eynollah_ocr: make work again, re-use Eynollah base class…

- re-use Eynollah base class
- use `ModelZoo.load_models()` instead of `load_model()`
- pass in `device` init kwarg, delegate to `ModelZoo.load_models()`
- `device`: return Torch device at loaded model tensors
  instead of ad-hoc selection
- make numeric init kwargs non-optional (only numeric)

src/eynollah/cli/cli_ocr.py

@@ -66,6 +66,10 @@ import click
     "--min_conf_value_of_textline_text",
     "-min_conf",
     help="minimum OCR confidence value. Text lines with a confidence value lower than this threshold will not be included in the output XML file.",
+@click.option(
+    "--device",
+    "-D",
+    help="placement of computations in predictors for each model type; if none (by default), will try to use first available GPU or fall back to CPU; set string to force using a device (e.g. 'GPU0', 'GPU1' or 'CPU'). Can also be a comma-separated list of model category to device mappings (e.g. 'col_classifier:CPU,page:GPU0,*:GPU1')",
 )
 @click.pass_context
 def ocr_cli(
@@ -81,18 +85,20 @@ def ocr_cli(
     do_not_mask_with_textline_contour,
     batch_size,
     min_conf_value_of_textline_text,
+    device,
 ):
     """
     Recognize text with a CNN/RNN or transformer ML model.
     """
-    assert bool(image) ^ bool(dir_in), "Either -i (single image) or -di (directory) must be provided, but not both."
+    assert bool(image) != bool(dir_in), "Either -i (single image) or -di (directory) must be provided, but not both."
     from ..eynollah_ocr import Eynollah_ocr
     eynollah_ocr = Eynollah_ocr(
         model_zoo=ctx.obj.model_zoo,
         tr_ocr=tr_ocr,
         do_not_mask_with_textline_contour=do_not_mask_with_textline_contour,
         batch_size=batch_size,
-        min_conf_value_of_textline_text=min_conf_value_of_textline_text)
+        min_conf_value_of_textline_text=min_conf_value_of_textline_text,
+        device=device)
     eynollah_ocr.run(overwrite=overwrite,
                      dir_in=dir_in,
                      dir_in_bin=dir_in_bin,

src/eynollah/eynollah_ocr.py

@@ -14,16 +14,17 @@ from cv2.typing import MatLike
 from xml.etree import ElementTree as ET
 from PIL import Image, ImageDraw
 import numpy as np
-from eynollah.model_zoo import EynollahModelZoo
-from eynollah.utils.font import get_font
-from eynollah.utils.xml import etree_namespace_for_element_tag
 try:
     import torch
 except ImportError:
     torch = None
 
 
+from .eynollah import Eynollah
+from .model_zoo import EynollahModelZoo
 from .utils import is_image_filename
+from .utils.font import get_font
+from .utils.xml import etree_namespace_for_element_tag
 from .utils.resize import resize_image
 from .utils.utils_ocr import (
     break_curved_line_into_small_pieces_and_then_merge,
@@ -44,45 +45,44 @@ class EynollahOcrResult:
     cropped_lines_region_indexer: List
     total_bb_coordinates:List
 
-class Eynollah_ocr:
+class Eynollah_ocr(Eynollah):
     def __init__(
         self,
         *,
         model_zoo: EynollahModelZoo,
         tr_ocr=False,
-        batch_size: Optional[int]=None,
+        batch_size: int=0,
         do_not_mask_with_textline_contour: bool=False,
-        min_conf_value_of_textline_text : Optional[float]=None,
+        min_conf_value_of_textline_text : float=0.3,
         logger: Optional[Logger]=None,
+        device: str = '',
     ):
         self.tr_ocr = tr_ocr
         # masking for OCR and GT generation, relevant for skewed lines and bounding boxes
         self.do_not_mask_with_textline_contour = do_not_mask_with_textline_contour
         self.logger = logger if logger else getLogger('eynollah.ocr')
-        self.model_zoo = model_zoo
         
-        self.min_conf_value_of_textline_text = min_conf_value_of_textline_text if min_conf_value_of_textline_text else 0.3
-        self.b_s = 2 if batch_size is None and tr_ocr else 8 if batch_size is None else batch_size
+        self.min_conf_value_of_textline_text = min_conf_value_of_textline_text
+        self.b_s = batch_size or 2 if tr_ocr else 8
 
-        if tr_ocr:
-            self.model_zoo.load_model('trocr_processor')
-            self.model_zoo.load_model('ocr', 'tr')
-            self.model_zoo.get('ocr').to(self.device)
+        self.model_zoo = model_zoo
+        self.setup_models(device=device)
+
+    def setup_models(self, device=''):
+        if self.tr_ocr:
+            self.model_zoo.load_models('trocr_processor',
+                                       ('ocr', 'tr'),
+                                       device=device)
         else:
-            self.model_zoo.load_model('ocr', '')
-            self.model_zoo.load_model('num_to_char')
-            self.model_zoo.load_model('characters')
-            self.end_character = len(self.model_zoo.get('characters', list)) + 2
+            self.model_zoo.load_models('ocr',
+                                       'num_to_char',
+                                       'characters',
+                                       device=device)
+            self.end_character = len(self.model_zoo.get('characters')) + 2
 
     @property
     def device(self):
-        assert torch
-        if torch.cuda.is_available():
-            self.logger.info("Using GPU acceleration")
-            return torch.device("cuda:0")
-        else:
-            self.logger.info("Using CPU processing")
-            return torch.device("cpu")
+        return self.model_zoo.get('ocr').device
 
     def run_trocr(
         self,