Hi HN! hietalajulius and I have been working on a toolkit for solving computer vision problems.<p>These days, there are a lot of fancy solutions to many computer vision problems, but there aren't good implementations of the algorithms, getting to a working solution requires figuring out lots of different steps, tools are buggy and not well maintained and often, you need a lot of training data to feed the algorithms. Projects easily balloon into months long R&D projects, even when done by seasoned computer vision engineers. With the Stray Robots toolkit, we aim to lower the barrier for deploying computer vision solutions.<p>Currently, the toolkit allows you to build 3D scenes from a stream of depth camera images, annotate the scenes using a GUI and fit computer vision algorithms to infer the labels from single images, among a few other things. In this project, we used the toolkit to build a simple electric scooter detector using only 25 short video clips of electric scooters.<p>If you want to try it out, you can install the toolkit by following the instructions here: <a href="https://docs.strayrobots.io/installing/index.html" rel="nofollow">https://docs.strayrobots.io/installing/index.html</a><p>Going forward we plan to add other components such as 3D keypoint detection, semantic segmentation and 6D object pose estimation.<p>Let us know what you think! Both of us are here to answer any questions you may have.
Using video to automatically build a large training set is smart! Well done! I was thinking about making a properly free and open dataset from just walking around London, and this gives me some ideas...
Super cool, especially the way it was able to differentiate that Posti box from the scooters, even though they have vaguely the same shape. Just out of curiosity, what confidence level did the classifier assign to the Posti box as a scooter?
Just so I understand the idealized pipeline here, a user does the following:<p>1. Use the Scanner app to take the images and camera pose data<p>2. Export the scene directory (color and depth images and json files) somehow to your computer<p>3. Import (integrate, open) the directory via the Stray CLI<p>4. Annotate voxels via 3D bounding box in Studio GUI<p>5. Generate labels from the annotated voxels<p>6. Import data and labels, train and test a detectron model with pytorch<p>7. Export trained model in torchscript format<p>8. Profit<p>I assume you require users to "ETL" the scene directory from your phone to your desktop/laptop via some manual transfer process?<p>Is there any reason I couldn't stop at step 5 and push my new labeled date to my own training system?
Newbie here, where's the intersection between object detection and OCR?<p>For example, if I have images in different pdf files that I want to compare or trying to identify information on the wine label, what are criteria to consider on which method to use?
Heads up for anyone else, I was interested in the strayscanner app to try on my iPhone 11, but I’m getting an error when trying to record: “unsupported device: this device doesn’t seem to have the required level of ARKit support”.