scikit-image库-- 过滤区域最大值（三）

女爷i 2021-10-18 11:16 290阅读 0赞

#### 过滤区域最大值 ####

import numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline
    from scipy.ndimage import gaussian_filter
    from skimage import data
    from skimage import img_as_float
    from skimage.morphology import reconstruction
    
    # Convert to float: Important for subtraction later which won't work with uint8
    image = img_as_float(data.coins())
    image = gaussian_filter(image, 1)
    
    seed = np.copy(image)
    seed[1:-1, 1:-1] = image.max()
    mask = image
    
    seed1 = np.copy(image)
    seed1[1:-1, 1:-1] = image.min()
    
    erosion = reconstruction(seed, mask, method='erosion')
    ''' reconstruction(seed, mask, method='dilation', selem=None, offset=None) erosion(腐蚀) seed 种子图像值必须大于mask值 腐蚀本质选取领域最小值，降低图像高亮区域 dilation (膨胀) seed种子图像值必须小于 mask值 膨胀本质选取区域最大值， 增加图像高亮区域 '''
    dilation=reconstruction(seed1, mask,method='dilation')

#### 画图 ####

fig = plt.figure(figsize=(18, 5))
    axes = np.zeros((1, 3), dtype=np.object)
    axes[0,0]=plt.subplot(1,3,1)
    
    axes[0,1]=plt.subplot(1,3,2)
    axes[0,2]=plt.subplot(1,3,3)
    axes[0,0].imshow(dilation, 'gray')
    axes[0,0].set_title('dilation')
    axes[0,1].imshow(erosion, 'gray')
    axes[0,1].set_title('erosion')
    axes[0,2].imshow(image1,'gray')
    axes[0,2].set_title('ground')
    plt.show()

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

image=image1-dilation  #原图减去膨胀后的图
    plot.imshow(image)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 1]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70]: /images/20211018/f6d250065ee042699171e22a8e7f663a.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 1]: /images/20211018/ba92c5546d304ccf88749302c9386b95.png