Object Tracking in IoT
Thresholding: Binarization or thresholding is one problem that have to solve in pattern recognition methods and applications. Moreover, it has a very important influence on the sequent steps in computer vision applications such as, Optical Character Recognition (OCR), image segmentation, and tracking objects.
Binarization or thresholding is one problem that must be solved in pattern recognition and it has a very important influence on the sequent steps in imaging applications. Thresholding is used to separate objects from the background, and diminish the amount of data alter the computational speed. Recently, interest in multilevel thresholding has been altered. However, when the levels are altered, the computation time alters so single threshold methods are accelerated than multilevel methods. Moreover, for every new application, new methods are is acquired.
Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, separating objects from background, decreasing the capacity of data consequently increases speed. Handwritten recognition is one of the important issues, which have various applications in mobile devices.
Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) http:https://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf
GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf
Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). http:https://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf
Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) http:https://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf
Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics https://doi.org/10.1109/ICPAIR.2011.5976918
License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) https://doi.org/10.1109/ICEEI.2011.6021627
Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) https://doi.org/10.1109/ICCAIE.2010.5735125 http:https://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf
- Visual code
- Bracket Pair Colorizer 2
- Prettier - Code formatter
- indent-rainbow
- GitHub
- Shell
bpython https://bpython-interpreter.org/downloads.html pip install bpython
Terminal
_ iTerm2
_ https://iterm2.com/downloads.html
_ themes
_ Oh My Zsh
_ sh -c "$(curl -fsSL https://raw.githubusercontent.com/ohmyzsh/ohmyzsh/master/tools/install.sh)"
_ Prezto,
_ git clone --recursive https://github.com/sorin-ionescu/prezto.git "${ZDOTDIR:-$HOME}/.zprezto"
_ source "${ZDOTDIR:-$HOME}/.zprezto/init.zsh"
_ zstyle ':prezto:load' pmodule-dirs $HOME/.zprezto-contrib
_ pip install --user powerline-status
_ Spaceship-prompt
_ git clone https://github.com/denysdovhan/spaceship-prompt.git "$ZSH_CUSTOM/themes/spaceship-prompt"
* ln -s "$ZSH*CUSTOM/themes/spaceship-prompt/spaceship.zsh-theme" "$ZSH_CUSTOM/themes/spaceship.zsh-theme"
- prezto _ nano ~/.zpreztorc _ Follow prezto-contrib#usage to clone prezto-contrib to the proper location. * zstyle ':prezto:load' pmodule-dirs $HOME/.zprezto-contrib
- cd $ZPREZTODIR _ git clone --recurse-submodules https://github.com/belak/prezto-contrib contrib _ Enable the contrib-prompt module (before the prompt module). _ Set zstyle ':prezto:module:prompt' theme 'spaceship' in your .zpreztorc. _ Plugins _ Pipeline Brew install pipeline Fzf: A command-line fuzzy finder _ https://github.com/junegunn/fzf _ brew install fzf _ Simplified and community-driven man pages _ brew install tldr _ rg — recursively search current directory for lines matching a pattern _ brew install ripgrep _ https://github.com/BurntSushi/ripgrep _ Bat * brew install bat
brew install pyenv
vim ~/.zshrc
eval "$(pyenv init -)"
https://blog.jayway.com/2019/12/28/pyenv-poetry-saviours-in-the-python-chaos/
curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python
poetry config virtualenvs.in-project true
source $HOME/.poetry/env
poetry init
poetry new poetry_projcet
poetry run python test.py
poetry new "name of projces"
poetry install
poetry run which python
poetry add `cat requirements.txt`
Computer Vision with Machine Learning Pipline: Docker, AWS, Kubernetes, TensorFlow, Seldon, Kubeflow, ...
Command + Shift + P Shell Command : Install code in PATH code .
Config GitHub: https://github.com 1. ssh-keygen -t rsa -b 4096 -C "[email protected]" 2. pbcopy < ~/.ssh/id_rsa.pub 3. GitHub->Personal Setting->SSH and GPG keys->Add new : past 4. ssh-add ~/.ssh/id_rsa_work_user1 5. ~/.ssh/config
1: install docker : https://docs.docker.com/docker-for-mac/install/ docker run Docker file: -> Docker image: package, template, planet Docker container:
3: install the requirements.txt
Computer Vision with Machine Learning Pipline: Docker, AWS, Kubernetes, TensorFlow, Seldon, Kubeflow, ...
Command + Shift + P Shell Command : Install code in PATH code .