Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/shu223/ios-experiments-contents
Table of Contents of my repo "iOS-Experiments"
https://github.com/shu223/ios-experiments-contents
Last synced: 5 days ago
JSON representation
Table of Contents of my repo "iOS-Experiments"
- Host: GitHub
- URL: https://github.com/shu223/ios-experiments-contents
- Owner: shu223
- Created: 2020-04-02T04:51:06.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-03T03:55:02.000Z (over 4 years ago)
- Last Synced: 2024-11-07T19:35:59.060Z (about 2 months ago)
- Size: 26.4 MB
- Stars: 20
- Watchers: 5
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
Awesome Lists containing this project
README
# iOS-Experiments Contents
Table of Contents of [iOS-Experiments](https://github.com/shu223/iOS-Experiments) repository.
## How to access to `iOS-Experiments`?
Here are some way to get access to the private repo:
- Become a sponsor (`premium` or higher): https://github.com/sponsors/shu223
- Let me know that. [Here](https://github.com/shu223) is my email address.
## Table of Contents
### Google Cloud Speech API sample
- A sample of Google Cloud Speech-to-Text API
- Modified `ios-docs-samples/speech/Objective-C/Speech-gRPC-Nonstreaming` in the official repository to recognize the audio of audio files.
- 公式リポジトリの`ios-docs-samples/speech/Objective-C/Speech-gRPC-Nonstreaming`を改変し、オーディオファイルの音声を認識するようにしたもの
- You should replace `API_KEY` when you use it.
- 利用にあたっては`API_KEY`を差し替えること
### vottcsv2createml
- Tool to convert annotation files as VoTT CSV format to Create ML JSON format.
- VoTT CSVフォーマットのアノテーションファイルをCreate MLのObject Detection用のJSONフォーマットに変換するツール
- I made this for myself, so it's not very kind and incomplete.
- 自分用につくったのでいろいろ不親切＆不完全
### yolo2createml
- Tool to convert annotation files as YOLO format (.txt) in YOLO format to Create ML JSON format.
- YOLOフォーマットのアノテーションファイル（.txt）をCreate MLのObject Detection用のJSONフォーマットに変換するツール
- I made this for myself, so it's not very kind and incomplete (for example, labels are hard coded)
- 自分用につくったのでいろいろ不親切＆不完全（ラベルがハードコーディングされてたり）
### ClockGestureClassification
**Activity Classification demo on watchOS**. The watchOS app can classify clockwise gesture and counter-clockwise gesture.
Here is the demo video: [youtu.be/I09hUyz1oG8?t=1939](https://youtu.be/I09hUyz1oG8?t=1939)
- Source code: [iOS-Experiments/ClockGestureClassification](https://github.com/shu223/iOS-Experiments/tree/master/ClockGestureClassification)
- 学習データ収集用に作成したwatchOSアプリ、学習データ、Create MLプロジェクトは含んでいません
`watchOS`, `Activity Classification`, `Machine Learning`, `Deep Learning`, `Core ML`, `Create ML`
https://github.com/shu223/iOS-Experiments/tree/master/
### Metal on watchOS
Metal shader examples on watchOS. Though Metal framework is NOT supported on watchOS, SceneKit framework and `SCNShadable` protocol is supported and it actually works! It contains toon shading sample and grayscale sample written in MSL (Metal Shading Language).
- Source code: [iOS-Experiments/watchOS-Metal](https://github.com/shu223/iOS-Experiments/tree/master/watchOS-Metal)
`watchOS`, `Metal`, `Metal Shading Language`, `shader modifier`, `SceneKit`, `MSL`, `GPU`
### AnimeSpeakerClassifier
An experimental app to classify speakers in an anime movie using `SoundAnalysis` framework. It worked well. Used Create ML to train the model.
SoundAnalysisフレームワークを用いてアニメ動画の話者を識別できるか試した。かなりうまくいった。学習はCreate MLを使用。
- Source code: [iOS-Experiments/AnimeSpeakerClassifier](https://github.com/shu223/iOS-Experiments/tree/master/AnimeSpeakerClassifier)
- The Core ML model and video data have been removed from the project in consideration of copyright, so you can **NOT** build & run this project.
- 著作権に配慮してCore MLモデルと動画データをプロジェクトから除去しているので**ビルド不可**
- The Create ML project file to train the model and the training data are **NOT** contained in the repo.
- Create MLのプロジェクトと学習用の音声データも同梱していません
`SoundAnalysis`, `Speaker Classification`, `Video Processing`, `iOS 13`
### PodcastSpeakerClassifier
An experimental app to classify speakers in a podcast audio using `SoundAnalysis` framework. It did **NOT** well. Used Create ML to train the model.
SoundAnalysisフレームワークを用いてPodcastの話者を識別できるか試した。あまりうまくいってない。学習用の音声データが少なかったかもしれない。学習はCreate MLを使用。
- Source code: [iOS-Experiments/PodcastSpeakerClassifier](https://github.com/shu223/iOS-Experiments/tree/master/PodcastSpeakerClassifier)
- The Create ML project file to train the model and the training data are **NOT** contained in the repo.
- Create MLのプロジェクトと学習用の音声データは同梱していない。どこかにあるはずなので見つけたらアップするかも。
`SoundAnalysis`, `Sound Analysis`, `Speaker Classification`, `Audio Processing`, `iOS 13`
### Scene Reconstruction with SceneKit + ARKit
Visualizing `ARMeshGeometry` from LiDAR **without RealityKit** (with SceneKit)
- Source code: [iOS-Experiments/ARKit_SceneReconstruction](https://github.com/shu223/iOS-Experiments/tree/master/ARKit_SceneReconstruction)
`ARKit 3.5`, `LiDAR`, `Depth`, `iOS 13.4`
### Core Haptics samples
#### HapticsMaker
A sample to generate custom haptic patterns using Core Haptics framework which is added in iOS 13.
- Source code: [iOS-Experiments/CoreHaptics/HapticsMaker](https://github.com/shu223/iOS-Experiments/tree/master/CoreHaptics/HapticsMaker)
#### AHAPPlayer
A very simple example to play an AHAP file.
- Source code: [iOS-Experiments/CoreHaptics/AHAPPlayer](https://github.com/shu223/iOS-Experiments/tree/master/CoreHaptics/AHAPPlayer)
`CoreHaptics`, `Core Haptics`, `iOS 13`
### MSL from GLSL
MSL (Metal Shader Language) samples which are ported from GLSL.
- Source code: [iOS-Experiments/MSLfromGLSL/MSLfromGLSL1](https://github.com/shu223/iOS-Experiments/tree/master/MSLfromGLSL/MSLfromGLSL1)
- Original: http://glslsandbox.com/e#36694.0
- Source code: [iOS-Experiments/MSLfromGLSL/MSLfromGLSL2](https://github.com/shu223/iOS-Experiments/tree/master/MSLfromGLSL/MSLfromGLSL2)
- Original: http://glslsandbox.com/e#36614.0
- Source code: [iOS-Experiments/MSLfromGLSL/MSLfromGLSL3](https://github.com/shu223/iOS-Experiments/tree/master/MSLfromGLSL/MSLfromGLSL3)
- Original: http://glslsandbox.com/e#36538.3
`Metal`, `Metal Shading Language`, `MSL`, `GPU`
### Rectangle Detection & Correction
A real-time camera sample to detect a rectangle using Vision (`VNDetectRectanglesRequest`, `VNRectangleObservation`) and correct it using Core Image (`CIPerspectiveCorrection`).
- Source code: [iOS-Experiments/RectangleDetection](https://github.com/shu223/iOS-Experiments/tree/master/RectangleDetection)
`Vision`, `Core Image`, `Image Processing`
### PhysicalFaceTracker
An one-day project to make a face tracking camera using iPhone and Raspberry Pi ([bleno](https://github.com/noble/bleno)).
- Source code: [iOS-Experiments/PhysicalFaceTracker](https://github.com/shu223/iOS-Experiments/tree/master/PhysicalFaceTracker)
- iOS code only. The code on Raspberry Pi and the 3D model data of the hardware are **NOT** included.
`Vision`, `CoreBluetooth`, `Image Processing`, `IoT`, `Hardware`
### Processing with Swift
#### Solar System 2D
Original(Processing): https://youtu.be/l8SiJ-RmeHU
- Source code: [iOS-Experiments/Processing/SolarSystem2D](https://github.com/shu223/iOS-Experiments/tree/master/Processing/SolarSystem2D)
`SpriteKit`, `Animation`
#### Solar System 3D
Original(Processing): https://github.com/CodingTrain/website/tree/master/CodingChallenges/CC_008_SolarSystemGenerator3D
- Source code: [iOS-Experiments/Processing/SolarSystem3D](https://github.com/shu223/iOS-Experiments/tree/master/Processing/SolarSystem3D)
`SceneKit`, `Animation`, `3D`
### ZundokoBLE
- https://twitter.com/shu223/status/715946579585314820
- https://qiita.com/shu223/items/2c7b2e1ed058a1e3b33f
- Source code: [iOS-Experiments/ZundokoBLE](https://github.com/shu223/iOS-Experiments/tree/master/ZundokoBLE)
- It can **NOT** be built with the latest Xcode because written in Swift 3.x.
`CoreBluetooth`, `Core Bluetooth`, `BLE`