# cmd
conda create --name sfm python=3.8 -y
conda activate sfm
pip install "git+https://github.com/Jordan-Pierce/Metashape-Azure.git"
If you are also running Metashape locally
, you will need to run the install.py
script as well:
# cmd
python install.py
This will add dependencies to the Metashape
python environment. Finally, to run the application, use the following
command:
# cmd
metashape-azure-mls
# cmd
conda activate sfm
pip install -U "git+https://github.com/Jordan-Pierce/Metashape-Azure.git"
- Open Azure Machine Learning Studio in Microsoft Edge and login
- Navigate to your workspace
- Copy the following credentials from the top-right of the page:
- Subscription ID
- Resource Group
- Workspace Name
- Enter credentials in the application:
- Use "Save Credentials" to store for future use
- Click "Authenticate"
- Select your compute cluster from the dropdown
- Configure project paths:
- Input Path: Azure URI to image folder
- Output Path: Azure URI for project destination
- Project Name: Unique name for your project
Note:
{output path}/{project name}
must not already exist
- Select desired SfM parameters
- Click "Run on Azure" to start processing
- Azure Machine Learning Studio and Authentication must be done in
Microsoft Edge
- You must be connected to the network (i.e.,
VPN
) to access theAzure
services.