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，不用greycomatrix和greycoprops，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\us2’，自然图像的文件路径是‘D:\zzz\na2’，两种图像都有84张，图片形状都不同，图像名称是随机的，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

以下是基于概率分布的超声图像与自然图像性质差异分析的Python代码，使用了OpenCV库：

import cv2
import numpy as np
import os

# 文件路径
us_path = 'D:\\zzz\\us2\\'
na_path = 'D:\\zzz\\na2\\'

# 读取图像
us_images = []
na_images = []
for filename in os.listdir(us_path):
    img = cv2.imread(us_path + filename)
    us_images.append(img)
for filename in os.listdir(na_path):
    img = cv2.imread(na_path + filename)
    na_images.append(img)

# 颜色特征分析
us_color_hist = []
na_color_hist = []
for img in us_images:
    hist = cv2.calcHist([img], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
    hist = cv2.normalize(hist, hist).flatten()
    us_color_hist.append(hist)
for img in na_images:
    hist = cv2.calcHist([img], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
    hist = cv2.normalize(hist, hist).flatten()
    na_color_hist.append(hist)

# 纹理特征分析
us_texture_hist = []
na_texture_hist = []
win_size = 21
for img in us_images:
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    texture = cv2.calcHist([cv2.Laplacian(gray, cv2.CV_64F)], [0], None, [256], [-127, 128])
    texture = cv2.normalize(texture, texture).flatten()
    us_texture_hist.append(texture)
for img in na_images:
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    texture = cv2.calcHist([cv2.Laplacian(gray, cv2.CV_64F)], [0], None, [256], [-127, 128])
    texture = cv2.normalize(texture, texture).flatten()
    na_texture_hist.append(texture)

# 形状特征分析
us_shape_hist = []
na_shape_hist = []
for img in us_images:
    contours, _ = cv2.findContours(cv2.Canny(img, 100, 200), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    hist = np.zeros((1, len(contours)))
    for i in range(len(contours)):
        hist[0, i] = cv2.contourArea(contours[i])
    hist = cv2.normalize(hist, hist).flatten()
    us_shape_hist.append(hist)
for img in na_images:
    contours, _ = cv2.findContours(cv2.Canny(img, 100, 200), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    hist = np.zeros((1, len(contours)))
    for i in range(len(contours)):
        hist[0, i] = cv2.contourArea(contours[i])
    hist = cv2.normalize(hist, hist).flatten()
    na_shape_hist.append(hist)

# 特征差异性分析
color_diff = cv2.compareHist(us_color_hist[0], na_color_hist[0], cv2.HISTCMP_CHISQR_ALT)
texture_diff = cv2.compareHist(us_texture_hist[0], na_texture_hist[0], cv2.HISTCMP_CHISQR_ALT)
shape_diff = cv2.compareHist(us_shape_hist[0], na_shape_hist[0], cv2.HISTCMP_CHISQR_ALT)

print('颜色特征差异性分析:', color_diff)
print('纹理特征差异性分析:', texture_diff)
print('形状特征差异性分析:', shape_diff)

以上代码中，我们首先使用`cv2.imread()`函数读取文件夹中的图像，然后使用`cv2.calcHist()`计算图像的颜色和纹理直方图，以及形状特征的轮廓面积，使用`cv2.normalize()`对直方图进行归一化处理，最后使用`cv2.compareHist()`计算不同图像特征之间的差异性。