{"id":108898,"date":"2025-05-14T03:54:12","date_gmt":"2025-05-14T00:24:12","guid":{"rendered":"https:\/\/nabfollower.com\/blog\/conversion-with-pil-image-pytorch-tensor-numpy-array-152c\/"},"modified":"2025-05-14T03:54:12","modified_gmt":"2025-05-14T00:24:12","slug":"conversion-with-pil-image-pytorch-tensor-numpy-array-152c","status":"publish","type":"post","link":"https:\/\/nabfollower.com\/blog\/conversion-with-pil-image-pytorch-tensor-numpy-array-152c\/","title":{"rendered":"\u062a\u0628\u062f\u06cc\u0644 \u0628\u0627 PIL Image \u060c Pytorch Tensor & Numpy Array"},"content":{"rendered":"
\n

\u0628\u0631\u0627\u06cc \u0645\u0646 \u06cc\u06a9 \u0642\u0647\u0648\u0647 \u0628\u062e\u0631<\/p>\n

*\u06cc\u0627\u062f\u062f\u0627\u0634\u062a \u0647\u0627:<\/p>\n

\u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0627 \u062a\u0635\u0648\u06cc\u0631 PIL (\u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0628\u0627\u0644\u0634) \u060c Pytorch Tensor \u0648 Numpy Array \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u062f\u0631 \u0632\u06cc\u0631 \u0622\u0645\u062f\u0647 \u0627\u0633\u062a \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u062f:<\/p>\n

\n
from<\/span> torchvision.datasets<\/span> import<\/span> OxfordIIITPet<\/span>\n\norigin_data<\/span> =<\/span> OxfordIIITPet<\/span>(<\/span>\n    root<\/span>=<\/span>\"<\/span>data<\/span>\"<\/span>,<\/span>\n    transform<\/span>=<\/span>None<\/span>\n)<\/span>\n\nimport<\/span> matplotlib.pyplot<\/span> as<\/span> plt<\/span>\n\nplt<\/span>.<\/span>figure<\/span>(<\/span>figsize<\/span>=<\/span>[<\/span>7<\/span>,<\/span> 9<\/span>])<\/span>\nplt<\/span>.<\/span>title<\/span>(<\/span>label<\/span>=<\/span>\"<\/span>s500_394origin_data<\/span>\"<\/span>,<\/span> fontsize<\/span>=<\/span>14<\/span>)<\/span>\nplt<\/span>.<\/span>imshow<\/span>(<\/span>X<\/span>=<\/span>origin_data<\/span>[<\/span>0<\/span>][<\/span>0<\/span>])<\/span>\nplt<\/span>.<\/span>show<\/span>()<\/span>\n<\/code><\/pre>\n
\n
\n    \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
<\/p>\n\n
\u062a\u0635\u0648\u06cc\u0631 PIL[H, W, C]<\/code> => \u062a\u0627\u0646\u0633\u0648\u0631 Pytorch[C, H, W]<\/code> => \u0622\u0631\u0627\u06cc\u0647 numpy[H, W, C]<\/code> => \u062a\u0635\u0648\u06cc\u0631 PIL[H, W, C]<\/code>:<\/p>\n
\n
from<\/span> torchvision.datasets<\/span> import<\/span> OxfordIIITPet<\/span>\nfrom<\/span> torchvision.transforms.v2<\/span> import<\/span> PILToTensor<\/span>,<\/span> ToPILImage<\/span>\nimport<\/span> numpy<\/span> as<\/span> np<\/span>\nfrom<\/span> PIL<\/span> import<\/span> Image<\/span>\n\norigin_data<\/span> =<\/span> OxfordIIITPet<\/span>(<\/span>\n    root<\/span>=<\/span>\"<\/span>data<\/span>\"<\/span>,<\/span>\n    transform<\/span>=<\/span>None<\/span>\n)<\/span>\n\n# PIL image to PyTorch tensor\n<\/span>ptt<\/span> =<\/span> PILToTensor<\/span>()<\/span>\n\npytorchimagetensor<\/span> =<\/span> ptt<\/span>(<\/span>origin_data<\/span>[<\/span>0<\/span>][<\/span>0<\/span>])<\/span>\n# tensor([[[ 37,  35,  36,  ..., 247, 249, 249],\n#          [ 35,  35,  37,  ..., 246, 248, 249],\n#          ...,\n#          [ 28,  28,  27,  ...,  59,  65,  76]],\n#         [[ 20,  18,  19,  ..., 248, 248, 248],\n#          [ 18,  18,  20,  ..., 247, 247, 248],\n#          ...,\n#          [ 27,  27,  27,  ...,  94, 106, 117]],\n#         [[ 12,  10,  11,  ..., 253, 253, 253],\n#          [ 10,  10,  12,  ..., 251, 252, 253],\n#          ...,\n#          [ 35,  35,  35,  ..., 214, 232, 223]]], dtype=torch.uint8)\n<\/span>\n# PyTorch tensor to NumPy array\n<\/span>numpyimagearray<\/span> =<\/span> pytorchimagetensor<\/span>.<\/span>permute<\/span>(<\/span>1<\/span>,<\/span> 2<\/span>,<\/span> 0<\/span>).<\/span>numpy<\/span>()<\/span>\nnumpyimagearray<\/span> =<\/span> np<\/span>.<\/span>array<\/span>(<\/span>object<\/span>=<\/span>pytorchimagetensor<\/span>.<\/span>permute<\/span>(<\/span>1<\/span>,<\/span> 2<\/span>,<\/span> 0<\/span>))<\/span>\nnumpyimagearray<\/span> =<\/span> np<\/span>.<\/span>asarray<\/span>(<\/span>pytorchimagetensor<\/span>.<\/span>permute<\/span>(<\/span>1<\/span>,<\/span> 2<\/span>,<\/span> 0<\/span>))<\/span>\n\nnumpyimagearray<\/span>\n# array([[[ 37  20  12]\n#         [ 35  18  10]\n#         ...\n#         [249 248 253]]\n#        [[ 35  18  10]\n#         [ 35  18  10]\n#         ...\n#         [249 248 253]]\n#        [[ 35  18  10]\n#         [ 36  19  11]\n#         ...\n#         [250 249 254]]\n#         ...\n#        [[  5   6  24]\n#         [  4   5  23]\n#         ...\n#         [ 69 110 224]]\n#        [[  4   3  19]\n#         [  3   2  18]\n#         ...\n#         [ 64 108 229]]\n#        [[ 28  27  35]\n#         [ 28  27  35]\n#         ...\n#         [ 76 117 223]]], dtype=uint8)\n<\/span>\n# NumPy array to PIL image\n<\/span>tpi<\/span> =<\/span> ToPILImage<\/span>()<\/span>\npilimage<\/span> =<\/span> tpi<\/span>(<\/span>numpyimagearray<\/span>)<\/span>\npilimage<\/span> =<\/span> Image<\/span>.<\/span>fromarray<\/span>(<\/span>obj<\/span>=<\/span>numpyimagearray<\/span>)<\/span>\n\nimport<\/span> matplotlib.pyplot<\/span> as<\/span> plt<\/span>\n\nplt<\/span>.<\/span>figure<\/span>(<\/span>figsize<\/span>=<\/span>[<\/span>7<\/span>,<\/span> 9<\/span>])<\/span>\nplt<\/span>.<\/span>title<\/span>(<\/span>label<\/span>=<\/span>\"<\/span>s500_394origin_data<\/span>\"<\/span>,<\/span> fontsize<\/span>=<\/span>14<\/span>)<\/span>\nplt<\/span>.<\/span>imshow<\/span>(<\/span>X<\/span>=<\/span>pilimage<\/span>)<\/span>\nplt<\/span>.<\/span>show<\/span>()<\/span>\n<\/code><\/pre>\n
\n
\n    \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
\"\u0634\u0631\u062d<\/p>\n\n
\u062a\u0635\u0648\u06cc\u0631 PIL[H, W, C]<\/code> => \u0622\u0631\u0627\u06cc\u0647 numpy[H, W, C]<\/code> => \u062a\u0627\u0646\u0633\u0648\u0631 Pytorch[C, H, W]<\/code> => \u062a\u0635\u0648\u06cc\u0631 PIL[H, W, C]<\/code>:<\/p>\n
\n
from<\/span> torchvision.datasets<\/span> import<\/span> OxfordIIITPet<\/span>\nfrom<\/span> torchvision.transforms.v2<\/span> import<\/span> ToPILImage<\/span>\nimport<\/span> numpy<\/span> as<\/span> np<\/span>\nimport<\/span> torch<\/span>\n\norigin_data<\/span> =<\/span> OxfordIIITPet<\/span>(<\/span>\n    root<\/span>=<\/span>\"<\/span>data<\/span>\"<\/span>,<\/span>\n    transform<\/span>=<\/span>None<\/span>\n)<\/span>\n\n# PIL image to NumPy array\n<\/span>numpyimagearray<\/span> =<\/span> np<\/span>.<\/span>array<\/span>(<\/span>object<\/span>=<\/span>origin_data<\/span>[<\/span>0<\/span>][<\/span>0<\/span>])<\/span>\nnumpyimagearray<\/span> =<\/span> np<\/span>.<\/span>asarray<\/span>(<\/span>origin_data<\/span>[<\/span>0<\/span>][<\/span>0<\/span>])<\/span>\n\nnumpyimagearray<\/span>\n# array([[[ 37  20  12]\n#         [ 35  18  10]\n#         ...\n#         [249 248 253]]\n#        [[ 35  18  10]\n#         [ 35  18  10]\n#         ...\n#         [249 248 253]]\n#        [[ 35  18  10]\n#         [ 36  19  11]\n#         ...\n#         [250 249 254]]\n#         ...\n#        [[  5   6  24]\n#         [  4   5  23]\n#         ...\n#         [ 69 110 224]]\n#        [[  4   3  19]\n#         [  3   2  18]\n#         ...\n#         [ 64 108 229]]\n#        [[ 28  27  35]\n#         [ 28  27  35]\n#         ...\n#         [ 76 117 223]]], dtype=uint8)\n<\/span>\n# NumPy array to PyTorch tensor\n<\/span>pytorchimagetensor<\/span> =<\/span> torch<\/span>.<\/span>from_numpy<\/span>(<\/span>numpyimagearray<\/span>).<\/span>permute<\/span>(<\/span>dims<\/span>=<\/span>[<\/span>2<\/span>,<\/span> 0<\/span>,<\/span> 1<\/span>])<\/span>\npytorchimagetensor<\/span> =<\/span> torch<\/span>.<\/span>tensor<\/span>(<\/span>numpyimagearray<\/span>).<\/span>permute<\/span>(<\/span>dims<\/span>=<\/span>[<\/span>2<\/span>,<\/span> 0<\/span>,<\/span> 1<\/span>])<\/span>\n\npytorchimagetensor<\/span>\n# tensor([[[ 37,  35,  36,  ..., 247, 249, 249],\n#          [ 35,  35,  37,  ..., 246, 248, 249],\n#          ...,\n#          [ 28,  28,  27,  ...,  59,  65,  76]],\n#         [[ 20,  18,  19,  ..., 248, 248, 248],\n#          [ 18,  18,  20,  ..., 247, 247, 248],\n#          ...,\n#          [ 27,  27,  27,  ...,  94, 106, 117]],\n#         [[ 12,  10,  11,  ..., 253, 253, 253],\n#          [ 10,  10,  12,  ..., 251, 252, 253],\n#          ...,\n#          [ 35,  35,  35,  ..., 214, 232, 223]]], dtype=torch.uint8)\n<\/span>\n# PyTorch tensor to PIL image\n<\/span>tpi<\/span> =<\/span> ToPILImage<\/span>()<\/span>\npilimage<\/span> =<\/span> tpi<\/span>(<\/span>pytorchimagetensor<\/span>)<\/span>\n\nimport<\/span> matplotlib.pyplot<\/span> as<\/span> plt<\/span>\n\nplt<\/span>.<\/span>figure<\/span>(<\/span>figsize<\/span>=<\/span>[<\/span>7<\/span>,<\/span> 9<\/span>])<\/span>\nplt<\/span>.<\/span>title<\/span>(<\/span>label<\/span>=<\/span>\"<\/span>s500_394origin_data<\/span>\"<\/span>,<\/span> fontsize<\/span>=<\/span>14<\/span>)<\/span>\nplt<\/span>.<\/span>imshow<\/span>(<\/span>X<\/span>=<\/span>pilimage<\/span>)<\/span>\nplt<\/span>.<\/span>show<\/span>()<\/span>\n<\/code><\/pre>\n
\n
\n    \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n
    \u0627\u0632 \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u062e\u0627\u0631\u062c \u0634\u0648\u06cc\u062f<\/title>\n    \n<\/svg><\/p>\n<\/div>\n<\/div>\n<\/div>\n
\"\u0634\u0631\u062d<\/p>\n<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"
\u0628\u0631\u0627\u06cc \u0645\u0646 \u06cc\u06a9 \u0642\u0647\u0648\u0647 \u0628\u062e\u0631 *\u06cc\u0627\u062f\u062f\u0627\u0634\u062a \u0647\u0627: \u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0627 \u062a\u0635\u0648\u06cc\u0631 PIL (\u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0628\u0627\u0644\u0634) \u060c Pytorch Tensor \u0648 Numpy Array \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u062f\u0631 \u0632\u06cc\u0631 \u0622\u0645\u062f\u0647 \u0627\u0633\u062a \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u062f: 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() \u062d\u0627\u0644\u062a \u062a\u0645\u0627\u0645 \u0635\u0641\u062d\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u062f …<\/p>\n","protected":false},"author":2,"featured_media":108899,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"","fifu_image_alt":"","footnotes":""},"categories":[339],"tags":[],"class_list":["post-108898","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-dev"],"_links":{"self":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/108898","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/comments?post=108898"}],"version-history":[{"count":0,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/posts\/108898\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media\/108899"}],"wp:attachment":[{"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/media?parent=108898"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/categories?post=108898"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nabfollower.com\/blog\/wp-json\/wp\/v2\/tags?post=108898"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}