Function predict

posenet/src/base_model.ts:58–79

* Predicts intermediate Tensor representations. * * @param input The input RGB image of the base model. * A Tensor of shape: [`inputResolution`, `inputResolution`, 3]. * * @return A dictionary of base model's intermediate predictions. * The returned dictionary should contains the f

(input: tf.Tensor3D)

   * displacementBwd: A Tensor3D that represents the backward displacement.
   */
  predict(input: tf.Tensor3D): {
    heatmapScores: tf.Tensor3D,
    offsets: tf.Tensor3D,
    displacementFwd: tf.Tensor3D,
    displacementBwd: tf.Tensor3D
  } {
    return tf.tidy(() => {
      const asFloat = this.preprocessInput(tf.cast(input, 'float32'));
      const asBatch = tf.expandDims(asFloat, 0);
      const results = this.model.predict(asBatch) as tf.Tensor4D[];
      const results3d: tf.Tensor3D[] = results.map(y => tf.squeeze(y, [0]));

      const namedResults = this.nameOutputResults(results3d);

      return {
        heatmapScores: tf.sigmoid(namedResults.heatmap),
        offsets: namedResults.offsets,
        displacementFwd: namedResults.displacementFwd,
        displacementBwd: namedResults.displacementBwd
      };
    });
  }

  // Because MobileNet and ResNet predict() methods output a different order for
  // these values, we have a method that needs to be implemented to order them.