Python使用Tkinter实现机器人走迷宫python,gstreamer,launcherpython爬虫程序学习教程-

@本文来源于公众号：csdn2299，喜欢可以关注公众号 程序员学府

这本是课程的一个作业研究搜索算法，当时研究了一下Tkinter，然后写了个很简单的机器人走迷宫的界面，并且使用了各种搜索算法来进行搜索，如下图
使用A*寻找最优路径：
由于时间关系，不分析了，我自己贴代码吧。希望对一些也要用Tkinter的人有帮助。

from Tkinter import * from random import * import time import numpy as np import util   class Directions:  NORTH = 'North'  SOUTH = 'South'  EAST = 'East'  WEST = 'West'   # Detect elements in the map       window = Tk() window.title('CityBusPlanner') window.resizable(0,0) width = 25 (x, y) = (22, 22)   totalsteps = 0   buidings = [(0, 0), (1, 0), (2, 0), (3, 0), (7, 0), (8, 0), (11, 0), (12, 0), (13, 0),  (17, 0), (18, 0), (21, 0), (21, 1), (2, 2), (5, 2), (8, 2), (9, 2), (12, 2),  (14, 2), (15, 2), (16, 2), (17, 2), (21, 2), (2, 3), (4, 3), (5, 3), (7, 3),  (8, 3), (11, 3), (17, 3), (18, 3), (19, 3), (2, 4), (4, 4), (5, 4), (8, 4),  (9, 4), (14, 4), (15, 4),(17, 4), (18, 4), (19, 4), (0, 6), (2, 6), (4, 6),  (7, 6), (8, 6), (11, 6), (12, 6), (14, 6), (15, 6),(16, 6), (18, 6), (19, 6),  (2, 7), (5, 7), (21, 7), (0, 8), (2, 8), (11, 8), (14, 8), (15, 8), (17, 8),  (18, 8), (21, 8), (4, 9), (5, 9), (7, 9), (9, 9), (11, 9), (14, 9), (21, 9),  (2, 10), (7, 10), (14, 10), (17, 10), (19, 10), (0, 11), (2, 11), (4, 11),  (5, 11), (7, 11), (8, 11), (9, 11), (11, 11), (12, 11), (14, 11), (15, 11),  (16, 11), (17, 11), (18, 11), (19, 11), (0, 13), (2, 13), (3, 13), (5, 13),  (7, 13), (8, 13), (9, 13), (14, 13), (17, 13), (18, 13), (21, 13), (2, 14),  (3, 14), (5, 14), (7, 14),(9, 14), (12, 14), (14, 14), (15, 14), (17, 14),  (18, 14), (21, 14), (2, 15), (3, 15), (5, 15), (7, 15), (9, 15), (12, 15),  (15, 15), (19, 15), (21, 15), (0, 16), (21, 16), (0, 17), (3, 17), (5, 17),  (7, 17),(9, 17), (11, 17), (14, 17), (15, 17), (17, 17), (18, 17), (21, 17),  (2, 18), (3, 18), (5, 18), (7, 18),(9, 18), (11, 18), (14, 18), (17, 18),  (18, 18), (3, 19), (5, 19), (7, 19), (9, 19), (11, 19), (12, 19), (14, 19),  (17, 19), (18, 19), (0, 21), (1, 21), (2, 21), (5, 21), (6, 21), (9, 21),  (10, 21), (11, 21), (12, 21), (15, 21), (16, 21), (18, 21), (19, 21), (21, 21)]   walls = [(10, 0), (0, 12), (21, 12), (14, 21)] park = [(14, 0), (15, 0), (16, 0)] robotPos = (21, 12)   view = Canvas(window, width=x * width, height=y * width) view.grid(row=0, column=0) searchMapButton = Button(window,text = 'search') searchMapButton.grid(row = 0,column = 1) robotView = Canvas(window,width=x * width, height=y * width) robotView.grid(row = 0,column = 2)   def formatColor(r, g, b):  return '#%02x%02x%02x' % (int(r * 255), int(g * 255), int(b * 255))   def cityMap():  global width, x, y, buidings,walls,park,robot  for i in range(x):  for j in range(y):  view.create_rectangle(  i * width, j * width, (i + 1) * width, (j + 1) * width, fill='white', outline='gray', width=1)  for (i, j) in buidings:  view.create_rectangle(  i * width, j * width, (i + 1) * width, (j + 1) * width, fill='black', outline='gray', width=1)  for (i,j) in walls:  view.create_rectangle(  i * width, j * width, (i + 1) * width, (j + 1) * width, fill='blue', outline='gray', width=1)  for (i,j) in park:  view.create_rectangle(  i * width, j * width, (i + 1) * width, (j + 1) * width, fill='red', outline='gray', width=1)   def robotCityMap():  global width, x, y, buidings,walls,park,robot,robotPos  for i in range(x):  for j in range(y):  robotView.create_rectangle(  i * width, j * width, (i + 1) * width, (j + 1) * width, fill='black', width=1)  robotView.create_rectangle(  robotPos[0] * width, robotPos[1] * width, (robotPos[0] + 1) * width, (robotPos[1] + 1) * width, fill='white', outline='gray', width=1) # Create City Map cityMap()   # Create Robot View robotCityMap() # Create a robot robot = view.create_rectangle(robotPos[0] * width + width * 2 / 10, robotPos[1] * width + width * 2 / 10,   robotPos[0] * width + width * 8 / 10, robotPos[1] * width + width * 8 / 10, fill="orange", width=1, tag="robot") robotSelf = robotView.create_rectangle(robotPos[0] * width + width * 2 / 10, robotPos[1] * width + width * 2 / 10,   robotPos[0] * width + width * 8 / 10, robotPos[1] * width + width * 8 / 10, fill="orange", width=1, tag="robot")   visited = [robotPos]   def move(dx,dy):  global robot,x,y,robotPos,robotSelf,view  global totalsteps  totalsteps = totalsteps + 1  newX = robotPos[0] + dx  newY = robotPos[1] + dy  if (not isEdge(newX, newY)) and (not isBlock(newX, newY)):  #print "move %d" % totalsteps  view.coords(robot, (newX) * width + width * 2 / 10, (newY) * width + width * 2 / 10,   (newX) * width + width * 8 / 10, (newY) * width + width * 8 / 10)  robotView.coords(robotSelf, (newX) * width + width * 2 / 10, (newY) * width + width * 2 / 10,   (newX) * width + width * 8 / 10, (newY) * width + width * 8 / 10)  robotPos = (newX, newY)  if robotPos not in visited:  visited.append(robotPos)  visitedPanel = robotView.create_rectangle(  robotPos[0] * width, robotPos[1] * width, (robotPos[0] + 1) * width, (robotPos[1] + 1) * width, fill='white', outline='gray', width=1)  robotView.tag_lower(visitedPanel,robotSelf)  else:  print "move error"   def callUp(event):  move(0,-1)   def callDown(event):  move(0, 1)   def callLeft(event):  move(-1, 0)   def callRight(event):  move(1, 0)   def isBlock(newX,newY):  global buidings,x,y      for (i,j) in buidings:  if (i == newX) and (j == newY):  return True  return False   def isEdge(newX,newY):  global x,y    if newX >= x or newY >= y or newX < 0 or newY < 0 :  return True  return False   def getSuccessors(robotPos):  n = Directions.NORTH  w = Directions.WEST  s = Directions.SOUTH  e = Directions.EAST  successors = []  posX = robotPos[0]  posY = robotPos[1]    if not isBlock(posX - 1, posY) and not isEdge(posX - 1,posY):  successors.append(w)  if not isBlock(posX, posY + 1) and not isEdge(posX,posY + 1):  successors.append(s)  if not isBlock(posX + 1, posY) and not isEdge(posX + 1,posY):  successors.append(e)  if not isBlock(posX, posY -1) and not isEdge(posX,posY - 1):  successors.append(n)    return successors   def getNewPostion(position,action):  posX = position[0]  posY = position[1]  n = Directions.NORTH  w = Directions.WEST  s = Directions.SOUTH  e = Directions.EAST  if action == n:  return (posX,posY - 1)  elif action == w:  return (posX - 1,posY)  elif action == s:  return (posX,posY + 1)  elif action == e:  return (posX + 1,posY)   delay = False def runAction(actions):  global delay  n = Directions.NORTH  w = Directions.WEST  s = Directions.SOUTH  e = Directions.EAST  for i in actions:  if delay:  time.sleep(0.05)  if i == n:  #print "North"  move(0, -1)  elif i == w:  #print "West"  move(-1, 0)  elif i == s:  #print "South"  move(0, 1)  elif i == e:  #sprint "East"  move(1, 0)  view.update()   def searchMapTest(event):  global robotPos  actions = []  position = robotPos  for i in range(100):  successors = getSuccessors(position)  successor = successors[randint(0, len(successors) - 1)]  actions.append(successor)  position = getNewPostion(position, successor)  print actions  runAction(actions)   def reverseSuccessor(successor):  n = Directions.NORTH  w = Directions.WEST  s = Directions.SOUTH  e = Directions.EAST  if successor == n:  return s  elif successor == w:  return e  elif successor == s:  return n  elif successor == e:  return w   roads = set()   detectedBuildings = {} blockColors = {} blockIndex = 0     def updateBuildings(detectedBuildings):  global robotView,width  for block,buildings in detectedBuildings.items():  color = blockColors[block]  for building in buildings:  robotView.create_rectangle(  building[0] * width, building[1] * width, (building[0] + 1) * width, (building[1] + 1) * width, fill=color, outline=color, width=1)   def addBuilding(position):  global blockIndex,detectedBuildings  isAdd = False  addBlock = ''  for block,buildings in detectedBuildings.items():  for building in buildings:  if building == position:  return  if util.manhattanDistance(position, building) == 1:  if not isAdd:   buildings.add(position)   isAdd = True   addBlock = block   break  else:   #merge two block   for building in detectedBuildings[block]:   detectedBuildings[addBlock].add(building)   detectedBuildings.pop(block)    if not isAdd:  newBlock = set([position])  blockIndex = blockIndex + 1  detectedBuildings['Block %d' % blockIndex] = newBlock  color = formatColor(random(), random(), random())  blockColors['Block %d' % blockIndex] = color  updateBuildings(detectedBuildings)   def addRoad(position):  global robotView,width,robotSelf  visitedPanel = robotView.create_rectangle(  position[0] * width, position[1] * width, (position[0] + 1) * width, (position[1] + 1) * width, fill='white', outline='gray', width=1)  robotView.tag_lower(visitedPanel,robotSelf)   def showPath(positionA,positionB,path):  global robotView,width,view  view.create_oval(positionA[0] * width + width * 3 / 10, positionA[1] * width + width * 3 / 10,   positionA[0] * width + width * 7 / 10, positionA[1] * width + width * 7 / 10, fill='yellow', width=1)  nextPosition = positionA  for action in path:  nextPosition = getNewPostion(nextPosition, action)  view.create_oval(nextPosition[0] * width + width * 4 / 10, nextPosition[1] * width + width * 4 / 10,   nextPosition[0] * width + width * 6 / 10, nextPosition[1] * width + width * 6 / 10, fill='yellow', width=1)  view.create_oval(positionB[0] * width + width * 3 / 10, positionB[1] * width + width * 3 / 10,   positionB[0] * width + width * 7 / 10, positionB[1] * width + width * 7 / 10, fill='yellow', width=1) hasDetected = set()     def detectLocation(position):  if position not in hasDetected:  hasDetected.add(position)  if isBlock(position[0],position[1]):  addBuilding(position)  elif not isEdge(position[0],position[1]):  addRoad(position)   def detect(position):  posX = position[0]  posY = position[1]    detectLocation((posX,posY + 1))  detectLocation((posX,posY - 1))  detectLocation((posX + 1,posY))  detectLocation((posX - 1,posY))     def heuristic(positionA,positionB):  return util.manhattanDistance(positionA,positionB)   def AstarSearch(positionA,positionB):  "Step 1: define closed: a set"  closed = set()  "Step 2: define fringe: a PriorityQueue "  fringe = util.PriorityQueue()  "Step 3: insert initial node to fringe"  "Construct node to be a tuple (location,actions)"  initialNode = (positionA,[])  initCost = 0 + heuristic(initialNode[0],positionB)  fringe.push(initialNode,initCost)  "Step 4: Loop to do search"  while not fringe.isEmpty():  node = fringe.pop()  if node[0] == positionB:  return node[1]  if node[0] not in closed:  closed.add(node[0])  for successor in getSuccessors(node[0]):  actions = list(node[1])  actions.append(successor)  newPosition = getNewPostion(node[0], successor)  childNode = (newPosition,actions)  cost = len(actions) + heuristic(childNode[0],positionB)  fringe.push(childNode,cost)  return []   def AstarSearchBetweenbuildings(building1,building2):  "Step 1: define closed: a set"  closed = set()  "Step 2: define fringe: a PriorityQueue "  fringe = util.PriorityQueue()  "Step 3: insert initial node to fringe"  "Construct node to be a tuple (location,actions)"  initialNode = (building1,[])  initCost = 0 + heuristic(initialNode[0],building2)  fringe.push(initialNode,initCost)  "Step 4: Loop to do search"  while not fringe.isEmpty():  node = fringe.pop()  if util.manhattanDistance(node[0],building2) == 1:  return node[1]  if node[0] not in closed:  closed.add(node[0])  for successor in getSuccessors(node[0]):  actions = list(node[1])  actions.append(successor)  newPosition = getNewPostion(node[0], successor)  childNode = (newPosition,actions)  cost = len(actions) + heuristic(childNode[0],building2)  fringe.push(childNode,cost)  return []   def calculatePositions(buildingA,path):  positions = set()  positions.add(buildingA)  nextPosition = buildingA  for action in path:  nextPosition = getNewPostion(nextPosition, action)  positions.add(nextPosition)  return positions   def showRoad(fullRoad):  global view,width  for road in fullRoad:  view.create_oval(road[0] * width + width * 4 / 10, road[1] * width + width * 4 / 10,   road[0] * width + width * 6 / 10, road[1] * width + width * 6 / 10, fill='yellow', width=1)  view.update()     def search(node):  successors = getSuccessors(node[0])  detect(node[0])  for successor in successors:  nextPosition = getNewPostion(node[0], successor)  if nextPosition not in roads:  runAction([successor]) # to the next node  roads.add(nextPosition)  search((nextPosition,[successor],[reverseSuccessor(successor)]))  runAction(node[2]) #back to top node   def searchConsiderTopVisit(node,topWillVisit):  successors = getSuccessors(node[0])  detect(node[0])  newTopWillVisit = set(topWillVisit)  for successor in successors:  nextPosition = getNewPostion(node[0], successor)  newTopWillVisit.add(nextPosition)  for successor in successors:  nextPosition = getNewPostion(node[0], successor)  if nextPosition not in roads and nextPosition not in topWillVisit:  runAction([successor]) # to the next node  roads.add(nextPosition)  newTopWillVisit.remove(nextPosition)  searchConsiderTopVisit((nextPosition,[successor],[reverseSuccessor(successor)]),newTopWillVisit)  runAction(node[2]) #back to top node     def searchShortestPathBetweenBlocks(block1,block2):  shortestPath = []  buildingA = (0,0)  buildingB = (0,0)  for building1 in block1:  for building2 in block2:  path = AstarSearchBetweenbuildings(building1, building2)  if len(shortestPath) == 0:  shortestPath = path  buildingA = building1  buildingB = building2  elif len(path) < len(shortestPath):  shortestPath = path  buildingA = building1  buildingB = building2  return (buildingA,buildingB,shortestPath)   def addBuildingToBlocks(linkedBlock,buildingA):  global detectedBuildings  newLinkedBlock = linkedBlock.copy()  for block,buildings in detectedBuildings.items():  for building in buildings:  if util.manhattanDistance(buildingA, building) == 1:   newLinkedBlock[block] = buildings   break  return newLinkedBlock   def bfsSearchNextBlock(initBuilding,linkedBlock):  global detectedBuildings  closed = set()  fringe = util.Queue()  initNode = (initBuilding,[])  fringe.push(initNode)  while not fringe.isEmpty():  node = fringe.pop()  newLinkedBlock = addBuildingToBlocks(linkedBlock,node[0])  if len(newLinkedBlock) == len(detectedBuildings):  return node[1]  if len(newLinkedBlock) > len(linkedBlock): # find a new block  actions = list(node[1])  '''  if len(node[1]) > 0:  lastAction = node[1][len(node[1]) - 1]  for successor in getSuccessors(node[0]):   if successor == lastAction:   nextPosition = getNewPostion(node[0], successor)   actions.append(successor)   return actions + bfsSearchNextBlock(nextPosition, newLinkedBlock)  '''  return node[1] + bfsSearchNextBlock(node[0], newLinkedBlock)    if node[0] not in closed:  closed.add(node[0])  for successor in getSuccessors(node[0]):  actions = list(node[1])  actions.append(successor)  nextPosition = getNewPostion(node[0], successor)    childNode = (nextPosition,actions)  fringe.push(childNode)  return []   def isGoal(node):  global detectedBuildings,robotPos  linkedBlock = {}  positions = calculatePositions(robotPos, node[1])  for position in positions:  for block,buildings in detectedBuildings.items():  for building in buildings:   if util.manhattanDistance(position, building) == 1:   linkedBlock[block] = buildings  print len(linkedBlock)  if len(linkedBlock) == 17:  return True  else:  return False   def roadHeuristic(road):  return 0   def AstarSearchRoad():  global robotPos,detectedBuildings  "Step 1: define closed: a set"  closed = set()  "Step 2: define fringe: a PriorityQueue "  fringe = util.PriorityQueue()  "Step 3: insert initial node to fringe"  "Construct node to be a tuple (location,actions)"  initRoad = (robotPos,[])  initCost = 0 + roadHeuristic(initRoad)  fringe.push(initRoad,initCost)  "Step 4: Loop to do search"  while not fringe.isEmpty():  node = fringe.pop()  if isGoal(node):  print len(closed)  return node[1]  if node[0] not in closed:  closed.add(node[0])  for successor in getSuccessors(node[0]):  actions = list(node[1])  actions.append(successor)  newPosition = getNewPostion(node[0], successor)  childNode = (newPosition,actions)  cost = len(actions) + roadHeuristic(childNode)  fringe.push(childNode,cost)    return []   def searchRoad(building):  global detectedBuildings,robotPos  linkedBlock = {}  initBuilding = building    return bfsSearchNextBlock(initBuilding,linkedBlock)   def searchShortestRoad():  shortestRoad = []  shortestPositions = set()  for block,buildings in detectedBuildings.items():  for building in buildings:  road = searchRoad(building)  positions = calculatePositions(building, road)  if len(shortestPositions) == 0 or len(positions) < len(shortestPositions):  shortestRoad = road  shortestPositions = positions  print len(shortestPositions)  showRoad(shortestPositions)   def searchMap(event):  print "Search Map"  global robotPos,roads,detectedBuildings,delay  actions = []  #roads = set()s  #roads.add(robotPos)  #fringe = util.Stack()  initNode = (robotPos,[],[]) # (position,forwardActions,backwarsdActions)  #fringe.push(initNode)  roads.add(robotPos)  search(initNode)  #searchConsiderTopVisit(initNode, set())  print detectedBuildings  print len(detectedBuildings)  #path = AstarSearchBetweenbuildings((6,21), (2, 18))  #showPath((6,21),(2,18), path)  '''  shortestRoad = set()  for block1 in detectedBuildings.values():  roads = set()  for block2 in detectedBuildings.values():  if not block1 == block2:  (buildingA,buildingB,path) = searchShortestPathBetweenBlocks(block1, block2)  #showPath(buildingA,buildingB,path)  positions = calculatePositions(buildingA,buildingB,path)  roads = roads | positions  if len(shortestRoad) == 0 or len(roads) < len(shortestRoad):  shortestRoad = roads  print len(shortestRoad)  showRoad(shortestRoad)  '''  '''  block1 = detectedBuildings.values()[3]  print block1  block2 = detectedBuildings.values()[5]  print block2  (buildingA,buildingB,path) = searchShortestPathBetweenBlocks(block1, block2)  print buildingA,buildingB,path  showPath(buildingA,buildingB,path)    block1 = detectedBuildings.values()[10]  print block1  block2 = detectedBuildings.values()[20]  print block2  (buildingA,buildingB,path) = searchShortestPathBetweenBlocks(block1, block2)  print buildingA,buildingB,path  showPath(buildingA,buildingB,path)  '''  searchShortestRoad()    '''  path = searchRoad()  #path = AstarSearchRoad()  positions = calculatePositions(robotPos, path)  print len(positions)  showRoad(positions)  delay = True  #runAction(path)  '''     window.bind("<Up>", callUp) window.bind("<Down>", callDown) window.bind("<Right>", callRight) window.bind("<Left>", callLeft) window.bind("s", searchMap) searchMapButton.bind("<Button-1>",searchMap) window.mainloop() 

下面的util.py使用的是加州伯克利的代码：

# util.py # ------- # Licensing Information: You are free to use or extend these projects for # educational purposes provided that (1) you do not distribute or publish # solutions, (2) you retain this notice, and (3) you provide clear # attribution to UC Berkeley, including a link to https://ai.berkeley.edu. # # Attribution Information: The Pacman AI projects were developed at UC Berkeley. # The core projects and autograders were primarily created by John DeNero # (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu). # Student side autograding was added by Brad Miller, Nick Hay, and # Pieter Abbeel (pabbeel@cs.berkeley.edu).     import sys import inspect import heapq, random     """  Data structures useful for implementing SearchAgents """   class Stack:  "A container with a last-in-first-out (LIFO) queuing policy."  def __init__(self):  self.list = []    def push(self,item):  "Push 'item' onto the stack"  self.list.append(item)    def pop(self):  "Pop the most recently pushed item from the stack"  return self.list.pop()    def isEmpty(self):  "Returns true if the stack is empty"  return len(self.list) == 0   class Queue:  "A container with a first-in-first-out (FIFO) queuing policy."  def __init__(self):  self.list = []    def push(self,item):  "Enqueue the 'item' into the queue"  self.list.insert(0,item)    def pop(self):  """  Dequeue the earliest enqueued item still in the queue. This  operation removes the item from the queue.  """  return self.list.pop()    def isEmpty(self):  "Returns true if the queue is empty"  return len(self.list) == 0   class PriorityQueue:  """  Implements a priority queue data structure. Each inserted item  has a priority associated with it and the client is usually interested  in quick retrieval of the lowest-priority item in the queue. This  data structure allows O(1) access to the lowest-priority item.    Note that this PriorityQueue does not allow you to change the priority  of an item. However, you may insert the same item multiple times with  different priorities.  """  def __init__(self):  self.heap = []  self.count = 0    def push(self, item, priority):  # FIXME: restored old behaviour to check against old results better  # FIXED: restored to stable behaviour  entry = (priority, self.count, item)  # entry = (priority, item)  heapq.heappush(self.heap, entry)  self.count += 1    def pop(self):  (_, _, item) = heapq.heappop(self.heap)  # (_, item) = heapq.heappop(self.heap)  return item    def isEmpty(self):  return len(self.heap) == 0   class PriorityQueueWithFunction(PriorityQueue):  """  Implements a priority queue with the same push/pop signature of the  Queue and the Stack classes. This is designed for drop-in replacement for  those two classes. The caller has to provide a priority function, which  extracts each item's priority.  """  def __init__(self, priorityFunction):  "priorityFunction (item) -> priority"  self.priorityFunction = priorityFunction # store the priority function  PriorityQueue.__init__(self) # super-class initializer    def push(self, item):  "Adds an item to the queue with priority from the priority function"  PriorityQueue.push(self, item, self.priorityFunction(item))     def manhattanDistance( xy1, xy2 ):  "Returns the Manhattan distance between points xy1 and xy2"  return abs( xy1[0] - xy2[0] ) + abs( xy1[1] - xy2[1] ) 

非常感谢你的阅读
大学的时候选择了自学python，工作了发现吃了计算机基础不好的亏，学历不行这是
没办法的事，只能后天弥补，于是在编码之外开启了自己的逆袭之路，不断的学习python核心知识，深入的研习计算机基础知识，整理好了，如果你也不甘平庸，那就与我一起在编码之外，不断成长吧！
其实这里不仅有技术，更有那些技术之外的东西，比如，如何做一个精致的程序员，而不是“屌丝”，程序员本身就是高贵的一种存在啊，难道不是吗？[点击加入]想做你自己想成为高尚人，加油！