trellixvulnteam / opencvprojects_ebkr

Implementations in ML using OpenCV

OpenCV Projects

OPENCV : OpenCV (Open Source Computer Vision Library) is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

Adopted all around the world, OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 14 million. Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics. Get it installed on your Windows 10 computer:

1. Install Via PIP pip install opencv-contrib-python --upgrade or without extra modules: pip install opencv-python

2. Test OpenCV Installation C:> python

import cv2 print(cv2.version) '3.4.0' # your version may be a newer one

This repository includes a variety of projects that are implemented in Python using opencv :

1. Facial Detection : Detects the face, eyes of a person.

2. Background Subtraction : Background subtraction, also known as foreground detection, is a technique in the fields of image processing and computer vision wherein an image's foreground is extracted for further processing (object recognition etc.).

3. Iris Dection : Implementation to detect the iris on a static image.

4. Motion Detection : Motion detection is the process of detecting a change in the position of an object relative to its surroundings or a change in the surroundings relative to an object. When motion detection is accomplished by natural organisms, it is called motion perception.

5. Feature Extraction : In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. Feature extraction is related to dimensionality reduction.