Updated the mednist notebooks adding the dependencies of pydicom and highdicom

examples/apps/mednist_classifier_monaideploy/mednist_classifier_monaideploy.py

@@ -95,6 +95,7 @@ def compute(self, op_input: InputContext, op_output: OutputContext, context: Exe
 
 
 @md.resource(cpu=1, gpu=1, memory="1Gi")
+@md.env(pip_packages=["pydicom >= 2.3.0", "highdicom>=0.18.2", "typeguard~=2.12.1"])
 class App(Application):
     """Application class for the MedNIST classifier."""
 

examples/apps/simple_imaging_app/gaussian_operator.py

@@ -25,12 +25,18 @@ class GaussianOperator(Operator):
     """
 
     def compute(self, op_input: InputContext, op_output: OutputContext, context: ExecutionContext):
+        import numpy as np
         from skimage.filters import gaussian
         from skimage.io import imsave
 
         data_in = op_input.get().asnumpy()
         data_out = gaussian(data_in, sigma=0.2, channel_axis=2)  # Add the param introduced in 0.19.
 
+        # Make sure the data type is what PIL Image can support, as the imsave function calls PIL Image fromarray()
+        # Some details can be found at https://stackoverflow.com/questions/55319949/pil-typeerror-cannot-handle-this-data-type
+        if np.max(data_out) <= 1:
+            data_out = (data_out * 255).astype(np.uint8)
+
         output_folder = op_output.get().path
         output_path = output_folder / "final_output.png"
         imsave(output_path, data_out)