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import numpy as np
class Utils:
@staticmethod
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)
@staticmethod
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
@staticmethod
def show(image):
import cv2
cv2.namedWindow("view", cv2.WINDOW_AUTOSIZE)
cv2.imshow("view", image)
cv2.waitKey(0)
cv2.destroyWindow("view")