          elif not 0.0 < kwargs['factor'] < 1.0:
              valid = False
  
          if not valid:
              print("Invalid MTCNN arguments received. Running with defaults")
              return {"minsize": 20,                 # minimum size of face
                      "threshold": [0.6, 0.7, 0.7],  # three steps threshold
                      "factor": 0.709}               # scale factor
          return kwargs
  
      def set_model_path(self):
          """ Load the mtcnn models """
          for model in ("det1.npy", "det2.npy", "det3.npy"):
 
</s> remove         print("Initializing MTCNN Detector...")
 </s> add  </s> remove         if self.verbose and self.scale < 1.0:
            print("Resizing image from {}x{} to {}.".format(
                str(width), str(height), "x".join(str(i) for i in dims)))
 </s> add         if self.scale < 1.0:
            logger.verbose("Resizing image from %sx%s to %s.",
                           width, height, "x".join(str(i) for i in dims))
            kwargs = {"minsize": 20,                 # minimum size of face
                      "threshold": [0.6, 0.7, 0.7],  # three steps threshold
                      "factor": 0.709}               # scale factor
            logger.warning("Invalid MTCNN arguments received. Running with defaults")
        logger.debug("Using mtcnn kwargs: %s", kwargs)
          elif not 0.0 < kwargs['factor'] < 1.0:
              valid = False
  
          if not valid:
            kwargs = {"minsize": 20,                 # minimum size of face
                      "threshold": [0.6, 0.7, 0.7],  # three steps threshold
                      "factor": 0.709}               # scale factor
            logger.warning("Invalid MTCNN arguments received. Running with defaults")
        logger.debug("Using mtcnn kwargs: %s", kwargs)
            kwargs = {"minsize": 20,                 # minimum size of face
                      "threshold": [0.6, 0.7, 0.7],  # three steps threshold
                      "factor": 0.709}               # scale factor
            logger.warning("Invalid MTCNN arguments received. Running with defaults")
        logger.debug("Using mtcnn kwargs: %s", kwargs)
            kwargs = {"minsize": 20,                 # minimum size of face
                      "threshold": [0.6, 0.7, 0.7],  # three steps threshold
                      "factor": 0.709}               # scale factor
            logger.warning("Invalid MTCNN arguments received. Running with defaults")
        logger.debug("Using mtcnn kwargs: %s", kwargs)
            kwargs = {"minsize": 20,                 # minimum size of face
                      "threshold": [0.6, 0.7, 0.7],  # three steps threshold
                      "factor": 0.709}               # scale factor
            logger.warning("Invalid MTCNN arguments received. Running with defaults")
        logger.debug("Using mtcnn kwargs: %s", kwargs)
          return kwargs
  
      def set_model_path(self):
          """ Load the mtcnn models """
          for model in ("det1.npy", "det2.npy", "det3.npy"):
keep keep keep keep replace replace replace replace keep keep keep keep keep
 Logging (#541)

* Convert prints to logger. Further logging improvements. Tidy  up

* Fix system verbosity. Allow SystemExit

* Fix reload extract bug

* Child Traceback handling

* Safer shutdown procedure

* Add shutdown event to queue manager

* landmarks_as_xy > property. GUI notes + linting. Aligner bugfix

* fix FaceFilter. Enable nFilter when no Filter is supplied

* Fix blurry face filter

* Continue on IO error. Better error handling

* Explicitly print stack trace tocrash log

* Windows Multiprocessing bugfix

* Add git info and conda version to crash log

* Windows/Anaconda mp bugfix

* Logging fixes for training 
plugins/extract/detect/mtcnn.py