Function Differences with torchvision.transforms.RandomSolarize

torchvision.transforms.RandomSolarize

class torchvision.transforms.RandomSolarize(
    threshold,
    p=0.5
    )

For more information, see torchvision.transforms.RandomSolarize.

mindspore.dataset.vision.RandomSolarize

class mindspore.dataset.vision.RandomSolarize(
    threshold=(0, 255)
    )

For more information, see mindspore.dataset.vision.RandomSolarize.

Differences

PyTorch: Randomly expose the image with a given probability by inverting all pixel values above the threshold.

MindSpore: Select a random subrange from the specified threshold range and adjust the image pixel values within the subrange.

Code Example

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