Keras vs OpenCV – Differences Between OpenCv and Keras

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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.

Keras vs OpenCV

Keras Vs OpenCV

First, we will see both the technologies, their application, and then the differences between keras and OpenCv.

About OpenCV

Computer Vision is defined for understanding meaningful descriptions of physical objects from the image.

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.

The common features in OpenCV 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.

Applications of OpenCV

  • In Robotics, OpenCV is useful in domains like navigation, obstacle avoiding, and in human-robot interaction.
  • In the medical industry it is useful for classification and detection of diseases, for analyzing brain MRI scans and in surgeries.
  • For security purposes, like in biometric scan and video surveillance.
  • In transportation and autonomous vehicles, self-driving cars.

About Keras

Keras was developed to design neural networks with user-friendliness, for being easy to extend, to be modular, and easy to work with. Keras focuses on the easy deployment of neural layers, cost functions, activation functions, optimizers, and regularization schemes.

We can deploy Keras models over a range of platforms and there are different modules for different platforms. Such as CoreML to deploy on IOS,TensorFlow Android runtime for Android, Keras.js for browser.

Keras has a wide range of predefined layers and we can also define our own custom layers. Using Keras, we have to define minimum structure to provide a clean way to create deep learning models.

Do not forget to check Important Keras Features.

Applications of Keras

Keras provides ten pre-trained deep learning models with weights trained on ImageNet dataset. These models are Xception, VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, MobileNet, DenseNet, NASNet, MobileNetV2TK.

Keras provides seven common datasets in deep learning. These are available in keras.datasets module. These datasets are IMDB movie reviews, MNIST digits, MNIST fashion image, cifar10, cifar100 small color images, Reuters newswire topics and Boston housing price.

Comparison between Keras and OpenCv

This article aims to judge the OpenCV library and Keras frameworks. So let’s see the difference between Keras and OpenCv for proper understanding:

1. History

Computer vision started in the 1960s, aiming to mimic human vision systems. We were trying to teach computers to count pixels, measuring shades of color, and estimating relations between objects. In 1999, OpenCV was launched by Intel. Initially, it was focused on real-time ray tracing.

While the first step in neural networks came in 1943 when researchers were trying to explain the functionality of neurons, they made a network of electric circuits. The initial release of Keras came in 2015, it was developed by a Google engineer.

2. Functionalities

Keras provides the implementation of all the common neural network layers, activation functions, and the optimizers. It also provides support for convolution and recurrent neural nets. This allows the use of distributed training of neural networks on a cluster of GPUs.

Major functionalities of OpenCV are image and video processing, object and feature detection, computational photography.

Both Keras and OpenCV provide CUDA support for GPU.

3. Popularity and Community Support

Here, we consider activity on github as the criteria of popularity. Keras has almost 48k stars and about 18k forks on github.  Whereas OpenCV has about 44 k stars and 35k forks on github.

OpenCV has almost double the number of forks, but OpenCV was released in 2000’s while Keras was only released in 2015. In only 5 years, Keras have shown a very high growth trajectory.

Both OpenCV and Keras have great community support. Though Keras has some competitors in the deep learning field like Tensorflow and Pytorch. OpenCV stands alone and is far the best library for real-time computer vision.

Companies like Intel, AMD & Google have funded OpenCV development. Keras is in use at Netflix, Uber, Instacart, and many others.

4. Speed and Performance

Keras deep learning framework is written in python. The pythonic nature gives Keras the ability of easy to code, easy to debug, and being modular but it also affects its performance.

OpenCV library is written in C++ and its primary interface is C++. It also provides binding in Python, Java, and MATLAB/OCTAVE. According to a study, OpenCV is about 1.5 times faster than Keras.

5. Quality Documentation

There is no doubt that Keras wins this race. Keras has one of the best documentation among all the other frameworks.
OpenCV does not have decent reviews for its documentation. Even staunch developers of OpenCV admit that it has bad documentation. Many times it’s very confusing to follow the documentation examples.

Summary

Keras is a framework for deep learning whereas OpenCV is a computer vision library. This article compares Keras vs OpenCV on their major functions, popularity, performance, and quality of documentation. It concludes that OpenCV has better performance results, Keras has better documentation. Both are popular among their developer community and have different functionalities in their respective fields.

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