4_azure_download_dataset.py

import azureml.core
from azureml.core import Workspace, Dataset

import os                                   # To extract path directory


# Load the workspace from the saved config file
ws = Workspace.from_config()
print('Ready to use Azure ML {} to work with {}'.format(azureml.core.VERSION, ws.name))

# Get the default datastore
default_ds = ws.get_default_datastore()

# A dataset is used to reference the data you uploaded to Azure Blob Storage.
# Datasets are an abstraction layer on top of your data that are designed to improve reliability and trustworthiness.
# From the default datastore, we want to extract the uploaded file (.mat) available in the Azure Blob Storage
# The path of the file should be the one available in the Azure Blob Container directory and not the one created locally on Azure Machine Learning > Author > Notebooks
# Dataset.File.from_files() returns a 'FileDataset' object.

#Create a file dataset from the path on the datastore (this may take a short while) for the Ground Truth Maps
files_gt = Dataset.File.from_files(path=(default_ds, 'NEMESIS_images/GroundTruthMaps/*.mat'))

# Download file paths available in the connected Azure Blob Storage.
# It returns an array with all file paths downloaded locally in a temp folder.
arrayDataset_gt = files_gt.download()

gt_path = os.path.dirname(arrayDataset_gt[0])
print(gt_path)
gt_path = gt_path + '\\'
print(gt_path)
    
#Create a file dataset from the path on the datastore (this may take a short while) for the preProcessedImages
files_preProcessed = Dataset.File.from_files(path=(default_ds, 'NEMESIS_images/preProcessedImages/*.mat'))

# Download file paths available in the connected Azure Blob Storage.
# It returns an array with all file paths downloaded locally in a temp folder.
arrayDataset_preProcessed = files_preProcessed.download()

preProcessed_path = os.path.dirname(arrayDataset_preProcessed[0])
print(preProcessed_path)
preProcessed_path = preProcessed_path + '\\'
print(preProcessed_path)