          self.scale = scale
          self.zoom = zoom
  
      def minibatchAB(self, images, batchsize, doShuffle=True):
          batch = lib.utils.BackgroundGenerator(self.minibatch(images, batchsize, doShuffle), 1)
          for ep1, warped_img, target_img in batch.iterator():
              yield ep1, warped_img, target_img
  
      # A generator function that yields epoch, batchsize of warped_img and batchsize of target_img
      def minibatch(self, data, batchsize, doShuffle=True):
 
</s> remove         generator = lib.training_data.TrainingDataGenerator(random_transform_args, 160, zoom)
        batch = generator.minibatchAB(input_images, batch_size, doShuffle=False)
 </s> add         generator = TrainingDataGenerator(random_transform_args, 160, zoom)
        batch = generator.minibatchAB(input_images, batch_size,
                                      doShuffle=False) </s> remove         zoom = self.trainer.model.IMAGE_SHAPE[0] // 64 if hasattr(self.trainer.model, 'IMAGE_SHAPE') else 1
 </s> add         zoom = 1
        if hasattr(self.trainer.model, 'IMAGE_SHAPE'):
            zoom = self.trainer.model.IMAGE_SHAPE[0] // 64 </s> remove method = None
 </s> add class BackgroundGenerator(threading.Thread):
    """ Run a queue in the background. From:
        https://stackoverflow.com/questions/7323664/ """
    # See below why prefetch count is flawed
    def __init__(self, generator, prefetch=1):
        threading.Thread.__init__(self)
        self.queue = Queue.Queue(maxsize=prefetch)
        self.generator = generator
        self.daemon = True
        self.start() </s> remove     for i in pool.imap_unordered(runner, data):
        yield i if i is not None else 0
    
def runner(item):
    return method(item)
 </s> add     def iterator(self):
        """ Iterate items out of the queue """
        while True:
            next_item = self.queue.get()
            if next_item is None:
                break
            yield next_item </s> remove     def have_face(self, filename):
        """ return path of images that have faces """
        return os.path.basename(filename) in self.faces_detected

    def get_faces(self, image, rotation=0):
        """ Extract the faces from an image """
        faces_count = 0
        faces = detect_faces(image,
                             self.args.detector,
                             self.args.verbose,
                             rotation=rotation,
                             dlib_buffer=self.args.dlib_buffer,
                             mtcnn_kwargs=self.mtcnn_kwargs)

        for face in faces:
            if self.filter and not self.filter.check(face):
                if self.args.verbose:
                    print("Skipping not recognized face!")
                continue
            yield faces_count, face

            self.num_faces_detected += 1
            faces_count += 1

        if faces_count > 1 and self.args.verbose:
            self.verify_output = True

    def get_faces_alignments(self, filename, image):
        """ Retrieve the face alignments from an image """
        faces_count = 0
        faces = self.faces_detected[os.path.basename(filename)]
        for rawface in faces:
            face = DetectedFace(**rawface)
            # Rotate the image if necessary
            # NB: Rotation of landmarks now occurs at extract stage
            # This is here for legacy alignments
            if face.r != 0:
                image, _ = rotate_image_by_angle(image, face.r)
            face.image = image[face.y: face.y + face.h,
                               face.x: face.x + face.w]
            if self.filter and not self.filter.check(face):
                if self.args.verbose:
                    print("Skipping not recognized face!")
                continue
 </s> add class Images():
    """ Holds the full frames/images """
    def __init__(self, arguments):
        self.args = arguments
        self.input_images = self.get_input_images()
        self.images_found = len(self.input_images)
        batch = BackgroundGenerator(self.minibatch(images, batchsize, doShuffle), 1)

          self.scale = scale
          self.zoom = zoom
  
      def minibatchAB(self, images, batchsize, doShuffle=True):
        batch = BackgroundGenerator(self.minibatch(images, batchsize, doShuffle), 1)
          for ep1, warped_img, target_img in batch.iterator():
              yield ep1, warped_img, target_img
  
      # A generator function that yields epoch, batchsize of warped_img and batchsize of target_img
      def minibatch(self, data, batchsize, doShuffle=True):
keep keep keep keep replace keep keep keep keep keep
 Extraction - Speed improvements (#522) (#523)

* Extraction - Speed improvements (#522)

* Initial Plugin restructure

* Detectors to plugins. Detector speed improvements

* Re-implement dlib aligner, remove models, FAN to TF. Parallel processing

* Update manual, update convert, implement parallel/serial switching

* linting + fix cuda check (setup.py). requirements update keras 2.2.4

* Add extract size option. Fix dlib hog init

* GUI: Increase tooltip width

* Update alignment tool to support new DetectedFace

* Add skip existing faces option

* Fix sort tool to new plugin structure

* remove old align plugin

* fix convert -skip faces bug

* Fix convert skipping no faces frames

* Convert - draw onto transparent layer

* Fix blur threshold bug

* fix skip_faces convert bug

* Fix training 
lib/training_data.py