Displaying 1 to 20 from 79 results

google-cloud-node - Google Cloud Client Library for Node.js

  •    Javascript

Node.js idiomatic client for Google Cloud Platform services.If you need support for other Google APIs, check out the Google Node.js API Client library.

iOS-11-by-Examples - 👨🏻‍💻 Examples of new iOS 11 APIs

  •    Swift

Code examples for new APIs of iOS 11. Note: The project requires Xcode 9, Swift 4 and iOS 11.

NextLevel - ⬆️ Rad Media Capture in Swift

  •    Swift

NextLevel is a Swift camera system designed for easy integration, customized media capture, and image streaming in iOS. Integration can optionally leverage AVFoundation or ARKit. Alternatively, drop the NextLevel source files or project file into your Xcode project.

PaddleHub - Awesome pre-trained models toolkit based on PaddlePaddle

  •    Python

If you have any questions during the use of the model, you can join the official WeChat group to get more efficient questions and answers, and fully communicate with developers from all walks of life. We look forward to your joining.

donkeycar - Open source hardware and software platform to build a small scale self driving car.

  •    Python

Donkeycar is minimalist and modular self driving library for Python. It is developed for hobbyists and students with a focus on allowing fast experimentation and easy community contributions. After building a Donkey2 you can turn on your car and go to http://localhost:8887 to drive.

ios-learning-materials - 📚Curated list of articles, web-resources, tutorials and code repositories that may help you dig a little bit deeper into iOS

  •    Swift

Last Update: 10/October/2018. Curated list of articles, web-resources, tutorials, Stack Overflow and Quora Q&A, GitHubcode repositories and useful resources that may help you dig a little bit deeper into iOS. All the resources are split into sub-categories which simlifies navigation and management. Feel free to use and suggest something to learn (iOS related of course 😜).

pytorch-dense-correspondence - Code for "Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation"

  •    Python

Abstract: What is the right object representation for manipulation? We would like robots to visually perceive scenes and learn an understanding of the objects in them that (i) is task-agnostic and can be used as a building block for a variety of manipulation tasks, (ii) is generally applicable to both rigid and non-rigid objects, (iii) takes advantage of the strong priors provided by 3D vision, and (iv) is entirely learned from self-supervision. This is hard to achieve with previous methods: much recent work in grasping does not extend to grasping specific objects or other tasks, whereas task-specific learning may require many trials to generalize well across object configurations or other tasks. In this paper we present Dense Object Nets, which build on recent developments in self-supervised dense descriptor learning, as a consistent object representation for visual understanding and manipulation. We demonstrate they can be trained quickly (approximately 20 minutes) for a wide variety of previously unseen and potentially non-rigid objects. We additionally present novel contributions to enable multi-object descriptor learning, and show that by modifying our training procedure, we can either acquire descriptors which generalize across classes of objects, or descriptors that are distinct for each object instance. Finally, we demonstrate the novel application of learned dense descriptors to robotic manipulation. We demonstrate grasping of specific points on an object across potentially deformed object configurations, and demonstrate using class general descriptors to transfer specific grasps across objects in a class. To prevent the repo from growing in size, recommend always "restart and clear outputs" before committing any Jupyter notebooks. If you'd like to save what your notebook looks like, you can always "download as .html", which is a great way to snapshot the state of that notebook and share.

FaceCropper - :scissors: Crop faces, inside of your image, with iOS 11 Vision api.

  •    Swift

To run the example project, clone the repo, and run pod install from the Example directory first. FaceCropper is available under the MIT license. See the LICENSE file for more info.

The Kinectinator


The Kinectinator is a Kinect-controlled turret. If a human walks in range of the Kinect, the Kinectinator starts tracking the human with a pan/tilt foam dart

3dmatch-toolbox - 3DMatch - a 3D ConvNet-based local geometric descriptor for aligning 3D meshes and point clouds

  •    C++

Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3DMatch, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose an unsupervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. This code is released under the Simplified BSD License (refer to the LICENSE file for details).

CudaSift - A CUDA implementation of SIFT for NVidia GPUs (1.6 ms on a GTX 1060)

  •    Cuda

This is the fourth version of a SIFT (Scale Invariant Feature Transform) implementation using CUDA for GPUs from NVidia. The first version is from 2007 and GPUs have evolved since then. This version is slightly more precise and considerably faster than the previous versions and has been optimized for Kepler and later generations of GPUs. On a GTX 1060 GPU the code takes about 1.6 ms on a 1280x960 pixel image and 2.4 ms on a 1920x1080 pixel image. There is also code for brute-force matching of features that takes about 2.2 ms for two sets of around 1900 SIFT features each.

VisionFaceDetection - An example of use a Vision framework for face landmarks detection in iOS 11

  •    Swift

First one is face rectangle detection by using VNDetectFaceRectanglesRequest based on pixelBuffer provided by delegate function captureOutput. Next we need to setup the property inputFaceObservations of VNDetectFaceLandmarksRequest object, to provide the input. Now we are redy to start landmarks detection.

apriltag_ros - A ROS wrapper of the AprilTag 3 visual fiducial detector

  •    C++

apriltag_ros is a Robot Operating System (ROS) wrapper of the AprilTag 3 visual fiducial detector. For details and tutorials, please see the ROS wiki. apriltag_ros depends on the latest release of the AprilTag library. Clone it into your catkin workspace before building.

cylon-opencv - Cylon adaptor and driver for OpenCV

  •    Javascript

Cylon.js (http://cylonjs.com) is a JavaScript framework for robotics, physical computing, and the Internet of Things (IoT).Want to use the Go programming language to power your robots? Check out our sister project Gobot (http://gobot.io).

seminar-convolutional-neural-networks - Seminar paper "Understanding Convolutional Neural Networks".

  •    TeX

Note: Several of the TikZ figures in this seminar paper are also available in the following repository: LaTeX Resources.This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Android-OCRSample - Android OCR example application which uses Google Text Recognition API

  •    Java

This is an example Android application for OCR. The current version uses Text Recognition API Overview while the old version used Tesseract.

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