Merge pull request #153 from odinsbane/pyramid_dim_reduction

Added a failing test and the code to make it not fail.

Author: thewtex

ngff_zarr/methods/_itk.py

@@ -8,6 +8,7 @@
     _align_chunks,
     _compute_sigma,
     _dim_scale_factors,
+    _update_previous_dim_factors,
     _get_block,
     _spatial_dims,
 )
@@ -136,7 +137,9 @@ def _downsample_itk_bin_shrink(
     spatial_dims = _image_dims[: len(spatial_dims)]
     for scale_factor in scale_factors:
         dim_factors = _dim_scale_factors(dims, scale_factor, previous_dim_factors)
-        previous_dim_factors = dim_factors
+        previous_dim_factors = _update_previous_dim_factors(
+            scale_factor, spatial_dims, previous_dim_factors
+        )
         previous_image = _align_chunks(previous_image, default_chunks, dim_factors)
 
         shrink_factors = [dim_factors[sd] for sd in spatial_dims]
@@ -222,7 +225,9 @@ def _downsample_itk_gaussian(
     spatial_dims = _image_dims[: len(spatial_dims)]
     for scale_factor in scale_factors:
         dim_factors = _dim_scale_factors(dims, scale_factor, previous_dim_factors)
-        previous_dim_factors = dim_factors
+        previous_dim_factors = _update_previous_dim_factors(
+            scale_factor, spatial_dims, previous_dim_factors
+        )
         previous_image = _align_chunks(previous_image, default_chunks, dim_factors)
 
         shrink_factors = [dim_factors[sd] for sd in spatial_dims]

ngff_zarr/methods/_itkwasm.py

@@ -10,6 +10,7 @@
     _align_chunks,
     _compute_sigma,
     _dim_scale_factors,
+    _update_previous_dim_factors,
     _get_block,
     _spatial_dims,
     _spatial_dims_last_zyx,
@@ -93,7 +94,9 @@ def _downsample_itkwasm(
     transposed_dims = False
     for scale_factor in scale_factors:
         dim_factors = _dim_scale_factors(dims, scale_factor, previous_dim_factors)
-        previous_dim_factors = dim_factors
+        previous_dim_factors = _update_previous_dim_factors(
+            scale_factor, spatial_dims, previous_dim_factors
+        )
         previous_image = _align_chunks(previous_image, default_chunks, dim_factors)
         # Operate on a contiguous spatial block
         previous_image = _spatial_dims_last_zyx(previous_image)

ngff_zarr/methods/_support.py

@@ -113,6 +113,12 @@ def _dim_scale_factors(dims, scale_factor, previous_dim_factors):
         }
     return result_scale_factors
 
+def _update_previous_dim_factors(scale_factor, spatial_dims, previous_dim_factors):
+    if isinstance(scale_factor, int):
+        previous_dim_factors = { d : scale_factor for d in spatial_dims }
+    else:
+        previous_dim_factors = scale_factor
+    return previous_dim_factors
 
 def _align_chunks(previous_image, default_chunks, dim_factors):
     block_0_shape = [c[0] for c in previous_image.data.chunks]

test/test_pyramid_integrity.py

@@ -0,0 +1,33 @@
+import dask.array as da
+from ngff_zarr import Multiscales, to_multiscales, NgffImage, Methods
+
+def test_check_pyramid():
+    """
+    When creating downscaled versions of images, subsequent resolutions were
+    not correctly being calculated. This test checks that the image volume
+    occupies the same space, and it checks that the scale factors are consistent
+    with the provided scale factors.
+    """
+    data = da.zeros(shape=(512, 512))
+    for method in Methods:
+        image = NgffImage(
+            data=data,
+            dims=["y", "x"],
+            scale={"y": 0.25, "x": 0.25},
+            translation={"y": 0, "x": 0},
+        )
+        scale_factors = [2, 4, 8, 16, 32]
+        multiscales: Multiscales = to_multiscales(
+            image,
+            scale_factors=scale_factors,
+            method=method,
+        )
+        dims = image.data.shape
+
+        for sf, image in enumerate(multiscales.images):
+            if sf == 0:
+                continue
+            scales = [image.scale[k] for k in image.scale]
+            for i, d in enumerate(dims):
+                assert d * 0.25 == scales[i]*image.data.shape[i]
+                assert scales[i] == scale_factors[sf - 1] * 0.25