Displaying 1 to 20 from 55 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.

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 😜).




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.

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.

hugh-detector - detect the color red in an image

  •    Javascript

Return whether the image data in buf with dimensions width and height contains sufficient redness as a boolean.

node-matchengine - Node module for using the TinEye MatchEngine API.

  •    Javascript

Node module for using the TinEye MatchEngine API. You should end up with a file with an ID of: "sample/test.jpg" in the MatchEngine index.

node-pastec - Node module for using a Pastec server.

  •    Javascript

Node module for interacting with a Pastec server. You should end up with a file with an ID of: 1234 in the Pastec index.

motion - A node.js motion detection library that supports node.js streams.

  •    Javascript

A node.js motion detection library that supports node.js streams. Using motion streams requires ImageMagick CLI tools to be installed. There's plenty of ways to install ImageMagick, choose what's right for you.

caraweb - Face Detection With HTML5's WebSockets, WebRTC's getUserMedia() and OpenCV on Node.js

  •    Javascript

Explorando detección facial en el browser con HTML5 y WebRTC (getUserMedia), usando WebSockets (con socket.io), con procesamiento de imagenes y detección facial rápida con caracteristicas pseudo-Haar en OpenCV en el backend con Node.js. Probado con Firefox y Google Chrome.