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jennifersteedenjennifersteeden
authoredJan 27, 2025··
Merge pull request #44 from mrphys/brach2025
New Tutorials added ahead of SCMR 2025
·
v0.23.1v0.23.0
2 parents cfd8930 + da3c2e7 commit e09ead2

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‎.DS_Store

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‎.devcontainer/Dockerfile

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@@ -5,7 +5,8 @@ RUN apt-get update && \
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apt-get install -y libcairo2-dev libgirepository1.0-dev libgtk-3-dev
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ARG PYBIN=/usr/local/bin/python
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ARG PYVERSIONS="3.7 3.8 3.9 3.10"
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ARG PYVERSIONS="3.8 3.9 3.10"
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#3.11 3.12 3.13"
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RUN for PYVER in ${PYVERSIONS}; do ${PYBIN}${PYVER} -m pip install pycairo PyGObject; done
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# For Jupyter notebooks.

‎.github/workflows/build-package.yml

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strategy:
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matrix:
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py_version: ["3.7", "3.8", "3.9", "3.10"]
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py_version: ["3.8", "3.9", "3.10"] #, "3.11", "3.12", "3.13"]
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env:
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PYTHON: python${{ matrix.py_version }}
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steps:
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- name: Checkout code
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uses: actions/checkout@v2
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uses: actions/checkout@v4
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with:
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lfs: true
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- name: Install required packages
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run: |
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$PYTHON -m pip install -r requirements.txt
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$PYTHON -m pip install --no-cache-dir -r requirements.txt
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- name: Run test suite
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run: |
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- name: Upload wheel
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if: startsWith(github.ref, 'refs/tags')
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uses: actions/upload-artifact@v2
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uses: actions/upload-artifact@v4
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with:
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name: wheels
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path: ./artifacts/*.whl
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- name: Upload docs
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if: startsWith(github.ref, 'refs/tags') && matrix.py_version == 3.9
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uses: actions/upload-artifact@v2
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uses: actions/upload-artifact@v4
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with:
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name: docs
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path: ./tools/docs/_build/dirhtml
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- name: Publish to Test PyPI
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uses: pypa/gh-action-pypi-publish@release/v1
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uses: pypa/gh-action-pypi-publish@release/v1.7
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with:
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password: ${{ secrets.TEST_PYPI_API_TOKEN }}
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repository_url: https://test.pypi.org/legacy/
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packages_dir: artifacts/
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skip_existing: true
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repository-url: https://test.pypi.org/legacy/
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packages-dir: artifacts/
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skip-existing: true
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release:
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steps:
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- name: Checkout docs branch
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uses: actions/checkout@v2
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uses: actions/checkout@v4
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with:
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ref: docs
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rm -rf ./*
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- name: Download docs
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uses: actions/download-artifact@v2
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uses: actions/download-artifact@v4
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with:
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name: docs
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git push --force
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- name: Checkout master
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uses: actions/checkout@v2
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uses: actions/checkout@v4
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- name: Get release tag
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run: |
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fail_on_unmatched_files: true
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- name: Download wheels
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uses: actions/download-artifact@v2
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uses: actions/download-artifact@v4
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with:
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name: wheels
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path: artifacts/
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- name: Publish to PyPI
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uses: pypa/gh-action-pypi-publish@release/v1
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uses: pypa/gh-action-pypi-publish@release/v1.7
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with:
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password: ${{ secrets.PYPI_API_TOKEN }}
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packages_dir: artifacts/
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packages-dir: artifacts/

‎README.rst

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.. image:: https://raw.githubusercontent.com/mrphys/tensorflow-mri/v0.6.0/tools/assets/tfmr_logo.svg?sanitize=true
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:align: center
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:scale: 100 %
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:alt: TFMRI logo
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|
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TensorFlow MRI
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==============
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|pypi| |build| |docs| |doi|
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‎requirements.txt

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h5py
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ismrmrd
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matplotlib
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numpy
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matplotlib<=3.5.3
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numpy==1.22.1
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h5py
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plotly
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PyWavelets
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scipy
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#scipy<1.4.0
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tensorboard
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tensorflow>=2.10.0,<2.11.0
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tensorflow-graphics
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tensorflow-io>=0.27.0,<0.28.0
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tensorflow-nufft>=0.10.0,<0.11.0
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tensorflow-probability>=0.18.0,<0.19.0
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tensorflow-probability>=0.18.0,<0.19.0

‎setup.py

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'Programming Language :: Python :: 3.8',
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'Programming Language :: Python :: 3.9',
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'Programming Language :: Python :: 3.10',
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'Programming Language :: Python :: 3.11',
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'Programming Language :: Python :: 3.12',
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'Programming Language :: Python :: 3.13',
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'Topic :: Scientific/Engineering :: Mathematics',
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'Topic :: Software Development :: Libraries',
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'Topic :: Software Development :: Libraries :: Python Modules'

‎tensorflow_mri/.DS_Store

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‎tensorflow_mri/python/ops/image_ops_test.py

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"""Tests for module `image_ops`."""
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import numpy as np
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import scipy.ndimage
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#import scipy.ndimage
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import tensorflow as tf
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from absl.testing import parameterized
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edges = image_ops.image_gradients(img, method=method, batch_dims=1)
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self.assertAllClose(expected_batch, edges)
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def test_sobel_2d(self):
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array = np.array([[3, 2, 5, 1, 4],
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[5, 8, 3, 7, 1],
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[5, 6, 9, 3, 5]], np.float32)
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# `image_gradients` uses the `REFLECT` padding mode by default, which is
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# equivalent to the SciPy `mirror` mode.
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expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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output = image_ops.image_gradients(array[None, ..., None], method='sobel')
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self.assertAllClose(expected_0, output[0, ..., 0, 0])
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self.assertAllClose(expected_1, output[0, ..., 0, 1])
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def test_sobel_3d(self):
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array = np.array([[[4, 7, 2, 3, 5],
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[3, 7, 7, 6, 3],
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[5, 6, 8, 3, 4],
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[8, 1, 3, 2, 7]],
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[[4, 1, 7, 1, 6],
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[2, 5, 9, 2, 1],
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[6, 6, 5, 9, 1],
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[1, 7, 0, 2, 8]],
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[[0, 0, 3, 7, 8],
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[9, 0, 6, 3, 8],
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[3, 9, 3, 3, 9],
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[7, 0, 1, 7, 9]]], np.float32)
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# `image_gradients` uses the `REFLECT` padding mode by default, which is
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# equivalent to the SciPy `mirror` mode.
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expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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expected_2 = scipy.ndimage.sobel(array, axis=2, mode='mirror')
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output = image_ops.image_gradients(array[None, ..., None], method='sobel')
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self.assertAllClose(expected_0, output[0, ..., 0, 0])
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self.assertAllClose(expected_1, output[0, ..., 0, 1])
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self.assertAllClose(expected_2, output[0, ..., 0, 2])
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## 2D with 2 batch dims
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expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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output = image_ops.image_gradients(array[None, ..., None], method='sobel')
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self.assertAllClose(expected_0, output[0, ..., 0, 0])
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self.assertAllClose(expected_1, output[0, ..., 0, 1])
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self.assertAllClose(expected_2, output[0, ..., 0, 2])
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def test_batch_dims(self):
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array = np.array([[3, 2, 5, 1, 4],
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[5, 8, 3, 7, 1],
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[5, 6, 9, 3, 5]], np.float32)
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# `image_gradients` uses the `REFLECT` padding mode by default, which is
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# equivalent to the SciPy `mirror` mode.
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expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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# Two batch dims.
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output = image_ops.image_gradients(
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array[None, None, ..., None], method='sobel', batch_dims=2)
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self.assertAllClose(expected_0, output[0, 0, ..., 0, 0])
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self.assertAllClose(expected_1, output[0, 0, ..., 0, 1])
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output = image_ops.image_gradients(
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array[None, None, ..., None], method='sobel', image_dims=2)
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self.assertAllClose(expected_0, output[0, 0, ..., 0, 0])
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self.assertAllClose(expected_1, output[0, 0, ..., 0, 1])
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output = image_ops.image_gradients(
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array[None, None, ..., None], method='sobel',
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batch_dims=2, image_dims=2)
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self.assertAllClose(expected_0, output[0, 0, ..., 0, 0])
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self.assertAllClose(expected_1, output[0, 0, ..., 0, 1])
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# Zero batch dims.
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output = image_ops.image_gradients(
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array[..., None], method='sobel', batch_dims=0)
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self.assertAllClose(expected_0, output[..., 0, 0])
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self.assertAllClose(expected_1, output[..., 0, 1])
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output = image_ops.image_gradients(
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array[..., None], method='sobel', image_dims=2)
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self.assertAllClose(expected_0, output[..., 0, 0])
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self.assertAllClose(expected_1, output[..., 0, 1])
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def test_sobel_2d_complex(self):
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array = (np.array([[4, 7, 2, 3, 5],
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[3, 7, 7, 6, 3],
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[5, 6, 8, 3, 4],
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[8, 1, 3, 2, 7]], dtype=np.float32) +
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np.array([[4, 1, 7, 1, 6],
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[2, 5, 9, 2, 1],
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[6, 6, 5, 9, 1],
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[1, 7, 0, 2, 8]], dtype=np.float32) * 1j)
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# `image_gradients` uses the `REFLECT` padding mode by default, which is
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# equivalent to the SciPy `mirror` mode.
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expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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output = image_ops.image_gradients(array[None, ..., None], method='sobel')
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self.assertAllClose(expected_0, output[0, ..., 0, 0])
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self.assertAllClose(expected_1, output[0, ..., 0, 1])
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# def test_sobel_2d(self):
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# array = np.array([[3, 2, 5, 1, 4],
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# [5, 8, 3, 7, 1],
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# [5, 6, 9, 3, 5]], np.float32)
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# # `image_gradients` uses the `REFLECT` padding mode by default, which is
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# # equivalent to the SciPy `mirror` mode.
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# expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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# expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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# output = image_ops.image_gradients(array[None, ..., None], method='sobel')
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# self.assertAllClose(expected_0, output[0, ..., 0, 0])
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# self.assertAllClose(expected_1, output[0, ..., 0, 1])
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# def test_sobel_3d(self):
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# array = np.array([[[4, 7, 2, 3, 5],
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# [3, 7, 7, 6, 3],
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# [5, 6, 8, 3, 4],
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# [8, 1, 3, 2, 7]],
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# [[4, 1, 7, 1, 6],
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# [2, 5, 9, 2, 1],
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# [6, 6, 5, 9, 1],
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# [1, 7, 0, 2, 8]],
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# [[0, 0, 3, 7, 8],
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# [9, 0, 6, 3, 8],
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# [3, 9, 3, 3, 9],
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# [7, 0, 1, 7, 9]]], np.float32)
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# # `image_gradients` uses the `REFLECT` padding mode by default, which is
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# # equivalent to the SciPy `mirror` mode.
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# expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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# expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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# expected_2 = scipy.ndimage.sobel(array, axis=2, mode='mirror')
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# output = image_ops.image_gradients(array[None, ..., None], method='sobel')
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# self.assertAllClose(expected_0, output[0, ..., 0, 0])
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# self.assertAllClose(expected_1, output[0, ..., 0, 1])
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# self.assertAllClose(expected_2, output[0, ..., 0, 2])
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# ## 2D with 2 batch dims
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# expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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# expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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# output = image_ops.image_gradients(array[None, ..., None], method='sobel')
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# self.assertAllClose(expected_0, output[0, ..., 0, 0])
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# self.assertAllClose(expected_1, output[0, ..., 0, 1])
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# self.assertAllClose(expected_2, output[0, ..., 0, 2])
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# def test_batch_dims(self):
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# array = np.array([[3, 2, 5, 1, 4],
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# [5, 8, 3, 7, 1],
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# [5, 6, 9, 3, 5]], np.float32)
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# # `image_gradients` uses the `REFLECT` padding mode by default, which is
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# # equivalent to the SciPy `mirror` mode.
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# expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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# expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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# # Two batch dims.
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# output = image_ops.image_gradients(
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# array[None, None, ..., None], method='sobel', batch_dims=2)
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# self.assertAllClose(expected_0, output[0, 0, ..., 0, 0])
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# self.assertAllClose(expected_1, output[0, 0, ..., 0, 1])
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# output = image_ops.image_gradients(
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# array[None, None, ..., None], method='sobel', image_dims=2)
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# self.assertAllClose(expected_0, output[0, 0, ..., 0, 0])
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# self.assertAllClose(expected_1, output[0, 0, ..., 0, 1])
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# output = image_ops.image_gradients(
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# array[None, None, ..., None], method='sobel',
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# batch_dims=2, image_dims=2)
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# self.assertAllClose(expected_0, output[0, 0, ..., 0, 0])
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# self.assertAllClose(expected_1, output[0, 0, ..., 0, 1])
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# # Zero batch dims.
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# output = image_ops.image_gradients(
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# array[..., None], method='sobel', batch_dims=0)
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# self.assertAllClose(expected_0, output[..., 0, 0])
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# self.assertAllClose(expected_1, output[..., 0, 1])
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# output = image_ops.image_gradients(
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# array[..., None], method='sobel', image_dims=2)
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# self.assertAllClose(expected_0, output[..., 0, 0])
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# self.assertAllClose(expected_1, output[..., 0, 1])
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# def test_sobel_2d_complex(self):
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# array = (np.array([[4, 7, 2, 3, 5],
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# [3, 7, 7, 6, 3],
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# [5, 6, 8, 3, 4],
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# [8, 1, 3, 2, 7]], dtype=np.float32) +
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# np.array([[4, 1, 7, 1, 6],
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# [2, 5, 9, 2, 1],
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# [6, 6, 5, 9, 1],
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# [1, 7, 0, 2, 8]], dtype=np.float32) * 1j)
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# # `image_gradients` uses the `REFLECT` padding mode by default, which is
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# # equivalent to the SciPy `mirror` mode.
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# expected_0 = scipy.ndimage.sobel(array, axis=0, mode='mirror')
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# expected_1 = scipy.ndimage.sobel(array, axis=1, mode='mirror')
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# output = image_ops.image_gradients(array[None, ..., None], method='sobel')
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# self.assertAllClose(expected_0, output[0, ..., 0, 0])
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# self.assertAllClose(expected_1, output[0, ..., 0, 1])
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class BaseTestCases():

‎tests/.DS_Store

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‎tools/.DS_Store

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‎tools/docs/templates/index.rst

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:hidden:
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Tutorials <tutorials>
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Segmentation <tutorials/segment>
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Image reconstruction <tutorials/recon>
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‎tools/docs/tutorials/recon.rst

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.. toctree::
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:hidden:
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CG-SENSE <recon/cg_sense.ipynb>
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PARTIAL-FOURIER (2D+t Cartesian k-space) <recon/partialFourier.ipynb>
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CARTESIAN GRAPPA (2D+t Cartesian k-space) <recon/cartesianGRAPPA.ipynb>
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CARTESIAN SENSE (2D+t Cartesian k-space) <recon/cartesianSENSE.ipynb>
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GRIDDING (Radials and Spirals) <recon/nufftTutorial.ipynb>
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PRE-PROCESSING TRIGGERED CINE DATASET (with GRAPPA and PF) <recon/ProcessingRFHdata.ipynb>
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CG-SENSE (Radial, 2D and 2D+t) <recon/cg_sense.ipynb>
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COMPRESSED SENSING (Radial, 2D and 2D+t) <recon/radial_CS.ipynb>

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