{"id":999960,"date":"2024-12-27T09:45:47","date_gmt":"2024-12-27T01:45:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/999960.html"},"modified":"2024-12-27T09:45:49","modified_gmt":"2024-12-27T01:45:49","slug":"python%e5%a6%82%e4%bd%95%e8%af%bb%e5%8f%96nrrd%e6%a0%bc%e5%bc%8f","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/999960.html","title":{"rendered":"python\u5982\u4f55\u8bfb\u53d6nrrd\u683c\u5f0f"},"content":{"rendered":"

\"python\u5982\u4f55\u8bfb\u53d6nrrd\u683c\u5f0f\"<\/p>\n

\u8981\u8bfb\u53d6NRRD\u683c\u5f0f\u7684\u6587\u4ef6\uff0cPython\u63d0\u4f9b\u4e86\u51e0\u4e2a\u5e38\u7528\u7684\u5e93\uff0c\u5982pynrrd<\/code>\u3001SimpleITK<\/code>\uff0c\u4ee5\u53canibabel<\/code>\u3002\u63a8\u8350\u4f7f\u7528pynrrd<\/code>\u5e93\uff0c\u56e0\u4e3a\u5b83\u4e13\u95e8\u7528\u4e8e\u5904\u7406NRRD\u683c\u5f0f\uff0c\u4f7f\u7528\u8d77\u6765\u7b80\u5355\u76f4\u89c2\u3002\u9996\u5148\uff0c\u9700\u8981\u5b89\u88c5pynrrd<\/code>\u5e93\uff0c\u7136\u540e\u4f7f\u7528nrrd.read<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6NRRD\u6587\u4ef6\u5185\u5bb9\u3001\u63d0\u53d6\u6570\u636e\u548c\u5934\u4fe1\u606f\u3002<\/strong>pynrrd<\/code>\u652f\u6301\u7684\u529f\u80fd\u5305\u62ec\u8bfb\u53d6NRRD\u6587\u4ef6\u7684\u6570\u636e\u90e8\u5206\u548c\u5934\u4fe1\u606f\u3001\u5904\u7406\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\u548c\u7ef4\u5ea6\u3001\u652f\u6301gzip\u538b\u7f29\u7684NRRD\u6587\u4ef6\u7b49\u3002<\/p>\n<\/p>\n

\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528pynrrd<\/code>\u5e93\u8bfb\u53d6NRRD\u6587\u4ef6\uff0c\u5e76\u63a2\u8ba8\u5176\u4ed6\u76f8\u5173\u5de5\u5177\u548c\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5b89\u88c5\u548c\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/p>\n<\/p>\n

\u5728\u5f00\u59cb\u8bfb\u53d6NRRD\u6587\u4ef6\u4e4b\u524d\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u5b89\u88c5pynrrd<\/code>\u5e93\u3002\u53ef\u4ee5\u901a\u8fc7pip<\/code>\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install pynrrd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u5728Python\u811a\u672c\u4e2d\u5bfc\u5165pynrrd<\/code>\u5e93\uff1a<\/p>\n<\/p>\n
import nrrd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u8bfb\u53d6NRRD\u6587\u4ef6<\/p>\n<\/p>\n
\u4f7f\u7528pynrrd<\/code>\u8bfb\u53d6NRRD\u6587\u4ef6\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u8c03\u7528nrrd.read()<\/code>\u51fd\u6570\u5373\u53ef\u3002\u8be5\u51fd\u6570\u8fd4\u56de\u4e24\u4e2a\u5bf9\u8c61\uff1a\u6570\u636e\u6570\u7ec4\u548c\u5934\u4fe1\u606f\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
# \u8bfb\u53d6NRRD\u6587\u4ef6<\/p>\n
data, header = nrrd.read('example.nrrd')<\/p>\n
\u6253\u5370\u6570\u636e\u548c\u5934\u4fe1\u606f<\/strong><\/h2>\n
print(data)<\/p>\n
print(header)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0cdata<\/code>\u53d8\u91cf\u5305\u542b\u4e86NRRD\u6587\u4ef6\u7684\u6570\u636e\u90e8\u5206\uff0c\u800cheader<\/code>\u5219\u662f\u4e00\u4e2a\u5b57\u5178\uff0c\u5305\u542b\u4e86NRRD\u6587\u4ef6\u7684\u5934\u4fe1\u606f\u3002<\/p>\n<\/p>\n
\u4e09\u3001NRRD\u5934\u4fe1\u606f\u89e3\u6790<\/p>\n<\/p>\n
NRRD\u6587\u4ef6\u7684\u5934\u4fe1\u606f\u5305\u542b\u4e86\u5173\u4e8e\u6570\u636e\u7684\u5404\u79cd\u5143\u6570\u636e\uff0c\u5982\u6570\u636e\u7c7b\u578b\u3001\u7ef4\u5ea6\u3001\u5927\u5c0f\u3001\u5355\u4f4d\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u5934\u4fe1\u606f\u5b57\u6bb5\u53ca\u5176\u610f\u4e49\uff1a<\/p>\n<\/p>\n
    \n
  • type<\/code>\uff1a\u6570\u636e\u7c7b\u578b\uff0c\u5982float<\/code>\u3001double<\/code>\u3001int<\/code>\u7b49\u3002<\/li>\n
  • dimension<\/code>\uff1a\u6570\u636e\u7684\u7ef4\u5ea6\u3002<\/li>\n
  • sizes<\/code>\uff1a\u6bcf\u4e2a\u7ef4\u5ea6\u4e0a\u7684\u6570\u636e\u5927\u5c0f\u3002<\/li>\n
  • space<\/code>\uff1a\u6570\u636e\u7684\u7a7a\u95f4\u5750\u6807\u7cfb\u3002<\/li>\n
  • space directions<\/code>\uff1a\u6bcf\u4e2a\u7ef4\u5ea6\u7684\u65b9\u5411\u5411\u91cf\u3002<\/li>\n
  • endian<\/code>\uff1a\u5b57\u8282\u5e8f\uff0c\u901a\u5e38\u4e3alittle<\/code>\u6216big<\/code>\u3002<\/li>\n
  • encoding<\/code>\uff1a\u6570\u636e\u7684\u7f16\u7801\u65b9\u5f0f\uff0c\u5982raw<\/code>\u3001gzip<\/code>\u7b49\u3002<\/li>\n<\/ul>\n
    \u901a\u8fc7\u89e3\u6790\u8fd9\u4e9b\u5934\u4fe1\u606f\uff0c\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3NRRD\u6587\u4ef6\u4e2d\u7684\u6570\u636e\u7ed3\u6784\u548c\u7279\u6027\u3002<\/p>\n<\/p>\n
    \u56db\u3001\u5904\u7406\u4e0d\u540c\u6570\u636e\u7c7b\u578b\u548c\u7ef4\u5ea6<\/p>\n<\/p>\n
    NRRD\u6587\u4ef6\u53ef\u4ee5\u5305\u542b\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\u548c\u7ef4\u5ea6\uff0cpynrrd<\/code>\u5e93\u80fd\u591f\u81ea\u52a8\u5904\u7406\u8fd9\u4e9b\u5dee\u5f02\u3002\u8bfb\u53d6NRRD\u6587\u4ef6\u540e\uff0c\u6570\u636e\u901a\u5e38\u4ee5NumPy\u6570\u7ec4\u7684\u5f62\u5f0f\u8fd4\u56de\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528NumPy\u7684\u64cd\u4f5c\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406\u3002<\/p>\n<\/p>\n
    \u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8ba1\u7b97\u6570\u636e\u7684\u5e73\u5747\u503c\uff1a<\/p>\n<\/p>\n
    import numpy as np<\/p>\n
    \u8ba1\u7b97\u6570\u636e\u7684\u5e73\u5747\u503c<\/strong><\/h2>\n
    mean_value = np.mean(data)<\/p>\n
    print("Mean value:", mean_value)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u5904\u7406\u538b\u7f29\u7684NRRD\u6587\u4ef6<\/p>\n<\/p>\n
    NRRD\u6587\u4ef6\u652f\u6301\u591a\u79cd\u7f16\u7801\u65b9\u5f0f\uff0c\u5176\u4e2d\u5305\u62ecgzip\u538b\u7f29\u3002pynrrd<\/code>\u80fd\u591f\u81ea\u52a8\u68c0\u6d4b\u5e76\u5904\u7406\u538b\u7f29\u7684NRRD\u6587\u4ef6\uff0c\u56e0\u6b64\u7528\u6237\u65e0\u9700\u989d\u5916\u7684\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
    \u516d\u3001\u5176\u4ed6\u8bfb\u53d6NRRD\u6587\u4ef6\u7684\u5de5\u5177<\/p>\n<\/p>\n
    \u9664\u4e86pynrrd<\/code>\uff0c\u8fd8\u6709\u5176\u4ed6\u4e00\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u8bfb\u53d6NRRD\u6587\u4ef6\uff0c\u4f8b\u5982SimpleITK<\/code>\u548cnibabel<\/code>\u3002\u8fd9\u4e9b\u5e93\u901a\u5e38\u7528\u4e8e\u533b\u5b66\u5f71\u50cf\u5904\u7406\uff0c\u652f\u6301\u591a\u79cd\u56fe\u50cf\u683c\u5f0f\uff0c\u5305\u62ecNRRD\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528SimpleITK<\/code>\u8bfb\u53d6NRRD\u6587\u4ef6\u7684\u793a\u4f8b\uff1a<\/p>\n<\/p>\n
    import SimpleITK as sitk<\/p>\n
    \u8bfb\u53d6NRRD\u6587\u4ef6<\/strong><\/h2>\n
    image = sitk.ReadImage('example.nrrd')<\/p>\n
    \u83b7\u53d6\u56fe\u50cf\u6570\u636e\u548c\u5143\u6570\u636e<\/strong><\/h2>\n
    array = sitk.GetArrayFromImage(image)<\/p>\n
    size = image.GetSize()<\/p>\n
    spacing = image.GetSpacing()<\/p>\n
    print("Array shape:", array.shape)<\/p>\n
    print("Size:", size)<\/p>\n
    print("Spacing:", spacing)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e03\u3001\u4fdd\u5b58\u6570\u636e\u4e3aNRRD\u6587\u4ef6<\/p>\n<\/p>\n
    \u9664\u4e86\u8bfb\u53d6NRRD\u6587\u4ef6\uff0cpynrrd<\/code>\u5e93\u8fd8\u652f\u6301\u5c06\u6570\u636e\u4fdd\u5b58\u4e3aNRRD\u683c\u5f0f\u3002\u53ef\u4ee5\u4f7f\u7528nrrd.write()<\/code>\u51fd\u6570\u5c06NumPy\u6570\u7ec4\u548c\u5934\u4fe1\u606f\u5199\u5165NRRD\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
    # \u521b\u5efa\u5934\u4fe1\u606f<\/p>\n
    new_header = {<\/p>\n
        'type': 'float',<\/p>\n
        'dimension': 3,<\/p>\n
        'sizes': data.shape,<\/p>\n
        'space': 'left-posterior-superior'<\/p>\n
    }<\/p>\n
    \u4fdd\u5b58\u4e3aNRRD\u6587\u4ef6<\/strong><\/h2>\n
    nrrd.write('output.nrrd', data, header=new_header)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u60a8\u53ef\u4ee5\u8f7b\u677e\u5730\u4f7f\u7528Python\u8bfb\u53d6\u548c\u5904\u7406NRRD\u683c\u5f0f\u7684\u6587\u4ef6\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u548c\u65b9\u6cd5\u53ef\u4ee5\u63d0\u9ad8\u5904\u7406\u6548\u7387\u548c\u51c6\u786e\u6027\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u5982\u4f55\u5728Python\u4e2d\u5b89\u88c5\u8bfb\u53d6NRRD\u683c\u5f0f\u6240\u9700\u7684\u5e93\uff1f<\/strong>
    \u8981\u5728Python\u4e2d\u8bfb\u53d6NRRD\u683c\u5f0f\u6587\u4ef6\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u76f8\u5e94\u7684\u5e93\u3002\u6700\u5e38\u7528\u7684\u5e93\u662fnrrd<\/code>\u548cnumpy<\/code>\u3002\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5728\u7ec8\u7aef\u6216\u547d\u4ee4\u63d0\u793a\u7b26\u4e2d\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff1a<\/p>\n
    pip install nrrd numpy\n<\/code><\/pre>\n
    \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u5bfc\u5165\u8fd9\u4e9b\u5e93\u6765\u8fdb\u884cNRRD\u6587\u4ef6\u7684\u8bfb\u53d6\u548c\u5904\u7406\u3002<\/p>\n
    \u8bfb\u53d6NRRD\u6587\u4ef6\u65f6\uff0c\u5982\u4f55\u5904\u7406\u6587\u4ef6\u4e2d\u7684\u5143\u6570\u636e\uff1f<\/strong>
    NRRD\u6587\u4ef6\u4e0d\u4ec5\u5305\u542b\u6570\u636e\u672c\u8eab\uff0c\u8fd8\u5305\u542b\u5173\u4e8e\u6570\u636e\u7684\u5143\u4fe1\u606f\uff0c\u5982\u7a7a\u95f4\u7ef4\u5ea6\u3001\u5355\u4f4d\u548c\u6570\u636e\u7c7b\u578b\u7b49\u3002\u4f7f\u7528nrrd<\/code>\u5e93\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u65b9\u5f0f\u8bfb\u53d6\u6570\u636e\u548c\u5143\u6570\u636e\uff1a<\/p>\n
    import nrrd\n\ndata, header = nrrd.read('path_to_file.nrrd')\n<\/code><\/pre>\n
    \u8fd9\u91cc\uff0cdata<\/code>\u5305\u542b\u56fe\u50cf\u6570\u636e\uff0c\u800cheader<\/code>\u5219\u4fdd\u5b58\u4e86\u5173\u4e8e\u6570\u636e\u7684\u8be6\u7ec6\u4fe1\u606f\uff0c\u53ef\u4ee5\u6839\u636e\u9700\u8981\u8fdb\u884c\u8bbf\u95ee\u548c\u4f7f\u7528\u3002<\/p>\n
    \u5728Python\u4e2d\u8bfb\u53d6NRRD\u6587\u4ef6\u540e\uff0c\u5982\u4f55\u5bf9\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\uff1f<\/strong>
    \u8bfb\u53d6NRRD\u6587\u4ef6\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u6216mayavi<\/code>\u7b49\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002\u5982\u679c\u6570\u636e\u662f\u4e09\u7ef4\u7684\uff0c\u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u7684imshow<\/code>\u6216mayavi<\/code>\u7684mlab<\/code>\u6a21\u5757\u8fdb\u884c\u5c55\u793a\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u4f8b\u5b50\uff1a<\/p>\n
    import matplotlib.pyplot as plt\n\nplt.imshow(data[:, :, slice_index], cmap='gray')\nplt.show()\n<\/code><\/pre>\n
    \u786e\u4fdd\u5728\u53ef\u89c6\u5316\u4e4b\u524d\u9009\u62e9\u5408\u9002\u7684\u5207\u7247\u7d22\u5f15\uff0c\u4ee5\u4fbf\u6e05\u6670\u5730\u5c55\u793a\u6570\u636e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u8bfb\u53d6NRRD\u683c\u5f0f\u7684\u6587\u4ef6\uff0cPython\u63d0\u4f9b\u4e86\u51e0\u4e2a\u5e38\u7528\u7684\u5e93\uff0c\u5982pynrrd\u3001SimpleITK\uff0c\u4ee5\u53canibabe […]","protected":false},"author":3,"featured_media":999971,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/999960"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=999960"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/999960\/revisions"}],"predecessor-version":[{"id":999972,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/999960\/revisions\/999972"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/999971"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=999960"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=999960"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=999960"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}