This repo aims to automate the workflow to generate timelapses and do shoreline mapping with CoastSnap (Spotteron) images. Check out `workflow.pptx` for an informative schematic of the intended workflow. Batch downloading code is an extension on the Leaman CoastSnap Toolbox (https://git.wrl.unsw.edu.au/chrisl/coastsnap.git)
This package has only been tested on a Windows system.
Using the [Anaconda distribution](https://www.anaconda.com/products/individual) of Python is recommended for easiest installation of the toolbox requirements.
3. Change your working directory into the repo: `cd CoastsnapAuto`
4. Update or create a new conda environment called "coastsnap" with the required dependencies. This is (almost) the same environment as for the Leaman CoastSnap Toolbox, so you may already have it. If you're not sure, run: `conda info --envs` and look for "coastsnap". If you already have the environment, run: `conda env update --name coastsnap --file environment.yml` to update it. Otherwise, create the environment by runnning:
`conda env create -f environment.yml`.
5. Check if you can activate the conda environment with `activate coastsnap`. If not, you may need to update your environment `Path` variable to include paths to the Anaconda install, like the following:
If `activate coastsnap` is still not working, you will have to edit each of the .bat files and substitute the first line `call activate coastsnap` with the direct path to the `activate.bat` file, such as "C:\Users\z5079346\Applications\Anaconda\Scripts\activate.bat". Substitute the third line `call deactivate` with the path to `deactivate.bat`
The following is a demo to make sure all of the code is working and for the user to get a sense of the intended workflow. It'll take approx. 10 mins (assuming everything works). The code will run on a demo CoastSnap directory (`CoastSnap_DEMO`) with 3 sites.
1. Open up `coastsnap_sites.csv`. Change the path name in cell E2, to the
- This tags the photoshop registered images and stores them in CoastSnap_DEMO/Images/Site_Name/Registered. Note: Loading .mat tide data in Python takes a while.
NOTE: `coastsnap_sites.csv` is called by all of the scripts in this repo. DON'T CHANGE THE STRUCTURE OF THE CSV, as it'll cause the registration.jsx scripts to die. Feel free to add more sites (rows) and change the limits though.
Script Logic: For every site in `coastsnap_sites.csv`, download the latest Spotteron images and save to Processed.
* Starts downloading from the most recent image and stops downloading for the site when an image has already been downloaded, to avoid overwriting images.
Script Logic: For every site in `coastsnap_sites.csv`, iterate through the sites' Processed images (Images/Site/Processed). For any image that hasn't already been photoshop registered (in Images/Site/Photoshop), register it.
* To get rid of the popup error "You are about to run the script containing...." when running this script, which you'll want to do if running this as a scheduled task:
* This script is based on the manual photoshop registration process, but additionally resizes images to the same width or height of the Target image prior to registration: https://unsw.sharepoint.com/:w:/r/sites/Coastsnap/_layouts/15/doc2.aspx?action=edit&sourcedoc=%7B49c9d377-f450-40bf-93e4-6ff0f289e889%7D&wdOrigin=TEAMS-ELECTRON.teams.chiclet&wdExp=TEAMS-CONTROL
* Output images are all 1280px wide and will have the same aspect ratio as the sites' Target.jpg image
* Batch Size: Images are registered with the target and seed images, in batches of 15. This is set on line 13 of `photoshop_registration_all_sites.jsx` and can be changed. Images may be registered in smaller batch sizes if there are less than 15 images in the Site/Processed/Year folder.
Script Logic: For every site in oneDrive CoastSnap directory, iterate through the sites' registered images (Images/Site/Photoshop). Tag the images with their Date, Time, Contributor and Tide (if tide data for site exists), suffix the filename with "_registered" and save at Images/Site/Registered.
* Starts tagging from the most recent image and stops for the site when an image has already been tagged. This way, the user can manually remove bad registered/tagged images, and they will not be automatically replaced.
* Retrieves tide data for the site from the .mat file specified in Database/CoastSnapDB.xlsx
If you want a bit more control over your image tagging, you'll have to do it from the command line. `cd` to this repo and activate the coastsnap conda environment with `activate coastsnap`. Now `cd` to the `CoastsnapAuto/coastsnap` directory. From here, your options are:
* stability (%) = # registered / # photoshop. This formula is based on the assumption that someone will manually remove poorly registered images in `Images/Registered`. Thus stability represents the percentage of images that had good registration.
* most recently deleted. This is the image date of the most recently deleted in `Images/Registered`. Could be useful to know for manual refining, so the user doesn't have to check every image each time.
Currently (22/6/22) it would appear that images downloaded from Spotteron do not retain the images' metadata. This is based on looking in windows file explorer image->properties, as well as using the exif python package. Note: The metadata presented in file explorer is IPTC data. There is a python package to interact with this data, but I had issues with it.
