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stardist · Apr 23, 2024 · d9869d5 · d9869d5
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diff --git a/examples/other2D/bioimageio.ipynb b/examples/other2D/bioimageio.ipynb
@@ -158,7 +158,7 @@
      "text": [
       "Help on function export_bioimageio in module stardist.bioimageio_utils:\n",
       "\n",
-      "export_bioimageio(model, outpath, test_input, test_input_axes=None, test_input_norm_axes='ZYX', name=None, mode='tensorflow_saved_model_bundle', min_percentile=1.0, max_percentile=99.8, overwrite_spec_kwargs=None)\n",
+      "export_bioimageio(model, outpath, test_input, test_input_axes=None, test_input_norm_axes='ZYX', name=None, mode='tensorflow_saved_model_bundle', min_percentile=1.0, max_percentile=99.8, overwrite_spec_kwargs=None, generate_default_deps=False)\n",
       "    Export stardist model into bioimage.io format, https://github.com/bioimage-io/spec-bioimage-io.\n",
       "    \n",
       "    Parameters\n",
@@ -186,6 +186,10 @@
       "        max percentile to be used for image normalization (default: 99.8)\n",
       "    overwrite_spec_kwargs: dict or None\n",
       "        spec keywords that should be overloaded (default: None)\n",
+      "    generate_default_deps: bool\n",
+      "        not required for bioimage.io, i.e. StarDist models don't need a dependencies field in rdf.yaml (default: False)\n",
+      "        if True, generate an environment.yaml file recording the python, bioimageio.core, stardist and tensorflow requirements\n",
+      "        from which a conda environment can be recreated to run this export\n",
       "\n"
      ]
     }

diff --git a/examples/other2D/multiclass.ipynb b/examples/other2D/multiclass.ipynb
@@ -107,7 +107,7 @@
     "# generate synthetic samples of 2D images, label masks, and class dicts \n",
     "\n",
     "def generate_sample(n=256):\n",
-    "    from scipy.ndimage.morphology import distance_transform_edt\n",
+    "    from scipy.ndimage import distance_transform_edt\n",
     "    from skimage.segmentation import watershed, relabel_sequential \n",
     "    from skimage.morphology import disk, binary_erosion, binary_closing\n",
     "    from scipy import ndimage as ndi\n",
@@ -128,13 +128,13 @@
     "    m1 = np.isin(y,c1)\n",
     "    m2 = np.isin(y,c2)    \n",
     "    \n",
-    "    x =  ndi.filters.gaussian_filter((y>0).astype(np.float32),2)\n",
+    "    x =  ndi.gaussian_filter((y>0).astype(np.float32),2)\n",
     "        \n",
     "    noise1 = ndi.zoom(np.random.uniform(0,1,(n//4,n//4)), (4,4), order=3)\n",
     "    noise2 = ndi.zoom(np.random.uniform(0,1,(n//16,n//16)), (16,16), order=3)\n",
     "    x[m1] = .2*(1+np.sin(y[m1]))+noise1[m1]\n",
     "    x[m2] = .2*(1+np.sin(y[m2]))+noise2[m2]\n",
-    "    x = ndi.filters.gaussian_filter(x,1)+2*ndi.filters.gaussian_filter(x,20)\n",
+    "    x = ndi.gaussian_filter(x,1)+2*ndi.gaussian_filter(x,20)\n",
     "    gain = 100 \n",
     "    x = np.random.poisson((1+gain*x).astype(int))/gain\n",
     "    x += .08*np.random.normal(0,1,x.shape)\n",

diff --git a/examples/other2D/predict_big_data.ipynb b/examples/other2D/predict_big_data.ipynb
@@ -38,7 +38,7 @@
     "%matplotlib inline\n",
     "%config InlineBackend.figure_format = 'retina'\n",
     "\n",
-    "from tifffile import imread, imsave\n",
+    "from tifffile import imread, imwrite as imsave\n",
     "from csbdeep.utils import Path, normalize\n",
     "from csbdeep.utils.tf import keras_import\n",
     "keras = keras_import()\n",
@@ -453,7 +453,7 @@
    "source": [
     "from zipfile import ZIP_DEFLATED\n",
     "\n",
-    "imsave(str(datadir/'labels.tif'), labels, compress=ZIP_DEFLATED)\n",
+    "imsave(str(datadir/'labels.tif'), labels, compression=ZIP_DEFLATED)\n",
     "export_imagej_rois(str(datadir/'labels_roi.zip'), polys['coord'], compression=ZIP_DEFLATED)"
    ]
   },