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  • skimage.measure.label函数
    万次阅读
    2018-12-19 21:44:55

    功能:实现连通区域标记

    函数调用形式:

    skimage.measure.label(input, neighbors = None, background = None, return_num = False, connectivity = None)[source]**

    参数介绍:

    Parameters:
    input : Image to label 需要被标记的图片,输入的数据结构不作要求
    neighbors : 这个参数将被移除,被下面的connectivity替代。可以忽略不看
    background : 选择背景像素,指定像素作为背景,全部相同像素标记为0
    return_num : 是一个bool值,如果为True的话返回值是一个元组(labelsnum );如果为False的话就只返回labels
    connectivity : Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor. Accepted values are ranging from 1 to input.ndim. If None, a full connectivity of input.ndim is used. [int, optional]。如果input是一个二维的图片,那么connectivity的值范围选择{1,2},如果是None则默认是取最高的值,对于二维来说,当connectivity=1时代表4连通,当connectivity=2时代表8连通.
    Returns:
    labels : 和input形状一样,但是数值是标记号,所以这是一个已经标记的图片
    num : 标记的种类数,如果输出0则只有背景,如果输出2则有两个种类或者说是连通域

    举例:

    x=np.array([[1,0,0,0,0],[0,1,8,8,0],[0,0,1,1,8],[0,0,0,0,1]])
    x
    '''Out[109]: 
    array([[1, 0, 0, 0, 0],
           [0, 1, 8, 8, 0],
           [0, 0, 1, 1, 8],
           [0, 0, 0, 0, 1]])'''
    label(x,connectivity = 1, return_num=True)
    '''Out[110]: 
    (array([[1, 0, 0, 0, 0],
            [0, 2, 3, 3, 0],
            [0, 0, 4, 4, 5],
            [0, 0, 0, 0, 6]]), 6)'''
    label(x,connectivity = 2, return_num=True)
    '''Out[111]: 
    (array([[1, 0, 0, 0, 0],
            [0, 1, 2, 2, 0],
            [0, 0, 1, 1, 2],
            [0, 0, 0, 0, 1]]), 2)'''
    label(x,return_num=True)
    '''Out[112]: 
    (array([[1, 0, 0, 0, 0],
            [0, 1, 2, 2, 0],
            [0, 0, 1, 1, 2],
            [0, 0, 0, 0, 1]]), 2)'''

     

    更多相关内容
  • 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...

    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
            them as 0. By default, 0-valued pixels are considered as background
            pixels.
        return_num : bool, optional
            Whether to return the number of assigned labels.
        connectivity : int, optional
            Maximum number of orthogonal hops to consider a pixel/voxel
            as a neighbor.
            Accepted values are ranging from  1 to input.ndim. If ``None``, a full
            connectivity of ``input.ndim`` is used.
        Returns
        -------
        labels : ndarray of dtype int
            Labeled array, where all connected regions are assigned the
            same integer value.
        num : int, optional
            Number of labels, which equals the maximum label index and is only
            returned if return_num is `True`.
        See Also
        --------
        regionprops
        regionprops_table
        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
        Examples
        --------
        >>> import numpy as np
        >>> x = np.eye(3).astype(int)
        >>> print(x)
        [[1 0 0]
         [0 1 0]
         [0 0 1]]
        >>> print(label(x, connectivity=1))
        [[1 0 0]
         [0 2 0]
         [0 0 3]]
        >>> print(label(x, connectivity=2))
        [[1 0 0]
         [0 1 0]
         [0 0 1]]
        >>> print(label(x, background=-1))
        [[1 2 2]
         [2 1 2]
         [2 2 1]]
        >>> x = np.array([[1, 0, 0],
        ...               [1, 1, 5],
        ...               [0, 0, 0]])
        >>> print(label(x))
        [[1 0 0]
         [1 1 2]
         [0 0 0]]
        """
        if label_image.dtype == bool:
            return _label_bool(label_image, background=background,
                               return_num=return_num, connectivity=connectivity)
        else:
            return clabel(label_image, background, return_num, connectivity)
    © 2021 GitHub, Inc.
    

    当两个像素相邻时,两个像素连接在一起并且具有相同的值。在2D模式下,它们可以是1或2连通的邻居。该值是指将像素视为邻居的最大正交跳数

    1-connectivity     2-connectivity     diagonal connection close-up
    
         [ ]           [ ]  [ ]  [ ]             [ ]
          |               \  |  /                 |  <- hop 2
    [ ]--[x]--[ ]      [ ]--[x]--[ ]        [x]--[ ]
          |               /  |  \             hop 1
         [ ]           [ ]  [ ]  [ ]
    

    示例:

    >>> import numpy as np
    >>> x = np.eye(3).astype(int)
    >>> print(x)
    [[1 0 0]
     [0 1 0]
     [0 0 1]]
    >>> print(label(x, connectivity=1))
    [[1 0 0]
     [0 2 0]
     [0 0 3]]
    >>> print(label(x, connectivity=2))
    [[1 0 0]
     [0 1 0]
     [0 0 1]]
    >>> print(label(x, background=-1))
    [[1 2 2]
     [2 1 2]
     [2 2 1]]
    >>> x = np.array([[1, 0, 0],
    ...               [1, 1, 5],
    ...               [0, 0, 0]])
    >>> print(label(x))
    [[1 0 0]
     [1 1 2]
     [0 0 0]]
    
    展开全文
  • skimage.measure.labelskimage.measure.regionprops()

    万次阅读 多人点赞 2019-06-29 22:25:39
    skimage.measure.label()函数 对二值图像进行连通区域进行标记,它的返回值就是标记,并没有对二值图像进行改变 在二值图像中,如果两个像素点相邻且值相同(同为0或同为1),那么就认为这两个像素点在一个相互...

    原博客

    https://www.cnblogs.com/denny402/p/5166258.html

    skimage.measure.label()函数

    对二值图像进行连通区域进行标记,它的返回值就是标记,并没有对二值图像进行改变

    在二值图像中,如果两个像素点相邻且值相同(同为0或同为1),那么就认为这两个像素点在一个相互连通的区域内。而同一个连通区域的所有像素点,都用同一个数值来进行标记,这个过程就叫连通区域标记。在判断两个像素是否相邻时,我们通常采用4连通或8连通判断。在图像中,最小的单位是像素,每个像素周围有8个邻接像素,常见的邻接关系有2种:4邻接与8邻接。4邻接一共4个点,即上下左右,如下左图所示。8邻接的点一共有8个,包括了对角线位置的点,如下右图所示。

    在skimage包中,我们采用measure子模块下的label()函数来实现连通区域标记。

    函数格式:

    skimage.measure.label(image,connectivity=None)

    参数中的image表示需要处理的二值图像,connectivity表示连接的模式,1代表4邻接,2代表8邻接。

    输出一个标记数组(labels), 从0开始标记。

    #coding=utf-8
    import numpy as np
    import scipy.ndimage as ndi
    from skimage import measure,color
    import matplotlib.pyplot as plt
    
    #编写一个函数来生成原始二值图像
    def microstructure(l=256):
        n = 5
        x, y = np.ogrid[0:l, 0:l]  #生成网络
        mask = np.zeros((l, l))
        generator = np.random.RandomState(1)  #随机数种子
        points = l * generator.rand(2, n**2)
        mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
        mask = ndi.gaussian_filter(mask, sigma=l/(4.*n)) #高斯滤波
        return mask > mask.mean()
    
    data = microstructure(l=128)*1 #生成测试图片
    
    labels=measure.label(data,connectivity=2)  #8连通区域标记
    dst=color.label2rgb(labels)  #根据不同的标记显示不同的颜色
    print('regions number:',labels.max()+1)  #显示连通区域块数(从0开始标记)
    
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
    ax1.imshow(data, plt.cm.gray, interpolation='nearest')
    ax1.axis('off')
    ax2.imshow(dst,interpolation='nearest')
    ax2.axis('off')
    
    fig.tight_layout()
    plt.show()

     

    skimage.measure.regionprops()函数

    如果想分别上面的的每一个连通区域进行操作,比如计算面积、外接矩形、凸包面积等,则需要调用measure子模块的regionprops()函数。该函数格式为:

    返回所有连通区块的属性列表,常用的属性列表如下表:

    属性名称类型描述
    areaint区域内像素点总数
    bboxtuple边界外接框(min_row, min_col, max_row, max_col)
    centroidarray  质心坐标
    convex_areaint凸包内像素点总数
    convex_imagendarray和边界外接框同大小的凸包  
    coordsndarray区域内像素点坐标
    Eccentricity float离心率
    equivalent_diameter float和区域面积相同的圆的直径
    euler_numberint  区域欧拉数
    extent float区域面积和边界外接框面积的比率
    filled_areaint区域和外接框之间填充的像素点总数
    perimeter float区域周长
    labelint区域标记

    代码

    #coding=utf-8
    import numpy as np
    import scipy.ndimage as ndi
    from skimage import measure,color
    import matplotlib.pyplot as plt
    
    #编写一个函数来生成原始二值图像
    def microstructure(l=256):
        n = 5
        x, y = np.ogrid[0:l, 0:l]  #生成网络
        mask = np.zeros((l, l))
        generator = np.random.RandomState(1)  #随机数种子
        points = l * generator.rand(2, n**2)
        mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1
        mask = ndi.gaussian_filter(mask, sigma=l/(4.*n)) #高斯滤波
        return mask > mask.mean()
    
    data = microstructure(l=128)*1 #生成测试图片
    
    labels = measure.label(data,connectivity=2)  #
    
    #筛选连通区域大于500的
    properties = measure.regionprops(labels)
    valid_label = set()
    for prop in properties:
        if prop.area > 500:
            valid_label.add(prop.label)
    current_bw = np.in1d(labels, list(valid_label)).reshape(labels.shape)
    
    
    dst = color.label2rgb(current_bw)  #根据不同的标记显示不同的颜色
    print('regions number:', current_bw.max()+1)  #显示连通区域块数(从0开始标记)
    
    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 4))
    ax1.imshow(data, plt.cm.gray, interpolation='nearest')
    ax1.axis('off')
    ax2.imshow(current_bw, plt.cm.gray, interpolation='nearest')
    ax2.axis('off')
    ax3.imshow(dst,interpolation='nearest')
    ax3.axis('off')
    
    fig.tight_layout()
    plt.show()

     

     

    skimage.segmentation.clear_border函数

    https://blog.csdn.net/qq_36401512/article/details/88252649

    clear_border(labels, buffer_size=0, bgval=0, in_place=False)主要作用是清除二值图像中边界的1值。例如

    >>> import numpy as np
    >>> from skimage.segmentation import clear_border
    >>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
    ...                    [0, 0, 0, 0, 1, 0, 0, 0, 0],
    ...                    [1, 0, 0, 1, 0, 1, 0, 0, 0],
    ...                    [0, 0, 1, 1, 1, 1, 1, 0, 0],
    ...                    [0, 1, 1, 1, 1, 1, 1, 1, 0],
    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0]])
    >>> clear_border(labels)
    array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
           [0, 0, 0, 0, 1, 0, 0, 0, 0],
           [0, 0, 0, 1, 0, 1, 0, 0, 0],
           [0, 0, 1, 1, 1, 1, 1, 0, 0],
           [0, 1, 1, 1, 1, 1, 1, 1, 0],
           [0, 0, 0, 0, 0, 0, 0, 0, 0]])

     

    展开全文
  • 鹅妹子的skimage.measure.regionprops

    千次阅读 2019-07-04 19:55:20
    参考:https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops skimage的强大无需多言,但是木有想到厉害成这个亚子!简直是宝藏函数! 今天简单记录skimage.measure使用中遇到的...

    参考:https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops

    skimage的强大无需多言,但是木有想到厉害成这个亚子!简直是宝藏函数!
    今天简单记录skimage.measure使用中遇到的惊喜。

    一、汇总

    函数功能
    skimage.measure.find_contours(array, level)Find iso-valued contours in a 2D array for a given level value.
    skimage.measure.regionprops(label_image[, …])Measure properties of labeled image regions.
    skimage.measure.regionprops_table(label_image)Find image properties and convert them into a dictionary
    skimage.measure.perimeter(image[, neighbourhood])Calculate total perimeter of all objects in binary image.
    skimage.measure.approximate_polygon(coords, …)Approximate a polygonal chain with the specified tolerance.
    skimage.measure.subdivide_polygon(coords[, …])Subdivision of polygonal curves using B-Splines.
    skimage.measure.ransac(data, model_class, …)Fit a model to data with the RANSAC (random sample consensus) algorithm.
    skimage.measure.block_reduce(image, block_size)Down-sample image by applying function to local blocks.
    skimage.measure.moments(image[, order])Calculate all raw image moments up to a certain order.
    skimage.measure.moments_central(image[, …])Calculate all central image moments up to a certain order.
    skimage.measure.moments_coords(coords[, order])Calculate all raw image moments up to a certain order.
    skimage.measure.moments_coords_central(coords)Calculate all central image moments up to a certain order.
    skimage.measure.moments_normalized(mu[, order])Calculate all normalized central image moments up to a certain order.
    skimage.measure.moments_hu(nu)Calculate Hu’s set of image moments (2D-only).
    skimage.measure.marching_cubes_lewiner(volume)Lewiner marching cubes algorithm to find surfaces in 3d volumetric data.
    skimage.measure.marching_cubes_classic(volume)Classic marching cubes algorithm to find surfaces in 3d volumetric data.
    skimage.measure.mesh_surface_area(verts, faces)Compute surface area, given vertices & triangular faces
    skimage.measure.profile_line(image, src, dst)Return the intensity profile of an image measured along a scan line.
    skimage.measure.label(input[, neighbors, …])Label connected regions of an integer array.
    skimage.measure.points_in_poly(points, verts)Test whether points lie inside a polygon.
    skimage.measure.grid_points_in_poly(shape, verts)Test whether points on a specified grid are inside a polygon.
    skimage.measure.compare_ssim(X, Y[, …])Compute the mean structural similarity index between two images.
    skimage.measure.compare_mse(im1, im2)Compute the mean-squared error between two images.
    skimage.measure.compare_nrmse(im_true, im_test)Compute the normalized root mean-squared error (NRMSE) between two images.
    skimage.measure.compare_psnr(im_true, im_test)Compute the peak signal to noise ratio (PSNR) for an image.
    skimage.measure.shannon_entropy(image[, base])Calculate the Shannon entropy of an image.
    skimage.measure.LineModelND()Total least squares estimator for N-dimensional lines.
    skimage.measure.CircleModel()Total least squares estimator for 2D circles.
    skimage.measure.EllipseModel()Total least squares estimator for 2D ellipses.

    下面捡两个自己用的比较多的函数记录一下,后续用到其他会继续更新。

    二、skimage.measure.find_contours

    对于给定的水平值,在二维数组中找到等值的轮廓,可以用来检测二值图像的边缘轮廓。

    skimage.measure.find_contours(array, level, fully_connected='low', positive_orientation='low')
    

    参数
    数组:2D输入数据的二维图像,用于查找轮廓。
    level:其中查找数组中的轮廓的值。
    fully_connected:str,{‘low’,‘high’}指示给定级别值以下的数组元素是否被视为完全连接(并且因此值之上的元素将仅面向连接),反之亦然。(详情请参见下面的注释。)
    positive_orientation:‘low’或’high’表示输出轮廓是否会在低或高值元素的岛周围产生正向多边形。如果’低’,那么等高线将围绕低于等值的元素逆时针旋转。或者,这意味着低值元素总是在轮廓的左侧。

    返回
    轮廓:(n,2)列表的列表每个轮廓都是形状(n,2)的状态,由沿着轮廓的n(行,列)坐标组成。 |

    三、skimage.measure.regionprops

    skimage.measure.regionprops(label_image, intensity_image=None, cache=True)
    

    测量标记的图像区域的属性。

    参数
    label_image:(N,M)ndarray标记的输入图像。值为0的标签将被忽略。intensity_image:(N,M)ndarray,可选强度(即输入)与标记图像大小相同的图像。缺省值是None。
    cache:bool,可选确定是否缓存计算的属性。缓存属性的计算速度要快得多,而内存消耗增加。

    返回
    属性:RegionProperties列表每个项目描述一个带标签的区域,并且可以使用下面列出的属性进行访问。

    以下是可以访问的属性

    area : int
    区域的像素数
    bbox : tuple
    Bounding box (min_row, min_col, max_row, max_col).
    Pixels belonging to the bounding box are in the half-open interval
    [min_row; max_row) and [min_col; max_col).
    bbox_area : int
    Number of pixels of bounding box.
    centroid : array
    质心坐标 tuple (row, col).
    convex_area : int
    凸包图像的像素数
    convex_image : (H, J) ndarray
    Binary convex hull image which has the same size as bounding box.
    coords : (N, 2) ndarray
    Coordinate list (row, col) of the region.
    eccentricity : float
    Eccentricity of the ellipse that has the same second-moments as the
    region. The eccentricity is the ratio of the focal distance
    (distance between focal points) over the major axis length.
    The value is in the interval [0, 1).
    When it is 0, the ellipse becomes a circle.
    equivalent_diameter : float
    The diameter of a circle with the same area as the region.
    euler_number : int
    Euler characteristic of region. Computed as number of objects (= 1)
    subtracted by number of holes (8-connectivity).
    extent : float
    Ratio of pixels in the region to pixels in the total bounding box.
    Computed as area / (rows * cols)
    filled_area : int
    Number of pixels of filled region.
    filled_image : (H, J) ndarray
    Binary region image with filled holes which has the same size as
    bounding box.
    image : (H, J) ndarray
    Sliced binary region image which has the same size as bounding box.
    inertia_tensor : (2, 2) ndarray
    Inertia tensor of the region for the rotation around its mass.
    inertia_tensor_eigvals : tuple
    The two eigen values of the inertia tensor in decreasing order.
    intensity_image : ndarray
    Image inside region bounding box.
    label : int
    The label in the labeled input image.
    local_centroid : array
    Centroid coordinate tuple (row, col), relative to region bounding
    box.
    major_axis_length : float
    The length of the major axis of the ellipse that has the same
    normalized second central moments as the region.
    max_intensity : float
    Value with the greatest intensity in the region.
    mean_intensity : float
    Value with the mean intensity in the region.
    min_intensity : float
    Value with the least intensity in the region.
    minor_axis_length : float
    The length of the minor axis of the ellipse that has the same
    normalized second central moments as the region.
    moments : (3, 3) ndarray
    Spatial moments up to 3rd order::

            m_ji = sum{ array(x, y) * x^j * y^i }
    
        where the sum is over the `x`, `y` coordinates of the region.
    **moments_central** : (3, 3) ndarray
        Central moments (translation invariant) up to 3rd order::
    
            mu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i }
    
        where the sum is over the `x`, `y` coordinates of the region,
        and `x_c` and `y_c` are the coordinates of the region's centroid.
    **moments_hu** : tuple
        Hu moments (translation, scale and rotation invariant).
    **moments_normalized** : (3, 3) ndarray
        Normalized moments (translation and scale invariant) up to 3rd order::
    
            nu_ji = mu_ji / m_00^[(i+j)/2 + 1]
    
        where `m_00` is the zeroth spatial moment.
    **orientation** : float
        与该区域具有相同二阶矩的椭圆的x轴与主轴之间的夹角。
        Angle between the X-axis and the major axis of the ellipse that has
        the same second-moments as the region. Ranging from `-pi/2` to
        `pi/2` in counter-clockwise direction.
    **perimeter** : float
        Perimeter of object which approximates the contour as a line
        through the centers of border pixels using a 4-connectivity.
    **solidity** : float
        Ratio of pixels in the region to pixels of the convex hull image.
    **weighted_centroid** : array
        Centroid coordinate tuple ``(row, col)`` weighted with intensity
        image.
    **weighted_local_centroid** : array
        Centroid coordinate tuple ``(row, col)``, relative to region bounding
        box, weighted with intensity image.
    **weighted_moments** : (3, 3) ndarray
        Spatial moments of intensity image up to 3rd order::
    
            wm_ji = sum{ array(x, y) * x^j * y^i }
    
        where the sum is over the `x`, `y` coordinates of the region.
    **weighted_moments_central** : (3, 3) ndarray
        Central moments (translation invariant) of intensity image up to
        3rd order::
    
            wmu_ji = sum{ array(x, y) * (x - x_c)^j * (y - y_c)^i }
    
        where the sum is over the `x`, `y` coordinates of the region,
        and `x_c` and `y_c` are the coordinates of the region's weighted
        centroid.
    **weighted_moments_hu** : tuple
        Hu moments (translation, scale and rotation invariant) of intensity
        image.
    **weighted_moments_normalized** : (3, 3) ndarray
        Normalized moments (translation and scale invariant) of intensity
        image up to 3rd order::
    
            wnu_ji = wmu_ji / wm_00^[(i+j)/2 + 1]
    
        where ``wm_00`` is the zeroth spatial moment (intensity-weighted area).
    
    展开全文
  • measure.label操作后,会将二值化(全True或Flase)的图片值全部重新赋值,当做为一种标记,具体赋值方式是:比如二值化图像中,有8个单独的连通区域,那么第一个连通区域内所有像素点值变为1,第二个连通区域内...
  • measure.label此类报错意味着measure模块中没有label 这个错误的原因是skimage版本太低,需要pip升级一下 给一个通用方法都能奏效,指定清华的源解决 sudo pip install scikit-image -U -i ...
  • skimage.measure求最大连通区域

    千次阅读 2019-08-01 15:52:09
    from skimage import measure list0 = [0,0,0,0,2,0,0,3,0,0,0,0,3,3,0,0,0,0,3,0,0,0,0,0,0,3,0,0,0,0,3,3,0,0,0,0] testa0 = np.array(list0).reshape(6,6)+1 #0会被忽略掉,但是我这里会要使用,所以+1 ...
  • 一.skimage.measure.label(input,background= None,return_num= False,connectivity= None) 功能:标记图中的连通区域 参数解释:input:输入二值图 background:指定北京元素像素值,默认为0 return_num:bool...
  • z1vg ——————————————————————2020.11.5更新——————————————————— skimage.measure.label 函数 import pywt import xlrd import numpy as np from skimage import measure ...
  • C++有没有类似python skimage库里measure.label()连通域标记的函数?
  • skimage.feature函数使用说明

    千次阅读 2021-12-23 13:57:11
    featureGenerate noisy image of a squareFirst trial with the Canny filter, with the default smoothingIncrease the ...skimage.feature.blob_dog(image[, min_sigma, …]) Finds blobs in the given grayscale
  • skimagemeasure.marching_cubes函数报错valueerror: too many values to unpack (expected 2) 我的scikit-image 版本为 0.17.2 报错原文 verts, faces = measure.marching_cubes(p, threshold) valueerror: too ...
  • Measure的英文学习链接:http://scikit-image.org/docs/dev/api/skimage.measure.html 1、Measure中所有的函数功能做一个简单的介绍: skimage.measure.find_contours(array,level) 对于给定的...
  • measureskimage.measure....skimage.measure.block_reduce(image,block_size)通过对局部块应用函数来下采样图像。skimage.measure.compare_mse(im1,im2)计算两幅图像之间的均方差。skimage.measure.comp...
  • skimagemeasure方法

    千次阅读 2019-04-04 22:38:21
    from skimage.measure import label,regionprops regionprops方法与label方法使用更好。 可以标记出每个联通区域。 label方法将图像的每个联通区域使用不同的像素标记出来,regionprops计算每个联通区域的属性...
  • measure.label寻找最大连通域

    千次阅读 2020-01-09 15:03:24
    网上看了几个帖子,自己试了...用skimage.measure.label寻找最大连通域 from skimage import measure # 输入二值图像mask def largeConnectComponent(bw_image): labeled_img, num = measure.label(bw_imag...
  • 使用remove_small_objects函数移除...即如果输入图像本身就是一个二值图像,该函数仍然会先进行二值标记,而这个标记的过程可能并不是像我们原来产生二值图像的过程一样balabala,总而言之,可以借助skimage.measure.la
  • 本文介绍了使用skimage完成二值图像连通区域标记及属性计算的过程,并给出了详细的文档。
  • 1.skimage.segmentation.clear_border函数 clear_border(labels, buffer_size=0, bgval=0, in_place=False)主要作用是清除二值图像中边界的1值。例如 >>> import numpy as np >>> from ...
  • python skimage图像处理(三)

    千次阅读 2020-04-23 15:00:03
    python skimage图像处理(三) This blog is from:https://www.jianshu.com/p/7693222523c0 霍夫线变换 在图片处理中,霍夫变换主要是用来检测图片中的几何形状,包括直线、圆、椭圆等。 在skimage中,霍夫变换...
  • 导入:from skimage.measure import label,regionprops 1、Skimage中的label参数解释: 作用:实现连通区域标记 output=label(input,neighbors= None,background= None,return_num= False,connectivity= None) ...
  • Examples: >>> from skimage import data, util >>> from skimage.measure import label >>> img = util.img_as_ubyte(data.coins()) > 110 >>> label_img = label(img, connectivity = img.ndim) >>> props = ...
  • skimage函数学习

    2021-01-26 17:31:44
    from skimage importmorphology covex_hull_image convex_hull_image将图片中所有目标看作一个整体,计算一个最小凸多边形,如果图中有多个目标物体,每一个物体需要计算一个最小凸多边形,则需要使用convex_hull_...
  • skimage.measure.approximate_polygon(coords,...) 近似具有指定公差的多边形链。 skimage.measure.block_reduce(image,block_size) 通过对局部块应用函数来下采样图像。
  • Overview 对于二值图像来说,每个像素点的值只有类似0/1的两种可能性,一般为 ...skimage.measure.labelskimage v0.13dev docs [2] skimage.measure.regionprops — skimage v0.13dev docs [3] ...
  • 获取图像连通域是图像处理中比较高级的功能,matlab可以通过函数直接获取图像的连通域,在skimage包中,我们同样可以采用measure子模块下的label()函数来实现相同的效果。 函数格式: from skimage ...
  • skimage图像处理库

    千次阅读 2018-06-26 10:19:56
    深度学习的一些模型中常常需要import skimage,以下是转自他人博客的内容,觉得写得很不错参考:...原文作者教大家怎么使用help来查看skimage中的各个包以及各个函数,需要大家自己花时间去阅读源...
  • skimage常用命令整理

    2020-06-07 09:40:40
    morphology.convex_hull_image(image) 计算凸包 morphology.convex_hull_object(img,neighbors=8) 计算凸包 measure.label(image,connectivity) 标记凸包 measure.regionprops(image_label) 返回连通域的属性列表 ...
  • measure 图像属性的测量,如相似性或等高线等 segmentation 图像分割 restoration 图像恢复 util 通用函数 从外部读取图片并显示 读取单张彩色rgb图片,使用skimage.io.imread(fname)函数,带一个参数,表示...

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