有效地调整np.array图像的批大小

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from skimage.transform import resize

imgs_in = np.random.rand(100, 32, 32, 3)
imgs_out = np.zeros((100,224,224,3))

for n,i in enumerate(imgs_in):
    imgs_out[n,:,:,:] = resize(imgs_in[n,:,:,:], imgs_out.shape[1:], anti_aliasing=True)

print(imgs_out.shape)

似乎比以前快了7-8倍 ndi.zoom multiprocessing

一种可能性是 scipy.ndimage.zoom

import numpy as np
import scipy.ndimage as ndi

imgs_in = np.random.rand(100, 32, 32, 3)
factor = 224/imgs_in.shape[1]
imgs_out = ndi.zoom(imgs_in, (1, factor, factor, 1), order=2)
print(imgs_out.shape)

生成的形状是 (100, 224, 224, 3) 一如预期。

您必须检查运行时和结果是否符合您的需要。旋转插值顺序可能会影响这一点:二阶和(默认)三阶样条曲线之间存在明显的速度差异,这是以插值质量为代价的。