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