From 425a9bf3e2235e157f4af5748546bc8cf60b8f4a Mon Sep 17 00:00:00 2001 From: s473558 Date: Mon, 17 Apr 2023 04:05:42 +0200 Subject: [PATCH 01/21] provisionary limit to the succ function --- graph_search.py | 32 ++++++++++++++++++++++---------- 1 file changed, 22 insertions(+), 10 deletions(-) diff --git a/graph_search.py b/graph_search.py index 14638220..57a37bca 100644 --- a/graph_search.py +++ b/graph_search.py @@ -9,19 +9,31 @@ class Search: def __init__(self, cell_size): self.cell_size = cell_size - # WARNING! IT EXCEEDS THE PLANE!!! - def succ(self, state): # successor function + #this method needs to check the size of the plane - for now, it assumes 15x15, like in main.py + def succ(self, state): x = state[0] y = state[1] angle = state[2] - if angle == 0: - return [['move', x, y - self.cell_size, 0], ['left', x, y, 270], ['right', x, y, 90]] - if angle == 90: - return [['move', x + self.cell_size, y, 90], ['left', x, y, 0], ['right', x, y, 180]] - if angle == 180: - return [['move', x, y + self.cell_size, 180], ['left', x, y, 90], ['right', x, y, 270]] - if angle == 270: - return [['move', x - self.cell_size, y, 270], ['left', x, y, 180], ['right', x, y, 0]] + if angle == 0: # up + if y != 0: #this can stay as 0, negative values are not tolerated + return [['move', x, y - self.cell_size, 0], ['left', x, y, 270], ['right', x, y, 90]] + else: + return [['left', x, y, 270], ['right', x, y, 90]] + if angle == 90: # right + if x != 700: #this needs to be changed to relative once size of plane is read by succ (parse in Search constructor?) + return [['move', x + self.cell_size, y, 90], ['left', x, y, 0], ['right', x, y, 180]] + else: + return [['left', x, y, 0], ['right', x, y, 180]] + if angle == 180: # down + if y != 700: #this needs to be changed to relative once size of plane is read by succ (parse in Search constructor?) + return [['move', x, y + self.cell_size, 180], ['left', x, y, 90], ['right', x, y, 270]] + else: + return [['left', x, y, 90], ['right', x, y, 270]] + if angle == 270: # left + if x != 0: #this can stay as 0, negative values are not tolerated + return [['move', x - self.cell_size, y, 270], ['left', x, y, 180], ['right', x, y, 0]] + else: + return [['left', x, y, 180], ['right', x, y, 0]] def graphsearch(self, istate, goaltest): x = istate[0] From 24894482e455d0b415d5f005eb34bc8eaf8ecb00 Mon Sep 17 00:00:00 2001 From: mikoMaz Date: Mon, 17 Apr 2023 10:20:05 +0200 Subject: [PATCH 02/21] add tractor event; tractor moves using graph_search --- main.py | 27 +++++++++++++++++---------- 1 file changed, 17 insertions(+), 10 deletions(-) diff --git a/main.py b/main.py index b9ab2292..45c8b6c3 100644 --- a/main.py +++ b/main.py @@ -1,12 +1,11 @@ import os - import pygame import random import land import tractor import blocks +import graph_search from pygame.locals import * -from datetime import datetime class Game: @@ -66,12 +65,14 @@ class Game: # print(self.potato.get_soil_info().get_irrigation()) running = True clock = pygame.time.Clock() - # last_time = datetime.now() + + move_tractor_event = pygame.USEREVENT + 1 + pygame.time.set_timer(move_tractor_event, 1000) # tractor moves every 1000 ms + tractor_next_moves = [] + graph_search_object = graph_search.Search(self.cell_size) while running: clock.tick(60) # manual fps control not to overwork the computer - # time_now = datetime.now() - for event in pygame.event.get(): if event.type == KEYDOWN: if pygame.key.get_pressed()[K_ESCAPE]: @@ -92,8 +93,14 @@ class Game: if pygame.key.get_pressed()[K_q]: self.tractor.harvest(self.fawn_seed_body, self.fawn_wheat_body, self.cell_size) self.tractor.put_seed(self.fawn_soil_body, self.fawn_seed_body, self.cell_size) - - + if event.type == move_tractor_event: + if len(tractor_next_moves) == 0: + random_x = random.randrange(0, self.cell_number * self.cell_size, 50) + random_y = random.randrange(0, self.cell_number * self.cell_size, 50) + tractor_next_moves = graph_search_object.graphsearch( + [self.tractor.x, self.tractor.y, self.tractor.angle], [random_x, random_y]) + else: + self.tractor.move(tractor_next_moves.pop(0)[0], self.cell_size, self.cell_number) elif event.type == QUIT: running = False @@ -105,16 +112,16 @@ class Game: self.fawn_soil.place_soil(self.fawn_soil_body, 'fawn_soil') self.fen_soil.place_soil(self.fen_soil_body, 'fen_soil') - #plants examples + # plants examples self.blocks.place_blocks(self.surface, self.cell_size, self.dead_leaf_body, 'leaf') self.blocks.place_blocks(self.surface, self.cell_size, self.green_leaf_body, 'alive') self.blocks.place_blocks(self.surface, self.cell_size, self.stone_body, 'stone') self.blocks.place_blocks(self.surface, self.cell_size, self.flower_body, 'flower') - #seeds + # seeds self.blocks.place_blocks(self.surface, self.cell_size, self.fawn_seed_body, 'fawn_seed') - #wheat + # wheat self.blocks.place_blocks(self.surface, self.cell_size, self.fawn_wheat_body, 'fawn_wheat') self.tractor.draw() From c5d86faade2b7486dbdb6fce2358bdb08fafb06f Mon Sep 17 00:00:00 2001 From: Szymon Szczubkowski Date: Mon, 17 Apr 2023 15:46:17 +0200 Subject: [PATCH 03/21] add: display target field in console --- main.py | 1 + 1 file changed, 1 insertion(+) diff --git a/main.py b/main.py index 45c8b6c3..6aceca28 100644 --- a/main.py +++ b/main.py @@ -97,6 +97,7 @@ class Game: if len(tractor_next_moves) == 0: random_x = random.randrange(0, self.cell_number * self.cell_size, 50) random_y = random.randrange(0, self.cell_number * self.cell_size, 50) + print("Generated target: ",random_x, random_y) tractor_next_moves = graph_search_object.graphsearch( [self.tractor.x, self.tractor.y, self.tractor.angle], [random_x, random_y]) else: From 2cb44dcb01d55240e73210e6afa0eeb0a5c3f71b Mon Sep 17 00:00:00 2001 From: Szymon Szczubkowski Date: Mon, 17 Apr 2023 16:00:22 +0200 Subject: [PATCH 04/21] partial cleanup of graph_search.py --- graph_search.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/graph_search.py b/graph_search.py index 57a37bca..c26652c6 100644 --- a/graph_search.py +++ b/graph_search.py @@ -76,7 +76,3 @@ class Search: x.action = action fringe.append(x) fringe_state.append(x.state) - - -se = Search(50) -se.graphsearch(istate=[50, 50, 0], goaltest=[150, 250]) From eaf7ed46fe38696475e8dba676ad997bd63c34fd Mon Sep 17 00:00:00 2001 From: Szymon Szczubkowski Date: Mon, 17 Apr 2023 16:24:08 +0200 Subject: [PATCH 05/21] improve limits of succ(), comments cleanup --- graph_search.py | 48 ++++++++++++++++++++++++------------------------ main.py | 2 +- 2 files changed, 25 insertions(+), 25 deletions(-) diff --git a/graph_search.py b/graph_search.py index c26652c6..a855adf9 100644 --- a/graph_search.py +++ b/graph_search.py @@ -6,34 +6,36 @@ class Node: class Search: - def __init__(self, cell_size): + def __init__(self, cell_size, cell_number): self.cell_size = cell_size + self.cell_number = cell_number - #this method needs to check the size of the plane - for now, it assumes 15x15, like in main.py def succ(self, state): x = state[0] y = state[1] angle = state[2] - if angle == 0: # up - if y != 0: #this can stay as 0, negative values are not tolerated - return [['move', x, y - self.cell_size, 0], ['left', x, y, 270], ['right', x, y, 90]] - else: - return [['left', x, y, 270], ['right', x, y, 90]] - if angle == 90: # right - if x != 700: #this needs to be changed to relative once size of plane is read by succ (parse in Search constructor?) - return [['move', x + self.cell_size, y, 90], ['left', x, y, 0], ['right', x, y, 180]] - else: - return [['left', x, y, 0], ['right', x, y, 180]] - if angle == 180: # down - if y != 700: #this needs to be changed to relative once size of plane is read by succ (parse in Search constructor?) - return [['move', x, y + self.cell_size, 180], ['left', x, y, 90], ['right', x, y, 270]] - else: - return [['left', x, y, 90], ['right', x, y, 270]] - if angle == 270: # left - if x != 0: #this can stay as 0, negative values are not tolerated - return [['move', x - self.cell_size, y, 270], ['left', x, y, 180], ['right', x, y, 0]] - else: - return [['left', x, y, 180], ['right', x, y, 0]] + angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'} + match(angles[angle]): + case 'UP': + if y != 0: + return [['move', x, y - self.cell_size, 0], ['left', x, y, 270], ['right', x, y, 90]] + else: + return [['left', x, y, 270], ['right', x, y, 90]] + case 'RIGHT': + if x != self.cell_size*(self.cell_number-1): + return [['move', x + self.cell_size, y, 90], ['left', x, y, 0], ['right', x, y, 180]] + else: + return [['left', x, y, 0], ['right', x, y, 180]] + case 'DOWN': + if y != self.cell_size*(self.cell_number-1): + return [['move', x, y + self.cell_size, 180], ['left', x, y, 90], ['right', x, y, 270]] + else: + return [['left', x, y, 90], ['right', x, y, 270]] + case 'LEFT': + if x != 0: + return [['move', x - self.cell_size, y, 270], ['left', x, y, 180], ['right', x, y, 0]] + else: + return [['left', x, y, 180], ['right', x, y, 0]] def graphsearch(self, istate, goaltest): x = istate[0] @@ -67,8 +69,6 @@ class Search: explored.append(elem.state) for (action, state_x, state_y, state_angle) in self.succ(elem.state): - if state_x < 0 or state_y < 0: # check if any of the values are negative - continue if [state_x, state_y, state_angle] not in fringe_state and \ [state_x, state_y, state_angle] not in explored: x = Node([state_x, state_y, state_angle]) diff --git a/main.py b/main.py index 6aceca28..61a3d487 100644 --- a/main.py +++ b/main.py @@ -69,7 +69,7 @@ class Game: move_tractor_event = pygame.USEREVENT + 1 pygame.time.set_timer(move_tractor_event, 1000) # tractor moves every 1000 ms tractor_next_moves = [] - graph_search_object = graph_search.Search(self.cell_size) + graph_search_object = graph_search.Search(self.cell_size, self.cell_number) while running: clock.tick(60) # manual fps control not to overwork the computer From ab37f8af99259045e324e1e80d4e7944f7761ef3 Mon Sep 17 00:00:00 2001 From: Szymon Szczubkowski Date: Mon, 17 Apr 2023 16:35:22 +0200 Subject: [PATCH 06/21] improve succ() switch tree --- graph_search.py | 28 ++++++++++++---------------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/graph_search.py b/graph_search.py index a855adf9..a703c010 100644 --- a/graph_search.py +++ b/graph_search.py @@ -17,25 +17,21 @@ class Search: angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'} match(angles[angle]): case 'UP': - if y != 0: - return [['move', x, y - self.cell_size, 0], ['left', x, y, 270], ['right', x, y, 90]] - else: - return [['left', x, y, 270], ['right', x, y, 90]] + possible = [['left', x, y, 270], ['right', x, y, 90]] + if y != 0: possible.append(['move', x, y - self.cell_size, 0]) + return possible case 'RIGHT': - if x != self.cell_size*(self.cell_number-1): - return [['move', x + self.cell_size, y, 90], ['left', x, y, 0], ['right', x, y, 180]] - else: - return [['left', x, y, 0], ['right', x, y, 180]] + possible = [['left', x, y, 0], ['right', x, y, 180]] + if x != self.cell_size*(self.cell_number-1): possible.append(['move', x + self.cell_size, y, 90]) + return possible case 'DOWN': - if y != self.cell_size*(self.cell_number-1): - return [['move', x, y + self.cell_size, 180], ['left', x, y, 90], ['right', x, y, 270]] - else: - return [['left', x, y, 90], ['right', x, y, 270]] + possible = [['left', x, y, 90], ['right', x, y, 270]] + if y != self.cell_size*(self.cell_number-1): possible.append(['move', x, y + self.cell_size, 180]) + return possible case 'LEFT': - if x != 0: - return [['move', x - self.cell_size, y, 270], ['left', x, y, 180], ['right', x, y, 0]] - else: - return [['left', x, y, 180], ['right', x, y, 0]] + possible = [['left', x, y, 180], ['right', x, y, 0]] + if x != 0: possible.append(['move', x - self.cell_size, y, 270]) + return possible def graphsearch(self, istate, goaltest): x = istate[0] From 08b318d1e679416ba01030efadabdf9b9127e799 Mon Sep 17 00:00:00 2001 From: Szymon Szczubkowski Date: Mon, 17 Apr 2023 16:53:54 +0200 Subject: [PATCH 07/21] purge magic numbers from graph_search, TODO: do the same in tractor.py --- graph_search.py | 21 ++++++++++----------- main.py | 4 +++- tractor.py | 16 ---------------- 3 files changed, 13 insertions(+), 28 deletions(-) diff --git a/graph_search.py b/graph_search.py index a703c010..622d3c86 100644 --- a/graph_search.py +++ b/graph_search.py @@ -14,23 +14,22 @@ class Search: x = state[0] y = state[1] angle = state[2] - angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'} - match(angles[angle]): + match(angle): case 'UP': - possible = [['left', x, y, 270], ['right', x, y, 90]] - if y != 0: possible.append(['move', x, y - self.cell_size, 0]) + possible = [['left', x, y, 'LEFT'], ['right', x, y, 'RIGHT']] + if y != 0: possible.append(['move', x, y - self.cell_size, 'UP']) return possible case 'RIGHT': - possible = [['left', x, y, 0], ['right', x, y, 180]] - if x != self.cell_size*(self.cell_number-1): possible.append(['move', x + self.cell_size, y, 90]) + possible = [['left', x, y, 'UP'], ['right', x, y, 'DOWN']] + if x != self.cell_size*(self.cell_number-1): possible.append(['move', x + self.cell_size, y, 'RIGHT']) return possible case 'DOWN': - possible = [['left', x, y, 90], ['right', x, y, 270]] - if y != self.cell_size*(self.cell_number-1): possible.append(['move', x, y + self.cell_size, 180]) + possible = [['left', x, y, 'RIGHT'], ['right', x, y, 'LEFT']] + if y != self.cell_size*(self.cell_number-1): possible.append(['move', x, y + self.cell_size, 'DOWN']) return possible case 'LEFT': - possible = [['left', x, y, 180], ['right', x, y, 0]] - if x != 0: possible.append(['move', x - self.cell_size, y, 270]) + possible = [['left', x, y, 'DOWN'], ['right', x, y, 'UP']] + if x != 0: possible.append(['move', x - self.cell_size, y, 'LEFT']) return possible def graphsearch(self, istate, goaltest): @@ -54,7 +53,7 @@ class Search: # print(elem.state[0], elem.state[1], elem.state[2]) if elem.state[0] == goaltest[0] and elem.state[1] == goaltest[1]: # checks if we reached the given point steps = [] - while elem.parent != '': + while elem.parent: steps.append([elem.action, elem.state[0], elem.state[1]]) # should return only elem.action in prod elem = elem.parent diff --git a/main.py b/main.py index 61a3d487..3bdc21bc 100644 --- a/main.py +++ b/main.py @@ -98,8 +98,10 @@ class Game: random_x = random.randrange(0, self.cell_number * self.cell_size, 50) random_y = random.randrange(0, self.cell_number * self.cell_size, 50) print("Generated target: ",random_x, random_y) + # below line should be later moved into tractor.py + angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'} tractor_next_moves = graph_search_object.graphsearch( - [self.tractor.x, self.tractor.y, self.tractor.angle], [random_x, random_y]) + [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y]) else: self.tractor.move(tractor_next_moves.pop(0)[0], self.cell_size, self.cell_number) elif event.type == QUIT: diff --git a/tractor.py b/tractor.py index ca8545ee..f9c5f623 100644 --- a/tractor.py +++ b/tractor.py @@ -31,22 +31,6 @@ class Tractor: def move(self, direction, cell_size, cell_number): - # if direction == 'up': - # if self.y != 0: - # self.y -= cell_size - # self.image = self.up - # if direction == 'down': - # if self.y != (cell_number-1)*cell_size: - # self.y += cell_size - # self.image = self.down - # if direction == 'left': - # if self.x != 0: - # self.x -= cell_size - # self.image = self.left - # if direction == 'right': - # if self.x != (cell_number-1)*cell_size: - # self.x += cell_size - # self.image = self.right if direction == 'move': if self.angle == 0 and self.y != 0: self.y -= cell_size From e6dd642006521f16341c6963461dc53af0901c65 Mon Sep 17 00:00:00 2001 From: s473558 Date: Mon, 8 May 2023 08:23:04 +0200 Subject: [PATCH 08/21] implement astar. TODO: fix the stones bandaids --- astar_search.py | 85 +++++++++++++++++++++++++++++++++++++++++++++++++ blocks.py | 6 ++++ main.py | 9 +++--- 3 files changed, 96 insertions(+), 4 deletions(-) create mode 100644 astar_search.py diff --git a/astar_search.py b/astar_search.py new file mode 100644 index 00000000..fac3a294 --- /dev/null +++ b/astar_search.py @@ -0,0 +1,85 @@ +class Node: + def __init__(self, state, parent='', action=''): + self.state = state + self.parent = parent + self.action = action + +class Search: + def __init__(self, cell_size, cell_number): + self.cell_size = cell_size + self.cell_number = cell_number + + def succ(self, state): + x = state[0] + y = state[1] + angle = state[2] + match(angle): + case 'UP': + possible = [['left', x, y, 'LEFT'], ['right', x, y, 'RIGHT']] + if y != 0: possible.append(['move', x, y - self.cell_size, 'UP']) + return possible + case 'RIGHT': + possible = [['left', x, y, 'UP'], ['right', x, y, 'DOWN']] + if x != self.cell_size*(self.cell_number-1): possible.append(['move', x + self.cell_size, y, 'RIGHT']) + return possible + case 'DOWN': + possible = [['left', x, y, 'RIGHT'], ['right', x, y, 'LEFT']] + if y != self.cell_size*(self.cell_number-1): possible.append(['move', x, y + self.cell_size, 'DOWN']) + return possible + case 'LEFT': + possible = [['left', x, y, 'DOWN'], ['right', x, y, 'UP']] + if x != 0: possible.append(['move', x - self.cell_size, y, 'LEFT']) + return possible + #bandaid to know about stones + def astarsearch(self, istate, goaltest, stones): + + #to be expanded + def cost(x, y): + if (x, y) in stones: + return 10 + else: + return 1 + + + x = istate[0] + y = istate[1] + angle = istate[2] + + fringe = [(Node([x, y, angle]), cost(x, y))] # queue (moves/states to check) + explored = [] + + while True: + if len(fringe) == 0: + return False + + fringe.sort(key=lambda x: x[1]) + elem = fringe.pop(0)[0] + + # if goal_test(elem.state): + # return + # print(elem.state[0], elem.state[1], elem.state[2]) + if elem.state[0] == goaltest[0] and elem.state[1] == goaltest[1]: # checks if we reached the given point + steps = [] + while elem.parent: + steps.append([elem.action, elem.state[0], elem.state[1]]) # should return only elem.action in prod + elem = elem.parent + + steps.reverse() + print(steps) # only for dev + return steps + + explored.append(elem.state) + + for (action, state_x, state_y, state_angle) in self.succ(elem.state): + x = Node([state_x, state_y, state_angle], elem, action) + + priority = cost(state_x, state_y) + fringe_states = [node.state for (node, p) in fringe] + + if x.state not in fringe_states and x.state not in explored: + fringe.append((x, priority)) + elif x.state in fringe_states: + for i in range(len(fringe)): + if fringe[i][0].state == x.state: + if fringe[i][1] > priority: + fringe[i] = (x, priority) \ No newline at end of file diff --git a/blocks.py b/blocks.py index 9412f51b..9f59a225 100644 --- a/blocks.py +++ b/blocks.py @@ -5,6 +5,7 @@ import soil class Blocks: + def __init__(self, parent_screen,cell_size): self.parent_screen = parent_screen self.flower_image = pygame.image.load(r'resources/flower.png').convert_alpha() @@ -28,6 +29,9 @@ class Blocks: self.soil = soil.Soil() + self.stones = set() + #bandaid to let astar know about stones + def locate_blocks(self, blocks_number, cell_number, body): for i in range(blocks_number): self.x = random.randint(0, cell_number-1) @@ -47,6 +51,8 @@ class Blocks: self.parent_screen.blit(self.alive_leaf_image, (x, y)) if color == 'stone': self.parent_screen.blit(self.stone_image, (x, y)) + #bandaid to let astar know about stones + self.stones.add((x, y)) if color == 'flower': self.parent_screen.blit(self.flower_image, (x, y)) if color == 'fawn_seed': diff --git a/main.py b/main.py index 3bdc21bc..5a3aba1a 100644 --- a/main.py +++ b/main.py @@ -4,7 +4,7 @@ import random import land import tractor import blocks -import graph_search +import astar_search from pygame.locals import * @@ -69,7 +69,7 @@ class Game: move_tractor_event = pygame.USEREVENT + 1 pygame.time.set_timer(move_tractor_event, 1000) # tractor moves every 1000 ms tractor_next_moves = [] - graph_search_object = graph_search.Search(self.cell_size, self.cell_number) + astar_search_object = astar_search.Search(self.cell_size, self.cell_number) while running: clock.tick(60) # manual fps control not to overwork the computer @@ -100,8 +100,9 @@ class Game: print("Generated target: ",random_x, random_y) # below line should be later moved into tractor.py angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'} - tractor_next_moves = graph_search_object.graphsearch( - [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y]) + #bandaid to know about stones + tractor_next_moves = astar_search_object.astarsearch( + [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.blocks.stones) else: self.tractor.move(tractor_next_moves.pop(0)[0], self.cell_size, self.cell_number) elif event.type == QUIT: From 243712510054f2d2f28bb6d905a45e7d1ac05bff Mon Sep 17 00:00:00 2001 From: mikoMaz Date: Mon, 8 May 2023 16:23:21 +0200 Subject: [PATCH 09/21] add heuristic; fix cost method --- astar_search.py | 53 ++++++++++++++++++++++++++++++++++++------------- main.py | 2 +- 2 files changed, 40 insertions(+), 15 deletions(-) diff --git a/astar_search.py b/astar_search.py index fac3a294..018654c2 100644 --- a/astar_search.py +++ b/astar_search.py @@ -1,8 +1,9 @@ class Node: - def __init__(self, state, parent='', action=''): + def __init__(self, state, parent='', action='', distance=0): self.state = state self.parent = parent self.action = action + self.distance = distance class Search: def __init__(self, cell_size, cell_number): @@ -30,11 +31,27 @@ class Search: possible = [['left', x, y, 'DOWN'], ['right', x, y, 'UP']] if x != 0: possible.append(['move', x - self.cell_size, y, 'LEFT']) return possible + + def cost(self, node, stones, goal, flowers): + # cost = node.distance + cost = 0 + # cost += 10 if stones[node.state[0], node.state[1]] == 1 else 1 + cost += 1000000 if (node.state[0], node.state[1]) in stones else 1 + cost += 30 if (node.state[0], node.state[1]) in flowers else 1 + + if node.parent: + node = node.parent + cost += node.distance # should return only elem.action in prod + return cost + + def heuristic(self, node, goal): + return abs(node.state[0] - goal[0]) + abs(node.state[1] - goal[1]) + #bandaid to know about stones - def astarsearch(self, istate, goaltest, stones): + def astarsearch(self, istate, goaltest, stones, flowers): #to be expanded - def cost(x, y): + def cost_old(x, y): if (x, y) in stones: return 10 else: @@ -45,7 +62,12 @@ class Search: y = istate[1] angle = istate[2] - fringe = [(Node([x, y, angle]), cost(x, y))] # queue (moves/states to check) + # fringe = [(Node([x, y, angle]), cost_old(x, y))] # queue (moves/states to check) + fringe = [(Node([x, y, angle]))] # queue (moves/states to check) + fringe[0].distance = self.cost(fringe[0], stones, goaltest, flowers) + fringe.append((Node([x, y, angle]), self.cost(fringe[0], stones, goaltest, flowers))) + fringe.pop(0) + explored = [] while True: @@ -71,15 +93,18 @@ class Search: explored.append(elem.state) for (action, state_x, state_y, state_angle) in self.succ(elem.state): - x = Node([state_x, state_y, state_angle], elem, action) + x = Node([state_x, state_y, state_angle], elem, action) + x.parent = elem - priority = cost(state_x, state_y) - fringe_states = [node.state for (node, p) in fringe] + priority = self.cost(elem, stones, goaltest, flowers) + self.heuristic(elem, goaltest) + elem.distance = priority + # priority = cost_old(x, y) + self.heuristic(elem, goaltest) + fringe_states = [node.state for (node, p) in fringe] - if x.state not in fringe_states and x.state not in explored: - fringe.append((x, priority)) - elif x.state in fringe_states: - for i in range(len(fringe)): - if fringe[i][0].state == x.state: - if fringe[i][1] > priority: - fringe[i] = (x, priority) \ No newline at end of file + if x.state not in fringe_states and x.state not in explored: + fringe.append((x, priority)) + elif x.state in fringe_states: + for i in range(len(fringe)): + if fringe[i][0].state == x.state: + if fringe[i][1] > priority: + fringe[i] = (x, priority) diff --git a/main.py b/main.py index 5a3aba1a..2e34e0b1 100644 --- a/main.py +++ b/main.py @@ -102,7 +102,7 @@ class Game: angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'} #bandaid to know about stones tractor_next_moves = astar_search_object.astarsearch( - [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.blocks.stones) + [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.blocks.stones, self.flower_body) else: self.tractor.move(tractor_next_moves.pop(0)[0], self.cell_size, self.cell_number) elif event.type == QUIT: From 0c025a857dbd03cf323a797c06e56c0dba9ef791 Mon Sep 17 00:00:00 2001 From: Aliaksei Brown Date: Mon, 8 May 2023 21:15:37 +0200 Subject: [PATCH 10/21] changes: added a visible mark where the chicken goes to --- .DS_Store | Bin 0 -> 8196 bytes .idea/.gitignore | 8 ++++++++ .idea/inspectionProfiles/profiles_settings.xml | 6 ++++++ .idea/misc.xml | 4 ++++ .idea/modules.xml | 8 ++++++++ .idea/si23traktor.iml | 10 ++++++++++ .idea/vcs.xml | 6 ++++++ __pycache__/astar_search.cpython-310.pyc | Bin 0 -> 3262 bytes __pycache__/blocks.cpython-310.pyc | Bin 0 -> 2750 bytes __pycache__/field.cpython-310.pyc | Bin 0 -> 1112 bytes __pycache__/graph_search.cpython-310.pyc | Bin 0 -> 2036 bytes __pycache__/land.cpython-310.pyc | Bin 0 -> 2297 bytes __pycache__/soil.cpython-310.pyc | Bin 0 -> 1249 bytes __pycache__/tractor.cpython-310.pyc | Bin 0 -> 2771 bytes astar_search.py | 4 ++-- blocks.py | 4 ++++ main.py | 15 +++++++++++---- resources/.DS_Store | Bin 0 -> 10244 bytes resources/redBush.png | Bin 0 -> 1327 bytes 19 files changed, 59 insertions(+), 6 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pygame.time.set_timer(move_tractor_event, 150) # tractor moves every 1000 ms + pygame.time.set_timer(move_tractor_event, 100) # tractor moves every 1000 ms tractor_next_moves = [] astar_search_object = astar_search.Search(self.cell_size, self.cell_number) @@ -108,7 +108,7 @@ class Game: angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'} #bandaid to know about stones tractor_next_moves = astar_search_object.astarsearch( - [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.stone_body, self.flower_body) + [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.blocks.stones, self.flower_body) else: self.tractor.move(tractor_next_moves.pop(0)[0], self.cell_size, self.cell_number) elif event.type == QUIT: From d16267826d257b0ea4d5f88f60f27002915a1092 Mon Sep 17 00:00:00 2001 From: Szymon Szczubkowski Date: Mon, 15 May 2023 16:58:23 +0200 Subject: [PATCH 13/21] remove bandaid stones, TODO: fix astar --- astar_search.py | 9 +++++++-- blocks.py | 4 ---- main.py | 2 +- 3 files changed, 8 insertions(+), 7 deletions(-) diff --git a/astar_search.py b/astar_search.py index 806afab4..223e343d 100644 --- a/astar_search.py +++ b/astar_search.py @@ -37,7 +37,7 @@ class Search: cost = 0 # cost += 10 if stones[node.state[0], node.state[1]] == 1 else 1 cost += 1000 if (node.state[0], node.state[1]) in stones else 1 - cost += 30 if ((node.state[0]), (node.state[1])) in flowers else 1 + cost += 300 if ((node.state[0]), (node.state[1])) in flowers else 1 if node.parent: node = node.parent @@ -48,7 +48,7 @@ class Search: return abs(node.state[0] - goal[0]) + abs(node.state[1] - goal[1]) #bandaid to know about stones - def astarsearch(self, istate, goaltest, stones, flowers): + def astarsearch(self, istate, goaltest, cStones, cFlowers): #to be expanded def cost_old(x, y): @@ -62,6 +62,11 @@ class Search: y = istate[1] angle = istate[2] + stones = [(x*50, y*50) for (x, y) in cStones] + flowers = [(x*50, y*50) for (x, y) in cFlowers] + + print(stones) + # fringe = [(Node([x, y, angle]), cost_old(x, y))] # queue (moves/states to check) fringe = [(Node([x, y, angle]))] # queue (moves/states to check) fringe[0].distance = self.cost(fringe[0], stones, goaltest, flowers) diff --git a/blocks.py b/blocks.py index 4c1f49dc..606da61d 100644 --- a/blocks.py +++ b/blocks.py @@ -31,8 +31,6 @@ class Blocks: self.soil = soil.Soil() - self.stones = set() - #bandaid to let astar know about stones def locate_blocks(self, blocks_number, cell_number, body): for i in range(blocks_number): @@ -53,8 +51,6 @@ class Blocks: self.parent_screen.blit(self.alive_leaf_image, (x, y)) if color == 'stone': self.parent_screen.blit(self.stone_image, (x, y)) - #bandaid to let astar know about stones - self.stones.add((x, y)) if color == 'flower': self.parent_screen.blit(self.flower_image, (x, y)) if color == 'fawn_seed': diff --git a/main.py b/main.py index 9f32a0ed..e3f233f5 100644 --- a/main.py +++ b/main.py @@ -108,7 +108,7 @@ class Game: angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'} #bandaid to know about stones tractor_next_moves = astar_search_object.astarsearch( - [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.blocks.stones, self.flower_body) + [self.tractor.x, self.tractor.y, angles[self.tractor.angle]], [random_x, random_y], self.stone_body, self.flower_body) else: self.tractor.move(tractor_next_moves.pop(0)[0], self.cell_size, self.cell_number) elif event.type == QUIT: From b9fba2067641f08a1244df58927eab35bc803b38 Mon Sep 17 00:00:00 2001 From: Aliaksei Brown Date: Sun, 4 Jun 2023 10:35:05 +0200 Subject: [PATCH 14/21] scratch for nn --- learn_tree.py | 51 ++++++++++++++++++ neural_network/nueralnet.py | 103 ++++++++++++++++++++++++++++++++++++ 2 files changed, 154 insertions(+) create mode 100644 learn_tree.py create mode 100644 neural_network/nueralnet.py diff --git a/learn_tree.py b/learn_tree.py new file mode 100644 index 00000000..22443ea8 --- /dev/null +++ b/learn_tree.py @@ -0,0 +1,51 @@ +from collections import Counter + +def tree_learn(examples, attributes, default_class): + if len(examples) == 0: + return default_class + + if all(examples[0][-1] == example[-1] for example in examples): + return examples[0][-1] + + if len(attributes) == 0: + class_counts = Counter(example[-1] for example in examples) + majority_class = class_counts.most_common(1)[0][0] + return majority_class + + # Choose the attribute A as the root of the decision tree + A = select_attribute(attributes, examples) + + tree = {A: {}} + new_attributes = [attr for attr in attributes if attr != A] + new_default_class = Counter(example[-1] for example in examples).most_common(1)[0][0] + + for value in get_attribute_values(A): + new_examples = [example for example in examples if example[attributes.index(A)] == value] + subtree = tree_learn(new_examples, new_attributes, new_default_class) + tree[A][value] = subtree + + return tree + +# Helper function: Select the best attribute based on a certain criterion (e.g., information gain) +def select_attribute(attributes, examples): + # Implement your attribute selection criterion here + pass + +# Helper function: Get the possible values of an attribute from the examples +def get_attribute_values(attribute): + # Implement your code to retrieve the attribute values from the examples here + pass + +# Example usage with coordinates +examples = [ + [1, 2, 'A'], + [3, 4, 'A'], + [5, 6, 'B'], + [7, 8, 'B'] +] + +attributes = ['x', 'y'] +default_class = 'unknown' + +decision_tree = tree_learn(examples, attributes, default_class) +print(decision_tree) \ No newline at end of file diff --git a/neural_network/nueralnet.py b/neural_network/nueralnet.py new file mode 100644 index 00000000..d08bb488 --- /dev/null +++ b/neural_network/nueralnet.py @@ -0,0 +1,103 @@ +#imports +import torch +import torch.nn as nn +import torch.optim as optim +import torch.nn.functional as F +from torch.utils.data import DataLoader +import torchvision.datasets as datasets +import torchvision.transforms as transforms + +#create fully connected network +class NN(nn.Module): + def __init__(self, input_size, num_classes): #1 layer (28x28 = 784 nodes) + super(NN,self)._init_() + self.fc1 = nn.Linear(input_size, 50) + self.fc2 = nn.Linear(50,num_classes) + + def forward(self, x): + x = F.relu(self.fc1(x)) + x = self.fc2(x) + return x + +# model = NN(784, 10) +# x = torch.rand(64, 784) +# print(model(x).shape) + +#set device +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +#hyperparameters +input_size = 784 +num_classes = 10 +learning_rate = 0.001 +batch_size = 64 +num_epochs = 1 + +#load data +train_dataset = datasets.MNIST(root='dataset/', train = True, transform = transforms.toTensor(), download = True) +train_loader = DataLoader(dataset= train_dataset, batch_size = batch_size, shuffle = True) +test_dataset = datasets.MNIST(root='dataset/', train = False, transform = transforms.toTensor(), download = True) +test_loader = DataLoader(dataset= test_dataset, batch_size = batch_size, shuffle = True) + + +#initialize network +model = NN(input_size=input_size, num_classes=num_classes).to(device) + +#loss and optimizer +criterion = nn.CrossEntropyLoss() +optimizer = optim.Adam(model.parameters(), lr=learning_rate) + +#train network +for epoch in range(num_epochs): + for batch_idx, (data, targets) in enumerate(train_loader): + #get data to cuda if possible + data = data.to(device = device) + targets = targets.to(device = device) + + #get to correct shape + data = data.reshape(data.shape[0], -1) + + # forward + scores = model(data) + loss = criterion(scores, targets) + + #backward + optimizer.zero_grad() + loss.backward() + + #gradient descent or adam step + optimizer.step() + + +#check accuracy on training and test to see how good our model + +def check_accuracy(loader, model): + if loader.dataset.train: + print("Checking accuracy on training data") + else: + print("Checking accuracy on test data") + + num_correct = 0 + num_samples = 0 + model.eval() + + with torch.no_grad(): + for x,y in loader: + x = x.to(device = device) + y = y.to(device = device) + x = x.reshape(x.shape[0], -1) + + scores = model(x) + _, predictions = scores.max(1) + num_correct += (predictions == y).sum() + num_samples += predictions.size(0) + + print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}') + + model.train() + return acc + +check_accuracy(train_loader, model) +check_accuracy(test_loader, model) + + From d4e382a7f0d48694c9daa22af0178b4b5c2e2aa9 Mon Sep 17 00:00:00 2001 From: s473558 Date: Mon, 5 Jun 2023 03:35:16 +0200 Subject: [PATCH 15/21] begin image recognition neural network implementation --- neural_network/datasets.py | 42 ++++++++++ neural_network/inference.py | 70 ++++++++++++++++ neural_network/model.py | 24 ++++++ neural_network/outputs/accuracy.png | Bin 0 -> 40402 bytes neural_network/outputs/loss.png | Bin 0 -> 42092 bytes neural_network/outputs/model.pth | Bin 0 -> 11528985 bytes neural_network/train.py | 119 ++++++++++++++++++++++++++++ neural_network/utils.py | 49 ++++++++++++ 8 files changed, 304 insertions(+) create mode 100644 neural_network/datasets.py create mode 100644 neural_network/inference.py create mode 100644 neural_network/model.py create mode 100644 neural_network/outputs/accuracy.png create mode 100644 neural_network/outputs/loss.png create mode 100644 neural_network/outputs/model.pth create mode 100644 neural_network/train.py create mode 100644 neural_network/utils.py diff --git a/neural_network/datasets.py b/neural_network/datasets.py new file mode 100644 index 00000000..33c7f162 --- /dev/null +++ b/neural_network/datasets.py @@ -0,0 +1,42 @@ +import torchvision +import torch +import torchvision.transforms as transforms + +from torch.utils.data import DataLoader + +BATCH_SIZE = 64 + + +train_transform = transforms.Compose([ + transforms.Resize((224, 224)), #validate that all images are 224x244 + transforms.RandomHorizontalFlip(p=0.5), + transforms.RandomVerticalFlip(p=0.5), + transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 5)), + transforms.RandomRotation(degrees=(30, 70)), #random effects are applied to prevent overfitting + transforms.ToTensor(), + transforms.Normalize( + mean=[0.5, 0.5, 0.5], + std=[0.5, 0.5, 0.5] + ) +]) + +valid_transform = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.5, 0.5, 0.5], + std=[0.5, 0.5, 0.5] + ) +]) + +train_dataset = torchvision.datasets.ImageFolder(root='./Vegetable Images/train', transform=train_transform) + +validation_dataset = torchvision.datasets.ImageFolder(root='./Vegetable Images/validation', transform=valid_transform) + +train_loader = DataLoader( + train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=0, pin_memory=True +) + +valid_loader = DataLoader( + validation_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=0, pin_memory=True +) \ No newline at end of file diff --git a/neural_network/inference.py b/neural_network/inference.py new file mode 100644 index 00000000..debffe7f --- /dev/null +++ b/neural_network/inference.py @@ -0,0 +1,70 @@ +import torch +import cv2 +import torchvision.transforms as transforms +import argparse +from model import CNNModel +# construct the argument parser +parser = argparse.ArgumentParser() +parser.add_argument('-i', '--input', + default='', + help='path to the input image') +args = vars(parser.parse_args()) + +# the computation device +device = ('cuda' if torch.cuda.is_available() else 'cpu') +# list containing all the class labels +labels = [ + 'bean', 'bitter gourd', 'bottle gourd', 'brinjal', 'broccoli', + 'cabbage', 'capsicum', 'carrot', 'cauliflower', 'cucumber', + 'papaya', 'potato', 'pumpkin', 'radish', 'tomato' + ] + +# initialize the model and load the trained weights +model = CNNModel().to(device) +checkpoint = torch.load('outputs/model.pth', map_location=device) +model.load_state_dict(checkpoint['model_state_dict']) +model.eval() + +# define preprocess transforms +transform = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(224), + transforms.ToTensor(), + transforms.Normalize( + mean=[0.5, 0.5, 0.5], + std=[0.5, 0.5, 0.5] + ) +]) + + +# read and preprocess the image +image = cv2.imread(args['input']) +# get the ground truth class +gt_class = args['input'].split('/')[-2] +orig_image = image.copy() +# convert to RGB format +image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) +image = transform(image) +# add batch dimension +image = torch.unsqueeze(image, 0) +with torch.no_grad(): + outputs = model(image.to(device)) +output_label = torch.topk(outputs, 1) +pred_class = labels[int(output_label.indices)] +cv2.putText(orig_image, + f"GT: {gt_class}", + (10, 25), + cv2.FONT_HERSHEY_SIMPLEX, + 0.6, (0, 255, 0), 2, cv2.LINE_AA +) +cv2.putText(orig_image, + f"Pred: {pred_class}", + (10, 55), + cv2.FONT_HERSHEY_SIMPLEX, + 0.6, (0, 0, 255), 2, cv2.LINE_AA +) +print(f"GT: {gt_class}, pred: {pred_class}") +cv2.imshow('Result', orig_image) +cv2.waitKey(0) +cv2.imwrite(f"outputs/{gt_class}{args['input'].split('/')[-1].split('.')[0]}.png", + orig_image) \ No newline at end of file diff --git a/neural_network/model.py b/neural_network/model.py new file mode 100644 index 00000000..2fe5ff01 --- /dev/null +++ b/neural_network/model.py @@ -0,0 +1,24 @@ +import torch.nn as nn +import torch.nn.functional as F + +class CNNModel(nn.Module): #model of the CNN type + def __init__(self): + super(CNNModel, self).__init__() + self.conv1 = nn.Conv2d(3, 32, 5) + self.conv2 = nn.Conv2d(32, 64, 5) + self.conv3 = nn.Conv2d(64, 128, 3) + self.conv4 = nn.Conv2d(128, 256, 5) + + self.fc1 = nn.Linear(256, 50) + + self.pool = nn.MaxPool2d(2, 2) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = 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