添加评价指标

image_fusion/Image_Registration_test.py

@@ -8,12 +8,41 @@ import cv2
 import numpy as np
 
 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.to('cuda')  # 启用GPU加速
 
 
+def calculate_en(img):
+    """计算信息熵（处理灰度图）"""
+    hist = cv2.calcHist([img], [0], None, [256], [0, 256])
+    hist = hist / hist.sum()
+    return -np.sum(hist * np.log2(hist + 1e-10))
+
+
+def calculate_sf(img):
+    """计算空间频率（处理灰度图）"""
+    rf = np.sqrt(np.mean(np.square(np.diff(img, axis=0))))
+    cf = np.sqrt(np.mean(np.square(np.diff(img, axis=1))))
+    return np.sqrt(rf ** 2 + cf ** 2)
+
+
+def calculate_mi(img1, img2):
+    """计算互信息（处理灰度图）"""
+    hist_2d = np.histogram2d(img1.ravel(), img2.ravel(), 256)[0]
+    pxy = hist_2d / hist_2d.sum()
+    px = np.sum(pxy, axis=1)
+    py = np.sum(pxy, axis=0)
+    return np.sum(pxy * np.log2(pxy / (px[:, None] * py[None, :] + 1e-10) + 1e-10))
+
+
+def calculate_ssim(img1, img2):
+    """计算SSIM（处理灰度图）"""
+    return ssim(img1, img2, data_range=255)
+
+
 # 裁剪线性RGB对比度拉伸：（去掉2%百分位以下的数，去掉98%百分位以上的数，上下百分位数一般相同，并设置输出上下限）
 def truncated_linear_stretch(image, truncated_value=2, maxout=255, min_out=0):
     """
@@ -145,20 +174,42 @@ def main(matchimg_vi, matchimg_in):
         orimg_vi = matchimg_vi
         orimg_in = matchimg_in
         h, w = orimg_vi.shape[:2]  # 480 640
-        flag, H, dot = Images_matching(matchimg_vi, matchimg_in)  # (3, 3)//获取对应的配准坐标点
+        # (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
         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(orimg_vi, matched_ni)
+            fusion = fusions(cropped_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)
+            matched_ni_gray = matched_ni  # 红外图已经是灰度
+            
+            # 计算指标
+            en = calculate_en(fusion_gray)
+            sf = calculate_sf(fusion_gray)
+            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
             
             # YOLOv8目标检测
             results = yolo_model(fusion)  # 输入融合后的图像
             annotated_image = results[0].plot()  # 绘制检测框
             
-            return 1, annotated_image, dot  # 返回带检测结果的图像
+            # 返回带检测结果的图像
+            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
@@ -272,9 +323,16 @@ if __name__ == '__main__':
         img_inf_gray = cv2.cvtColor(img_infrared, cv2.COLOR_BGR2GRAY)
         
         # 执行融合与检测
-        flag, fusion_result, _ = main(img_visible, img_inf_gray)
+        flag, fusion_result, dot, en, sf, mi, ssim_val = main(img_visible, img_inf_gray)
         
         if flag == 1:
+            # 展示评价指标
+            print("\n======== 融合质量评价 ========")
+            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}")
+            
             # 显示并保存结果
             cv2.imshow("Fusion with Detection", fusion_result)
             cv2.imwrite("output/fusion_result.jpg", fusion_result)