fix merge issues

src/eynollah/cli.py

@@ -426,13 +426,11 @@ def layout(
     threshold_art_class_layout,
     skip_layout_and_reading_order,
     ignore_page_extraction,
-    log_level,
-    setup_logging,
 ):
     """
     Detect Layout (with optional image enhancement and reading order detection)
     """
-    from eynollah.eynollah import Eynollah
+    from .eynollah import Eynollah
     assert enable_plotting or not save_layout, "Plotting with -sl also requires -ep"
     assert enable_plotting or not save_deskewed, "Plotting with -sd also requires -ep"
     assert enable_plotting or not save_all, "Plotting with -sa also requires -ep"
@@ -452,7 +450,6 @@ def layout(
     assert not extract_only_images or not right2left, "Image extraction -eoi can not be set alongside right2left -r2l"
     assert not extract_only_images or not headers_off, "Image extraction -eoi can not be set alongside headers_off -ho"
     assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
-    from .eynollah import Eynollah
     eynollah = Eynollah(
         model_zoo=ctx.obj.model_zoo,
         extract_only_images=extract_only_images,

src/eynollah/eynollah.py

@@ -1,12 +1,22 @@
+"""
+document layout analysis (segmentation) with output in PAGE-XML
+"""
 # pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring,missing-class-docstring,too-many-branches
 # pylint: disable=too-many-locals,wrong-import-position,too-many-lines,too-many-statements,chained-comparison,fixme,broad-except,c-extension-no-member
 # pylint: disable=too-many-public-methods,too-many-arguments,too-many-instance-attributes,too-many-public-methods,
 # pylint: disable=consider-using-enumerate
+# FIXME: fix all of those...
 # pyright: reportUnnecessaryTypeIgnoreComment=true
 # pyright: reportPossiblyUnboundVariable=false
-"""
+# pyright: reportMissingImports=false
-document layout analysis (segmentation) with output in PAGE-XML
+# pyright: reportCallIssue=false
-"""
+# pyright: reportOperatorIssue=false
+# pyright: reportUnboundVariable=false
+# pyright: reportArgumentType=false
+# pyright: reportAttributeAccessIssue=false
+# pyright: reportOptionalMemberAccess=false
+# pyright: reportGeneralTypeIssues=false
+# pyright: reportOptionalSubscript=false
 
 import logging
 import sys
@@ -21,8 +31,7 @@ from difflib import SequenceMatcher as sq
 import math
 import os
 import time
-from typing import List, Optional, Tuple
+from typing import Optional
-import warnings
 from functools import partial
 from pathlib import Path
 from multiprocessing import cpu_count
@@ -39,17 +48,8 @@ from skimage.morphology import skeletonize
 from ocrd_utils import tf_disable_interactive_logs
 import statistics
 
-try:
-    import torch # type: ignore
-except ImportError:
-    torch = None
-try:
-    import matplotlib.pyplot as plt
-except ImportError:
-    plt = None
-
-#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 tf_disable_interactive_logs()
+
 import tensorflow as tf
 # warnings.filterwarnings("ignore")
 from tensorflow.python.keras import backend as K
@@ -58,6 +58,14 @@ from tensorflow.keras.models import load_model
 from tensorflow.compat.v1.keras.backend import set_session
 from tensorflow.keras import layers
 from tensorflow.keras.layers import StringLookup
+try:
+    import torch
+except ImportError:
+    torch = None
+try:
+    import matplotlib.pyplot as plt
+except ImportError:
+    plt = None
 
 from .model_zoo import EynollahModelZoo
 from .utils.contour import (
@@ -3667,8 +3675,8 @@ class Eynollah:
     def return_ocr_of_textline_without_common_section(
         self,
         textline_image,
-        model_ocr: KerasModel,
+        model_ocr,
-        processor: TrOCRProcessor,
+        processor,
         device,
         width_textline,
         h2w_ratio,

src/eynollah/image_enhancer.py

@@ -2,6 +2,11 @@
 Image enhancer. The output can be written as same scale of input or in new predicted scale.
 """
 
+# FIXME: fix all of those...
+# pyright: reportUnboundVariable=false
+# pyright: reportCallIssue=false
+# pyright: reportArgumentType=false
+
 import logging
 import os
 import time

src/eynollah/mb_ro_on_layout.py

@@ -2,6 +2,10 @@
 Machine learning based reading order detection
 """
 
+# pyright: reportCallIssue=false
+# pyright: reportUnboundVariable=false
+# pyright: reportArgumentType=false
+
 import logging 
 import os
 import time

src/eynollah/model_zoo/default_specs.py

@@ -1,5 +1,4 @@
 from .specs import EynollahModelSpec, EynollahModelSpecSet
-from .types import KerasModel
 
 # NOTE: This needs to change whenever models/versions change
 ZENODO = "https://zenodo.org/records/17295988/files"

src/eynollah/model_zoo/specs.py

@@ -1,6 +1,5 @@
 from dataclasses import dataclass
-from typing import Dict, List, Set, Tuple, Type
+from typing import Dict, List, Set, Tuple
-from .types import AnyModel
 
 
 @dataclass

src/eynollah/model_zoo/types.py

@@ -1,5 +1,4 @@
-from typing import List, TypeVar, Union
+from typing import TypeVar
-from keras.models import Model as KerasModel
 
 # NOTE: Creating an actual union type requires loading transformers which is expensive and error-prone
 # from transformers import TrOCRProcessor, VisionEncoderDecoderModel

src/eynollah/sbb_binarize.py

@@ -2,10 +2,15 @@
 Tool to load model and binarize a given image.
 """
 
+# pyright: reportIndexIssue=false
+# pyright: reportCallIssue=false
+# pyright: reportArgumentType=false
+# pyright: reportPossiblyUnboundVariable=false
+
 import os
 import logging
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Dict, Optional
 
 import numpy as np
 import cv2
@@ -326,7 +331,7 @@ class SbbBinarizer:
                 image = cv2.imread(image_path)
             img_last = 0
             for n, (model_file, model) in enumerate(self.models.items()):
-                self.log.info('Predicting %s with model %s [%s/%s]', image_path if image_path else '[image]', model_file, n + 1, len(self.models.keys()))
+                self.logger.info('Predicting %s with model %s [%s/%s]', image_path if image_path else '[image]', model_file, n + 1, len(self.models.keys()))
                 res = self.predict(model, image, use_patches)
 
                 img_fin = np.zeros((res.shape[0], res.shape[1], 3))

tests/conftest.py

@@ -31,7 +31,7 @@ def eynollah_subcommands():
         'layout',
         'ocr',
         'enhancement',
-        'machine-based-reading-order'
+        'machine-based-reading-order',
-        'models'
+        'models',
     ]