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          "value": "1-connectivity     2-connectivity     diagonal connection close-up\n\n     [ ]           [ ]  [ ]  [ ]             [ ]\n      |               \\  |  /                 |  <- hop 2\n[ ]--[x]--[ ]      [ ]--[x]--[ ]        [x]--[ ]\n      |               /  |  \\             hop 1\n     [ ]           [ ]  [ ]  [ ]",
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        "value": "import numpy as np\nx = np.eye(3).astype(int)\nprint(x)\nprint(label(x, connectivity=1))\nprint(label(x, connectivity=2))\nprint(label(x, background=-1))\nx = np.array([[1, 0, 0],\n              [1, 1, 5],\n              [0, 0, 0]])\nprint(label(x))\n",
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    {
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      }
    ],
    "return_annotation": {
      "__type": "Empty",
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    },
    "target_name": "label"
  },
  "references": [
    ".. [1] Christophe Fiorio and Jens Gustedt, \"Two linear time Union-Find",
    "       strategies for image processing\", Theoretical Computer Science",
    "       154 (1996), pp. 165-181.",
    ".. [2] Kensheng Wu, Ekow Otoo and Arie Shoshani, \"Optimizing connected",
    "       component labeling algorithms\", Paper LBNL-56864, 2005,",
    "       Lawrence Berkeley National Laboratory (University of California),",
    "       http://repositories.cdlib.org/lbnl/LBNL-56864"
  ],
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