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master
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neural_net
Author | SHA1 | Date | |
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5b2a499631 | |||
3b2342a6b4 | |||
ebcecf4279 |
BIN
Tiles/Base.jpg
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After Width: | Height: | Size: 209 KiB |
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Tiles/Bend.jpg
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After Width: | Height: | Size: 192 KiB |
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Tiles/End.jpg
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After Width: | Height: | Size: 193 KiB |
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Tiles/Intersection.jpg
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After Width: | Height: | Size: 187 KiB |
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Tiles/Junction.jpg
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After Width: | Height: | Size: 178 KiB |
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Tiles/Straight.jpg
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After Width: | Height: | Size: 186 KiB |
Before Width: | Height: | Size: 9.3 KiB After Width: | Height: | Size: 9.3 KiB |
Before Width: | Height: | Size: 3.5 KiB After Width: | Height: | Size: 3.5 KiB |
Before Width: | Height: | Size: 26 KiB After Width: | Height: | Size: 26 KiB |
Before Width: | Height: | Size: 9.8 KiB After Width: | Height: | Size: 9.8 KiB |
22
collect
@ -24,7 +24,7 @@ edge [fontname="helvetica"] ;
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6 -> 10 ;
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6 -> 10 ;
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11 [label="garbage_weight <= 0.612\ngini = 0.094\nsamples = 61\nvalue = [3, 58]\nclass = no-collect"] ;
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11 [label="garbage_weight <= 0.612\ngini = 0.094\nsamples = 61\nvalue = [3, 58]\nclass = no-collect"] ;
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10 -> 11 ;
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10 -> 11 ;
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12 [label="distance <= 10.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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12 [label="garbage_type <= 2.0\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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11 -> 12 ;
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11 -> 12 ;
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13 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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13 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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12 -> 13 ;
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12 -> 13 ;
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@ -36,7 +36,7 @@ edge [fontname="helvetica"] ;
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15 -> 16 ;
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15 -> 16 ;
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17 [label="garbage_weight <= 15.925\ngini = 0.26\nsamples = 13\nvalue = [2, 11]\nclass = no-collect"] ;
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17 [label="garbage_weight <= 15.925\ngini = 0.26\nsamples = 13\nvalue = [2, 11]\nclass = no-collect"] ;
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15 -> 17 ;
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15 -> 17 ;
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18 [label="odour_intensity <= 5.724\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = collect"] ;
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18 [label="fuel <= 13561.0\ngini = 0.444\nsamples = 3\nvalue = [2, 1]\nclass = collect"] ;
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17 -> 18 ;
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17 -> 18 ;
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19 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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19 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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18 -> 19 ;
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18 -> 19 ;
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@ -54,11 +54,11 @@ edge [fontname="helvetica"] ;
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23 -> 25 ;
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23 -> 25 ;
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26 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = collect"] ;
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26 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]\nclass = collect"] ;
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25 -> 26 ;
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25 -> 26 ;
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27 [label="space_occupied <= 0.936\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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27 [label="days_since_last_collection <= 22.0\ngini = 0.5\nsamples = 2\nvalue = [1, 1]\nclass = collect"] ;
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25 -> 27 ;
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25 -> 27 ;
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28 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
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28 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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27 -> 28 ;
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27 -> 28 ;
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29 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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29 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = no-collect"] ;
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27 -> 29 ;
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27 -> 29 ;
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30 [label="odour_intensity <= 7.156\ngini = 0.292\nsamples = 107\nvalue = [88, 19]\nclass = collect"] ;
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30 [label="odour_intensity <= 7.156\ngini = 0.292\nsamples = 107\nvalue = [88, 19]\nclass = collect"] ;
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0 -> 30 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
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0 -> 30 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
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@ -88,18 +88,14 @@ edge [fontname="helvetica"] ;
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40 -> 42 ;
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40 -> 42 ;
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43 [label="gini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = no-collect"] ;
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43 [label="gini = 0.0\nsamples = 8\nvalue = [0, 8]\nclass = no-collect"] ;
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42 -> 43 ;
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42 -> 43 ;
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44 [label="days_since_last_collection <= 20.0\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = no-collect"] ;
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44 [label="distance <= 24.0\ngini = 0.48\nsamples = 10\nvalue = [4, 6]\nclass = no-collect"] ;
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42 -> 44 ;
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42 -> 44 ;
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45 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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45 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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44 -> 45 ;
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44 -> 45 ;
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46 [label="paid_on_time <= 0.5\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = no-collect"] ;
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46 [label="space_occupied <= 0.243\ngini = 0.375\nsamples = 8\nvalue = [2, 6]\nclass = no-collect"] ;
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44 -> 46 ;
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44 -> 46 ;
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47 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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47 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = collect"] ;
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46 -> 47 ;
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46 -> 47 ;
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48 [label="space_occupied <= 0.243\ngini = 0.245\nsamples = 7\nvalue = [1, 6]\nclass = no-collect"] ;
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48 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = no-collect"] ;
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46 -> 48 ;
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46 -> 48 ;
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49 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = collect"] ;
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48 -> 49 ;
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50 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]\nclass = no-collect"] ;
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48 -> 50 ;
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}
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}
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BIN
collect.pdf
@ -1,11 +1,16 @@
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from heuristicfn import heuristicfn
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from heuristicfn import heuristicfn
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FIELDWIDTH = 50
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FIELDWIDTH = 50
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TURN_FUEL_COST = 10
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TURN_FUEL_COST = 10
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MOVE_FUEL_COST = 200
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MOVE_FUEL_COST = 200
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MAX_FUEL = 20000
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MAX_FUEL = 20000
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MAX_SPACE = 5
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MAX_SPACE = 5
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MAX_WEIGHT = 200
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MAX_WEIGHT = 400
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MAX_WEIGHT_GLASS = 100
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MAX_WEIGHT_MIXED = 100
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MAX_WEIGHT_PAPER = 100
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MAX_WEIGHT_PLASTIC = 100
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class GarbageTruck:
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class GarbageTruck:
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@ -18,6 +23,10 @@ class GarbageTruck:
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self.fuel = MAX_FUEL
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self.fuel = MAX_FUEL
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self.free_space = MAX_SPACE
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self.free_space = MAX_SPACE
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self.weight_capacity = MAX_WEIGHT
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self.weight_capacity = MAX_WEIGHT
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self.weight_capacity_glass = MAX_WEIGHT_GLASS
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self.weight_capacity_mixed = MAX_WEIGHT_MIXED
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self.weight_capacity_paper = MAX_WEIGHT_PAPER
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self.weight_capacity_plastic = MAX_WEIGHT_PLASTIC
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self.rect = rect
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self.rect = rect
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self.orientation = orientation
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self.orientation = orientation
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self.request_list = request_list #lista domów do odwiedzenia
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self.request_list = request_list #lista domów do odwiedzenia
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@ -78,10 +87,33 @@ class GarbageTruck:
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def collect(self):
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def collect(self, garbage_type):
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if self.rect.x == self.dump_x and self.rect.y == self.dump_y:
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if self.rect.x == self.dump_x and self.rect.y == self.dump_y:
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self.fuel = MAX_FUEL
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self.fuel = MAX_FUEL
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self.free_space = MAX_SPACE
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self.free_space = MAX_SPACE
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self.weight_capacity = MAX_WEIGHT
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self.weight_capacity = MAX_WEIGHT
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print(f'agent at ({self.rect.x}, {self.rect.y}); fuel: {self.fuel}; free space: {self.free_space}; weight capacity: {self.weight_capacity}')
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self.weight_capacity_plastic = MAX_WEIGHT_PLASTIC
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self.weight_capacity_mixed = MAX_WEIGHT_MIXED
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self.weight_capacity_glass = MAX_WEIGHT_GLASS
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self.weight_capacity_paper = MAX_WEIGHT_PAPER
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request = self.request_list[0]
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if garbage_type == "glass":
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if request.weight > self.weight_capacity_glass:
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return 1
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self.weight_capacity_glass -= request.weight
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elif garbage_type == "mixed":
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if request.weight > self.weight_capacity_mixed:
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return 1
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self.weight_capacity_mixed -= request.weight
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elif garbage_type == "paper":
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if request.weight > self.weight_capacity_paper:
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return 1
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self.weight_capacity_paper -= request.weight
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elif garbage_type == "plastic":
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if request.weight > self.weight_capacity_plastic:
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return 1
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self.weight_capacity_plastic -= request.weight
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print(f'agent at ({self.rect.x}, {self.rect.y}); fuel: {self.fuel}; free space: {self.free_space}; weight capacity: {self.weight_capacity}, glass_capacity: {self.weight_capacity_glass}, mixed_capacity: {self.weight_capacity_mixed}, paper_capacity: {self.weight_capacity_paper}, plastic_capacity: {self.weight_capacity_plastic}')
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return 0
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pass
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pass
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@ -1,3 +1,2 @@
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def heuristicfn(startx, starty, goalx, goaly):
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def heuristicfn(startx, starty, goalx, goaly):
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return abs(startx - goalx) + abs(starty - goaly)
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return abs(startx - goalx) + abs(starty - goaly)
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# return pow(((startx//50)-(starty//50)),2) + pow(((goalx//50)-(goaly//50)),2)
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44
loadmodel.py
Normal file
@ -0,0 +1,44 @@
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import torch
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import torchvision
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import torchvision.transforms as transforms
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import PIL.Image as Image
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import os
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def classify(image_path):
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model = torch.load('./model_training/garbage_model.pth')
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mean = [0.6908, 0.6612, 0.6218]
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std = [0.1947, 0.1926, 0.2086]
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classes = [
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"glass",
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"mixed",
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"paper",
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"plastic",
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]
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image_transforms = transforms.Compose([
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transforms.Resize((128, 128)),
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transforms.ToTensor(),
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transforms.Normalize(torch.Tensor(mean), torch.Tensor(std))
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])
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model = model.eval()
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image = Image.open(image_path)
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image = image_transforms(image).float()
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image = image.unsqueeze(0)
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output = model(image)
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_, predicted = torch.max(output.data, 1)
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label = os.path.basename(os.path.dirname(image_path))
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prediction = classes[predicted.item()]
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print(f"predicted: {prediction}")
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if label == prediction:
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print("predicted correctly.")
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else:
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print("predicted incorrectly.")
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return prediction
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# classify("./model_training/test.jpg")
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61
main.py
@ -1,7 +1,6 @@
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import pygame
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import pygame
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from treelearn import treelearn
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from treelearn import treelearn
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import loadmodel
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from astar import astar
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from astar import astar
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from state import State
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from state import State
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import time
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import time
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@ -9,6 +8,7 @@ from garbage_truck import GarbageTruck
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from heuristicfn import heuristicfn
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from heuristicfn import heuristicfn
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from map import randomize_map
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from map import randomize_map
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pygame.init()
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pygame.init()
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WIDTH, HEIGHT = 800, 800
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WIDTH, HEIGHT = 800, 800
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window = pygame.display.set_mode((WIDTH, HEIGHT))
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window = pygame.display.set_mode((WIDTH, HEIGHT))
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@ -18,14 +18,18 @@ AGENT = pygame.transform.scale(AGENT_IMG, (50, 50))
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FPS = 10
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FPS = 10
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FIELDCOUNT = 16
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FIELDCOUNT = 16
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FIELDWIDTH = 50
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FIELDWIDTH = 50
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BASE_IMG = pygame.image.load("Tiles/Base.jpg")
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BASE = pygame.transform.scale(BASE_IMG, (50, 50))
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GRASS_IMG = pygame.image.load("grass.png")
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def draw_window(agent, fields, flip, turn):
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GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
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def draw_window(agent, fields, flip):
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if flip:
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if flip:
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direction = pygame.transform.flip(AGENT, True, False)
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direction = pygame.transform.flip(AGENT, True, False)
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if turn:
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direction = pygame.transform.rotate(AGENT, -90)
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else:
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else:
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direction = pygame.transform.flip(AGENT, False, False)
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direction = pygame.transform.flip(AGENT, False, False)
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if turn:
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direction = pygame.transform.rotate(AGENT, 90)
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for i in range(16):
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for i in range(16):
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for j in range(16):
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for j in range(16):
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window.blit(fields[i][j], (i * 50, j * 50))
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window.blit(fields[i][j], (i * 50, j * 50))
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@ -37,40 +41,63 @@ def main():
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clf = treelearn()
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clf = treelearn()
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clock = pygame.time.Clock()
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clock = pygame.time.Clock()
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run = True
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run = True
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fields, priority_array, request_list = randomize_map()
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fields, priority_array, request_list, imgpath_array = randomize_map()
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agent = GarbageTruck(0, 0, pygame.Rect(0, 0, 50, 50), 0, request_list, clf) # tworzenie pola dla agenta
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agent = GarbageTruck(0, 0, pygame.Rect(0, 0, 50, 50), 0, request_list, clf) # tworzenie pola dla agenta
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low_space = 0
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while run:
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while run:
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clock.tick(FPS)
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clock.tick(FPS)
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for event in pygame.event.get():
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for event in pygame.event.get():
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if event.type == pygame.QUIT:
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if event.type == pygame.QUIT:
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run = False
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run = False
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draw_window(agent, fields, False) # false = kierunek east (domyslny), true = west
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draw_window(agent, fields, False, False) # false = kierunek east (domyslny), true = west
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x, y = agent.next_destination()
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x, y = agent.next_destination()
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if x == agent.rect.x and y == agent.rect.y:
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if x == agent.rect.x and y == agent.rect.y:
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print('out of jobs')
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print('out of jobs')
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break
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break
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steps = astar(State(None, None, agent.rect.x, agent.rect.y, agent.orientation, priority_array[agent.rect.x//50][agent.rect.y//50], heuristicfn(agent.rect.x, agent.rect.y, x, y)), x, y, priority_array)
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if low_space == 1:
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x, y = 0, 0
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steps = astar(State(None, None, agent.rect.x, agent.rect.y, agent.orientation,
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priority_array[agent.rect.x//50][agent.rect.y//50],
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heuristicfn(agent.rect.x, agent.rect.y, x, y)), x, y, priority_array)
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for interm in steps:
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for interm in steps:
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if interm.action == 'LEFT':
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if interm.action == 'LEFT':
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agent.turn_left()
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agent.turn_left()
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draw_window(agent, fields, True)
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if agent.orientation == 0:
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draw_window(agent, fields, False, False)
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elif agent.orientation == 2:
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draw_window(agent, fields, True, False)
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elif agent.orientation == 1:
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draw_window(agent, fields, True, True)
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else:
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draw_window(agent, fields, False, True)
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elif interm.action == 'RIGHT':
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elif interm.action == 'RIGHT':
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agent.turn_right()
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agent.turn_right()
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draw_window(agent, fields, False)
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if agent.orientation == 0:
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draw_window(agent, fields, False, False)
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elif agent.orientation == 2:
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draw_window(agent, fields, True, False)
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elif agent.orientation == 1:
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draw_window(agent, fields, True, True)
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else:
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draw_window(agent, fields, False, True)
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elif interm.action == 'FORWARD':
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elif interm.action == 'FORWARD':
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agent.forward()
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agent.forward()
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if agent.orientation == 0:
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if agent.orientation == 0:
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draw_window(agent, fields, False)
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draw_window(agent, fields, False, False)
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elif agent.orientation == 2:
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elif agent.orientation == 2:
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draw_window(agent, fields, True)
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draw_window(agent, fields, True, False)
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elif agent.orientation == 1:
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draw_window(agent, fields, True, True)
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else:
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else:
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draw_window(agent, fields, False)
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draw_window(agent, fields, False, True)
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time.sleep(0.3)
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time.sleep(0.3)
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agent.collect()
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if (agent.rect.x // 50 != 0) or (agent.rect.y // 50 != 0):
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fields[agent.rect.x//50][agent.rect.y//50] = GRASS
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garbage_type = loadmodel.classify(imgpath_array[agent.rect.x // 50][agent.rect.y // 50])
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priority_array[agent.rect.x//50][agent.rect.y//50] = 1
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low_space = agent.collect(garbage_type)
|
||||||
time.sleep(0.5)
|
|
||||||
|
|
||||||
|
fields[agent.rect.x//50][agent.rect.y//50] = BASE
|
||||||
|
priority_array[agent.rect.x//50][agent.rect.y//50] = 100
|
||||||
|
time.sleep(0.5)
|
||||||
|
|
||||||
pygame.quit()
|
pygame.quit()
|
||||||
|
|
||||||
|
131
map.py
@ -1,44 +1,127 @@
|
|||||||
import pygame, random
|
import pygame as pg
|
||||||
|
import random
|
||||||
from request import Request
|
from request import Request
|
||||||
|
|
||||||
DIRT_IMG = pygame.image.load("dirt.jpg")
|
|
||||||
DIRT = pygame.transform.scale(DIRT_IMG, (50, 50))
|
|
||||||
GRASS_IMG = pygame.image.load("grass.png")
|
|
||||||
GRASS = pygame.transform.scale(GRASS_IMG, (50, 50))
|
|
||||||
SAND_IMG = pygame.image.load("sand.jpeg")
|
|
||||||
SAND = pygame.transform.scale(SAND_IMG, (50, 50))
|
|
||||||
COBBLE_IMG = pygame.image.load("cobble.jpeg")
|
|
||||||
COBBLE = pygame.transform.scale(COBBLE_IMG, (50, 50))
|
|
||||||
|
|
||||||
def randomize_map(): # tworzenie mapy z losowymi polami
|
STRAIGHT_IMG = pg.image.load("Tiles/Straight.jpg")
|
||||||
|
STRAIGHT_VERTICAL = pg.transform.scale(STRAIGHT_IMG, (50, 50))
|
||||||
|
STRAIGHT_HORIZONTAL = pg.transform.rotate(STRAIGHT_VERTICAL, 270)
|
||||||
|
BASE_IMG = pg.image.load("Tiles/Base.jpg")
|
||||||
|
BASE = pg.transform.scale(BASE_IMG, (50, 50))
|
||||||
|
BEND_IMG = pg.image.load("Tiles/Bend.jpg")
|
||||||
|
BEND1 = pg.transform.scale(BEND_IMG, (50, 50))
|
||||||
|
BEND2 = pg.transform.rotate(BEND1, 90)
|
||||||
|
BEND3 = pg.transform.rotate(pg.transform.flip(pg.transform.rotate(BEND1, 180), True, True), 180)
|
||||||
|
BEND4 = pg.transform.rotate(BEND1, -90)
|
||||||
|
INTERSECTION_IMG = pg.image.load("Tiles/Intersection.jpg")
|
||||||
|
INTERSECTION = pg.transform.scale(INTERSECTION_IMG, (50, 50))
|
||||||
|
JUNCTION_IMG = pg.image.load("Tiles/Junction.jpg")
|
||||||
|
JUNCTION_SOUTH = pg.transform.scale(JUNCTION_IMG, (50, 50))
|
||||||
|
JUNCTION_NORTH = pg.transform.rotate(pg.transform.flip(JUNCTION_SOUTH, True, False), 180)
|
||||||
|
JUNCTION_EAST = pg.transform.rotate(JUNCTION_SOUTH, -90)
|
||||||
|
JUNCTION_WEST = pg.transform.rotate(JUNCTION_SOUTH, 90)
|
||||||
|
END_IMG = pg.image.load("Tiles/End.jpg")
|
||||||
|
END1 = pg.transform.flip(pg.transform.rotate(pg.transform.scale(END_IMG, (50, 50)), 180), False, True)
|
||||||
|
END2 = pg.transform.rotate(END1, 90)
|
||||||
|
DIRT_IMG = pg.image.load("Tiles/dirt.jpg")
|
||||||
|
DIRT = pg.transform.scale(DIRT_IMG, (50, 50))
|
||||||
|
GRASS_IMG = pg.image.load("Tiles/grass.png")
|
||||||
|
GRASS = pg.transform.scale(GRASS_IMG, (50, 50))
|
||||||
|
SAND_IMG = pg.image.load("Tiles/sand.jpeg")
|
||||||
|
SAND = pg.transform.scale(SAND_IMG, (50, 50))
|
||||||
|
COBBLE_IMG = pg.image.load("Tiles/cobble.jpeg")
|
||||||
|
COBBLE = pg.transform.scale(COBBLE_IMG, (50, 50))
|
||||||
|
|
||||||
|
|
||||||
|
def randomize_map(): # tworzenie mapy z losowymi polami
|
||||||
request_list = []
|
request_list = []
|
||||||
field_array_1 = []
|
field_array_1 = []
|
||||||
field_array_2 = []
|
field_array_2 = []
|
||||||
|
imgpath_array = [[0 for x in range(16)] for x in range(16)]
|
||||||
field_priority = []
|
field_priority = []
|
||||||
|
map_array = [['b', 'sh', 'sh', 'sh', 'sh', 'jw', 'sh', 'sh', 'sh', 'sh', 'jw', 'sh', 'sh', 'sh', 'b3', 'g'],
|
||||||
|
['sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'g', 'g', 'sv', 'g'],
|
||||||
|
['sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'gr', 'g', 'sv', 'g'],
|
||||||
|
['js', 'sh', 'sh', 'sh', 'sh', 'i', 'sh', 'sh', 'sh', 'sh', 'jn', 'g', 'gr', 'g', 'sv', 'g'],
|
||||||
|
['sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'gr', 'g', 'sv', 'g'],
|
||||||
|
['sv', 'g', 'gr', 'gr', 'g', 'sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'g', 'g', 'sv', 'g'],
|
||||||
|
['sv', 'g', 'gr', 'gr', 'g', 'js', 'sh', 'sh', 'sh', 'sh', 'i', 'sh', 'sh', 'sh', 'jn', 'g'],
|
||||||
|
['sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'g', 'g', 'sv', 'g'],
|
||||||
|
['b1', 'sh', 'jw', 'sh', 'sh', 'jn', 'g', 'gr', 'gr', 'g', 'sv', 'g', 'gr', 'g', 'sv', 'g'],
|
||||||
|
['g', 'g', 'sv', 'g', 'g', 'sv', 'g', 'gr', 'gr', 'g', 'sv', 'g', 'g', 'g', 'sv', 'g'],
|
||||||
|
['gr', 'g', 'sv', 'g', 'g', 'sv', 'g', 'gr', 'gr', 'g', 'js', 'sh', 'sh', 'sh', 'jn', 'g'],
|
||||||
|
['gr', 'g', 'sv', 'g', 'g', 'sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'g', 'g', 'sv', 'g'],
|
||||||
|
['gr', 'g', 'js', 'sh', 'sh', 'i', 'sh', 'sh', 'sh', 'sh', 'jn', 'g', 'gr', 'g', 'sv', 'g'],
|
||||||
|
['gr', 'g', 'sv', 'g', 'g', 'sv', 'g', ' g', 'g', 'g', 'sv', 'g', 'gr', 'g', 'sv', 'g'],
|
||||||
|
['gr', 'g', 'sv', 'g', 'g', 'sv', 'g', 'g', 'g', 'g', 'sv', 'g', 'g', 'g', 'sv', 'g'],
|
||||||
|
['gr', 'g', 'b1', 'sh', 'sh', 'je', 'sh', 'sh', 'sh', 'sh', 'je', 'sh', 'sh', 'sh', 'b4', 'g'],
|
||||||
|
]
|
||||||
|
|
||||||
for i in range(16):
|
for i in range(16):
|
||||||
temp_priority = []
|
temp_priority = []
|
||||||
for j in range(16):
|
for j in range(16):
|
||||||
if i in (0, 1) and j in (0, 1):
|
if map_array[i][j] == 'b':
|
||||||
field_array_2.append(GRASS)
|
field_array_2.append(BASE)
|
||||||
temp_priority.append(1)
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'b3':
|
||||||
|
field_array_2.append(BEND3)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'b4':
|
||||||
|
field_array_2.append(BEND4)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'b1':
|
||||||
|
field_array_2.append(BEND1)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'sh':
|
||||||
|
field_array_2.append(STRAIGHT_VERTICAL)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'sv':
|
||||||
|
field_array_2.append(STRAIGHT_HORIZONTAL)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'i':
|
||||||
|
field_array_2.append(INTERSECTION)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'je':
|
||||||
|
field_array_2.append(JUNCTION_EAST)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'jw':
|
||||||
|
field_array_2.append(JUNCTION_WEST)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'js':
|
||||||
|
field_array_2.append(JUNCTION_SOUTH)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'jn':
|
||||||
|
field_array_2.append(JUNCTION_NORTH)
|
||||||
|
temp_priority.append(1)
|
||||||
|
elif map_array[i][j] == 'gr':
|
||||||
|
field_array_2.append(BASE)
|
||||||
|
temp_priority.append(1000)
|
||||||
else:
|
else:
|
||||||
prob = random.uniform(0, 100)
|
prob = random.uniform(0, 100)
|
||||||
if 0 <= prob <= 12:
|
if 0 <= prob <= 20:
|
||||||
field_array_2.append(COBBLE)
|
garbage_type = random.choice(['glass', 'mixed', 'paper', 'plastic'])
|
||||||
|
garbage_image_number = random.randrange(1, 100)
|
||||||
|
GARBAGE_IMG = pg.image.load(
|
||||||
|
f"./model_training/test_dataset/{garbage_type}/{garbage_type} ({str(garbage_image_number)}).jpg")
|
||||||
|
GARBAGE = pg.transform.scale(GARBAGE_IMG, (50, 50))
|
||||||
|
field_array_2.append(GARBAGE)
|
||||||
|
imgpath_array[i][j] = (
|
||||||
|
f"./model_training/test_dataset/{garbage_type}/{garbage_type} ({str(garbage_image_number)}).jpg")
|
||||||
|
|
||||||
temp_priority.append(100)
|
temp_priority.append(100)
|
||||||
request_list.append(Request(
|
request_list.append(Request(
|
||||||
i*50,j*50, #lokacja
|
i * 50, j * 50, # lokacja
|
||||||
random.randint(0,3), #typ śmieci
|
random.randint(0, 3), # typ śmieci
|
||||||
random.random(), #objętość śmieci
|
random.random(), # objętość śmieci
|
||||||
random.randint(0,30), #ostatni odbiór
|
random.randint(0, 30), # ostatni odbiór
|
||||||
random.randint(0,1), #czy opłacone w terminie
|
random.randint(0, 1), # czy opłacone w terminie
|
||||||
random.random() * 10, #intensywność odoru
|
random.random() * 10, # intensywność odoru
|
||||||
random.random() * 50 #waga śmieci
|
random.random() * 50 # waga śmieci
|
||||||
))
|
))
|
||||||
else:
|
else:
|
||||||
field_array_2.append(GRASS)
|
field_array_2.append(BASE)
|
||||||
temp_priority.append(1)
|
temp_priority.append(1000)
|
||||||
field_array_1.append(field_array_2)
|
field_array_1.append(field_array_2)
|
||||||
field_array_2 = []
|
field_array_2 = []
|
||||||
field_priority.append(temp_priority)
|
field_priority.append(temp_priority)
|
||||||
return field_array_1, field_priority, request_list
|
return field_array_1, field_priority, request_list, imgpath_array
|
||||||
|
BIN
model_training/garbage_model.pth
Normal file
177
model_training/main.py
Normal file
@ -0,0 +1,177 @@
|
|||||||
|
import os
|
||||||
|
import torch
|
||||||
|
import torchvision
|
||||||
|
import torchvision.transforms as transforms
|
||||||
|
from torch.utils.data import Dataset, random_split, DataLoader
|
||||||
|
from torchvision.transforms import Compose, Lambda, ToTensor, Resize, CenterCrop, Normalize
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import numpy as np
|
||||||
|
import torchvision.models as models
|
||||||
|
import torch.nn as nn
|
||||||
|
import torch.optim as optim
|
||||||
|
|
||||||
|
def main():
|
||||||
|
torch.manual_seed(42)
|
||||||
|
# input_size = 49152
|
||||||
|
# hidden_sizes = [64, 128]
|
||||||
|
# output_size = 10
|
||||||
|
|
||||||
|
classes = os.listdir('./train_dataset')
|
||||||
|
print(classes)
|
||||||
|
mean = [0.6908, 0.6612, 0.6218]
|
||||||
|
std = [0.1947, 0.1926, 0.2086]
|
||||||
|
|
||||||
|
training_dataset_path = './train_dataset'
|
||||||
|
training_transforms = transforms.Compose([Resize((128,128)), ToTensor(), Normalize(torch.Tensor(mean), torch.Tensor(std))])
|
||||||
|
train_dataset = torchvision.datasets.ImageFolder(root=training_dataset_path, transform=training_transforms)
|
||||||
|
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=32, shuffle=True)
|
||||||
|
|
||||||
|
testing_dataset_path = './test_dataset'
|
||||||
|
testing_transforms = transforms.Compose([Resize((128,128)), ToTensor(), Normalize(torch.Tensor(mean), torch.Tensor(std))])
|
||||||
|
test_dataset = torchvision.datasets.ImageFolder(root=testing_dataset_path, transform=testing_transforms)
|
||||||
|
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=32, shuffle=False)
|
||||||
|
|
||||||
|
# Mean and Standard Deviation approximations
|
||||||
|
def get_mean_and_std(loader):
|
||||||
|
mean = 0.
|
||||||
|
std = 0.
|
||||||
|
total_images_count = 0
|
||||||
|
for images, _ in loader:
|
||||||
|
image_count_in_a_batch = images.size(0)
|
||||||
|
#print(images.shape)
|
||||||
|
images = images.view(image_count_in_a_batch, images.size(1), -1)
|
||||||
|
#print(images.shape)
|
||||||
|
mean += images.mean(2).sum(0)
|
||||||
|
std += images.std(2).sum(0)
|
||||||
|
total_images_count += image_count_in_a_batch
|
||||||
|
mean /= total_images_count
|
||||||
|
std /= total_images_count
|
||||||
|
return mean, std
|
||||||
|
|
||||||
|
print(get_mean_and_std(train_loader))
|
||||||
|
|
||||||
|
# Show images with applied transformations
|
||||||
|
def show_transformed_images(dataset):
|
||||||
|
loader = torch.utils.data.DataLoader(dataset, batch_size=6, shuffle=True)
|
||||||
|
batch = next(iter(loader))
|
||||||
|
images, labels = batch
|
||||||
|
|
||||||
|
grid = torchvision.utils.make_grid(images, nrow=3)
|
||||||
|
plt.figure(figsize=(11,11))
|
||||||
|
plt.imshow(np.transpose(grid, (1,2,0)))
|
||||||
|
print('labels: ', labels)
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
show_transformed_images(train_dataset)
|
||||||
|
|
||||||
|
# Neural network training:
|
||||||
|
def set_device():
|
||||||
|
if torch.cuda.is_available():
|
||||||
|
dev = "cuda:0"
|
||||||
|
else:
|
||||||
|
dev = "cpu"
|
||||||
|
return torch.device(dev)
|
||||||
|
|
||||||
|
|
||||||
|
def train_nn(model,train_loader,test_loader,criterion,optimizer,n_epochs):
|
||||||
|
device = set_device()
|
||||||
|
best_acc = 0
|
||||||
|
|
||||||
|
for epoch in range(n_epochs):
|
||||||
|
print("Epoch number %d " % (epoch+1))
|
||||||
|
model.train()
|
||||||
|
running_loss = 0.0
|
||||||
|
running_correct = 0.0
|
||||||
|
total = 0
|
||||||
|
|
||||||
|
for data in train_loader:
|
||||||
|
images, labels = data
|
||||||
|
images = images.to(device)
|
||||||
|
labels = labels.to(device)
|
||||||
|
total += labels.size(0)
|
||||||
|
|
||||||
|
# Back propagation
|
||||||
|
optimizer.zero_grad()
|
||||||
|
outputs = model(images)
|
||||||
|
_, predicted = torch.max(outputs.data, 1)
|
||||||
|
loss = criterion(outputs, labels)
|
||||||
|
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
running_loss += loss.item()
|
||||||
|
running_correct += (labels==predicted).sum().item()
|
||||||
|
|
||||||
|
epoch_loss = running_loss/len(train_loader)
|
||||||
|
epoch_acc = 100.00 * running_correct / total
|
||||||
|
|
||||||
|
print(" - Training dataset. Got %d out of %d images correctly (%.3f%%). Epoch loss: %.3f" % (running_correct, total, epoch_acc, epoch_loss))
|
||||||
|
test_dataset_acc = evaluate_model_on_test_set(model, test_loader)
|
||||||
|
|
||||||
|
if(test_dataset_acc > best_acc):
|
||||||
|
best_acc = test_dataset_acc
|
||||||
|
save_checkpoint(model, epoch, optimizer, best_acc)
|
||||||
|
|
||||||
|
print("Finished")
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def evaluate_model_on_test_set(model, test_loader):
|
||||||
|
model.eval()
|
||||||
|
predicted_correctly_on_epoch = 0
|
||||||
|
total = 0
|
||||||
|
device = set_device()
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
for data in test_loader:
|
||||||
|
images, labels = data
|
||||||
|
images = images.to(device)
|
||||||
|
labels = labels.to(device)
|
||||||
|
total += labels.size(0)
|
||||||
|
|
||||||
|
outputs = model(images)
|
||||||
|
_, predicted = torch.max(outputs.data, 1)
|
||||||
|
predicted_correctly_on_epoch += (predicted == labels).sum().item()
|
||||||
|
|
||||||
|
epoch_acc = 100.0 * predicted_correctly_on_epoch / total
|
||||||
|
print(" - Testing dataset. Got %d out of %d images correctly (%.3f%%)" % (predicted_correctly_on_epoch, total, epoch_acc))
|
||||||
|
|
||||||
|
return epoch_acc
|
||||||
|
|
||||||
|
|
||||||
|
# Saving the checkpoint:
|
||||||
|
def save_checkpoint(model, epoch, optimizer, best_acc):
|
||||||
|
state = {
|
||||||
|
'epoch': epoch+1,
|
||||||
|
'model': model.state_dict(),
|
||||||
|
'best_accuracy': best_acc,
|
||||||
|
'optimizer': optimizer.state_dict(),
|
||||||
|
}
|
||||||
|
torch.save(state, 'model_best_checkpoint.zip')
|
||||||
|
|
||||||
|
|
||||||
|
resnet18_model = models.resnet18(pretrained=True) #Increase n_epochs if False
|
||||||
|
num_features = resnet18_model.fc.in_features
|
||||||
|
number_of_classes = 4
|
||||||
|
resnet18_model.fc = nn.Linear(num_features, number_of_classes)
|
||||||
|
device = set_device()
|
||||||
|
resnet_18_model = resnet18_model.to(device)
|
||||||
|
loss_fn = nn.CrossEntropyLoss() #criterion
|
||||||
|
|
||||||
|
optimizer = optim.SGD(resnet_18_model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.003)
|
||||||
|
train_nn(resnet_18_model, train_loader, test_loader, loss_fn, optimizer, 5)
|
||||||
|
|
||||||
|
|
||||||
|
# Saving the model:
|
||||||
|
checkpoint = torch.load('model_best_checkpoint.pth.zip')
|
||||||
|
|
||||||
|
resnet18_model = models.resnet18()
|
||||||
|
num_features = resnet18_model.fc.in_features
|
||||||
|
number_of_classes = 4
|
||||||
|
resnet18_model.fc = nn.Linear(num_features, number_of_classes)
|
||||||
|
resnet18_model.load_state_dict(checkpoint['model'])
|
||||||
|
|
||||||
|
torch.save(resnet18_model, 'garbage_model.pth')
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
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