| #set m, n for resized image size | |
| m = 256 | |
| n = 256 | |
| data = [] | |
| expandedPosClass = resizeAndExpandDataset(posImageList, m, n) | |
| expandedNegClass = resizeAndExpandDataset(negImageList, m, n) | |
| for im in expandedPosClass: | |
| data.append([np.array(im), 1]) | |
| for im in expandedNegClass: | |
| data.append([np.array(im), 0]) | |
| #randomly shuffle the data | |
| np.random.shuffle(data) | |
| #split the data into training and testing data | |
| train_data, test_data = train_test_split(data, test_size=0.3) | |
| # split the data into features and labels | |
| train_x = np.array([i[0] for i in train_data]) | |
| # train_x = tf.expand_dims(train_x, axis=-1) | |
| train_y = np.array([i[1] for i in train_data]) | |
| test_x = np.array([i[0] for i in test_data]) | |
| test_y = np.array([i[1] for i in test_data]) |