@codait/max-vis
Image annotation library for MAX image models
Last updated a month ago by vabarbosa .
Apache-2.0 · Repository · Bugs · Original npm · Tarball · package.json
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max-vis

max-vis is a JavaScript library and command-line utility to help render the predictions returned by some of the deep learning models of the Model Asset eXchange (MAX).

Given the JSON result (prediction) from one of the MAX image models and the source image, with max-vis you can render a new version of the image with predictions (i.e., bounding box, pose lines, etc) annotated on it.

Install

  • browser

    <script src="https://cdn.jsdelivr.net/npm/@codait/max-vis"></script>
    
  • Node.js

    npm install @codait/max-vis
    
  • command-line

    npm install -g @codait/max-vis
    

Usage

See working examples for browser, Node.js, and command-line environments in the /examples directory.

  • browser

    // the prediction (JSON object) returned by a MAX image model
    const prediction = ... 
    
    // the source image used to get the prediction
    const image = document.getElementById('myimage')
    
    // returns a Promise that resolves to a copy of the source image annotated with the prediction
    maxvis.annotate(prediction, image)
       .then(annotatedImageBlob => {
          // the argument passed is a Blob of a PNG image
          let img = document.createElement('img')
          img.src = URL.createObjectURL(annotatedImageBlob)
          document.body.appendChild(img)
       })
    

    Note: When loaded in a browser, the global variable maxvis will be available to access the API.

  • Node.js

    const maxvis = require('@codait/max-vis')
    
    // the prediction (JSON object) returned by a MAX image model
    const prediction = ... 
    
    // the source image used to get the prediction
    const image = 'images/myImage.jpg'
    
    // returns a Promise that resolves to a copy of the source image annotated with the prediction
    maxvis.annotate(prediction, image)
       .then(annotatedImageBuffer => {
          // the argument passed is a Buffer of a PNG image
          fs.writeFile('myAnnotatedImage.png', annotatedImageBuffer, (err) => {
             if (err) {
                console.error(err)
             }
          })
       })
    
  • command-line

    Pass prediction directly from a file

    $ maxvis images/myImage.jpg -p maxImageModelPrediction.json
    

    or pipe prediction from curl

    $ curl -X POST "http://max-image-model-endpoint/model/predict" \
    -F "image=@images/myImage.jpg" \
    | maxvis images/myImage.jpg
    

    Note: When installed as a command-line utility, the global command maxvis will be available.

API

overlay(prediction, image, [options])

Processes the prediction against the image and renders the prediction (in a Canvas overlay) on top of the image. Not applicable when running in Node.js.

prediction - (Required) the prediction output from a MAX image model
image - (Required) an HTMLImageElement or the id of an HTMLImageElement
options - (Optional) a JSON object of options to customize rendering. See Options for more info.

annotate(prediction, image, [options])

Processes the prediction against the image and creates a new version of the image that includes the rendered prediction.

prediction - (Required) the prediction output from a MAX image model
image - (Required) an HTMLImageElement or HTMLCanvasElement or the id of an HTMLImageElement or HTMLCanvasElement.
options - (Optional) a JSON object of options to customize rendering. See Options for more info.

Returns a Promise that resolves to a Blob (in browsers) or Buffer (in Node.js) of a PNG image containing the input image annotated with the prediction.

extract(prediction, image, [options])

Processes the prediction against the image, extracts the components from the image.

prediction - (Required) the prediction output from a MAX image model
image - (Required) an HTMLImageElement or HTMLCanvasElement or the id of an HTMLImageElement or HTMLCanvasElement.
options - (Optional) a JSON object of options to customize rendering. See Options for more info.

Returns a Promise that resolves to an array of objects representing each item of the prediction. Each object in the array contains:

  • image: a Blob (in browsers) or Buffer (in Node.js) of a PNG image containing the cropped out area of the input image identified in prediction.
  • label: a label for the image

version

Returns the max-vis version number

API Options

Available options to pass to the API. All are optional and by default, max-vis will try to determine the appropriate values from the prediction object.

Option Type Description
type String The name of type of rendering the prediction conforms to. Acceptable types are boxes (for bounding boxes), lines (for pose lines), or segments (for image segmentation).
height Number The height (in pixels) of the image represented by the prediction
width Number The width (in pixels) of the image represented by the prediction
colors 2D array or Object An array of RGB values to use for rendering (e.g., [[255,0,200], [125,125,125], ...]). Alternatively, for bounding boxes an object of label names mapped to preferred RGB values (e.g., {person: [255,0,200], horse: [125,125,125], ...}) can be passed.
segments Array An array of segmentation IDs to process (e.g., [0, 15]). If not provided, all segments will be processed. This is only applicable for predictions of type segments.
exclude Boolean Set to true if segments option indicates segmentation that should be excluded instead of included in processing. Default is false. This is only applicable for predictions of type segments.
lineWidth Number The thickness of the lines in the rendering. Default is 2. This is only applicable for predictions of type boxes or lines.

CLI Parameters

Available parameters to pass to the CLI

Parameter Description
--type Same as type API option
--extract Extract and save each component of the prediction from the image instead of saving a single image will all components rendered
--prediction The path to a JSON file containing the prediction returned by a MAX image model

Examples

The /examples directory contains working examples for the browser, Node.js, and command-line environments.

  • annotate bounding boxes
    jockey

  • extract bounding boxes
    jockey

  • annotate segmentation maps
    soccer

  • extract segmentation maps
    soccer

  • annotate pose lines
    pilots

Links

License

Apache-2.0

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