Method segment

deeplab/src/index.ts:182–206

* Segments an arbitrary image and generates a two-dimensional tensor with * class labels assigned to each cell of the grid overlayed on the image ( the * maximum number of cells on the side is fixed to 513). * * @param image :: `ImageData | HTMLImageElement | HTMLCanvasElement | * HTM

(input: DeepLabInput, config: PredictionConfig = {})

   */

  public async segment(input: DeepLabInput, config: PredictionConfig = {}):
      Promise<DeepLabOutput> {
    if (!((config.colormap && config.labels) || this.base)) {
      throw new Error(
          `Calling the 'segment' method requires either the 'base'` +
          ` attribute to be defined ` +
          `(e.g. 'pascal', 'cityscapes' or'ade20k'),` +
          ` or 'colormap' and 'labels' options to be set. ` +
          `Aborting, since neither has been provided.`);
    } else if (!(config.colormap && config.labels)) {
      config.colormap = getColormap(this.base);
      config.labels = getLabels(this.base);
    }

    const {colormap, labels, canvas} = config;
    const rawSegmentationMap = tf.tidy(() => this.predict(input));

    const [height, width] = rawSegmentationMap.shape;
    const {legend, segmentationMap} =
        await toSegmentationImage(colormap, labels, rawSegmentationMap, canvas);

    tf.dispose(rawSegmentationMap);

    return {legend, height, width, segmentationMap};
  }

  /**
   * Dispose of the tensors allocated by the model.