RTObjectDetection.lf

/**
 * @file RTObjectDetection.lf
 * @author Vincenzo Barbuto
 * @brief Examples of how to perform real-time object detection 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 DetectionVisualizer from "lib/Display.lf"
import ObjectDetector from "lib/ComputerVision.lf"

/**
 * @brief This reactor tests the object detection functionality by connecting a Camera, an
 * ObjectDetectior, and a DetectionVisualizer reactor.
 *
 * The `DetectionVisualizer` reactor receives the detections results and inference time from the
 * `ObjectDetector`, shows detectios results in a video window and prints the inference time to the
 * console.
 *
 * The `main` reactor creates instances of the `Camera`, `ObjectDetector`, and `DetectionVisualizer`
 * reactors, and connects them together to form the test pipeline.
 *
 * @note Remember to set the `model` parameter to the absolute path of the object detection model
 * you want to use.
 */
main reactor {
  camera = new Camera()
  display = new DetectionVisualizer()
  obj = new ObjectDetector(
      model = {= os.path.join(os.getcwd(),"models/vision/detection/ssd_mobilenet_v1.tflite") =})
  camera.camera_frame -> display.original_frame
  camera.camera_frame ~> camera.trigger
  camera.camera_frame -> obj.input_data
  obj.inference_time -> display.inference_time
  obj.results -> display.results
}