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3c5b05a7bb
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3c5b05a7bb | ||
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ffe1ff9565 |
20
main.py
@ -4,8 +4,10 @@ import random
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import land
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import tractor
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import blocks
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import nn
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import astar_search
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from pygame.locals import *
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import numpy as np
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class Game:
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@ -23,6 +25,8 @@ class Game:
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self.grass_body = []
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self.red_block = [] #aim block
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#self.one_body = []
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self.fawn_seed_body = []
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self.fawn_wheat_body = []
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@ -59,6 +63,15 @@ class Game:
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# self.potato = blocks.Blocks(self.surface, self.cell_size)
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# self.potato.locate_soil('black earth', 6, 1, [])
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#class_names = ['Pumpkin', 'Tomato', 'Carrot']
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self.neural_network = nn.NNModel("neural_network/save/second_model.pth")
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# self.pumpkin_batch = self.neural_network.input_image("resources/pampkin.png")
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# self.tomato_batch = self.neural_network.input_image("resources/tomato.png")
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# self.carrot_batch = self.neural_network.input_image("resources/carrot.png")
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self.tractor = tractor.Tractor(self.surface, self.cell_size)
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self.tractor.draw()
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@ -101,9 +114,16 @@ class Game:
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random_x = random.randrange(0, self.cell_number * self.cell_size, 50)
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random_y = random.randrange(0, self.cell_number * self.cell_size, 50)
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print("Generated target: ",random_x, random_y)
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#aim-blue block
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if self.red_block:
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self.red_block.pop()
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self.red_block.append([random_x/50, random_y/50])
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self.path_image = "resources/2.png"
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self.aim_batch = self.neural_network.input_image(self.path_image)
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self.predicate = self.neural_network.predicte(self.aim_batch)
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# below line should be later moved into tractor.py
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angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'}
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#bandaid to know about stones
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