比较与tf.keras.preprocessing.image.random_shear的功能差异
tf.keras.preprocessing.image.random_shear
tf.keras.preprocessing.image.random_shear(
x,
intensity,
row_axis=1,
col_axis=2,
channel_axis=0,
fill_mode='nearest',
cval=0.0,
interpolation_order=1
)
mindspore.dataset.vision.RandomAffine
class mindspore.dataset.vision.RandomAffine(
degrees,
translate=None,
scale=None,
shear=None,
resample=Inter.NEAREST,
fill_value=0
)
使用方式
TensorFlow:对图像进行随机剪切,图像的行、列及通道轴索引可通过入参指定。
MindSpore:对图像进行随机仿射变换,其中包括随机剪切,图像需按照行、列、通道的轴顺序排列。
代码示例
# The following implements RandomAffine with MindSpore.
import numpy as np
import mindspore.dataset as ds
from mindspore.dataset.vision import Inter
image = np.random.random((28, 28, 3))
result = ds.vision.RandomAffine(0, shear=30, resample=Inter.NEAREST)(image)
print(result.shape)
# (28, 28, 3)
# The following implements random_shear with TensorFlow.
import tensorflow as tf
image = np.random.random((28, 28, 3))
result = tf.keras.preprocessing.image.random_shear(
image, intensity=30, row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest')
print(result.shape)
# (28, 28, 3)