Deploy on your server:

1. change gypsum.json to your configuration
2. git clone the repo and put it to the processor_path on your server
3. create tmp/scripts folder on your processor_path
4. write your own template_path

You can also create multiple configurations, remember to change registeredServerConfig.json to your configuration file.

Run on your local machine:

1. follow the following commands to create a conda environment
   • conda create -n your_env_name python=3.8
   • conda activate your_env_name
   • pip install PyQt5
   • pip install numpy
   • pip install Pillow
   • pip install opencv-python
   • pip install opencv-contrib-python
   • pip install decord
   • pip install pexpect
   • brew install rsync / sudo apt install rsync
2. To run the program, follow the following commands
   • conda activate your_env_name
   • go to the root folder
   • python ImageBrowser.py

Transformer module specific required packages:

• optical flow:
• PWC-NET: pytorch, cupy (pip install cupy-cuda111), flow_viz (pip install flow_viz)
• RAFT: scipy

Add your own image transformation modules:

1. create your own class derived from Transform_base class
2. implement getArgParser and processImageCollection methods in your own class
3. register your class to registeredTransformer.json