Recognize objects in images

🤖/image/describe recognizes objects in images and returns them as English words.

As mentioned this Robot enables you to recognize objects on images.

You can use the labels that we return in your application to automatically classify images. You can also pass the labels down to other Robots to filter images that contain (or do not contain) certain content.

Warning: Transloadit aims to be deterministic, but this Robot uses third-party AI services. The providers (AWS, GCP) will evolve their models over time, giving different responses for the same input images. Avoid relying on exact responses in your tests and application.

Parameters

use

String / Array of Strings / Objectrequired
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"
]
```
💡 That’s likely all you need to know about use, but you can view advanced use cases:
› Advanced use cases
- Step bundling. Some Robots can gather several Step results for a single invocation. For example, 🤖/file/compress would normally create one archive for each file passed to it. If you'd set bundle_steps to true, however, it will create one archive containing all the result files from all Steps you give it. To enable bundling, provide an object like the one below to the use parameter:
```
"use": {
  "steps": [
    ":original",
    "encoded",
    "resized"
  ],
  "bundle_steps": true
}
```
  This is also a crucial parameter for 🤖/video/adaptive, otherwise you'll generate 1 playlist for each viewing quality.
  Keep in mind that all input Steps must be present in your Template. If one of them is missing (for instance it is rejected by a filter), no result is generated because the Robot waits indefinitely for all input Steps to be finished.
  
  Here’s a demo that showcases Step bundling.
- Group by original. Sticking with 🤖/file/compress example, you can set group_by_original to true, in order to create a separate archive for each of your uploaded or imported files, instead of creating one archive containing all originals (or one per resulting file). This is important for for 🤖/media/playlist where you'd typically set:
```
"use": {
  "steps": [
    "segmented"
  ],
  "bundle_steps": true,
  "group_by_original": true
}
```
- Fields. You can be more discriminatory by only using files that match a field name by setting the fields property. When this array is specified, the corresponding Step will only be executed for files submitted through one of the given field names, which correspond with the strings in the name attribute of the HTML file input field tag for instance. When using a back-end SDK, it corresponds with myFieldName1 in e.g.: $transloadit->addFile('myFieldName1', './chameleon.jpg').
  
  This parameter is set to true by default, meaning all fields are accepted.
  
  Example:
```
"use": {
  "steps": [ ":original" ],
  "fields": [ "myFieldName1" ]
}
```
- Use as. Sometimes Robots take several inputs. For instance, 🤖/video/merge can create a slideshow from audio and images. You can map different Steps to the appropriate inputs.
  
  Example:
```
"use": {
  "steps": [
    { "name": "audio_encoded", "as": "audio" },
    { "name": "images_resized", "as": "image" }
  ]
}
```
  Sometimes the ordering is important, for instance, with our concat Robots. In these cases, you can add an index that starts at 1. You can also optionally filter by the multipart field name. Like in this example, where all files are coming from the same source (end-user uploads), but with different <input> names:
  
  Example:
```
"use": {
  "steps": [
    { "name": ":original", "fields": "myFirstVideo", "as": "video_1" },
    { "name": ":original", "fields": "mySecondVideo", "as": "video_2" },
    { "name": ":original", "fields": "myThirdVideo", "as": "video_3" }
  ]
}
```
  For times when it is not apparent where we should put the file, you can use Assembly Variables to be specific. For instance, you may want to pass a text file to 🤖/image/resize to burn the text in an image, but you are burning multiple texts, so where do we put the text file? We specify it via ${use.text_1}, to indicate the first text file that was passed.
  
  Example:
```
"watermarked": {
  "robot": "/image/resize",
  "use"  : {
    "steps": [
      { "name": "resized", "as": "base" },
      { "name": "transcribed", "as": "text" },
    ],
  },
  "text": [
    {
      "text"  : "Hi there",
      "valign": "top",
      "align" : "left",
    },
    {
      "text"    : "From the 'transcribed' Step: ${use.text_1}",
      "valign"  : "bottom",
      "align"   : "right",
      "x_offset": 16,
      "y_offset": -10,
    }
  ]
}
```
provider

Stringrequired

Which AI provider to leverage. Valid values are "aws" and "gcp".

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

String ⋅ default: "full"

Whether to return a flow blown response ("full"), or a flat list of descriptions ("list").
format

String ⋅ 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 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.

Recognize objects in images

Parameters

`use`

`provider`

`granularity`

`format`

`explicit_descriptions`

Demos

Related blog posts