matlab中slic实现

以下是一个简单的Matlab代码实现slic算法的示例：

function [labels, centers] = slic(I, numSuperpixels, compactness)
    % 参数设置
    if nargin < 3
        compactness = 20;
    end
    if nargin < 2
        numSuperpixels = 1000;
    end
    
    % 初始化超像素中心点
    [height, width, ~] = size(I);
    step = sqrt(height * width / numSuperpixels);
    x = round(step/2):round(step):width;
    y = round(step/2):round(step):height;
    [X, Y] = meshgrid(x, y);
    centers = [X(:) Y(:)];
    
    % 初始化超像素标签
    labels = zeros(height, width);
    labelIndex = 1;
    
    % 迭代更新超像素中心点和标签
    while true
        newLabels = zeros(height, width);
        for i = 1:size(centers, 1)
            center = centers(i,:);
            xStart = max(center(1) - 2*step, 1);
            xEnd = min(center(1) + 2*step, width);
            yStart = max(center(2) - 2*step, 1);
            yEnd = min(center(2) + 2*step, height);
            patch = I(yStart:yEnd, xStart:xEnd, :);
            [patchHeight, patchWidth, ~] = size(patch);
            [patchX, patchY] = meshgrid(xStart:xEnd, yStart:yEnd);
            patchPoints = [patchX(:) patchY(:)];
            patchColors = reshape(patch, patchHeight*patchWidth, []);
            patchDistances = sum(bsxfun(@minus, patchColors, center).^2, 2);
            patchLabels = labelIndex * ones(patchHeight*patchWidth, 1);
            [~, idx] = min(patchDistances);
            patchLabels(idx) = 0;
            newLabels(yStart:yEnd, xStart:xEnd) = reshape(patchLabels, patchHeight, patchWidth);
        end
        
        % 计算超像素中心点的更新值
        newCenters = zeros(size(centers));
        for i = 1:size(centers, 1)
            center = centers(i,:);
            xStart = max(center(1) - 2*step, 1);
            xEnd = min(center(1) + 2*step, width);
            yStart = max(center(2) - 2*step, 1);
            yEnd = min(center(2) + 2*step, height);
            patchLabels = newLabels(yStart:yEnd, xStart:xEnd);
            [patchX, patchY] = meshgrid(xStart:xEnd, yStart:yEnd);
            patchPoints = [patchX(:) patchY(:)];
            patchColors = reshape(I(yStart:yEnd, xStart:xEnd, :), [], 3);
            patchLabels = patchLabels(:);
            patchPoints = double(patchPoints);
            patchColors = double(patchColors);
            patchCenters = accumarray(patchLabels+1, [patchPoints patchColors], [labelIndex+1 8], @sum);
            patchSizes = accumarray(patchLabels+1, ones(size(patchLabels)));
            patchCenters(:,1:2) = patchCenters(:,1:2) ./ patchSizes;
            patchCenters(:,3:5) = patchCenters(:,3:5) ./ patchCenters(:,8);
            patchCenters(:,6:8) = sqrt(patchCenters(:,6:8) ./ patchSizes - patchCenters(:,3:5).^2);
            patchCenters(2:end,6:8) = patchCenters(2:end,6:8) * compactness;
            [~, idx] = min(sum((patchCenters(2:end,3:5) - repmat(center, labelIndex, 1)).^2, 2));
            newCenters(i,:) = patchCenters(idx+1,1:2);
        end
        
        % 判断是否收敛
        if sum(sum(newLabels ~= labels)) < 0.1 * height * width
            break;
        end
        
        % 更新超像素中心点和标签
        labels = newLabels;
        centers = newCenters;
        labelIndex = labelIndex + 1;
    end
    
    % 调整超像素标签编号
    [~, ~, labels] = unique(labels);
    labels = reshape(labels, height, width);
end

在这个示例中，我们首先定义了一个名为slic的函数，它接受三个输入参数：I为原始图像，numSuperpixels为期望得到的超像素数目，compactness为控制超像素形状的参数。如果用户没有提供这些参数，则使用默认值。然后，我们初始化了超像素中心点和标签，并开始迭代更新它们。在每次迭代中，我们首先使用当前的超像素中心点对图像进行分割，得到新的超像素标签。然后，我们计算新的超像素中心点的位置，以便下一次迭代使用。最后，我们检查当前的超像素标签是否与上一次迭代相同，如果是，则认为算法已经收敛，退出循环。最后，我们将超像素标签进行重新编号，以便它们可以被更方便地使用。

请注意，这个示例代码只是slic算法的一种简单实现，可能不够高效或准确。实际使用时应该根据具体情况进行调整和优化。