def main():
# Parse arguments.
parser = argparse.ArgumentParser()
kwargs = {
'type': str,
'help': 'The model file path.',
'required': True
}
parser.add_argument('-m', '--model', **kwargs)
kwargs = {
'type': int,
'default': 10,
'help': 'The number of samples to evaluate. default: 10'
}
parser.add_argument('-n', '--num', **kwargs)
kwargs = {
'type': str,
'default': 'val',
'help': 'Type of dataset to use. "val" or "test". default: "val"'
}
parser.add_argument('-t', '--type', **kwargs)
args = parser.parse_args()
# Prepare training data.
dataset = np.load('./temp/dataset.npz')
val_x = dataset[f'{args.type}_x']
val_y = dataset[f'{args.type}_y']
if args.num < 0 or len(val_x) < args.num:
args.num = len(val_x)
# Prepare tensorflow.
config = tf.ConfigProto(device_count={'GPU': 0})
session = tf.Session(config=config)
keras.backend.tensorflow_backend.set_session(session)
# Prepare model.
model = SegNet(shape=(360, 480, 3))
model.load_weights(args.model)
# Output results.
head, tail = os.path.split(args.model)
filename, ext = os.path.splitext(tail)
os.makedirs(f'{head}/{filename}/', exist_ok=True)
for i, x, y, t in zip(range(args.num), val_x, model.predict(val_x[:args.num]), val_y):
cv2.imwrite(f'{head}/{filename}/{args.type}-{i}-input.png', x * 255)
cv2.imwrite(f'{head}/{filename}/{args.type}-{i}-prediction.png', y * 255)
cv2.imwrite(f'{head}/{filename}/{args.type}-{i}-teacher.png', t * 255)
x_test = np.delete(x_test, len(x_test) - 1, axis=0)
y_train = sc.fit_transform(np.asarray(controls))
# Prepare model for training
model = SegNet((IMG_H, IMG_W))
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, epochs=EPOCHS, batch_size=BATCH_SIZE)
if not os.path.exists('models'):
os.mkdir('models')
model.save_weights('models/res.h5', save_format='h5')
# Predict on testing dataset
predictions = model.predict(x_test)
predictions = sc.inverse_transform(predictions)
print(predictions)
# Show results and compare
plt.plot(testControls, color='blue', label=f'Real steering')
plt.plot(predictions, color='red', label=f'Predicted steering')
plt.title(f"Steering Angle Prediction")
plt.xlabel('Frame')
plt.ylabel('Steering Angle')
plt.legend()
plt.show()