main.py

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
import numpy as np
from tensorflow.keras.preprocessing import image

# Define image size and batch size
IMG_SIZE = 224
BATCH_SIZE = 32

# Define train and validation directories
train_dir = 'm2rncvif2arzs1w3q44gfn\images.cv_m2rncvif2arzs1w3q44gfn\data\train\burrito'
val_dir = 'm2rncvif2arzs1w3q44gfn\images.cv_m2rncvif2arzs1w3q44gfn\data\val\burrito'

# Use ImageDataGenerator for data augmentation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest')

val_datagen = ImageDataGenerator(rescale=1./255)

# Generate batches of augmented data from directories
train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(IMG_SIZE, IMG_SIZE),
    batch_size=BATCH_SIZE,
    class_mode='categorical')

val_generator = val_datagen.flow_from_directory(
    val_dir,
    target_size=(IMG_SIZE, IMG_SIZE),
    batch_size=BATCH_SIZE,
    class_mode='categorical')

# Define the model architecture
model = Sequential()

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 3)))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(256, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

# Compile the model with categorical crossentropy loss and Adam optimizer
model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# Define the number of training and validation steps per epoch
train_steps_per_epoch = train_generator.samples // BATCH_SIZE
val_steps_per_epoch = val_generator.samples // BATCH_SIZE

# Train the model with fit_generator
history = model.fit_generator(
    train_generator,
    steps_per_epoch=train_steps_per_epoch,
    epochs=10,
    validation_data=val_generator,
    validation_steps=val_steps_per_epoch)

# Path to directory with burrito images
dir_path = 'm2rncvif2arzs1w3q44gfn\images.cv_m2rncvif2arzs1w3q44gfn\data\test\burrito'

# Loop through all images in the directory
for img_file in os.listdir(dir_path):
    # Load and preprocess the image
    img_path = os.path.join(dir_path, img_file)
    img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array /= 255.0

    # Make a prediction
    prediction = model.predict(img_array)

    # Print the prediction result
    if prediction[0][0] > prediction[0][1]:
        print('{}: Not a burrito'.format(img_file))
    else:
        print('{}: Burrito!'.format(img_file))