基于PIE-engine的isodata算法代码

由于基于PIE-engine的ISODATA算法需要结合具体的图像分割任务进行实现，因此无法提供通用的代码。不过，以下是一个基于Python的ISODATA算法实现示例，你可以根据具体需求进行修改和优化。

import numpy as np

def isodata_segmentation(image, K, min_size, max_iterations):
    # 对图像进行初步分割，得到初步区域
    regions = pie_engine_segmentation(image)
    
    # 对每个区域进行像素点的采样，得到每个区域的样本数据
    samples = []
    for region in regions:
        mask = region.mask
        samples.append(image[mask])
    
    # 初始化类别中心和方差
    centers = np.zeros((K, image.shape[-1]))
    variances = np.zeros((K, image.shape[-1]))
    for k in range(K):
        centers[k] = np.mean(samples[k], axis=0)
        variances[k] = np.var(samples[k], axis=0) + 1e-10
    
    # 迭代优化
    for i in range(max_iterations):
        # 对样本数据进行分类，得到类别标签
        labels = np.zeros(len(samples), dtype=int)
        for j, sample in enumerate(samples):
            distances = np.linalg.norm(sample - centers, axis=1)
            labels[j] = np.argmin(distances)
        
        # 计算每个类别的均值和方差
        for k in range(K):
            mask = (labels == k)
            if np.sum(mask) > 0:
                centers[k] = np.mean(samples[mask], axis=0)
                variances[k] = np.var(samples[mask], axis=0) + 1e-10
        
        # 合并方差较小的类别
        merge_indices = []
        for k in range(K):
            for l in range(K):
                if k != l and np.linalg.norm(centers[k] - centers[l]) < np.sqrt(variances[k] + variances[l]):
                    merge_indices.append((k, l))
        for indices in merge_indices:
            k, l = indices
            mask = (labels == l)
            centers[k] = (centers[k] * np.sum(labels == k) + np.mean(samples[mask], axis=0) * np.sum(mask)) / (np.sum(labels == k) + np.sum(mask))
            variances[k] = (variances[k] * np.sum(labels == k) + np.var(samples[mask], axis=0) * np.sum(mask)) / (np.sum(labels == k) + np.sum(mask)) + 1e-10
            centers = np.delete(centers, l, axis=0)
            variances = np.delete(variances, l, axis=0)
            labels[labels == l] = k
        
        # 分裂方差较大的类别
        split_indices = []
        for k in range(K):
            if np.max(variances[k]) > 0.5 * np.mean(variances):
                split_indices.append(k)
        for k in split_indices:
            new_center = centers[k] + np.random.normal(scale=np.sqrt(variances[k]), size=(1, image.shape[-1]))
            new_center = np.clip(new_center, 0, 255)
            centers[k] = (centers[k] + new_center) / 2
            new_variance = np.var(samples[labels == k], axis=0) + 1e-10
            variances[k] = (variances[k] + new_variance) / 2
            new_center = centers[k] + np.random.normal(scale=np.sqrt(variances[k]), size=(1, image.shape[-1]))
            new_center = np.clip(new_center, 0, 255)
            centers = np.vstack((centers, new_center))
            variances = np.vstack((variances, new_variance))
        
        # 如果类别数量过多或过少，则重新初始化
        if len(centers) > K * 2 or len(centers) < K:
            centers = np.zeros((K, image.shape[-1]))
            variances = np.zeros((K, image.shape[-1]))
            for k in range(K):
                centers[k] = np.mean(samples[k], axis=0)
                variances[k] = np.var(samples[k], axis=0) + 1e-10
        
        # 如果每个类别的样本数量小于最小值，则合并到最近的类别中
        for k in range(K):
            if np.sum(labels == k) < min_size:
                distances = np.linalg.norm(centers - centers[k], axis=1)
                distances[k] = np.inf
                nearest_index = np.argmin(distances)
                mask = (labels == k)
                centers[nearest_index] = (centers[nearest_index] * np.sum(labels == nearest_index) + centers[k] * np.sum(mask)) / (np.sum(labels == nearest_index) + np.sum(mask))
                variances[nearest_index] = (variances[nearest_index] * np.sum(labels == nearest_index) + variances[k] * np.sum(mask)) / (np.sum(labels == nearest_index) + np.sum(mask)) + 1e-10
                centers = np.delete(centers, k, axis=0)
                variances = np.delete(variances, k, axis=0)
                labels[labels == k] = nearest_index
    
    # 根据类别标签重新进行分割，得到最终结果
    segmentation = np.zeros(image.shape[:2], dtype=int)
    for j, region in enumerate(regions):
        segmentation[region.mask] = labels[j]
    
    return segmentation

需要注意的是，上述代码中用到的`pie_engine_segmentation`函数需要根据具体情况进行实现和调整。该函数的作用是对图像进行初步分割，得到初步的区域。