matlab如何实现以下步骤的代码： 1.将excel中每个数据点的数据值与该点所在行的阈值0.02进行比较。如果当前值比阈值高10%，且至少有一个相邻的数据点也满足这个条件，则判断该点为有效的缺陷数据。然后，一组有效的缺陷数据点被作为缺陷集。如果两个有效缺陷数据点之间的非缺陷数据点的数量超过一定距离(10个数据点或2行数据)，则判断这些缺陷点属于两个不同的缺陷集。然后将判定的数据集的起始和结束点的行列信息导出，并将数据集导出为excel。 2.将excel数据利用imagesc函数生成伪彩色图并显示图像，将excel数据中判定为缺陷集的数据轮廓进行追踪，提取轮廓线并显示结果图像； 3.设定上一步生成的伪彩色图像的长度为20mm,宽度为10mm,通过计算像素点与实际长度的比例来获取上一步提取的轮廓的长度和宽度尺寸。

以下是实现这个过程的代码：

% 读取Excel数据
data = xlsread('data.xlsx');

% 计算阈值
threshold = 0.02 * ones(size(data,1),1);

% 找到满足条件的数据点
valid_points = false(size(data));
for i = 1:size(data,1)
    for j = 1:size(data,2)
        if data(i,j) > 1.1 * threshold(i) && ...
           (j > 1 && data(i,j-1) > 1.1 * threshold(i) || ...
            j < size(data,2) && data(i,j+1) > 1.1 * threshold(i))
            valid_points(i,j) = true;
        end
    end
end

% 找到缺陷集
[labels,num_labels] = bwlabel(valid_points,8);
defect_sets = cell(num_labels,1);
for i = 1:num_labels
    [rows,cols] = find(labels == i);
    min_row = min(rows);
    max_row = max(rows);
    min_col = min(cols);
    max_col = max(cols);
    defect_sets{i} = [min_row,min_col,max_row,max_col];
end

% 判断缺陷点是否属于同一缺陷集
for i = 1:num_labels
    for j = i+1:num_labels
        set1 = defect_sets{i};
        set2 = defect_sets{j};
        if set1(1) > set2(3) || set1(3) < set2(1) || ...
           set1(2) > set2(4) || set1(4) < set2(2)
            continue;
        end
        non_defect_points = ~valid_points(max(set1(1),set2(1)):min(set1(3),set2(3)), ...
                                          max(set1(2),set2(2)):min(set1(4),set2(4)));
        if sum(non_defect_points(:)) > 10 || size(non_defect_points,1) > 2
            continue;
        end
        if set1(1) < set2(1)
            defect_sets{j} = [set1(1),min(set1(2),set2(2)),set2(3),max(set1(4),set2(4))];
            defect_sets(i) = [];
        else
            defect_sets{i} = [set2(1),min(set1(2),set2(2)),set1(3),max(set1(4),set2(4))];
            defect_sets(j) = [];
        end
        num_labels = num_labels - 1;
    end
end

% 输出缺陷集到Excel
xlswrite('defect_sets.xlsx',defect_sets);

% 生成伪彩色图像
image_data = zeros(size(data));
for i = 1:num_labels
    set = defect_sets{i};
    image_data(set(1):set(3),set(2):set(4)) = i;
end
imagesc(image_data);
colormap(jet);
colorbar;

% 提取轮廓线
contours = cell(num_labels,1);
for i = 1:num_labels
    set = defect_sets{i};
    contour = bwtraceboundary(valid_points(set(1):set(3),set(2):set(4)),[set(1),set(2)],'N');
    contour(:,1) = contour(:,1) + set(2) - 1;
    contour(:,2) = contour(:,2) + set(1) - 1;
    contours{i} = contour;
    hold on;
    plot(contour(:,1),contour(:,2),'w','LineWidth',2);
end

% 计算像素点与实际长度的比例
scale = 20 / size(data,1); % 假设数据的长度为20mm