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Tag: Python

#002 How to draw lines, rectangles, circles and write text on images with OpenCV in Python?

08.05.2020

Highlight: Hello and welcome back to our Hacking OpenCV series in Python. In this post we are going to explain how to draw basic and more advanced shapes, as well as how to write text on images. You will learn to draw lines, rectangles, circles, ellipses, polylines and how to write a textual content on the image. This knowledge will be very useful later on when we start to develop our facial recognition algorithms with…
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#000 How to access and edit pixel values in OpenCV with Python?

22.04.2020

Highlight: Welcome to another datahacker.rs post series! We are going to talk about digital image processing using OpenCV in Python. In this series, you will be introduced to the basic concepts of OpenCV and you will be able to start writing your first scripts in Python. Our first post will provide you with an introduction to the OpenCV library and some basic concepts that are necessary for building your computer vision applications. You will learn…
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#015 TF Implementing AlexNet in TensorFlow 2.0

29.02.2020

Highlights: In this post we will show how to implement a fundamental Convolutional Neural Network \(AlexNet\) in TensorFlow 2.0. The AlexNet architecture is designed by Alex Krizhevsky and published with Ilya Sutskever and Geoffrey Hinton. It competed in the ImageNet Large Scale Visual Recognition Challenge in 2012. Tutorial Overview: Review of the Theory Implementation in TensorFlow 2.0 1. Review of the Theory Real life Computer Vision problems requires big amount of quality data to be trained on. In the past, people…
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#012 TF Transfer Learning in TensorFlow 2.0

16.02.2020

Highlights: In this post we are going to show how to build a computer vision model without building it from scratch. The idea behind transfer learning is that a neural network that has been trained on a large dataset can apply its knowledge to a dataset that it has never seen before. That is, why it’s called a transfer learning; we transfer the learning of an existing model to a new dataset. Tutorial Overview: Introduction Transfer…
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#011 TF How to improve the model performance with Data Augmentation?

16.02.2020

Highlights: In this post we will show the benefits of data augmentation techniques as a way to improve performance of a model. This method will be very beneficial when we do not have enough data at our disposal. Tutorial Overview: Training without data augmentation What is data augmentation? Training with data augmentation Visualization 1. Training without data augmentation A familiar question is “why should we use data augmentation?”. So, let’s see the answer. In order…
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