删除实例模块

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)
-