评价指标测试

image_fusion/evaluate_module/evaluate.py

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+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

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+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)
+