neural network integration
This commit is contained in:
parent
56ca4bc891
commit
b26f7b244e
@ -1,49 +1,49 @@
|
||||
import tensorflow as tf
|
||||
from keras import layers
|
||||
from keras.models import Sequential
|
||||
from keras.optimizers import Adam
|
||||
from keras.utils import to_categorical
|
||||
from keras.preprocessing.image import ImageDataGenerator
|
||||
|
||||
# Normalizes the pixel values of an image to the range [0, 1].
|
||||
# Normalizes the pixel values of an image to the range [0, 1].
|
||||
|
||||
|
||||
def normalize(image, label):
|
||||
return image / 255, label
|
||||
|
||||
|
||||
# Set the paths to the folder containing the training data
|
||||
train_data_dir = "Network/Training/"
|
||||
# Set the number of classes and batch size
|
||||
# Set the number of classes and batch size
|
||||
num_classes = 3
|
||||
batch_size = 32
|
||||
# Set the image size and input shape
|
||||
# Set the image size and input shape
|
||||
img_width, img_height = 100, 100
|
||||
input_shape = (img_width, img_height, 1)
|
||||
# Load the training and validation data
|
||||
# Load the training and validation data
|
||||
train_ds = tf.keras.utils.image_dataset_from_directory(
|
||||
train_data_dir,
|
||||
validation_split=0.2,
|
||||
subset="training",
|
||||
shuffle=True,
|
||||
seed=123,
|
||||
image_size=(img_height, img_width),
|
||||
batch_size=batch_size)
|
||||
train_data_dir,
|
||||
validation_split=0.2,
|
||||
subset="training",
|
||||
shuffle=True,
|
||||
seed=123,
|
||||
image_size=(img_height, img_width),
|
||||
batch_size=batch_size)
|
||||
|
||||
val_ds = tf.keras.utils.image_dataset_from_directory(
|
||||
train_data_dir,
|
||||
validation_split=0.2,
|
||||
subset="validation",
|
||||
shuffle=True,
|
||||
seed=123,
|
||||
image_size=(img_height, img_width),
|
||||
batch_size=batch_size)
|
||||
# Get the class names
|
||||
train_data_dir,
|
||||
validation_split=0.2,
|
||||
subset="validation",
|
||||
shuffle=True,
|
||||
seed=123,
|
||||
image_size=(img_height, img_width),
|
||||
batch_size=batch_size)
|
||||
# Get the class names
|
||||
class_names = train_ds.class_names
|
||||
print(class_names)
|
||||
# Normalize the training and validation data
|
||||
# Normalize the training and validation data
|
||||
train_ds = train_ds.map(normalize)
|
||||
val_ds = val_ds.map(normalize)
|
||||
# Define the model architecture
|
||||
# Define the model architecture
|
||||
model = tf.keras.Sequential([
|
||||
layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(img_height, img_width, 1)),
|
||||
layers.Conv2D(16, 3, padding='same', activation='relu',
|
||||
input_shape=(img_height, img_width, 1)),
|
||||
layers.MaxPooling2D(),
|
||||
layers.Conv2D(32, 3, padding='same', activation='relu'),
|
||||
layers.MaxPooling2D(),
|
||||
@ -53,16 +53,17 @@ model = tf.keras.Sequential([
|
||||
layers.Dense(128, activation='relu'),
|
||||
layers.Dense(num_classes, activation='softmax')
|
||||
])
|
||||
# Compile the model
|
||||
# Compile the model
|
||||
model.compile(optimizer='adam',
|
||||
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
|
||||
loss=tf.keras.losses.SparseCategoricalCrossentropy(
|
||||
from_logits=True),
|
||||
metrics=['accuracy'])
|
||||
# Print the model summary
|
||||
# Print the model summary
|
||||
model.summary()
|
||||
# Train the model
|
||||
epochs=10
|
||||
# Train the model
|
||||
epochs = 10
|
||||
model.fit(train_ds,
|
||||
validation_data=val_ds,
|
||||
epochs=epochs)
|
||||
# Save the trained model
|
||||
# Save the trained model
|
||||
model.save('Network/trained_model.h5')
|
46
Network/Predictor.py
Normal file
46
Network/Predictor.py
Normal file
@ -0,0 +1,46 @@
|
||||
import os
|
||||
import random
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from tensorflow import keras
|
||||
|
||||
|
||||
class Predictor:
|
||||
def __init__(self):
|
||||
# Load the trained model
|
||||
self.model = keras.models.load_model('Network/trained_model.h5')
|
||||
|
||||
# Load the class names
|
||||
self.class_names = ['table', 'done', 'order']
|
||||
|
||||
# Path to the folder containing test images
|
||||
self.test_images_folder = 'Network/Testing/'
|
||||
|
||||
def predict(self, image_path):
|
||||
# Load and preprocess the test image
|
||||
test_image = keras.preprocessing.image.load_img(
|
||||
image_path, target_size=(100, 100))
|
||||
test_image = keras.preprocessing.image.img_to_array(test_image)
|
||||
test_image = np.expand_dims(test_image, axis=0)
|
||||
test_image = test_image / 255.0 # Normalize the image
|
||||
|
||||
# Reshape the image array to (1, height, width, channels)
|
||||
test_image = np.reshape(test_image, (1, 100, 100, 3))
|
||||
|
||||
# Make predictions
|
||||
predictions = self.model.predict(test_image)
|
||||
predicted_class_index = np.argmax(predictions[0])
|
||||
predicted_class = self.class_names[predicted_class_index]
|
||||
|
||||
print(predicted_class)
|
||||
return predicted_class
|
||||
|
||||
def random_path_img(self) -> str:
|
||||
folder_name = random.choice(os.listdir(self.test_images_folder))
|
||||
folder_path = os.path.join(self.test_images_folder, folder_name)
|
||||
filename = ""
|
||||
while not (filename.endswith('.jpg') or filename.endswith('.jpeg')):
|
||||
filename = random.choice(os.listdir(folder_path))
|
||||
image_path = os.path.join(folder_path, filename)
|
||||
return image_path
|
@ -1,4 +1,5 @@
|
||||
import os
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from tensorflow import keras
|
||||
@ -7,7 +8,7 @@ from tensorflow import keras
|
||||
model = keras.models.load_model('Network/trained_model.h5')
|
||||
|
||||
# Load the class names
|
||||
class_names = ['Table', 'Done','Order']
|
||||
class_names = ['table', 'done', 'order']
|
||||
|
||||
# Path to the folder containing test images
|
||||
test_images_folder = 'Network/Testing/'
|
||||
@ -22,25 +23,26 @@ for folder_name in os.listdir(test_images_folder):
|
||||
|
||||
# True class based on folder name
|
||||
if folder_name == 'Empty':
|
||||
true_class = 'Table'
|
||||
true_class = 'table'
|
||||
elif folder_name == 'Food':
|
||||
true_class = 'Done'
|
||||
true_class = 'done'
|
||||
elif folder_name == 'People':
|
||||
true_class = 'Order'
|
||||
true_class = 'order'
|
||||
|
||||
# Iterate over the files in the subfolder
|
||||
for filename in os.listdir(folder_path):
|
||||
if filename.endswith('.jpg') or filename.endswith('.jpeg'):
|
||||
i+=1
|
||||
i += 1
|
||||
# Load and preprocess the test image
|
||||
image_path = os.path.join(folder_path, filename)
|
||||
test_image = keras.preprocessing.image.load_img(image_path, target_size=(100, 100))
|
||||
test_image = keras.preprocessing.image.load_img(
|
||||
image_path, target_size=(100, 100))
|
||||
test_image = keras.preprocessing.image.img_to_array(test_image)
|
||||
test_image = np.expand_dims(test_image, axis=0)
|
||||
test_image = test_image / 255.0 # Normalize the image
|
||||
|
||||
# Reshape the image array to (1, height, width, channels)
|
||||
test_image = np.reshape(test_image, (1,100, 100, 3))
|
||||
test_image = np.reshape(test_image, (1, 100, 100, 3))
|
||||
|
||||
# Make predictions
|
||||
predictions = model.predict(test_image)
|
||||
@ -48,9 +50,11 @@ for folder_name in os.listdir(test_images_folder):
|
||||
predicted_class = class_names[predicted_class_index]
|
||||
|
||||
direct = 'Network/Results/'
|
||||
filename = str(i) + predicted_class + '.jpeg'
|
||||
filename = str(i) + predicted_class + '.jpeg'
|
||||
test_image = np.reshape(test_image, (100, 100, 3))
|
||||
tf.keras.preprocessing.image.save_img(direct+filename, test_image)
|
||||
Path(direct).mkdir(parents=True, exist_ok=True)
|
||||
tf.keras.preprocessing.image.save_img(
|
||||
direct+filename, test_image)
|
||||
if predicted_class != true_class:
|
||||
errorcount += 1
|
||||
print('Image:', filename)
|
||||
|
@ -1,4 +1,4 @@
|
||||
import os
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
from tensorflow import keras
|
||||
@ -7,7 +7,7 @@ from tensorflow import keras
|
||||
model = keras.models.load_model('Network/trained_model.h5')
|
||||
|
||||
# Load the class names
|
||||
class_names = ['Table', 'Done','Order']
|
||||
class_names = ['table', 'done', 'order']
|
||||
|
||||
# Load and preprocess the validation dataset
|
||||
data_dir = "Network/Training/"
|
||||
@ -47,6 +47,7 @@ for i in range(60):
|
||||
|
||||
direct = 'Network/Results/'
|
||||
filename = predicted_class + str(i) + '.jpeg'
|
||||
Path(direct).mkdir(parents=True, exist_ok=True)
|
||||
tf.keras.preprocessing.image.save_img(direct+filename, val_images[i])
|
||||
if predicted_class != true_class:
|
||||
errorcount += 1
|
||||
|
@ -49,6 +49,6 @@
|
||||
|
||||
---
|
||||
|
||||
- [ ] **Sieci neuronowe: wymagania dot. czwartego przyrostu**
|
||||
- [ ] Należy przygotować zbiór uczący zawierający co najmniej 1000 przykładów dla każdej klasy.
|
||||
- [ ] Agent powinien wykorzystywać wyuczoną sieć w procesie podejmowania decyzji.
|
||||
- [x] **Sieci neuronowe: wymagania dot. czwartego przyrostu**
|
||||
- [x] Należy przygotować zbiór uczący zawierający co najmniej 1000 przykładów dla każdej klasy.
|
||||
- [x] Agent powinien wykorzystywać wyuczoną sieć w procesie podejmowania decyzji.
|
||||
|
@ -3,3 +3,5 @@ pygame==2.3.0
|
||||
pandas
|
||||
scikit-learn
|
||||
graphviz
|
||||
tensorflow
|
||||
pillow
|
@ -1,5 +1,6 @@
|
||||
import random
|
||||
from src.obj.Object import Object
|
||||
from Network.Predictor import Predictor
|
||||
|
||||
|
||||
class Table(Object):
|
||||
@ -8,6 +9,7 @@ class Table(Object):
|
||||
self.waiting_time = 0
|
||||
self.cooking_time = 0
|
||||
self.is_actual = False
|
||||
self.p = Predictor()
|
||||
|
||||
def isActual(self):
|
||||
return self.is_actual
|
||||
@ -17,7 +19,7 @@ class Table(Object):
|
||||
return
|
||||
self.is_actual = True
|
||||
# here must be neural network choise
|
||||
new_role = random.choice(["table", "order", "wait", "done"])
|
||||
new_role = self.p.predict(self.p.random_path_img())
|
||||
self.change_role(new_role, current_time)
|
||||
|
||||
if self.agent_role == "table":
|
||||
|
Loading…
Reference in New Issue
Block a user