maweigert

maweigert

Member Since 6 years ago

EPFL, Lausanne, Switzerland

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258 contributions in the last year

Pinned
⚡ GPU accelerated volume rendering / processing in Python
⚡ Image restoration for fluorescence microscopy
⚡ GPU accelerated image/volume processing in Python
⚡ StarDist - Object Detection with Star-convex Shapes
Activity
Oct
27
1 day ago
Activity icon
issue

maweigert issue comment napari/napari

maweigert
maweigert

Fix contrast_limit artefacts for images with large data range

Description

For images with large data ranges (e.g. hot pixels) the manual changing of contrast limits towards lower values doesn't update the texture limits thus leading to image quantization artefacts.

Example:

import numpy as np
import napari
from skimage.data import camera

x = camera().astype(np.uint16)
x[100,100] = 2**16-1

napari.view_image(x)

Manually changing the contrasts limits to values like (0,217) leads to this:

Screenshot 2021-10-27 at 02 24 27

Type of change

  • Bug-fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to not work as expected)
  • This change requires a documentation update

References

Every pixel is sacred, every pixel is great. If a pixel is wasted, god gets quite irate.

How has this been tested?

  • example: the test suite for my feature covers cases x, y, and z
  • example: all tests pass with my change

(N.B. napari/_tests depends on tensorstore which seems not available for Apple Silicon arm64)

Final checklist:

  • My PR is the minimum possible work for the desired functionality
  • I have commented my code, particularly in hard-to-understand areas
  • I have made corresponding changes to the documentation
  • I have added tests that prove my fix is effective or that my feature works
  • If I included new strings, I have used trans. to make them localizable. For more information see our translations guide.
maweigert
maweigert

Hi @tlambert03 ,

thanks for the explanation, totally makes sense to wait for the float texture integration... (I simply created a PR instead of an issue to point to the code change).

pull request

maweigert pull request napari/napari

maweigert
maweigert

Fix contrast_limit artefacts for images with large data range

Description

For images with large data ranges (e.g. hot pixels) the manual changing of contrast limits towards lower values doesn't update the texture limits thus leading to image quantization artefacts.

Example:

import numpy as np
import napari
from skimage.data import camera

x = camera().astype(np.uint16)
x[100,100] = 2**16-1

napari.view_image(x)

Manually changing the contrasts limits to values like (0,217) leads to this:

Screenshot 2021-10-27 at 02 24 27

Type of change

  • Bug-fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to not work as expected)
  • This change requires a documentation update

References

Every pixel is sacred, every pixel is great. If a pixel is wasted, god gets quite irate.

How has this been tested?

  • example: the test suite for my feature covers cases x, y, and z
  • example: all tests pass with my change

(N.B. napari/_tests depends on tensorstore which seems not available for Apple Silicon arm64)

Final checklist:

  • My PR is the minimum possible work for the desired functionality
  • I have commented my code, particularly in hard-to-understand areas
  • I have made corresponding changes to the documentation
  • I have added tests that prove my fix is effective or that my feature works
  • If I included new strings, I have used trans. to make them localizable. For more information see our translations guide.
Activity icon
created branch

maweigert in maweigert/napari create branch contrast_texture_limits_adjust

createdAt 1 day ago
Activity icon
fork

maweigert forked napari/napari

⚡ napari: a fast, interactive, multi-dimensional image viewer for python
maweigert BSD 3-Clause "New" or "Revised" License Updated
fork time in 1 day ago
Oct
25
3 days ago
Activity icon
issue

maweigert issue comment stardist/stardist-napari

maweigert
maweigert

stardist-napari requires `tensorflow` but pip doesn't reacognize tensorflow-macos

I was reminded about this when I went to update my stardist-napari. The setup.py includes tensorflow but on macOS the package is tensorflow-macos. pip doesn't recognize the later as the former and the install/update fails—even though it's imported as tensorflow. Is there a way to flag that? by platform or something? Or maybe remove it? I do notice that stardist doesn't list tensorflow as a requirement, but of course it's in the README. Here the situation is different, due to the Plugin menu install...

maweigert
maweigert

Indeed, it seems that platform dependent requirements could be include like so:

    install_requires=[
        'tensorflow;  platform_system!="Darwin" or platform_machine!="arm64"',
        'tensorflow-macos;  platform_system=="Darwin" and platform_machine=="arm64"',
    ],

But then again, maybe its best to remove tf as explicit dependency altogether...

Oct
24
4 days ago
Activity icon
created branch

maweigert in stardist/stardist create branch backbones2

createdAt 4 days ago
Oct
22
6 days ago
Activity icon
issue

maweigert issue comment stardist/stardist-napari

maweigert
maweigert

Installing stardist-napari downgrades numpy - any ideas why?

Hi Uwe and Martin @uschmidt83 @maweigert ,

I had repeatedly issues with installing stardist-napari because it downgraded numpy and then breaks environments. I'm not 100%ly sure if the breaking-environment issue is related to numpy, but I'm quite sure that it happens when installing stardist-napari.

Do you have any idea which dependency pins numpy to 1.19.5 and would it maybe be possible to upgrade that dependency and/or create an issue there? Closing this issue because it's not related directly to stardist-napari would also be fine. Anyway, any hint is welcome!

Thanks! Robert

Here's the log of a recent installation in a new environment. All I did after setting up a fresh conda environment was 'pip install napari[all]' and then the following output is produced by 'pip install stardist-napari':

(bio_38) C:\Users\rober>pip install stardist-napari
Collecting stardist-napari
  Using cached stardist_napari-2021.6.28-py3-none-any.whl (28 kB)
Collecting stardist>=0.7.0
  Using cached stardist-0.7.3-cp38-cp38-win_amd64.whl (757 kB)
Requirement already satisfied: napari>=0.4.8 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from stardist-napari) (0.4.12)
Requirement already satisfied: magicgui>=0.2.9 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from stardist-napari) (0.3.2)
Collecting tensorflow
  Using cached tensorflow-2.6.0-cp38-cp38-win_amd64.whl (423.2 MB)
Requirement already satisfied: qtpy>=1.7.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from magicgui>=0.2.9->stardist-napari) (1.11.2)
Requirement already satisfied: psygnal>=0.1.3 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from magicgui>=0.2.9->stardist-napari) (0.1.4)
Requirement already satisfied: docstring-parser in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from magicgui>=0.2.9->stardist-napari) (0.12)
Requirement already satisfied: typing-extensions in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from magicgui>=0.2.9->stardist-napari) (3.10.0.2)
Requirement already satisfied: tifffile>=2020.2.16 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (2021.10.12)
Requirement already satisfied: appdirs>=1.4.4 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (1.4.4)
Requirement already satisfied: pydantic>=1.8.1 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (1.8.2)
Requirement already satisfied: napari-console>=0.0.4 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (0.0.4)
Requirement already satisfied: toolz>=0.10.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (0.11.1)
Requirement already satisfied: vispy!=0.8.0,>=0.6.4 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (0.9.2)
Requirement already satisfied: wrapt>=1.11.1 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (1.13.2)
Requirement already satisfied: psutil>=5.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (5.8.0)
Requirement already satisfied: jsonschema>=3.2.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (4.1.2)
Requirement already satisfied: Pillow!=7.1.0,!=7.1.1 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (8.4.0)
Requirement already satisfied: scipy>=1.2.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (1.7.1)
Requirement already satisfied: imageio>=2.5.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (2.9.0)
Requirement already satisfied: dask[array]!=2.28.0,>=2.15.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (2021.9.1)
Requirement already satisfied: tqdm>=4.56.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (4.62.3)
Requirement already satisfied: cachey>=0.2.1 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (0.2.1)
Requirement already satisfied: numpydoc>=0.9.2 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (1.1.0)
Requirement already satisfied: PyYAML>=5.1 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (6.0)
Requirement already satisfied: numpy>=1.18.5 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (1.21.3)
Requirement already satisfied: pint>=0.17 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (0.17)
Requirement already satisfied: certifi>=2018.1.18 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (2021.10.8)
Requirement already satisfied: PyOpenGL>=3.1.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (3.1.5)
Requirement already satisfied: superqt>=0.2.4 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (0.2.4)
Requirement already satisfied: napari-svg>=0.1.4 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (0.1.5)
Requirement already satisfied: napari-plugin-engine>=0.1.9 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari>=0.4.8->stardist-napari) (0.2.0)
Requirement already satisfied: scikit-image in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from stardist>=0.7.0->stardist-napari) (0.18.3)
Collecting numba
  Using cached numba-0.54.1-cp38-cp38-win_amd64.whl (2.3 MB)
Collecting csbdeep>=0.6.3
  Using cached csbdeep-0.6.3-py2.py3-none-any.whl (73 kB)
Requirement already satisfied: wheel~=0.35 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from tensorflow->stardist-napari) (0.37.0)
Collecting tensorboard~=2.6
  Using cached tensorboard-2.7.0-py3-none-any.whl (5.8 MB)
Collecting numpy>=1.18.5
  Using cached numpy-1.19.5-cp38-cp38-win_amd64.whl (13.3 MB)
Collecting google-pasta~=0.2
  Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)
Collecting flatbuffers~=1.12.0
  Using cached flatbuffers-1.12-py2.py3-none-any.whl (15 kB)
Collecting termcolor~=1.1.0
  Using cached termcolor-1.1.0-py3-none-any.whl
Collecting h5py~=3.1.0
  Using cached h5py-3.1.0-cp38-cp38-win_amd64.whl (2.7 MB)
Collecting gast==0.4.0
  Using cached gast-0.4.0-py3-none-any.whl (9.8 kB)
Collecting clang~=5.0
  Using cached clang-5.0.tar.gz (30 kB)
  Preparing metadata (setup.py) ... done
Collecting tensorflow-estimator~=2.6
  Using cached tensorflow_estimator-2.6.0-py2.py3-none-any.whl (462 kB)
Collecting wrapt>=1.11.1
  Using cached wrapt-1.12.1-py3-none-any.whl
Collecting typing-extensions
  Using cached typing_extensions-3.7.4.3-py3-none-any.whl (22 kB)
Collecting protobuf>=3.9.2
  Downloading protobuf-3.19.0-cp38-cp38-win_amd64.whl (895 kB)
     |████████████████████████████████| 895 kB 726 kB/s
Collecting keras~=2.6
  Using cached keras-2.6.0-py2.py3-none-any.whl (1.3 MB)
Collecting absl-py~=0.10
  Using cached absl_py-0.15.0-py3-none-any.whl (132 kB)
Collecting astunparse~=1.6.3
  Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)
Collecting opt-einsum~=3.3.0
  Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)
Collecting grpcio<2.0,>=1.37.0
  Downloading grpcio-1.41.0-cp38-cp38-win_amd64.whl (3.2 MB)
     |████████████████████████████████| 3.2 MB ...
Collecting six~=1.15.0
  Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting keras-preprocessing~=1.1.2
  Using cached Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)
Requirement already satisfied: heapdict in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from cachey>=0.2.1->napari>=0.4.8->stardist-napari) (1.0.1)
INFO: pip is looking at multiple versions of clang to determine which version is compatible with other requirements. This could take a while.
INFO: pip is looking at multiple versions of certifi to determine which version is compatible with other requirements. This could take a while.
Collecting certifi>=2018.1.18
  Using cached certifi-2021.10.8-py2.py3-none-any.whl (149 kB)
INFO: pip is looking at multiple versions of cachey to determine which version is compatible with other requirements. This could take a while.
Collecting cachey>=0.2.1
  Using cached cachey-0.2.1-py3-none-any.whl (6.4 kB)
INFO: pip is looking at multiple versions of astunparse to determine which version is compatible with other requirements. This could take a while.
INFO: pip is looking at multiple versions of appdirs to determine which version is compatible with other requirements. This could take a while.
Collecting appdirs>=1.4.4
  Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
INFO: pip is looking at multiple versions of absl-py to determine which version is compatible with other requirements. This could take a while.
Collecting absl-py~=0.10
  Using cached absl_py-0.14.1-py3-none-any.whl (131 kB)
INFO: pip is looking at multiple versions of gast to determine which version is compatible with other requirements. This could take a while.
INFO: pip is looking at multiple versions of tensorflow to determine which version is compatible with other requirements. This could take a while.
Collecting tensorflow
  Using cached tensorflow-2.5.1-cp38-cp38-win_amd64.whl (422.6 MB)
Collecting keras-nightly~=2.5.0.dev
  Using cached keras_nightly-2.5.0.dev2021032900-py2.py3-none-any.whl (1.2 MB)
Collecting grpcio~=1.34.0
  Using cached grpcio-1.34.1-cp38-cp38-win_amd64.whl (2.9 MB)
Collecting tensorflow-estimator<2.6.0,>=2.5.0
  Using cached tensorflow_estimator-2.5.0-py2.py3-none-any.whl (462 kB)
Collecting tensorflow
  Using cached tensorflow-2.5.0-cp38-cp38-win_amd64.whl (422.6 MB)
  Using cached tensorflow-2.4.3-cp38-cp38-win_amd64.whl (370.9 MB)
Collecting tensorflow-estimator<2.5.0,>=2.4.0
  Using cached tensorflow_estimator-2.4.0-py2.py3-none-any.whl (462 kB)
Collecting h5py~=2.10.0
  Using cached h5py-2.10.0-cp38-cp38-win_amd64.whl (2.5 MB)
Collecting gast==0.3.3
  Using cached gast-0.3.3-py2.py3-none-any.whl (9.7 kB)
Collecting grpcio~=1.32.0
  Using cached grpcio-1.32.0-cp38-cp38-win_amd64.whl (2.6 MB)
Requirement already satisfied: matplotlib in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from csbdeep>=0.6.3->stardist>=0.7.0->stardist-napari) (3.4.3)
Requirement already satisfied: cloudpickle>=1.1.1 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from dask[array]!=2.28.0,>=2.15.0->napari>=0.4.8->stardist-napari) (2.0.0)
Requirement already satisfied: partd>=0.3.10 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from dask[array]!=2.28.0,>=2.15.0->napari>=0.4.8->stardist-napari) (1.2.0)
Requirement already satisfied: packaging>=20.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from dask[array]!=2.28.0,>=2.15.0->napari>=0.4.8->stardist-napari) (21.0)
Requirement already satisfied: fsspec>=0.6.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from dask[array]!=2.28.0,>=2.15.0->napari>=0.4.8->stardist-napari) (2021.10.1)
Requirement already satisfied: attrs>=17.4.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from jsonschema>=3.2.0->napari>=0.4.8->stardist-napari) (21.2.0)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from jsonschema>=3.2.0->napari>=0.4.8->stardist-napari) (0.18.0)
Requirement already satisfied: ipykernel>=5.2.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari-console>=0.0.4->napari>=0.4.8->stardist-napari) (6.4.2)
Requirement already satisfied: qtconsole!=4.7.6,>=4.5.1 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari-console>=0.0.4->napari>=0.4.8->stardist-napari) (5.1.1)
Requirement already satisfied: IPython>=7.7.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from napari-console>=0.0.4->napari>=0.4.8->stardist-napari) (7.28.0)
Requirement already satisfied: Jinja2>=2.3 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from numpydoc>=0.9.2->napari>=0.4.8->stardist-napari) (3.0.2)
Requirement already satisfied: sphinx>=1.6.5 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from numpydoc>=0.9.2->napari>=0.4.8->stardist-napari) (4.2.0)
Collecting google-auth<3,>=1.6.3
  Using cached google_auth-2.3.0-py2.py3-none-any.whl (154 kB)
Requirement already satisfied: requests<3,>=2.21.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from tensorboard~=2.6->tensorflow->stardist-napari) (2.26.0)
Collecting markdown>=2.6.8
  Using cached Markdown-3.3.4-py3-none-any.whl (97 kB)
Requirement already satisfied: setuptools>=41.0.0 in c:\users\rober\appdata\roaming\python\python38\site-packages (from tensorboard~=2.6->tensorflow->stardist-napari) (57.1.0)
Collecting google-auth-oauthlib<0.5,>=0.4.1
  Using cached google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)
Collecting tensorboard-data-server<0.7.0,>=0.6.0
  Using cached tensorboard_data_server-0.6.1-py3-none-any.whl (2.4 kB)
Collecting tensorboard-plugin-wit>=1.6.0
  Using cached tensorboard_plugin_wit-1.8.0-py3-none-any.whl (781 kB)
Collecting werkzeug>=0.11.15
  Using cached Werkzeug-2.0.2-py3-none-any.whl (288 kB)
Requirement already satisfied: colorama in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from tqdm>=4.56.0->napari>=0.4.8->stardist-napari) (0.4.4)
Requirement already satisfied: hsluv in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from vispy!=0.8.0,>=0.6.4->napari>=0.4.8->stardist-napari) (5.0.2)
Requirement already satisfied: kiwisolver in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from vispy!=0.8.0,>=0.6.4->napari>=0.4.8->stardist-napari) (1.3.2)
Requirement already satisfied: freetype-py in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from vispy!=0.8.0,>=0.6.4->napari>=0.4.8->stardist-napari) (2.2.0)
Collecting llvmlite<0.38,>=0.37.0rc1
  Using cached llvmlite-0.37.0-cp38-cp38-win_amd64.whl (17.0 MB)
Requirement already satisfied: networkx>=2.0 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from scikit-image->stardist>=0.7.0->stardist-napari) (2.6.3)
Requirement already satisfied: PyWavelets>=1.1.1 in c:\users\rober\miniconda3\envs\bio_38\lib\site-packages (from scikit-image->stardist>=0.7.0->stardist-napari) (1.1.1)
Collecting rsa<5,>=3.1.4
  Using cached rsa-4.7.2-py3-none-any.whl (34 kB)
Collecting pyasn1-modules>=0.2.1
  Using cached pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)
Collecting cachetools<5.0,>=2.0.0
  Using cached cachetools-4.2.4-py3-none-any.whl (10 kB)
Collecting requests-oauthlib>=0.7.0
  Using cached requests_oauthlib-1.3.0-py2.py3-none-any.whl (23 kB)
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Collecting pyasn1<0.5.0,>=0.4.6
  Using cached pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)
Collecting oauthlib>=3.0.0
  Using cached oauthlib-3.1.1-py2.py3-none-any.whl (146 kB)
Installing collected packages: six, pyasn1, rsa, pyasn1-modules, oauthlib, cachetools, typing-extensions, requests-oauthlib, numpy, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, llvmlite, h5py, grpcio, google-auth-oauthlib, absl-py, wrapt, termcolor, tensorflow-estimator, tensorboard, opt-einsum, numba, keras-preprocessing, google-pasta, gast, flatbuffers, csbdeep, astunparse, tensorflow, stardist, stardist-napari
  Attempting uninstall: six
    Found existing installation: six 1.16.0
    Uninstalling six-1.16.0:
      Successfully uninstalled six-1.16.0
  Attempting uninstall: typing-extensions
    Found existing installation: typing-extensions 3.10.0.2
    Uninstalling typing-extensions-3.10.0.2:
      Successfully uninstalled typing-extensions-3.10.0.2
  Attempting uninstall: numpy
    Found existing installation: numpy 1.21.3
    Uninstalling numpy-1.21.3:
      Successfully uninstalled numpy-1.21.3
  Attempting uninstall: wrapt
    Found existing installation: wrapt 1.13.2
    Uninstalling wrapt-1.13.2:
      Successfully uninstalled wrapt-1.13.2
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
readme-renderer 29.0 requires bleach>=2.1.0, which is not installed.
Successfully installed absl-py-0.15.0 astunparse-1.6.3 cachetools-4.2.4 csbdeep-0.6.3 flatbuffers-1.12 gast-0.3.3 google-auth-2.3.0 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.32.0 h5py-2.10.0 keras-preprocessing-1.1.2 llvmlite-0.37.0 markdown-3.3.4 numba-0.54.1 numpy-1.19.5 oauthlib-3.1.1 opt-einsum-3.3.0 protobuf-3.19.0 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-oauthlib-1.3.0 rsa-4.7.2 six-1.15.0 stardist-0.7.3 stardist-napari-2021.6.28 tensorboard-2.7.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.0 tensorflow-2.4.3 tensorflow-estimator-2.4.0 termcolor-1.1.0 typing-extensions-3.7.4.3 werkzeug-2.0.2 wrapt-1.12.1
maweigert
maweigert

Hi,

Yes, I these numpy version issues are quite annoying. Almost always they are related to version pins by either tensorflow or numba, see

https://github.com/tensorflow/tensorflow/issues/50204 https://github.com/numba/numba/issues/7176

I normally simply upgrade numpy directly after installing tensorflow, numba, stardist etc and keep my fingers crossed :)

push

maweigert push stardist/stardist

maweigert
maweigert

Add matching labels functionility/test

commit sha: 8e4b2fe347fcd1e8826dce2a970254ab66495e35

push time in 6 days ago
Oct
21
1 week ago
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created branch
createdAt 6 days ago
Oct
20
1 week ago
Activity icon
issue

maweigert issue stardist/stardist

maweigert
maweigert

about annotations for multi-label classification

The multiclass.ipynb shows a way to apply stardist to multi-label segmentation/classification. The example uses synthesized images for demo purpose. One should supply the class info, together with the image label during training. The qupath_export_annotations script only export the pixel label. I am wondering if it is possible to export a seperate json or xml file containing the class info from qupath. I am not familiar with qupath tool, I see that QuPath allows one to set class info to annotations, and wondering where it saves the class info for each annotation.

Many thanks!

Activity icon
issue

maweigert issue comment stardist/stardist

maweigert
maweigert

Windows 10 - StarDist-V073 - save_weights requires h5py when saving in hdf5

Dear All,

I am trying to run stardist_V073 in a jupyter notebook on a local Windows 10 workstation with CUDA 11.0, and TF 2.4.0 but unfortunately I get the following error message:

Epoch 1/400
WARNING:tensorflow:AutoGraph could not transform <function _gcd_import at 0x000001F72047E430> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: Unable to locate the source code of <function _gcd_import at 0x000001F72047E430>. Note that functions defined in certain environments, like the interactive Python shell do not expose their source code. If that is the case, you should to define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.do_not_convert. Original error: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <function _gcd_import at 0x000001F72047E430> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: Unable to locate the source code of <function _gcd_import at 0x000001F72047E430>. Note that functions defined in certain environments, like the interactive Python shell do not expose their source code. If that is the case, you should to define them in a .py source file. If you are certain the code is graph-compatible, wrap the call using @tf.autograph.do_not_convert. Original error: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
100/100 [==============================] - 6s 35ms/step - loss: 2.7778 - prob_loss: 0.3665 - dist_loss: 9.0414 - prob_class_loss: 0.6031 - prob_kld: 0.3175 - dist_relevant_mae: 9.0412 - dist_relevant_mse: 121.1503 - dist_dist_iou_metric: 0.0486 - val_loss: 2.0168 - val_prob_loss: 0.2446 - val_dist_loss: 6.4863 - val_prob_class_loss: 0.4749 - val_prob_kld: 0.1732 - val_dist_relevant_mae: 6.4857 - val_dist_relevant_mse: 70.6139 - val_dist_dist_iou_metric: 0.2198

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
C:\Users\CARLOB~1\AppData\Local\Temp/ipykernel_9564/1463295039.py in <module>
     13     model = StarDist2D.from_pretrained("2D_versatile_fluo")
     14 else:
---> 15     model.train(X_trn, Y_trn, classes=C_trn, validation_data=(X_val,Y_val,C_val), augmenter=augmenter)
     16 None;

~\anaconda3\envs\stardist-V073\lib\site-packages\stardist\models\model2d.py in train(self, X, Y, validation_data, classes, augmenter, seed, epochs, steps_per_epoch, workers)
    450 
    451         fit = self.keras_model.fit_generator if IS_TF_1 else self.keras_model.fit
--> 452         history = fit(iter(self.data_train), validation_data=data_val,
    453                       epochs=epochs, steps_per_epoch=steps_per_epoch,
    454                       workers=workers, use_multiprocessing=workers>1,

~\anaconda3\envs\stardist-V073\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1143           epoch_logs.update(val_logs)
   1144 
-> 1145         callbacks.on_epoch_end(epoch, epoch_logs)
   1146         training_logs = epoch_logs
   1147         if self.stop_training:

~\anaconda3\envs\stardist-V073\lib\site-packages\tensorflow\python\keras\callbacks.py in on_epoch_end(self, epoch, logs)
    426     for callback in self.callbacks:
    427       if getattr(callback, '_supports_tf_logs', False):
--> 428         callback.on_epoch_end(epoch, logs)
    429       else:
    430         if numpy_logs is None:  # Only convert once.

~\anaconda3\envs\stardist-V073\lib\site-packages\tensorflow\python\keras\callbacks.py in on_epoch_end(self, epoch, logs)
   1342     # pylint: disable=protected-access
   1343     if self.save_freq == 'epoch':
-> 1344       self._save_model(epoch=epoch, logs=logs)
   1345 
   1346   def _should_save_on_batch(self, batch):

~\anaconda3\envs\stardist-V073\lib\site-packages\tensorflow\python\keras\callbacks.py in _save_model(self, epoch, logs)
   1391               self.best = current
   1392               if self.save_weights_only:
-> 1393                 self.model.save_weights(
   1394                     filepath, overwrite=True, options=self._options)
   1395               else:

~\anaconda3\envs\stardist-V073\lib\site-packages\tensorflow\python\keras\engine\training.py in save_weights(self, filepath, overwrite, save_format, options)
   2093 
   2094     if save_format == 'h5' and h5py is None:
-> 2095       raise ImportError(
   2096           '`save_weights` requires h5py when saving in hdf5.')
   2097     if save_format == 'tf':

ImportError: `save_weights` requires h5py when saving in hdf5.

I tried to reinstall h5py, different versions (above and below 3.0) and change the TF version from 2.4.0 to 2.5.0. Unfortunately I could not solve the problem.

conda list return the following:

(stardist-V073) C:\Users\CarloBeretta\Documents\AI\stardist-gpu\notebook_Obj>conda list
# packages in environment at C:\Users\CarloBeretta\anaconda3\envs\stardist-V073:
#
# Name                    Version                   Build  Channel
absl-py                   0.13.0                   pypi_0    pypi
argon2-cffi               20.1.0           py38h2bbff1b_1
astunparse                1.6.3                    pypi_0    pypi
async_generator           1.10               pyhd3eb1b0_0
attrs                     21.2.0             pyhd3eb1b0_0
backcall                  0.2.0              pyhd3eb1b0_0
bleach                    4.0.0              pyhd3eb1b0_0
ca-certificates           2021.7.5             haa95532_1
cachetools                4.2.2                    pypi_0    pypi
certifi                   2021.5.30        py38haa95532_0
cffi                      1.14.6           py38h2bbff1b_0
charset-normalizer        2.0.4                    pypi_0    pypi
colorama                  0.4.4              pyhd3eb1b0_0
csbdeep                   0.6.3                    pypi_0    pypi
cycler                    0.10.0                   pypi_0    pypi
debugpy                   1.4.1            py38hd77b12b_0
decorator                 5.0.9              pyhd3eb1b0_0
defusedxml                0.7.1              pyhd3eb1b0_0
entrypoints               0.3                      py38_0
flatbuffers               1.12                     pypi_0    pypi
gast                      0.3.3                    pypi_0    pypi
google-auth               1.35.0                   pypi_0    pypi
google-auth-oauthlib      0.4.6                    pypi_0    pypi
google-pasta              0.2.0                    pypi_0    pypi
grpcio                    1.32.0                   pypi_0    pypi
h5py                      2.10.0                   pypi_0    pypi
icu                       58.2                 ha925a31_3
idna                      3.2                      pypi_0    pypi
imageio                   2.9.0                    pypi_0    pypi
importlib-metadata        3.10.0           py38haa95532_0
importlib_metadata        3.10.0               hd3eb1b0_0
ipykernel                 6.2.0            py38haa95532_1
ipython                   7.26.0           py38hd4e2768_0
ipython_genutils          0.2.0              pyhd3eb1b0_1
ipywidgets                7.6.3              pyhd3eb1b0_1
jedi                      0.18.0           py38haa95532_1
jinja2                    3.0.1              pyhd3eb1b0_0
jpeg                      9b                   hb83a4c4_2
jsonschema                3.2.0              pyhd3eb1b0_2
jupyter                   1.0.0                    py38_7
jupyter_client            7.0.1              pyhd3eb1b0_0
jupyter_console           6.4.0              pyhd3eb1b0_0
jupyter_core              4.7.1            py38haa95532_0
jupyterlab_pygments       0.1.2                      py_0
jupyterlab_widgets        1.0.0              pyhd3eb1b0_1
keras-preprocessing       1.1.2                    pypi_0    pypi
kiwisolver                1.3.2                    pypi_0    pypi
libpng                    1.6.37               h2a8f88b_0
llvmlite                  0.37.0                   pypi_0    pypi
m2w64-gcc-libgfortran     5.3.0                         6
m2w64-gcc-libs            5.3.0                         7
m2w64-gcc-libs-core       5.3.0                         7
m2w64-gmp                 6.1.0                         2
m2w64-libwinpthread-git   5.0.0.4634.697f757               2
markdown                  3.3.4                    pypi_0    pypi
markupsafe                2.0.1            py38h2bbff1b_0
matplotlib                3.4.3                    pypi_0    pypi
matplotlib-inline         0.1.2              pyhd3eb1b0_2
mistune                   0.8.4           py38he774522_1000
msys2-conda-epoch         20160418                      1
nbclient                  0.5.3              pyhd3eb1b0_0
nbconvert                 6.1.0            py38haa95532_0
nbformat                  5.1.3              pyhd3eb1b0_0
nest-asyncio              1.5.1              pyhd3eb1b0_0
networkx                  2.6.3                    pypi_0    pypi
notebook                  6.4.3            py38haa95532_0
numba                     0.54.0                   pypi_0    pypi
numpy                     1.19.5                   pypi_0    pypi
oauthlib                  3.1.1                    pypi_0    pypi
openssl                   1.1.1l               h2bbff1b_0
opt-einsum                3.3.0                    pypi_0    pypi
packaging                 21.0               pyhd3eb1b0_0
pandocfilters             1.4.3            py38haa95532_1
parso                     0.8.2              pyhd3eb1b0_0
pickleshare               0.7.5           pyhd3eb1b0_1003
pillow                    8.3.2                    pypi_0    pypi
pip                       21.2.2           py38haa95532_0
prometheus_client         0.11.0             pyhd3eb1b0_0
prompt-toolkit            3.0.17             pyhca03da5_0
prompt_toolkit            3.0.17               hd3eb1b0_0
protobuf                  3.17.3                   pypi_0    pypi
pyasn1                    0.4.8                    pypi_0    pypi
pyasn1-modules            0.2.8                    pypi_0    pypi
pycparser                 2.20                       py_2
pygments                  2.10.0             pyhd3eb1b0_0
pyparsing                 2.4.7              pyhd3eb1b0_0
pyqt                      5.9.2            py38ha925a31_4
pyrsistent                0.17.3           py38he774522_0
python                    3.8.0                hff0d562_2
python-dateutil           2.8.2              pyhd3eb1b0_0
pywavelets                1.1.1                    pypi_0    pypi
pywin32                   228              py38hbaba5e8_1
pywinpty                  0.5.7                    py38_0
pyzmq                     22.2.1           py38hd77b12b_1
qt                        5.9.7            vc14h73c81de_0
qtconsole                 5.1.0              pyhd3eb1b0_0
qtpy                      1.10.0             pyhd3eb1b0_0
requests                  2.26.0                   pypi_0    pypi
requests-oauthlib         1.3.0                    pypi_0    pypi
rsa                       4.7.2                    pypi_0    pypi
scikit-image              0.18.3                   pypi_0    pypi
scipy                     1.7.1                    pypi_0    pypi
send2trash                1.5.0              pyhd3eb1b0_1
setuptools                52.0.0           py38haa95532_0
sip                       4.19.13          py38ha925a31_0
six                       1.15.0                   pypi_0    pypi
sqlite                    3.36.0               h2bbff1b_0
stardist                  0.7.3                    pypi_0    pypi
tensorboard               2.6.0                    pypi_0    pypi
tensorboard-data-server   0.6.1                    pypi_0    pypi
tensorboard-plugin-wit    1.8.0                    pypi_0    pypi
tensorflow-estimator      2.4.0                    pypi_0    pypi
tensorflow-gpu            2.4.0                    pypi_0    pypi
termcolor                 1.1.0                    pypi_0    pypi
terminado                 0.9.4            py38haa95532_0
testpath                  0.5.0              pyhd3eb1b0_0
tifffile                  2021.8.30                pypi_0    pypi
tornado                   6.1              py38h2bbff1b_0
tqdm                      4.62.2                   pypi_0    pypi
traitlets                 5.0.5              pyhd3eb1b0_0
typing-extensions         3.7.4.3                  pypi_0    pypi
urllib3                   1.26.6                   pypi_0    pypi
vc                        14.2                 h21ff451_1
vs2015_runtime            14.27.29016          h5e58377_2
wcwidth                   0.2.5              pyhd3eb1b0_0
webencodings              0.5.1                    py38_1
werkzeug                  2.0.1                    pypi_0    pypi
wheel                     0.37.0             pyhd3eb1b0_1
widgetsnbextension        3.5.1                    py38_0
wincertstore              0.2                      py38_0
winpty                    0.4.3                         4
wrapt                     1.12.1                   pypi_0    pypi
zipp                      3.5.0              pyhd3eb1b0_0
zlib                      1.2.11               h62dcd97_4

Do you have any suggestion how to solve this issue?

Please let me know if you need any further information.

Thank you for your help.

Carlo

maweigert
maweigert

ok, thats weird. Did the error persist?

Activity icon
issue

maweigert issue stardist/stardist

maweigert
maweigert

255 labels

What if I have more than 255 labels in an image how can I label all of them distinctly ?

Activity icon
issue

maweigert issue comment stardist/stardist

maweigert
maweigert

255 labels

What if I have more than 255 labels in an image how can I label all of them distinctly ?

maweigert
maweigert

Thanks @saskra , now I finally understood the question 😄 Closing...

Oct
14
2 weeks ago
push

maweigert push stardist/stardist

maweigert
maweigert
maweigert
maweigert

Merge branch 'dev' of github.com:stardist/stardist into dev

commit sha: f5c45d07597628aa47aab8fc18826b9d85991e4e

push time in 2 weeks ago
Oct
6
3 weeks ago
Activity icon
issue

maweigert issue comment stardist/stardist

maweigert
maweigert

Image scale / resolution and size

Hi I'm attempting to use a pre-trained model for nuclei segmentation. Is there any general guidance re: image scale and resolution or are there published statistics for the training sets? My images are 0.65 um/pixel.

Similarly is there a recommendation to tile images to specific sizes before segmenting?

maweigert
maweigert

Is there any general guidance re: image scale and resolution or are there published statistics for the training sets?

The dsb2018 training data had objects in the range of 10-70 pixels in diameter. So if your objects are considerably larger, I would suggest downscaling them

See as well the FAQ

Similarly is there a recommendation to tile images to specific sizes before segmenting?

You can use any image size, no need to tile.

Sep
20
1 month ago
Activity icon
issue

maweigert issue comment stardist/stardist

maweigert
maweigert

255 labels

What if I have more than 255 labels in an image how can I label all of them distinctly ?

maweigert
maweigert

Hi, I still don't understand the nature of your problem. Is it about converting a binary image into a label image? Any case, this seems more like a question for the image.sc forum.

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