Latest 0.0.5
License MIT
Platforms ios 10.0, osx 10.10

Carpaccio Carthage compatible Build Status Cocoapods compatible

Pure Swift goodness for RAW and other image + metadata handling

Carpaccio is a Swift library for macOS and iOS that allows fast decoding of image data & EXIF metadata from file formats supported by CoreImage (including all the various RAW file formats supported, using the CoreImage RAW decoding capability).

  • thumbnails
  • metadata
  • full sized image

Carpaccio uses multiple CPU cores efficiently in parallel for all of metadata, thumbnail and image data decoding.

There are no 3rd party dependencies (CoreImage filter is used for RAW decoding).



Carthage is a decentralized dependency manager that builds your dependencies and provides you with binary frameworks.

You can install Carthage with Homebrew using the following command:

$ brew update
$ brew install carthage

To integrate Carpaccio into your Xcode project using Carthage, specify it in your Cartfile:

github "mz2/Carpaccio" ~> 0.0.1

Run carthage update to build the framework and drag the built Carpaccio.framework into your Xcode project.


If you prefer not to use either of the aforementioned dependency managers, you can integrate Carpaccio into your project manually.

Embedded Framework

  • Open up Terminal, cd into your top-level project directory, and run the following command "if" your project is not initialized as a git repository:
$ git init
  • Add Carpaccio as a git submodule by running the following command:
$ git submodule add
  • Open the new Carpaccio folder, and drag the Carpaccio.xcodeproj into the Project Navigator of your application’s Xcode project.

    It should appear nested underneath your application’s blue project icon. Whether it is above or below all the other Xcode groups does not matter.

  • Select the Carpaccio.xcodeproj in the Project Navigator and verify the deployment target matches that of your application target.
  • Next, select your application project in the Project Navigator (blue project icon) to navigate to the target configuration window and select the application target under the "Targets" heading in the sidebar.
  • In the tab bar at the top of that window, open the "General" panel.
  • Click on the + button under the "Embedded Binaries" section.
  • Select the Carpaccio.xcodeproj nested inside a Products folder now visible.

The Carpaccio.framework is automagically added as a target dependency, linked framework and embedded framework in a copy files build phase which is all you need to build on the simulator and a device.

(This manual installation section was shamelessly ripped from the excellent Alamofire instructions.)


Adapting from a test included in the test suite for the framework, here’s how you can use Carpaccio:

    let converter = RAWImageLoader(imageURL: img1URL, thumbnailScheme: .fullImageWhenThumbnailMissing)

    converter.loadThumbnailImage(handler: { thumb, imageMetadata in
        // deal with thumbnail + metadata 
    }) { error in
        // deal with the error 

There’s a lot more to it though, including different schemes for loading thumbnails or using full size images when thumbnails are not found or are too small, and decoding thumbnails / full images at a specified maximum resolution.

Documentation and tests are minimal so for now you’ll just need to explore the API to discover all the good stuff. Please feel free to make suggestions as issues on GitHub.


Carpaccio is still a very fresh and raw (har har) library and there are many tasks to make this a more generally useful library.

  • [ ] Add tests for RAWs from a number of different camera vendors.
  • [x] Travis CI support.
  • [x] iOS support.

Latest podspec

    "name": "Carpaccio",
    "version": "0.0.5",
    "summary": "A pure Swift library for decoding image data and EXIF metadata (including RAW files).",
    "description": "Carpaccio is a Swift library that allows decoding image data from file formats supported by CoreImage (including all the various RAW file formats supported by CoreImage). It is built for efficient use of multiple CPU cores.",
    "homepage": "",
    "license": {
        "type": "MIT",
        "file": "LICENSE"
    "authors": {
        "Matias Piipari": "[email protected]"
    "source": {
        "git": "",
        "tag": "0.0.5"
    "social_media_url": "",
    "module_name": "Carpaccio",
    "ios": {
        "frameworks": "UIKit",
        "source_files": [
    "osx": {
        "frameworks": "AppKit",
        "source_files": [
    "platforms": {
        "ios": "10.0",
        "osx": "10.10"

Pin It on Pinterest

Share This