RTImageSegmentation.lf

/**
 * @file RTImageSegmentation.lf
 * @author Vincenzo Barbuto
 * @brief Examples of how to perform real-time image segmentation using a TensorFlow Lite model.
 */
target Python {
  keepalive: true,
  single-threaded: true  # OpenCV crashes if we use the multithreaded version.
}

import Camera from "lib/Input.lf"
import SegmentationVisualizer from "lib/Display.lf"
import ImageSegmenter from "lib/ComputerVision.lf"

/**
 * @brief This reactor tests the real-time image segmentation functionality by connecting a Camera,
 * an ImageSegmenter, and a SegmentationVisualizer reactor.
 *
 * The `SegmentationVisualizer` reactor receives the segmentations results and inference time from
 * the `ImageSegmenter`, shows results in a video window and prints the inference time to the
 * console.
 *
 * The `main` reactor creates instances of the `Camera`, `ImageSegmenter`, and
 * `SegmentationVisualizer` reactors, and connects them together to form the test pipeline.
 *
 * @note Remember to set the `model` parameter to the absolute path of the image segmentation model
 * you want to use.
 */
main reactor {
  camera = new Camera()
  display = new SegmentationVisualizer()
  segmenter = new ImageSegmenter(
      model = {= os.path.join(os.getcwd(),"models/vision/segmentation/deeplab.tflite") =})
  camera.camera_frame -> segmenter.input_data
  camera.camera_frame -> display.original_frame
  camera.camera_frame ~> camera.trigger
  segmenter.inference_time -> display.inference_time
  segmenter.results -> display.results
}