An objective image analysis method for estimation of canopy attributes from digital cover photography.
- is based on the paper of Alivernini, A., Fares, S., Ferrara, C. Chianucci, F., Trees 2018. https://doi.org/10.1007/s00468-018-1666-3
- processes every file in the input directory as a photo
- returns an xls spreadsheet with the gap fraction of each photo
- is a Free and Open Source software released under MIT licence
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LET'S HEAT UP THE ENVIRONMENTS:
- for Windows, please check/install:
* Microsoft build tools 2015
http://landinghub.visualstudio.com/visual-cpp-build-tools
* your favourite version of Python (recommended versions are: v2.7.8 or v3.6.4)
https://www.python.org/downloads/windows/
- For Mac OS, please check/install:
* your favourite version of Python (recommended versions are: v2.7.8 or v3.6.4)
https://www.python.org/downloads/mac-osx/
- For Linux:
* You are ready to go
LET'S INSTALL AND USE CACO
- In your terminal emulator (or command prompt):
* install CaCo:
> python -m pip install --user caco
* run CaCo
> python -m caco
If you use python3 and you get an error, try typing "python3" instead of "python"
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LET'S HAVE A LOOK AT THE RESULTS OF A TUTORIAL PROJECT
* download and extract the tutorial project "caco_myprj"
https://github.com/alivernini/caco/releases/download/v0.2.4/caco_myprj.zip
* take a look at the input images
* in the folder "output" you can see an excel spreadsheet
with the computed gap fraction and other statistics
* right there, inside the "th_img" subfolder, have a peek
at the thresholded images, ... closer... zoom one:
- small gaps are identified in light grey
- big gaps are white
- vegetation is black
* this output was given by the "test_myprj.py"
* can you set up caco to have the same results?
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Now you are ready to analyze your images with CaCo!
Just one tip to set up CaCo: copy the project paths from your file-manager
Buona giornata, Alessandro