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ddec2b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import tensorflow as tf
import numpy as np
import os
import cv2
from sklearn.model_selection import train_test_split
print("=" * 70)
print("👁️ TRAINING EYE MODEL - OPEN vs CLOSED")
print("=" * 70)
def load_eye_data(data_path):
images, labels = [], []
for split in ['train', 'test']:
for class_name, class_idx in [('open', 0), ('closed', 1)]:
class_path = os.path.join(data_path, 'yawn_eye', split, class_name)
if os.path.exists(class_path):
img_files = [f for f in os.listdir(class_path) if f.endswith(('.jpg', '.png', '.jpeg'))]
print(f" Loading {split}/{class_name}: {len(img_files)} images")
for img_name in img_files:
img_path = os.path.join(class_path, img_name)
img = cv2.imread(img_path)
if img is not None:
img = cv2.resize(img, (224, 224))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
images.append(img)
labels.append(class_idx)
return np.array(images), np.array(labels)
# Load data
data_path = 'data/processed'
print("\n[1/4] Loading Eye dataset...")
X, y = load_eye_data(data_path)
if len(X) == 0:
print("\n⚠️ No eye images found. Creating synthetic data for testing...")
X = np.random.random((200, 224, 224, 3)) * 255
y = np.random.randint(0, 2, 200)
else:
print(f"\n✅ Total eye images: {len(X)}")
print(f" Open: {np.sum(y==0)} | Closed: {np.sum(y==1)}")
X = X.astype('float32') / 255.0
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y if len(y)>0 else None)
y_train = tf.keras.utils.to_categorical(y_train, 2)
y_val = tf.keras.utils.to_categorical(y_val, 2)
# Build model
print("\n[2/4] Building Eye Model (MobileNetV2)...")
base_model = tf.keras.applications.MobileNetV2(
weights='imagenet', include_top=False, input_shape=(224, 224, 3)
)
base_model.trainable = False
model = tf.keras.Sequential([
base_model,
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.4),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dropout(0.3),
tf.keras.layers.Dense(2, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Train
print("\n[3/4] Training Eye Model...")
history = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, batch_size=16, verbose=1)
os.makedirs('data/models', exist_ok=True)
model.save('data/models/eye_model.h5')
print("\n[4/4] ✅ Eye Model saved to: data/models/eye_model.h5")
print(f" Validation Accuracy: {history.history['val_accuracy'][-1]:.2%}")
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