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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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import tensorflow as tf
import numpy as np
import os
import cv2
from sklearn.model_selection import train_test_split
print("=" * 70)
print("🔵 QUICK CNN MODEL - DROWSY vs NON-DROWSY")
print("=" * 70)
def load_small_sample(data_path, samples_per_class=1000):
"""Load a small balanced sample for quick training"""
images = []
labels = []
for class_name, class_idx in [('drowsy', 1), ('non_drowsy', 0)]:
class_path = os.path.join(data_path, 'ddd', class_name)
if os.path.exists(class_path):
img_files = [f for f in os.listdir(class_path) if f.endswith(('.jpg', '.png', '.jpeg'))]
# Take random sample
if len(img_files) > samples_per_class:
img_files = np.random.choice(img_files, samples_per_class, replace=False)
print(f" Loading {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 small sample
data_path = 'data/processed'
print("\n[1/4] Loading small balanced sample...")
X, y = load_small_sample(data_path, samples_per_class=1000)
print(f"\n✅ Total images: {len(X)}")
print(f" Drowsy: {np.sum(y==1)} | Non-Drowsy: {np.sum(y==0)}")
# Preprocess
X = X.astype('float32') / 255.0
# Split
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
y_train_cat = tf.keras.utils.to_categorical(y_train, 2)
y_val_cat = tf.keras.utils.to_categorical(y_val, 2)
print(f"\n[2/4] Data split: Train={len(X_train)}, Val={len(X_val)}")
# Build model
print("\n[3/4] Building model...")
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.Dropout(0.5),
tf.keras.layers.Dense(2, activation='softmax')
])
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),
loss='categorical_crossentropy',
metrics=['accuracy']
)
# Train
print("\n[4/4] Training...")
history = model.fit(
X_train, y_train_cat,
validation_data=(X_val, y_val_cat),
epochs=10,
batch_size=16,
verbose=1
)
# Save
os.makedirs('data/models', exist_ok=True)
model.save('data/models/cnn_ddd_quick.h5')
print("\n" + "=" * 70)
print("✅ TRAINING COMPLETE!")
print("=" * 70)
print(f"📊 Best Validation Accuracy: {max(history.history['val_accuracy']):.2%}")
print(f"📁 Model saved to: data/models/cnn_ddd_quick.h5")
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