For some Lens Creators, a monumental step is making the jump from a familiar workflow to a brand new one.
While finding your strong suits and your art style is important, it is equally as important to be a flexible and well-rounded creator.
Continue reading “How to Use Blender to Create Powerful Lenses”
Google’s on-device ML Kit recently introduced another API for barcode scanning. It can read and scan almost a dozen different types of barcodes including Codabar, Code 39, Code 93, EAN-8, EAN-13, QR code, PDF417, and more.
The barcode scanning API also tells you the scanned format and it will automatically detect the formats. The other main feature of this API is that you can use it in your application without a network connection and it will support the different orientation.
Continue reading Scan Barcodes on Android Using Google’s On-Device ML Kit
Augmented Reality (AR) is a digital virtual interface that overlaps created images on top of the real world, generally through a phone. AR necessitates the use of an app to provide the consumer with a superior experience. But people can be adamant about not installing another app. But there’s a solution to reaching your audience without asking them to download — using their already existing apps.
Continue reading Best Features Of Spark AR Studio to Build AR-Powered Lens
In photograph and video editing, color grading is an important step in the process to enrich the content and make it look more appealing. This is done by adjusting the image/video in specific ways to create a stylistic effect, giving the content a cinematic look. One common way of achieving this consistently across various editing software is by using a LUT (Look Up Table).
The introduction of iOS 14 brought in a slew of enhancements and interesting new features in Apple’s computer vision framework.
Vision framework was released in 2017 in a bid to allow mobile application developers to leverage complex computer vision algorithms with ease. Specifically, the framework incorporates a host of pre-trained deep learning models whilst also acting as a wrapper to quickly run your own custom Core ML models.
Continue reading Swipeless Tinder Using iOS 14 Vision Hand Pose Estimation
This article highlights the best photo editing, video editing, and GIF-making apps currently available in the App Store.
From basic contrast and brightness settings to advanced deep learning-based filters, these apps have it all covered.
Cloud computing is very much a buzzword in the machine learning community, but what does it really mean?
It turns out, cloud computing can be a real lifesaver for your machine learning needs, and it doesn’t need to break the bank.
In this tutorial, you’ll learn how to quickly set up an Amazon EC2 instance for under a dollar and connect to it via SSH.
Now, if none of that made sense, don’t sweat it. This tutorial is intended for beginners, so you don’t need to know anything about Amazon Web Services to follow along.
By the end, you’ll have a computer running in the cloud, which you can connect to and use for almost anything!
The recently-concluded CVPR 2020 had quite a large number of contributions in pushing object detection forward. In this piece, we’ll look at a couple of the especially impressive papers.
Continue reading “CVPR 2020: The Top Object Detection Papers”
Sensor fusion is one of the key aspects of self-driving cars.
If we take a look at the 4 main elements of self-driving vehicles, we can categorize sensor fusion a part of both the perception and the localization world. The reason is simple: both these disciplines use multiple sensors to function.
For example, perception is using cameras, LiDARs, and RADARs to detect obstacles’ classes, positions, and velocities with high accuracy. Localization, on the other hand, fuses GPS, LiDAR, and camera data to get an accurate position with centimeter-level accuracy.
Continue reading LiDAR and Camera Fusion in Self-Driving Cars
Today we’re excited to announce the Fritz AI Dataset Collection System, an important piece in our effort to help ML teams and developers generate, collect, and use data for mobile machine learning projects.
Data is a critical component to AI workflows, and the best data comes from the real world, where your models are (or will be) deployed.
For mobile machine learning projects, that means a user’s phone. The new Dataset Collection system makes it easy to gather this data through the same convenient SDK that delivers and runs your models.
Continue reading “Introducing the Fritz AI Dataset Collection System”
