scikit-image库-- 图像块视图（十四）

╰+哭是因爲堅強的太久メ 2021-10-15 15:39 150阅读 0赞

此示例说明了如何使用skimage.util（）中的view\_as\_blocks。 当想要在非重叠图像块上执行本地操作时，块视图非常有用。  
我们使用来自skimage.data的宇航员并将其“切片”成方块。 然后，在每个块上，我们或者汇集该块的平均值，最大值或中值。 结果一起显示，以及原始宇航员图像的3阶重新缩放的样条插值。

import numpy as np
    from scipy import ndimage as ndi
    from matplotlib import pyplot as plt
    import matplotlib.cm as cm
    %matplotlib inline
    from skimage import data
    from skimage import color
    from skimage.util import view_as_blocks
    
    
    # get astronaut from skimage.data in grayscale
    l = color.rgb2gray(data.astronaut())
    
    # size of blocks
    block_shape = (4, 4)
    
    # see astronaut as a matrix of blocks (of shape block_shape)
    view = view_as_blocks(l, block_shape)
    
    # collapse the last two dimensions in one
    flatten_view = view.reshape(view.shape[0], view.shape[1], -1)
    
    # resampling the image by taking either the `mean`,
    # the `max` or the `median` value of each blocks.
    mean_view = np.mean(flatten_view, axis=2)# np对数组图像操作
    max_view = np.max(flatten_view, axis=2)
    median_view = np.median(flatten_view, axis=2)
    
    # display resampled images
    fig, axes = plt.subplots(2, 2, figsize=(18, 13), sharex=True, sharey=True)
    ax = axes.ravel()
    
    l_resized = ndi.zoom(l, 2, order=3)  #放大函数， 2代表放大2倍 3代表【三次样条插值】
    ax[0].set_title("Original rescaled with\n spline interpolation (order=3)")
    ax[0].imshow(l_resized, extent=(0, 128, 128, 0), interpolation='nearest',
                 cmap=cm.Greys_r)
    
    ax[1].set_title("Block view with\n local mean pooling")
    ax[1].imshow(mean_view, interpolation='nearest', cmap=cm.Greys_r)
    
    ax[2].set_title("Block view with\n local max pooling")
    ax[2].imshow(max_view, interpolation='nearest', cmap=cm.Greys_r)
    
    ax[3].set_title("Block view with\n local median pooling")
    ax[3].imshow(median_view, interpolation='nearest', cmap=cm.Greys_r)
    
    for a in ax:
        a.set_axis_off()
    
    fig.tight_layout()
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70]

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