AStar changes optimalisation
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UI/Apath.py
216
UI/Apath.py
@ -2,70 +2,182 @@ import numpy as np
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from heapq import * # pylint: disable=unused-wildcard-import
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def heuristic(a, b):
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x = abs(a[0]-b[0])
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y = abs(a[1]-b[1])
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def astar(table, start, end):
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"""Returns a list of tuples as a path from the given start to the given end in the given table"""
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if x > y:
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return 14*y + 10*(x - y)
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else:
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return 14*x + 10*(y - x)
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# Create start and end node
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start_node = table[start[0]][start[1]]
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start_node.g = start_node.h = start_node.f = 0
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end_node = table[end[0]][end[1]]
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end_node.g = end_node.h = end_node.f = 0
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# Initialize both open and closed list
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open_list = []
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closed_list = []
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# Add the start node
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open_list.append(start_node)
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def Astar(array, start, goal):
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# Loop until you find the end
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while len(open_list) > 0:
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# Get the current node
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current_node = open_list[0]
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current_index = 0
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for index, item in enumerate(open_list):
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if item.f < current_node.f:
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current_node = item
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current_index = index
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neighbors = [(0, 1), (0, -1), (1, 0), (-1, 0), (1, 1), (1, -1), (-1, 1), (-1, -1)]
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# Pop current off open list, add to closed list
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open_list.pop(current_index)
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closed_list.append(current_node)
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# Found the goal
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if current_node == end_node:
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path = []
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current = current_node
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while current is not None:
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path.append((current.row,current.col))
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current = current.parent
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return path[::-1] # Return reversed path
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came_from = {}
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gscore = {start: 0}
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fscore = {start: heuristic(start, goal)}
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oheap = []
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checked = []
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# Generate children
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children = []
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for new_position in [(0, -1), (0, 1), (-1, 0), (1, 0), (-1, -1), (-1, 1), (1, -1), (1, 1)]: # Adjacent squares
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heappush(oheap, (fscore[start], start))
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# Get node position #check
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node_position = (current_node.row + new_position[0], current_node.col + new_position[1])
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while oheap:
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current = heappop(oheap)[1]
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checked.append(current)
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if current == goal:
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data = []
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while current in came_from:
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data.append(current)
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current = came_from[current]
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return list(reversed(data)), checked
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print("array current",array[current[0],current[1]])
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array[current[0], current[1]]=2
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for i, j in neighbors:
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neighbor = current[0] + i, current[1] + j
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tentative_g_score = gscore[current] + heuristic(current, neighbor)
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if 0 <= neighbor[0] < array.shape[0]:
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if 0 <= neighbor[1] < array.shape[1]:
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if array.flat[array.shape[1] * neighbor[0]+neighbor[1]] == 1:
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continue
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else:
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# array bound y walls
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continue
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else:
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# array bound x walls
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# Make sure within range
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if node_position[0] > (len(table) - 1) or node_position[0] < 0 or node_position[1] > (len(table[len(table)-1]) -1) or node_position[1] < 0:
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continue
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if array[neighbor[0]][neighbor[1]] == 2 and tentative_g_score >= gscore.get(neighbor, 0):
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# Make sure walkable terrain
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if table[node_position[0]][node_position[1]].field_type == 3:
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continue
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if tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1]for i in oheap]:
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came_from[neighbor] = current
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gscore[neighbor] = tentative_g_score
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fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal)
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heappush(oheap, (fscore[neighbor], neighbor))
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return False
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# Create new node
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table[node_position[0]][node_position[1]].parent = current_node
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#new_node = table[node_position[0]][node_position[1]]
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print("Dla :",node_position[0],node_position[1],"rodzicem jest:",current_node.row,current_node.col)
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# Append
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children.append(table[node_position[0]][node_position[1]])
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# Loop through children
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for child in children:
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# Child is on the closed list
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for closed_child in closed_list:
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if child == closed_child:
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continue
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# Create the f, g, and h values
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child.g = current_node.g + 1
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child.h = ((child.row - end_node.row) ** 2) + ((child.col - end_node.col) ** 2)
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child.f = child.g + child.h
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# Child is already in the open list
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for open_node in open_list:
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if child == open_node and child.g > open_node.g:
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continue
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# Add the child to the open list
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open_list.append(child)
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class AStarNode():
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def __init__(self, parent=None, position=None):
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self.parent = parent
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self.position = position
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self.g = 0
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self.h = 0
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self.f = 0
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def __eq__(self, other):
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return self.position == other.position
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def APath(table, start, end):
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"""Returns a list of tuples as a path from the given start to the given end in the given table"""
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# Create start and end node
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start_node = AStarNode(None, start)
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start_node.g = start_node.h = start_node.f = 0
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end_node = AStarNode(None, end)
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end_node.g = end_node.h = end_node.f = 0
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# Initialize both open and closed list
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open_list = []
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closed_list = []
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# Add the start node
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open_list.append(start_node)
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# Loop until you find the end
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while len(open_list) > 0:
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# Get the current node
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current_node = open_list[0]
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current_index = 0
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for index, item in enumerate(open_list):
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if item.f < current_node.f:
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current_node = item
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current_index = index
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# Pop current off open list, add to closed list
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open_list.pop(current_index)
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closed_list.append(current_node)
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# Found the goal
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if current_node == end_node:
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path = []
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current = current_node
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while current is not None:
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path.append(current.position)
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current = current.parent
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return path[::-1] # Return reversed path
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# Generate children
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children = []
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for new_position in [(0, -1), (0, 1), (-1, 0), (1, 0), (-1, -1), (-1, 1), (1, -1), (1, 1)]: # Adjacent squares
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# Get node position
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node_position = (current_node.position[0] + new_position[0], current_node.position[1] + new_position[1])
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# Make sure within range
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if node_position[0] > (len(table) - 1) or node_position[0] < 0 or node_position[1] > (len(table[len(table)-1]) -1) or node_position[1] < 0:
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continue
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# Make sure walkable terrain
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if table[node_position[0]][node_position[1]].field_type == 3:
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continue
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# Create new node
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new_node = AStarNode(current_node, node_position)
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# Append
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children.append(new_node)
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# Loop through children
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for child in children:
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# Child is on the closed list
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for closed_child in closed_list:
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if child == closed_child:
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continue
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# Create the f, g, and h values
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child.g = current_node.g + 1
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child.h = ((child.position[0] - end_node.position[0]) ** 2) + ((child.position[1] - end_node.position[1]) ** 2)
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child.f = child.g + child.h
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# Child is already in the open list
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for open_node in open_list:
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if child == open_node and child.g > open_node.g:
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continue
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# Add the child to the open list
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open_list.append(child)
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@ -41,15 +41,12 @@ class Node:
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ORANGE = (255, 165, 0)
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def __init__(self, row: int, col: int,
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field_type: int = 0, reachable: bool = True):
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field_type: int = 0):
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self.row = row
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self.col = col
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self.field_type = field_type
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self.reachable = reachable
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self.visited = False
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def visit(self):
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self.visited = True
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def draw(self, screen):
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color = self.get_field_color()
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21
UI/window.py
21
UI/window.py
@ -2,7 +2,7 @@ import pygame as pg
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import numpy as np
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import random
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from UI.grid import Grid, Node
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from UI.Apath import Astar
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from UI.Apath import APath, astar
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@ -32,7 +32,7 @@ class Window():
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grid.change_field(19, 19, 2)
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#random obsticle
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for x in range(40):
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for x in range(70):
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grid.change_field(random.randint(1,18),random.randint(1,18),3)
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#path
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@ -41,17 +41,20 @@ class Window():
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#convert table to support Apath algoritm
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array = [[self.grid.table[col][row] for row in range(cols)] for col in range(rows)]
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for i,x in enumerate(array):
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for j,y in enumerate(x):
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if y.field_type == 3:
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array[i][j] = None
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nodes_array = np.array(array)
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#for i,x in enumerate(array):
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# for j,y in enumerate(x):
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# if y.field_type == 3:
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# array[i][j] = None
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#nodes_array = np.array(array)
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#Run A star
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path, check = Astar(nodes_array, (0,0), (19, 19))
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print(path,"\n\n",check,"\n\n")
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#path, check = Astar(nodes_array, (0,0), (19, 19))
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#print(path,"\n\n",check,"\n\n")
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path = APath(array,(0,0),(19,19))
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print(path,"\n\n")
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for t in path:
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