{"id":79139,"date":"2020-07-11T09:00:22","date_gmt":"2020-07-11T03:30:22","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=79139"},"modified":"2021-08-25T13:49:43","modified_gmt":"2021-08-25T08:19:43","slug":"keras-vs-opencv","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/keras-vs-opencv\/","title":{"rendered":"Keras vs OpenCV – Differences Between OpenCv and Keras"},"content":{"rendered":"

OpenCV is the open-source library for computer vision and image processing tasks in machine learning. OpenCV provides a huge suite of algorithms and aims at real-time computer vision. Keras, on the other hand, is a deep learning framework to enable fast experimentation with deep learning. In this Keras Tutorial, we will learn about Keras Vs OpenCV.<\/p>\n

\"Keras<\/a><\/p>\n

Keras Vs OpenCV<\/h2>\n

First, we will see both the technologies, their application, and then the differences between keras and OpenCv.<\/p>\n

About OpenCV<\/h3>\n

Computer Vision is defined for understanding meaningful descriptions of physical objects from the image.<\/p>\n

OpenCV was built to provide an infrastructure for computer vision. This library has a huge range of optimized machine learning and computer vision algorithms. These algorithms include object identification, detecting and recognizing faces, object movement tracking, etc. OpenCV provides support for C++, Python, Java and MATLAB programming languages and works on Windows, Linux, Android and Mac Operating Systems.<\/p>\n

The common features in OpenCV<\/a> are read and write images, save and capture images\/videos, filter or transform the image, detecting faces,eyes,cars in images or videos, perform feature detection, background subtraction, and tracking objects.<\/p>\n

Applications of OpenCV<\/h3>\n