Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information.
I tried to solve a small problem concerning computer vision: Realization of a disktop python application which establishes filters on the photos chosen by the user:
video1542072357.mp4
OpenCV Open Source Computer Vision Library is an open-source library that includes several hundreds of computer vision algorithms. OpenCV-Python makes use of Numpy, which is a highly optimized library for numerical operations with a MATLAB-style syntax. All the OpenCV array structures are converted to and from Numpy arrays. This also makes it easier to integrate with other libraries that use Numpy such as SciPy and Matplotlib.
NumPy arrays are faster and more compact than Python lists. An array consumes less memory and is convenient to use. NumPy uses much less memory to store data and it provides a mechanism of specifying the data types. This allows the code to be optimized even further.
The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. NumPy can be installed with conda , with pip
If you use conda, you can install NumPy from the defaults or conda-forge channels:
#Best practice, use an environment rather than install in the base env
conda create -n my-env
conda activate my-env
#If you want to install from conda-forge
conda config --env --add channels conda-forge
#The actual install command
conda install numpyIf you use pip, you can install NumPy with:
pip install numpy 
