src/rose_model.py

import os
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report
import tensorflow as tf

# Set the paths to your image folders
BASE_DIR = os.path.join(os.getcwd(), 'dataset')
train_dir = os.path.join(BASE_DIR, 'train')
validation_dir = os.path.join(BASE_DIR, 'validation')
test_dir = os.path.join(BASE_DIR, 'test')

# Set the parameters for the data generators
batch_size = 32
img_height, img_width = 256, 256

# Create data generators with data augmentation for training and validation
train_datagen = ImageDataGenerator(
    rescale=1.0 / 255.0,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    vertical_flip=True
)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical'
)

validation_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
validation_generator = validation_datagen.flow_from_directory(
    validation_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical'
)

# Create a CNN model
cnn_model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(train_generator.num_classes, activation='softmax')
])

# Compile the CNN model
cnn_model.compile(optimizer='adam',
                 loss='categorical_crossentropy',
                 metrics=['accuracy'])

# Train the CNN model
epochs = 10
history = cnn_model.fit(
    train_generator,
    epochs=epochs,
    validation_data=validation_generator
)

# Save the trained model
model_path = os.path.join(os.getcwd(), 'models', 'rose_model.h5')
os.makedirs(os.path.dirname(model_path), exist_ok=True)
cnn_model.save(model_path)

# Save class names
class_names = list(train_generator.class_indices.keys())
class_names_path = os.path.join(os.getcwd(), 'models', 'class_names.json')
import json
with open(class_names_path, 'w') as f:
    json.dump(class_names, f)

# Evaluate the model
test_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode='categorical',
    shuffle=False
)

# Make predictions
test_predictions = cnn_model.predict(test_generator)
predicted_labels = np.argmax(test_predictions, axis=1)
true_labels = test_generator.classes

# Calculate and print metrics
print("\nClassification Report:")
print(classification_report(true_labels, predicted_labels, target_names=class_names))

# Plot training history
plt.figure(figsize=(12, 4))

# Plot training & validation accuracy
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')

# Plot training & validation loss
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Validation'], loc='upper left')

plt.tight_layout()
plt.savefig(os.path.join(os.getcwd(), 'models', 'training_history.png'))
plt.close()

print("\nModel saved to:", model_path)
print("Class names saved to:", class_names_path)
print("Training history plot saved to:", os.path.join(os.getcwd(), 'models', 'training_history.png'))