添加评价指标

README.md

@@ -1,3 +1,8 @@
 # Graduation-Project
 
-毕业设计：基于YOLO和图像融合技术的无人机检测系统及安全性研究
+毕业设计：基于YOLO和图像融合技术的无人机检测系统及安全性研究
+
+Linux 运行训练
+```bash
+nohup python -u yolov8_fed.py >> runtime.log 2>&1 &
+```

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)

image_fusion/evaluate_module/evaluate.py

@@ -1,74 +0,0 @@
-import numpy as np
-import cv2
-from skimage.metrics import structural_similarity as ssim
-from skimage.filters import sobel
-from sklearn.metrics import mutual_info_score
-
-
-# Helper to compute mutual information between two grayscale images
-def evaluate_mutual_information(img1_gray, img2_gray):
-    hist_2d, _, _ = np.histogram2d(img1_gray.ravel(), img2_gray.ravel(), bins=256)
-    pxy = hist_2d / float(np.sum(hist_2d))
-    px = np.sum(pxy, axis=1)
-    py = np.sum(pxy, axis=0)
-    px_py = np.outer(px, py)
-    nzs = pxy > 0
-    mi = np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs]))
-    return mi
-
-
-# Compute SSIM between two grayscale images
-def evaluate_registration_ssim(img1_gray, img2_gray):
-    return ssim(img1_gray, img2_gray)
-
-
-# Entropy of grayscale image (fusion quality)
-def evaluate_fusion_entropy(fusion_img):
-    gray = cv2.cvtColor(fusion_img, cv2.COLOR_RGB2GRAY)
-    hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
-    hist = hist.ravel() / hist.sum()
-    entropy = -np.sum(hist * np.log2(hist + 1e-9))
-    return entropy
-
-
-# Edge strength using Sobel (fusion quality)
-def evaluate_fusion_edges(fusion_img):
-    gray = cv2.cvtColor(fusion_img, cv2.COLOR_RGB2GRAY)
-    edges = sobel(gray.astype(float) / 255.0)
-    return np.mean(edges)
-
-
-# SSIM between fused image and one of the sources
-def evaluate_fusion_ssim(fusion_img, reference_img):
-    fusion_gray = cv2.cvtColor(fusion_img, cv2.COLOR_RGB2GRAY)
-    ref_gray = cv2.cvtColor(reference_img, cv2.COLOR_RGB2GRAY)
-    return ssim(fusion_gray, ref_gray)
-
-
-# Return all in one place (stub images would be required to test)
-def summarize_evaluation(img1_gray, img2_gray, fusion_img, ref_img_for_ssim):
-    return {
-        "Registration SSIM": evaluate_registration_ssim(img1_gray, img2_gray),
-        "Mutual Information": evaluate_mutual_information(img1_gray, img2_gray),
-        "Fusion Entropy": evaluate_fusion_entropy(fusion_img),
-        "Fusion Edge Strength": evaluate_fusion_edges(fusion_img),
-        "Fusion SSIM (vs Ref)": evaluate_fusion_ssim(fusion_img, ref_img_for_ssim),
-    }
-
-# 将所有评价封装成一个高层函数 evaluate_all
-def evaluate_all(img1_gray, img2_gray, fusion_img, ref_img_for_ssim, verbose=True):
-    """
-    评估图像配准和融合质量的通用函数
-    :param img1_gray: 可见光灰度图像（原图）
-    :param img2_gray: 红外灰度图像（配准后）
-    :param fusion_img: 融合图像（RGB）
-    :param ref_img_for_ssim: 可见光RGB图，用于对比SSIM
-    :param verbose: 是否打印结果
-    :return: dict 评价指标结果
-    """
-    results = summarize_evaluation(img1_gray, img2_gray, fusion_img, ref_img_for_ssim)
-    if verbose:
-        print("图像评价指标如下：")
-        for k, v in results.items():
-            print(f"{k}: {v:.4f}")
-    return results

image_fusion/evaluate_module/evaluation_test.py

@@ -1,26 +0,0 @@
-from evaluate import *
-
-# 创建模拟图像数据用于测试
-# img1_gray：原始灰度图像（可见光）
-# img2_gray：变换后的灰度图像（红外模拟）
-# fusion_img：融合图像（可见光 + 红外）
-# ref_img_for_ssim：参考图像（可见光RGB）
-
-# 创建基础灰度图像（梯度）
-img1_gray = np.tile(np.linspace(50, 200, 256).astype(np.uint8), (256, 1))
-
-# 模拟配准后的图像：加一点噪声和平移
-img2_gray = np.roll(img1_gray, shift=5, axis=1)  # 平移模拟配准偏差
-noise = np.random.normal(0, 5, img2_gray.shape).astype(np.uint8)
-img2_gray = cv2.add(img2_gray, noise)
-
-# 创建 RGB 可见光图（重复三个通道）
-ref_img_for_ssim = cv2.merge([img1_gray] * 3)
-
-# 创建融合图像（取两个灰度图平均后合并入RGB）
-fusion_Y = cv2.addWeighted(img1_gray, 0.5, img2_gray, 0.5, 0)
-fusion_img = cv2.merge([fusion_Y, img1_gray, img2_gray])
-
-# 运行评价函数
-scores = evaluate_all(img1_gray, img2_gray, fusion_img, ref_img_for_ssim)
-