OpenCV Learning libraries and modules are available. Some



Open Source Computer Vision (OpenCV) is a
cross-platform, free library used to build real-time computer vision
applications. It predominantly focuses on processing and analyzing images and video
captures. Identifying similar images, face detection, and object detection are
some of the commonly used features. It supports Machine Learning and Deep
Learning frameworks. OpenCV runs on different platforms such as Windows, Linux,
macOS, Android, and iOS. It supports programming languages such as C++, C,
Python, Java, and so on.

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OpenCV using

OpenCV-Python is a library set of Python
bindings optimized to address computer vision needs. Leveraging the best
features of the OpenCV, C++ API, and the Python language it serves as the
Python API for OpenCV. OpenCV-Python extensively uses the NumPy Python package
which is designed for numerical operations and scientific computing. It also uses
python packages such as SciPy and Matplotlib that are compatible and easy to

OpenCV using
Python in Machine Learning

Machine Learning (ML) enables computers,
mobile devices, and other machines to access, learn, and interpret data.
Machine Learning uses smart algorithms to learn and make data-driven decisions.
It aids other technologies such as Artificial Intelligence, Robotics, and so
on. Numerous Python Machine Learning libraries and modules are available.

Some of the popular and widely used OpenCV
Python algorithms are:

Artificial Neural Networks (ANN): It is
a collection of connected units called artificial neurons. A signal
transmission can happen between artificial neurons. Most commonly used neural
networks type is multi-layer perceptrons (MLP). One or more neurons in each MLP
layer is directly connected with the neurons from the preceding and the subsequent
layers. A classical random sequential back-propagation algorithm and a batch
RPROP algorithm (default) are implemented to train the MLPs.

Support Vector Machines (SVM): It is a
supervised classification algorithm that creates a dividing line between
different categories of data. This technique is a representation of the
examples as points in space. The examples are mapped such that the separate
categories are divided by a clear zone. Upcoming examples are then mapped into
the same space and identified to fit into a category based on which side of the
zone they are present.

K-Nearest Neighbors (KNN): It is a
supervised learning algorithm that analyzes all the training samples and
predicts the response for a new sample. It makes predictions based on the
analysis of a K value of the nearest neighbors of the sample. KNN is also
called as “Learning by Example” technique.

K-Means Clustering: It is an unsupervised
learning algorithm used to address clustering problems. The algorithm initiates
with randomly selected points and uses a distance formula to find the best
grouping of data points to optimize the clusters.