wymiary obrazków, tiles
Tomasz Adamczyk
@ -3,53 +3,110 @@
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<change beforePath="$PROJECT_DIR$/resources/neural_network/sliced/screen_10_10.png" beforeDir="false" />
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</list>
|
||||
<option name="SHOW_DIALOG" value="false" />
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<option name="HIGHLIGHT_CONFLICTS" value="true" />
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||||
@ -214,6 +271,9 @@
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<workItem from="1622544012547" duration="1597000" />
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<workItem from="1622545712675" duration="82000" />
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<workItem from="1622546872446" duration="905000" />
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<workItem from="1622548476938" duration="57000" />
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<workItem from="1622755977663" duration="282000" />
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<workItem from="1622801431771" duration="2057000" />
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</task>
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<servers />
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</component>
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@ -230,30 +290,30 @@
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</state>
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<state x="628" y="343" key="#com.intellij.fileTypes.FileTypeChooser/0.0.1920.1080@0.0.1920.1080" timestamp="1621109431051" />
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<screen x="0" y="0" width="1920" height="1080" />
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</state>
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<state x="690" y="287" key="#com.intellij.refactoring.safeDelete.UnsafeUsagesDialog/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622536049152" />
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<state x="690" y="287" key="#com.intellij.refactoring.safeDelete.UnsafeUsagesDialog/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622802726751" />
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<state x="690" y="287" key="#com.intellij.refactoring.safeDelete.UnsafeUsagesDialog/0.0.1920.1080@0.0.1920.1080" timestamp="1621429221570" />
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<screen x="0" y="0" width="1920" height="1080" />
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</state>
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<state width="1879" height="295" key="GridCell.Tab.0.bottom/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622548325392" />
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<state width="1879" height="295" key="GridCell.Tab.0.bottom/0.0.1920.1080@0.0.1920.1080" timestamp="1621429389327" />
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<screen x="0" y="0" width="1920" height="1080" />
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<state width="1879" height="295" key="GridCell.Tab.0.center/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622548325391" />
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<state width="1879" height="295" key="GridCell.Tab.0.center/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622803709514" />
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<state width="1879" height="295" key="GridCell.Tab.0.center/0.0.1920.1080@0.0.1920.1080" timestamp="1621429389327" />
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<state width="1879" height="295" key="GridCell.Tab.0.left" timestamp="1622548325391">
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<state width="1879" height="295" key="GridCell.Tab.0.left" timestamp="1622803709514">
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<screen x="0" y="0" width="1920" height="1080" />
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</state>
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<state width="1879" height="295" key="GridCell.Tab.0.left/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622548325391" />
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<state width="1879" height="295" key="GridCell.Tab.0.left/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622803709514" />
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<state width="1879" height="295" key="GridCell.Tab.0.left/0.0.1920.1080@0.0.1920.1080" timestamp="1621429389326" />
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<state width="1879" height="295" key="GridCell.Tab.0.right" timestamp="1622548325391">
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<state width="1879" height="295" key="GridCell.Tab.0.right" timestamp="1622803709514">
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<screen x="0" y="0" width="1920" height="1080" />
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</state>
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<state width="1879" height="295" key="GridCell.Tab.0.right/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622548325391" />
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<state width="1879" height="295" key="GridCell.Tab.0.right/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622803709514" />
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<state width="1879" height="295" key="GridCell.Tab.0.right/0.0.1920.1080@0.0.1920.1080" timestamp="1621429389327" />
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<state width="1879" height="364" key="GridCell.Tab.1.bottom" timestamp="1622538962724">
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<screen x="0" y="0" width="1920" height="1080" />
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@ -275,10 +335,10 @@
|
||||
</state>
|
||||
<state width="1879" height="364" key="GridCell.Tab.1.right/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622538962724" />
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<state width="1879" height="364" key="GridCell.Tab.1.right/0.0.1920.1080@0.0.1920.1080" timestamp="1619289752045" />
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<state x="0" y="0" key="com.intellij.ide.util.TipDialog" timestamp="1622546873603">
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<state x="0" y="0" key="com.intellij.ide.util.TipDialog" timestamp="1622801461112">
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<screen x="0" y="0" width="1920" height="1080" />
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</state>
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<state x="0" y="0" key="com.intellij.ide.util.TipDialog/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622546873603" />
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<state x="0" y="0" key="com.intellij.ide.util.TipDialog/0.0.1920.1080/-1920.0.1920.1080@0.0.1920.1080" timestamp="1622801461112" />
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<state x="0" y="0" key="com.intellij.ide.util.TipDialog/0.0.1920.1080@0.0.1920.1080" timestamp="1621439087317" />
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<state x="623" y="235" width="672" height="678" key="search.everywhere.popup" timestamp="1622494466933">
|
||||
<screen x="0" y="0" width="1920" height="1080" />
|
||||
@ -300,6 +360,6 @@
|
||||
<SUITE FILE_PATH="coverage/SmartCart$copy.coverage" NAME="copy Coverage Results" MODIFIED="1622503170538" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/SmartCart$glue.coverage" NAME="glue Coverage Results" MODIFIED="1622501306368" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/SmartTractor$py.coverage" NAME="py Coverage Results" MODIFIED="1622469837941" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/SmartCart$py.coverage" NAME="py Coverage Results" MODIFIED="1622548262057" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/SmartCart$py.coverage" NAME="py Coverage Results" MODIFIED="1622803709507" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
</component>
|
||||
</project>
|
||||
@ -46,15 +46,16 @@ CART_WATER_LEVEL = 40
|
||||
DIRT = pygame.image.load(os.path.join('resources/images', 'dirt.png'))
|
||||
DIRT = pygame.transform.scale(DIRT, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
DIRT_COST = 2
|
||||
HEIGHT_AMOUNT, WIDTH_AMOUNT = 10, 10
|
||||
HEIGHT, WIDTH = BLOCK_SIZE * HEIGHT_AMOUNT, BLOCK_SIZE * WIDTH_AMOUNT
|
||||
FARMLAND_DRY = pygame.image.load(os.path.join('resources/images', 'farmland_dry.png'))
|
||||
FARMLAND_DRY = pygame.transform.scale(FARMLAND_DRY, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
FARMLAND_DRY_COST = 3
|
||||
FARMLAND_WET = pygame.image.load(os.path.join('resources/images', 'farmland_wet.png'))
|
||||
FARMLAND_WET = pygame.transform.scale(FARMLAND_WET, (BLOCK_SIZE, BLOCK_SIZE))
|
||||
FARMLAND_WET_COST = 1
|
||||
FPS = 1
|
||||
FPS = 10
|
||||
HEIGHT_AMOUNT, WIDTH_AMOUNT = 10, 10
|
||||
HEIGHT, WIDTH = BLOCK_SIZE * HEIGHT_AMOUNT, BLOCK_SIZE * WIDTH_AMOUNT
|
||||
IMAGE_SIZE_NEURAL_NETWORK = 16
|
||||
POTATOES_ADULT_COST = 4
|
||||
POTATOES_GROW_COST = 10
|
||||
POTATOES_GROW_TIME = 5
|
||||
|
||||
@ -3,6 +3,7 @@ from torch.autograd import Variable
|
||||
from torch.optim import Adam
|
||||
from torch.utils.data import DataLoader
|
||||
from torchvision.transforms import transforms
|
||||
import definitions
|
||||
import glob
|
||||
import graph
|
||||
import os
|
||||
@ -10,7 +11,7 @@ import pathlib
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torchvision
|
||||
transformer1 = transforms.Compose([transforms.Resize((150, 150)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
|
||||
transformer1 = transforms.Compose([transforms.Resize((definitions.IMAGE_SIZE_NEURAL_NETWORK, definitions.IMAGE_SIZE_NEURAL_NETWORK)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
|
||||
class ConvNet(nn.Module):
|
||||
def __init__(self, num_classes=6):
|
||||
super(ConvNet, self).__init__()
|
||||
@ -23,7 +24,7 @@ class ConvNet(nn.Module):
|
||||
self.conv3 = nn.Conv2d(in_channels=20, out_channels=32, kernel_size=3, stride=1, padding=1)
|
||||
self.bn3 = nn.BatchNorm2d(num_features=32)
|
||||
self.relu3 = nn.ReLU()
|
||||
self.fc = nn.Linear(in_features=75 * 75 * 32, out_features=num_classes)
|
||||
self.fc = nn.Linear(in_features=int(definitions.IMAGE_SIZE_NEURAL_NETWORK / 2) * int(definitions.IMAGE_SIZE_NEURAL_NETWORK / 2) * 32, out_features=num_classes)
|
||||
def forward(self, input):
|
||||
output = self.conv1(input)
|
||||
output = self.bn1(output)
|
||||
@ -34,12 +35,12 @@ class ConvNet(nn.Module):
|
||||
output = self.conv3(output)
|
||||
output = self.bn3(output)
|
||||
output = self.relu3(output)
|
||||
output = output.view(-1, 32 * 75 * 75)
|
||||
output = output.view(-1, 32 * int(definitions.IMAGE_SIZE_NEURAL_NETWORK / 2) * int(definitions.IMAGE_SIZE_NEURAL_NETWORK / 2))
|
||||
output = self.fc(output)
|
||||
return output
|
||||
def create_neural_network(): #tworzenie sieci neuronowej
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') #użyj cuda jeśli możliwe
|
||||
transformer = transforms.Compose([transforms.Resize((150, 150)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
|
||||
transformer = transforms.Compose([transforms.Resize((definitions.IMAGE_SIZE_NEURAL_NETWORK, definitions.IMAGE_SIZE_NEURAL_NETWORK)), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
|
||||
train_path = os.path.join('resources/neural_network/train/') #ścieżka do obrazków do treningu
|
||||
test_path = os.path.join('resources/neural_network/test/') #ścieżka do obrazków do testu
|
||||
train_loader = DataLoader(torchvision.datasets.ImageFolder(train_path, transform=transformer), batch_size=64, shuffle=True)
|
||||
@ -97,19 +98,21 @@ def create_neural_network(): #tworzenie sieci neuronowej
|
||||
model.eval()
|
||||
return classes, model
|
||||
def predfield(classes, istate, model): #zwraca najbliższe miejsce pola z wyrośniętą rośliną na podstawie wykrywania obrazu
|
||||
pred_path = os.path.join('resources/neural_network/sliced/') #ścieżka do obrazków do sprawdzenia
|
||||
pred_path = os.path.join('resources/neural_network/tiles/') #ścieżka do obrazków do sprawdzenia
|
||||
pred_dict = {}
|
||||
images_path = glob.glob(pred_path + '/*.png')
|
||||
x = None #x'owa pola
|
||||
y = None #y'kowa pola
|
||||
x_position = 15
|
||||
y_position = 12
|
||||
min = None
|
||||
for i in images_path: #dodajemy pocięte obrazki do listy i ustawiamy im przewidywaną metkę
|
||||
pred_dict[i[i.rfind('/') + 1:]] = prediction1(classes, i, model, transformer1)
|
||||
for img_name, field in pred_dict.items():
|
||||
if field != "random": #jeżeli metka nie jest 'random' to przypisz do x'a i y'a miejsce wyrośniętej rośliny
|
||||
if x is None and y is None:
|
||||
x = img_name[18]
|
||||
y = img_name[15]
|
||||
x = img_name[x_position]
|
||||
y = img_name[y_position]
|
||||
x = int(x)
|
||||
y = int(y)
|
||||
if x == 0:
|
||||
@ -122,8 +125,8 @@ def predfield(classes, istate, model): #zwraca najbliższe miejsce pola z wyroś
|
||||
y = y - 1
|
||||
min = len((graph.graphsearch([], [], (x, y), istate, graph.succ)))
|
||||
else:
|
||||
temp_x = img_name[18]
|
||||
temp_y = img_name[15]
|
||||
temp_x = img_name[x_position]
|
||||
temp_y = img_name[y_position]
|
||||
temp_x = int(temp_x)
|
||||
temp_y = int(temp_y)
|
||||
if temp_x == 0:
|
||||
|
||||
7
py.py
@ -35,9 +35,10 @@ def main():
|
||||
run = False
|
||||
map1.draw_window(cart1, cart1_rect)
|
||||
if not move_list: #jeżeli są jakieś ruchy do wykonania w move_list
|
||||
pygame.image.save(pygame.display.get_surface(), os.path.join('resources/neural_network/sliced/', 'screen.jpg')) #zrzut obecnego ekranu
|
||||
image_slicer.slice(os.path.join('resources/neural_network/sliced/', 'screen.jpg'), 100) #pocięcie ekranu na sto części
|
||||
os.remove('resources/neural_network/sliced/screen.jpg')
|
||||
pygame.image.save(pygame.display.get_surface(), os.path.join('resources/neural_network/tiles/', 'screen.jpg')) #zrzut obecnego ekranu
|
||||
tiles = image_slicer.slice(os.path.join('resources/neural_network/tiles/', 'screen.jpg'), 100, save=False) #pocięcie ekranu na sto części
|
||||
image_slicer.save_tiles(tiles, directory=os.path.join('resources/neural_network/tiles/'), prefix='tile', format='png') #zapisanie części do folderu tiles
|
||||
os.remove('resources/neural_network/tiles/screen.jpg')
|
||||
istate = graph.Istate(cart1.get_direction(), cart1.get_x() / definitions.BLOCK_SIZE, cart1.get_y() / definitions.BLOCK_SIZE) #stan początkowy wózka (jego orientacja oraz jego aktualne miejsce)
|
||||
if neuralnetwork.predfield(classes, istate, model) is not False: #jeżeli istnieje jakaś dojrzała roślina
|
||||
random_movement = False
|
||||
|
||||
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.3 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.4 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.5 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.2 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.4 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.5 KiB |
|
Before Width: | Height: | Size: 6.2 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.5 KiB |
|
Before Width: | Height: | Size: 6.2 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.2 KiB |
|
Before Width: | Height: | Size: 6.0 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 7.2 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB |
BIN
resources/neural_network/tiles/screen.jpg
Normal file
|
After Width: | Height: | Size: 131 KiB |
|
Before Width: | Height: | Size: 7.0 KiB After Width: | Height: | Size: 7.0 KiB |
|
Before Width: | Height: | Size: 6.7 KiB After Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.3 KiB After Width: | Height: | Size: 6.3 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.7 KiB After Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.7 KiB After Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.7 KiB After Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.7 KiB After Width: | Height: | Size: 6.7 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
|
Before Width: | Height: | Size: 6.2 KiB After Width: | Height: | Size: 6.2 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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resources/neural_network/tiles/tile_03_06.png
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After Width: | Height: | Size: 6.6 KiB |
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resources/neural_network/tiles/tile_03_07.png
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After Width: | Height: | Size: 6.6 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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Before Width: | Height: | Size: 6.7 KiB After Width: | Height: | Size: 6.7 KiB |
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Before Width: | Height: | Size: 6.7 KiB After Width: | Height: | Size: 6.7 KiB |
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Before Width: | Height: | Size: 6.5 KiB After Width: | Height: | Size: 6.5 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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resources/neural_network/tiles/tile_04_05.png
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After Width: | Height: | Size: 6.6 KiB |
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resources/neural_network/tiles/tile_04_06.png
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After Width: | Height: | Size: 6.5 KiB |
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resources/neural_network/tiles/tile_04_07.png
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After Width: | Height: | Size: 6.6 KiB |
0
resources/neural_network/tiles/tile_04_08.png
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |
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resources/neural_network/tiles/tile_05_01.png
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After Width: | Height: | Size: 6.6 KiB |
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resources/neural_network/tiles/tile_05_02.png
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After Width: | Height: | Size: 6.7 KiB |
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Before Width: | Height: | Size: 6.4 KiB After Width: | Height: | Size: 6.4 KiB |
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Before Width: | Height: | Size: 6.7 KiB After Width: | Height: | Size: 6.7 KiB |
BIN
resources/neural_network/tiles/tile_05_05.png
Normal file
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After Width: | Height: | Size: 6.6 KiB |
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resources/neural_network/tiles/tile_05_06.png
Normal file
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After Width: | Height: | Size: 6.6 KiB |
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resources/neural_network/tiles/tile_05_07.png
Normal file
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After Width: | Height: | Size: 6.7 KiB |
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Before Width: | Height: | Size: 6.6 KiB After Width: | Height: | Size: 6.6 KiB |