Oil Deposits

The GeoSurvComp geologic survey company is responsible for detecting underground oil deposits. GeoSurvComp works with one large rectangular region of land at a time, and creates a grid that divides the land into numerous square plots. It then analyzes each plot separately, using sensing equipment to determine whether or not the plot contains oil. A plot containing oil is called a pocket. If two pockets are adjacent, then they are part of the same oil deposit. Oil deposits can be quite large and may contain numerous pockets. Your job is to determine how many different oil deposits are contained in a grid. 

Input

The input file contains one or more grids. Each grid begins with a line containing m and n, the number of rows and columns in the grid, separated by a single space. If m = 0 it signals the end of the input; otherwise 1 <= m <= 100 and 1 <= n <= 100. Following this are m lines of n characters each (not counting the end-of-line characters). Each character corresponds to one plot, and is either `*', representing the absence of oil, or `@', representing an oil pocket. 

Output

For each grid, output the number of distinct oil deposits. Two different pockets are part of the same oil deposit if they are adjacent horizontally, vertically, or diagonally. An oil deposit will not contain more than 100 pockets. 

Sample Input

1 1
*
3 5
*@*@*
**@**
*@*@*
1 8
@@****@*
5 5 
****@
*@@*@
*@**@
@@@*@
@@**@
0 0 

Sample Output

0
1
2
2
/*
怎么把油田找到并且确定相邻? 
	从石油开始只走有石油的路
走过的路都变成不存在石油的路 
我是用vis标记
*/
#include<stdio.h>
#include<string.h>
#include<queue>

using namespace std;

const int maxn = 100 + 10;
int vis[maxn][maxn];
char str[maxn][maxn];
int n, m, ans = 0;

int d[8][2] = {1, 0, -1, 0, 0, 1, 0, -1, -1, -1, 1, -1, -1, 1, 1, 1};

void DFS(int x, int y){
	if (str[x][y] == '*' || x < 0|| x >= n || y < 0 || y >= m || vis[x][y]){
		return;
	}
	vis[x][y] = 1;
	for (int i = 0; i < 8; i++){
		vis[x][y] = 1;
		int dx = x + d[i][0];
		int dy = y + d[i][1];
		DFS(dx, dy);
	}
}

int main()
{
	while (scanf ("%d %d", &n, &m) != EOF && (n + m)){
		for (int i = 0; i < n; i++){
			scanf ("%s", str[i]);
		}
		int x, y;
		ans = 0;
		memset(vis, 0, sizeof(vis));
		for (int i = 0; i < n; i++)
			for (int j = 0; j < m; j++)
				if (str[i][j] == '@' && vis[i][j] != 1){
					DFS(i, j);
					ans ++;
				}		
		printf ("%d\n", ans);
	}
	return 0;
}

 

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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