          placeholder = np.array(placeholder)
          self._previewcache["placeholder"] = placeholder
          logger.debug("Created placeholder. shape: %s", placeholder.shape)
  
      def load_training_preview(self) -> None:
          """ Load the training preview images.
  
          Reads the training image currently stored in the cache folder and loads them to
          :attr:`previewtrain` for retrieval in the GUI.
          """
          logger.debug("Loading Training preview images")
 
</s> remove         asamples = np.array(samples)
        if not np.any(asamples):
            logger.debug("No preview images collected.")
            return False

        self._previewcache["filenames"] = (cast(List[str], self._previewcache["filenames"]) +
                                           filenames)[-num_images:]
        cache = cast(Optional[np.ndarray], self._previewcache["images"])
        if cache is None:
            logger.debug("Creating new cache")
            cache = asamples[-num_images:]
        else:
            logger.debug("Appending to existing cache")
            cache = np.concatenate((cache, asamples))[-num_images:]
        self._previewcache["images"] = cache
        logger.debug("Cache shape: %s", cast(np.ndarray, self._previewcache["images"]).shape)
        return True
 </s> add         logger.debug("Creating placeholder. thumbnail_size: %s", thumbnail_size)
        placeholder = Image.new("RGB", (thumbnail_size, thumbnail_size))
        draw = ImageDraw.Draw(placeholder)
        draw.rectangle(((0, 0), (thumbnail_size, thumbnail_size)), outline="#E5E5E5", width=1)
        placeholder = np.array(placeholder)
        self._placeholder = placeholder
        logger.debug("Created placeholder. shape: %s", placeholder.shape) </s> remove         Returns
        -------
        dict:
            The icons formatted as described in :attr:`icons`
 </s> add         self._batch_mode = False
        self._output_path = "" </s> remove     def previewoutput(self) -> Tuple[Image.Image, ImageTk.PhotoImage]:
        """ Tuple: First item in the tuple is the extract or convert preview image
        (:class:`PIL.Image`), the second item is the image in a format that tkinter can display
        (:class:`PIL.ImageTK.PhotoImage`).
 </s> add     def buffer(self) -> PreviewBuffer:
        """ :class:`~lib.training.PreviewBuffer` The preview buffer for the training preview
        image. """
        return self._buffer

    def load(self) -> bool:
        """ Load the latest training preview image(s) from disk and add to :attr:`buffer` """
        logger.trace("Loading Training preview images")  # type:ignore
        image_files = _get_previews(self._cache_path)
        filename = next((fname for fname in image_files
                         if os.path.basename(fname) == TRAININGPREVIEW), "")
        if not filename:
            logger.trace("No preview to display")  # type:ignore
            return False
        try:
            modified = os.path.getmtime(filename)
            if modified <= self._modified:
                logger.trace("preview '%s' not updated. Current timestamp: %s, "  # type:ignore
                             "existing timestamp: %s", filename, modified, self._modified)
                return False

            logger.debug("Loading preview: '%s'", filename)
            img = cv2.imread(filename, cv2.IMREAD_UNCHANGED)
            self._modified = modified
            self._buffer.add_image(os.path.basename(filename), img)
            self._error_count = 0
        except ValueError:
            # This is probably an error reading the file whilst it's being saved so ignore it
            # for now and only pick up if there have been multiple consecutive fails
            logger.warning("Unable to display preview: (image: '%s', attempt: %s)",
                           img, self._error_count)
            if self._error_count < 10:
                self._error_count += 1
            else:
                logger.error("Error reading the preview file for '%s'", filename)
            return False </s> remove         self._pathoutput = location
 </s> add         self._output_path = location </s> remove     def __init__(self) -> None:
        logger.debug("Initializing %s", self.__class__.__name__)
        self._pathpreview = os.path.join(PATHCACHE, "preview")
        self._pathoutput: Optional[str] = None
        self._batch_mode = False
        self._previewoutput: Optional[Tuple[Image.Image, ImageTk.PhotoImage]] = None
        self._previewtrain: Dict[str, List[Union[Image.Image,
                                                 ImageTk.PhotoImage,
                                                 None,
                                                 float]]] = {}
        self._previewcache: Dict[str, Union[None, float, np.ndarray, List[str]]] = dict(
            modified=None,  # cache for extract and convert
            images=None,
            filenames=[],
            placeholder=None)
        self._errcount = 0
        self._icons = self._load_icons()
 </s> add     logger.debug("Getting images: '%s'", image_path)
    if not os.path.isdir(image_path):
        logger.debug("Folder does not exist")
        return []
    files = [os.path.join(image_path, f)
             for f in os.listdir(image_path) if f.lower().endswith((".png", ".jpg"))]
    logger.debug("Image files: %s", files)
    return files


class PreviewTrain():
    """ Handles the loading of the training preview image(s) and adding to the display buffer

    Parameters
    ----------
    cache_path: str
        Full path to the cache folder that contains the preview images
    """
    def __init__(self, cache_path: str) -> None:
        logger.debug("Initializing %s: (cache_path: '%s')", self.__class__.__name__, cache_path)
        self._buffer = PreviewBuffer()
        self._cache_path = cache_path
        self._modified: float = 0.0
        self._error_count: int = 0
          placeholder = np.array(placeholder)
          self._previewcache["placeholder"] = placeholder
          logger.debug("Created placeholder. shape: %s", placeholder.shape)
  


  
          Reads the training image currently stored in the cache folder and loads them to
          :attr:`previewtrain` for retrieval in the GUI.
          """
          logger.debug("Loading Training preview images")
keep keep keep keep replace replace keep keep keep keep keep
 GUI - Preview updates
  - Training preview. Embed preview pop-out window
  - Bugfix - convert/extract previews 
lib/gui/utils/image.py