101 lines
4.0 KiB
Python
101 lines
4.0 KiB
Python
import random
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from mesa.visualization.ModularVisualization import ModularServer
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from mesa.visualization.modules import CanvasGrid
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from tensorflow import keras
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from ForkliftAgent import ForkliftAgent
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from GameModel import GameModel
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from PatchAgent import PatchAgent
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from PatchType import PatchType
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from PictureVisualizationAgent import PictureVisualizationAgent
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from data.enum.Direction import Direction
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from util.PathDefinitions import GridWithWeights
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from visualization.DisplayAttributeElement import DisplayAttributeElement
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from visualization.DisplayItemListAttribute import DisplayItemListAttributeElement
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from visualization.DisplayOrderList import DisplayOrderList
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colors = [
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'blue', 'cyan', 'orange', 'yellow', 'magenta', 'purple', '#103d3e', '#9fc86c',
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'#b4c2ed', '#31767d', '#31a5fa', '#ba96e0', '#fef3e4', '#6237ac', '#f9cacd', '#1e8123'
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]
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def agent_portrayal(agent):
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if isinstance(agent, ForkliftAgent):
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shape = ""
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if agent.current_rotation == Direction.top:
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shape = "img/image_top.png"
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elif agent.current_rotation == Direction.right:
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shape = "img/image_right.png"
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elif agent.current_rotation == Direction.down:
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shape = "img/image_down.png"
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elif agent.current_rotation == Direction.left:
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shape = "img/image_left.png"
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portrayal = {"Shape": shape, "scale": 1.0, "Layer": 0}
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if isinstance(agent, PatchAgent):
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color = colors[0]
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if agent.patch_type == PatchType.wall:
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portrayal = {"Shape": "img/brick.webp", "scale": 1.0, "Layer": 0}
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elif agent.patch_type == PatchType.dropOff:
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portrayal = {"Shape": "img/truck.png", "scale": 1.0, "Layer": 0}
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elif agent.patch_type == PatchType.pickUp:
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portrayal = {"Shape": "img/okB00mer.png", "scale": 1.0, "Layer": 0}
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elif agent.patch_type == PatchType.diffTerrain:
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portrayal = {"Shape": "img/puddle.png", "scale": 1.0, "Layer": 0}
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elif agent.patch_type == PatchType.divider:
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portrayal = \
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{"Shape": "rect",
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"Filled": "true",
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"Layer": 0,
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"Color": "black",
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"w": 1,
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"h": 1}
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else:
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color = colors[random.randrange(13) + 3]
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portrayal = {"Shape": "rect",
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"Filled": "true",
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"Layer": 0,
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"Color": color,
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"w": 1,
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"h": 1}
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if isinstance(agent, PictureVisualizationAgent):
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portrayal = {"Shape": f"{agent.img}", "scale": 3.0, "Layer": 0}
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return portrayal
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if __name__ == '__main__':
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base = 512
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gridWidth = 10
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gridHeight = 13
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scale = base / gridWidth
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diagram = GridWithWeights(gridWidth, gridHeight)
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diagram.walls = [(6, 5), (6, 6), (6, 7), (6, 8), (2, 3), (2, 4), (3, 4), (4, 4), (6, 4)]
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diagram.puddles = [(2, 2), (2, 5), (2, 6), (5, 4)]
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diagram.packingStations = [(PatchType.packingA, (4, 8)), (PatchType.packingB, (4, 6)), (PatchType.packingC, (4, 2))]
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grid = CanvasGrid(agent_portrayal, gridWidth, gridHeight, scale * gridWidth, scale * gridHeight)
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readyText = DisplayAttributeElement("phase")
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# current_item = DisplayPictureElement("current_item_recognition")
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provided_itesm = DisplayItemListAttributeElement("provided_items")
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recognised_items = DisplayItemListAttributeElement("recognised_items")
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ordersText = DisplayOrderList("orderList")
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fulfilled_orders = DisplayOrderList("fulfilled_orders")
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loaded_model = keras.models.load_model("./imageClasification/my_model")
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server = ModularServer(GameModel,
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[grid, readyText, provided_itesm, recognised_items, ordersText,
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fulfilled_orders],
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"Automatyczny Wózek Widłowy",
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dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=3, classificator=loaded_model))
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server.port = 8888
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server.launch()
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