def generator(dataset_name, batch_size, classes):
data_loader = DataManager(dataset_name)
ground_truth_data = data_loader.get_data()
images_path = data_loader.dataset_path
train_keys, val_keys = split_data(ground_truth_data, 0)
image_generator = ImageGenerator(ground_truth_data,
batch_size, [224, 224, 3],
train_keys,
val_keys,
classes,
None,
path_prefix=images_path,
grayscale=False)
train_generator = image_generator.flow(mode='train')
train_num = len(train_keys) / batch_size
return train_generator, train_num
#callbacks
log_base_path = base_path + dataset_name + '_emotion_training.log'
csv_logger = CSVLogger(log_base_path, append=False)
early_stop = EarlyStopping('val_loss', patience=patience)
reduce_lr = ReduceLROnPlateau('val_loss',
factor=0.1,
patience=int(patience / 4),
verbose=1)
trained_models_path = base_path + dataset_name + '_mini_XCEPTION'
model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'
model_checkpoint = ModelCheckpoint(model_names,
'val_loss',
verbose=1,
save_best_only=True)
callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]
# loading dataset
data_loader = DataManager(dataset_name, image_size=input_shape[:2])
faces, emotions = data_loader.get_data()
faces = preprocess_input(faces)
num_samples, num_classes = emotions.shape
train_data, val_data = split_data(faces, emotions, validation_split)
train_faces, train_emotions = train_data
model.fit_generator(data_generator.flow(train_faces, train_emotions,
batch_size),
steps_per_epoch=len(train_faces) / batch_size,
epochs=num_epochs,
verbose=1,
callbacks=callbacks,
validation_data=val_data)
from keras.preprocessing import image
from keras.applications.imagenet_utils import preprocess_input
sys.path.append('../utils')
from data_augmentation_reid import ImageGenerator
from datasets import DataManager
from datasets import split_data
if __name__ == '__main__':
dataset_name = 'market'
batch_size = 32
input_shape = (224, 224, 3)
classes = 751
data_loader = DataManager(dataset_name)
images_path = data_loader.dataset_path
ground_truth_data = data_loader.get_data()
train_keys, val_keys = split_data(ground_truth_data, 0.2)
#print ground_truth_data
f = open('../result/market_ground_truth.txt', 'w')
f.write(str(ground_truth_data))
f.close()
#np.savetxt('../result/market_ground_truth.txt',ground_truth_data)
#print len(ground_truth_data)
#print len(train_keys)
#print train_keys[0]
#print ground_truth_data[train_keys[0]]
#print images_path
#print ground_truth_data[train_keys[1]]
#print ground_truth_data[train_keys[0]][0]
#print ground_truth_data[train_keys[1]][0]
