Fundamentals of Computer Vision

This project is developed in Python + OpenCV for implementing some techinques of image processing and feature extraction algorithms.

Outline

01 Image Fundamentals

• Geometric Transformations(e.g. rotation, image stitching)
• Histogram Equalization
• Interpolation

02 Contrast Adjustment

• Image Adjusting
• Histogram Equalization
• Local Histogram Equalization(LHE)

03 Filters

• Box Filter
• Median Filter
• Noises(e.g. salt-and-pepper, gaussian)
• Sharpening, Blurring and Noise Removal
• Edge Detection(first-order difference, robert, sobel)
• Unsharp Masking

04 Frequency Domain

• Discrete Fourier Transform(DFT)
• Separable Filters
• Filtering

05 Color

• Color Spaces(e.g. RGB, HSI)
• Quantization
• Clustering

06 Wavelet

• Discrete Wavelet Transform(DWT)
• Multi-level DWT
• Approximation & Prediction Residual Pyramids
• Denoising with Wavelet

07 Features

• Estimate Geometry
• Corner Detection(with Harris algorithm)

Environment

• Python: 3.9
• OpenCV: 4.5
• Numpy: 1.22
• Pandas: 1.3
• Matplotlib: 3.4
• Scikit-image: 0.18
• PyWavelets: 1.2

Setup Guide

• Clone the repository:

git clone https://github.com/mohsenMahmoodzadeh/image-processing-basic-methods.git

• Set up Git-LFS:

# install the package
apt-get install git-lfs

# initialize it on the project
git lfs install

# modify the git config not to download large files at first 
git config --global filter.lfs.smudge "git-lfs smudge --skip -- %f"
git config --global filter.lfs.process "git-lfs filter-process --skip"

# pull the large files into the project
git lfs pull

• Create a virtual environment (to avoid conflicts):

virtualenv -p python3.9 fcv

# this may vary depending on your shell
. fcv/bin/activate

Install the dependencies:

pip install -r requirements.txt

If you want to work on jupyter notebook, you may need to set up a kernel on your virtual environment to make sure all your modules execute correctly.

python -m ipykernel install --name fcvkernel

# Now you get a kernel named `fcvkernel` in your jupyter notebook

Future Works

• Correct and improve the image registration algorithm in 01 Image Fundamentals.ipynb notebook.

• Improve the algorithms in 02 Contrast Adjustment.ipynb and 03 Filter.ipynb

• Apply OOP to algorithms and visualization plots to gain reusability.

Contributing

Fixes and improvements are more than welcome, so raise an issue or send a PR!