This is an attempt to put together a collection of resources and scripts I put together while working on various Computer Vision projects, hope they can help you save your time if you come across tasks these resources can handle.
In Terminal
git clone https://github.com/r3stl355/cv-utils.git
cd cv-utils
I often use Conda environments for my projects but any other equivalent will do. Here is an example of setting up a Conda environment to test the scripts
- First, install Conda (my personal preferece is Miniconda)
- Then
conda create -n cv-utils python=3.7
conda activate cv-utils
pip install -r requirements.txt
Most of the resources here are implemented as Jupyter Notebooks, with others being just loose script files.
Run a Jupyter server and check the Notebook(s) of interest in the Jupyter instance that opens in your browser (if it does not launch automatically, just copy/paste the URL shown in the Terminal to your browser address bar)
jupyter notebook
Check out voc_2_rec.ipynb Notebook for details.
Check out coco_2_rec.ipynb Notebook for details.
Script is using fixed split of 90/10, adjust as needed to fit your needs within the script or extend the script to
accept more parameters. Third parameter determines the shuffle tool to use, e.g. gshuf on Mac OS installed as part
of coreutils
source split_train_val.sh data/voc_like_sample test.lst gshuf
and so on...