def __reformat_and_normalize(img: np.array,
mean=None,
std=None) -> np.array:
"""
Reformats the image to match the input dimensions (224, 224, 3) of the standard networks
downloaded from gluon model zoo, see ImageFeaturizer
:param img: image as numpy array
:param mean: mean of the image for each color channel
:param std: standard deviation of each color channel
:return: reformatted and normalized image
"""
if not mean:
mean = [0.485, 0.456, 0.406]
if not std:
std = [0.229, 0.224, 0.225]
if img.shape != (224, 224, 3):
img = pad_to_square(img)
img = mx.image.imresize(img, 224, 224)
img = img.astype(float) / 255
img = mx.image.color_normalize(img,
mean=mx.nd.array(mean).astype(float),
std=mx.nd.array(std).astype(float))
img = img.transpose((2, 0, 1))
img = img.expand_dims(axis=0)
return img.asnumpy()
