ToPILImage in PyTorch (5)

#python #pytorch #topilimage #v2

*Memos:

My post explains ToPILImage() about no arguments.
My post explains ToPILImage() about mode argument (1).
My post explains ToPILImage() about mode argument (2).
My post explains ToPILImage() about mode argument (3).

ToPILImage() can convert an Image([..., C, H, W]), tensor or ndarray to a PIL(Pillow library) image([H, W, C]) and doesn't scale its values to [0.0, 1.0] as shown below. *It's about mode argument (4):

from torchvision.datasets import OxfordIIITPet
from torchvision.transforms.v2 import ToPILImage, ToImage, PILToTensor
import numpy as np
import matplotlib.pyplot as plt

def show_images(im, m=None):
    if m == None:
        m = [None for _ in range(len(im))]
    if len(im) > len(m):
        for _ in range(len(im)-len(m)):
            m.append(None)
    plt.figure(figsize=[14, 6])
    for i in range(len(im)):
        image = im[i]
        if torch.is_tensor(image):
            dpart = str(image.dtype).split(".")[1]
        elif isinstance(image, np.ndarray):
            dpart = str(image.dtype)
        title = "m" + str(m[i]) + "_PILImage from " \
                + type(image).__name__+ "(" + dpart + ")"
        plt.subplot(1, 3, (i+1))
        tp = ToPILImage(mode=m[i])
        plt.title(label=title, y=1, fontsize=14)
        plt.imshow(X=tp(image))
        plt.xticks(ticks=[])
        plt.yticks(ticks=[])
    plt.tight_layout()
    plt.show()

show_images(im=[np.array([[[0]]]), np.array([[[0, 1]]])], # int32
            m=["I", "LA"])
show_images(im=[np.array([[[0, 1, 2]]]), np.array([[[0, 1, 2]]]),
                np.array([[[0, 1, 2]]])], m=["RGB", "YCbCr", "HSV"])
show_images(im=[np.array([[[0, 1, 2, 3]]]), np.array([[[0, 1, 2, 3]]]),
                np.array([[[0, 1, 2, 3]]])], m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[np.array([[[0, 1]]], dtype=np.int64)], m=["LA"])
show_images(im=[np.array([[[0, 1, 2]]], dtype=np.int64),
                np.array([[[0, 1, 2]]], dtype=np.int64),
                np.array([[[0, 1, 2]]], dtype=np.int64)],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[np.array([[[0, 1, 2, 3]]], dtype=np.int64),
                np.array([[[0, 1, 2, 3]]], dtype=np.int64),
                np.array([[[0, 1, 2, 3]]], dtype=np.int64)],
            m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[np.array([[[0.]]]), np.array([[[0., 1.]]])], # float64
            m=["L", "LA"])
show_images(im=[np.array([[[0., 1., 2.]]]), np.array([[[0., 1., 2.]]]),
                np.array([[[0., 1., 2.]]])], m=["RGB", "YCbCr", "HSV"])
show_images(im=[np.array([[[0., 1., 2., 3.]]]),
                np.array([[[0., 1., 2., 3.]]]),
                np.array([[[0., 1., 2., 3.]]])],
            m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[np.array([[[0.]]], dtype=np.float32),
                np.array([[[0., 1.]]], dtype=np.float32)], m=["L", "LA"])
show_images(im=[np.array([[[0., 1., 2.]]], dtype=np.float32),
                np.array([[[0., 1., 2.]]], dtype=np.float32),
                np.array([[[0., 1., 2.]]], dtype=np.float32)],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[np.array([[[0., 1., 2., 3.]]], dtype=np.float32),
                np.array([[[0., 1., 2., 3.]]], dtype=np.float32),
                np.array([[[0., 1., 2., 3.]]], dtype=np.float32)],
            m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[np.array([[[0.+0.j, 1.+0.j]]])], m=["LA"]) # complex128
show_images(im=[np.array([[[0.+0.j, 1.+0.j, 2.+0.j]]]),
                np.array([[[0.+0.j, 1.+0.j, 2.+0.j]]]),
                np.array([[[0.+0.j, 1.+0.j, 2.+0.j]]])],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[np.array([[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]]),
                np.array([[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]]),
                np.array([[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]])],
            m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[np.array([[[0.+0.j, 1.+0.j]]], dtype=np.complex64)],
            m=["LA"])
show_images(im=[np.array([[[0.+0.j, 1.+0.j, 2.+0.j]]], dtype=np.complex64),
                np.array([[[0.+0.j, 1.+0.j, 2.+0.j]]], dtype=np.complex64),
                np.array([[[0.+0.j, 1.+0.j, 2.+0.j]]], dtype=np.complex64)],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[np.array([[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]], 
                         dtype=np.complex64),
                np.array([[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]], 
                         dtype=np.complex64),
                np.array([[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]], 
                         dtype=np.complex64)],
            m=["RGBA", "CMYK", "RGBX"])
print()
show_images(im=[np.array([[[True, False]]])], m=["LA"]) # bool
show_images(im=[np.array([[[True, False, True]]]),
                np.array([[[True, False, True]]]),
                np.array([[[True, False, True]]])],
            m=["RGB", "YCbCr", "HSV"])
show_images(im=[np.array([[[True, False, True, False]]]),
                np.array([[[True, False, True, False]]]),
                np.array([[[True, False, True, False]]])],
            m=["RGBA", "CMYK", "RGBX"])

DEV Community

ToPILImage in PyTorch (5)

Top comments (0)