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A C++ SDK for 3D computer vision, paired with Cocoa frameworks for TrueDepth-based 3D scanning, meshing, and ML landmarking models + analysis for foot and ear

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StandardCyborgSDK

A C++ SDK for 3D computer vision, paired with Cocoa frameworks for TrueDepth-based 3D scanning, meshing, and ML landmarking models + analysis for face, foot, and ear

Introduction

A native (iOS/macOS) C++ library containing data structures, I/O, and algorithms, as well as a Cocoa framework for iOS and Mac clients to use publicly.

This project generates StandardCyborgFusion.framework

This code was developed by the Standard Cyborg team, primarily in 2018 and the middle of 2019. Standard Cyborg powered applications for custom 3d smart glasses, football helmet fitting, custom medical glasses, shoe sizing, and more. Development was paused on this code when Standard Cyborg began working on horizontal tooling, versus developing specific applications.

License

This codebase is released under the MIT license, with the exception that commercial applications in the field of prosthetics are prohibited until Jan 1, 2023.

See LICENSE file

Installation

  1. Make sure you have installed git lfs before cloning this repo
  2. Run this shell command
$ ./install-dependencies.sh
  1. Open StandardCyborgSDK.xcworkspace in Xcode

Targets

  • StandardCyborgFusion: iOS + macOS framework for 3D scanning and meshing using TrueDepth
  • StandardCyborgFusionTests: unit tests for the above
  • VisualTesterMac: a macOS app for helping develop and test StandardCyborgFusion
  • VisualTesterMac: ditto, but for iOS; also useful for on-device benchmarking
  • TrueDepthFusion: an iOS app for exercising the StandardCyborgFusion framework
  • StandardCyborgAlgorithmsTestbed: an iOS app for testing the SC C++ algorithms and data structures

Development

For debugging via lldb in Xcode, it is recommended to install the LLDB Eigen Data Formatter.

Note on building with bitcode support: https://medium.com/@heitorburger/static-libraries-frameworks-and-bitcode-6d8f784478a9

Deployment

To build StandardCyborgFusion.framework for public release:

  1. Run archive-build-standardcyborgfusion.sh, which will both update the compiled copy in ../StandardCyborgCocoa/StandardCyborgFusion and generate a .zip file for you to upload to the StandardCyborgFusion release in GitHub
  2. Commit the updated StandardCyborgFusion/Info.plist (which now has a new version number)
  3. Merge updated Info.plist commit into main
  4. Tag this commit in the format git tag v1.2.3-StandardCyborgFusion

Deploying StandardCyborgFusion to CocoaPods

  1. Commit the changes with a nice public-facing message and a prefix of StandardCyborgFusion: , e.g. StandardCyborgFusion: Adds SCMesh class
  2. Create a git tag for this commit in the format v1.2.3-StandardCyborgFusion
  3. Push the commit and the tag git push origin main, git push origin v1.2.3-StandardCyborgFusion
  4. Open this repo's releases on GitHub and draft a new release: https://github.com/StandardCyborg/StandardCyborgCocoa/releases/new a) For Tag version, specify the git tag you just created in step 2 a) For Release title, use the commit message from step 1 a) In "Attach binaries by dropping them here or selecting them", drag in the StandardCyborgFusion.framework.zip file that was generated inside StandardCyborgSDK/build. a) Publish release
  5. Push to CocoaPods: pod trunk push StandardCyborgFusion

Registering with CocoaPods

  1. Register: pod trunk register [email protected] 'Your Name' --description='MacBook Pro 13 2019'
  2. Click the link in your email
  3. Get someone who has access to add you. pod trunk add-owner StandardCyborgFusion [email protected]

Integrating External CocoaPods

How To

Our SCCocoaPods private registry (which is just a github repo) provides the most scalable way for deploying common dependencies.

Using a local podspec

Sometimes you only want to use a dependency for a single project. In this scenario, CocoaPods supports local podspecs— the podspec file is a file locally on disk rather than in a CocoaPods registry (as discussed above). See for example StandardCyborgFusion/PoissonRecon.podspec, which is a local dependency for the StandardCyborgFusionOSX target of the SDK.

We use a mix of local Podspecs and registry-served Podspecs. Local podspecs are best for internal-only usage.

Developing against the SDK locally

You may develop locally against the SDK as CocoaPod. For example, to develop a command line app which uses the SDK via local CocoaPods:

  1. Clone StandardCyborgCocoa as a sibling directory to this repo
  2. cd StandardCyborgSDK
  3. Build the CocoaPod into StandardCyborgCocoa/StandardCyborgFusion by running ./archive-build-standardcyborgfusion.sh
  4. Create a new command line project in Xcode, for example, FusionTest
  5. cd /path/to/FusionTest
  6. pod init
  7. Add to your podfile something like:
    target 'FusionTest' do
      platform :osx, '11.0'
    
      use_frameworks!
    
      pod 'StandardCyborgFusion', path: '/path/to/your/StandardCyborgCocoa/StandardCyborgFusion'
    end
  8. pod install
  9. We need to be able to locate the headers, but local CocoaPods don't actually get copied into the Pods directory. You'll need to symlink the local CocoaPod directory into Pods/. For example from your project root, ln -s /path/to/StandardCyborgCocoa/StandardCyborgFusion Pods/StandardCyborgFusion
  10. open FusionTest.xcworkspace
  11. For some unknown reason, Hardened Runtime conflicts with the loading of our dynamic library. Open the FusionTest command line target settings and go to the Signing & Capabilities tab. Click the × next to Hardened Runtime to disable it.
  12. Change the main.m extension to main.mm and add some code like the following:
    #import <iostream>
    #import <StandardCyborgData/StandardCyborgData.hpp>
    
    int main(int argc, const char * argv[]) {
        StandardCyborg::Vec3 v {1, 2, 3};
        std::cout << "v = " << v << std::endl;
        return 0;
    }
  13. Build and run!

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A C++ SDK for 3D computer vision, paired with Cocoa frameworks for TrueDepth-based 3D scanning, meshing, and ML landmarking models + analysis for foot and ear

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