Shuffle'm Up(map)

本文介绍了一种模拟扑克筹码洗牌过程的算法实现。通过不断交错叠加两堆筹码来达到洗牌的效果,并探讨如何通过该算法确定特定洗牌结果所需的最少操作次数。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

Shuffle’m Up
A common pastime for poker players at a poker table is to shuffle stacks of chips. Shuffling chips is performed by starting with two stacks of poker chips, S1 and S2, each stack containing C chips. Each stack may contain chips of several different colors.

The actual shuffle operation is performed by interleaving a chip from S1 with a chip from S2 as shown below for C = 5:

The single resultant stack, S12, contains 2 * C chips. The bottommost chip of S12 is the bottommost chip from S2. On top of that chip, is the bottommost chip from S1. The interleaving process continues taking the 2nd chip from the bottom of S2 and placing that on S12, followed by the 2nd chip from the bottom of S1 and so on until the topmost chip from S1 is placed on top of S12.

After the shuffle operation, S12 is split into 2 new stacks by taking the bottommost C chips from S12 to form a new S1 and the topmost C chips from S12 to form a new S2. The shuffle operation may then be repeated to form a new S12.

For this problem, you will write a program to determine if a particular resultant stack S12 can be formed by shuffling two stacks some number of times.

Input
The first line of input contains a single integer N, (1 ≤ N ≤ 1000) which is the number of datasets that follow.

Each dataset consists of four lines of input. The first line of a dataset specifies an integer C, (1 ≤ C ≤ 100) which is the number of chips in each initial stack (S1 and S2). The second line of each dataset specifies the colors of each of the C chips in stack S1, starting with the bottommost chip. The third line of each dataset specifies the colors of each of the C chips in stack S2 starting with the bottommost chip. Colors are expressed as a single uppercase letter (A through H). There are no blanks or separators between the chip colors. The fourth line of each dataset contains 2 * C uppercase letters (A through H), representing the colors of the desired result of the shuffling of S1 and S2 zero or more times. The bottommost chip’s color is specified first.

Output
Output for each dataset consists of a single line that displays the dataset number (1 though N), a space, and an integer value which is the minimum number of shuffle operations required to get the desired resultant stack. If the desired result can not be reached using the input for the dataset, display the value negative 1 (−1) for the number of shuffle operations.

Sample Input
2
4
AHAH
HAHA
HHAAAAHH
3
CDE
CDE
EEDDCC
Sample Output
1 2
2 -1

map函数,模拟~

#include <iostream>
#include <cstring>
#include <map>
#include <sstream>
#include <cstdio>
#include <algorithm>
using namespace std;
#define MAXN 12121
char a[MAXN], b[MAXN], c[MAXN], d[MAXN];
int main()
{
    int T, n;
    cin>>T;//组数
    map<string, int> ma;
    for(int t=1;t<=T;t++)
    {
        scanf("%d", &n);//元素个数
        scanf("%s %s %s", a, b, c);
        int flag = 0;//标记是否可以
        int ans = 0;//记录需要的步数
        while(1)
        {
            ans++;
            int top = 0;
            for(int i=0;i<n;i++)//合并
            {
                d[top] = b[i];
                top++;
                d[top] = a[i];
                top++;
            }
            d[top] = '\0';
            if(strcmp(d, c)==0)//判断是否与所给的字符串相等
            {
                flag = 1;
                break;
            }
            if(ma.find(d)!=ma.end())//如果找到了就跳出,不可能会有答案~一直循环
            {
                break;
            }
            ma[d] = 0;
            for(int i=0;i<n;i++)//更新a,b
            {
                a[i] = d[i];
                b[i] = d[i+n];
            }
        }
         if(flag)
            printf("%d %d\n", t, ans);
        else
            printf("%d -1\n", t);
    }
    return 0;
}
clear;close all %% read pictures directory = ['6/']; files = dir(directory); files = files(3:end); %% shuffle randIndex = randperm(numel(files)); files = files(randIndex); %% preprocess N = numel(files); dataset = {}; cnt = 1; for i = 1:N if files(i).name(1) ~= '.' im = imread(strcat(directory,files(i).name)); im = double(imrotate(imresize(im, [480, 640]), 0))/255; dataset{cnt} = im; cnt = cnt + 1; end end %% load dataset and run order_list = get_order_list(dataset); panorama = mymosaic(dataset, order_list); imshow(panorama); imwrite(panorama,'panorama.png'); %% 质量评估 if size(panorama, 3) == 3 grayPanorama = rgb2gray(panorama); else grayPanorama = panorama; end meanValue = mean2(grayPanorama); stdValue = std2(grayPanorama); imageEntropy = entropy(grayPanorama); [Gx, Gy] = imgradientxy(grayPanorama, 'prewitt'); gradientMagnitude = mean2(sqrt(double(Gx).^2 + double(Gy).^2)); brisqueScore = brisque(panorama); niqeScore = niqe(panorama); piqeScore = piqe(grayPanorama); fprintf('\n===== 图像质量评估结果 =====\n'); fprintf('基础指标:\n'); fprintf(' 图像均值 (亮度): %.2f\n', meanValue); fprintf(' 图像标准差 (对比度): %.2f\n', stdValue); fprintf(' 图像熵 (信息量): %.4f\n', imageEntropy); fprintf(' 梯度幅值 (清晰度): %.2f\n', gradientMagnitude); fprintf('\n=====无参考质量指标=====\n'); fprintf(' BRISQUE: %.2f (越低越好)\n', brisqueScore); fprintf(' NIQE: %.2f (越低越好)\n', niqeScore); fprintf(' PIQE: %.2f (越低越好)\n', piqeScore); %% 特征提取函数 function [features, points] = get_features(img) if ndims(img) == 3 gray_img = rgb2gray(img); else gray_img = img; end points = detectSURFFeatures(gray_img); [features, points] = extractFeatures(gray_img, points); end %% 生成拼接顺序 function [order_list] = get_order_list(dataset) level_matrix = get_level_matrix(dataset); N = numel(dataset); for i = 1:N [~, index] = sort(level_matrix(i,:), 'descend'); rank_matrix(i,:) = index; end base = find_best(rank_matrix); order_list(1) = base; j = base; while length(order_list) < N k = 1; while ismember(rank_matrix(j,k), order_list) k = k + 1; end order_list(end+1) = rank_matrix(j,k); j = order_list(end); end end %% 选择基准图像 function [base] = find_best(rank_matrix) N = length(rank_matrix(:,1)); rank_list = zeros(1, N); freq_matrix = zeros(N, N); for i = 1:N table = tabulate(rank_matrix(:, i)); j = length(table(:, 2)) + 1; for k = j:N table(k, :) = 0; end freq_matrix(:, i) = table(:, 2); end for i = 1:N for j = 1:N-1 rank_list(i) = rank_list(i) + (N - j) * freq_matrix(i, j); end end [~, base] = max(rank_list); end %% 生成相似度矩阵 function [level_matrix] = get_level_matrix(dataset) N = numel(dataset); level_matrix = zeros(N, N); [feature_map, ~] = get_feature_map(dataset); for i = 1:N for j = 1:N if i ~= j level_matrix(i, j) = kd_match(feature_map(i).cluster', feature_map(j).cluster'); end end end end %% KD树匹配 function [match_points_num] = kd_match(descs1, descs2) n1 = size(descs1,2); match = zeros(n1, 1); kdtree = KDTreeSearcher(descs2'); for i = 1:size(descs1,2) desc = descs1(:, i); [idx, ~] = knnsearch(kdtree, desc', 'K', 2); nn_1 = descs2(:,idx(1)); nn_2 = descs2(:,idx(2)); if sum((desc - nn_1).^2)/sum((desc - nn_2).^2) < 0.6 match(i) = idx(1); else match(i) = 0; end end match_points_num = sum(match ~= 0); end %% 提取特征 function [feature_map, points_map] = get_feature_map(dataset) N = numel(dataset); for i = 1:N [feature, points] = get_features(dataset{i}); feature_map(i)=struct('cluster',feature); points_map(i)=struct('cluster',points); end end %% 普通融合:替换基于边界距离的融合 function [img_b] = map_pairs(H, img_i, img_b) hei_i = size(img_i,1); wid_i = size(img_i,2); hei_b = size(img_b,1); wid_b = size(img_b,2); % 计算图像i映射到图像b后的四角坐标(用于确定扩展范围) ul = H*[1 1 1]'; ul = ul/ul(end); % 左上 ur = H*[wid_i, 1, 1]'; ur = ur/ur(end); % 右上 bl = H*[1, hei_i, 1]'; bl = bl/bl(end); % 左下 br = H*[wid_i, hei_i, 1]'; br = br/br(end); % 右下 % 扩展图像b,确保能容纳映射后的图像i pad_up = 0; pad_left = 0; pad_down = 0; pad_right = 0; if max(br(1),ur(1)) > wid_b pad_right = round(max(br(1),ur(1)) - wid_b + 30); img_b = padarray(img_b, [0, pad_right], 'post'); end if max(br(2), bl(2)) > hei_b pad_down = round(max(br(2), bl(2)) - hei_b + 30); img_b = padarray(img_b, [pad_down, 0], 'post'); end if min(ul(1), bl(1)) <= 0 pad_left = round(-min(ul(1), bl(1)) + 30); img_b = padarray(img_b, [0, pad_left], 'pre'); end if min(ul(2), ur(2)) <= 0 pad_up = round(-min(ul(2), ur(2)) + 30); img_b = padarray(img_b, [pad_up, 0], 'pre'); end H_inv = inv(H); % 逆单应矩阵 % 生成图像b的坐标网格并映射到图像i [y_b, x_b] = meshgrid(... round(pad_up + min(ul(2), ur(2))):round(pad_up + max(bl(2), br(2))), ... round(pad_left + min(ul(1), bl(1))):round(pad_left + max(br(1), ur(1)))); y_b = y_b(:); x_b = x_b(:); xy = H_inv * [x_b - pad_left, y_b - pad_up, ones(size(x_b,1),1)]'; x_i = int64(xy(1,:)' ./ xy(3,:)'); % 图像i的x坐标 y_i = int64(xy(2,:)' ./ xy(3,:)'); % 图像i的y坐标 % 普通融合:重叠区域取平均,非重叠区域直接覆盖 % 1. 重叠区域(同时在img_i和img_b范围内):平均融合 indices = x_i > 0 & x_i <= wid_i & y_i > 0 & y_i <= hei_i ... & x_b > 0 & x_b <= size(img_b,2) & y_b > 0 & y_b <= size(img_b,1); % 对RGB三通道取平均 img_b(sub2ind(size(img_b), y_b(indices), x_b(indices))) = ... (img_i(sub2ind(size(img_i), y_i(indices), x_i(indices))) + ... img_b(sub2ind(size(img_b), y_b(indices), x_b(indices)))) / 2; if ndims(img_b) == 3 % 确保所有索引向量具有相同的大小 y_idx = y_b(indices); x_idx = x_b(indices); len = length(indices); % 绿色通道 - 修正索引维度 green_indices = sub2ind(size(img_b), y_idx, x_idx, 2*ones(len, 1)); img_b(green_indices) = ... (img_i(sub2ind(size(img_i), y_i(indices), x_i(indices), 2*ones(len, 1))) + ... img_b(green_indices)) / 2; % 蓝色通道 - 修正索引维度 blue_indices = sub2ind(size(img_b), y_idx, x_idx, 3*ones(len, 1)); img_b(blue_indices) = ... (img_i(sub2ind(size(img_i), y_i(indices), x_i(indices), 3*ones(len, 1))) + ... img_b(blue_indices)) / 2; end % 2. 非重叠区域(仅在img_i范围内):直接复制img_i的像素 indices = x_i > 0 & x_i <= wid_i & y_i > 0 & y_i <= hei_i ... & ~(x_b > 0 & x_b <= size(img_b,2) & y_b > 0 & y_b <= size(img_b,1)); % 确保所有索引向量具有相同的大小 y_idx = y_b(indices); x_idx = x_b(indices); len = length(indices); img_b(sub2ind(size(img_b), y_idx, x_idx)) = ... img_i(sub2ind(size(img_i), y_i(indices), x_i(indices))); if ndims(img_b) == 3 green_indices = sub2ind(size(img_b), y_idx, x_idx, 2*ones(len, 1)); img_b(green_indices) = img_i(sub2ind(size(img_i), y_i(indices), x_i(indices), 2*ones(len, 1))); blue_indices = sub2ind(size(img_b), y_idx, x_idx, 3*ones(len, 1)); img_b(blue_indices) = img_i(sub2ind(size(img_i), y_i(indices), x_i(indices), 3*ones(len, 1))); end end %% 全景拼接主函数 function [img_b] = mymosaic(img_input, order_list) N = numel(img_input); fprintf('Image %d is initial...\n',order_list(1)); img_b = img_input{order_list(1)}; for i = 2:N fprintf('Blending image %d...\n',order_list(i)); img_i = img_input{order_list(i)}; img_b = stitch(img_i, img_b); end fprintf('Done!\n'); end %% 单图拼接函数 function [img_b] = stitch(img_i, img_b) [features_i, points_i] = get_features(img_i); [features_b, points_b] = get_features(img_b); pairs = matchFeatures(features_i, features_b); matchedBoxPoints = points_i(pairs(:, 1), :); matchedScenePoints = points_b(pairs(:, 2), :); x_i = matchedBoxPoints.Location(:,1); y_i = matchedBoxPoints.Location(:,2); x_b = matchedScenePoints.Location(:,1); y_b = matchedScenePoints.Location(:,2); [H, ~] = ransac_est_homography(x_i, y_i, x_b, y_b, 10); img_b = map_pairs(H, img_i, img_b); end %% RANSAC估计单应矩阵 function [H, inlier_ind] = ransac_est_homography(x1, y1, x2, y2, thresh) N = numel(x1); max_inliers = 0; H = eye(3); ssd = @(x,y) sum((x-y).^2); for t = 1:1000 r_idx = randi(N,4,1); H_t = est_homography(x2(r_idx),y2(r_idx),x1(r_idx),y1(r_idx)); inliers = 0; for i = 1:N t_xy = H_t*[x1(i), y1(i), 1]'; t_xy = t_xy/t_xy(end); if ssd([x2(i), y2(i), 1]', t_xy) < thresh inliers = inliers + 1; end end if inliers > max_inliers max_inliers = inliers; H = H_t; end end inlier_ind = find(arrayfun(@(i) ssd(H*[x1(i);y1(i);1], [x2(i);y2(i);1]) < thresh, 1:N)); end %% 计算单应矩阵 function H = est_homography(X,Y,x,y) A = zeros(2*length(x),9); for i = 1:length(x) A(2*i-1,:) = [x(i), y(i), 1, 0, 0, 0, -X(i)*x(i), -X(i)*y(i), -X(i)]; A(2*i,:) = [0, 0, 0, x(i), y(i), 1, -Y(i)*x(i), -Y(i)*y(i), -Y(i)]; end [~,~,V] = svd(A); H = reshape(V(:,9),3,3)'; H = H/H(3,3); end错误使用 sub2ind (第 61 行) 下标参量必须为标量或大小相同的数组。 出错 pingjunjiaquanronghe>map_pairs (第 213 行) green_indices = sub2ind(size(img_b), y_idx, x_idx, 2*ones(len, 1)); ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 出错 pingjunjiaquanronghe>stitch (第 271 行) img_b = map_pairs(H, img_i, img_b); ^^^^^^^^^^^^^^^^^^^^^^^^^^ 出错 pingjunjiaquanronghe>mymosaic (第 254 行) img_b = stitch(img_i, img_b); ^^^^^^^^^^^^^^^^^^^^ 出错 pingjunjiaquanronghe (第 26 行) panorama = mymosaic(dataset, order_list); ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 代码修正,然后输出全套代码
最新发布
07-18
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值