From d1ed958db500b7f37002f2fcecec9d383f04921e Mon Sep 17 00:00:00 2001 From: myh Date: Tue, 22 Apr 2025 16:35:19 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=E5=AE=9E=E4=BE=8B=E6=A8=A1?= =?UTF-8?q?=E5=9D=97?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- image_fusion/evaluate_module/evaluate.py | 74 ------------------- .../evaluate_module/evaluation_test.py | 26 ------- 2 files changed, 100 deletions(-) delete mode 100644 image_fusion/evaluate_module/evaluate.py delete mode 100644 image_fusion/evaluate_module/evaluation_test.py diff --git a/image_fusion/evaluate_module/evaluate.py b/image_fusion/evaluate_module/evaluate.py deleted file mode 100644 index 1ac811f..0000000 --- a/image_fusion/evaluate_module/evaluate.py +++ /dev/null @@ -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 diff --git a/image_fusion/evaluate_module/evaluation_test.py b/image_fusion/evaluate_module/evaluation_test.py deleted file mode 100644 index 64b6f00..0000000 --- a/image_fusion/evaluate_module/evaluation_test.py +++ /dev/null @@ -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) -