WGU-Capstone/quickstart.txt

NixOS/Nix: 
If you run on a Nix or NixOS environment, you can use the included shell.nix file to create a nix shell to run this in.

Windows/Other Linux:
This program was developed with python 3.11, so please use that version of python to create the virtual environment. After making sure you are using the correct python version, run the following commands:

python -m venv venv

to create a new virtual environment ".\venv" 

now enter the the virtual environment by running either .\venv\Scripts\Activate.ps1 or ./venv/Scripts/activate depending on if you use windows and install the following packages (found in requirements.txt)

pip install numpy
pip install opencv-python

Now you can run the program. It is recommended to run the program with -d and -o set while testing. This enables the dashboard which shows live statistics, and output, which shows the calculated adjustments required to center the face in the frame.


Training Data:
https://www.kaggle.com/datasets/utkarshsaxenadn/landscape-recognition-image-dataset-12k-images


create positives from the negatives: \opencv\build\x64\vc15\bin\opencv_createsamples.exe -img .\positives\face_1.png -bg .\bg.txt -info info/info.lst -pngoutput info -maxxangle 0.8 -maxyangle 0.8 -maxzangle 0.8 -num 1950
Create vec files from positives: .\opencv\build\x64\vc15\bin\opencv_createsamples.exe -info .\info\info.lst -num 1950 -w 80 -h 80 -vec positives-80.vec
(I created a 20, 40, and 80) we have 1650 positives