Normalizer unit tests, bug fixes, and badges

.github/workflows/tests.yml

@@ -0,0 +1,65 @@
+name: tests
+
+on:
+  push:
+    branches:
+      - '*'
+  pull_request:
+    branches:
+      - '*'
+
+jobs:
+  build:
+    runs-on: ubuntu-18.04
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python 3.6
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.6
+
+      - name: Install dependencies
+        run: pip install wheel setuptools torch tensorflow numpy
+
+      - name: Build wheel
+        run: python setup.py bdist_wheel
+
+      - name: Upload Python wheel
+        uses: actions/upload-artifact@v2
+        with:
+          name: Python wheel
+          path: ${{github.workspace}}/dist/torchstain-*.whl
+          if-no-files-found: error
+
+  test:
+    needs: build
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [windows-2019, ubuntu-18.04, macos-11]
+        python-version: [3.6, 3.7, 3.8, 3.9]
+
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: pip install wheel setuptools torch tensorflow numpy
+
+      - name: Install test dependencies
+        run: pip install opencv-python matplotlib torchvision scikit-image pytest
+
+      - name: Download artifact
+        uses: actions/download-artifact@master
+        with:
+          name: "Python wheel"
+
+      - name: Install wheel
+        run: pip install --find-links=${{github.workspace}} torchstain
+
+      - name: Run tests
+        run: |
+          pytest -v tests

.gitignore

@@ -110,6 +110,10 @@ ENV/
 env.bak/
 venv.bak/
 
+# IDE
+.vs/
+.idea/
+
 # Spyder project settings
 .spyderproject
 .spyproject

README.md

@@ -1,5 +1,9 @@
 # torchstain
 
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
+[![tests](https://github.com/andreped/torchstain/workflows/tests/badge.svg)](https://github.com/andreped/torchstain/actions)
+[![Pip Downloads](https://img.shields.io/pypi/dm/torchstain?label=pip%20downloads&logo=python)](https://pypi.org/project/torchstain/)
+
 Pytorch-compatible normalization tools for histopathological images.
 Normalization algorithms currently implemented:
 

setup.py

@@ -21,5 +21,16 @@
         'numpy',
         'tensorflow'
     ],
+    classifiers=[
+        'Development Status :: 4 - Beta',
+        'Intended Audience :: Developers',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        "Programming Language :: Python :: 3.6",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent"
+    ],
     python_requires='>=3.6'
 )

tests/test_normalizers.py

@@ -0,0 +1,46 @@
+import os
+import cv2
+import matplotlib.pyplot as plt
+import torchstain
+import torch
+from torchvision import transforms
+import time
+from skimage.metrics import structural_similarity as ssim
+import numpy as np
+
+
+size = 1024
+curr_file_path = os.path.dirname(os.path.realpath(__file__))
+target = cv2.resize(cv2.cvtColor(cv2.imread(os.path.join(curr_file_path, "../data/target.png")), cv2.COLOR_BGR2RGB), (size, size))
+to_transform = cv2.resize(cv2.cvtColor(cv2.imread(os.path.join(curr_file_path, "../data/source.png")), cv2.COLOR_BGR2RGB), (size, size))
+
+# setup preprocessing and preprocess image to be normalized
+T = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Lambda(lambda x: x*255)
+])
+t_to_transform = T(to_transform)
+
+# initialize normalizers for each backend and fit to target image
+normalizer = torchstain.MacenkoNormalizer(backend='numpy')
+normalizer.fit(target)
+
+torch_normalizer = torchstain.MacenkoNormalizer(backend='torch')
+torch_normalizer.fit(T(target))
+
+tf_normalizer = torchstain.MacenkoNormalizer(backend='tensorflow')
+tf_normalizer.fit(T(target))
+
+# transform
+result_numpy, _, _ = normalizer.normalize(I=to_transform, stains=True)
+result_torch, _, _ = torch_normalizer.normalize(I=t_to_transform, stains=True)
+result_tf, _, _ = tf_normalizer.normalize(I=t_to_transform, stains=True)
+
+# convert to numpy and set dtype
+result_numpy = result_numpy.astype("float32")
+result_torch = result_torch.numpy().astype("float32")
+result_tf = result_tf.numpy().astype("float32")
+
+# assess whether the normalized images are identical across backends
+np.testing.assert_almost_equal(ssim(result_numpy.flatten(), result_torch.flatten()), 1.0, decimal=4, verbose=True)
+np.testing.assert_almost_equal(ssim(result_numpy.flatten(), result_tf.flatten()), 1.0, decimal=4, verbose=True)

tests/test_utils.py

@@ -16,4 +16,4 @@ def test_percentile():
     p_np = np.percentile(x, p, interpolation='nearest')
     p_t = torchstain.utils.percentile(torch.tensor(x), p)
 
-    np.testing.assert_equal(p_np, p_t)
+    np.testing.assert_almost_equal(p_np, p_t)

torchstain/normalizers/torch_macenko_normalizer.py

@@ -1,5 +1,4 @@
 import torch
-import cv2
 from torchstain.normalizers.he_normalizer import HENormalizer
 from torchstain.utils import cov, percentile