Merge branch 'main' into mixed-dimensions

.pre-commit-config.yaml

@@ -0,0 +1,6 @@
+repos:
+- repo: https://github.com/google/yapf
+  rev: 'v0.40.2'  # Use the sha / tag you want to point at
+  hooks:
+  - id: yapf
+    exclude: docs/conf.py

src/affinder/_tests/test_affinder.py

@@ -6,6 +6,7 @@
 import zarr
 import napari
 import pytest
+from pathlib import Path
 from copy import copy
 from scipy import ndimage as ndi
 
@@ -45,6 +46,12 @@
         './src/affinder/_tests/nuclei3D_labels.zarr', mode='r'
         )  #########
 
+im0 = data.camera()
+im1 = transform.rotate(im0[100:, 32:496], 60)
+this_dir = Path(__file__).parent.absolute()
+labels0 = zarr.open(this_dir / 'labels0.zarr', mode='r')
+labels1 = zarr.open(this_dir / 'labels1.zarr', mode='r')
+
 
 def make_vector_border(layer_pts):
     vectors = np.zeros((layer_pts.shape[0], 2, layer_pts.shape[1]))
@@ -199,10 +206,12 @@ def test_2D_3D(make_napari_viewer, tmp_path, reference, moving):
     # type Points of Vectors and not same dimensions as reference layer - so l1
     # is a redundant layer that is no longer used as the real moving layer -
     # this is why we use viewer.layers['layer1] instead of l1
-    expected_affine = np.array([[1.00000000e+00,  0.00000000e+00,  0.00000000e+00, 0.00000000e+00],
-                                [0.00000000e+00, 1.000000e+00, 2.890235e-17, 0.000000e+00],
-                                [0.00000000e+00, -7.902889e-18, 1.000000e+00, 1.421085e-14],
-                                [0.00000000e+00, 0.000000e+00, 0.000000e+00, 1.000000e+00]])
+    expected_affine = np.array(
+            [[1.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
+             [0.00000000e+00, 1.000000e+00, 2.890235e-17, 0.000000e+00],
+             [0.00000000e+00, -7.902889e-18, 1.000000e+00, 1.421085e-14],
+             [0.00000000e+00, 0.000000e+00, 0.000000e+00, 1.000000e+00]]
+            )
 
     np.testing.assert_allclose(
             actual_affine, expected_affine, rtol=10, atol=1e-10
@@ -248,6 +257,20 @@ def test_apply_affine():
     np.testing.assert_allclose(res_layer[0], ref_im)
 
 
+def test_apply_affine_with_scale():
+    ref_im = np.random.random((5, 5))
+    mov_im = ndi.zoom(ref_im, 2, order=0)
+
+    ref_layer = napari.layers.Image(ref_im, scale=(0.2, 0.2))
+    mov_layer = napari.layers.Image(mov_im, scale=(0.4, 0.4))
+    mov_layer.affine = np.array([[0.25, 0, 0], [0, 0.25, 0], [0, 0, 1]])
+
+    widget = apply_affine()
+    res_layer = widget(ref_layer, mov_layer)
+
+    np.testing.assert_allclose(res_layer[0], ref_im)
+
+
 def test_apply_affine_nonimage():
     ref_im = np.random.random((5, 5))
     mov_pts = np.random.random((5, 2))
@@ -271,7 +294,4 @@ def test_load_affine(tmp_path):
     widget = load_affine()
     widget(layer, affile)
 
-    np.testing.assert_allclose(
-        layer.affine, affine
-    )
-
+    np.testing.assert_allclose(layer.affine, affine)

src/affinder/apply_tf.py

@@ -1,5 +1,6 @@
 from magicgui import magic_factory
 from skimage import transform
+from napari.utils.transforms import CompositeAffine
 import numpy as np
 
 
@@ -62,8 +63,24 @@ def apply_affine(
                 'Only image transforms supported at this point.'
                 )
 
+    reference_meta = CompositeAffine(
+            scale=reference_layer.scale,
+            translate=reference_layer.translate,
+            rotate=reference_layer.rotate,
+            shear=reference_layer.shear,
+            )
+    moving_meta = CompositeAffine(
+            scale=moving_layer.scale,
+            translate=moving_layer.translate,
+            rotate=moving_layer.rotate,
+            shear=moving_layer.shear,
+            )
     # Find the transformation relative to the reference image
-    affine = np.linalg.inv(reference_layer.affine) @ moving_layer.affine
+    affine = (
+            np.linalg.inv(reference_meta)
+            @ np.linalg.inv(reference_layer.affine) @ moving_layer.affine
+            @ moving_meta
+            )
 
     # Apply the transformation
     transformed = _apply_affine_image(
@@ -74,7 +91,7 @@ def apply_affine(
     layertype = 'image'
     ref_metadata = {
             n: getattr(reference_layer, n)
-            for n in ['scale', 'translate', 'rotate', 'shear']
+            for n in ['scale', 'translate', 'rotate', 'shear', 'affine']
             }
     mov_metadata = moving_layer.as_layer_data_tuple()[1]
     name = {'name': moving_layer.name + '_transformed'}