比较与torchvision.transforms.RandomSolarize的功能差异
torchvision.transforms.RandomSolarize
class torchvision.transforms.RandomSolarize(
threshold,
p=0.5
)
mindspore.dataset.vision.RandomSolarize
class mindspore.dataset.vision.RandomSolarize(
threshold=(0, 255)
)
使用方式
PyTorch:通过反转高于阈值的所有像素值,以给定的概率随机对图像进行曝光操作。
MindSpore:从指定阈值范围内随机选择一个子范围,并将图像像素值调整在子范围内。
代码示例
from PIL import Image
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
import torchvision.transforms as T
import mindspore.dataset.vision as vision
orig_img = Image.open(Path('.') / 'test.jpg')
def show_diff_image(image_original, image_transformed):
num = 2
plt.subplot(1, num, 1)
plt.imshow(image_original)
plt.title("Original image")
plt.subplot(1, num, 2)
plt.imshow(image_transformed)
plt.title("Random Solaried image")
plt.show()
# In MindSpore, randomly selects a subrange within the specified threshold range and sets the pixel value within the subrange to (255 - pixel).
solarizer = vision.RandomSolarize(threshold=(10,100))
rand_sola_img = solarizer(orig_img)
show_diff_image(orig_img, rand_sola_img)
# Out:
# Original image and Solarized image are showed with matplotlib tools
# In torch, the RandomSolarize transform randomly solarizes the image by inverting all pixel values above the threshold.
solarizer = T.RandomSolarize(threshold=192.0)
solarized_imgs = solarizer(orig_img)
show_diff_image(orig_img, solarized_imgs)
# Out:
# Original image and Solarized image are showed with matplotlib tools