Added test for multimacenko tf vs numpy

tests/test_tf.py

@@ -5,13 +5,15 @@
 import tensorflow as tf
 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
@@ -20,6 +22,7 @@ def test_percentile():
 
     np.testing.assert_almost_equal(p_np, p_t)
 
+
 def test_macenko_tf():
     size = 1024
     curr_file_path = os.path.dirname(os.path.realpath(__file__))
@@ -48,6 +51,7 @@ def test_macenko_tf():
     # assess whether the normalized images are identical across backends
     np.testing.assert_almost_equal(result_numpy.flatten(), result_tf.flatten(), decimal=2, verbose=True)
 
+
 def test_reinhard_tf():
     size = 1024
     curr_file_path = os.path.dirname(os.path.realpath(__file__))
@@ -75,3 +79,37 @@ def test_reinhard_tf():
 
     # assess whether the normalized images are identical across backends
     np.testing.assert_almost_equal(result_numpy.flatten(), result_tf.flatten(), decimal=2, verbose=True)
+
+
+def test_multistain_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(np.moveaxis(x, -1, 0).astype("float32"))  #  * 255
+    t_to_transform = T(to_transform)
+    target_transformed = T(target)
+
+    # move channel to first
+    target_numpy = np.moveaxis(target, -1, 0)
+    to_transform_numpy = np.moveaxis(to_transform, -1, 0)
+
+    # initialize normalizers for each backend and fit to target image
+    normalizer = torchstain.normalizers.MultiMacenkoNormalizer(backend='numpy')
+    normalizer.fit([target_numpy, target_numpy, target_numpy])
+
+    tf_normalizer = torchstain.normalizers.MultiMacenkoNormalizer(backend='tensorflow')
+    tf_normalizer.fit([target_transformed, target_transformed, target_transformed])
+
+    # transform
+    result_numpy, _, _ = normalizer.normalize(I=to_transform_numpy, 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") / 255.
+    result_tf = result_tf.numpy().astype("float32") / 255.
+
+    # assess whether the normalized images are identical across backends
+    np.testing.assert_almost_equal(result_numpy.flatten(), result_tf.flatten(), decimal=2, verbose=True)