I'm bringing dependencies up to date in an old project of mine and recently ran into an issue when using RandomWalker as follows:
import numpy as np
from skimage import color as skc
from skimage.segmentation import random_walker
def my_func(img):
img = skc.rgb2gray(img)
markers = np.zeros_like(img, dtype=np.uint)
markers[img < (img.mean() - img.std()) / 2.0] = 1
markers[img > (img.mean() img.std()) / 2.0] = 2
try:
rw = random_walker(img, markers) # ValueError: need at least one array to concatenate
except RuntimeError:
...
The dependencies I previously got and that are currently working just fine are (if there is any other version required that can help troubleshooting just ask):
# Python 3.6
numpy==1.14.5
scikit-image==0.13.0
scikit-learn==0.19.2
And upgraded ones where the ValueError: need at least one array to concatenate is thrown are:
# Python 3.9
numpy==1.21.0
scikit-image==0.19.1
scikit-learn==1.1.2
Example for testing - install previously mentioned dependencies from requirements.txt, with the Python versions mentioned:
import numpy as np
from skimage.segmentation import random_walker
from skimage import color as skc
img_array = np.array(
[
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
[
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
[255, 255, 255],
],
]
).astype(np.uint8)
def walker_texas_ranger():
image = skc.rgb2gray(img_array)
markers = np.zeros_like(image, dtype=np.uint)
markers[image < image.mean() - image.std() / 2.0] = 1
markers[image > image.mean() image.std() / 2.0] = 2
try:
rw = random_walker(image, markers)
except RuntimeError:
return None
return rw
if __name__ == '__main__':
walker_texas_ranger()
Sample execution stack trace running latest dependencies with Python 3.9:
File "/Users/me/examples/main.py", line 188, in walker_texas_ranger
rw = random_walker(image, markers)
File "/usr/local/lib/python3.9/site-packages/skimage/_shared/utils.py", line 338, in fixed_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.9/site-packages/skimage/_shared/utils.py", line 293, in fixed_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.9/site-packages/skimage/segmentation/random_walker_segmentation.py", line 490, in random_walker
lap_sparse, B = _build_linear_system(data, spacing, labels, nlabels, mask,
File "/usr/local/lib/python3.9/site-packages/skimage/segmentation/random_walker_segmentation.py", line 157, in _build_linear_system
seeds_mask = sparse.csc_matrix(np.hstack(
File "<__array_function__ internals>", line 5, in hstack
File "/usr/local/lib/python3.9/site-packages/numpy/core/shape_base.py", line 345, in hstack
return _nx.concatenate(arrs, 1)
File "<__array_function__ internals>", line 5, in concatenate
ValueError: need at least one array to concatenate
Any clue on what has changed so that it is no longer working?
CodePudding user response：
The issue is here:
markers = np.zeros_like(image, dtype=np.uint)
markers[image < image.mean() - image.std() / 2.0] = 1
markers[image > image.mean() image.std() / 2.0] = 2
Your image only contains values of 255. So image.mean() - image.std() / 2.0 is 255, and image.mean() image.std() / 2.0 is also 255, and no image pixels are < or > these values. Therefore, your markers array contains all zeros and there are no walkers, which causes the error. I'm not sure what the old behaviour would have been.
Here's a more minimal reproducer causing the same error:
import numpy as np
from skimage import segmentation
image = np.random.random((5, 5))
labels = np.zeros((5, 5), dtype=int)
segmented = segmentation.random_walker(image, labels)
Either way, the error raised should be less obscure, so I've created an issue in the scikit-image repo: scikit-image/scikit-image#6561.