python skimage.measure.label标记不同连通域

最新推荐文章于 2021-07-31 22:52:43 发布

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最新推荐文章于 2021-07-31 22:52:43 发布 · 4.7k 阅读

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本文介绍了如何使用skimage.measure.label函数在Python中对图像进行连通域标记，演示了如何定义背景、选择连接方式，并通过实例展示了如何处理不同类型的输入数据。此功能对于图像处理和分析至关重要，例如在计算机视觉和机器学习任务中区分不同的对象区域。

python 的skimage库中的measure.label可用于标记不同连通域，从而方便图像分析

skimage.measure.label(label_image, background=None, return_num=False, connectivity=None)

源码如下：

@deprecate_kwarg({
   
   "input": "label_image"}, removed_version="1.0")
def label(label_image, background=None, return_num=False, connectivity=None):
    r"""Label connected regions of an integer array.
    Two pixels are connected when they are neighbors and have the same value.
    In 2D, they can be neighbors either in a 1- or 2-connected sense.
    The value refers to the maximum number of orthogonal hops to consider a
    pixel/voxel a neighbor::
      1-connectivity     2-connectivity     diagonal connection close-up
           [ ]           [ ]  [ ]  [ ]             [ ]
            |               \  |  /                 |  <- hop 2
      [ ]--[x]--[ ]      [ ]--[x]--[ ]        [x]--[ ]
            |               /  |  \             hop 1
           [ ]           [ ]  [ ]  [ ]
    Parameters
    ----------
    label_image : ndarray of dtype int
        Image to label.
    background : int, optional
        Consider all pixels with this value as background pixels, and label