Content Delivery Network

What a group is

Groups are file collections. Use them to organize content in your Uploadcare project. A common use case is to use them in single content upload or delivery transactions.

For instance, each time you use multi-upload with File Uploader, a new group is created. It stores UUIDs of uploaded files. You can access file collections via :group_uuid:

https://ucarecdn.com/cd334b26-c641-4393-bcce-b5041546430d~11/

:group_uuid is similar to a single file UUID, but it has the file number ~N at the end. A group can contain up to 1000 files.

A group URL will show a list of individual file URLs with their UUIDs, and index numbers in that group.

Technically, our File Uploader will create a group on multi-upload using Uploadcare APIs. Learn more about creating and managing groups.

Accessing single files in a group

Request a specific file in a group by adding /nth/i/, where i is a file index, starting from 0:

/:group_uuid/nth/0/
/:group_uuid/nth/1/
/:group_uuid/nth/2/

Note, there is no /-/ separator after a group UUID. It's required for transformations only.

By the way, you can apply Image Transformations to indivitual files within a group:

/:group_uuid/nth/0/-/resize/256x/

You can also group the processed files with the respective operation sequences. When you request a file by its group URL, it'll include all operations before adding to that group. Adding more operations after /-/ will apply them over the existing ones.

Get a group as an archive

Getting a group as an archive is done via the archive group processing operation.

The operation limits are:

• A total size of uncompressed files ≤ 2 GB.
• Processing operations will be discarded. Only original files will be archived.

Here's how to get an archived file group:

/:group_uuid/archive/:format/:filename

• :group_uuid — UUID of a file group you want to get as an archive.
• :format — the format of that output archive, we support zip and tar.
• :filename (optional) — output archive filename.

Gallery from in-group files

A gallery will display a group of images on a single page. The gallery feature is powered by the Fotorama JavaScript library.

Making a gallery is also based on a group processing operation, gallery. Here’s how it works,

/:group_uuid/gallery/

You can customize the layout and behavior of images in a gallery by adding Fotorama options to their URLs. The workflow is pretty much the same as with Image Transformations except for image size, dimensions, and ratio manipulations.

/:group_uuid/gallery/-/nav/thumbs/-/fit/cover/-/autoplay/1000/

The most common use case with gallery is to embed a collection of images into your page via <iframe>:

<iframe
  src="/:group_uuid/gallery/-/nav/thumbs/-/fit/cover/-/loop/true/-/allowfullscreen/native/-/thumbwidth/100/"
  width="100%"
  height="450"
  allowfullscreen="true"
  frameborder="0">
</iframe>

Get file info as JSON

-/json/

Returns file-related information, such as image dimensions or geotagging data in the JSON format.

/:uuid/-/json/
/:uuid/-/preview/-/json/

In the second option, json response will contain the original key with the info on your original image.

Get file info as application/javascript

-/jsonp/

Same as the example above, but it returns data as the application/javascript type. Callback name should always be uploadcare_callback. It should receive the two following arguments: id and a response object. For original files, id is na equivalent to their UUIDs. For processed files, id is an equivalent to their URLs, excluding the last operation, jsonp.

/:uuid/-/jsonp/
/:uuid/-/preview/-/jsonp/

In the first case id will be c5b7dd84-c0e2-48ff-babc-d23939f2c6b4, in the second: /c5b7dd84-c0e2-48ff-babc-d23939f2c6b4/-/preview/.

Perceptual hash

File info response includes a value of a perceptual hash calculated using pixel contents of an image. Perceptual hashing is a common fingerprinting technique to quickly compare images and find duplicates or similar images.

Uploadcare automatically calculates a 64-bit long perceptual hash value and returns it as a HEX string. In this example, the perceptual hash value is 940f5fd09aa48ddc:

{
  "id": "1b192edb-212d-401a-ad9b-529047272e1b",
  "datetime_original":null, 
  "orientation":null, 
  "height":1600,
  "width":2400,
  "geo_location":null,
  "format":"JPEG",
  "hash":"940f5fd09aa48ddc"
}

It’s easy to find image duplicates by quickly comparing their perceptual hash values. To find similar images, it's important to compare perceptual hash values bitwise (Hamming distance). A small number of unmatched bits (e.g., up to 8 bits) will correspond to subtle changes in the visual contents, while non-similar images will usually have more than 8 different bits.