一笔画小游戏,好玩的很
就是到了后面比较麻烦,手动找路径太慢了,作为程序员,这又是一个锻炼的好机会是不是!于是乎,了解了一下dfs和bfs算法(都是路径搜索算法),然后就开撸:
# pointArr=[[1,0,0,3], # [0,2,2,0], # [0,1,0,0], # [0,0,0,3]] pointArr=[[0,0,0,0,0,0], [-1,0,0,0,0,1], [0,0,0,0,0,0], [0,0,0,0,0,0], [-1,-1,0,0,-1,0], [0,0,0,0,-1,0], [0,0,0,0,0,0], ] class solutiondfs(): def __init__(self,arr): self.arr=arr self.rows=len(arr) self.cols=len(arr[0]) self.nowPositionRow=None self.nowPositionCol=None self.steps=[] #寻找开始的点 def startPoint(self): for i in range(len(self.arr)): for j in range(len(self.arr[i])): if self.arr[i][j]==1: return i,j #判断是否结束 def isFinished(self): for i in pointArr: for j in i: if j ==0: return False return True #获取下一步的位置 def getNextEle(self,now_row,now_col): #顺序是上,右,下,左边 nextArr=[] if now_row>=1 and self.arr[now_row-1][now_col]==0: nextArr.append([now_row-1,now_col]) if now_col<self.cols-1 and self.arr[now_row][now_col+1]==0: nextArr.append( [now_row,now_col+1]) if now_row<self.rows-1 and self.arr[now_row+1][now_col]==0: nextArr.append( [now_row+1,now_col]) if now_col>=1 and self.arr[now_row][now_col-1]==0: nextArr.append( [now_row,now_col-1]) return nextArr #递归,深度优先 def step_to_next(self): if self.isFinished(): return True next_steps=self.getNextEle(self.steps[-1][0],self.steps[-1][1]) for i in next_steps: self.arr[i[0]][i[1]]=1 self.steps.append(i) if self.step_to_next(): return True else: self.steps.pop() self.arr[i[0]][i[1]]=0 return False def start_bfs(self): self.nowPositionRow,self.nowPositionCol=self.startPoint() self.steps.append([self.nowPositionRow,self.nowPositionCol]) if self.step_to_next(): print(self.steps) else: print('hehe') s=solutiondfs(pointArr) s.start_bfs()
嘛,计算出来的路径打印出来就是
[[1, 5], [0, 5], [0, 4], [1, 4], [1, 3], [0, 3], [0, 2], [1, 2], [2, 2], [2, 3], [3, 3], [3, 4], [2, 4], [2, 5], [3, 5], [4, 5], [5, 5], [6, 5], [6, 4], [6, 3], [6, 2], [6, 1], [6, 0], [5, 0], [5, 1], [5, 2], [5, 3], [4, 3], [4, 2], [3, 2], [3, 1], [3, 0], [2, 0], [2, 1], [1, 1], [0, 1], [0, 0]]
速度还不赖,嘿嘿。
然后博主又改了一下,运用在autojs上,这就需要加一些图像识别和手势转化,这里就不细说了,效果如下,总体来说不慢,但是有的关卡因为初始路径没选对,会计算很久,还是需要优化。