Recognize objects in images

Usage example

{
  "steps": {
    "described": {
      "robot": "/image/describe",
      "use": ":original",
      "provider": "aws"
    }
  }
}

Parameters

• interpolate

  boolean | Record<string, boolean>

  Controls whether Assembly Variables are interpolated for individual instruction fields.

  By default, most Robot instruction fields interpolate Assembly Variables. Set this to false to treat every instruction field as literal text, or set an individual field path to false to treat only that field as literal text. For Robot-specific fields that are literal by default, set this to true or set that field path to true to opt back into interpolation.

  Use field names such as path, or dotted paths such as ffmpeg.vf for nested objects.

• output_meta

  Record<string, boolean> | boolean | Array<string>

  Allows you to specify a set of metadata that is more expensive on CPU power to calculate, and thus is disabled by default to keep your Assemblies processing fast.

  For images, you can add "has_transparency": true in this object to extract if the image contains transparent parts and "dominant_colors": true to extract an array of hexadecimal color codes from the image.

  For images, you can also add "blurhash": true to extract a BlurHash string — a compact representation of a placeholder for the image, useful for showing a blurred preview while the full image loads.

  For videos, you can add the "colorspace: true" parameter to extract the colorspace of the output video.

  For videos, you can also add "interlaced": true to detect whether the video is interlaced. This combines the cheap ffprobe field_order flag with a bounded idet sampling pass over the first frames of the source, exposing interlaced, field_order, and a diagnostic interlace_detection object under file.meta. This is computationally expensive and billed accordingly.

  For audio, you can add "mean_volume": true to get a single value representing the mean average volume of the audio file.

  You can also set this to false to skip metadata extraction and speed up transcoding.

• result

  boolean (default: false)

  Whether the results of this Step should be present in the Assembly Status JSON

• queue

  batch

  Setting the queue to 'batch', manually downgrades the priority of jobs for this step to avoid consuming Priority job slots for jobs that don't need zero queue waiting times

• force_accept

  boolean (default: false)

  Force a Robot to accept a file type it would have ignored.

  By default, Robots ignore files they are not familiar with. 🤖/video/encode, for example, will happily ignore input images.

  With the force_accept parameter set to true, you can force Robots to accept all files thrown at them. This will typically lead to errors and should only be used for debugging or combatting edge cases.

• ignore_errors

  boolean | Array<meta | execute> (default: [])

  Ignore errors during specific phases of processing.

  Setting this to ["meta"] will cause the Robot to ignore errors during metadata extraction.

  Setting this to ["execute"] will cause the Robot to ignore errors during the main execution phase.

  Setting this to true is equivalent to ["meta", "execute"] and will ignore errors in both phases.

• use

  string | Array<string> | Array<object> | object

  Specifies which Step(s) to use as input.

  • You can pick any names for Steps except ":original" (reserved for user uploads handled by Transloadit)
  • You can provide several Steps as input with arrays:
    

    {
      "use": [
        ":original",
        "encoded",
        "resized"
      ]
    }
    

  • You can also tag an input Step with as to pass semantic intent to robots:
  • You can also tag input Steps with as to pass semantic intent to robots:
    

    {
      "use": [
        {
          "name": ":original",
          "as": "image"
        },
        {
          "name": ":original",
          "as": "mask"
        }
      ]
    }
    

  Tip

  That's likely all you need to know about use, but you can view Advanced use cases.

• provider

  aws | gcp | replicate | fal | transloadit

  Which AI provider to leverage.

  Transloadit outsources this task and abstracts the interface so you can expect the same data structures, but different latencies and information being returned. Different cloud vendors have different areas they shine in, and we recommend to try out and see what yields the best results for your use case.

• granularity

  full | list (default: "full")

  Whether to return a full response ("full") including confidence percentages for each found label, or just a flat list of labels ("list").

• format

  json | meta | text (default: "json")

  In what format to return the descriptions.

  • "json" returns a JSON file.
  • "meta" does not return a file, but stores the data inside Transloadit's file object (under ${file.meta.descriptions}) that's passed around between encoding Steps, so that you can use the values to burn the data into videos, filter on them, etc.

• explicit_descriptions

  boolean (default: false)

  Whether to return only explicit or only non-explicit descriptions of the provided image. Explicit descriptions include labels for NSFW content (nudity, violence, etc). If set to false, only non-explicit descriptions (such as human or chair) will be returned. If set to true, only explicit descriptions will be returned.

  The possible descriptions depend on the chosen provider. The list of labels from AWS can be found in their documentation. GCP labels the image based on five categories, as described in their documentation.

  For an example of how to automatically reject NSFW content and malware, please check out this blog post.

Demos

Related blog posts