From ece6e7c4d0ff182fc62295962a146d7e92eee2a8 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Mon, 8 Aug 2022 14:19:02 +0200
Subject: [PATCH 01/63] update backend comparison

---
 README.md  | 20 ++++++++++----------
 compare.py | 34 +++++++++++++++++++++++++---------
 2 files changed, 35 insertions(+), 19 deletions(-)

diff --git a/README.md b/README.md
index 6d45a3e..860d4fc 100644
--- a/README.md
+++ b/README.md
@@ -46,16 +46,16 @@ norm, H, E = normalizer.normalize(I=t_to_transform, stains=True)
 Results with 10 runs per size on a Intel(R) Core(TM) i5-8365U CPU @ 1.60GHz
 
 
-|   size | numpy avg. time   | numpy tot. time   | torch avg. time   | torch tot. time   |
-|--------|-------------------|-------------------|-------------------|-------------------|
-|    224x224 | 0.0323s ± 0.0032  | 0.3231s           | 0.0234s ± 0.0384  | 0.2340s           |
-|    448x448 | 0.1228s ± 0.0042  | 1.2280s           | 0.0395s ± 0.0168  | 0.3954s           |
-|    672x672 | 0.2653s ± 0.0106  | 2.6534s           | 0.0753s ± 0.0157  | 0.7527s           |
-|    896x896 | 0.4940s ± 0.0208  | 4.9397s           | 0.1262s ± 0.0159  | 1.2622s           |
-|   1120x1120 | 0.6888s ± 0.0081  | 6.8883s           | 0.2002s ± 0.0141  | 2.0021s           |
-|   1344x1344 | 1.0145s ± 0.0089  | 10.1448s          | 0.2703s ± 0.0136  | 2.7026s           |
-|   1568x1568 | 1.2620s ± 0.0133  | 12.6200s          | 0.3680s ± 0.0128  | 3.6795s           |
-|   1792x1792 | 1.4289s ± 0.0128  | 14.2886s          | 0.5968s ± 0.0160  | 5.9676s           |
+|   size | numpy avg. time   | torch avg. time   | tf avg. time     |
+|--------|-------------------|-------------------|------------------|
+|    224 | 0.0182s ± 0.0016  | 0.0180s ± 0.0390  | 0.0048s ± 0.0002 |
+|    448 | 0.0880s ± 0.0224  | 0.0283s ± 0.0172  | 0.0210s ± 0.0025 |
+|    672 | 0.1810s ± 0.0139  | 0.0463s ± 0.0301  | 0.0354s ± 0.0018 |
+|    896 | 0.3013s ± 0.0377  | 0.0820s ± 0.0329  | 0.0713s ± 0.0008 |
+|   1120 | 0.4694s ± 0.0350  | 0.1321s ± 0.0237  | 0.1036s ± 0.0042 |
+|   1344 | 0.6640s ± 0.0553  | 0.1665s ± 0.0026  | 0.1663s ± 0.0021 |
+|   1568 | 1.1935s ± 0.0739  | 0.2590s ± 0.0088  | 0.2531s ± 0.0031 |
+|   1792 | 1.4523s ± 0.0207  | 0.3402s ± 0.0114  | 0.3080s ± 0.0188 |
 
 
 ## Reference
diff --git a/compare.py b/compare.py
index 0fbf4f8..cacab8c 100644
--- a/compare.py
+++ b/compare.py
@@ -14,24 +14,26 @@ def measure(size, N):
     target = cv2.resize(cv2.cvtColor(cv2.imread("./data/target.png"), cv2.COLOR_BGR2RGB), (size, size))
     to_transform = cv2.resize(cv2.cvtColor(cv2.imread("./data/source.png"), cv2.COLOR_BGR2RGB), (size, size))
 
-    normalizer = torchstain.MacenkoNormalizer(backend='numpy')
+    normalizer = torchstain.normalizers.MacenkoNormalizer(backend='numpy')
     normalizer.fit(target)
 
-
     T = transforms.Compose([
         transforms.ToPILImage(),
         transforms.ToTensor(),
         transforms.Lambda(lambda x: x*255)
     ])
 
-    torch_normalizer = torchstain.MacenkoNormalizer(backend='torch')
+    torch_normalizer = torchstain.normalizers.MacenkoNormalizer(backend='torch')
     torch_normalizer.fit(T(target))
 
+    tf_normalizer = torchstain.normalizers.MacenkoNormalizer(backend='tensorflow')
+    tf_normalizer.fit(T(target))
+
     t_to_transform = T(to_transform)
 
     t_np = []
     start_np = time.perf_counter()
-    for i in range(N):
+    for _ in range(N):
         tic = time.perf_counter()
         _ = normalizer.normalize(to_transform)
         toc = time.perf_counter()
@@ -42,7 +44,7 @@ def measure(size, N):
 
     t_torch = []
     start_torch = time.perf_counter()
-    for i in range(N):
+    for _ in range(N):
         tic = time.perf_counter()
         _ = torch_normalizer.normalize(t_to_transform)
         toc = time.perf_counter()
@@ -50,18 +52,32 @@ def measure(size, N):
     end_torch = time.perf_counter()
     t_torch = np.array(t_torch)
 
+
+    t_tf = []
+    start_tf = time.perf_counter()
+    for _ in range(N):
+        tic = time.perf_counter()
+        _ = torch_normalizer.normalize(t_to_transform)
+        toc = time.perf_counter()
+        t_tf.append(toc-tic)
+    end_tf = time.perf_counter()
+    t_tf = np.array(t_tf)
+
     """
     print(f'Results of {N} runs:')
     print(f'numpy: {t_np.mean():.4f}s ± {t_np.std():.4f} (tot: {end_np-start_np:.4f}s)')
     print(f'torch: {t_torch.mean():.4f}s ± {t_torch.std():.4f} (tot: {end_torch-start_torch:.4f}s)')
     """
 
-    return t_np, end_np-start_np, t_torch, end_torch-start_torch
+    return t_np, end_np-start_np, t_torch, end_torch-start_torch, t_tf, end_tf-start_tf
 
 table = []
 for size in [224, 448, 672, 896, 1120, 1344, 1568, 1792]:
-    t_np, tot_np, t_torch, tot_torch = measure(size, N=10)
-    row = [size, f'{t_np.mean():.4f}s ± {t_np.std():.4f}', f'{tot_np:.4f}s', f'{t_torch.mean():.4f}s ± {t_torch.std():.4f}', f'{tot_torch:.4f}s']
+    t_np, tot_np, t_torch, tot_torch, t_tf, tot_tf = measure(size, N=10)
+    # row = [size, f'{t_np.mean():.4f}s ± {t_np.std():.4f}', f'{tot_np:.4f}s', f'{t_torch.mean():.4f}s ± {t_torch.std():.4f}', f'{tot_torch:.4f}s']
+    row = [size, f'{t_np.mean():.4f}s ± {t_np.std():.4f}', f'{t_torch.mean():.4f}s ± {t_torch.std():.4f}', f'{t_tf.mean():.4f}s ± {t_tf.std():.4f}']
     table.append(row)
 
-print(tabulate(table, headers=['size', 'numpy avg. time', 'numpy tot. time', 'torch avg. time', 'torch tot. time'], tablefmt='github'))
+# print(tabulate(table, headers=['size', 'numpy avg. time', 'numpy tot. time', 'torch avg. time', 'torch tot. time'], tablefmt='github'))
+print(tabulate(table, headers=['size', 'numpy avg. time', 'torch avg. time', 'tf avg. time'], tablefmt='github'))
+

From 5e2fdf525caa114c322aed492afdcb453df93882 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Mon, 8 Aug 2022 14:19:30 +0200
Subject: [PATCH 02/63] bump version to 1.2.0

---
 setup.py               | 2 +-
 torchstain/__init__.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index 7eb0b9e..46c0d67 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 
 setup(
     name='torchstain',
-    version='1.1.0',
+    version='1.2.0',
     description='Pytorch stain normalization utils',
     long_description=README,
     long_description_content_type='text/markdown',
diff --git a/torchstain/__init__.py b/torchstain/__init__.py
index 980e3a6..dab8618 100644
--- a/torchstain/__init__.py
+++ b/torchstain/__init__.py
@@ -1,3 +1,3 @@
-__version__ = '1.1.0'
+__version__ = '1.2.0'
 
 from torchstain.base import normalizers
\ No newline at end of file

From a8b57f2119290a58032a1ce68774d54f7171655e Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Mon, 8 Aug 2022 14:25:36 +0200
Subject: [PATCH 03/63] update README

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 860d4fc..1dcb32e 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,8 @@ Normalization algorithms currently implemented:
 pip install torchstain
 ```
 
+To install a specific backend use either ```torchstain[torch]``` or ```torchstain[tf]```. The numpy backend is included by default in both.
+
 ## Example Usage
 
 ```python

From 838a806aa9f80b2b98f0919fa2be669bc657c1c7 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Mon, 8 Aug 2022 14:29:53 +0200
Subject: [PATCH 04/63] update README

---
 README.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/README.md b/README.md
index 1dcb32e..b1c7e2e 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,12 @@ norm, H, E = normalizer.normalize(I=t_to_transform, stains=True)
 
 ![alt text](result.png)
 
+## Implemented algorithms
+
+| Algorithm | numpy | torch | tensorflow |
+|-|-|-|-|
+| Macenko | &check; | &check; | &check; |
+
 
 ## Backend comparison
 

From bbdf449ae38bbd50394c4b7a3f1126f81d3cd178 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Mon, 8 Aug 2022 15:23:15 +0200
Subject: [PATCH 05/63] rename cov_tf and percentile_tf to cov and percentile

---
 torchstain/tf/normalizers/macenko.py | 10 +++++-----
 torchstain/tf/utils/__init__.py      |  4 ++--
 torchstain/tf/utils/cov.py           |  2 +-
 torchstain/tf/utils/percentile.py    |  2 +-
 4 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/torchstain/tf/normalizers/macenko.py b/torchstain/tf/normalizers/macenko.py
index e9a69ce..bf21666 100644
--- a/torchstain/tf/normalizers/macenko.py
+++ b/torchstain/tf/normalizers/macenko.py
@@ -1,6 +1,6 @@
 import tensorflow as tf
 from torchstain.base.normalizers.he_normalizer import HENormalizer
-from torchstain.tf.utils import cov_tf, percentile_tf, solveLS
+from torchstain.tf.utils import cov, percentile, solveLS
 import numpy as np
 import tensorflow.keras.backend as K
 
@@ -35,8 +35,8 @@ def __find_HE(self, ODhat, eigvecs, alpha):
         That = tf.linalg.matmul(ODhat, eigvecs)
         phi = tf.math.atan2(That[:, 1], That[:, 0])
 
-        minPhi = percentile_tf(phi, alpha)
-        maxPhi = percentile_tf(phi, 100 - alpha)
+        minPhi = percentile(phi, alpha)
+        maxPhi = percentile(phi, 100 - alpha)
 
         vMin = tf.matmul(eigvecs, tf.expand_dims(tf.stack((tf.math.cos(minPhi), tf.math.sin(minPhi))), axis=-1))
         vMax = tf.matmul(eigvecs, tf.expand_dims(tf.stack((tf.math.cos(maxPhi), tf.math.sin(maxPhi))), axis=-1))
@@ -58,13 +58,13 @@ def __compute_matrices(self, I, Io, alpha, beta):
         OD, ODhat = self.__convert_rgb2od(I, Io=Io, beta=beta)
 
         # compute eigenvectors
-        _, eigvecs = tf.linalg.eigh(cov_tf(tf.transpose(ODhat)))
+        _, eigvecs = tf.linalg.eigh(cov(tf.transpose(ODhat)))
         eigvecs = eigvecs[:, 1:3]
 
         HE = self.__find_HE(ODhat, eigvecs, alpha)
 
         C = self.__find_concentration(OD, HE)
-        maxC = tf.stack([percentile_tf(C[0, :], 99), percentile_tf(C[1, :], 99)])
+        maxC = tf.stack([percentile(C[0, :], 99), percentile(C[1, :], 99)])
 
         return HE, C, maxC
 
diff --git a/torchstain/tf/utils/__init__.py b/torchstain/tf/utils/__init__.py
index 1c65f31..4ac551d 100644
--- a/torchstain/tf/utils/__init__.py
+++ b/torchstain/tf/utils/__init__.py
@@ -1,3 +1,3 @@
-from torchstain.tf.utils.cov import cov_tf
-from torchstain.tf.utils.percentile import percentile_tf
+from torchstain.tf.utils.cov import cov
+from torchstain.tf.utils.percentile import percentile
 from torchstain.tf.normalizers.solveLS import solveLS
diff --git a/torchstain/tf/utils/cov.py b/torchstain/tf/utils/cov.py
index c0ce049..3429d90 100644
--- a/torchstain/tf/utils/cov.py
+++ b/torchstain/tf/utils/cov.py
@@ -1,6 +1,6 @@
 import tensorflow as tf
 
-def cov_tf(x):
+def cov(x):
     """
     https://en.wikipedia.org/wiki/Covariance_matrix
     """
diff --git a/torchstain/tf/utils/percentile.py b/torchstain/tf/utils/percentile.py
index c6697dc..2db4aeb 100644
--- a/torchstain/tf/utils/percentile.py
+++ b/torchstain/tf/utils/percentile.py
@@ -1,7 +1,7 @@
 from typing import Union
 import tensorflow as tf
 
-def percentile_tf(t: tf.Tensor, q: float) -> Union[int, float]:
+def percentile(t: tf.Tensor, q: float) -> Union[int, float]:
     """
     Return the ``q``-th percentile of the flattened input tensor's data.
 

From a34fc47fb1a5cf1ff58cc31976d6bf7de6e58bba Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Mon, 8 Aug 2022 15:30:09 +0200
Subject: [PATCH 06/63] update tests

---
 .github/workflows/tests.yml                   | 40 ++---------------
 tests/test_normalizers.py                     | 45 -------------------
 tests/{test_tf_normalizer.py => test_tf.py}   | 17 +++++++
 ...test_torch_normalizer.py => test_torch.py} | 21 ++++++++-
 tests/test_utils.py                           | 19 --------
 5 files changed, 40 insertions(+), 102 deletions(-)
 delete mode 100644 tests/test_normalizers.py
 rename tests/{test_tf_normalizer.py => test_tf.py} (78%)
 rename tests/{test_torch_normalizer.py => test_torch.py} (77%)
 delete mode 100644 tests/test_utils.py

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 3849cee..44f455e 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -31,46 +31,13 @@ jobs:
           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: ${{ matrix.python-version }}
-
-      - name: Download artifact
-        uses: actions/download-artifact@master
-        with:
-          name: "Python wheel"
-
-      - name: Install deps and wheel
-        run: |
-          pip install tensorflow torch
-          pip install --find-links=${{github.workspace}} torchstain
-
-      - name: Install test dependencies
-        run: pip install opencv-python torchvision scikit-image pytest
-
-      - name: Run tests
-        run: |
-          pytest -v tests/test_utils.py
-          pytest -v tests/test_normalizers.py
-
   test-tf:
     needs: build
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
         os: [ windows-2019, ubuntu-18.04, macos-11 ]
-        python-version: [ 3.7 ]
+        python-version: [ 3.7, 3.8, 3.9 ]
 
     steps:
       - uses: actions/checkout@v1
@@ -101,7 +68,8 @@ jobs:
     strategy:
       matrix:
         os: [ windows-2019, ubuntu-18.04, macos-11 ]
-        python-version: [ 3.7 ]
+        python-version: [ 3.7, 3.8, 3.9 ]
+        pytorch-version: [1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0]
 
     steps:
       - uses: actions/checkout@v1
@@ -117,7 +85,7 @@ jobs:
 
       - name: Install deps and wheel wheel
         run: |
-          pip install torch
+          pip install torch==${{ matrix.pytorch-version }}
           pip install --find-links=${{github.workspace}} torchstain
 
       - name: Install test dependencies
diff --git a/tests/test_normalizers.py b/tests/test_normalizers.py
deleted file mode 100644
index 5769b96..0000000
--- a/tests/test_normalizers.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import os
-import cv2
-import torchstain
-import torch
-from torchvision import transforms
-import time
-from skimage.metrics import structural_similarity as ssim
-import numpy as np
-
-def test_normalize_all():
-    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.normalizers.MacenkoNormalizer(backend='numpy')
-    normalizer.fit(target)
-
-    torch_normalizer = torchstain.normalizers.MacenkoNormalizer(backend='torch')
-    torch_normalizer.fit(T(target))
-
-    tf_normalizer = torchstain.normalizers.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)
diff --git a/tests/test_tf_normalizer.py b/tests/test_tf.py
similarity index 78%
rename from tests/test_tf_normalizer.py
rename to tests/test_tf.py
index c67d8e9..075093d 100644
--- a/tests/test_tf_normalizer.py
+++ b/tests/test_tf.py
@@ -1,11 +1,28 @@
 import os
 import cv2
 import torchstain
+import torchstain.tf
 import tensorflow as tf
 import time
 from skimage.metrics import structural_similarity as ssim
 import numpy as np
 
+def test_cov():
+    x = np.random.randn(10, 10)
+    cov_np = np.cov(x)
+    cov_t = torchstain.tf.utils.cov(x)
+
+    np.testing.assert_almost_equal(cov_np, cov_t.numpy())
+
+def test_percentile():
+    x = np.random.randn(10, 10)
+    p = 20
+    p_np = np.percentile(x, p, interpolation='nearest')
+    p_t = torchstain.tf.utils.percentile(x, p)
+
+    np.testing.assert_almost_equal(p_np, p_t)
+
+
 def test_normalize_tf():
     size = 1024
     curr_file_path = os.path.dirname(os.path.realpath(__file__))
diff --git a/tests/test_torch_normalizer.py b/tests/test_torch.py
similarity index 77%
rename from tests/test_torch_normalizer.py
rename to tests/test_torch.py
index aa493fc..0d79ed1 100644
--- a/tests/test_torch_normalizer.py
+++ b/tests/test_torch.py
@@ -1,11 +1,28 @@
 import os
 import cv2
 import torchstain
+import torchstain.torch
 import torch
-from torchvision import transforms
 import time
-from skimage.metrics import structural_similarity as ssim
 import numpy as np
+from torchvision import transforms
+from skimage.metrics import structural_similarity as ssim
+
+
+def test_cov():
+    x = np.random.randn(10, 10)
+    cov_np = np.cov(x)
+    cov_t = torchstain.torch.utils.cov(torch.tensor(x))
+
+    np.testing.assert_almost_equal(cov_np, cov_t.numpy())
+
+def test_percentile():
+    x = np.random.randn(10, 10)
+    p = 20
+    p_np = np.percentile(x, p, interpolation='nearest')
+    p_t = torchstain.torch.utils.percentile(torch.tensor(x), p)
+
+    np.testing.assert_almost_equal(p_np, p_t)
 
 def test_normalize_torch():
     size = 1024
diff --git a/tests/test_utils.py b/tests/test_utils.py
deleted file mode 100644
index 266215e..0000000
--- a/tests/test_utils.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import torch
-import torchstain
-import torchstain.torch
-import numpy as np
-
-def test_cov():
-    x = np.random.randn(10, 10)
-    cov_np = np.cov(x)
-    cov_t = torchstain.torch.utils.cov(torch.tensor(x))
-
-    np.testing.assert_almost_equal(cov_np, cov_t.numpy())
-
-def test_percentile():
-    x = np.random.randn(10, 10)
-    p = 20
-    p_np = np.percentile(x, p, interpolation='nearest')
-    p_t = torchstain.torch.utils.percentile(torch.tensor(x), p)
-
-    np.testing.assert_almost_equal(p_np, p_t)

From c2c39b40c26c7c66645d617298d0cbb6a546b0ac Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Mon, 8 Aug 2022 15:31:30 +0200
Subject: [PATCH 07/63] Use CPU flavor of torch and fix tf and protobuf version

---
 .github/workflows/tests.yml | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 44f455e..1e22526 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -36,8 +36,10 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ windows-2019, ubuntu-18.04, macos-11 ]
+        # os: [ windows-2019, ubuntu-18.04, macos-11 ]
+        os: [ubuntu-18.04, ubuntu-20.04]
         python-version: [ 3.7, 3.8, 3.9 ]
+        tf-version: [2.7.0, 2.8.0, 2.9.0]
 
     steps:
       - uses: actions/checkout@v1
@@ -53,21 +55,22 @@ jobs:
 
       - name: Install deps and wheel
         run: |
-          pip install tensorflow
+          pip install tensorflow==${{ matrix.tf-version }} protobuf==3.20.*
           pip install --find-links=${{github.workspace}} torchstain
 
       - name: Install test dependencies
         run: pip install opencv-python scikit-image pytest
 
       - name: Run tests
-        run: pytest -v tests/test_tf_normalizer.py
+        run: pytest -v tests/test_tf.py
 
   test-torch:
     needs: build
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ windows-2019, ubuntu-18.04, macos-11 ]
+        # os: [ windows-2019, ubuntu-18.04, macos-11 ]
+        os: [ubuntu-18.04, ubuntu-20.04]
         python-version: [ 3.7, 3.8, 3.9 ]
         pytorch-version: [1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0]
 
@@ -85,11 +88,11 @@ jobs:
 
       - name: Install deps and wheel wheel
         run: |
-          pip install torch==${{ matrix.pytorch-version }}
+          pip install --index-url https://download.pytorch.org/whl/cpu/torch/ --extra-index-url https://pypi.org/simple/ torch==${{ matrix.pytorch-version }}
           pip install --find-links=${{github.workspace}} torchstain
 
       - name: Install test dependencies
         run: pip install opencv-python torchvision scikit-image pytest
 
       - name: Run tests
-        run: pytest -v tests/test_torch_normalizer.py
+        run: pytest -v tests/test_torch.py

From ecacfb8dd42e44c49d844de89b9be699249de5ed Mon Sep 17 00:00:00 2001
From: andreped <andrped94@gmail.com>
Date: Mon, 8 Aug 2022 19:22:03 +0200
Subject: [PATCH 08/63] fixed tf percentile test - off by one

---
 tests/test_tf.py                     | 3 +--
 tests/test_torch.py                  | 1 -
 torchstain/tf/utils/percentile.py    | 4 ++--
 torchstain/torch/utils/percentile.py | 2 +-
 4 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/tests/test_tf.py b/tests/test_tf.py
index 075093d..b38ef74 100644
--- a/tests/test_tf.py
+++ b/tests/test_tf.py
@@ -18,11 +18,10 @@ def test_percentile():
     x = np.random.randn(10, 10)
     p = 20
     p_np = np.percentile(x, p, interpolation='nearest')
-    p_t = torchstain.tf.utils.percentile(x, p)
+    p_t = torchstain.tf.utils.percentile(tf.convert_to_tensor(x), p)
 
     np.testing.assert_almost_equal(p_np, p_t)
 
-
 def test_normalize_tf():
     size = 1024
     curr_file_path = os.path.dirname(os.path.realpath(__file__))
diff --git a/tests/test_torch.py b/tests/test_torch.py
index 0d79ed1..168d2f1 100644
--- a/tests/test_torch.py
+++ b/tests/test_torch.py
@@ -8,7 +8,6 @@
 from torchvision import transforms
 from skimage.metrics import structural_similarity as ssim
 
-
 def test_cov():
     x = np.random.randn(10, 10)
     cov_np = np.cov(x)
diff --git a/torchstain/tf/utils/percentile.py b/torchstain/tf/utils/percentile.py
index 2db4aeb..de8c6ac 100644
--- a/torchstain/tf/utils/percentile.py
+++ b/torchstain/tf/utils/percentile.py
@@ -14,5 +14,5 @@ def percentile(t: tf.Tensor, q: float) -> Union[int, float]:
     :param q: Percentile to compute, which must be between 0 and 100 inclusive.
     :return: Resulting value (scalar).
     """
-    k = 1 + tf.math.round(.01 * tf.cast(q, tf.float32) * (tf.cast(tf.size(t), tf.float32) - 1))
-    return tf.sort(tf.reshape(t, [-1]))[tf.cast(k, tf.int32)]
+    k = 1 + tf.math.round(.01 * tf.cast(q, tf.float32) * (tf.cast(tf.math.reduce_prod(tf.size(t)), tf.float32) - 1))
+    return tf.sort(tf.reshape(t, [-1]))[tf.cast(k - 1, tf.int32)]
diff --git a/torchstain/torch/utils/percentile.py b/torchstain/torch/utils/percentile.py
index 188f1bb..08273d4 100644
--- a/torchstain/torch/utils/percentile.py
+++ b/torchstain/torch/utils/percentile.py
@@ -6,7 +6,7 @@
 """
 def percentile(t: torch.tensor, q: float) -> Union[int, float]:
     """
-    Return the ``q``-th percentile of the flattened input tensor's data.
+    Return the ``q``-th percentile of the flattenepip d input tensor's data.
 
     CAUTION:
      * Needs PyTorch >= 1.1.0, as ``torch.kthvalue()`` is used.

From 89a84075f818a51570329403a54474ec5f47f724 Mon Sep 17 00:00:00 2001
From: andreped <andrped94@gmail.com>
Date: Mon, 8 Aug 2022 19:24:31 +0200
Subject: [PATCH 09/63] removed redundant tensor conversion

---
 tests/test_tf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_tf.py b/tests/test_tf.py
index b38ef74..b24757a 100644
--- a/tests/test_tf.py
+++ b/tests/test_tf.py
@@ -18,7 +18,7 @@ def test_percentile():
     x = np.random.randn(10, 10)
     p = 20
     p_np = np.percentile(x, p, interpolation='nearest')
-    p_t = torchstain.tf.utils.percentile(tf.convert_to_tensor(x), p)
+    p_t = torchstain.tf.utils.percentile(x, p)
 
     np.testing.assert_almost_equal(p_np, p_t)
 

From 5d1b729f1e8cc98a700ce2ab1679d958edfe1b3e Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Mon, 8 Aug 2022 21:23:02 +0200
Subject: [PATCH 10/63] move solveLS to torchstain.tf.utils

---
 torchstain/tf/utils/__init__.py                 | 2 +-
 torchstain/tf/{normalizers => utils}/solveLS.py | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename torchstain/tf/{normalizers => utils}/solveLS.py (100%)

diff --git a/torchstain/tf/utils/__init__.py b/torchstain/tf/utils/__init__.py
index 4ac551d..253a3d0 100644
--- a/torchstain/tf/utils/__init__.py
+++ b/torchstain/tf/utils/__init__.py
@@ -1,3 +1,3 @@
 from torchstain.tf.utils.cov import cov
 from torchstain.tf.utils.percentile import percentile
-from torchstain.tf.normalizers.solveLS import solveLS
+from torchstain.tf.utils.solveLS import solveLS
diff --git a/torchstain/tf/normalizers/solveLS.py b/torchstain/tf/utils/solveLS.py
similarity index 100%
rename from torchstain/tf/normalizers/solveLS.py
rename to torchstain/tf/utils/solveLS.py

From 32f6f67524d7ea983e17ace3e19d904ee2ddbedd Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 10:31:19 +0200
Subject: [PATCH 11/63] fix type hint

---
 .github/workflows/tests.yml          | 2 +-
 torchstain/torch/utils/percentile.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 1e22526..df40da1 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -92,7 +92,7 @@ jobs:
           pip install --find-links=${{github.workspace}} torchstain
 
       - name: Install test dependencies
-        run: pip install opencv-python torchvision scikit-image pytest
+        run: pip install --no-dependencies opencv-python torchvision scikit-image pytest
 
       - name: Run tests
         run: pytest -v tests/test_torch.py
diff --git a/torchstain/torch/utils/percentile.py b/torchstain/torch/utils/percentile.py
index 08273d4..08c28ef 100644
--- a/torchstain/torch/utils/percentile.py
+++ b/torchstain/torch/utils/percentile.py
@@ -4,7 +4,7 @@
 """
 Author: https://gist.github.com/spezold/42a451682422beb42bc43ad0c0967a30
 """
-def percentile(t: torch.tensor, q: float) -> Union[int, float]:
+def percentile(t: torch.Tensor, q: float) -> Union[int, float]:
     """
     Return the ``q``-th percentile of the flattenepip d input tensor's data.
 

From 163d10b249ad57c09d573256a7ed82815fd958b4 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 10:37:44 +0200
Subject: [PATCH 12/63] print version

---
 tests/test_torch.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tests/test_torch.py b/tests/test_torch.py
index 168d2f1..f4363e7 100644
--- a/tests/test_torch.py
+++ b/tests/test_torch.py
@@ -3,11 +3,16 @@
 import torchstain
 import torchstain.torch
 import torch
+import torchvision
 import time
 import numpy as np
 from torchvision import transforms
 from skimage.metrics import structural_similarity as ssim
 
+
+def setup_function(fn):
+    print("torch version:", torch.__version__, "torchvision version:", torchvision.__version__)
+
 def test_cov():
     x = np.random.randn(10, 10)
     cov_np = np.cov(x)

From 45b98dabe62d3962ec928e5d8899adf33705f92e Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 10:38:09 +0200
Subject: [PATCH 13/63] disable stdout capture

---
 .github/workflows/tests.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index df40da1..e703af9 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -62,7 +62,7 @@ jobs:
         run: pip install opencv-python scikit-image pytest
 
       - name: Run tests
-        run: pytest -v tests/test_tf.py
+        run: pytest -vs tests/test_tf.py
 
   test-torch:
     needs: build
@@ -92,7 +92,7 @@ jobs:
           pip install --find-links=${{github.workspace}} torchstain
 
       - name: Install test dependencies
-        run: pip install --no-dependencies opencv-python torchvision scikit-image pytest
+        run: pip install opencv-python torchvision scikit-image pytest
 
       - name: Run tests
-        run: pytest -v tests/test_torch.py
+        run: pytest -vs tests/test_torch.py

From 0616dea591ed906a725cb8e4b3792ad1bd7db835 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 10:56:30 +0200
Subject: [PATCH 14/63] fix tests

---
 .github/workflows/tests.yml | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index df40da1..b2a3d76 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -53,16 +53,16 @@ jobs:
         with:
           name: "Python wheel"
 
-      - name: Install deps and wheel
+      - name: Install dependencies
         run: |
-          pip install tensorflow==${{ matrix.tf-version }} protobuf==3.20.*
-          pip install --find-links=${{github.workspace}} torchstain
+          pip install tensorflow==${{ matrix.tf-version }} protobuf==3.20.* opencv-python-headless scikit-image
+          pip install pytest
 
-      - name: Install test dependencies
-        run: pip install opencv-python scikit-image pytest
+      - name: Install wheel
+        run: pip install --find-links=${{github.workspace}} torchstain
 
       - name: Run tests
-        run: pytest -v tests/test_tf.py
+        run: pytest -vs tests/test_tf.py
 
   test-torch:
     needs: build
@@ -86,13 +86,13 @@ jobs:
         with:
           name: "Python wheel"
 
-      - name: Install deps and wheel wheel
+      - name: Install dependencies
         run: |
-          pip install --index-url https://download.pytorch.org/whl/cpu/torch/ --extra-index-url https://pypi.org/simple/ torch==${{ matrix.pytorch-version }}
-          pip install --find-links=${{github.workspace}} torchstain
+          pip install torch==${{ matrix.pytorch-version }} torchvision opencv-python-headless scikit-image
+          pip install pytest
 
-      - name: Install test dependencies
-        run: pip install --no-dependencies opencv-python torchvision scikit-image pytest
+      - name: Install wheel
+        run: pip install --find-links=${{github.workspace}} torchstain
 
       - name: Run tests
-        run: pytest -v tests/test_torch.py
+        run: pytest -vs tests/test_torch.py

From 3bcbd7207ecd54b1263a0f6b3bc12c3cfd3113fc Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 11:12:28 +0200
Subject: [PATCH 15/63] update README

---
 README.md | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/README.md b/README.md
index b1c7e2e..f831348 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,9 @@
 [![License](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
 [![tests](https://github.com/EIDOSLAB/torchstain/workflows/tests/badge.svg)](https://github.com/EIDOSLAB/torchstain/actions)
 [![Pip Downloads](https://img.shields.io/pypi/dm/torchstain?label=pip%20downloads&logo=python)](https://pypi.org/project/torchstain/)
+[![DOI](https://zenodo.org/badge/323590093.svg)](https://zenodo.org/badge/latestdoi/323590093)
+
+
 
 GPU-accelerated stain normalization tools for histopathological images. Compatible with PyTorch, TensorFlow, and Numpy.
 Normalization algorithms currently implemented:
@@ -69,3 +72,23 @@ Results with 10 runs per size on a Intel(R) Core(TM) i5-8365U CPU @ 1.60GHz
 ## Reference
 
 - [1] Macenko, Marc, et al. "A method for normalizing histology slides for quantitative analysis." 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2009.
+
+
+## Citing
+
+If you find this software useful for your research, please cite it as: 
+
+
+```bibtex
+@software{barbano2022torchstain,
+  author       = {Carlo Alberto Barbano and
+                  André Pedersen},
+  title        = {EIDOSLAB/torchstain: v1.2.0-rc1},
+  month        = aug,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {v1.2.0-rc1},
+  doi          = {10.5281/zenodo.6976410},
+  url          = {https://doi.org/10.5281/zenodo.6976410}
+}
+```

From cb99e5e9e69dfb35dd081b283d2cf92917cc1deb Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 13:12:46 +0200
Subject: [PATCH 16/63] fix docstring

---
 torchstain/tf/utils/percentile.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/torchstain/tf/utils/percentile.py b/torchstain/tf/utils/percentile.py
index de8c6ac..32f7c67 100644
--- a/torchstain/tf/utils/percentile.py
+++ b/torchstain/tf/utils/percentile.py
@@ -6,7 +6,6 @@ def percentile(t: tf.Tensor, q: float) -> Union[int, float]:
     Return the ``q``-th percentile of the flattened input tensor's data.
 
     CAUTION:
-     * Needs PyTorch >= 1.1.0, as ``torch.kthvalue()`` is used.
      * Values are not interpolated, which corresponds to
        ``numpy.percentile(..., interpolation="nearest")``.
 

From a4246c26ec9aed2f514849f013b7d78ec2d11071 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 13:14:41 +0200
Subject: [PATCH 17/63] update description

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 46c0d67..8380aa1 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
 setup(
     name='torchstain',
     version='1.2.0',
-    description='Pytorch stain normalization utils',
+    description='Stain normalization utils for pytorch and tensorflow',
     long_description=README,
     long_description_content_type='text/markdown',
     url='https://github.com/EIDOSlab/torchstain',

From 7298fc478743770251d05ab5ec1923cb8c65fea5 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 13:15:30 +0200
Subject: [PATCH 18/63] add python 3.6

---
 .github/workflows/tests.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index b2a3d76..ef8d0ae 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -38,7 +38,7 @@ jobs:
       matrix:
         # os: [ windows-2019, ubuntu-18.04, macos-11 ]
         os: [ubuntu-18.04, ubuntu-20.04]
-        python-version: [ 3.7, 3.8, 3.9 ]
+        python-version: [ 3.6, 3.7, 3.8, 3.9 ]
         tf-version: [2.7.0, 2.8.0, 2.9.0]
 
     steps:
@@ -71,7 +71,7 @@ jobs:
       matrix:
         # os: [ windows-2019, ubuntu-18.04, macos-11 ]
         os: [ubuntu-18.04, ubuntu-20.04]
-        python-version: [ 3.7, 3.8, 3.9 ]
+        python-version: [ 3.6, 3.7, 3.8, 3.9 ]
         pytorch-version: [1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0]
 
     steps:

From 899e41f19a75849e3ad5206d9aae0edce7e31053 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 13:24:53 +0200
Subject: [PATCH 19/63] fix test configurations

---
 .github/workflows/tests.yml | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index ef8d0ae..f5d4d10 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -37,9 +37,12 @@ jobs:
     strategy:
       matrix:
         # os: [ windows-2019, ubuntu-18.04, macos-11 ]
-        os: [ubuntu-18.04, ubuntu-20.04]
-        python-version: [ 3.6, 3.7, 3.8, 3.9 ]
+        os: [ubuntu-18.04]
+        python-version: [ 3.7, 3.8, 3.9 ]
         tf-version: [2.7.0, 2.8.0, 2.9.0]
+        include:
+          - python-version: 3.6
+            tf-version: 2.6.0
 
     steps:
       - uses: actions/checkout@v1
@@ -70,9 +73,14 @@ jobs:
     strategy:
       matrix:
         # os: [ windows-2019, ubuntu-18.04, macos-11 ]
-        os: [ubuntu-18.04, ubuntu-20.04]
+        os: [ubuntu-18.04]
         python-version: [ 3.6, 3.7, 3.8, 3.9 ]
         pytorch-version: [1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0]
+        exclude:
+          - python-version: 3.6
+            pytorch-version: 1.11.0
+          - python-version: 3.6
+            pytorch-version: 1.12.0
 
     steps:
       - uses: actions/checkout@v1

From 036d1eec13fc8eb5f56f0b6c7a33b32c58f16948 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 13:30:50 +0200
Subject: [PATCH 20/63] fix test configurations

---
 .github/workflows/tests.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index f5d4d10..684066f 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -41,7 +41,8 @@ jobs:
         python-version: [ 3.7, 3.8, 3.9 ]
         tf-version: [2.7.0, 2.8.0, 2.9.0]
         include:
-          - python-version: 3.6
+          - os: ubuntu-18.04
+            python-version: 3.6
             tf-version: 2.6.0
 
     steps:

From 78e42aaf5829bda375eced267034e5e9a67619d9 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 13:36:14 +0200
Subject: [PATCH 21/63] remove python 3.6 from tensorflow tests

---
 .github/workflows/tests.yml | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 684066f..5ab6cd8 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -40,10 +40,6 @@ jobs:
         os: [ubuntu-18.04]
         python-version: [ 3.7, 3.8, 3.9 ]
         tf-version: [2.7.0, 2.8.0, 2.9.0]
-        include:
-          - os: ubuntu-18.04
-            python-version: 3.6
-            tf-version: 2.6.0
 
     steps:
       - uses: actions/checkout@v1

From 803c0141c8a7312719b53f39dcf061fcc1e4d93f Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 13:43:00 +0200
Subject: [PATCH 22/63] enable all systems

---
 .github/workflows/tests.yml | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 5ab6cd8..13b7f31 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -36,8 +36,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        # os: [ windows-2019, ubuntu-18.04, macos-11 ]
-        os: [ubuntu-18.04]
+        os: [ windows-2019, ubuntu-18.04, macos-11 ]
         python-version: [ 3.7, 3.8, 3.9 ]
         tf-version: [2.7.0, 2.8.0, 2.9.0]
 
@@ -69,8 +68,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        # os: [ windows-2019, ubuntu-18.04, macos-11 ]
-        os: [ubuntu-18.04]
+        os: [ windows-2019, ubuntu-18.04, macos-11 ]
         python-version: [ 3.6, 3.7, 3.8, 3.9 ]
         pytorch-version: [1.8.0, 1.9.0, 1.10.0, 1.11.0, 1.12.0]
         exclude:

From 856051316ce4ffae7e8c0c7679a4468ea5fac918 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 14:23:08 +0200
Subject: [PATCH 23/63] perform all tests for main branch only

---
 .../workflows/{tests.yml => tests_full.yml}   |  4 +-
 .github/workflows/tests_quick.yml             | 86 +++++++++++++++++++
 2 files changed, 88 insertions(+), 2 deletions(-)
 rename .github/workflows/{tests.yml => tests_full.yml} (99%)
 create mode 100644 .github/workflows/tests_quick.yml

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests_full.yml
similarity index 99%
rename from .github/workflows/tests.yml
rename to .github/workflows/tests_full.yml
index 13b7f31..ba105d9 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests_full.yml
@@ -3,10 +3,10 @@ name: tests
 on:
   push:
     branches:
-      - '*'
+      - main
   pull_request:
     branches:
-      - '*'
+      - main
 
 jobs:
   build:
diff --git a/.github/workflows/tests_quick.yml b/.github/workflows/tests_quick.yml
new file mode 100644
index 0000000..3e08828
--- /dev/null
+++ b/.github/workflows/tests_quick.yml
@@ -0,0 +1,86 @@
+name: tests
+
+on:
+  push:
+    branches-ignore:
+      - main
+  pull_request:
+    branches-ignore:
+      - main
+
+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
+
+      - name: Build wheel
+        run: python setup.py bdist_wheel
+
+      - name: Upload Python wheel
+        uses: actions/upload-artifact@v2
+        with:
+          name: Python wheel
+          path: ${{github.workspace}}/dist/torchstain-*.whl
+          if-no-files-found: error
+
+  test-tf:
+    needs: build
+    runs-on: ubuntu-18.04
+
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: Download artifact
+        uses: actions/download-artifact@master
+        with:
+          name: "Python wheel"
+
+      - name: Install dependencies
+        run: |
+          pip install tensorflow protobuf==3.20.* opencv-python-headless scikit-image
+          pip install pytest
+
+      - name: Install wheel
+        run: pip install --find-links=${{github.workspace}} torchstain
+
+      - name: Run tests
+        run: pytest -vs tests/test_tf.py
+
+  test-torch:
+    needs: build
+    runs-on: ubuntu-18.04
+
+    steps:
+      - uses: actions/checkout@v1
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: Download artifact
+        uses: actions/download-artifact@master
+        with:
+          name: "Python wheel"
+
+      - name: Install dependencies
+        run: |
+          pip install torch torchvision opencv-python-headless scikit-image
+          pip install pytest
+
+      - name: Install wheel
+        run: pip install --find-links=${{github.workspace}} torchstain
+
+      - name: Run tests
+        run: pytest -vs tests/test_torch.py

From 1282a02a2779a0d19daea00a5502da7ffb67ff77 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 14:43:18 +0200
Subject: [PATCH 24/63] update description

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 8380aa1..c535b9c 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
 setup(
     name='torchstain',
     version='1.2.0',
-    description='Stain normalization utils for pytorch and tensorflow',
+    description=' Stain normalization tools for histological analysis and computational pathology',
     long_description=README,
     long_description_content_type='text/markdown',
     url='https://github.com/EIDOSlab/torchstain',

From 365a4949d7fb83f8631e324001157e76b74196b1 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Tue, 9 Aug 2022 21:38:27 +0200
Subject: [PATCH 25/63] remove leading space

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index c535b9c..d27acf7 100644
--- a/setup.py
+++ b/setup.py
@@ -7,7 +7,7 @@
 setup(
     name='torchstain',
     version='1.2.0',
-    description=' Stain normalization tools for histological analysis and computational pathology',
+    description='Stain normalization tools for histological analysis and computational pathology',
     long_description=README,
     long_description_content_type='text/markdown',
     url='https://github.com/EIDOSlab/torchstain',

From ecf0ade421fabfb77a0b18e714653383d4a046e2 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Wed, 10 Aug 2022 09:10:51 +0200
Subject: [PATCH 26/63] add build action

---
 .github/workflows/build.yaml | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 .github/workflows/build.yaml

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
new file mode 100644
index 0000000..5af1972
--- /dev/null
+++ b/.github/workflows/build.yaml
@@ -0,0 +1,27 @@
+name: Build
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build_wheels:
+    name: Build wheels on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-20.04, windows-2019, macOS-11]
+
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v3
+
+      - name: Install cibuildwheel
+        run: python -m pip install cibuildwheel==2.8.1
+      - name: Build wheels
+        run: python -m cibuildwheel --output-dir dist
+  
+      - uses: actions/upload-artifact@v3
+        with:
+          path: ./dist/*.whl
\ No newline at end of file

From bc7c6a5c4b40bea8eee26f4ffe1a4968642f444f Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Wed, 10 Aug 2022 09:29:52 +0200
Subject: [PATCH 27/63] add build&deploy action on release created

---
 .github/workflows/build.yaml | 42 +++++++++++++++++++++++++-----------
 1 file changed, 29 insertions(+), 13 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 5af1972..eaa4ff0 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -1,27 +1,43 @@
-name: Build
+name: Build and upload to PyPI
 
 on:
-  push:
-    branches:
-      - main
+  release:
+    types: [created]
 
 jobs:
   build_wheels:
-    name: Build wheels on ${{ matrix.os }}
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: [ubuntu-20.04, windows-2019, macOS-11]
+    name: Build release
+    runs-on: ubuntu-18.04
 
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v3
+        with:
+          python-version: '3.x'
+
+      - name: Install deps
+        run: |
+          pip install setuptools wheel
 
-      - name: Install cibuildwheel
-        run: python -m pip install cibuildwheel==2.8.1
       - name: Build wheels
-        run: python -m cibuildwheel --output-dir dist
+        run: python setup.py sdist bdist_wheel
   
       - uses: actions/upload-artifact@v3
         with:
-          path: ./dist/*.whl
\ No newline at end of file
+          path: ./dist/*
+    
+  upload_pypi:
+    needs: build_wheels
+    runs-on: ubuntu-18.04
+
+    steps:
+      - uses: actions/download-artifact@v3
+        with:
+          name: artifact
+          path: dist
+      
+      - uses: pypa/gh-action-pypi-publish@v1.5.0
+        with:
+          user: __token__
+          password: ${{ secrets.torchstain_deploy_token }}
+      
\ No newline at end of file

From 0248a936eb886375ef0dda09d348f6c63d4b1a9c Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Wed, 10 Aug 2022 09:43:51 +0200
Subject: [PATCH 28/63] add build&deploy action on release created

---
 .github/workflows/build.yaml | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index eaa4ff0..99a671a 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -1,8 +1,12 @@
 name: Build and upload to PyPI
 
 on:
-  release:
-    types: [created]
+  push:
+    branches:
+      main
+  relase:
+    types:
+      - published
 
 jobs:
   build_wheels:
@@ -29,6 +33,8 @@ jobs:
   upload_pypi:
     needs: build_wheels
     runs-on: ubuntu-18.04
+    
+    if: github.event_name == 'release' && github.event.action == 'published'
 
     steps:
       - uses: actions/download-artifact@v3

From e588a63398cf4e1077176173659f7bc5f8cea9eb Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Wed, 10 Aug 2022 09:44:29 +0200
Subject: [PATCH 29/63] fix typo in action yaml

---
 .github/workflows/build.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 99a671a..11967cb 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -4,7 +4,7 @@ on:
   push:
     branches:
       main
-  relase:
+  release:
     types:
       - published
 

From 93fd522e3f75bde17a52b43b277d4f2b83d5478c Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Wed, 10 Aug 2022 10:13:18 +0200
Subject: [PATCH 30/63] deploy on tagged version commit

---
 .github/workflows/build.yaml     | 5 +----
 .github/workflows/tests_full.yml | 3 +++
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index 11967cb..f9d872f 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -4,9 +4,6 @@ on:
   push:
     branches:
       main
-  release:
-    types:
-      - published
 
 jobs:
   build_wheels:
@@ -34,7 +31,7 @@ jobs:
     needs: build_wheels
     runs-on: ubuntu-18.04
     
-    if: github.event_name == 'release' && github.event.action == 'published'
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
 
     steps:
       - uses: actions/download-artifact@v3
diff --git a/.github/workflows/tests_full.yml b/.github/workflows/tests_full.yml
index ba105d9..e550c88 100644
--- a/.github/workflows/tests_full.yml
+++ b/.github/workflows/tests_full.yml
@@ -11,6 +11,9 @@ on:
 jobs:
   build:
     runs-on: ubuntu-18.04
+    
+    if: startsWith(github.ref, 'refs/tags/v') != true
+
     steps:
       - uses: actions/checkout@v1
       - name: Set up Python 3.6

From 978c84ce57ea0315d28d212fd16d0737b56774a7 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Wed, 10 Aug 2022 10:21:47 +0200
Subject: [PATCH 31/63] deploy on tagged version commit

---
 .github/workflows/build.yaml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index f9d872f..df8be46 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -30,7 +30,6 @@ jobs:
   upload_pypi:
     needs: build_wheels
     runs-on: ubuntu-18.04
-    
     if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
 
     steps:

From 3fbf493adea957707d47e216377a1a4712a9b5ef Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Wed, 10 Aug 2022 10:27:18 +0200
Subject: [PATCH 32/63] deploy on tagged version commit

---
 .github/workflows/build.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index df8be46..f9d872f 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -30,6 +30,7 @@ jobs:
   upload_pypi:
     needs: build_wheels
     runs-on: ubuntu-18.04
+    
     if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
 
     steps:

From a104b345e1fbe29121aa1c10936b28b19c96b88d Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Wed, 10 Aug 2022 10:38:38 +0200
Subject: [PATCH 33/63] do not specify main branch for push

---
 .github/workflows/build.yaml | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml
index f9d872f..e6719d7 100644
--- a/.github/workflows/build.yaml
+++ b/.github/workflows/build.yaml
@@ -1,9 +1,7 @@
 name: Build and upload to PyPI
 
 on:
-  push:
-    branches:
-      main
+  push
 
 jobs:
   build_wheels:

From b2571847b4760c5909085d69e27827d0f0c5b022 Mon Sep 17 00:00:00 2001
From: Carlo Alberto Barbano <carlo.alberto.barbano@outlook.com>
Date: Wed, 10 Aug 2022 11:03:11 +0200
Subject: [PATCH 34/63] update README [skip ci]

---
 README.md | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index f831348..f777711 100644
--- a/README.md
+++ b/README.md
@@ -83,12 +83,25 @@ If you find this software useful for your research, please cite it as:
 @software{barbano2022torchstain,
   author       = {Carlo Alberto Barbano and
                   André Pedersen},
-  title        = {EIDOSLAB/torchstain: v1.2.0-rc1},
+  title        = {EIDOSLAB/torchstain: v1.2.0-stable},
   month        = aug,
   year         = 2022,
   publisher    = {Zenodo},
-  version      = {v1.2.0-rc1},
-  doi          = {10.5281/zenodo.6976410},
-  url          = {https://doi.org/10.5281/zenodo.6976410}
+  version      = {v1.2.0-stable},
+  doi          = {10.5281/zenodo.6979540},
+  url          = {https://doi.org/10.5281/zenodo.6979540}
+}
+```
+
+Torchstain was originally developed within the [UNITOPATHO](https://github.com/EIDOSLAB/UNITOPATHO) data collection, which you can cite as:
+
+```bibtex
+@inproceedings{barbano2021unitopatho,
+  title={UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading},
+  author={Barbano, Carlo Alberto and Perlo, Daniele and Tartaglione, Enzo and Fiandrotti, Attilio and Bertero, Luca and Cassoni, Paola and Grangetto, Marco},
+  booktitle={2021 IEEE International Conference on Image Processing (ICIP)},
+  pages={76--80},
+  year={2021},
+  organization={IEEE}
 }
 ```

From 97ea7657d507735f53f050d90a2527baed9c8f4b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 13:47:43 +0100
Subject: [PATCH 35/63] added experimental numpy support for Reinhard

---
 torchstain/base/normalizers/__init__.py  |  1 +
 torchstain/base/normalizers/reinhard.py  | 10 ++++++++
 torchstain/numpy/normalizers/__init__.py |  3 ++-
 torchstain/numpy/normalizers/reinhard.py | 29 +++++++++++++++++++++
 torchstain/numpy/utils/__init__.py       |  0
 torchstain/numpy/utils/rgb2lab.py        | 32 ++++++++++++++++++++++++
 torchstain/numpy/utils/split.py          | 21 ++++++++++++++++
 torchstain/numpy/utils/stats.py          |  9 +++++++
 8 files changed, 104 insertions(+), 1 deletion(-)
 create mode 100644 torchstain/base/normalizers/reinhard.py
 create mode 100644 torchstain/numpy/normalizers/reinhard.py
 create mode 100644 torchstain/numpy/utils/__init__.py
 create mode 100644 torchstain/numpy/utils/rgb2lab.py
 create mode 100644 torchstain/numpy/utils/split.py
 create mode 100644 torchstain/numpy/utils/stats.py

diff --git a/torchstain/base/normalizers/__init__.py b/torchstain/base/normalizers/__init__.py
index aceb595..a8f8bb7 100644
--- a/torchstain/base/normalizers/__init__.py
+++ b/torchstain/base/normalizers/__init__.py
@@ -1,2 +1,3 @@
 from .he_normalizer import HENormalizer
 from .macenko import MacenkoNormalizer
+from .reinhard import ReinhardNormalizer
diff --git a/torchstain/base/normalizers/reinhard.py b/torchstain/base/normalizers/reinhard.py
new file mode 100644
index 0000000..f6bb6ff
--- /dev/null
+++ b/torchstain/base/normalizers/reinhard.py
@@ -0,0 +1,10 @@
+def ReinhardNormalizer(backend='numpy'):
+    if backend == 'numpy':
+        from torchstain.numpy.normalizers import NumpyReinhardNormalizer
+        return NumpyReinhardNormalizer()
+    elif backend == "torch":
+        raise NotImplementedError
+    elif backend == "tensorflow":
+        raise NotImplementedError
+    else:
+        raise Exception(f'Unknown backend {backend}')
diff --git a/torchstain/numpy/normalizers/__init__.py b/torchstain/numpy/normalizers/__init__.py
index 2220c20..d453cf1 100644
--- a/torchstain/numpy/normalizers/__init__.py
+++ b/torchstain/numpy/normalizers/__init__.py
@@ -1 +1,2 @@
-from .macenko import NumpyMacenkoNormalizer
\ No newline at end of file
+from .macenko import NumpyMacenkoNormalizer
+from .reinhard import NumpyReinhardNormalizer
\ No newline at end of file
diff --git a/torchstain/numpy/normalizers/reinhard.py b/torchstain/numpy/normalizers/reinhard.py
new file mode 100644
index 0000000..bd36609
--- /dev/null
+++ b/torchstain/numpy/normalizers/reinhard.py
@@ -0,0 +1,29 @@
+import numpy as np
+from torchstain.base.normalizers import HENormalizer
+from torchstain.numpy.utils.rgb2lab import rgb2lab
+from torchstain.numpy.utils.stats import get_mean_std, standardize
+
+"""
+Source code adapted from:
+https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_normalization/reinhard.py
+https://github.com/Peter554/StainTools/blob/master/staintools/reinhard_color_normalizer.py
+"""
+class NumpyReinhardNormalizer(HENormalizer):
+    def __init__(self):
+        super().__init__()
+        # unless fit() is applied, no transfer is performed
+        self.target_mus = np.ones(3)
+        self.target_stds = np.ones(3)
+    
+    def fit(self, target):
+        self.target_mus, self.target_stds = get_mean_std(target)
+
+    def normalize(self, I):
+        # convert to LAB
+        lab = rgb2lab(I)
+
+        # get summary statistics from LAB
+        mus, stds = get_mean_std(lab)
+
+        # standardize intensities channel-wise and normalize using target means and standard deviations (one for each channel)
+        return np.dstack([standardize(x, mu_, std_) for x, mu, std_, mu_T, std_T in zip(csplit(lab), mus, stds, target_mus, target_stds)])
diff --git a/torchstain/numpy/utils/__init__.py b/torchstain/numpy/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/torchstain/numpy/utils/rgb2lab.py b/torchstain/numpy/utils/rgb2lab.py
new file mode 100644
index 0000000..6706424
--- /dev/null
+++ b/torchstain/numpy/utils/rgb2lab.py
@@ -0,0 +1,32 @@
+import numpy as np
+
+
+# constant conversion matrices between color spaces
+_rgb2lms = np.array([[0.3811, 0.5783, 0.0402],
+                     [0.1967, 0.7244, 0.0782],
+                     [0.0241, 0.1288, 0.8444]])
+
+_lms2lab = np.dot(
+    np.array([[1 / (3 ** 0.5), 0, 0],
+              [0, 1 / (6 ** 0.5), 0],
+              [0, 0, 1 / (2 ** 0.5)]]),
+    np.array([[1, 1, 1],
+              [1, 1, -2],
+              [1, -1, 0]])
+)
+
+
+"""
+Implementation adapted from https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_conversion/rgb_to_lab.py
+"""
+def rgb2lab(I):
+    m, n = I.shape[:2]
+    
+    # get LMS from RGB
+    rgb = np.reshape(I, (m * n, 3))
+    lms = np.dot(_rgb2lms, rgb.T)
+    lms[lms == 0] = np.spacing(1)
+
+    # get LAB from LMS and reshape to three channel image
+    lab = np.dot(_lms2lab, np.log(lms))
+    return np.reshape(lab.T, (m, n, 3))
diff --git a/torchstain/numpy/utils/split.py b/torchstain/numpy/utils/split.py
new file mode 100644
index 0000000..6ca19af
--- /dev/null
+++ b/torchstain/numpy/utils/split.py
@@ -0,0 +1,21 @@
+import numpy as np
+from torchstain.numpy.utils.rgb2lab import rgb2lab
+
+
+def csplit(I):
+    return np.dsplit(I, I.shape[-1])
+
+
+def cmerge(I1, I2, I3):
+    return np.dstack((I1, I2, I3))
+
+
+def lab_split(I):
+    I = rgb2lab(I)
+    I1, I2, I3 = csplit(I)
+    return I1 / 2.55, I2 - 128, I3 - 128
+
+
+def lab_merge(I1, I2, I3):
+    merged = cmerge((I1 * 2.55, I2 + 128, I3 + 128))
+    return np.clip(merged, 0, 255)
diff --git a/torchstain/numpy/utils/stats.py b/torchstain/numpy/utils/stats.py
new file mode 100644
index 0000000..e420dd7
--- /dev/null
+++ b/torchstain/numpy/utils/stats.py
@@ -0,0 +1,9 @@
+import numpy as np
+
+
+def get_mean_std(I):
+    return np.mean(I), np.std(I)
+
+
+def standardize(x, mu, std):
+    return (x - mu) / std

From ba229051afe139adec3583fa4d174ef4ee53cf45 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 19:06:13 +0100
Subject: [PATCH 36/63] reinhard works, but required opencv for rgb/lab
 conversion

---
 torchstain/numpy/__init__.py             |  1 +
 torchstain/numpy/normalizers/reinhard.py | 31 ++++++++++++++++++------
 torchstain/numpy/utils/__init__.py       |  4 +++
 torchstain/numpy/utils/lab2rgb.py        | 28 +++++++++++++++++++++
 torchstain/numpy/utils/rgb2lab.py        | 31 +++++++++++++++++-------
 torchstain/numpy/utils/split.py          | 10 ++++----
 6 files changed, 84 insertions(+), 21 deletions(-)
 create mode 100644 torchstain/numpy/utils/lab2rgb.py

diff --git a/torchstain/numpy/__init__.py b/torchstain/numpy/__init__.py
index e69de29..113e6f1 100644
--- a/torchstain/numpy/__init__.py
+++ b/torchstain/numpy/__init__.py
@@ -0,0 +1 @@
+from torchstain.numpy import normalizers, utils
diff --git a/torchstain/numpy/normalizers/reinhard.py b/torchstain/numpy/normalizers/reinhard.py
index bd36609..162d26c 100644
--- a/torchstain/numpy/normalizers/reinhard.py
+++ b/torchstain/numpy/normalizers/reinhard.py
@@ -1,8 +1,12 @@
 import numpy as np
 from torchstain.base.normalizers import HENormalizer
 from torchstain.numpy.utils.rgb2lab import rgb2lab
+from torchstain.numpy.utils.lab2rgb import lab2rgb
+from torchstain.numpy.utils.split import csplit, cmerge, lab_split, lab_merge
 from torchstain.numpy.utils.stats import get_mean_std, standardize
 
+import cv2 as cv
+
 """
 Source code adapted from:
 https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_normalization/reinhard.py
@@ -11,19 +15,32 @@
 class NumpyReinhardNormalizer(HENormalizer):
     def __init__(self):
         super().__init__()
-        # unless fit() is applied, no transfer is performed
-        self.target_mus = np.ones(3)
-        self.target_stds = np.ones(3)
+        self.target_mus = None
+        self.target_stds = None
     
     def fit(self, target):
-        self.target_mus, self.target_stds = get_mean_std(target)
+        target = target.astype("float32")
+        lab = rgb2lab(target)
+        stack_ = np.array([get_mean_std(x) for x in lab_split(lab)])
+        self.target_means = stack_[:, 0]
+        self.target_stds = stack_[:, 1]
 
     def normalize(self, I):
         # convert to LAB
         lab = rgb2lab(I)
+        labs = lab_split(lab)
 
         # get summary statistics from LAB
-        mus, stds = get_mean_std(lab)
+        stack_ = np.array([get_mean_std(x) for x in labs])
+        mus = stack_[:, 0]
+        stds = stack_[:, 1]
+
+        # standardize intensities channel-wise and normalize using target mus and stds
+        result = [standardize(x, mu_, std_) * std_T + mu_T for x, mu_, std_, mu_T, std_T \
+            in zip(labs, mus, stds, self.target_means, self.target_stds)]
+        
+        # rebuild LAB
+        lab = lab_merge(*result)
 
-        # standardize intensities channel-wise and normalize using target means and standard deviations (one for each channel)
-        return np.dstack([standardize(x, mu_, std_) for x, mu, std_, mu_T, std_T in zip(csplit(lab), mus, stds, target_mus, target_stds)])
+        # finally, convert back to RGB from LAB
+        return lab2rgb(lab)
diff --git a/torchstain/numpy/utils/__init__.py b/torchstain/numpy/utils/__init__.py
index e69de29..24a91f1 100644
--- a/torchstain/numpy/utils/__init__.py
+++ b/torchstain/numpy/utils/__init__.py
@@ -0,0 +1,4 @@
+from torchstain.numpy.utils.rgb2lab import rgb2lab
+from torchstain.numpy.utils.lab2rgb import lab2rgb
+from torchstain.numpy.utils.split import *
+from torchstain.numpy.utils.stats import *
diff --git a/torchstain/numpy/utils/lab2rgb.py b/torchstain/numpy/utils/lab2rgb.py
new file mode 100644
index 0000000..4045781
--- /dev/null
+++ b/torchstain/numpy/utils/lab2rgb.py
@@ -0,0 +1,28 @@
+import numpy as np
+from torchstain.numpy.utils.rgb2lab import _lms2lab, _rgb2lms
+import cv2 as cv
+
+
+_lms2rgb = np.linalg.inv(_rgb2lms)
+_lab2lms = np.linalg.inv(_lms2lab)
+
+
+def lab2rgb(lab):
+    return cv.cvtColor(np.clip(lab, 0, 255).astype("uint8"), cv.COLOR_LAB2RGB)
+
+
+"""
+Implementation adapted from https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_conversion/lab_to_rgb.py
+"""
+def lab2rgbX(lab):
+    m, n, c = lab.shape
+    
+    # get LMS from LAB
+    lab = np.reshape(lab, (m * n, c))
+    lms = np.dot(_lab2lms, lab.T)
+
+    # get RGB from LMS and reshape to three channel image
+    lms = np.exp(lms)
+    lms[lms == np.spacing(1)] = 0
+    rgb = np.dot(_lms2rgb, lms)
+    return np.reshape(rgb.T, (m, n, c))
diff --git a/torchstain/numpy/utils/rgb2lab.py b/torchstain/numpy/utils/rgb2lab.py
index 6706424..f7d227f 100644
--- a/torchstain/numpy/utils/rgb2lab.py
+++ b/torchstain/numpy/utils/rgb2lab.py
@@ -1,10 +1,12 @@
 import numpy as np
+import cv2 as cv
+import skimage
 
 
 # constant conversion matrices between color spaces
-_rgb2lms = np.array([[0.3811, 0.5783, 0.0402],
-                     [0.1967, 0.7244, 0.0782],
-                     [0.0241, 0.1288, 0.8444]])
+_rgb2lms = np.array([[0.412453, 0.357580, 0.180423],
+                     [0.212671, 0.715160, 0.072169],
+                     [0.019334, 0.119193, 0.950227]])
 
 _lms2lab = np.dot(
     np.array([[1 / (3 ** 0.5), 0, 0],
@@ -16,17 +18,28 @@
 )
 
 
+def rgb2lab(rgb):
+    return cv.cvtColor(rgb.astype("uint8"), cv.COLOR_RGB2LAB)
+
+
+def rgb2labX(rgb):
+    return skimage.color.rgb2lab(rgb)
+
+
 """
 Implementation adapted from https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_conversion/rgb_to_lab.py
 """
-def rgb2lab(I):
-    m, n = I.shape[:2]
-    
+def rgb2labY(rgb):
+    # rgb = rgb.astype("float32")
+    m, n, c = rgb.shape
+
     # get LMS from RGB
-    rgb = np.reshape(I, (m * n, 3))
+    rgb = np.reshape(rgb, (m * n, c))
     lms = np.dot(_rgb2lms, rgb.T)
     lms[lms == 0] = np.spacing(1)
 
-    # get LAB from LMS and reshape to three channel image
+    # get LAB from LMS and reshape to 3-channel image
     lab = np.dot(_lms2lab, np.log(lms))
-    return np.reshape(lab.T, (m, n, 3))
+    lab = np.reshape(lab.T, (m, n, c))
+
+    return lab[..., 0] * 255/100, lab[..., 1] + 128, lab[..., 2] + 128
diff --git a/torchstain/numpy/utils/split.py b/torchstain/numpy/utils/split.py
index 6ca19af..e2f2b60 100644
--- a/torchstain/numpy/utils/split.py
+++ b/torchstain/numpy/utils/split.py
@@ -1,21 +1,21 @@
 import numpy as np
 from torchstain.numpy.utils.rgb2lab import rgb2lab
+import cv2 as cv
 
 
 def csplit(I):
-    return np.dsplit(I, I.shape[-1])
+    return [I[..., i] for i in range(I.shape[-1])]
 
 
 def cmerge(I1, I2, I3):
-    return np.dstack((I1, I2, I3))
+    return np.stack([I1, I2, I3], axis=-1)
 
 
 def lab_split(I):
-    I = rgb2lab(I)
+    I = I.astype("float32")
     I1, I2, I3 = csplit(I)
     return I1 / 2.55, I2 - 128, I3 - 128
 
 
 def lab_merge(I1, I2, I3):
-    merged = cmerge((I1 * 2.55, I2 + 128, I3 + 128))
-    return np.clip(merged, 0, 255)
+    return cmerge(I1 * 2.55, I2 + 128, I3 + 128)

From 657d136e21dc3b0fe034b79e3babd4bd19c6390a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 20:03:40 +0100
Subject: [PATCH 37/63] replaced opencv with scikit-image -> towards numpy only

---
 torchstain/numpy/utils/lab2rgb.py | 10 +++++-
 torchstain/numpy/utils/rgb2lab.py | 57 +++++++++++++++++++++++++++----
 2 files changed, 59 insertions(+), 8 deletions(-)

diff --git a/torchstain/numpy/utils/lab2rgb.py b/torchstain/numpy/utils/lab2rgb.py
index 4045781..60a550b 100644
--- a/torchstain/numpy/utils/lab2rgb.py
+++ b/torchstain/numpy/utils/lab2rgb.py
@@ -1,16 +1,24 @@
 import numpy as np
 from torchstain.numpy.utils.rgb2lab import _lms2lab, _rgb2lms
 import cv2 as cv
+import skimage
 
 
 _lms2rgb = np.linalg.inv(_rgb2lms)
 _lab2lms = np.linalg.inv(_lms2lab)
 
 
-def lab2rgb(lab):
+def lab2rgbY(lab):
     return cv.cvtColor(np.clip(lab, 0, 255).astype("uint8"), cv.COLOR_LAB2RGB)
 
 
+def lab2rgb(lab):
+    lab[..., 0] /= 2.55
+    lab[..., 1] -= 128
+    lab[..., 2] -= 128
+    return skimage.color.lab2rgb(lab)
+
+
 """
 Implementation adapted from https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_conversion/lab_to_rgb.py
 """
diff --git a/torchstain/numpy/utils/rgb2lab.py b/torchstain/numpy/utils/rgb2lab.py
index f7d227f..12e898a 100644
--- a/torchstain/numpy/utils/rgb2lab.py
+++ b/torchstain/numpy/utils/rgb2lab.py
@@ -4,10 +4,15 @@
 
 
 # constant conversion matrices between color spaces
-_rgb2lms = np.array([[0.412453, 0.357580, 0.180423],
+# https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L392
+_rgb2xyz = np.array([[0.412453, 0.357580, 0.180423],
                      [0.212671, 0.715160, 0.072169],
                      [0.019334, 0.119193, 0.950227]])
 
+# "D65": {'2': (0.95047, 1., 1.08883),   # This was: `lab_ref_white`
+#              '10': (0.94809667673716, 1, 1.0730513595166162),
+#              'R': (0.9532057125493769, 1, 1.0853843816469158)},
+
 _lms2lab = np.dot(
     np.array([[1 / (3 ** 0.5), 0, 0],
               [0, 1 / (6 ** 0.5), 0],
@@ -18,19 +23,57 @@
 )
 
 
-def rgb2lab(rgb):
+def rgb2labCV(rgb):
     return cv.cvtColor(rgb.astype("uint8"), cv.COLOR_RGB2LAB)
 
 
-def rgb2labX(rgb):
-    return skimage.color.rgb2lab(rgb)
+def rgb2labY(rgb):
+    rgb = rgb.astype("float32") / 255
+    x = skimage.color.rgb2lab(rgb)
+
+    # OpenCV format
+    x[..., 0] *= 2.55
+    x[..., 1] += 128
+    x[..., 2] += 128
+    return x
+
+
+"""
+Implementation adapted from https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L704
+"""
+def rgb2lab(rgb):
+    # convert rgb -> xyz color domain
+    arr = rgb.copy()
+    mask = arr > 0.04045
+    arr[mask] = np.power((arr[mask] + 0.055) / 1.055, 2.4)
+    arr[~mask] /= 12.92
+    xyz = np.dot(arr, _rgb2xyz.T.astype(arr.dtype))
+
+    # convert xyz -> lab color domain
+
+    # scale by CIE XYZ tristimulus values of the reference white point
+    arr = arr / np.array((0.95047, 1., 1.08883))
+
+    # Nonlinear distortion and linear transformation
+    mask = arr > 0.008856
+    arr[mask] = np.cbrt(arr[mask])
+    arr[~mask] = 7.787 * arr[~mask] + 16. / 116.
+
+    x, y, z = arr[..., 0], arr[..., 1], arr[..., 2]
+
+    # Vector scaling
+    L = (116. * y) - 16.
+    a = 500.0 * (x - y)
+    b = 200.0 * (y - z)
+    return np.concatenate([x[..., np.newaxis] for x in [L, a, b]], axis=-1)
 
 
 """
 Implementation adapted from https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_conversion/rgb_to_lab.py
 """
-def rgb2labY(rgb):
-    # rgb = rgb.astype("float32")
+def rgb2lab_old(rgb):
+    # rgb = rgb.astype("float32") / 255
+    rgb = rgb.astype("float32")
     m, n, c = rgb.shape
 
     # get LMS from RGB
@@ -42,4 +85,4 @@ def rgb2labY(rgb):
     lab = np.dot(_lms2lab, np.log(lms))
     lab = np.reshape(lab.T, (m, n, c))
 
-    return lab[..., 0] * 255/100, lab[..., 1] + 128, lab[..., 2] + 128
+    return lab

From c2c46255952e2d76940591d1117f56bb79fa274d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 20:24:04 +0100
Subject: [PATCH 38/63] numpy rgb2lab working

---
 torchstain/numpy/utils/rgb2lab.py | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/torchstain/numpy/utils/rgb2lab.py b/torchstain/numpy/utils/rgb2lab.py
index 12e898a..d38d33f 100644
--- a/torchstain/numpy/utils/rgb2lab.py
+++ b/torchstain/numpy/utils/rgb2lab.py
@@ -9,6 +9,8 @@
                      [0.212671, 0.715160, 0.072169],
                      [0.019334, 0.119193, 0.950227]])
 
+_rgb2lms = _rgb2xyz
+
 # "D65": {'2': (0.95047, 1., 1.08883),   # This was: `lab_ref_white`
 #              '10': (0.94809667673716, 1, 1.0730513595166162),
 #              'R': (0.9532057125493769, 1, 1.0853843816469158)},
@@ -42,6 +44,9 @@ def rgb2labY(rgb):
 Implementation adapted from https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L704
 """
 def rgb2lab(rgb):
+    # first normalize
+    rgb = rgb.astype("float32") / 255
+
     # convert rgb -> xyz color domain
     arr = rgb.copy()
     mask = arr > 0.04045
@@ -49,10 +54,9 @@ def rgb2lab(rgb):
     arr[~mask] /= 12.92
     xyz = np.dot(arr, _rgb2xyz.T.astype(arr.dtype))
 
-    # convert xyz -> lab color domain
-
     # scale by CIE XYZ tristimulus values of the reference white point
-    arr = arr / np.array((0.95047, 1., 1.08883))
+    arr = xyz.copy()
+    arr = arr / np.asarray((0.95047, 1., 1.08883), dtype=xyz.dtype)
 
     # Nonlinear distortion and linear transformation
     mask = arr > 0.008856
@@ -65,6 +69,13 @@ def rgb2lab(rgb):
     L = (116. * y) - 16.
     a = 500.0 * (x - y)
     b = 200.0 * (y - z)
+
+    # OpenCV format
+    L *= 2.55
+    a += 128
+    b += 128
+
+    # finally, get LAB color domain
     return np.concatenate([x[..., np.newaxis] for x in [L, a, b]], axis=-1)
 
 

From 39bb65a6be5cd5e61b13b8a729e3fc459f567cde Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 20:25:11 +0100
Subject: [PATCH 39/63] refactored rgb2lab

---
 torchstain/numpy/utils/rgb2lab.py | 58 +++----------------------------
 1 file changed, 4 insertions(+), 54 deletions(-)

diff --git a/torchstain/numpy/utils/rgb2lab.py b/torchstain/numpy/utils/rgb2lab.py
index d38d33f..f5b012f 100644
--- a/torchstain/numpy/utils/rgb2lab.py
+++ b/torchstain/numpy/utils/rgb2lab.py
@@ -3,45 +3,15 @@
 import skimage
 
 
-# constant conversion matrices between color spaces
-# https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L392
+# constant conversion matrices between color spaces: https://gist.github.com/bikz05/6fd21c812ef6ebac66e1
 _rgb2xyz = np.array([[0.412453, 0.357580, 0.180423],
                      [0.212671, 0.715160, 0.072169],
                      [0.019334, 0.119193, 0.950227]])
 
-_rgb2lms = _rgb2xyz
-
-# "D65": {'2': (0.95047, 1., 1.08883),   # This was: `lab_ref_white`
-#              '10': (0.94809667673716, 1, 1.0730513595166162),
-#              'R': (0.9532057125493769, 1, 1.0853843816469158)},
-
-_lms2lab = np.dot(
-    np.array([[1 / (3 ** 0.5), 0, 0],
-              [0, 1 / (6 ** 0.5), 0],
-              [0, 0, 1 / (2 ** 0.5)]]),
-    np.array([[1, 1, 1],
-              [1, 1, -2],
-              [1, -1, 0]])
-)
-
-
-def rgb2labCV(rgb):
-    return cv.cvtColor(rgb.astype("uint8"), cv.COLOR_RGB2LAB)
-
-
-def rgb2labY(rgb):
-    rgb = rgb.astype("float32") / 255
-    x = skimage.color.rgb2lab(rgb)
-
-    # OpenCV format
-    x[..., 0] *= 2.55
-    x[..., 1] += 128
-    x[..., 2] += 128
-    return x
-
-
 """
-Implementation adapted from https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L704
+Implementation adapted from:
+https://gist.github.com/bikz05/6fd21c812ef6ebac66e1
+https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L704
 """
 def rgb2lab(rgb):
     # first normalize
@@ -77,23 +47,3 @@ def rgb2lab(rgb):
 
     # finally, get LAB color domain
     return np.concatenate([x[..., np.newaxis] for x in [L, a, b]], axis=-1)
-
-
-"""
-Implementation adapted from https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_conversion/rgb_to_lab.py
-"""
-def rgb2lab_old(rgb):
-    # rgb = rgb.astype("float32") / 255
-    rgb = rgb.astype("float32")
-    m, n, c = rgb.shape
-
-    # get LMS from RGB
-    rgb = np.reshape(rgb, (m * n, c))
-    lms = np.dot(_rgb2lms, rgb.T)
-    lms[lms == 0] = np.spacing(1)
-
-    # get LAB from LMS and reshape to 3-channel image
-    lab = np.dot(_lms2lab, np.log(lms))
-    lab = np.reshape(lab.T, (m, n, c))
-
-    return lab

From a597208215c9022d488642aa19e598bf14d7f88e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 20:28:32 +0100
Subject: [PATCH 40/63] removed sklearn + opencv from rgb2lab

---
 torchstain/numpy/utils/__init__.py |  4 ++--
 torchstain/numpy/utils/lab2rgb.py  | 22 ++--------------------
 torchstain/numpy/utils/rgb2lab.py  |  2 --
 3 files changed, 4 insertions(+), 24 deletions(-)

diff --git a/torchstain/numpy/utils/__init__.py b/torchstain/numpy/utils/__init__.py
index 24a91f1..f440077 100644
--- a/torchstain/numpy/utils/__init__.py
+++ b/torchstain/numpy/utils/__init__.py
@@ -1,4 +1,4 @@
-from torchstain.numpy.utils.rgb2lab import rgb2lab
-from torchstain.numpy.utils.lab2rgb import lab2rgb
+from torchstain.numpy.utils.rgb2lab import *
+from torchstain.numpy.utils.lab2rgb import *
 from torchstain.numpy.utils.split import *
 from torchstain.numpy.utils.stats import *
diff --git a/torchstain/numpy/utils/lab2rgb.py b/torchstain/numpy/utils/lab2rgb.py
index 60a550b..0672234 100644
--- a/torchstain/numpy/utils/lab2rgb.py
+++ b/torchstain/numpy/utils/lab2rgb.py
@@ -1,11 +1,10 @@
 import numpy as np
-from torchstain.numpy.utils.rgb2lab import _lms2lab, _rgb2lms
+from torchstain.numpy.utils.rgb2lab import _rgb2xyz
 import cv2 as cv
 import skimage
 
 
-_lms2rgb = np.linalg.inv(_rgb2lms)
-_lab2lms = np.linalg.inv(_lms2lab)
+_xyz2rgb = np.linalg.inv(_rgb2xyz)
 
 
 def lab2rgbY(lab):
@@ -17,20 +16,3 @@ def lab2rgb(lab):
     lab[..., 1] -= 128
     lab[..., 2] -= 128
     return skimage.color.lab2rgb(lab)
-
-
-"""
-Implementation adapted from https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_conversion/lab_to_rgb.py
-"""
-def lab2rgbX(lab):
-    m, n, c = lab.shape
-    
-    # get LMS from LAB
-    lab = np.reshape(lab, (m * n, c))
-    lms = np.dot(_lab2lms, lab.T)
-
-    # get RGB from LMS and reshape to three channel image
-    lms = np.exp(lms)
-    lms[lms == np.spacing(1)] = 0
-    rgb = np.dot(_lms2rgb, lms)
-    return np.reshape(rgb.T, (m, n, c))
diff --git a/torchstain/numpy/utils/rgb2lab.py b/torchstain/numpy/utils/rgb2lab.py
index f5b012f..f367f0d 100644
--- a/torchstain/numpy/utils/rgb2lab.py
+++ b/torchstain/numpy/utils/rgb2lab.py
@@ -1,6 +1,4 @@
 import numpy as np
-import cv2 as cv
-import skimage
 
 
 # constant conversion matrices between color spaces: https://gist.github.com/bikz05/6fd21c812ef6ebac66e1

From e8907d557167cb5814a92e881d142b91ccbc0f9d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 20:40:43 +0100
Subject: [PATCH 41/63] lab2rgb working, refactored to pure numpy

---
 torchstain/numpy/normalizers/reinhard.py |  8 ++++--
 torchstain/numpy/utils/lab2rgb.py        | 35 +++++++++++++++++++-----
 torchstain/numpy/utils/rgb2lab.py        |  1 +
 3 files changed, 34 insertions(+), 10 deletions(-)

diff --git a/torchstain/numpy/normalizers/reinhard.py b/torchstain/numpy/normalizers/reinhard.py
index 162d26c..cdb0332 100644
--- a/torchstain/numpy/normalizers/reinhard.py
+++ b/torchstain/numpy/normalizers/reinhard.py
@@ -5,7 +5,6 @@
 from torchstain.numpy.utils.split import csplit, cmerge, lab_split, lab_merge
 from torchstain.numpy.utils.stats import get_mean_std, standardize
 
-import cv2 as cv
 
 """
 Source code adapted from:
@@ -42,5 +41,8 @@ def normalize(self, I):
         # rebuild LAB
         lab = lab_merge(*result)
 
-        # finally, convert back to RGB from LAB
-        return lab2rgb(lab)
+        # convert back to RGB from LAB
+        lab = lab2rgb(lab)
+
+        # rescale to [0, 255] uint8
+        return (lab * 255).astype("uint8")
diff --git a/torchstain/numpy/utils/lab2rgb.py b/torchstain/numpy/utils/lab2rgb.py
index 0672234..0665b63 100644
--- a/torchstain/numpy/utils/lab2rgb.py
+++ b/torchstain/numpy/utils/lab2rgb.py
@@ -1,18 +1,39 @@
 import numpy as np
 from torchstain.numpy.utils.rgb2lab import _rgb2xyz
-import cv2 as cv
-import skimage
 
 
 _xyz2rgb = np.linalg.inv(_rgb2xyz)
 
 
-def lab2rgbY(lab):
-    return cv.cvtColor(np.clip(lab, 0, 255).astype("uint8"), cv.COLOR_LAB2RGB)
-
-
+"""
+Implementation is based on:
+https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L704
+"""
 def lab2rgb(lab):
+    # first rescale back from OpenCV format
     lab[..., 0] /= 2.55
     lab[..., 1] -= 128
     lab[..., 2] -= 128
-    return skimage.color.lab2rgb(lab)
+
+    # convert LAB -> XYZ color domain
+    L, a, b = lab[..., 0], lab[..., 1], lab[..., 2]
+    y = (L + 16.) / 116.
+    x = (a / 500.) + y
+    z = y - (b / 200.)
+
+    out = np.stack([x, y, z], axis=-1)
+
+    mask = out > 0.2068966
+    out[mask] = np.power(out[mask], 3.)
+    out[~mask] = (out[~mask] - 16.0 / 116.) / 7.787
+
+    # rescale to the reference white (illuminant)
+    out *= np.array((0.95047, 1., 1.08883), dtype=out.dtype)
+    
+    # convert XYZ -> RGB color domain
+    arr = out.copy()
+    arr = np.dot(arr, _xyz2rgb.T)
+    mask = arr > 0.0031308
+    arr[mask] = 1.055 * np.power(arr[mask], 1 / 2.4) - 0.055
+    arr[~mask] *= 12.92
+    return np.clip(arr, 0, 1)
diff --git a/torchstain/numpy/utils/rgb2lab.py b/torchstain/numpy/utils/rgb2lab.py
index f367f0d..26a11eb 100644
--- a/torchstain/numpy/utils/rgb2lab.py
+++ b/torchstain/numpy/utils/rgb2lab.py
@@ -6,6 +6,7 @@
                      [0.212671, 0.715160, 0.072169],
                      [0.019334, 0.119193, 0.950227]])
 
+
 """
 Implementation adapted from:
 https://gist.github.com/bikz05/6fd21c812ef6ebac66e1

From b3f7209b5dfaf699d86dc01c06fe95eb5f4c0e0e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 20:41:36 +0100
Subject: [PATCH 42/63] removed redundant cv2 import

---
 torchstain/numpy/utils/split.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/torchstain/numpy/utils/split.py b/torchstain/numpy/utils/split.py
index e2f2b60..8423334 100644
--- a/torchstain/numpy/utils/split.py
+++ b/torchstain/numpy/utils/split.py
@@ -1,6 +1,5 @@
 import numpy as np
 from torchstain.numpy.utils.rgb2lab import rgb2lab
-import cv2 as cv
 
 
 def csplit(I):

From 74c22e390d03a92bea899e414de7bda9c5bbaaad Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 20:43:29 +0100
Subject: [PATCH 43/63] refactored to keep consistent structure

---
 torchstain/numpy/normalizers/reinhard.py | 1 -
 torchstain/numpy/utils/lab2rgb.py        | 2 --
 torchstain/numpy/utils/rgb2lab.py        | 2 --
 torchstain/numpy/utils/split.py          | 4 ----
 torchstain/numpy/utils/stats.py          | 2 --
 5 files changed, 11 deletions(-)

diff --git a/torchstain/numpy/normalizers/reinhard.py b/torchstain/numpy/normalizers/reinhard.py
index cdb0332..2a7d874 100644
--- a/torchstain/numpy/normalizers/reinhard.py
+++ b/torchstain/numpy/normalizers/reinhard.py
@@ -5,7 +5,6 @@
 from torchstain.numpy.utils.split import csplit, cmerge, lab_split, lab_merge
 from torchstain.numpy.utils.stats import get_mean_std, standardize
 
-
 """
 Source code adapted from:
 https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_normalization/reinhard.py
diff --git a/torchstain/numpy/utils/lab2rgb.py b/torchstain/numpy/utils/lab2rgb.py
index 0665b63..d14f929 100644
--- a/torchstain/numpy/utils/lab2rgb.py
+++ b/torchstain/numpy/utils/lab2rgb.py
@@ -1,10 +1,8 @@
 import numpy as np
 from torchstain.numpy.utils.rgb2lab import _rgb2xyz
 
-
 _xyz2rgb = np.linalg.inv(_rgb2xyz)
 
-
 """
 Implementation is based on:
 https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L704
diff --git a/torchstain/numpy/utils/rgb2lab.py b/torchstain/numpy/utils/rgb2lab.py
index 26a11eb..cdcec1e 100644
--- a/torchstain/numpy/utils/rgb2lab.py
+++ b/torchstain/numpy/utils/rgb2lab.py
@@ -1,12 +1,10 @@
 import numpy as np
 
-
 # constant conversion matrices between color spaces: https://gist.github.com/bikz05/6fd21c812ef6ebac66e1
 _rgb2xyz = np.array([[0.412453, 0.357580, 0.180423],
                      [0.212671, 0.715160, 0.072169],
                      [0.019334, 0.119193, 0.950227]])
 
-
 """
 Implementation adapted from:
 https://gist.github.com/bikz05/6fd21c812ef6ebac66e1
diff --git a/torchstain/numpy/utils/split.py b/torchstain/numpy/utils/split.py
index 8423334..978e9a2 100644
--- a/torchstain/numpy/utils/split.py
+++ b/torchstain/numpy/utils/split.py
@@ -1,20 +1,16 @@
 import numpy as np
 from torchstain.numpy.utils.rgb2lab import rgb2lab
 
-
 def csplit(I):
     return [I[..., i] for i in range(I.shape[-1])]
 
-
 def cmerge(I1, I2, I3):
     return np.stack([I1, I2, I3], axis=-1)
 
-
 def lab_split(I):
     I = I.astype("float32")
     I1, I2, I3 = csplit(I)
     return I1 / 2.55, I2 - 128, I3 - 128
 
-
 def lab_merge(I1, I2, I3):
     return cmerge(I1 * 2.55, I2 + 128, I3 + 128)
diff --git a/torchstain/numpy/utils/stats.py b/torchstain/numpy/utils/stats.py
index e420dd7..b43ebe1 100644
--- a/torchstain/numpy/utils/stats.py
+++ b/torchstain/numpy/utils/stats.py
@@ -1,9 +1,7 @@
 import numpy as np
 
-
 def get_mean_std(I):
     return np.mean(I), np.std(I)
 
-
 def standardize(x, mu, std):
     return (x - mu) / std

From 25f1602053a492acb7118434e6cf1eb70d0c87b9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 21:00:10 +0100
Subject: [PATCH 44/63] refactored

---
 torchstain/numpy/normalizers/reinhard.py | 10 +++++++++-
 torchstain/numpy/utils/lab2rgb.py        |  1 +
 torchstain/numpy/utils/rgb2lab.py        |  3 +--
 3 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/torchstain/numpy/normalizers/reinhard.py b/torchstain/numpy/normalizers/reinhard.py
index 2a7d874..fbb8d13 100644
--- a/torchstain/numpy/normalizers/reinhard.py
+++ b/torchstain/numpy/normalizers/reinhard.py
@@ -17,13 +17,21 @@ def __init__(self):
         self.target_stds = None
     
     def fit(self, target):
-        target = target.astype("float32")
+        # normalize
+        target = target.astype("float32") / 255
+
+        # convert to LAB
         lab = rgb2lab(target)
+
+        # get summary statistics
         stack_ = np.array([get_mean_std(x) for x in lab_split(lab)])
         self.target_means = stack_[:, 0]
         self.target_stds = stack_[:, 1]
 
     def normalize(self, I):
+        # normalize
+        I = I.astype("float32") / 255
+        
         # convert to LAB
         lab = rgb2lab(I)
         labs = lab_split(lab)
diff --git a/torchstain/numpy/utils/lab2rgb.py b/torchstain/numpy/utils/lab2rgb.py
index d14f929..ddcd6c0 100644
--- a/torchstain/numpy/utils/lab2rgb.py
+++ b/torchstain/numpy/utils/lab2rgb.py
@@ -8,6 +8,7 @@
 https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L704
 """
 def lab2rgb(lab):
+    lab = lab.astype("float32")
     # first rescale back from OpenCV format
     lab[..., 0] /= 2.55
     lab[..., 1] -= 128
diff --git a/torchstain/numpy/utils/rgb2lab.py b/torchstain/numpy/utils/rgb2lab.py
index cdcec1e..e0edbed 100644
--- a/torchstain/numpy/utils/rgb2lab.py
+++ b/torchstain/numpy/utils/rgb2lab.py
@@ -11,8 +11,7 @@
 https://github.com/scikit-image/scikit-image/blob/00177e14097237ef20ed3141ed454bc81b308f82/skimage/color/colorconv.py#L704
 """
 def rgb2lab(rgb):
-    # first normalize
-    rgb = rgb.astype("float32") / 255
+    rgb = rgb.astype("float32")
 
     # convert rgb -> xyz color domain
     arr = rgb.copy()

From 5970a45df8a5a38615f9ab30023d353eb1282314 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 21:00:30 +0100
Subject: [PATCH 45/63] fixed spacing

---
 tests/test_torch.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/test_torch.py b/tests/test_torch.py
index f4363e7..74b6fd2 100644
--- a/tests/test_torch.py
+++ b/tests/test_torch.py
@@ -9,7 +9,6 @@
 from torchvision import transforms
 from skimage.metrics import structural_similarity as ssim
 
-
 def setup_function(fn):
     print("torch version:", torch.__version__, "torchvision version:", torchvision.__version__)
 

From 5cac2a50cd2929ac31f34790fc318e57e33b0160 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 21:00:48 +0100
Subject: [PATCH 46/63] started to add unit test for rgb/lab conversion

---
 tests/test_color_conv.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 tests/test_color_conv.py

diff --git a/tests/test_color_conv.py b/tests/test_color_conv.py
new file mode 100644
index 0000000..7261307
--- /dev/null
+++ b/tests/test_color_conv.py
@@ -0,0 +1,15 @@
+from torchstain.numpy.utils.rgb2lab import rgb2lab
+from torchstain.numpy.utils.lab2rgb import lab2rgb
+import numpy as np
+import cv2
+import os
+
+def test_rgb_to_lab():
+    size = 1024
+    curr_file_path = os.path.dirname(os.path.realpath(__file__))
+    img = cv2.resize(cv2.cvtColor(cv2.imread(os.path.join(curr_file_path, "../data/source.png")), cv2.COLOR_BGR2RGB), (size, size))
+
+    reconstructed_img = lab2rgb(rgb2lab(img))
+    val = np.mean(np.abs(reconstructed_img - img))
+    print("MAE:", val)
+    assert val < 0.1

From 5738b1faa68290342d29449bb1ebb29b8fefa6cc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Wed, 23 Nov 2022 21:07:52 +0100
Subject: [PATCH 47/63] added comment of Reinhard to README

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f777711..5e40dd6 100644
--- a/README.md
+++ b/README.md
@@ -6,11 +6,11 @@
 [![DOI](https://zenodo.org/badge/323590093.svg)](https://zenodo.org/badge/latestdoi/323590093)
 
 
-
 GPU-accelerated stain normalization tools for histopathological images. Compatible with PyTorch, TensorFlow, and Numpy.
 Normalization algorithms currently implemented:
 
 - Macenko et al. [\[1\]](#reference) (ported from [numpy implementation](https://github.com/schaugf/HEnorm_python))
+- Reinhard et al. [\[2\]](#reference) (experimental numpy support)
 
 ## Installation
 
@@ -72,6 +72,7 @@ Results with 10 runs per size on a Intel(R) Core(TM) i5-8365U CPU @ 1.60GHz
 ## Reference
 
 - [1] Macenko, Marc, et al. "A method for normalizing histology slides for quantitative analysis." 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2009.
+- [2] Reinhard, Erik, et al. "Color transfer between images." IEEE Computer Graphics and Applications. IEEE, 2001.
 
 
 ## Citing

From ee360c650f91a0a8554e76d5eb77d7049311684b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Thu, 24 Nov 2022 09:24:37 +0100
Subject: [PATCH 48/63] added Reinhard to alg table

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 5e40dd6..c848af6 100644
--- a/README.md
+++ b/README.md
@@ -50,6 +50,7 @@ norm, H, E = normalizer.normalize(I=t_to_transform, stains=True)
 | Algorithm | numpy | torch | tensorflow |
 |-|-|-|-|
 | Macenko | &check; | &check; | &check; |
+| Reinhard | &check; | &cross; | &cross; |
 
 
 ## Backend comparison

From 1ea3bade79336b092c515c8a6c0ce97eb14ed24c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Thu, 24 Nov 2022 12:06:49 +0100
Subject: [PATCH 49/63] added convenience functions for TF reinhard

---
 torchstain/tf/utils/split.py | 16 ++++++++++++++++
 torchstain/tf/utils/stats.py |  7 +++++++
 2 files changed, 23 insertions(+)
 create mode 100644 torchstain/tf/utils/split.py
 create mode 100644 torchstain/tf/utils/stats.py

diff --git a/torchstain/tf/utils/split.py b/torchstain/tf/utils/split.py
new file mode 100644
index 0000000..226cde0
--- /dev/null
+++ b/torchstain/tf/utils/split.py
@@ -0,0 +1,16 @@
+import tensorflow as tf
+from torchstain.tf.utils.rgb2lab import rgb2lab
+
+def csplit(I):
+    return [I[..., i] for i in range(I.shape[-1])]
+
+def cmerge(I1, I2, I3):
+    return tf.stack([I1, I2, I3], axis=-1)
+
+def lab_split(I):
+    I = tf.cast(I, tf.float32)
+    I1, I2, I3 = csplit(I)
+    return I1 / 2.55, I2 - 128, I3 - 128
+
+def lab_merge(I1, I2, I3):
+    return cmerge(I1 * 2.55, I2 + 128, I3 + 128)
diff --git a/torchstain/tf/utils/stats.py b/torchstain/tf/utils/stats.py
new file mode 100644
index 0000000..962afd5
--- /dev/null
+++ b/torchstain/tf/utils/stats.py
@@ -0,0 +1,7 @@
+import tensorflow as tf
+
+def get_mean_std(I):
+    return tf.math.reduce_mean(I), tf.math.reduce_std(I)
+
+def standardize(x, mu, std):
+    return (x - mu) / std

From fbf06275ca3c7392659a26773f2619ddab829bed Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Thu, 24 Nov 2022 12:07:03 +0100
Subject: [PATCH 50/63] added RGB/LAB converters for TF

---
 torchstain/tf/utils/__init__.py |  4 +++
 torchstain/tf/utils/rgb2lab.py  | 45 +++++++++++++++++++++++++++++++++
 2 files changed, 49 insertions(+)
 create mode 100644 torchstain/tf/utils/rgb2lab.py

diff --git a/torchstain/tf/utils/__init__.py b/torchstain/tf/utils/__init__.py
index 253a3d0..3e2035c 100644
--- a/torchstain/tf/utils/__init__.py
+++ b/torchstain/tf/utils/__init__.py
@@ -1,3 +1,7 @@
 from torchstain.tf.utils.cov import cov
 from torchstain.tf.utils.percentile import percentile
 from torchstain.tf.utils.solveLS import solveLS
+from torchstain.tf.utils.stats import *
+from torchstain.tf.utils.split import *
+from torchstain.tf.utils.rgb2lab import *
+from torchstain.tf.utils.lab2rgb import *
diff --git a/torchstain/tf/utils/rgb2lab.py b/torchstain/tf/utils/rgb2lab.py
new file mode 100644
index 0000000..d679a63
--- /dev/null
+++ b/torchstain/tf/utils/rgb2lab.py
@@ -0,0 +1,45 @@
+import tensorflow as tf
+
+# constant conversion matrices between color spaces: https://gist.github.com/bikz05/6fd21c812ef6ebac66e1
+_rgb2xyz = tf.constant([[0.412453, 0.357580, 0.180423],
+                        [0.212671, 0.715160, 0.072169],
+                        [0.019334, 0.119193, 0.950227]])
+                        
+_white = tf.constant([0.95047, 1., 1.08883])
+
+def rgb2lab(rgb):
+    arr = tf.cast(rgb, tf.float32)
+
+    # convert rgb -> xyz color domain
+    mask = arr > 0.04045
+    not_mask = tf.math.logical_not(mask)
+    arr = tf.tensor_scatter_nd_update(arr, tf.where(mask), tf.math.pow((tf.boolean_mask(arr, mask) + 0.055) / 1.055, 2.4))
+    arr = tf.tensor_scatter_nd_update(arr, tf.where(not_mask), tf.boolean_mask(arr, not_mask) / 12.92)
+
+    xyz = arr @ tf.cast(tf.transpose(_rgb2xyz), arr.dtype)
+
+    # scale by CIE XYZ tristimulus values of the reference white point
+    arr = tf.identity(xyz)
+    arr = arr / tf.cast(_white, xyz.dtype)
+
+    # nonlinear distortion and linear transformation
+    mask = arr > 0.008856
+    not_mask = tf.math.logical_not(mask)
+    arr = tf.tensor_scatter_nd_update(arr, tf.where(mask), tf.math.pow(tf.boolean_mask(arr, mask), 1.0 / 3.0))
+    arr = tf.tensor_scatter_nd_update(arr, tf.where(not_mask), 7.787 * tf.boolean_mask(arr, not_mask) + 16 / 116)
+
+    # get each channel as individual tensors
+    x, y, z = arr[..., 0], arr[..., 1], arr[..., 2]
+
+    # vector scaling
+    L = (116. * y) - 16.
+    a = 500.0 * (x - y)
+    b = 200.0 * (y - z)
+
+    # OpenCV format
+    L *= 2.55
+    a += 128
+    b += 128
+
+    # finally, get LAB color domain
+    return tf.stack([L, a, b], axis=-1)

From c379f436544c9f10a6ff5d0fed56b10d731fb255 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Thu, 24 Nov 2022 12:07:13 +0100
Subject: [PATCH 51/63] forgot lab2rgb

---
 torchstain/tf/utils/lab2rgb.py | 36 ++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 torchstain/tf/utils/lab2rgb.py

diff --git a/torchstain/tf/utils/lab2rgb.py b/torchstain/tf/utils/lab2rgb.py
new file mode 100644
index 0000000..b1d2638
--- /dev/null
+++ b/torchstain/tf/utils/lab2rgb.py
@@ -0,0 +1,36 @@
+import tensorflow as tf
+from torchstain.tf.utils.rgb2lab import _rgb2xyz, _white
+
+_xyz2rgb = tf.linalg.inv(_rgb2xyz)
+
+def lab2rgb(lab):
+    lab = tf.cast(lab, tf.float32)
+    
+    # rescale back from OpenCV format and extract LAB channel
+    L, a, b = lab[..., 0] / 2.55, lab[..., 1] - 128, lab[..., 2] - 128
+
+    # vector scaling to produce X, Y, Z
+    y = (L + 16.) / 116.
+    x = (a / 500.) + y
+    z = y - (b / 200.)
+
+    # merge back to get reconstructed XYZ color image
+    out = tf.stack([x, y, z], axis=-1)
+
+    # apply boolean transforms
+    mask = out > 0.2068966
+    not_mask = tf.math.logical_not(mask)
+    out = tf.tensor_scatter_nd_update(out, tf.where(mask), tf.pow(tf.boolean_mask(out, mask), 3))
+    out = tf.tensor_scatter_nd_update(out, tf.where(not_mask), (tf.boolean_mask(out, not_mask) - 16 / 116) / 7.787)
+
+    # rescale to the reference white (illuminant)
+    out = out * tf.cast(_white, out.dtype)
+    
+    # convert XYZ -> RGB color domain
+    arr = tf.identity(out)
+    arr = arr @ tf.transpose(_xyz2rgb)
+    mask = arr > 0.0031308
+    not_mask = tf.math.logical_not(mask)
+    arr = tf.tensor_scatter_nd_update(arr, tf.where(mask), 1.055 * tf.pow(tf.boolean_mask(arr, mask), 1 / 2.4) - 0.055)
+    arr = tf.tensor_scatter_nd_update(arr, tf.where(not_mask), tf.boolean_mask(out, not_mask) * 12.92)
+    return tf.clip_by_value(arr, 0, 1)

From 87dc6356d40ebd9a230058922eb2d4ffeea8f13f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Thu, 24 Nov 2022 12:07:48 +0100
Subject: [PATCH 52/63] added TF Reinhard

---
 torchstain/tf/normalizers/reinhard.py | 55 +++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 torchstain/tf/normalizers/reinhard.py

diff --git a/torchstain/tf/normalizers/reinhard.py b/torchstain/tf/normalizers/reinhard.py
new file mode 100644
index 0000000..8a2b601
--- /dev/null
+++ b/torchstain/tf/normalizers/reinhard.py
@@ -0,0 +1,55 @@
+import tensorflow as tf
+from torchstain.base.normalizers import HENormalizer
+from torchstain.tf.utils.rgb2lab import rgb2lab
+from torchstain.tf.utils.lab2rgb import lab2rgb
+from torchstain.tf.utils.split import csplit, cmerge, lab_split, lab_merge
+from torchstain.tf.utils.stats import get_mean_std, standardize
+
+"""
+Source code adapted from:
+https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_normalization/reinhard.py
+https://github.com/Peter554/StainTools/blob/master/staintools/reinhard_color_normalizer.py
+"""
+class TensorFlowReinhardNormalizer(HENormalizer):
+    def __init__(self):
+        super().__init__()
+        self.target_mus = None
+        self.target_stds = None
+    
+    def fit(self, target):
+        # normalize
+        target = tf.cast(target, tf.float32) / 255
+
+        # convert to LAB
+        lab = rgb2lab(target)
+
+        # get summary statistics
+        stack_ = tf.convert_to_tensor([get_mean_std(x) for x in lab_split(lab)])
+        self.target_means = stack_[:, 0]
+        self.target_stds = stack_[:, 1]
+
+    def normalize(self, I):
+        # normalize
+        I = tf.cast(I, tf.float32) / 255
+        
+        # convert to LAB
+        lab = rgb2lab(I)
+        labs = lab_split(lab)
+
+        # get summary statistics from LAB
+        stack_ = tf.convert_to_tensor([get_mean_std(x) for x in labs])
+        mus = stack_[:, 0]
+        stds = stack_[:, 1]
+
+        # standardize intensities channel-wise and normalize using target mus and stds
+        result = [standardize(x, mu_, std_) * std_T + mu_T for x, mu_, std_, mu_T, std_T \
+            in zip(labs, mus, stds, self.target_means, self.target_stds)]
+        
+        # rebuild LAB
+        lab = lab_merge(*result)
+
+        # convert back to RGB from LAB
+        lab = lab2rgb(lab)
+
+        # rescale to [0, 255] uint8
+        return tf.cast(lab * 255, tf.uint8)

From f30f02a9e788586696c47c2a6dc489f70a41126e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Thu, 24 Nov 2022 12:07:59 +0100
Subject: [PATCH 53/63] Reinhard TF works - added it to API

---
 torchstain/base/normalizers/reinhard.py | 3 ++-
 torchstain/tf/normalizers/__init__.py   | 3 ++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/torchstain/base/normalizers/reinhard.py b/torchstain/base/normalizers/reinhard.py
index f6bb6ff..96947aa 100644
--- a/torchstain/base/normalizers/reinhard.py
+++ b/torchstain/base/normalizers/reinhard.py
@@ -5,6 +5,7 @@ def ReinhardNormalizer(backend='numpy'):
     elif backend == "torch":
         raise NotImplementedError
     elif backend == "tensorflow":
-        raise NotImplementedError
+        from torchstain.tf.normalizers import TensorFlowReinhardNormalizer
+        return TensorFlowReinhardNormalizer()
     else:
         raise Exception(f'Unknown backend {backend}')
diff --git a/torchstain/tf/normalizers/__init__.py b/torchstain/tf/normalizers/__init__.py
index f835e4d..0916e1b 100644
--- a/torchstain/tf/normalizers/__init__.py
+++ b/torchstain/tf/normalizers/__init__.py
@@ -1 +1,2 @@
-from torchstain.tf.normalizers.macenko import TensorFlowMacenkoNormalizer
\ No newline at end of file
+from torchstain.tf.normalizers.macenko import TensorFlowMacenkoNormalizer
+from torchstain.tf.normalizers.reinhard import TensorFlowReinhardNormalizer
\ No newline at end of file

From 193041d349f37c8c206993768902621d961dd66f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Thu, 24 Nov 2022 12:15:22 +0100
Subject: [PATCH 54/63] updated README

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index c848af6..f26c796 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@ GPU-accelerated stain normalization tools for histopathological images. Compatib
 Normalization algorithms currently implemented:
 
 - Macenko et al. [\[1\]](#reference) (ported from [numpy implementation](https://github.com/schaugf/HEnorm_python))
-- Reinhard et al. [\[2\]](#reference) (experimental numpy support)
+- Reinhard et al. [\[2\]](#reference) (only numpy & TensorFlow backend support)
 
 ## Installation
 
@@ -50,7 +50,7 @@ norm, H, E = normalizer.normalize(I=t_to_transform, stains=True)
 | Algorithm | numpy | torch | tensorflow |
 |-|-|-|-|
 | Macenko | &check; | &check; | &check; |
-| Reinhard | &check; | &cross; | &cross; |
+| Reinhard | &check; | &cross; | &check; |
 
 
 ## Backend comparison

From d029fe47ed28b0f8408d22c6cc92389523d383dc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Fri, 25 Nov 2022 14:20:37 +0100
Subject: [PATCH 55/63] moved result.png to data/

---
 README.md                     |   2 +-
 result.png => data/result.png | Bin
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename result.png => data/result.png (100%)

diff --git a/README.md b/README.md
index f26c796..75976ea 100644
--- a/README.md
+++ b/README.md
@@ -43,7 +43,7 @@ t_to_transform = T(to_transform)
 norm, H, E = normalizer.normalize(I=t_to_transform, stains=True)
 ```
 
-![alt text](result.png)
+![alt text](data/result.png)
 
 ## Implemented algorithms
 
diff --git a/result.png b/data/result.png
similarity index 100%
rename from result.png
rename to data/result.png

From 946db087a87895fc7e641c05b4a2e3042a294445 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Fri, 25 Nov 2022 14:22:33 +0100
Subject: [PATCH 56/63] minor refactoring in README

---
 README.md | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/README.md b/README.md
index 75976ea..9047b49 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,6 @@
 [![Pip Downloads](https://img.shields.io/pypi/dm/torchstain?label=pip%20downloads&logo=python)](https://pypi.org/project/torchstain/)
 [![DOI](https://zenodo.org/badge/323590093.svg)](https://zenodo.org/badge/latestdoi/323590093)
 
-
 GPU-accelerated stain normalization tools for histopathological images. Compatible with PyTorch, TensorFlow, and Numpy.
 Normalization algorithms currently implemented:
 
@@ -52,12 +51,10 @@ norm, H, E = normalizer.normalize(I=t_to_transform, stains=True)
 | Macenko | &check; | &check; | &check; |
 | Reinhard | &check; | &cross; | &check; |
 
-
 ## Backend comparison
 
 Results with 10 runs per size on a Intel(R) Core(TM) i5-8365U CPU @ 1.60GHz
 
-
 |   size | numpy avg. time   | torch avg. time   | tf avg. time     |
 |--------|-------------------|-------------------|------------------|
 |    224 | 0.0182s ± 0.0016  | 0.0180s ± 0.0390  | 0.0048s ± 0.0002 |
@@ -69,18 +66,15 @@ Results with 10 runs per size on a Intel(R) Core(TM) i5-8365U CPU @ 1.60GHz
 |   1568 | 1.1935s ± 0.0739  | 0.2590s ± 0.0088  | 0.2531s ± 0.0031 |
 |   1792 | 1.4523s ± 0.0207  | 0.3402s ± 0.0114  | 0.3080s ± 0.0188 |
 
-
 ## Reference
 
 - [1] Macenko, Marc, et al. "A method for normalizing histology slides for quantitative analysis." 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2009.
 - [2] Reinhard, Erik, et al. "Color transfer between images." IEEE Computer Graphics and Applications. IEEE, 2001.
 
-
 ## Citing
 
 If you find this software useful for your research, please cite it as: 
 
-
 ```bibtex
 @software{barbano2022torchstain,
   author       = {Carlo Alberto Barbano and

From bde067e20d1b8df05412b7ae1da45cb64df0b638 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Sat, 3 Dec 2022 23:55:35 +0100
Subject: [PATCH 57/63] added torch reinhard base support

---
 torchstain/base/normalizers/reinhard.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/torchstain/base/normalizers/reinhard.py b/torchstain/base/normalizers/reinhard.py
index 96947aa..f96f087 100644
--- a/torchstain/base/normalizers/reinhard.py
+++ b/torchstain/base/normalizers/reinhard.py
@@ -3,7 +3,8 @@ def ReinhardNormalizer(backend='numpy'):
         from torchstain.numpy.normalizers import NumpyReinhardNormalizer
         return NumpyReinhardNormalizer()
     elif backend == "torch":
-        raise NotImplementedError
+        from torchstain.torch.normalizers import TorchReinhardNormalizer
+        return TorchReinhardNormalizer()
     elif backend == "tensorflow":
         from torchstain.tf.normalizers import TensorFlowReinhardNormalizer
         return TensorFlowReinhardNormalizer()

From 03d499a65c5c033c303c640187b134aa9787f5a6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Sat, 3 Dec 2022 23:56:18 +0100
Subject: [PATCH 58/63] removed redundant import and added newline in tf

---
 torchstain/tf/normalizers/__init__.py | 2 +-
 torchstain/tf/utils/split.py          | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/torchstain/tf/normalizers/__init__.py b/torchstain/tf/normalizers/__init__.py
index 0916e1b..fb0718f 100644
--- a/torchstain/tf/normalizers/__init__.py
+++ b/torchstain/tf/normalizers/__init__.py
@@ -1,2 +1,2 @@
 from torchstain.tf.normalizers.macenko import TensorFlowMacenkoNormalizer
-from torchstain.tf.normalizers.reinhard import TensorFlowReinhardNormalizer
\ No newline at end of file
+from torchstain.tf.normalizers.reinhard import TensorFlowReinhardNormalizer
diff --git a/torchstain/tf/utils/split.py b/torchstain/tf/utils/split.py
index 226cde0..5717b87 100644
--- a/torchstain/tf/utils/split.py
+++ b/torchstain/tf/utils/split.py
@@ -1,5 +1,4 @@
 import tensorflow as tf
-from torchstain.tf.utils.rgb2lab import rgb2lab
 
 def csplit(I):
     return [I[..., i] for i in range(I.shape[-1])]

From ce443d861cc845b2167ef080d2e203fb1931a3c6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Sat, 3 Dec 2022 23:56:46 +0100
Subject: [PATCH 59/63] added all utils for torch

---
 torchstain/torch/utils/__init__.py |  4 +++
 torchstain/torch/utils/lab2rgb.py  | 35 ++++++++++++++++++++++++
 torchstain/torch/utils/rgb2lab.py  | 44 ++++++++++++++++++++++++++++++
 torchstain/torch/utils/split.py    | 15 ++++++++++
 torchstain/torch/utils/stats.py    |  7 +++++
 5 files changed, 105 insertions(+)
 create mode 100644 torchstain/torch/utils/lab2rgb.py
 create mode 100644 torchstain/torch/utils/rgb2lab.py
 create mode 100644 torchstain/torch/utils/split.py
 create mode 100644 torchstain/torch/utils/stats.py

diff --git a/torchstain/torch/utils/__init__.py b/torchstain/torch/utils/__init__.py
index 5e0de3e..4acea5a 100644
--- a/torchstain/torch/utils/__init__.py
+++ b/torchstain/torch/utils/__init__.py
@@ -1,2 +1,6 @@
 from torchstain.torch.utils.cov import cov
 from torchstain.torch.utils.percentile import percentile
+from torchstain.torch.utils.stats import *
+from torchstain.torch.utils.split import *
+from torchstain.torch.utils.rgb2lab import *
+from torchstain.torch.utils.lab2rgb import *
diff --git a/torchstain/torch/utils/lab2rgb.py b/torchstain/torch/utils/lab2rgb.py
new file mode 100644
index 0000000..623be42
--- /dev/null
+++ b/torchstain/torch/utils/lab2rgb.py
@@ -0,0 +1,35 @@
+import torch
+from torchstain.torch.utils.rgb2lab import _rgb2xyz, _white
+
+_xyz2rgb = torch.linalg.inv(_rgb2xyz)
+
+def lab2rgb(lab):
+    lab = lab.type(torch.float32)
+    
+    # rescale back from OpenCV format and extract LAB channel
+    L, a, b = lab[0] / 2.55, lab[1] - 128, lab[2] - 128
+
+    # vector scaling to produce X, Y, Z
+    y = (L + 16.) / 116.
+    x = (a / 500.) + y
+    z = y - (b / 200.)
+
+    # merge back to get reconstructed XYZ color image
+    out = torch.stack([x, y, z], axis=0)
+
+    # apply boolean transforms
+    mask = out > 0.2068966
+    not_mask = torch.logical_not(mask)
+    out.masked_scatter_(mask, torch.pow(out, 3))
+    out.masked_scatter_(not_mask, (out - 16 / 116) / 7.787)
+
+    # rescale to the reference white (illuminant)
+    out = torch.mul(out, _white.type(out.dtype).unsqueeze(dim=-1).unsqueeze(dim=-1))
+
+    # convert XYZ -> RGB color domain
+    arr = torch.tensordot(out, torch.t(_xyz2rgb).type(out.dtype), dims=([0], [0]))
+    mask = arr > 0.0031308
+    not_mask = torch.logical_not(mask)
+    arr.masked_scatter(mask, 1.055 * torch.pow(arr, 1 / 2.4) - 0.055)
+    arr.masked_scatter(not_mask, arr * 12.92)
+    return torch.clamp(arr, 0, 1)
diff --git a/torchstain/torch/utils/rgb2lab.py b/torchstain/torch/utils/rgb2lab.py
new file mode 100644
index 0000000..1a2d9a8
--- /dev/null
+++ b/torchstain/torch/utils/rgb2lab.py
@@ -0,0 +1,44 @@
+import torch
+
+# constant conversion matrices between color spaces: https://gist.github.com/bikz05/6fd21c812ef6ebac66e1
+_rgb2xyz = torch.tensor([[0.412453, 0.357580, 0.180423],
+                        [0.212671, 0.715160, 0.072169],
+                        [0.019334, 0.119193, 0.950227]])
+                        
+_white = torch.tensor([0.95047, 1., 1.08883])
+
+def rgb2lab(rgb):
+    arr = rgb.type(torch.float32)
+
+    # convert rgb -> xyz color domain
+    mask = arr > 0.04045
+    not_mask = torch.logical_not(mask)
+    arr.masked_scatter_(mask, torch.pow((arr[mask] + 0.055) / 1.055, 2.4))
+    arr.masked_scatter_(not_mask, mask / 12.92)
+
+    xyz = torch.tensordot(torch.t(_rgb2xyz), arr, dims=([0], [0]))
+
+    # scale by CIE XYZ tristimulus values of the reference white point
+    arr = torch.mul(xyz, 1 / _white.unsqueeze(dim=-1).unsqueeze(dim=-1))
+
+    # nonlinear distortion and linear transformation
+    mask = arr > 0.008856
+    not_mask = torch.logical_not(mask)
+    arr.masked_scatter_(mask, torch.pow(arr[mask], 1. / 3.))
+    arr.masked_scatter_(not_mask, 7.787 * arr[~mask] + 16. / 166.)
+
+    # get each channel as individual tensors
+    x, y, z = arr[0], arr[1], arr[2]
+
+    # vector scaling
+    L = (116. * y) - 16.
+    a = 500.0 * (x - y)
+    b = 200.0 * (y - z)
+
+    # OpenCV format
+    L *= 2.55
+    a += 128
+    b += 128
+
+    # finally, get LAB color domain
+    return torch.stack([L, a, b], axis=0)
diff --git a/torchstain/torch/utils/split.py b/torchstain/torch/utils/split.py
new file mode 100644
index 0000000..a9c0dda
--- /dev/null
+++ b/torchstain/torch/utils/split.py
@@ -0,0 +1,15 @@
+import torch
+
+def csplit(I):
+    return [I[i] for i in range(I.shape[0])]
+
+def cmerge(I1, I2, I3):
+    return torch.stack([I1, I2, I3], dim=0)
+
+def lab_split(I):
+    I = I.type(torch.FloatTensor)
+    I1, I2, I3 = csplit(I)
+    return I1 / 2.55, I2 - 128, I3 - 128
+
+def lab_merge(I1, I2, I3):
+    return cmerge(I1 * 2.55, I2 + 128, I3 + 128)
diff --git a/torchstain/torch/utils/stats.py b/torchstain/torch/utils/stats.py
new file mode 100644
index 0000000..0fa45bb
--- /dev/null
+++ b/torchstain/torch/utils/stats.py
@@ -0,0 +1,7 @@
+import torch
+
+def get_mean_std(I):
+    return torch.mean(I), torch.std(I)
+
+def standardize(x, mu, std):
+    return (x - mu) / std

From 18b539eb395b8eafd4f8bc5f5b56860f700c2fe8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Sat, 3 Dec 2022 23:57:51 +0100
Subject: [PATCH 60/63] torch reinhard almost working

---
 torchstain/torch/normalizers/__init__.py |  1 +
 torchstain/torch/normalizers/reinhard.py | 55 ++++++++++++++++++++++++
 2 files changed, 56 insertions(+)
 create mode 100644 torchstain/torch/normalizers/reinhard.py

diff --git a/torchstain/torch/normalizers/__init__.py b/torchstain/torch/normalizers/__init__.py
index febcd90..c78c273 100644
--- a/torchstain/torch/normalizers/__init__.py
+++ b/torchstain/torch/normalizers/__init__.py
@@ -1 +1,2 @@
 from torchstain.torch.normalizers.macenko import TorchMacenkoNormalizer
+from torchstain.torch.normalizers.reinhard import TorchReinhardNormalizer
diff --git a/torchstain/torch/normalizers/reinhard.py b/torchstain/torch/normalizers/reinhard.py
new file mode 100644
index 0000000..0970764
--- /dev/null
+++ b/torchstain/torch/normalizers/reinhard.py
@@ -0,0 +1,55 @@
+import torch
+from torchstain.base.normalizers import HENormalizer
+from torchstain.torch.utils.rgb2lab import rgb2lab
+from torchstain.torch.utils.lab2rgb import lab2rgb
+from torchstain.torch.utils.split import csplit, cmerge, lab_split, lab_merge
+from torchstain.torch.utils.stats import get_mean_std, standardize
+
+"""
+Source code adapted from:
+https://github.com/DigitalSlideArchive/HistomicsTK/blob/master/histomicstk/preprocessing/color_normalization/reinhard.py
+https://github.com/Peter554/StainTools/blob/master/staintools/reinhard_color_normalizer.py
+"""
+class TorchReinhardNormalizer(HENormalizer):
+    def __init__(self):
+        super().__init__()
+        self.target_mus = None
+        self.target_stds = None
+    
+    def fit(self, target):
+        # normalize
+        target = target.type(torch.float32) / 255
+
+        # convert to LAB
+        lab = rgb2lab(target)
+
+        # get summary statistics
+        stack_ = torch.tensor([get_mean_std(x) for x in lab_split(lab)])
+        self.target_means = stack_[:, 0]
+        self.target_stds = stack_[:, 1]
+
+    def normalize(self, I):
+        # normalize
+        I = I.type(torch.float32) / 255
+        
+        # convert to LAB
+        lab = rgb2lab(I)
+        labs = lab_split(lab)
+
+        # get summary statistics from LAB
+        stack_ = torch.tensor([get_mean_std(x) for x in labs])
+        mus = stack_[:, 0]
+        stds = stack_[:, 1]
+
+        # standardize intensities channel-wise and normalize using target mus and stds
+        result = [standardize(x, mu_, std_) * std_T + mu_T for x, mu_, std_, mu_T, std_T \
+            in zip(labs, mus, stds, self.target_means, self.target_stds)]
+        
+        # rebuild LAB
+        lab = lab_merge(*result)
+
+        # convert back to RGB from LAB
+        lab = lab2rgb(lab)
+
+        # rescale to [0, 255] uint8
+        return (lab * 255).type(torch.uint8)

From ede14d71051d945bb42efa9a13243ecd907c851f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Sun, 4 Dec 2022 00:15:35 +0100
Subject: [PATCH 61/63] fixed bug - torch reinhard works

---
 torchstain/torch/utils/lab2rgb.py |  8 ++++----
 torchstain/torch/utils/rgb2lab.py | 10 +++++-----
 torchstain/torch/utils/split.py   |  2 +-
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/torchstain/torch/utils/lab2rgb.py b/torchstain/torch/utils/lab2rgb.py
index 623be42..250c1c9 100644
--- a/torchstain/torch/utils/lab2rgb.py
+++ b/torchstain/torch/utils/lab2rgb.py
@@ -20,8 +20,8 @@ def lab2rgb(lab):
     # apply boolean transforms
     mask = out > 0.2068966
     not_mask = torch.logical_not(mask)
-    out.masked_scatter_(mask, torch.pow(out, 3))
-    out.masked_scatter_(not_mask, (out - 16 / 116) / 7.787)
+    out.masked_scatter_(mask, torch.pow(torch.masked_select(out, mask), 3))
+    out.masked_scatter_(not_mask, (torch.masked_select(out, not_mask) - 16 / 116) / 7.787)
 
     # rescale to the reference white (illuminant)
     out = torch.mul(out, _white.type(out.dtype).unsqueeze(dim=-1).unsqueeze(dim=-1))
@@ -30,6 +30,6 @@ def lab2rgb(lab):
     arr = torch.tensordot(out, torch.t(_xyz2rgb).type(out.dtype), dims=([0], [0]))
     mask = arr > 0.0031308
     not_mask = torch.logical_not(mask)
-    arr.masked_scatter(mask, 1.055 * torch.pow(arr, 1 / 2.4) - 0.055)
-    arr.masked_scatter(not_mask, arr * 12.92)
+    arr.masked_scatter_(mask, 1.055 * torch.pow(torch.masked_select(arr, mask), 1 / 2.4) - 0.055)
+    arr.masked_scatter_(not_mask, torch.masked_select(arr, not_mask) * 12.92)
     return torch.clamp(arr, 0, 1)
diff --git a/torchstain/torch/utils/rgb2lab.py b/torchstain/torch/utils/rgb2lab.py
index 1a2d9a8..3b1aa50 100644
--- a/torchstain/torch/utils/rgb2lab.py
+++ b/torchstain/torch/utils/rgb2lab.py
@@ -13,19 +13,19 @@ def rgb2lab(rgb):
     # convert rgb -> xyz color domain
     mask = arr > 0.04045
     not_mask = torch.logical_not(mask)
-    arr.masked_scatter_(mask, torch.pow((arr[mask] + 0.055) / 1.055, 2.4))
-    arr.masked_scatter_(not_mask, mask / 12.92)
+    arr.masked_scatter_(mask, torch.pow((torch.masked_select(arr, mask) + 0.055) / 1.055, 2.4))
+    arr.masked_scatter_(not_mask, torch.masked_select(arr, not_mask) / 12.92)
 
     xyz = torch.tensordot(torch.t(_rgb2xyz), arr, dims=([0], [0]))
 
     # scale by CIE XYZ tristimulus values of the reference white point
-    arr = torch.mul(xyz, 1 / _white.unsqueeze(dim=-1).unsqueeze(dim=-1))
+    arr = torch.mul(xyz, 1 / _white.type(xyz.dtype).unsqueeze(dim=-1).unsqueeze(dim=-1))
 
     # nonlinear distortion and linear transformation
     mask = arr > 0.008856
     not_mask = torch.logical_not(mask)
-    arr.masked_scatter_(mask, torch.pow(arr[mask], 1. / 3.))
-    arr.masked_scatter_(not_mask, 7.787 * arr[~mask] + 16. / 166.)
+    arr.masked_scatter_(mask, torch.pow(torch.masked_select(arr, mask), 1 / 3))
+    arr.masked_scatter_(not_mask, 7.787 * torch.masked_select(arr, not_mask) + 16 / 166)
 
     # get each channel as individual tensors
     x, y, z = arr[0], arr[1], arr[2]
diff --git a/torchstain/torch/utils/split.py b/torchstain/torch/utils/split.py
index a9c0dda..d6f6fdb 100644
--- a/torchstain/torch/utils/split.py
+++ b/torchstain/torch/utils/split.py
@@ -7,7 +7,7 @@ def cmerge(I1, I2, I3):
     return torch.stack([I1, I2, I3], dim=0)
 
 def lab_split(I):
-    I = I.type(torch.FloatTensor)
+    I = I.type(torch.float32)
     I1, I2, I3 = csplit(I)
     return I1 / 2.55, I2 - 128, I3 - 128
 

From 51f944825fff19551a7ca3580f04651b49bd5952 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Sun, 4 Dec 2022 00:19:07 +0100
Subject: [PATCH 62/63] updated README as all backends are supported with
 Reinhard

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 9047b49..d16b5ea 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ GPU-accelerated stain normalization tools for histopathological images. Compatib
 Normalization algorithms currently implemented:
 
 - Macenko et al. [\[1\]](#reference) (ported from [numpy implementation](https://github.com/schaugf/HEnorm_python))
-- Reinhard et al. [\[2\]](#reference) (only numpy & TensorFlow backend support)
+- Reinhard et al. [\[2\]](#reference)
 
 ## Installation
 
@@ -49,7 +49,7 @@ norm, H, E = normalizer.normalize(I=t_to_transform, stains=True)
 | Algorithm | numpy | torch | tensorflow |
 |-|-|-|-|
 | Macenko | &check; | &check; | &check; |
-| Reinhard | &check; | &cross; | &check; |
+| Reinhard | &check; | &check; | &check; |
 
 ## Backend comparison
 

From 3af74774838a907a9d8491b65242559927cd1a13 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andr=C3=A9=20Pedersen?= <andrped94@gmail.com>
Date: Sun, 4 Dec 2022 00:26:41 +0100
Subject: [PATCH 63/63] added reinhard test for all backends

---
 tests/test_tf.py    | 30 +++++++++++++++++++++++++++++-
 tests/test_torch.py | 35 +++++++++++++++++++++++++++++++++--
 2 files changed, 62 insertions(+), 3 deletions(-)

diff --git a/tests/test_tf.py b/tests/test_tf.py
index b24757a..6098e37 100644
--- a/tests/test_tf.py
+++ b/tests/test_tf.py
@@ -22,7 +22,7 @@ def test_percentile():
 
     np.testing.assert_almost_equal(p_np, p_t)
 
-def test_normalize_tf():
+def test_macenko_tf():
     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))
@@ -49,3 +49,31 @@ def test_normalize_tf():
 
     # assess whether the normalized images are identical across backends
     np.testing.assert_almost_equal(ssim(result_numpy.flatten(), result_tf.flatten()), 1.0, decimal=4, verbose=True)
+
+def test_reinhard_tf():
+    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 = lambda x: tf.convert_to_tensor(x, dtype=tf.float32)
+    t_to_transform = T(to_transform)
+
+    # initialize normalizers for each backend and fit to target image
+    normalizer = torchstain.normalizers.ReinhardNormalizer(backend='numpy')
+    normalizer.fit(target)
+
+    tf_normalizer = torchstain.normalizers.ReinhardNormalizer(backend='tensorflow')
+    tf_normalizer.fit(T(target))
+
+    # transform
+    result_numpy = normalizer.normalize(I=to_transform)
+    result_tf = tf_normalizer.normalize(I=t_to_transform)
+
+    # convert to numpy and set dtype
+    result_numpy = result_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_tf.flatten()), 1.0, decimal=4, verbose=True)
diff --git a/tests/test_torch.py b/tests/test_torch.py
index 74b6fd2..0a7e99a 100644
--- a/tests/test_torch.py
+++ b/tests/test_torch.py
@@ -27,7 +27,7 @@ def test_percentile():
 
     np.testing.assert_almost_equal(p_np, p_t)
 
-def test_normalize_torch():
+def test_macenko_torch():
     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))
@@ -36,7 +36,7 @@ def test_normalize_torch():
     # setup preprocessing and preprocess image to be normalized
     T = transforms.Compose([
         transforms.ToTensor(),
-        transforms.Lambda(lambda x: x*255)
+        transforms.Lambda(lambda x: x * 255)
     ])
     t_to_transform = T(to_transform)
 
@@ -57,3 +57,34 @@ def test_normalize_torch():
 
     # 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)
+
+def test_reinhard_torch():
+    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.normalizers.ReinhardNormalizer(backend='numpy')
+    normalizer.fit(target)
+
+    torch_normalizer = torchstain.normalizers.ReinhardNormalizer(backend='torch')
+    torch_normalizer.fit(T(target))
+
+    # transform
+    result_numpy = normalizer.normalize(I=to_transform)
+    result_torch = torch_normalizer.normalize(I=t_to_transform)
+
+    # convert to numpy and set dtype
+    result_numpy = result_numpy.astype("float32")
+    result_torch = result_torch.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)