Add various improvements to nifti conversion and fix tiff-api transpose bug (#222)

* add various improvements to nifti conversion
* fix transpose error in high-level tiff api
* refactor and clean up convert-nifti code further
* format
* refactor wkw constants
* allow to flip nifti data again with --flip_axes
* refactor bounding box parsing
* format
* format
* try to fix CI by downgrading pyyaml

see: actions/runner-images#1202
* remove pyyaml dependency
* fix linting
* fix transpose again
* format

Author: philippotto

poetry.lock

@@ -10,7 +10,6 @@ python = "^3.6"
 scipy = "^1.4.0"
 numpy = "^1.17.4"
 pillow = "^6.2.1"
-pyyaml = "^5.2"
 wkw = "0.1.2"
 requests = "^2.22.0"
 cluster_tools = "1.49"

pyproject.toml

@@ -0,0 +1,27 @@
+import numpy as np
+from wkcuber.utils import pad_or_crop_to_size_and_topleft
+
+
+def test_pad_or_crop_to_size_and_topleft():
+    target_topleft = np.array((0, 50, 36, 1))
+    target_size = np.array((1, 156, 112, 30))
+
+    test_shapes = [(1, 117, 95, 16), (1, 128, 128, 16), (1, 196, 196, 12)]
+    expected_voxel_counts = [117 * 95 * 16, 128 * 112 * 16, 156 * 112 * 12]
+
+    for shape, expected_voxel_count in zip(test_shapes, expected_voxel_counts):
+        cube_data = np.ones(shape)
+        cube_data = pad_or_crop_to_size_and_topleft(
+            cube_data, target_size, target_topleft
+        )
+        assert cube_data.shape[1:3] == tuple((target_size + target_topleft)[1:3])
+
+        cube_data_from_top_left = cube_data[
+            :, target_topleft[1] :, target_topleft[2] :, :
+        ]
+        cube_data_from_top_left_to_size = cube_data_from_top_left[
+            :, : target_size[1], : target_size[2], :
+        ]
+        assert cube_data_from_top_left.shape[0:3] == tuple(target_size)[0:3]
+        assert np.sum(cube_data) == expected_voxel_count
+        assert np.sum(cube_data_from_top_left_to_size) == expected_voxel_count

tests/test_pad_or_crop.py

@@ -10,6 +10,7 @@
     TiledTiffMagDataset,
 )
 from wkcuber.mag import Mag
+from wkcuber.utils import DEFAULT_WKW_FILE_LEN
 
 
 class Layer:
@@ -68,7 +69,7 @@ def add_mag(
         if block_len is None:
             block_len = 32
         if file_len is None:
-            file_len = 32
+            file_len = DEFAULT_WKW_FILE_LEN
         if block_type is None:
             block_type = wkw.Header.BLOCK_TYPE_RAW
 

wkcuber/api/Layer.py

@@ -352,7 +352,7 @@ def assert_correct_data_format(self, data):
         if self.has_only_one_channel():
             if not len(data.shape) == 3:
                 raise AttributeError(
-                    "The shape of the provided data does not match the expected shape."
+                    "The shape of the provided data does not match the expected shape. Expected three-dimensional data shape, since target data is single-channel."
                 )
         else:
             if not len(data.shape) == 4:
@@ -399,6 +399,15 @@ def __init__(
         self.tile_size = tile_size
 
 
+def transpose_for_skimage(data):
+    if len(data.shape) == 2:
+        return data.transpose()
+    elif len(data.shape) == 3:
+        return data.transpose((1, 0, 2))
+    else:
+        raise ValueError("Cannot handle shape for data.")
+
+
 class TiffReader:
     def __init__(self, file_name):
         self.file_name = file_name
@@ -414,11 +423,12 @@ def open(cls, file_name):
         return cls(file_name)
 
     def read(self) -> np.array:
-        return io.imread(self.file_name)
+        data = io.imread(self.file_name)
+        return transpose_for_skimage(data)
 
     def write(self, pixels):
         os.makedirs(os.path.dirname(self.file_name), exist_ok=True)
-        io.imsave(self.file_name, pixels, check_contrast=False)
+        io.imsave(self.file_name, transpose_for_skimage(pixels), check_contrast=False)
 
     def merge_with_image(self, foreground_pixels, offset):
         background_pixels = self.read()

wkcuber/api/TiffData/TiffMag.py

@@ -75,12 +75,20 @@ def from_checkpoint_name(checkpoint_name: str) -> "BoundingBox":
         size = bbox_tuple[3:6]
         return BoundingBox.from_tuple2((topleft, size))
 
+    @staticmethod
+    def from_csv(csv_bbox: str) -> "BoundingBox":
+        bbox_tuple = tuple(int(x) for x in csv_bbox.split(","))
+        return BoundingBox.from_tuple6(bbox_tuple)
+
     @staticmethod
     def from_auto(obj) -> "BoundingBox":
         if isinstance(obj, BoundingBox):
             return obj
         elif isinstance(obj, str):
-            return BoundingBox.from_auto(json.loads(obj))
+            if ":" in obj:
+                return BoundingBox.from_auto(json.loads(obj))
+            else:
+                return BoundingBox.from_csv(obj)
         elif isinstance(obj, dict):
             return BoundingBox.from_wkw(obj)
         elif isinstance(obj, BoundingBoxNamedTuple):