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| __author__ = '' | |
| import numpy as np | |
| class Utils: | |
| def sparsify(label_vector, output_size): | |
| sparse_vector = [] | |
| for label in label_vector: | |
| sparse_label = np.zeros(output_size) | |
| sparse_label[label] = 1 | |
| sparse_vector.append(sparse_label) | |
| return np.array(sparse_vector) | |
| def get_preprocessed_img(img_path, image_size): | |
| import cv2 | |
| # from keras.preprocessing.image import array_to_img, img_to_array | |
| # img = array_to_img(img_path) | |
| # img = img_to_array(img) | |
| # img = cv2.imread(img_path) | |
| # don't need to read the image as we're now directly passing the | |
| # image as numpy array to this method | |
| img = cv2.resize(img_path, (image_size, image_size)) | |
| img = img.astype('float32') | |
| img /= 255 | |
| return img | |
| def show(image): | |
| import cv2 | |
| cv2.namedWindow("view", cv2.WINDOW_AUTOSIZE) | |
| cv2.imshow("view", image) | |
| cv2.waitKey(0) | |
| cv2.destroyWindow("view") | |