python 图像拼接融合质量评价代码

以下是使用 OpenCV 库对图像拼接融合质量进行评价的代码示例：

import cv2

# 读取原始图像和拼接后的图像
img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')
result = cv2.imread('result.jpg')

# 计算原始图像之间的重叠区域
overlap1 = img1[:, -100:]
overlap2 = img2[:, :100]

# 计算拼接后的图像重叠区域
result_overlap1 = result[:, -100:]
result_overlap2 = result[:, :100]

# 计算原始图像重叠区域和拼接后的图像重叠区域之间的差异
diff1 = cv2.absdiff(overlap1, result_overlap1)
diff2 = cv2.absdiff(overlap2, result_overlap2)

# 计算差异图像的均值和标准差
mean_diff1 = cv2.mean(diff1)
mean_diff2 = cv2.mean(diff2)
std_diff1 = cv2.meanStdDev(diff1)[0]
std_diff2 = cv2.meanStdDev(diff2)[0]

# 计算重叠区域的平均值和标准差
mean_overlap1 = cv2.mean(overlap1)
mean_overlap2 = cv2.mean(overlap2)
std_overlap1 = cv2.meanStdDev(overlap1)[0]
std_overlap2 = cv2.meanStdDev(overlap2)[0]

# 计算评价指标
quality = (mean_overlap1[0] + mean_overlap2[0]) / 2
similarity = (1 - mean_diff1[0] / std_overlap1[0]) * (1 - mean_diff2[0] / std_overlap2[0])

# 输出评价指标
print('Quality:', quality)
print('Similarity:', similarity)

该代码计算了原始图像之间的重叠区域和拼接后的图像重叠区域之间的差异，以及重叠区域的平均值和标准差。然后计算了两个评价指标：质量和相似度。其中，质量指标是重叠区域的平均值，而相似度指标是差异图像的标准差与重叠区域的标准差之比的乘积。