Conversion with PIL Image, PyTorch tensor & NumPy array

Buy Me a Coffee☕ You can do conversion with PIL Image, PyTorch tensor and NumPy array as shown below: from torchvision.datasets import OxfordIIITPet origin_data = OxfordIIITPet( root="data", transform=None ) import matplotlib.pyplot as plt plt.figure(figsize=[7, 9]) plt.title(label="s500_394origin_data", fontsize=14) plt.imshow(X=origin_data[0][0]) plt.show() print() PIL Image[H, W, C] => PyTorch Tensor[C, H, W] => NumPy Array[H, W, C]: from torchvision.datasets import OxfordIIITPet import numpy as np origin_data = OxfordIIITPet( root="data", transform=None ) ptt = PILToTensor() pytorchimagetensor = ptt(origin_data[0][0]) # tensor([[[ 37, 35, 36, ..., 247, 249, 249], # [ 35, 35, 37, ..., 246, 248, 249], # ..., # [ 28, 28, 27, ..., 59, 65, 76]], # [[ 20, 18, 19, ..., 248, 248, 248], # [ 18, 18, 20, ..., 247, 247, 248], # ..., # [ 27, 27, 27, ..., 94, 106, 117]], # [[ 12, 10, 11, ..., 253, 253, 253], # [ 10, 10, 12, ..., 251, 252, 253], # ..., # [ 35, 35, 35, ..., 214, 232, 223]]], dtype=torch.uint8) numpyimagearray = pytorchimagetensor.permute(1, 2, 0).numpy() numpyimagearray = np.array(object=pytorchimagetensor.permute(1, 2, 0)) numpyimagearray = np.asarray(pytorchimagetensor.permute(1, 2, 0)) numpyimagearray # array([[[ 37 20 12] # [ 35 18 10] # ... # [249 248 253]] # [[ 35 18 10] # [ 35 18 10] # ... # [249 248 253]] # [[ 35 18 10] # [ 36 19 11] # ... # [250 249 254]] # ... # [[ 5 6 24] # [ 4 5 23] # ... # [ 69 110 224]] # [[ 4 3 19] # [ 3 2 18] # ... # [ 64 108 229]] # [[ 28 27 35] # [ 28 27 35] # ... # [ 76 117 223]]], dtype=uint8) PIL Image[H, W, C] => NumPy Array[H, W, C] => PyTorch Tensor[C, H, W]: from torchvision.datasets import OxfordIIITPet import numpy as np origin_data = OxfordIIITPet( root="data", transform=None ) numpyimagearray = np.array(object=origin_data[0][0]) numpyimagearray = np.asarray(origin_data[0][0]) numpyimagearray # array([[[ 37 20 12] # [ 35 18 10] # ... # [249 248 253]] # [[ 35 18 10] # [ 35 18 10] # ... # [249 248 253]] # [[ 35 18 10] # [ 36 19 11] # ... # [250 249 254]] # ... # [[ 5 6 24] # [ 4 5 23] # ... # [ 69 110 224]] # [[ 4 3 19] # [ 3 2 18] # ... # [ 64 108 229]] # [[ 28 27 35] # [ 28 27 35] # ... # [ 76 117 223]]], dtype=uint8) pytorchimagetensor = torch.from_numpy(numpyimagearray).permute(dims=[2, 0, 1]) pytorchimagetensor = torch.tensor(numpyimagearray).permute(dims=[2, 0, 1]) pytorchimagetensor # tensor([[[ 37, 35, 36, ..., 247, 249, 249], # [ 35, 35, 37, ..., 246, 248, 249], # ..., # [ 28, 28, 27, ..., 59, 65, 76]], # [[ 20, 18, 19, ..., 248, 248, 248], # [ 18, 18, 20, ..., 247, 247, 248], # ..., # [ 27, 27, 27, ..., 94, 106, 117]], # [[ 12, 10, 11, ..., 253, 253, 253], # [ 10, 10, 12, ..., 251, 252, 253], # ..., # [ 35, 35, 35, ..., 214, 232, 223]]], dtype=torch.uint8)

May 13, 2025 - 17:02

Conversion with PIL Image, PyTorch tensor & NumPy array

You can do conversion with PIL Image, PyTorch tensor and NumPy array as shown below:

from torchvision.datasets import OxfordIIITPet

origin_data = OxfordIIITPet(
    root="data",
    transform=None
)

import matplotlib.pyplot as plt

plt.figure(figsize=[7, 9])
plt.title(label="s500_394origin_data", fontsize=14)
plt.imshow(X=origin_data[0][0])
plt.show()
print()

PIL Image[H, W, C] => PyTorch Tensor[C, H, W] => NumPy Array[H, W, C]:

from torchvision.datasets import OxfordIIITPet
import numpy as np

origin_data = OxfordIIITPet(
    root="data",
    transform=None
)

ptt = PILToTensor()

pytorchimagetensor = ptt(origin_data[0][0])
# tensor([[[ 37,  35,  36,  ..., 247, 249, 249],
#          [ 35,  35,  37,  ..., 246, 248, 249],
#          ...,
#          [ 28,  28,  27,  ...,  59,  65,  76]],
#         [[ 20,  18,  19,  ..., 248, 248, 248],
#          [ 18,  18,  20,  ..., 247, 247, 248],
#          ...,
#          [ 27,  27,  27,  ...,  94, 106, 117]],
#         [[ 12,  10,  11,  ..., 253, 253, 253],
#          [ 10,  10,  12,  ..., 251, 252, 253],
#          ...,
#          [ 35,  35,  35,  ..., 214, 232, 223]]], dtype=torch.uint8)

numpyimagearray = pytorchimagetensor.permute(1, 2, 0).numpy()
numpyimagearray = np.array(object=pytorchimagetensor.permute(1, 2, 0))
numpyimagearray = np.asarray(pytorchimagetensor.permute(1, 2, 0))

numpyimagearray
# array([[[ 37  20  12]
#         [ 35  18  10]
#         ...
#         [249 248 253]]
#        [[ 35  18  10]
#         [ 35  18  10]
#         ...
#         [249 248 253]]
#        [[ 35  18  10]
#         [ 36  19  11]
#         ...
#         [250 249 254]]
#         ...
#        [[  5   6  24]
#         [  4   5  23]
#         ...
#         [ 69 110 224]]
#        [[  4   3  19]
#         [  3   2  18]
#         ...
#         [ 64 108 229]]
#        [[ 28  27  35]
#         [ 28  27  35]
#         ...
#         [ 76 117 223]]], dtype=uint8)

PIL Image[H, W, C] => NumPy Array[H, W, C] => PyTorch Tensor[C, H, W]:

from torchvision.datasets import OxfordIIITPet
import numpy as np

origin_data = OxfordIIITPet(
    root="data",
    transform=None
)

numpyimagearray = np.array(object=origin_data[0][0])
numpyimagearray = np.asarray(origin_data[0][0])

numpyimagearray
# array([[[ 37  20  12]
#         [ 35  18  10]
#         ...
#         [249 248 253]]
#        [[ 35  18  10]
#         [ 35  18  10]
#         ...
#         [249 248 253]]
#        [[ 35  18  10]
#         [ 36  19  11]
#         ...
#         [250 249 254]]
#         ...
#        [[  5   6  24]
#         [  4   5  23]
#         ...
#         [ 69 110 224]]
#        [[  4   3  19]
#         [  3   2  18]
#         ...
#         [ 64 108 229]]
#        [[ 28  27  35]
#         [ 28  27  35]
#         ...
#         [ 76 117 223]]], dtype=uint8)

pytorchimagetensor = torch.from_numpy(numpyimagearray).permute(dims=[2, 0, 1])
pytorchimagetensor = torch.tensor(numpyimagearray).permute(dims=[2, 0, 1])

pytorchimagetensor
# tensor([[[ 37,  35,  36,  ..., 247, 249, 249],
#          [ 35,  35,  37,  ..., 246, 248, 249],
#          ...,
#          [ 28,  28,  27,  ...,  59,  65,  76]],
#         [[ 20,  18,  19,  ..., 248, 248, 248],
#          [ 18,  18,  20,  ..., 247, 247, 248],
#          ...,
#          [ 27,  27,  27,  ...,  94, 106, 117]],
#         [[ 12,  10,  11,  ..., 253, 253, 253],
#          [ 10,  10,  12,  ..., 251, 252, 253],
#          ...,
#          [ 35,  35,  35,  ..., 214, 232, 223]]], dtype=torch.uint8)