integrating neural network with project

This commit is contained in:
Dominik Cupał 2021-06-01 21:13:32 +02:00
parent 53daf29a98
commit 53fa8e0066
11 changed files with 168 additions and 19 deletions

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@ -35,14 +35,14 @@ VERTICAL_NUM_OF_FIELDS = 3
HORIZONTAL_NUM_OF_FIELDS = 3
```
\
#####4.1 Save generated map:
####4.1 Save generated map:
```bash
python main.py --save-map
```
Map will be saved in maps directory.
Generated filename: map-uuid
#####4.2 Load map
####4.2 Load map
```bash
python main.py --load-map=name_of_map
```

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@ -42,21 +42,21 @@ class App:
def keys_pressed_handler(self):
keys = pygame.key.get_pressed()
if keys[pygame.K_m]:
self.__tractor.move()
print(self.__tractor)
if keys[pygame.K_w]:
self.__tractor.move()
print(self.__tractor)
if keys[pygame.K_n]:
self.__bot_is_running.set()
self.__tractor.harvest_checked_fields_handler(self.__bot_is_running)
if keys[pygame.K_h]:
self.__tractor.harvest()
if keys[pygame.K_v]:
self.__tractor.sow()
if keys[pygame.K_n]:
if keys[pygame.K_j]:
self.__tractor.hydrate()
if keys[pygame.K_f]:

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@ -7,6 +7,9 @@ class BaseField:
def __init__(self, img_path: str):
self._img_path = img_path
def get_img_path(self):
return self._img_path
def draw_field(self, screen: pygame.Surface, pos_x: int,
pos_y: int, is_centered: bool = False,
size: tuple = None, angle: float = 0.0) -> None:

113
app/neural_network.py Normal file
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@ -0,0 +1,113 @@
#!/usr/bin/python3
import os
from tensorflow.keras.models import Sequential, save_model, load_model
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras.losses import sparse_categorical_crossentropy
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow import keras as k
import numpy as np
from app.base_field import BaseField
from config import *
class NeuralNetwork:
def __init__(self):
# Model config
self.batch_size = 25
self.img_width, self.img_height, self.img_num_channels = 25, 25, 3
self.loss_function = sparse_categorical_crossentropy
self.no_classes = 7
self.no_epochs = 40
self.optimizer = Adam()
self.verbosity = 1
# Determine shape of the data
self.input_shape = (self.img_width, self.img_height, self.img_num_channels)
# labels
self.labels = ["cabbage", "carrot", "corn", "lettuce", "paprika", "potato", "tomato"]
def init_model(self):
if not self.model_dir_is_empty():
# Load the model
self.model = load_model(
os.path.join(RESOURCE_DIR, "saved_model"),
custom_objects=None,
compile=True
)
else:
# Create the model
self.model = Sequential()
self.model.add(Conv2D(16, kernel_size=(5, 5), activation='relu', input_shape=self.input_shape))
self.model.add(Conv2D(32, kernel_size=(5, 5), activation='relu'))
self.model.add(Conv2D(64, kernel_size=(5, 5), activation='relu'))
self.model.add(Conv2D(128, kernel_size=(5, 5), activation='relu'))
self.model.add(Flatten())
self.model.add(Dense(16, activation='relu'))
self.model.add(Dense(self.no_classes, activation='softmax'))
# Display a model summary
self.model.summary()
def load_images(self):
# Create a generator
self.train_datagen = ImageDataGenerator(
rescale=1. / 255
)
self.train_datagen = self.train_datagen.flow_from_directory(
TRAINING_SET_DIR,
save_to_dir=ADAPTED_IMG_DIR,
save_format='jpeg',
batch_size=self.batch_size,
target_size=(25, 25),
class_mode='sparse')
def train(self):
self.model.compile(loss=self.loss_function,
optimizer=self.optimizer,
metrics=['accuracy'])
# Start training
self.model.fit(
self.train_datagen,
epochs=self.no_epochs,
shuffle=False)
def predict(self, field: BaseField) -> str:
print(field.get_img_path())
# corn_img_path = os.path.join(RESOURCE_DIR,'corn.png')
loaded_image = k.preprocessing.image.load_img(field.get_img_path(),
target_size=(
self.img_width, self.img_height, self.img_num_channels))
# convert to array and resample dividing by 255
img_array = k.preprocessing.image.img_to_array(loaded_image) / 255.
# add sample dimension. the predictor is expecting (1, CHANNELS, IMG_WIDTH, IMG_HEIGHT)
img_np_array = np.expand_dims(img_array, axis=0)
# print(img_np_array)
predictions = self.model.predict(img_np_array)
prediction = np.argmax(predictions[0])
label = self.labels[prediction]
print(f'Ground truth: {type(field).__name__} - Prediction: {label}')
return label
def model_dir_is_empty(self) -> bool:
if len(os.listdir(MODEL_DIR)) == 0:
return True
return False
def check(self, field: BaseField) -> str:
self.load_images()
self.init_model()
prediction = self.predict(field)
# Saving model
if not self.model_dir_is_empty():
save_model(self.model, MODEL_DIR)
return prediction

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@ -11,6 +11,7 @@ from typing import Union
from app.base_field import BaseField
from app.board import Board
from app.neural_network import NeuralNetwork
from app.utils import get_class
from app.fields import CROPS, PLANTS, Crops, Sand, Clay, Field
from config import *
@ -28,6 +29,7 @@ class Tractor(BaseField):
self.__board = board
self.__harvested_corps = []
self.__fuel = 10
self.__neural_network = None
def draw(self, screen: pygame.Surface) -> None:
self.draw_field(screen, self.__pos_x + FIELD_SIZE / 2, self.__pos_y + FIELD_SIZE / 2,
@ -185,7 +187,7 @@ class Tractor(BaseField):
def run_bot(self, moves: list[tuple[str, str]], is_running: threading.Event) -> None:
print(moves)
print(f"Length of Moves {len(moves)}") #- {3 ** len(moves)}")
print(f"Length of Moves {len(moves)}") # - {3 ** len(moves)}")
while len(moves) > 0:
movement, action = moves.pop(0)
# do action
@ -203,17 +205,20 @@ class Tractor(BaseField):
time.sleep(1)
# move
print(f"Move {movement}")
if movement == M_GO_FORWARD:
self.move()
elif movement == M_ROTATE_LEFT:
self.rotate_left()
elif movement == M_ROTATE_RIGHT:
self.rotate_right()
self.move_or_rotate(movement)
time.sleep(TIME_OF_MOVING)
is_running.clear()
def move_or_rotate(self, movement: str):
print(f"Move {movement}")
if movement == M_GO_FORWARD:
self.move()
elif movement == M_ROTATE_LEFT:
self.rotate_left()
elif movement == M_ROTATE_RIGHT:
self.rotate_right()
@staticmethod
def move_is_correct(x: int, y: int, direction: float) -> Union[(int, int), None]:
pos_x = x * FIELD_SIZE
@ -261,3 +266,26 @@ class Tractor(BaseField):
obj = get_class("app.fields", choosen_type)
board.get_fields()[x][y] = obj()
return obj()
def harvest_checked_fields_handler(self, is_running: threading.Event):
thread = threading.Thread(target=self.harvest_checked_fields, args=(is_running,), daemon=True)
thread.start()
def harvest_checked_fields(self, is_running: threading.Event):
moves = [M_GO_FORWARD, M_ROTATE_LEFT, M_ROTATE_RIGHT]
distribution=[0.6,0.2,0.2]
while True:
field = self.get_field_from_board()
self.__neural_network = NeuralNetwork()
prediction = self.__neural_network.check(field)
if prediction.capitalize() in CROPS:
self.harvest()
break
chosen_move = random.choices(moves,distribution)
self.move_or_rotate(chosen_move[0])
time.sleep(1)
is_running.clear()

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@ -12,7 +12,8 @@ __all__ = (
'A_SOW', 'A_HARVEST', 'A_HYDRATE', 'A_FERTILIZE', 'A_DO_NOTHING',
'D_NORTH', 'D_EAST', 'D_SOUTH', 'D_WEST',
'VALUE_OF_CROPS', 'VALUE_OF_PLANT', 'VALUE_OF_SAND', 'VALUE_OF_CLAY',
'MAP_FILE_NAME', 'JSON','SAVE_MAP', 'LOAD_MAP'
'MAP_FILE_NAME', 'JSON', 'SAVE_MAP', 'LOAD_MAP',
'TRAINING_SET_DIR', 'TEST_SET_DIR', 'ADAPTED_IMG_DIR', 'MODEL_DIR'
)
# Board settings:
@ -26,11 +27,15 @@ HEIGHT = VERTICAL_NUM_OF_FIELDS * FIELD_SIZE
FPS = 10
CAPTION = 'Tractor'
# Path
# Paths
BASE_DIR = os.path.dirname(__file__)
RESOURCE_DIR = os.path.join(BASE_DIR, 'resources')
MAP_DIR = os.path.join(BASE_DIR, 'maps')
MAP_FILE_NAME = 'map'
TRAINING_SET_DIR = os.path.join(RESOURCE_DIR, 'smaller_train')
TEST_SET_DIR = os.path.join(RESOURCE_DIR, 'smaller_test')
ADAPTED_IMG_DIR = os.path.join(RESOURCE_DIR, "adapted-images")
MODEL_DIR = os.path.join(RESOURCE_DIR, 'saved_model')
# Picture format
PNG = "png"

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@ -9,5 +9,5 @@
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Êroot.layer_with_weights-4"_tf_keras_layer*“{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 16, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 14}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 15}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 16, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 10368}}, "shared_object_id": 27}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 10368]}}2
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¹croot.keras_api.metrics.0"_tf_keras_metric*{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 29}2
ódroot.keras_api.metrics.1"_tf_keras_metric*¼{"class_name": "MeanMetricWrapper", "name": "accuracy", "dtype": "float32", "config": {"name": "accuracy", "dtype": "float32", "fn": "sparse_categorical_accuracy"}, "shared_object_id": 22}2
¹froot.keras_api.metrics.0"_tf_keras_metric*{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 29}2
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