wip

Makefile

@@ -73,7 +73,6 @@ install-dev:
 
 deps-test: $(EYNOLLAH_MODELS_ZIP)
 	$(PIP) install -r requirements-test.txt
-endif
 
 smoke-test: TMPDIR != mktemp -d
 smoke-test: tests/resources/kant_aufklaerung_1784_0020.tif

src/eynollah/eynollah.py

@@ -45,7 +45,7 @@ import tensorflow as tf
 tf.get_logger().setLevel("ERROR")
 warnings.filterwarnings("ignore")
 
-from .model_zoo import EynollahModelZoo
+from .model_zoo import (EynollahModelZoo, KerasModel, TrOCRProcessor)
 from .utils.contour import (
     filter_contours_area_of_image,
     filter_contours_area_of_image_tables,
@@ -178,6 +178,7 @@ class Eynollah:
         self.full_layout = full_layout
         self.tables = tables
         self.right2left = right2left
+        # --input-binary sensible if image is very dark, if layout is not working.
         self.input_binary = input_binary
         self.allow_scaling = allow_scaling
         self.headers_off = headers_off
@@ -3651,7 +3652,15 @@ class Eynollah:
             pass
 
     def return_ocr_of_textline_without_common_section(
-            self, textline_image, model_ocr, processor, device, width_textline, h2w_ratio,ind_tot):
+        self,
+        textline_image,
+        model_ocr: KerasModel,
+        processor: TrOCRProcessor,
+        device,
+        width_textline,
+        h2w_ratio,
+        ind_tot,
+    ):
 
         if h2w_ratio > 0.05:
             pixel_values = processor(textline_image, return_tensors="pt").pixel_values

src/eynollah/eynollah_ocr.py

@@ -63,8 +63,11 @@ class Eynollah_ocr:
         logger: Optional[Logger]=None,
     ):
         self.tr_ocr = tr_ocr
+        # For generating textline-image pairs for traning, move to generate_gt_for_training
         self.export_textline_images_and_text = export_textline_images_and_text
+        # 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
+        # prefix or dataset
         self.pref_of_dataset = pref_of_dataset
         self.logger = logger if logger else getLogger('eynollah')
         self.model_zoo = EynollahModelZoo(basedir=dir_models)
@@ -103,6 +106,7 @@ class Eynollah_ocr:
 
     def run(self, overwrite: bool = False,
             dir_in: Optional[str] = None,
+            # Prediction with RGB and binarized images for selected pages, should not be the default
             dir_in_bin: Optional[str] = None,
             image_filename: Optional[str] = None,
             dir_xmls: Optional[str] = None,

src/eynollah/model_zoo/__init__.py

@@ -1,4 +1,7 @@
 __all__ = [
     'EynollahModelZoo',
+    'KerasModel',
+    'TrOCRProcessor',
+    'VisionEncoderDecoderModel',
 ]
-from .model_zoo import EynollahModelZoo
+from .model_zoo import EynollahModelZoo, KerasModel, TrOCRProcessor, VisionEncoderDecoderModel

src/eynollah/model_zoo/model_zoo.py

@@ -9,7 +9,6 @@ from keras.models import Model as KerasModel
 from keras.models import load_model
 from tabulate import tabulate
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-
 from ..patch_encoder import PatchEncoder, Patches
 from .specs import EynollahModelSpecSet
 from .default_specs import DEFAULT_MODEL_SPECS
@@ -100,7 +99,7 @@ class EynollahModelZoo:
         elif model_category == 'characters':
             model = self._load_characters()
         elif model_category == 'trocr_processor':
-            return TrOCRProcessor.from_pretrained(self.model_path(...))
+            model = TrOCRProcessor.from_pretrained(model_path)
         else:
             try:
                 model = load_model(model_path, compile=False)
@@ -184,6 +183,5 @@ class EynollahModelZoo:
         Ensure that a loaded models is not referenced by ``self._loaded`` anymore
         """
         if hasattr(self, '_loaded') and getattr(self, '_loaded'):
-            for needle in self._loaded:
+            for needle in self._loaded.keys():
-                if self._loaded[needle]:
                 del self._loaded[needle]

tests/test_model_zoo.py

@@ -0,0 +1,19 @@
+from pathlib import Path
+
+from eynollah.model_zoo import EynollahModelZoo, TrOCRProcessor, VisionEncoderDecoderModel
+
+testdir = Path(__file__).parent.resolve()
+MODELS_DIR = testdir.parent
+
+def test_trocr1():
+    model_zoo = EynollahModelZoo(str(MODELS_DIR))
+    model_zoo.load_model('trocr_processor')
+    proc = model_zoo.get('trocr_processor', TrOCRProcessor)
+    assert isinstance(proc, TrOCRProcessor)
+
+    model_zoo.load_model('ocr', 'tr')
+    model = model_zoo.get('ocr')
+    assert isinstance(model, VisionEncoderDecoderModel)
+    print(proc)
+
+test_trocr1()