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，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，需要将图像归一化，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

好的，我可以帮您实现这个问题。首先，我们需要导入必要的库：cv2（OpenCV），numpy，os和matplotlib。代码如下：

import cv2
import numpy as np
import os
import matplotlib.pyplot as plt

接下来，我们需要定义一些函数来计算图像的颜色、纹理和形状特征。

对于颜色特征，我们可以使用图像的平均颜色值和标准差来表示。代码如下：

def get_color_features(image):
    # calculate mean and standard deviation of each channel
    mean, std = cv2.meanStdDev(image)
    mean = mean.flatten()
    std = std.flatten()
    # concatenate mean and standard deviation into a single feature vector
    return np.concatenate([mean, std])

对于纹理特征，我们可以使用灰度共生矩阵（GLCM）来表示。但是，由于您不希望使用greycomatrix和greycoprops函数，我们可以手动计算GLCM并使用统计量来表示纹理特征。具体来说，我们将计算每个方向的GLCM，然后计算每个GLCM的能量、熵、对比度和相关性。这些统计量将被用作纹理特征。代码如下：

def get_texture_features(image):
    # convert image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # calculate GLCM for each direction
    glcms = [cv2.calcGLCM(gray, d, 3, 0) for d in [0, np.pi/4, np.pi/2, 3*np.pi/4]]
    # calculate energy, entropy, contrast and correlation for each GLCM
    features = []
    for glcm in glcms:
        energy = np.sum(glcm**2)
        entropy = -np.sum(glcm*np.log2(glcm+1e-10))
        contrast = np.sum((np.arange(3)**2).reshape((1, 3, 1, 3)) * glcm[:, :, np.newaxis, np.newaxis])
        mu_x = np.sum(glcm * np.arange(3)[:, np.newaxis, np.newaxis], axis=(0, 1))
        mu_y = np.sum(glcm * np.arange(3)[np.newaxis, :, np.newaxis], axis=(0, 1))
        sigma_x = np.sqrt(np.sum(glcm * (np.arange(3)[:, np.newaxis, np.newaxis] - mu_x)**2, axis=(0, 1)))
        sigma_y = np.sqrt(np.sum(glcm * (np.arange(3)[np.newaxis, :, np.newaxis] - mu_y)**2, axis=(0, 1)))
        correlation = np.sum(glcm * (np.arange(3)[:, np.newaxis, np.newaxis] - mu_x) * (np.arange(3)[np.newaxis, :, np.newaxis] - mu_y), axis=(0, 1)) / (sigma_x * sigma_y)
        features.extend([energy, entropy, contrast, correlation])
    return np.array(features)

对于形状特征，我们可以使用图像的轮廓来表示。具体来说，我们将计算图像的轮廓，然后计算轮廓面积、周长和形状因子。这些统计量将被用作形状特征。代码如下：

def get_shape_features(image):
    # convert image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # threshold image to get binary mask
    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    # find contours in binary mask
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    # calculate area, perimeter and shape factor for each contour
    features = []
    for cnt in contours:
        area = cv2.contourArea(cnt)
        perimeter = cv2.arcLength(cnt, True)
        if perimeter == 0:
            shape_factor = 0
        else:
            shape_factor = 4*np.pi*area/perimeter**2
        features.extend([area, perimeter, shape_factor])
    return np.array(features)

最后，我们可以使用这些函数来计算两种图像的特征，并对比它们的差异。

具体来说，我们将遍历两个文件夹中的所有图像，对于每个图像，我们将计算其颜色、纹理和形状特征。然后，我们将使用t-SNE算法将每个图像的特征向量转换为二维空间中的点，并使用不同的颜色和标记表示两种图像。最后，我们将显示可视化结果并保存到文件中。代码如下：

# define paths to image folders
us_path = 'D:/zzz/us2'
na_path = 'D:/zzz/na2'
# define lists to store features and labels
features = []
labels = []
# loop over ultrasound images
for file in os.listdir(us_path):
    if file.endswith('.jpg'):
        # read image and resize to (256, 256)
        image = cv2.imread(os.path.join(us_path, file))
        image = cv2.resize(image, (256, 256))
        # normalize image
        image = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX)
        # calculate color, texture and shape features
        color_features = get_color_features(image)
        texture_features = get_texture_features(image)
        shape_features = get_shape_features(image)
        # concatenate features into a single feature vector
        features.append(np.concatenate([color_features, texture_features, shape_features]))
        # add label for ultrasound image
        labels.append(0)
# loop over natural images
for file in os.listdir(na_path):
    if file.endswith('.jpg'):
        # read image and resize to (256, 256)
        image = cv2.imread(os.path.join(na_path, file))
        image = cv2.resize(image, (256, 256))
        # normalize image
        image = cv2.normalize(image, None, 0, 255, cv2.NORM_MINMAX)
        # calculate color, texture and shape features
        color_features = get_color_features(image)
        texture_features = get_texture_features(image)
        shape_features = get_shape_features(image)
        # concatenate features into a single feature vector
        features.append(np.concatenate([color_features, texture_features, shape_features]))
        # add label for natural image
        labels.append(1)
# convert features and labels to numpy arrays
features = np.array(features)
labels = np.array(labels)
# apply t-SNE to features to get 2D embedding
from sklearn.manifold import TSNE
tsne = TSNE(n_components=2, perplexity=30, n_iter=1000, random_state=42)
embedding = tsne.fit_transform(features)
# plot embedding with different colors and markers for ultrasound and natural images
plt.scatter(embedding[labels==0, 0], embedding[labels==0, 1], c='r', marker='o', label='Ultrasound')
plt.scatter(embedding[labels==1, 0], embedding[labels==1, 1], c='b', marker='s', label='Natural')
plt.legend()
plt.show()
# save embedding plot to file
plt.savefig('embedding.png')

请注意，这个代码可能需要一些时间才能运行完毕，因为它需要计算每个图像的特征向量，并使用t-SNE算法进行降维。但是，一旦运行完毕，您将获得一个可视化结果，其中每个点表示一个图像，并且点的颜色和标记表示该图像是超声图像还是自然图像。通过观察可视化结果，您可以比较两种图像在颜色、纹理和形状方面的差异，并确定哪些特征对于区分两种图像最有用。