create_masks.py 文件源码

def calculate_distance(centers_of_mass, image):
    """
    takes the centers of each blob, and an image to be segmented. Divides the image according to the center of masses
    by a random walk

    :param image: a binarised image to be segmented
    :param centers_of_mass: the centres that will define the maxima of the watershed segmentation
    :return segmentation_labels: a labelled image/segmentation, where each index belongs do a different center of mass

    """
    # random walk segmentation of 2D image-mask array
    distance = ndimage.distance_transform_edt(np.abs(image-1))
    local_maxi = np.zeros_like(image)

    for c in centers_of_mass:
        local_maxi[int(c[0]), int(c[1])] = 1

    markers = ndimage.label(local_maxi)[0]
    segmentation_labels = segmentation.random_walker(distance, markers, beta=60)

    return segmentation_labels