F#, CUDA, Computer Vision, Deep Learning
We will explore running object detection on-device with HoloLens, using Unity game engine as our development platform. AR Academy is a great introduction to all aspects of HoloLens development. It has 300-level tutorials that demonstrate how to connect the device to Azure Cognitive Services to perform machine learning tasks. There are no samples of performing … Continue reading HoloLens Object Detection
I'm writing a series of posts on supercharging object detection inference performance in video streams using Tensorflow and cool tech from NVIDIA: step-by-step, starting from 6 fps all the way up to 230. But before I start, this small post is about a cool little gem, which I think is often overlooked. Anyone in the … Continue reading On the Margins: Non-maximum Suppression with Tensorflow
So I was bored, didn't know what to do, started solving problems on codewars (shut up!). There was this little problem there, where they are asking you to compute $latex \frac{\sum_{i=1}^n i!}{n!}&s=2$, for potentially large n's with $latex 10^{-6}$ precision. It's easy to see that this needs to be refactored (no need to actually compute … Continue reading F# Humor (kinda)
Computer vision should not be confused with image processing (as we all know). I love building computer vision pipelines, but sometimes menial tasks of pure image processing, automated editing come up. Suppose you had the same astronauts from one of the previous posts participating in a study, where they are actually filmed watching something, say … Continue reading Scripting Video Editing with F# and FFmpeg
Auto-writing Software Good software writes itself. We all know the patterns and following them makes things easy. Not because we have memorized any monumental volume on design patterns, but because years of experience taught us what should be done on an almost intuitive level. Also in a negative sense. We avoid duplication, globals, encapsulation violations. … Continue reading D3 Off-Label
The Choices I have experimented with OpenCV and Dlib face detection in my computer vision pipeline. Both work well, but the Dlib one worked better: it is more sensitive with (in my case) almost no false positives right out of the box! Dlib uses several HOG filters that account for profile as well as frontal … Continue reading Detecting Faces with Dlib from F#. IFSharp Notebook
The post on applying GPU to finding Eulerian path mentioned a stumbling block: partitioning a very specific kind of graph. In general, partitioning is a hard problem, NP-hard actually. The graphs we are dealing with are very specific, though, and may be partitioned in $latex O(|E|)$ time (E - the set of graph edges). Our … Continue reading HashSet, Graph, Cognac
I've been playing with Affectiva emotion, demographics, and face detection SDK, found it excellent, however, their sample gallery lacks a sample in F#! So here we are to correct that. I just wanted a simple F# script that would let me take all kinds of the SDK options for a ride. The script itself is … Continue reading Getting Emotional with Affectiva, F#, and Emgu
So, continuing where we left off: Walking the Euler Path: Intro Visualizing Graphs Walking the Euler Path: GPU for the Road Walking the Euler Path: PIN Cracking and DNA Sequencing For the Win And finally I ran the GPU-enabled algorithm for finding the Euler path. And the results: Generating euler graph: vertices = 10,485,760; avg … Continue reading Zooming Through Euler Path: Supercharging with GPU
Continuing on to some cool applications of Eulerian paths. Intro Visualization GPU/CUDA Injection The goal of this little graph experiment remains exploration of accelerating Eulerian path finding on the GPU. This is the final introductory post. Eulerian Path Hierholzer algorithm works great. It's linear in the number of edges, so as fast as we can … Continue reading Walking the Euler Path: PIN Cracking and DNA Sequencing
Viral F# 