用python实现基于概率分布的超声图像与自然图像性质差异分析这一课题，使用opencv，两种图像的文件类型都是jpg,超声图像的文件路径是‘D:\zzz\zus2\’，自然图像的文件路径是‘D:\zzz\zna2\’，两种图像都有84张，需要对比两种图像的特征必须有颜色，纹理，形状，差异性分析方法也需要多种，包括完整详细代码

首先需要安装Opencv库，可以使用以下指令安装：

!pip install opencv-python

然后开始实现特征提取和差异性分析方法。

1. 颜色特征提取

使用HSV颜色空间提取颜色特征，计算每张图像的颜色直方图，并将其归一化。

import cv2
import numpy as np

def color_histogram(image):
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
    cv2.normalize(hist, hist, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
    return hist.flatten()

2. 纹理特征提取

使用LBP算法提取纹理特征，计算每张图像的LBP直方图，并将其归一化。

def lbp_histogram(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    radius = 1
    numPoints = 8 * radius
    lbp = cv2.LBP(gray, numPoints, radius, method=cv2.LBP_UNIFORM)
    hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, numPoints + 3), range=(0, numPoints + 2))
    hist = hist.astype("float")
    hist /= (hist.sum() + 1e-7)
    return hist

3. 形状特征提取

使用Hu矩算法提取形状特征，计算每张图像的Hu矩，并将其归一化。

def hu_moments(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    moments = cv2.moments(gray)
    huMoments = cv2.HuMoments(moments)
    huMoments = -np.sign(huMoments) * np.log10(np.abs(huMoments) + 1e-7)
    huMoments = huMoments.flatten()
    return huMoments

4. 差异性分析

使用Kullback-Leibler散度计算两种图像的颜色、纹理、形状特征的差异性，并输出结果。

def kl_divergence(p, q):
    p = np.asarray(p, dtype=np.float)
    q = np.asarray(q, dtype=np.float)
    return np.sum(np.where(p != 0, p * np.log(p / q), 0))

def compare_images(path1, path2):
    image1 = cv2.imread(path1)
    image2 = cv2.imread(path2)
    color_hist1 = color_histogram(image1)
    color_hist2 = color_histogram(image2)
    texture_hist1 = lbp_histogram(image1)
    texture_hist2 = lbp_histogram(image2)
    shape_feat1 = hu_moments(image1)
    shape_feat2 = hu_moments(image2)
    color_diff = kl_divergence(color_hist1, color_hist2)
    texture_diff = kl_divergence(texture_hist1, texture_hist2)
    shape_diff = kl_divergence(shape_feat1, shape_feat2)
    print(f"Color difference: {color_diff:.6f}, Texture difference: {texture_diff:.6f}, Shape difference: {shape_diff:.6f}")

最后，使用以上方法计算两种图像的差异性，并输出结果。

for i in range(1, 85):
    print(f"Image {i}")
    ultrasound_path = f"D:/zzz/zus2/{i}.jpg"
    natural_path = f"D:/zzz/zna2/{i}.jpg"
    compare_images(ultrasound_path, natural_path)

完整代码如下：

import cv2
import numpy as np

def color_histogram(image):
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
    cv2.normalize(hist, hist, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
    return hist.flatten()

def lbp_histogram(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    radius = 1
    numPoints = 8 * radius
    lbp = cv2.LBP(gray, numPoints, radius, method=cv2.LBP_UNIFORM)
    hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, numPoints + 3), range=(0, numPoints + 2))
    hist = hist.astype("float")
    hist /= (hist.sum() + 1e-7)
    return hist

def hu_moments(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    moments = cv2.moments(gray)
    huMoments = cv2.HuMoments(moments)
    huMoments = -np.sign(huMoments) * np.log10(np.abs(huMoments) + 1e-7)
    huMoments = huMoments.flatten()
    return huMoments

def kl_divergence(p, q):
    p = np.asarray(p, dtype=np.float)
    q = np.asarray(q, dtype=np.float)
    return np.sum(np.where(p != 0, p * np.log(p / q), 0))

def compare_images(path1, path2):
    image1 = cv2.imread(path1)
    image2 = cv2.imread(path2)
    color_hist1 = color_histogram(image1)
    color_hist2 = color_histogram(image2)
    texture_hist1 = lbp_histogram(image1)
    texture_hist2 = lbp_histogram(image2)
    shape_feat1 = hu_moments(image1)
    shape_feat2 = hu_moments(image2)
    color_diff = kl_divergence(color_hist1, color_hist2)
    texture_diff = kl_divergence(texture_hist1, texture_hist2)
    shape_diff = kl_divergence(shape_feat1, shape_feat2)
    print(f"Color difference: {color_diff:.6f}, Texture difference: {texture_diff:.6f}, Shape difference: {shape_diff:.6f}")

for i in range(1, 85):
    print(f"Image {i}")
    ultrasound_path = f"D:/zzz/zus2/{i}.jpg"
    natural_path = f"D:/zzz/zna2/{i}.jpg"
    compare_images(ultrasound_path, natural_path)