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agent - item recognition loop

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This commit is contained in:
Aleksander Szamałek 2022-05-27 00:03:24 +02:00
parent bc5bdf6fa4
commit 13e5c5d62c
9 changed files with 65 additions and 41 deletions
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5
ForkliftAgent.py
View File

@ -32,6 +32,7 @@ class ForkliftAgent(AgentBase):
self.current_item = None
self.item_station_completed = False
self.provided_items: List[Item] = []
self.ready_for_execution = False
def queue_movement_actions(self, movement_actions: List[Action]):
self.action_queue.extend(movement_actions)
@ -102,7 +103,7 @@ class ForkliftAgent(AgentBase):
stations = dict(self.graph.packingStations)
if i.real_type == ItemType.SHELF:
packing_station = stations[PatchType.packingA]
elif i.real_type == ItemType.EGG:
elif i.real_type == ItemType.FRIDGE:
packing_station = stations[PatchType.packingB]
elif i.real_type == ItemType.DOOR:
packing_station = stations[PatchType.packingC]
@ -147,7 +148,7 @@ class ForkliftAgent(AgentBase):
def step(self) -> None:
if len(self.action_queue) > 0:
self.move()
elif len(self.orderList) > 0:
elif self.ready_for_execution and len(self.orderList) > 0:
if (self.current_order is not None and len(self.current_order.items)) == 0:
self.fulfilled_orders.append(self.current_order)
self.current_order = None

34
GameModel.py
View File

@ -1,3 +1,4 @@
import copy
from enum import Enum
from typing import List
@ -14,9 +15,12 @@ from PictureVisualizationAgent import PictureVisualizationAgent
from data.GameConstants import GameConstants
from data.Item import Item
from data.Order import Order
from data.enum.ItemType import ItemType
from decision.Action import Action
from decision.ActionType import ActionType
from imageClasification.Classificator import Classificator
from pathfinding.PathfinderOnStates import PathFinderOnStates, PathFinderState
from util.PathByEnum import PathByEnum
from util.PathDefinitions import GridLocation, GridWithWeights
@ -30,7 +34,7 @@ class Phase(Enum):
class GameModel(Model):
def __init__(self, width, height, graph: GridWithWeights):
def __init__(self, width, height, graph: GridWithWeights, items: int, orders: int, classificator):
# self.num_agents = 5
self.first = True
self.item_recognised = False
@ -67,14 +71,16 @@ class GameModel(Model):
print("############## INITIALIZATION ##############")
self.phase = Phase.INIT
self.initialize_grid(graph)
self.orderList: List[Order] = InitialStateFactory.generate_order_list(3)
self.orderList: List[Order] = InitialStateFactory.generate_order_list(orders)
self.fulfilled_orders: List[Order] = []
self.forklift_agent.orderList = self.orderList
self.forklift_agent.fulfilled_orders = self.fulfilled_orders
self.classificator = classificator
print("############## RECOGNISE ITEMS ##############")
self.phase = Phase.ITEM_RECOGNITION
self.provided_items = InitialStateFactory.generate_item_list(3)
self.provided_items = InitialStateFactory.generate_item_list(items)
self.items_for_recognization = copy.deepcopy(self.provided_items)
self.recognised_items: List[Item] = []
print("Relocate forklift agent to loading area for item recognition")
@ -115,7 +121,7 @@ class GameModel(Model):
def place_dividers(self):
for i in range(0, 10):
for j in range(10,13):
for j in range(10, 13):
agent = PatchAgent(self, (i, j), PatchType.divider)
self.agents.append(agent)
self.grid.place_agent(agent, (i, j))
@ -160,14 +166,15 @@ class GameModel(Model):
if self.phase == Phase.ITEM_RECOGNITION:
if not self.item_recognised and self.forklift_agent.current_position == self.drop_off.location:
if len(self.provided_items) == 0:
if len(self.items_for_recognization) == 0:
print("FINISHED ITEM RECOGNITION")
self.item_recognised = True
self.phase = Phase.CLIENT_SORTING
self.forklift_agent.ready_for_execution = True
else:
print("BEGIN ITEM RECOGNITION, left: {}".format(len(self.provided_items)))
item_to_recognise = self.provided_items.pop()
self.picture_visualization.img = "item_images/door/drzwi1.jpg"
print("BEGIN ITEM RECOGNITION, left: {}".format(len(self.items_for_recognization)))
item_to_recognise = self.items_for_recognization.pop()
self.picture_visualization.img = PathByEnum.get_random_path(item_to_recognise.real_type)
recognised = self.recognise_item(item_to_recognise)
self.recognised_items.append(recognised)
@ -182,5 +189,14 @@ class GameModel(Model):
def recognise_item(self, item: Item):
# TODO IMAGE PROCESSING
item.guessed_type = item.real_type
val = self.classificator.image_clasification(self.picture_visualization.img)
print("VAL: {}".format(val))
if val == ItemType.DOOR:
item.guessed_type = ItemType.DOOR
elif val == ItemType.FRIDGE:
item.guessed_type = ItemType.FRIDGE
elif val == ItemType.SHELF:
item.guessed_type = ItemType.SHELF
return item

6
data/enum/ItemType.py
View File

@ -2,6 +2,6 @@ from enum import Enum
class ItemType(Enum):
DOOR = 1
SHELF = 2
EGG = 3
DOOR = "door"
SHELF = "shelf"
FRIDGE = "fridge"

23
imageClasification/imageClasification.py → imageClasification/Classificator.py
View File

@ -9,8 +9,13 @@ from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
class imageClasification():
def train_model(data_dir):
class Classificator():
def __init__(self, data_dir: str) -> None:
super().__init__()
self.data = self.train_model(data_dir)
def train_model(self, data_dir):
batch_size = 32
img_height = 180
img_width = 180
@ -111,7 +116,7 @@ class imageClasification():
plt.show()
return model, class_names, img_width, img_height
def image_clasification(image_path, model, class_names):
def image_clasification(self,image_path):
# ścieżka do sprawdzanego obrazu
# image_path = "./th-367101945.jpg"
@ -121,11 +126,11 @@ class imageClasification():
img_array = tf.keras.utils.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch
predictions = model.predict(img_array)
predictions = self.data[0].predict(img_array)
score = tf.nn.softmax(predictions[0])
# print(
# "This image most likely belongs to {} with a {:.2f} percent confidence."
# .format(class_names[np.argmax(score)], 100 * np.max(score))
# )
return class_names[np.argmax(score)]
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(self.data[1][np.argmax(score)], 100 * np.max(score))
)
return self.data[1][np.argmax(score)]

7
imageClasification/how_to_use.py
View File

@ -1,13 +1,12 @@
from imageClasification import imageClasification
from Classificator import Classificator
#Training model
data = imageClasification.train_model("../SI_InteligentnyWozekWidlowy/imageClasification/images")
data = Classificator.train_model("../SI_InteligentnyWozekWidlowy/imageClasification/images")
#image_path = ścieżka do klasyfikowanego obrazka
#data[0] = model (wytrenowany model
#data[1] = class_names
image_classified = imageClasification.image_clasification("./th-367101945.jpg",
image_classified = Classificator.image_clasification("./th-367101945.jpg",
data[0],
data[1])

6
main.py
View File

@ -9,11 +9,11 @@ from PatchAgent import PatchAgent
from PatchType import PatchType
from PictureVisualizationAgent import PictureVisualizationAgent
from data.enum.Direction import Direction
from imageClasification.Classificator import Classificator
from util.PathDefinitions import GridWithWeights
from visualization.DisplayAttributeElement import DisplayAttributeElement
from visualization.DisplayItemListAttribute import DisplayItemListAttributeElement
from visualization.DisplayOrderList import DisplayOrderList
from visualization.DisplayPictureElement import DisplayPictureElement
colors = [
'blue', 'cyan', 'orange', 'yellow', 'magenta', 'purple', '#103d3e', '#9fc86c',
@ -88,11 +88,13 @@ if __name__ == '__main__':
ordersText = DisplayOrderList("orderList")
fulfilled_orders = DisplayOrderList("fulfilled_orders")
classificator = Classificator("imageClasification/images")
server = ModularServer(GameModel,
[grid, readyText, provided_itesm, recognised_items, ordersText,
fulfilled_orders],
"Automatyczny Wózek Widłowy",
{"width": gridHeight, "height": gridWidth, "graph": diagram}, )
dict(width=gridHeight, height=gridWidth, graph=diagram, items=50, orders=3, classificator=classificator))
server.port = 8888
server.launch()

7
util/PathByEnum.py
View File

@ -1,14 +1,15 @@
from random import random
import random
from data.enum.ItemType import ItemType
class PathByEnum:
def get_random_path(self, item:ItemType):
@staticmethod
def get_random_path(item: ItemType):
if item == ItemType.DOOR:
a = str(random.randint(1, 10))
return "item_images/door/drzwi" + a + ".jpg"
if item == ItemType.EGG:
if item == ItemType.FRIDGE:
a = str(random.randint(1, 10))
return "item_images/refrigerator/lodowka" + a + ".jpg"
if item == ItemType.SHELF:

2
visualization/DisplayItemListAttribute.py
View File

@ -41,7 +41,7 @@ class DisplayItemListAttributeElement(TextElement):
elif key == ItemType.SHELF:
key_str = "Shelf"
res += "<li>{}:{}</li>".format(key_str, val)
res += "<li>{}:{}</li>".format(key, val)
res += "</ul>"

12
visualization/DisplayOrderList.py
View File

@ -38,13 +38,13 @@ class DisplayOrderList(TextElement):
key = e
val = itemCounter[key]
key_str = ""
if key == ItemType.DOOR:
key_str = "Door"
elif key == ItemType.SHELF:
key_str = "Shelf"
# key_str = ""
# if key == ItemType.DOOR:
# key_str = "Door"
# elif key == ItemType.SHELF:
# key_str = "Shelf"
item_str += f"<li>{key_str}:{val}</li>"
item_str += f"<li>{str(key)}:{str(val)}</li>"
item_str += "</ul>"