img_bw_sg = img_bw[ c_ind[0]:c_ind[-1],r_ind[0]:r_ind[-1]] ~~~~~^^^ IndexError: list index out of range的原因是什么

在Python中，`IndexError: list index out of range`这个错误通常是当你试图访问列表元素，但是提供的索引超出了列表的实际长度范围。在这个代码片段中：

img_bw_sg = img_bw[c_ind[0]:c_ind[-1], r_ind[0]:r_ind[-1]]

变量`c_ind`和`r_ind`可能是两个用于切片`img_bw`图像的索引列表，分别对应列索引（column indices）和行索引（row indices）。如果`c_ind[-1]`或`r_ind[-1]`对应的值比列表本身的长度还要大，那么就会引发这个错误，因为它们尝试访问不存在的列表项。

例如，如果`c_ind`是一个只有一个元素的列表，而`c_ind[-1]`会返回列表的最后一个元素，如果这个元素大于实际图像的列数，就会出错。同样地，如果`r_ind`也遇到类似的问题，就可能导致整体的切片超出边界。

要解决这个问题，你需要检查`c_ind`和`r_ind`的长度是否合适，以及它们的最后一个元素是否在图像的有效范围内。可以添加条件判断或者确保索引操作之前对列表的长度进行了适当的校验。

相关问题

clear f = @(x,y) 20 + x.^2 + y.^2 - 10*cos(2*pi.*x) - 10*cos(2*pi.*y) ; x0 = [-5.12:0.05:5.12]; y0 = x0 ; [X,Y] = meshgrid(x0,y0); Z =f(X,Y) ; figure(1); mesh(X,Y,Z); colormap(parula(5)); n = 10; narvs = 2; c1 = 0.6; c2 = 0.6; w_max = 0.9; w_min = 0.4; K = 100; vmax = 1.2; x_lb = -5.12; x_ub = 5.12; x = zeros(n,narvs); x = x_lb + (x_ub-x_lb).*rand(n,narvs) v = -vmax + 2*vmax .* rand(n,narvs); fit = zeros(n,1); for i = 1:n fit(i) = Obj_fun1(x(i,:)); end pbest = x; ind = find(fit == max(fit), 1); gbest = x(ind,:); h = scatter(x,fit,80,'*r'); fitnessbest = ones(K,1); for d = 1:K for i = 1:n f_i = fit(i); f_avg = sum(fit)/n; f_max = max(fit); if f_i >= f_avg if f_avg ~= f_max w = w_min + (w_max - w_min)*(f_max - f_i)/(f_max - f_avg); else w = w_max; end else w = w_max; end v(i,:) = w*v(i,:) + c1*rand(1)*(pbest(i,:) - x(i,:)) + c2*rand(1)*(gbest - x(i,:)); for j = 1: narvs if v(i,j) < -vmax(j) v(i,j) = -vmax(j); elseif v(i,j) > vmax(j) v(i,j) = vmax(j); end end x(i,:) = x(i,:) + v(i,:); for j = 1: narvs if x(i,j) < x_lb(j) x(i,j) = x_lb(j); elseif x(i,j) > x_ub(j) x(i,j) = x_ub(j); end end fit(i) = Obj_fun1(x(i,:)); if fit(i) > Obj_fun1(pbest(i,:)) pbest(i,:) = x(i,:); end if fit(i) > Obj_fun1(gbest) gbest = pbest(i,:); end end fitnessbest(d) = Obj_fun1(gbest); pause(0.1) h.XData = x; h.YData = fit; end figure(2) plot(fitnessbest) xlabel('迭代次数'); disp('最佳的位置是：'); disp(gbest) disp('此时最优值是：'); disp(Obj_fun1(gbest)) function y = Obj_fun1(x) y = 7*cos(5*x) + 4*sin(x); end

代码中有两处错误：

1. 在初始化 x 变量时，应该使用矩阵赋值，而不是两次赋值。修改后的代码如下：

x = x_lb + (x_ub-x_lb).*rand(n,narvs);

2. 在迭代更新速度时，应该将速度限制在 [-vmax, vmax] 的范围内。修改后的代码如下：

for j = 1: narvs
    if v(i,j) < -vmax
        v(i,j) = -vmax;
    elseif v(i,j) > vmax
        v(i,j) = vmax;
    end
end

修改后的完整代码如下：

clear
f = @(x,y) 20 + x.^2 + y.^2 - 10*cos(2*pi.*x) - 10*cos(2*pi.*y);
x0 = [-5.12:0.05:5.12];
y0 = x0;
[X,Y] = meshgrid(x0,y0);
Z = f(X,Y);
figure(1);
mesh(X,Y,Z);
colormap(parula(5));
n = 10;
narvs = 2;
c1 = 0.6;
c2 = 0.6;
w_max = 0.9;
w_min = 0.4;
K = 100;
vmax = 1.2;
x_lb = -5.12;
x_ub = 5.12;
x = x_lb + (x_ub-x_lb).*rand(n,narvs);
v = -vmax + 2*vmax .* rand(n,narvs);
fit = zeros(n,1);
for i = 1:n
    fit(i) = Obj_fun1(x(i,:));
end
pbest = x;
ind = find(fit == max(fit), 1);
gbest = x(ind,:);
h = scatter(x(:,1),x(:,2),80,'*r');
fitnessbest = ones(K,1);
for d = 1:K
    for i = 1:n
        f_i = fit(i);
        f_avg = sum(fit)/n;
        f_max = max(fit);
        if f_i >= f_avg
            if f_avg ~= f_max
                w = w_min + (w_max - w_min)*(f_max - f_i)/(f_max - f_avg);
            else
                w = w_max;
            end
        else
            w = w_max;
        end
        v(i,:) = w*v(i,:) + c1*rand(1)*(pbest(i,:) - x(i,:)) + c2*rand(1)*(gbest - x(i,:));
        for j = 1: narvs
            if v(i,j) < -vmax
                v(i,j) = -vmax;
            elseif v(i,j) > vmax
                v(i,j) = vmax;
            end
        end
        x(i,:) = x(i,:) + v(i,:);
        for j = 1: narvs
            if x(i,j) < x_lb
                x(i,j) = x_lb;
            elseif x(i,j) > x_ub
                x(i,j) = x_ub;
            end
        end
        fit(i) = Obj_fun1(x(i,:));
        if fit(i) > Obj_fun1(pbest(i,:))
            pbest(i,:) = x(i,:);
        end
        if fit(i) > Obj_fun1(gbest)
            gbest = pbest(i,:);
        end
    end
    fitnessbest(d) = Obj_fun1(gbest);
    pause(0.1)
    h.XData = x(:,1);
    h.YData = x(:,2);
end
figure(2)
plot(fitnessbest)
xlabel('迭代次数');
disp('最佳的位置是：');
disp(gbest)
disp('此时最优值是：');
disp(Obj_fun1(gbest))
function y = Obj_fun1(x)
    y = 7*cos(5*x) + 4*sin(x);
end

解释如下代码： for pic_id1 in range(1,N_pic+1): print('matching ' + set_name +': ' +str(pic_id1).zfill(5)) N_CHANGE = 0 for T_id in range(1,16,3): for H_id in range(2,5): FAIL_CORNER = 0 data_mat1 = read_data(input_file,pic_id1,T_id,H_id) search_list = range( max((pic_id1-10),1),pic_id1)+ range(pic_id1+1, min((pic_id1 + 16),N_pic + 1 ) ) for cor_ind in range(0,N_cor): row_cent1 = cor_row_center[cor_ind] col_cent1 = cor_col_center[cor_ind] img_corner = data_mat1[(row_cent1-N_pad): (row_cent1+N_pad+1), (col_cent1-N_pad): (col_cent1+N_pad+1) ] if ((len(np.unique(img_corner))) >2)&(np.sum(img_corner ==1)< 0.8*(N_pad2+1)**2) : for pic_id2 in search_list: data_mat2 = read_data(input_file,pic_id2,T_id,H_id) match_result = cv2_based(data_mat2,img_corner) if len(match_result[0]) ==1: row_cent2 = match_result[0][0]+ N_pad col_cent2 = match_result[1][0]+ N_pad N_LEF = min( row_cent1 , row_cent2) N_TOP = min( col_cent1, col_cent2 ) N_RIG = min( L_img-1-row_cent1 , L_img-1-row_cent2) N_BOT = min( L_img-1-col_cent1 , L_img-1-col_cent2) IMG_CHECK1 = data_mat1[(row_cent1-N_LEF): (row_cent1+N_RIG+1), (col_cent1-N_TOP): (col_cent1+N_BOT+1) ] IMG_CHECK2 = data_mat2[(row_cent2-N_LEF): (row_cent2+N_RIG+1), (col_cent2-N_TOP): (col_cent2+N_BOT+1) ] if np.array_equal(IMG_CHECK1,IMG_CHECK2) : check_row_N = IMG_CHECK1.shape[0] check_col_N = IMG_CHECK1.shape[1] if (check_col_Ncheck_row_N>=25): match_all.append( (pic_id1, row_cent1, col_cent1, pic_id2 , row_cent2, col_cent2) ) search_list.remove(pic_id2) else: FAIL_CORNER = FAIL_CORNER +1 N_CHANGE = N_CHANGE + 1 #%% break if less than 1 useless corners, or have detected more than 10 images from 60 if(FAIL_CORNER <= 1): break match_all_pd = pd.DataFrame(match_all,columns = ['pic_id1','row_id1','col_id1','pic_id2','row_id2','col_id2']) pd_add = pd.DataFrame(np.arange(1,N_pic+1), columns = ['pic_id1']) pd_add['pic_id2'] = pd_add['pic_id1'] pd_add['row_id1'] = 0 pd_add['row_id2'] = 0 pd_add['col_id1'] = 0 pd_add['col_id2'] = 0 match_all_pd = pd.concat([match_all_pd,pd_add]) match_all_pd.index = np.arange(len(match_all_pd))

这段代码是一个图像匹配的算法，其主要功能是在一个图像序列中找到相互匹配的图像对。以下是代码的主要流程：

1. 对于每张图像，用read_data()函数读取图像数据，并依次对每个角点进行匹配。
2. 对于每个角点，提取其周围的图像块，并检查图像块的像素值是否合适。
3. 在当前图像的前10张和后15张图像中搜索匹配图像，并用cv2_based()函数进行匹配。
4. 如果找到匹配图像，则计算两个图像块之间的偏移量，并用该偏移量对匹配图像进行校准。
5. 如果两个图像块相似，则将它们标记为匹配，并将匹配结果添加到match_all列表中。
6. 最后，将匹配结果存储在一个名为match_all_pd的pandas数据框中，并返回该数据框。

整个算法的核心是cv2_based()函数，它使用OpenCV中的模板匹配算法来计算两个图像块之间的偏移量。此外，该算法还使用了pandas数据框来保存匹配结果，以方便后续的分析和可视化。