def testBinary():
k = 2
data = Data(k, 0, 0)
data.importDataFromMat()
data.normalize()
train = TrainerValidator(k, 70, 100, 10, 0.1, 0.2, 1, data)
train.trainAndClassify()
train.plotResults()
test = Test(train.getMLP(), data, k)
test.classify()
test.examples()
test.plot_confusion_matrix()
'1: to convert image to npy file.\n'
'2: to run the training.\n'
'3: to test the model.\n'
'action: ')
if (action == '0'):
print('INFO: Please provide the data path')
path = input('path to data: ')
list_categories(path)
elif (action == '1'):
print('INFO: Please provide the path to the images and the filename')
path = input('path to the images: ')
filename = input('the npy filename: ')
image_to_npy(filename=filename, path=path, img_size=(64, 64))
elif (action == '2'):
print('INFO: Please provide the data path')
data_path = input('data path: ')
data = np.load(data_path, allow_pickle=True)
images = np.array([i[0] for i in data])
labels = np.array([i[1] for i in data])
run_training = TrainModel(train_x=images, train_y=labels)
run_training.train()
elif (action == '3'):
print('INFO: Please provide the image to classify and the model path!')
image_path = input('image path: ')
model_path = input('modelpath: ')
run_classification = Test(image_path=image_path, graph_path=model_path)
category = run_classification.classify()
print(category)
else:
print('ERROR: Wrong choise of action!')
