【Pytorch】torchvision transforms

import torchvision
import matplotlib.pyplot as plt
from PIL import Image
from torchvision import transforms


def test(img=Image.open("./cat.png"), save_name=None):
    plt.subplot(1, 2, 1)
    w, h = img.size  # w, h
    plt.title("w="+str(w)+",h="+str(h))
    plt.imshow(img)
    plt.axis("off")

    plt.subplot(1, 2, 2)
    try:
        img = aug(img)
    except:
        img = aug(img.convert("RGB"))
    # img.convert("RGB").save("1.jpg")
    w, h = img.size  # w, h
    plt.title("w="+str(w)+",h="+str(h))
    plt.imshow(img)
    plt.axis("off")
    plt.savefig(save_name)
    plt.show()


def test5(img=Image.open("cat.png"), save_name=None):
    plt.subplot(2, 3, 1)
    w, h = img.size  # w, h
    plt.title("w="+str(w)+",h="+str(h))
    plt.imshow(img)
    plt.axis("off")

    img = aug(img)
    for i in range(2, 7):
        plt.subplot(2, 3, i)
        w, h = img[i-2].size  # w, h
        plt.title("w="+str(w)+",h="+str(h))
        plt.imshow(img[i-2])
        plt.axis("off")

    plt.savefig(save_name)
    plt.show()




if __name__ == "__main__":
    # aug = torchvision.transforms.CenterCrop([500, 400])  # h, w
    # test(save_name="centercrop.png")

    # aug = torchvision.transforms.ColorJitter(0.5, 0.5, 0.5, 0.5)  # brightness, contrast, saturation. hue
    # test(save_name="ColorJitter.png")

    # aug = torchvision.transforms.FiveCrop(350)
    # test5(save_name="FiveCrop.png")

    # aug = torchvision.transforms.Grayscale(3)  # output channel
    # test(save_name="Grayscale.png")

    # aug = torchvision.transforms.Pad([120, 60, 60, 120], padding_mode="reflect")  # top down left right
    # test(save_name="Pad-reflect.png")

    # aug = torchvision.transforms.Pad([120, 60, 60, 120], padding_mode="edge")  # top down left right
    # test(save_name="Pad-edge.png")

    # aug = torchvision.transforms.Pad([120, 60, 60, 120], fill=128, padding_mode="constant")  # top down left right
    # test(save_name="Pad.png")

    # aug = torchvision.transforms.RandomAffine(90)  # degree
    # test(save_name="RandomAffine.png")

    # aug = torchvision.transforms.RandomCrop(450)  # degree
    # test(save_name="RandomCrop.png")

    # aug = torchvision.transforms.RandomResizedCrop(450)  # degree
    # test(save_name="RandomResizedCrop.png")

    # aug = torchvision.transforms.RandomRotation(90)  # degree
    # test(save_name="RandomRotation.png")

    # aug = torchvision.transforms.Resize([250, 250])  # degree
    # test(save_name="Resize.png")

    # aug = torchvision.transforms.RandomInvert(0.9)
    # test(save_name="RandomInvert.png")

    # aug = torchvision.transforms.RandomPosterize(2, 0.9)
    # test(save_name="RandomPosterize.png")

    # aug = torchvision.transforms.RandomSolarize(125, 0.9)
    # test(save_name="RandomSolarize.png")

    aug = torchvision.transforms.RandomEqualize(0.9)
    test(save_name="RandomEqualize.png")

centercrop

ColorJitter

FiveCrop

Grayscale

Pad

Pad-edge

Pad-reflect

RandomAffine

RandomCrop

RandomEqualize

RandomInvert

RandomPosterize

RandomResizedCrop

RandomRotation

RandomSolarize

Resize