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Carthage/Checkouts
Classifier.playground
CrowdCount.xcodeproj
CrowdCount.xcworkspace
CrowdCount
CrowdCountApi
CrowdCountMac
Prediction.playground
readmeimages
.gitattributes
.gitignore
.swiftlint.yml
Cartfile
Cartfile.resolved
README.md

README.md

Overview

The Apple CoreML can be divided into two implementations:

  1. iOS Crowd Counting Application
  2. macOS App (Ported from Prediction.playground because playgrounds are too fickle)

iOS Crowd Counting

iOS App In Action

iOS Development

macOS

MacOS App

MacOS Prediction Playground

Ability to drag and drop an image to see the results of different prediction strategies.

Setup

  1. Use Xcode 10+ for Swift 4.2, and macOS Mojave for Core ML 2 and Create ML.
  2. Install carthage.
  3. carthage update --platform ios,macos or carthage bootstrap.
  4. Build CrowdCount and CrowdCountMac. For Prediction.playground, explicitly build the libraries CrowdCountApi and CrowdCountApiMac.
  5. For iOS app, use Google Firebase to create a standard bare bones GoogleService-Info.plist or just delete the file GoogleService-Info.plist from Xcode. Used for image upload on request.

iOS todo

  • Release v0.2
  • Allow users to select correct classification to help model training
  • Ability to select a photo from the library and segue to show page
  • Have previous prediction preview and count “roll” to the left to imply that it’s working over time (animation).