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  1. """ Wokers definition """
  2. # TODO Implement this with a queue and mutliprocessingt
  3. import threading
  4. from config import Config as Conf
  5. from transform.gan.mask import CorrectToMask, MaskrefToMaskdet, MaskfinToNude
  6. from transform.opencv.correct import DressToCorrect, ColorTransfer
  7. from transform.opencv.mask import MaskToMaskref, MaskdetToMaskfin
  8. from transform.opencv.resize import ImageToResized, ImageToCrop, ImageToOverlay, ImageToResizedCrop, ImageToRescale
  9. workers = {
  10. "gan": {
  11. CorrectToMask: [],
  12. MaskrefToMaskdet: [],
  13. MaskfinToNude: [],
  14. "sem": threading.Semaphore(1)
  15. },
  16. "opencv": {
  17. DressToCorrect: [],
  18. MaskToMaskref: [],
  19. ImageToResized: [],
  20. ImageToCrop: [],
  21. ImageToOverlay: [],
  22. ImageToResizedCrop: [],
  23. ImageToRescale: [],
  24. ColorTransfer: [],
  25. MaskdetToMaskfin: [],
  26. "sem": threading.Semaphore(Conf.args['n_cores'])
  27. }
  28. }
  29. select_sem = threading.Semaphore(1)
  30. def run_worker(klass, image_step, config=None):
  31. r = None
  32. for k in ("gan", "opencv"):
  33. if workers.get(k).get(klass) is not None:
  34. Conf.log.debug("wk {}".format(workers.get(k).get(klass)))
  35. workers.get(k).get("sem").acquire()
  36. select_sem.acquire()
  37. if len(workers.get(k).get(klass)) == 0:
  38. w = klass()
  39. else:
  40. w = workers.get(k).get(klass).pop(0)
  41. select_sem.release()
  42. r = w.run(*[image_step[i] for i in w.input_index], config=config)
  43. select_sem.acquire()
  44. workers.get(k).get(klass).append(w)
  45. select_sem.release()
  46. workers.get(k).get("sem").release()
  47. return r