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Unnecessary background within an image, photograph, or video makes the image lose its focal point. By removing the background, we can draw the attention of the viewer back to the focal point, thereby making the image more informative and easy to view.

In this article, we will learn to create segmentation and the camera texture to create an effect that replaces unnecessary background in Facebook’s Spark AR Studio. We will make use of a segmentation mask texture to separate the foreground and background of the scene. Then, we will implement the camera texture to a material in the foreground to stream a video of the person using the effect back into the scene. You can then transform the environment behind them and replace the background.

Continue reading Replace the Background in Facebook’s Spark AR Studio

Often in machine learning, and specifically with classification problems, we encounter imbalanced datasets. This typically refers to an issue where the classes are not represented equally, which can cause huge problems for some algorithms.

In this article, we’ll explore a technique called resampling, which is used to reduce this effect on our machine learning algorithms.

Continue reading Resampling to Properly Handle Imbalanced Datasets in Machine Learning

In these unprecedented times involving a worldwide pandemic, many changes have taken place in our daily lives. To ensure the safety and well being of all, many protocols and guidelines have been implemented across society, such as maintaining a minimum distance or limiting physical contact with exposed and shared surfaces.

Businesses are getting creative by adapting their shopping experiences to ensure the safety of their customers and employees. Contactless services have become more of a necessity than a fad, and technology has evolved to keep pace with these newfound demands. Many stores have standardized Scan & Go technology, which allows customers to scan item barcodes and pay using just their smartphones, thus limiting human contact and providing a faster, simpler alternative.

Continue reading Safer and Smarter: Contactless shopping with on-device object detection

At WWDC 2020, Apple introduced a bunch of great new updates to SwiftUI to make it even easier for developers to write apps for Apple platforms. In this tutorial, you’ll learn how to use those new features to make your app work on both iOS and macOS. By the end of this tutorial you will have created a fully functional HackerNews reader.

Continue reading Building a Multi-platform App with SwiftUI

If you’re tired of building and running your iOS apps via Xcode’s ▶️ button, let’s explore an exciting way to build and run your apps without touching the ▶️ button (ever again).

This is a four-step process.

Xcode comes with a number of command line tools. These tools are capable of performing pretty much every thing you can do via Xcode’s UI. While you need a human to point and click certain buttons in Xcode to make it work, these command line tools can help automate the whole process of building and running your project. Powerful, right?

Continue reading Ditching Xcode’s ▶️ button

The ability of a machine or program to identify spoken words and transcribe them to readable text is called speech recognition (speech-to-text). In this tutorial, I will be walking you through analyzing speech data and converting them to a useful text for sentiment analysis using Pydub and SpeechRecognition library in Python.

Sentiment analysis is the use of natural language to classify the opinion of people. It helps to classify words (written or spoken) into positive, negative, or neutral depending on the use case. The sentiment analyzed can help identify the pattern of a product; it helps to know what the users are saying and take the necessary steps to mitigate any problems.

Continue reading Sentiment Analysis of Speech Using PyDub and SpeechRecognition in Python

If our data isn’t good enough, there’s no machine learning tool, platform, or framework that exists that will work well—no matter how good the algorithm is.

So while debugging machine learning models, we also need to make sure our input data is prepared properly. For example the input data may not be a valid data type for a particular feature. Like in case of gender the allowed values are M or F, while the input data may contain other letter values for this feature.

Continue reading Debugging machine learning models

For years now, Snapchat has been at the forefront of mobile machine learning — their popular Lenses, which often combine on-device ML models with augmented reality, have become shining examples of the power and flexibility of on-device machine learning.

Given our respect and admiration for Snap’s work in this area, our team was thrilled to hear about the recent release of SnapML, Snap’s new ML framework inside their development platform Lens Studio (released with 3.0).

Continue reading Exploring SnapML: A Technical Overview