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              "value": "The footprint can also be a provided as a sequence of 2-tuples where the first element of each 2-tuple is a footprint ndarray and the second element is an integer describing the number of times it should be iterated. For example "
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              "value": "footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]"
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              "value": " would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net effect that is the same as "
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              "value": "footprint=np.ones((9, 9))"
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              "value": ", but with lower computational cost. Most of the builtin footprints such as "
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              "value": "skimage.morphology.disk",
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                      "value": "The result of the morphological black top hat."
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              "value": "Return black top hat of an image."
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                      "value": "Image array."
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                      "value": "The neighborhood expressed as a 2-D array of 1's and 0's. If None, use a cross-shaped footprint (connectivity=1). The footprint can also be provided as a sequence of smaller footprints as described in the notes below."
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                      "value": " parameter determines how the array borders are handled. Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap', 'max', 'min', or 'ignore'. See "
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              "value": "The black top hat of an image is defined as its morphological closing minus the original image. This operation returns the dark spots of the image that are smaller than the footprint. Note that dark spots in the original image are bright spots after the black top hat."
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        "value": "import numpy as np\nfrom skimage.morphology import footprint_rectangle\ndark_on_gray = np.array([[7, 6, 6, 6, 7],\n                         [6, 5, 4, 5, 6],\n                         [6, 4, 0, 4, 6],\n                         [6, 5, 4, 5, 6],\n                         [7, 6, 6, 6, 7]], dtype=np.uint8)\nblack_tophat(dark_on_gray, footprint_rectangle((3, 3)))\n",
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