def train_and_predict():
DataCreator()
print('-' * 30)
print('Loading and preprocessing train data...')
print('-' * 30)
imgs_train, imgs_mask_train = DataLoader()
imgs_train = preprocess(imgs_train)
imgs_mask_train = preprocess(imgs_mask_train)
imgs_train = imgs_train.astype('float32')
mean = np.mean(imgs_train) # mean for data centering
std = np.std(imgs_train) # std for data normalization
#imgs_train -= mean
#imgs_train /= std
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_mask_train /= 255. # scale masks to [0, 1]
print('-' * 30)
print('Creating and compiling model...')
print('-' * 30)
model = get_unet()
model_checkpoint = ModelCheckpoint('weights.h5',
monitor='val_loss',
save_best_only=True)
print('-' * 30)
print('Fitting model...')
print('-' * 30)
model.fit(imgs_train,
imgs_mask_train,
batch_size=32,
nb_epoch=numberOfEpochs,
verbose=1,
shuffle=True,
validation_split=0.2,
callbacks=[model_checkpoint])
#TESTS DATA --------------
print('-' * 30)
print('Loading and preprocessing test data...')
print('-' * 30)
imgs_test, imgs_id_test = load_test_data()
imgs_test = preprocess(imgs_test)
imgs_test = imgs_test.astype('float32')
# imgs_test -= mean
imgs_test /= std
print('-' * 30)
print('Loading saved weights...')
print('-' * 30)
model.load_weights('weights.h5')
print('-' * 30)
print('Predicting masks on test data...')
print('-' * 30)
imgs_mask_test = model.predict(imgs_test, verbose=1)
np.save('imgs_mask_test.npy', imgs_mask_test)
print('-' * 30)
print('Saving predicted masks to files...')
print('-' * 30)
pred_dir = 'preds'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
#for image, image_id in zip(imgs_mask_test, imgs_id_test):
i = 0
for image in imgs_mask_test:
image = (image[:, :, 0] * 255.).astype(np.uint8)
imsave(os.path.join(pred_dir, str(i) + '_pred.png'), image)
i += 1
imgt = Image.fromarray(imgs_train[6])
imgg = Image.fromarray(imgs_mask_train[6])
imgt.show()
imgg.show()
'''
imgs_train = preprocess(imgs_train)
imgs_mask_train = preprocess(imgs_mask_train)
imgs_train = imgs_train.astype('float32')
mean = np.mean(imgs_train) # mean for data centering
std = np.std(imgs_train) # std for data normalization
#imgs_train -= mean
#imgs_train /= std
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_mask_train /= 255. # scale masks to [0, 1]
'''
imgt = Image.fromarray(np.reshape(imgs_train[6], (48,48)))
imgg = Image.fromarray(np.reshape(imgs_mask_train[6], (48,48)))
imgt.show()
imgg.show()
