label
skimage.morphology.label(input, neighbors=None, background=None, return_num=False, connectivity=None)[source]
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: | input : ndarray of dtype int Image to label. neighbors : {4, 8}, int, optional Whether to use 4- or 8-“connectivity”. In 3D, 4-“connectivity” means connected pixels have to share face, whereas with 8-“connectivity”, they have to share only edge or vertex. Deprecated, use ``connectivity`` instead. 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 |
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| 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. |
labels = label(img)
if img.ndim > 2:
return [rle_encode(np.sum(labels == k, axis=2), **kwargs) for k in np.unique(labels[labels > 0])]
else:
return [rle_encode(labels == k, **kwargs) for k in np.unique(labels[labels > 0])]
# ref: https://www.kaggle.com/paulorzp/run-length-encode-and-decode
def rle_encode(img, min_max_threshold=1e-3, max_mean_threshold=None):
'''
img: numpy array, 1 - mask, 0 - background
Returns run length as string formated
'''
if np.max(img) < min_max_threshold:
return '' # no need to encode if it's all zeros
if max_mean_threshold and np.mean(img) > max_mean_threshold:
return '' # ignore overfilled mask
pixels = img.T.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return ' '.join(str(x) for x in runs)