Fix issues for 2d ome zarr data (mobie#137)

Fix issues for 2d ome zarr data

examples/create_mobie_project.ipynb

@@ -30,12 +30,12 @@
     "# location of the input data. \n",
     "# the example data used in this notebook is available via this link:\n",
     "# https://oc.embl.de/index.php/s/IV1709ZlcUB1k99\n",
-    "example_input_data = \"/home/pape/Work/data/mobie/mobie-example-data\"\n",
+    "example_input_data = \"./mobie-example-data\"\n",
     "\n",
     "# the location of the mobie project that will be created\n",
     "# we recommend that the mobie project folders have the structure <PROECJT_ROOT_FOLDER/data>\n",
     "# the folder 'data' will contain the sub-folders for individual datasets\n",
-    "mobie_project_folder = \"/home/pape/Work/data/mobie/mobie_example_project/data\"\n",
+    "mobie_project_folder = \"./mobie_example_project/data\"\n",
     "\n",
     "# name of the dataset that will be created.\n",
     "# one project can contain multiple datasets\n",
@@ -93,17 +93,15 @@
     "# - resolution: the size of one voxel in the physical unit, this needs to be a tuple/list of length 3,\n",
     "#               specifying the size for each of the 3 spatial dimensions\n",
     "# - chunks: the size of the chunks (in voxels) that are used to store the output file.\n",
-    "#           good choices are usually (1, 512, 512) for 2d data and (64, 64, 64) for 3d data\n",
+    "#           good choices are usually (512, 512) for 2d data and (64, 64, 64) for 3d data\n",
     "# - scale_factors: the scale factors used for downsampling the input when creating the image pyramid\n",
-    "#                  this needs to be a list, where each entry specifies the scale factors for the 3 axes.\n",
-    "# Note that axes are listed in the order ZYX for the resolution, chunks and scale factors\n",
+    "#                  this needs to be a list, where each entry specifies the scale factors for the 2 or 3 axes.\n",
+    "# Note that axes are listed in the order YX / ZYX for the resolution, chunks and scale factors\n",
     "# (in the java implementation of mobie / big-data-viewer the axis convention is XYZ).\n",
-    "# Also note that the values for all three axes (ZYX) need to be specified. In the case of 2d data, the value\n",
-    "# for Z should be set to 1.\n",
     "unit = \"nanometer\"\n",
-    "resolution = (1., 10., 10.)\n",
-    "chunks = (1, 512, 512)\n",
-    "scale_factors = 4 * [[1, 2, 2]]\n",
+    "resolution = (10., 10.)\n",
+    "chunks = (512, 512)\n",
+    "scale_factors = 4 * [[2, 2]]\n",
     "\n",
     "mobie.add_image(\n",
     "    input_path=input_file, \n",
@@ -215,7 +213,8 @@
     "        target=target,\n",
     "        max_jobs=max_jobs,\n",
     "        view=view,\n",
-    "        unit=unit\n",
+    "        unit=unit,\n",
+    "        file_format=\"bdv.n5\",  # We need to use bdv.n5 as file format because we specify transformations in the metadata.\n",
     "    )"
    ]
   },
@@ -234,11 +233,10 @@
     "# We choose 'lm' as menu name, because this is a lightmicroscopy source\n",
     "menu_name = \"lm\"\n",
     "\n",
-    "# This is again a 2d image, so we set all values for Z to 1.\n",
     "unit = \"nanometer\"\n",
-    "resolution = [1., 100., 100.]\n",
-    "scale_factors = [[1, 2, 2], [1, 2, 2], [1, 2, 2]]\n",
-    "chunks = (1, 512, 512)\n",
+    "resolution = [100., 100.]\n",
+    "scale_factors = [[2, 2], [2, 2], [2, 2]]\n",
+    "chunks = (512, 512)\n",
     "\n",
     "# we set the default display color to green.\n",
     "view = mobie.metadata.get_default_view(\"image\", im_name, color=\"green\")\n",
@@ -272,9 +270,9 @@
     "\n",
     "# again, the mask is 2d\n",
     "unit = \"nanometer\"\n",
-    "chunks = [1, 256, 256]\n",
-    "resolution = [1., 160., 160.]\n",
-    "scale_factors = [[1, 2, 2]]\n",
+    "chunks = [256, 256]\n",
+    "resolution = [160., 160.]\n",
+    "scale_factors = [[2, 2]]\n",
     "\n",
     "mobie.add_image(\n",
     "    input_path=input_path,\n",
@@ -304,19 +302,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# we add a segmentation for several of the cells visible in the em-overview image\n",
     "input_path = os.path.join(example_input_data, \"em_segmentation.tif\")\n",
     "segmentation_name = \"cells\"\n",
     "\n",
     "unit = \"nanometer\"\n",
-    "resolution = [1., 30., 30.]\n",
-    "chunks = [1, 256, 256]\n",
-    "scale_factors = [[1, 2, 2], [1, 2, 2], [1, 2, 2], [1, 2, 2]]\n",
+    "resolution = [30., 30.]\n",
+    "chunks = [256, 256]\n",
+    "scale_factors = [[2, 2], [2, 2], [2, 2], [2, 2]]\n",
     "\n",
     "mobie.add_segmentation(\n",
     "    input_path=input_path,\n",
@@ -328,7 +324,8 @@
     "    resolution=resolution,\n",
     "    chunks=chunks,\n",
     "    scale_factors=scale_factors,\n",
-    "    add_default_table=True  # add the default table with the properties mobie needs to interact with table and segmentation\n",
+    "    is_2d=True,  # We need to specify that this is a 2d segmentation.\n",
+    "    add_default_table=True,  # add the default table with the properties mobie needs to interact with table and segmentation\n",
     ")"
    ]
   },
@@ -451,7 +448,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

mobie/__version__.py

@@ -1,2 +1,2 @@
-__version__ = "0.5.1"
+__version__ = "0.5.2"
 SPEC_VERSION = "0.3.0"

mobie/metadata/source_metadata.py

@@ -235,9 +235,10 @@ def add_source_to_dataset(
     overwrite=True,
     channel=None,
     suppress_warnings=False,
+    is_2d=None,
     **kwargs,
 ):
-    """ Add source metadata to a MoBIE dataset.
+    """Add source metadata to a MoBIE dataset.
 
     Arguments:
         dataset_folder [str] - path to the dataset folder.
@@ -254,6 +255,7 @@ def add_source_to_dataset(
         channel [int] - the channel to load from the data.
             Currently only supported for the ome.zarr format (default: None)
         suppress_warnings [bool] - a flag to suppress warnings raised by the metadata validation (default: False)
+        is_2d [bool] - whether this is a 2d source. (default: None)
         kwargs - additional keyword arguments for spot source
     """
     dataset_metadata = read_dataset_metadata(dataset_folder)
@@ -284,7 +286,8 @@ def add_source_to_dataset(
     else:
         raise ValueError(f"Invalid source type: {source_type}, expect one of 'image', 'segmentation' or 'spots'")
 
-    is_2d = dataset_metadata.get("is2D", False)
+    if is_2d is None:
+        is_2d = dataset_metadata.get("is2D", False)
     validate_source_metadata(source_name, source_metadata, dataset_folder, is_2d=is_2d)
     sources_metadata[source_name] = source_metadata
     dataset_metadata["sources"] = sources_metadata

mobie/segmentation.py

@@ -23,7 +23,7 @@ def add_segmentation(input_path, input_key,
                      max_jobs=multiprocessing.cpu_count(),
                      add_default_table=True, view=None,
                      postprocess_config=None, unit="micrometer",
-                     is_default_dataset=False, description=None):
+                     is_default_dataset=False, description=None, is_2d=None):
     """ Add segmentation source to MoBIE dataset.
 
     Arguments:
@@ -53,6 +53,7 @@ def add_segmentation(input_path, input_key,
         is_default_dataset [bool] - whether to set new dataset as default dataset.
             Only applies if the dataset is being created. (default: False)
         description [str] - description for this segmentation (default: None)
+        is_2d [bool] - whether this is a 2d segmentation (default: None)
     """
     if isinstance(input_path, np.ndarray):
         input_path, input_key = mobie.utils.save_temp_input(input_path, tmp_folder, segmentation_name)
@@ -96,14 +97,16 @@ def add_segmentation(input_path, input_key,
                             source_name=segmentation_name,
                             file_format=file_format)
 
+    if is_2d is None:
+        is_2d = mobie.metadata.read_dataset_metadata(dataset_folder).get("is2D", False)
     # we initialize with an already computed default table
     if isinstance(add_default_table, (str, pd.DataFrame)):
         table_folder = os.path.join(dataset_folder, "tables", segmentation_name)
         table_path = os.path.join(table_folder, "default.tsv")
         os.makedirs(table_folder, exist_ok=True)
         input_table = add_default_table
-        is_2d = mobie.metadata.read_dataset_metadata(dataset_folder).get("is2D", False)
         check_and_copy_default_table(input_table, table_path, is_2d)
+
     # compute the default segmentation table
     elif add_default_table:
         table_folder = os.path.join(dataset_folder, "tables", segmentation_name)
@@ -119,7 +122,7 @@ def add_segmentation(input_path, input_key,
     # add the segmentation to the dataset metadata
     mobie.metadata.add_source_to_dataset(dataset_folder, "segmentation",
                                          segmentation_name, image_metadata_path,
-                                         table_folder=table_folder, view=view)
+                                         table_folder=table_folder, view=view, is_2d=is_2d)
 
 
 def main():