写一段代码，比较lbp直方图相似性

这里提供一个 Python3 的示例代码，比较两张图像的 LBP 直方图相似性：

import cv2
import numpy as np
from matplotlib import pyplot as plt

def calc_lbp(img):
    """Calculate LBP (Local Binary Pattern) image"""
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    radius = 1
    n_points = 8 * radius
    lbp = np.zeros_like(gray)
    for i in range(radius, gray.shape[0]-radius):
        for j in range(radius, gray.shape[1]-radius):
            center = gray[i, j]
            code = 0
            for k in range(n_points):
                x = i + int(radius * np.cos(2 * np.pi * k / n_points))
                y = j - int(radius * np.sin(2 * np.pi * k / n_points))
                if gray[x, y] > center:
                    code += 2 ** k
            lbp[i, j] = code
    return lbp

def calc_hist(lbp, num_bins=256):
    """Calculate LBP histogram"""
    hist, _ = np.histogram(lbp, bins=num_bins, range=(0, num_bins))
    return hist

def compare_lbp_hist(hist1, hist2):
    """Compare two LBP histograms using Chi-Squared distance"""
    return cv2.compareHist(hist1, hist2, cv2.HISTCMP_CHISQR)

# Load two images to compare
img1 = cv2.imread('image1.jpg')
img2 = cv2.imread('image2.jpg')

# Calculate LBP histograms for both images
lbp1 = calc_lbp(img1)
lbp2 = calc_lbp(img2)
hist1 = calc_hist(lbp1)
hist2 = calc_hist(lbp2)

# Compare the two histograms using Chi-Squared distance
similarity = compare_lbp_hist(hist1, hist2)

# Print the similarity score
print('Similarity score:', similarity)

# Plot the two LBP histograms for visualization
plt.plot(hist1, label='Image 1')
plt.plot(hist2, label='Image 2')
plt.legend()
plt.show()

这段代码首先定义了三个函数：`calc_lbp` 用于计算 LBP 图像，`calc_hist` 用于计算 LBP 直方图，`compare_lbp_hist` 用于比较两个 LBP 直方图的相似性。然后加载两张需要比较的图像，计算它们的 LBP 直方图，并使用 `compare_lbp_hist` 计算它们的相似性。最后将相似性打印出来，并使用 `matplotlib` 库将两张图像的 LBP 直方图可视化。