_visible = calculate_ssim(fusion_gray, cropped_vi_gray)
+                ssim_infrared = calculate_ssim(fusion_gray, matched_ni_gray)
+                ssim_avg = (ssim_visible + ssim_infrared) / 2
+            except Exception as ssim_error:
+                print(f"SSIM计算错误: {ssim_error}")
+                ssim_avg = -1  # 用-1表示计算失败
             
             # YOLOv8目标检测
             results = yolo_model(fusion)  # 输入融合后的图像
@@ -212,16 +218,16 @@ def main(matchimg_vi, matchimg_in):
             return 1, annotated_image, dot, en, sf, mi_total, ssim_avg
     except Exception as e:
         print(f"Error in fusion/detection: {e}")
-        return 0, None, 0
+        return 0, None, 0, 0.0, 0.0, 0.0, 0.0
 
 
 def parse_args():
     # 输入可见光和红外图像路径
-    visible_image_path = "test/visible.jpg"  # 可见光图片路径
-    infrared_image_path = "test/infrared.jpg"  # 红外图片路径
+    visible_image_path = "./test/visible/visibleI0195.jpg"  # 可见光图片路径
+    infrared_image_path = "./test/infrared/infraredI0195.jpg"  # 红外图片路径
     # 输入可见光和红外视频路径
-    visible_video_path = "test/visible.mp4"  # 可见光视频路径
-    infrared_video_path = "test/infrared.mp4"  # 红外视频路径
+    visible_video_path = "./test/visible.mp4"  # 可见光视频路径
+    infrared_video_path = "./test/infrared.mp4"  # 红外视频路径
     
     """解析命令行参数"""
     parser = argparse.ArgumentParser(description='图像融合与目标检测')
@@ -331,12 +337,18 @@ if __name__ == '__main__':
             print(f"信息熵（EN）: {en:.2f}")
             print(f"空间频率（SF）: {sf:.2f}")
             print(f"互信息（MI）: {mi:.2f}")
-            print(f"结构相似性（SSIM）: {ssim_val:.4f}")
             
+            # 条件显示SSIM
+            if ssim_val >= 0:
+                print(f"结构相似性（SSIM）: {ssim_val:.4f}")
+            else:
+                print("结构相似性（SSIM）: 计算失败（已跳过）")
+            
+            print(f"配准点数: {dot}")
             # 显示并保存结果
-            cv2.imshow("Fusion with Detection", fusion_result)
+            # cv2.imshow("Fusion with Detection", fusion_result)
             cv2.imwrite("output/fusion_result.jpg", fusion_result)
-            cv2.waitKey(0)
-            cv2.destroyAllWindows()
+            # cv2.waitKey(0)
+            # cv2.destroyAllWindows()
         else:
             print("融合失败！")