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from image_manipulation import *
from keras import layers
from keras import models
import matplotlib.pyplot as plt
import tensorflow as tf
import keras
import numpy as np
def data_process():
train_datagen = ImageDataGenerator(rescale = 1./255)
test_datagen = ImageDataGenerator(rescale = 1./255)
train_generator = train_datagen.flow_from_directory(train_dir, target_size = (300, 350), batch_size = 8, class_mode = 'sparse')
validation_generator = test_datagen.flow_from_directory(validation_dir, target_size = (300, 350), batch_size = 8, class_mode = 'sparse')
return train_generator, validation_generator
def CNN_model():
# creating CNN model
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation = 'relu', input_shape = (300, 350, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation = 'relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation = 'relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation = 'relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Dropout(0.2))
model.add(layers.Flatten())
model.add(layers.Dense(256, activation = 'relu'))
model.add(layers.Dense(2, activation = 'softmax'))
return model
def fit():
model = CNN_model()
model.compile(loss = 'sparse_categorical_crossentropy',
optimizer = keras.optimizers.RMSprop(learning_rate = 1e-5), metrics = ['acc'])
train_generator, validation_generator = data_process()
return model.fit(train_generator, steps_per_epoch = 25, epochs = 50,
validation_data = validation_generator, validation_steps = 6)
def plot(history):
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'r', label = 'Training acc')
plt.plot(epochs, val_acc, 'b', label = 'Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'r', label = 'Training loss')
plt.plot(epochs, val_loss, 'b', label = 'Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
def test_image(model):
image_dir = "C:\\Ryan\\PersonalProject\\FriendRecog\\bot\\resized_images"
class_names = ["Brandon", "Manuel"]
target_size = (300, 350)
# List all image files in directory
image_files = [os.path.join(image_dir, file) for file in os.listdir(image_dir) if file.endswith('.png')]
# Loop over each image file
for image_path in image_files:
# Load the image
img = keras.utils.load_img(image_path, target_size = target_size)
# Preprocess the image
img_array = keras.utils.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # create a batch
# Use the pre-trained model to predict
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
predicted_class = class_names[np.argmax(score)]
confidence = np.max(score)
return predicted_class, confidence |