From d139f5afcface3956269ab635696a3ddce2c4516 Mon Sep 17 00:00:00 2001
From: myh <yunhao.meng@outlook.com>
Date: Mon, 21 Apr 2025 17:51:32 +0800
Subject: [PATCH] =?UTF-8?q?=E8=AF=84=E4=BB=B7=E6=8C=87=E6=A0=87?=
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---
 image_fusion/evaluate_module/evaluate.py | 74 ++++++++++++++++++++++++
 1 file changed, 74 insertions(+)
 create mode 100644 image_fusion/evaluate_module/evaluate.py

diff --git a/image_fusion/evaluate_module/evaluate.py b/image_fusion/evaluate_module/evaluate.py
new file mode 100644
index 0000000..1ac811f
--- /dev/null
+++ b/image_fusion/evaluate_module/evaluate.py
@@ -0,0 +1,74 @@
+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