Merge 9801129aa6 into 1df32eba87

src/eynollah/cli/__init__.py

@@ -1,7 +1,3 @@
-# NOTE: For predictable order of imports of torch/shapely/tensorflow
-#       this must be the first import of the CLI!
-from ..eynollah_imports import imported_libs
-
 from .cli import main
 from .cli_binarize import binarize_cli
 from .cli_enhance import enhance_cli

src/eynollah/cli/cli.py

@@ -15,6 +15,7 @@ class EynollahCliCtx:
     Holds options relevant for all eynollah subcommands
     """
     model_zoo: EynollahModelZoo
+    device: str = ''
     log_level : Union[str, None] = 'INFO'
 
 
@@ -35,6 +36,11 @@ class EynollahCliCtx:
     type=(str, str, str),
     multiple=True,
 )
+@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.option(
     "--log_level",
     "-l",
@@ -42,7 +48,7 @@ class EynollahCliCtx:
     help="Override log level globally to this",
 )
 @click.pass_context
-def main(ctx, model_basedir, model_overrides, log_level):
+def main(ctx, model_basedir, model_overrides, device, log_level):
     """
     eynollah - Document Layout Analysis, Image Enhancement, OCR
     """
@@ -58,6 +64,7 @@ def main(ctx, model_basedir, model_overrides, log_level):
     # Initialize CLI context
     ctx.obj = EynollahCliCtx(
         model_zoo=model_zoo,
+        device=device,
         log_level=log_level,
     )
 

src/eynollah/cli/cli_binarize.py

@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     '--patches/--no-patches',
     default=True,
@@ -31,11 +33,6 @@ import click
     help="overwrite (instead of skipping) if output xml exists",
     is_flag=True,
 )
-@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 binarize_cli(
         ctx,
@@ -44,14 +41,14 @@ def binarize_cli(
         dir_in,
         output,
         overwrite,
-        device,
 ):
     """
     Binarize images with a ML model
     """
     from ..sbb_binarize import SbbBinarizer
     assert bool(input_image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
-    binarizer = SbbBinarizer(model_zoo=ctx.obj.model_zoo, device=device)
+    binarizer = SbbBinarizer(model_zoo=ctx.obj.model_zoo,
+                             device=ctx.obj.device)
     binarizer.run(
         image_filename=input_image,
         use_patches=patches,

src/eynollah/cli/cli_enhance.py

@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     "--image",
     "-i",
@@ -46,13 +48,8 @@ import click
     is_flag=True,
     help="save the enhanced image in original image size",
 )
-@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 enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower, save_org_scale, device):
+def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower, save_org_scale):
     """
     Enhance image
     """
@@ -60,10 +57,10 @@ def enhance_cli(ctx, image, out, overwrite, dir_in, num_col_upper, num_col_lower
     from ..image_enhancer import Enhancer
     enhancer = Enhancer(
         model_zoo=ctx.obj.model_zoo,
+        device=ctx.obj.device,
         num_col_upper=num_col_upper,
         num_col_lower=num_col_lower,
         save_org_scale=save_org_scale,
-        device=device,
     )
     enhancer.run(overwrite=overwrite,
                  dir_in=dir_in,

src/eynollah/cli/cli_extract_images.py

@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     "--image",
     "-i",
@@ -30,36 +32,40 @@ import click
 @click.option(
     "--save_images",
     "-si",
-    help="if a directory is given, images in documents will be cropped and saved there",
+    help="if a directory is given, cropped images of pages will be saved there",
     type=click.Path(exists=True, file_okay=False),
 )
 @click.option(
-    "--enable-plotting/--disable-plotting",
+    "--enable-plotting",
-    "-ep/-noep",
+    "-ep",
     is_flag=True,
-    help="If set, will plot intermediary files and images",
+    help="plot intermediary diagnostic images to files",
 )
 @click.option(
-    "--input_binary/--input-RGB",
+    "--input_binary",
-    "-ib/-irgb",
+    "-ib",
     is_flag=True,
-    help="In general, eynollah uses RGB as input but if the input document is very dark, very bright or for any other reason you can turn on input binarization. When this flag is set, eynollah will binarize the RGB input document, you should always provide RGB images to eynollah.",
+    help="In general, eynollah uses RGB as input, but if the input document is very dark, very bright or for any other reason you can turn on internal binarization here. When set, eynollah will binarize the RGB input document first.",
 )
 @click.option(
-    "--ignore_page_extraction/--extract_page_included",
+    "--ignore_page_extraction",
-    "-ipe/-epi",
+    "-ipe",
     is_flag=True,
-    help="if this parameter set to true, this tool would ignore page extraction",
+    help="ignore page extraction (cropping via page frame detection model)",
 )
 @click.option(
     "--num_col_upper",
     "-ncu",
-    help="lower limit of columns in document image",
+    default=0,
+    type=click.IntRange(min=0),
+    help="lower limit of columns in document image; 0 means autodetected from model",
 )
 @click.option(
     "--num_col_lower",
     "-ncl",
-    help="upper limit of columns in document image",
+    default=0,
+    type=click.IntRange(min=0),
+    help="upper limit of columns in document image; 0 means autodetected from model",
 )
 @click.pass_context
 def extract_images_cli(

src/eynollah/cli/cli_layout.py

@@ -172,11 +172,6 @@ import click
     type=click.FloatRange(min=0),
     help="abort when number of failed images exceeds this value (if >=1) or ratio of failed over total images exceeds this value (if <1); 0 means ignore failures",
 )
-@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 layout_cli(
     ctx,
@@ -207,7 +202,6 @@ def layout_cli(
     ignore_page_extraction,
     num_jobs,
     halt_fail,
-    device,
 ):
     """
     Detect Layout (with optional image enhancement and reading order detection)
@@ -223,7 +217,7 @@ def layout_cli(
     assert bool(image) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
     eynollah = Eynollah(
         model_zoo=ctx.obj.model_zoo,
-        device=device,
+        device=ctx.obj.device,
         enable_plotting=enable_plotting,
         allow_enhancement=allow_enhancement,
         curved_line=curved_line,

src/eynollah/cli/cli_ocr.py

@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     "--image",
     "-i",
@@ -16,7 +18,7 @@ import click
 @click.option(
     "--dir_in_bin",
     "-dib",
-    help=("directory of binarized images (in addition to --dir_in for RGB images; filename stems must match the RGB image files, with '.png' \n                                                                          Perform prediction using both RGB and binary images. (This does not necessarily improve results, however it may be beneficial for certain document images."),                                                 
+    help=("directory of binarized images (in addition to --dir_in for RGB images; filename stems must match the RGB image files, with '.png'. \n                                                                          Perform prediction using both RGB and binary images. (This may improve results for certain document images.)"),
     type=click.Path(exists=True, file_okay=False),
 )
 @click.option(
@@ -47,25 +49,29 @@ import click
 )
 @click.option(
     "--tr_ocr",
-    "-trocr/-notrocr",
+    "-trocr",
     is_flag=True,
-    help="if this parameter set to true, transformer ocr will be applied, otherwise cnn_rnn model.",
+    help="use transformer OCR (instead of classic CNN-RNN) model",
 )
 @click.option(
     "--do_not_mask_with_textline_contour",
-    "-nmtc/-mtc",
+    "-nmtc",
     is_flag=True,
-    help="if this parameter set to true, cropped textline images will not be masked with textline contour.",
+    help="skip masking each cropped textline image with its corresponding textline contour",
 )
 @click.option(
     "--batch_size",
     "-bs",
+    default=0,
+    type=click.IntRange(min=0),
     help="number of inference batch size. Default b_s for trocr and cnn_rnn models are 2 and 8 respectively",
 )
 @click.option(
     "--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.",
+    default=0.3,
+    type=click.FloatRange(min=0.0, max=1.0),
+    help="minimum OCR confidence threshold. Text lines with a lower confidence value will not be included in the output XML file.",
 )
 @click.pass_context
 def ocr_cli(
@@ -85,14 +91,16 @@ def ocr_cli(
     """
     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,
+        device=ctx.obj.device,
         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,
+    )
     eynollah_ocr.run(overwrite=overwrite,
                      dir_in=dir_in,
                      dir_in_bin=dir_in_bin,

src/eynollah/cli/cli_readingorder.py

@@ -1,6 +1,8 @@
 import click
 
-@click.command()
+@click.command(context_settings=dict(
+    help_option_names=['-h', '--help'],
+    show_default=True))
 @click.option(
     "--input",
     "-i",
@@ -25,9 +27,10 @@ def readingorder_cli(ctx, input, dir_in, out):
     """
     Generate ReadingOrder with a ML model
     """
-    from ..mb_ro_on_layout import machine_based_reading_order_on_layout
+    from ..mb_ro_on_layout import Reorder
     assert bool(input) != bool(dir_in), "Either -i (single input) or -di (directory) must be provided, but not both."
-    orderer = machine_based_reading_order_on_layout(model_zoo=ctx.obj.model_zoo)
+    orderer = Reorder(model_zoo=ctx.obj.model_zoo,
+                      device=ctx.obj.device)
     orderer.run(xml_filename=input,
                 dir_in=dir_in,
                 dir_out=out,

src/eynollah/extract_images.py

@@ -9,7 +9,6 @@ import os
 import time
 from typing import Optional
 from pathlib import Path
-import tensorflow as tf
 import numpy as np
 import cv2
 
@@ -64,12 +63,6 @@ class EynollahImageExtractor(Eynollah):
 
         t_start = time.time()
 
-        try:
-            for device in tf.config.list_physical_devices('GPU'):
-                tf.config.experimental.set_memory_growth(device, True)
-        except:
-            self.logger.warning("no GPU device available")
-            
         self.logger.info("Loading models...")
         self.setup_models()
         self.logger.info(f"Model initialization complete ({time.time() - t_start:.1f}s)")

src/eynollah/eynollah.py

@@ -1148,7 +1148,6 @@ class Eynollah:
             boxes,
             textline_mask_tot
     ):
-        assert np.any(textline_mask_tot)
         self.logger.debug("enter do_order_of_regions")
         contours_only_text_parent = ensure_array(contours_only_text_parent)
         contours_only_text_parent_h = ensure_array(contours_only_text_parent_h)

src/eynollah/eynollah_imports.py

@@ -1,13 +0,0 @@
-"""
-Load libraries with possible race conditions once. This must be imported as the first module of eynollah.
-"""
-import os
-os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
-
-from ocrd_utils import tf_disable_interactive_logs
-from torch import *
-tf_disable_interactive_logs()
-import tensorflow.keras
-from shapely import *
-imported_libs = True
-__all__ = ['imported_libs']

src/eynollah/eynollah_ocr.py

@@ -14,16 +14,14 @@ 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 ocrd_utils import polygon_from_points, xywh_from_polygon
-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,
@@ -34,6 +32,7 @@ from .utils.utils_ocr import (
     preprocess_and_resize_image_for_ocrcnn_model,
     return_textlines_split_if_needed,
     rotate_image_with_padding,
+    batched,
 )
 
 # TODO: refine typing
@@ -44,45 +43,44 @@ 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.min_conf_value_of_textline_text = min_conf_value_of_textline_text
+        self.b_s = batch_size or 2 if tr_ocr else 8
+
         self.model_zoo = model_zoo
+        self.setup_models(device=device)
 
-        self.min_conf_value_of_textline_text = min_conf_value_of_textline_text if min_conf_value_of_textline_text else 0.3
+    def setup_models(self, device=''):
-        self.b_s = 2 if batch_size is None and tr_ocr else 8 if batch_size is None else batch_size
+        if self.tr_ocr:
-
+            self.model_zoo.load_models('trocr_processor',
-        if tr_ocr:
+                                       ('ocr', 'tr'),
-            self.model_zoo.load_models('trocr_processor')
+                                       device=device)
-            self.model_zoo.load_models(['ocr', 'tr'])
-            self.model_zoo.get('ocr').to(self.device)
         else:
-            self.model_zoo.load_models('ocr')
+            self.model_zoo.load_models('ocr',
-            self.model_zoo.load_models('num_to_char')
+                                       'num_to_char',
-            self.model_zoo.load_models('characters')
+                                       'characters',
+                                       device=device)
             self.end_character = len(self.model_zoo.get('characters')) + 2
 
     @property
     def device(self):
-        assert torch
+        return self.model_zoo.get('ocr').device
-        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")
 
     def run_trocr(
         self,
@@ -94,174 +92,94 @@ class Eynollah_ocr:
     ) -> EynollahOcrResult:
         
         total_bb_coordinates = []
-
-            
         cropped_lines = []
         cropped_lines_region_indexer = []
         cropped_lines_meging_indexing = []
-        
         extracted_texts = []
+        extracted_confs = []
 
-        indexer_text_region = 0
+        for n_region, region in enumerate(page_tree.getroot().iter('{%s}TextRegion' % page_ns)):
-        indexer_b_s = 0
+            for n_line, line in enumerate(region.iter('{%s}TextLine' % page_ns)):
+                cropped_lines_region_indexer.append(n_region)
 
-        for nn in page_tree.getroot().iter(f'{{{page_ns}}}TextRegion'):
+                coords = line.find('{%s}Coords' % page_ns)
-            for child_textregion in nn:
+                if coords is None:
-                if child_textregion.tag.endswith("TextLine"):
+                    self.logger.warning("region '%s' line '%s' has no Coords", region.attrib['id'], line.attrib['id'])
+                    continue
+                poly = np.array(polygon_from_points(coords.attrib['points'])).astype(int)
+                cont = poly[:, np.newaxis]
+                xywh = xywh_from_polygon(poly)
+                x, y, w, h = xywh['x'], xywh['y'], xywh['w'], xywh['h']
 
-                    for child_textlines in child_textregion:
+                total_bb_coordinates.append([x, y, w, h])
-                        if child_textlines.tag.endswith("Coords"):
-                            cropped_lines_region_indexer.append(indexer_text_region)
-                            p_h=child_textlines.attrib['points'].split(' ')
-                            textline_coords =  np.array( [ [int(x.split(',')[0]),
-                                                            int(x.split(',')[1]) ]
-                                                            for x in p_h] )
-                            x,y,w,h = cv2.boundingRect(textline_coords)
 
-                            total_bb_coordinates.append([x,y,w,h])
+                img_crop = img[y: y + h, x: x + w]
+                if not self.do_not_mask_with_textline_contour:
+                    mask_poly = np.zeros(img_crop.shape[:2], dtype=np.uint8)
+                    mask_poly = cv2.fillPoly(mask_poly, pts=[cont - [x, y]], color=1)
+                    img_crop[mask_poly == 0] = 255 # FIXME: or median color?
 
-                            h2w_ratio = h/float(w)
+                if h > 0.1 * w:
-                            
+                    cropped_lines.append(resize_image(img_crop,
-                            img_poly_on_img = np.copy(img)
+                                                        tr_ocr_input_height_and_width,
-                            mask_poly = np.zeros(img.shape)
+                                                        tr_ocr_input_height_and_width)  )
-                            mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
+                    cropped_lines_meging_indexing.append(0)
-                            
+                else:
-                            mask_poly = mask_poly[y:y+h, x:x+w, :]
+                    splited_images, _ = return_textlines_split_if_needed(img_crop, None)
-                            img_crop = img_poly_on_img[y:y+h, x:x+w, :]
+                    if splited_images:
-                            img_crop[mask_poly==0] = 255
+                        cropped_lines.append(resize_image(splited_images[0],
-                            
+                                                            tr_ocr_input_height_and_width,
-                            self.logger.debug("processing %d lines for '%s'",
+                                                            tr_ocr_input_height_and_width))
-                                                len(cropped_lines), nn.attrib['id'])
+                        cropped_lines_meging_indexing.append(1)
-                            if h2w_ratio > 0.1:
+                        cropped_lines.append(resize_image(splited_images[1],
-                                cropped_lines.append(resize_image(img_crop,
+                                                            tr_ocr_input_height_and_width,
-                                                                    tr_ocr_input_height_and_width,
+                                                            tr_ocr_input_height_and_width))
-                                                                    tr_ocr_input_height_and_width)  )
+                        cropped_lines_meging_indexing.append(-1)
-                                cropped_lines_meging_indexing.append(0)
+                    else:
-                                indexer_b_s+=1
+                        cropped_lines.append(img_crop)
-                                if indexer_b_s==self.b_s:
+                        cropped_lines_meging_indexing.append(0)
-                                    imgs = cropped_lines[:]
-                                    cropped_lines = []
-                                    indexer_b_s = 0
-                                    
-                                    pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-                                    generated_ids_merged = self.model_zoo.get('ocr').generate(
-                                        pixel_values_merged.to(self.device))
-                                    generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
-                                        generated_ids_merged, skip_special_tokens=True)
-                                    
-                                    extracted_texts = extracted_texts + generated_text_merged
-                                    
-                            else:
-                                splited_images, _ = return_textlines_split_if_needed(img_crop, None)
-                                #print(splited_images)
-                                if splited_images:
-                                    cropped_lines.append(resize_image(splited_images[0],
-                                                                        tr_ocr_input_height_and_width,
-                                                                        tr_ocr_input_height_and_width))
-                                    cropped_lines_meging_indexing.append(1)
-                                    indexer_b_s+=1
-                                    
-                                    if indexer_b_s==self.b_s:
-                                        imgs = cropped_lines[:]
-                                        cropped_lines = []
-                                        indexer_b_s = 0
-                                        
-                                        pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-                                        generated_ids_merged = self.model_zoo.get('ocr').generate(
-                                            pixel_values_merged.to(self.device))
-                                        generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
-                                            generated_ids_merged, skip_special_tokens=True)
-                                        
-                                        extracted_texts = extracted_texts + generated_text_merged
 
 
-                                    cropped_lines.append(resize_image(splited_images[1],
+        self.logger.debug("processing %d lines for %d regions",
-                                                                        tr_ocr_input_height_and_width,
+                          len(cropped_lines), len(set(cropped_lines_region_indexer)))
-                                                                        tr_ocr_input_height_and_width))
+        for imgs in batched(cropped_lines, self.b_s):
-                                    cropped_lines_meging_indexing.append(-1)
+            pixel_values = self.model_zoo.get('trocr_processor')(
-                                    indexer_b_s+=1
+                imgs, return_tensors="pt").pixel_values
-                                    
+            output = self.model_zoo.get('ocr').generate(
-                                    if indexer_b_s==self.b_s:
+                pixel_values.to(self.device),
-                                        imgs = cropped_lines[:]
+                # beam search instead of greedy decoding:
-                                        cropped_lines = []
+                num_beams=4,
-                                        indexer_b_s = 0
+                # also return probability
-                                        
+                output_scores=True,
-                                        pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
+                return_dict_in_generate=True)
-                                        generated_ids_merged = self.model_zoo.get('ocr').generate(
+            if output.sequences_scores is not None:
-                                            pixel_values_merged.to(self.device))
+                # log-prob averaged over length
-                                        generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
+                conf = output.sequences_scores.exp().clamp(0.0, 1.0).tolist()
-                                            generated_ids_merged, skip_special_tokens=True)
+            else:
-                                        
+                conf = [1.0] * len(output.sequences)
-                                        extracted_texts = extracted_texts + generated_text_merged
+            text = self.model_zoo.get('trocr_processor').batch_decode(
-                                        
+                output.sequences,
-                                else:
+                skip_special_tokens=True,
-                                    cropped_lines.append(img_crop)
+                clean_up_tokenization_spaces=False)
-                                    cropped_lines_meging_indexing.append(0)
+            extracted_confs.extend(conf)
-                                    indexer_b_s+=1
+            extracted_texts.extend(text)
-                                    
-                                    if indexer_b_s==self.b_s:
-                                        imgs = cropped_lines[:]
-                                        cropped_lines = []
-                                        indexer_b_s = 0
-                                        
-                                        pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-                                        generated_ids_merged = self.model_zoo.get('ocr').generate(
-                                            pixel_values_merged.to(self.device))
-                                        generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
-                                            generated_ids_merged, skip_special_tokens=True)
-                                        
-                                        extracted_texts = extracted_texts + generated_text_merged
-                                        
-            
-                                    
-            indexer_text_region = indexer_text_region +1
-
-        if indexer_b_s!=0:
-            imgs = cropped_lines[:]
-            cropped_lines = []
-            indexer_b_s = 0
-            
-            pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-            generated_ids_merged = self.model_zoo.get('ocr').generate(pixel_values_merged.to(self.device))
-            generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(generated_ids_merged, skip_special_tokens=True)
-            
-            extracted_texts = extracted_texts + generated_text_merged
-            
-        ####extracted_texts = []
-        ####n_iterations  = math.ceil(len(cropped_lines) / self.b_s) 
-
-        ####for i in range(n_iterations):
-            ####if i==(n_iterations-1):
-                ####n_start = i*self.b_s
-                ####imgs = cropped_lines[n_start:]
-            ####else:
-                ####n_start = i*self.b_s
-                ####n_end = (i+1)*self.b_s
-                ####imgs = cropped_lines[n_start:n_end]
-            ####pixel_values_merged = self.model_zoo.get('trocr_processor')(imgs, return_tensors="pt").pixel_values
-            ####generated_ids_merged = self.model_ocr.generate(
-            ####    pixel_values_merged.to(self.device))
-            ####generated_text_merged = self.model_zoo.get('trocr_processor').batch_decode(
-            ####    generated_ids_merged, skip_special_tokens=True)
-            
-            ####extracted_texts = extracted_texts + generated_text_merged
-            
         del cropped_lines
         gc.collect()
 
         extracted_texts_merged = [extracted_texts[ind]
-                                    if cropped_lines_meging_indexing[ind]==0
+                                  if cropped_lines_meging_indexing[ind] == 0
-                                    else extracted_texts[ind]+" "+extracted_texts[ind+1]
+                                  else extracted_texts[ind] + " " + extracted_texts[ind + 1]
-                                    if cropped_lines_meging_indexing[ind]==1
+                                  for ind in range(len(cropped_lines_meging_indexing))
-                                    else None
+                                  if cropped_lines_meging_indexing[ind] >= 0]
-                                    for ind in range(len(cropped_lines_meging_indexing))]
+        extracted_confs_merged = [extracted_confs[ind]
-
+                                  if cropped_lines_meging_indexing[ind] == 0
-        extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
+                                  else 0.5 * (extracted_confs[ind] + extracted_confs[ind + 1])
-        #print(extracted_texts_merged, len(extracted_texts_merged))
+                                  for ind in range(len(cropped_lines_meging_indexing))
+                                  if cropped_lines_meging_indexing[ind] >= 0]
 
         return EynollahOcrResult(
             extracted_texts_merged=extracted_texts_merged,
-            extracted_conf_value_merged=None,
+            extracted_conf_value_merged=extracted_confs_merged,
             cropped_lines_region_indexer=cropped_lines_region_indexer,
             total_bb_coordinates=total_bb_coordinates,
         )
@@ -717,6 +635,7 @@ class Eynollah_ocr:
             
             has_textline = False
             for child_textregion in nn:
+                # FIXME: should remove Word level, if it already exists
                 if child_textregion.tag.endswith("TextLine"):
                     
                     is_textline_text = False
@@ -754,6 +673,7 @@ class Eynollah_ocr:
                 indexer_textregion = indexer_textregion + 1
                 
         ET.register_namespace("",page_ns)
+        self.logger.info("output filename: '%s'", out_file_ocr)
         page_tree.write(out_file_ocr, xml_declaration=True, method='xml', encoding="utf-8", default_namespace=None)
 
     def run(

src/eynollah/mb_ro_on_layout.py

@@ -17,9 +17,7 @@ import cv2
 import numpy as np
 import statistics
 
-os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
+from .eynollah import Eynollah
-import tensorflow as tf
-
 from .model_zoo import EynollahModelZoo
 from .utils.resize import resize_image
 from .utils.contour import (
@@ -33,23 +31,27 @@ DPI_THRESHOLD = 298
 KERNEL = np.ones((5, 5), np.uint8)
 
 
-class machine_based_reading_order_on_layout:
+class Reorder(Eynollah):
     def __init__(
-        self,
+            self,
-        *,
+            *,
-        model_zoo: EynollahModelZoo,
+            model_zoo: EynollahModelZoo,
-        logger : Optional[logging.Logger] = None,
+            logger : Optional[logging.Logger] = None,
+            device: str = '',
     ):
         self.logger = logger or logging.getLogger('eynollah.mbreorder')
         self.model_zoo = model_zoo
         
-        try:
+        self.model_zoo.load_model('reading_order')
-            for device in tf.config.list_physical_devices('GPU'):
+        self.setup_models(device=device)
-                tf.config.experimental.set_memory_growth(device, True)
+
-        except:
+    def setup_models(self, device=''):
-            self.logger.warning("no GPU device available")
+        loadable = ['reading_order']
+        self.model_zoo.load_models(*loadable, device=device)
+        for model in loadable:
+            self.logger.debug("model %s has input shape %s", model,
+                              self.model_zoo.get(model).input_shape)
         
-        self.model_zoo.load_models('reading_order')
 
     def read_xml(self, xml_file):
         tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8'))
@@ -675,7 +677,7 @@ class machine_based_reading_order_on_layout:
                 tot_counter += 1
                 batch.append(j)
                 if tot_counter % inference_bs == 0 or tot_counter == len(ij_list):
-                    y_pr = self.model_zoo.get('reading_order').predict(input_1 , verbose='0')
+                    y_pr = self.model_zoo.get('reading_order').predict(input_1, verbose=0)
                     for jb, j in enumerate(batch):
                         if y_pr[jb][0]>=0.5:
                             post_list.append(j)

src/eynollah/model_zoo/model_zoo.py

@@ -35,7 +35,7 @@ class EynollahModelZoo:
         self._overrides = []
         if model_overrides:
             self.override_models(*model_overrides)
-        self._loaded: Dict[str, Predictor] = {}
+        self._loaded: Dict[str, Union[Predictor, AnyModel]] = {}
 
     @property
     def model_overrides(self):
@@ -70,6 +70,9 @@ class EynollahModelZoo:
             model_path = Path(self.model_basedir).joinpath(spec.filename)
         else:
             model_path = Path(spec.filename)
+        if model_path.suffix == '.h5' and Path(model_path.stem).exists():
+            # prefer SavedModel over HDF5 format if it exists
+            model_path = Path(model_path.stem)
         return model_path
 
     def load_models(
@@ -82,32 +85,50 @@ class EynollahModelZoo:
         """
         ret = {} # cannot use self._loaded here, yet – first spawn all predictors
         for load_args in all_load_args:
+            load_kwargs = dict(device=device)
             if isinstance(load_args, str):
-                model_category = load_args
+                model_category, model_variant = load_args, ""
-                load_args = [model_category]
+            elif len(load_args) > 2:
+                # for calls to self.model_path
+                self.override_models(load_args)
+                # for calls to Predictor.load_model
+                model_category, model_variant, model_path = load_args
+                load_kwargs["model_variant"] = model_variant
+                load_kwargs["model_path_override"] = model_path
             else:
-                model_category = load_args[0]
+                model_category, model_variant = load_args
-            load_kwargs = {}
+                load_kwargs["model_variant"] = model_variant
+
             if model_category.endswith('_resized'):
-                load_args[0] = model_category[:-8]
+                model_category = model_category[:-8]
                 load_kwargs["resized"] = True
             elif model_category.endswith('_patched'):
-                load_args[0] = model_category[:-8]
+                model_category = model_category[:-8]
                 load_kwargs["patched"] = True
-            spec = self.specs.get(model_category, load_args[1] if len(load_args) > 1 else '')
+
-            if spec.type in ['Keras'] and spec.category != 'ocr':
+            if model_category == 'ocr':
-                ret[model_category] = Predictor(self.logger, self)
+                model = self._load_ocr_model(variant=model_variant, device=device)
-                ret[model_category].load_model(*load_args, **load_kwargs, device=device)
+            elif model_category == 'num_to_char':
+                model = self._load_num_to_char()
+            elif model_category == 'characters':
+                model = self._load_characters()
+            elif model_category == 'trocr_processor':
+                from transformers import TrOCRProcessor
+                model_path = self.model_path(model_category, model_variant)
+                model = TrOCRProcessor.from_pretrained(model_path)
             else:
-                ret[model_category] = self.load_model(*load_args, **load_kwargs, device=device)
+                model = Predictor(self.logger, self)
+                model.load_model(model_category, **load_kwargs)
+
+            ret[model_category] = model
         self._loaded.update(ret)
         return self._loaded
 
     def load_model(
-        self,
+            self,
-        model_category: str,
+            model_category: str,
-        model_variant: str = '',
+            model_variant: str = '',
-        model_path_override: Optional[str] = None,
+            model_path_override: Optional[str] = None,
             patched: bool = False,
             resized: bool = False,
             device: str = '',
@@ -121,6 +142,7 @@ class EynollahModelZoo:
 
         import tensorflow as tf
         from tensorflow.keras.models import load_model
+        from tensorflow.keras.models import Model as KerasModel
 
         from ..patch_encoder import (
             PatchEncoder,
@@ -132,7 +154,7 @@ class EynollahModelZoo:
         try:
             gpus = tf.config.list_physical_devices('GPU')
             if device:
-                if ',' in device:
+                if ':' in device:
                     for spec in device.split(','):
                         cat, dev = spec.split(':')
                         if fnmatchcase(model_category, cat):
@@ -147,7 +169,24 @@ class EynollahModelZoo:
                 gpus = gpus[:1] # TF will always use first allowable
             tf.config.set_visible_devices(gpus, 'GPU')
             for device in gpus:
-                tf.config.experimental.set_memory_growth(device, True)
+                # tf.config.experimental.set_memory_growth(device, True)
+                # dynamic growth never frees memory (to avoid fragmentation),
+                # so the VRAM requirements end up much larger than feasible
+                # (for small GPUs); so try hard (calibrated) limits instead:
+                tf.config.set_logical_device_configuration(
+                    device,
+                    [tf.config.LogicalDeviceConfiguration(memory_limit={
+                        "binarization": 868, # due to bs 5
+                        "enhancement": 980, # due to bs 3
+                        "col_classifier": 210,
+                        "page": 618,
+                        "textline": 1680, # 954 for bs 1
+                        "region_1_2": 1580,
+                        "region_fl_np": 1756,
+                        "table": 1818,
+                        "reading_order": 632,
+                        "ocr": 850,
+                    }[model_category])])
                 vendor_name = (
                     tf.config.experimental.get_device_details(device)
                     .get('device_name', 'unknown'))
@@ -166,65 +205,76 @@ class EynollahModelZoo:
         if model_path_override:
             self.override_models((model_category, model_variant, model_path_override))
         model_path = self.model_path(model_category, model_variant)
-        if model_path.suffix == '.h5' and Path(model_path.stem).exists():
+        try:
-            # prefer SavedModel over HDF5 format if it exists
+            if model_path.is_dir() and not (model_path / "keras_metadata.pb").exists():
-            model_path = Path(model_path.stem)
+                # short-cut to avoid warning for exported models
-        if model_category == 'ocr':
+                raise ValueError()
-            model = self._load_ocr_model(variant=model_variant)
+            model = load_model(model_path, compile=False)
-        elif model_category == 'num_to_char':
-            model = self._load_num_to_char()
-        elif model_category == 'characters':
-            model = self._load_characters()
-        elif model_category == 'trocr_processor':
-            from transformers import TrOCRProcessor
-            model = TrOCRProcessor.from_pretrained(model_path)
-        else:
-            try:
-                # avoid wasting VRAM on non-transformer models
-                model = load_model(model_path, compile=False)
-            except Exception as e:
-                self.logger.error(e)
-                model = load_model(
-                    model_path, compile=False,
-                    custom_objects=dict(PatchEncoder=PatchEncoder,
-                                        Patches=Patches))
-            model._name = model_category
-            if resized:
-                model = wrap_layout_model_resized(model)
-                model._name = model_category + '_resized'
-            elif patched:
-                model = wrap_layout_model_patched(model)
-                model._name = model_category + '_patched'
-            else:
-                model.jit_compile = True
             model.make_predict_function()
+        except (AttributeError, ValueError):
+            model = tf.saved_model.load(model_path)
+            model.predict_on_batch = model.serve
+            model.input_shape = tuple(model.signatures.get('serving_default').inputs[0].shape)
+        model._name = model_category
+        if resized:
+            model = wrap_layout_model_resized(model)
+            model._name = model_category + '_resized'
+        elif patched:
+            model = wrap_layout_model_patched(model)
+            model._name = model_category + '_patched'
+        else:
+            # increases required VRAM, does not always work
+            # (depending on CUDA/libcudnn/TF version):
+            #model.jit_compile = True
+            pass
+
+        if model_category == 'ocr':
+            model = KerasModel(
+                model.get_layer(name="image").input,   # type: ignore
+                model.get_layer(name="dense2").output, # type: ignore
+        )
+
         return model
 
-    def get(self, model_category: str) -> Predictor:
+    def get(self, model_category: str) -> Union[Predictor, AnyModel]:
         if model_category not in self._loaded:
             raise ValueError(f'Model "{model_category}" not previously loaded with "load_model(..)"')
         return self._loaded[model_category]
 
-    def _load_ocr_model(self, variant: str) -> AnyModel:
+    def _load_ocr_model(self, variant: str, device: str = "") -> AnyModel:
         """
         Load OCR model
         """
-        from tensorflow.keras.models import Model as KerasModel
+        model_dir = self.model_path('ocr', variant)
-        from tensorflow.keras.models import load_model
-
-        ocr_model_dir = self.model_path('ocr', variant)
         if variant == 'tr':
             from transformers import VisionEncoderDecoderModel
-            ret = VisionEncoderDecoderModel.from_pretrained(ocr_model_dir)
+            import torch
-            assert isinstance(ret, VisionEncoderDecoderModel)
+            model = VisionEncoderDecoderModel.from_pretrained(model_dir)
-            return ret
+            assert isinstance(model, VisionEncoderDecoderModel)
-        else:
+            device0 = torch.device('cpu')
-            ocr_model = load_model(ocr_model_dir, compile=False)
+            if not device and torch.cuda.is_available():
-            assert isinstance(ocr_model, KerasModel)
+                device = 'GPU' # try
-            return KerasModel(
+            if device and ':' in device:
-                ocr_model.get_layer(name="image").input,   # type: ignore
+                for spec in device.split(','):
-                ocr_model.get_layer(name="dense2").output, # type: ignore
+                    cat, dev = spec.split(':')
-            )
+                    if fnmatchcase('ocr', cat):
+                        device = dev
+                        break
+            if device and device.startswith('GPU'):
+                try:
+                    device0 = torch.device('cuda', int(device[3:] or 0))
+                    name = torch.cuda.get_device_name(device0)
+                    self.logger.info("using GPU %s (%s) for model ocr:tr", device0, name)
+                except:
+                    self.logger.exception("cannot configure GPU device")
+                    device0 = torch.device('cpu')
+            if device0.type == 'cuda':
+                model.to(device0)
+            else:
+                self.logger.warning("no GPU device available")
+            return model
+
+        return self.load_model('ocr', model_variant=variant, device=device)
 
     def _load_characters(self) -> List[str]:
         """
@@ -237,6 +287,10 @@ class EynollahModelZoo:
         """
         Load decoder for OCR
         """
+        os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
+        from ocrd_utils import tf_disable_interactive_logs
+        tf_disable_interactive_logs()
+
         from tensorflow.keras.layers import StringLookup
 
         characters = self._load_characters()
@@ -277,5 +331,5 @@ class EynollahModelZoo:
         """
         if hasattr(self, '_loaded') and getattr(self, '_loaded'):
             for needle in list(self._loaded.keys()):
-                self._loaded[needle].shutdown()
+                if isinstance(self._loaded[needle], Predictor):
-                del self._loaded[needle]
+                    self._loaded[needle].shutdown()

src/eynollah/ocrd_cli.py

@@ -1,10 +1,8 @@
-# NOTE: For predictable order of imports of torch/shapely/tensorflow
-#       this must be the first import of the CLI!
-from .eynollah_imports import imported_libs
-from .processor import EynollahProcessor
 from click import command
 from ocrd.decorators import ocrd_cli_options, ocrd_cli_wrap_processor
 
+from .processor import EynollahProcessor
+
 @command()
 @ocrd_cli_options
 def main(*args, **kwargs):

src/eynollah/predictor.py

@@ -194,17 +194,18 @@ class Predictor(mp.context.SpawnProcess):
 
     def shutdown(self):
         # do not terminate from forked processor instances
-        if mp.parent_process() is None:
+        if not hasattr(self, 'model'):
             self.stopped.set()
+            self.join()
             self.taskq.close()
             self.taskq.cancel_join_thread()
             self.resultq.close()
             self.resultq.cancel_join_thread()
             self.logq.close()
-            self.terminate()
+            #self.terminate()
         else:
             del self.model
 
     def __del__(self):
-        #self.logger.debug(f"deinit of {self} in {mp.current_process().name}")
+        #self.logger.debug(f"deinit of {self.name} in {mp.current_process().name}")
         self.shutdown()

src/eynollah/training/models.py

@@ -309,11 +309,10 @@ def transformer_block(img,
         # Skip connection 2.
         encoded_patches = Add()([x3, x2])
 
-    encoded_patches = tf.reshape(encoded_patches,
+    encoded_patches = Reshape(target_shape=(img.shape[1],
-                                 [-1,
+                                            img.shape[2],
-                                  img.shape[1],
+                                            projection_dim // (patchsize_x * patchsize_y)),
-                                  img.shape[2],
+                              name="reshape_patches")(encoded_patches)
-                                  projection_dim // (patchsize_x * patchsize_y)])
     return encoded_patches
 
 def vit_resnet50_unet(num_patches,

src/eynollah/training/reload-models-v0.8.mk

@@ -26,16 +26,17 @@ RELOADABLE_MODELS = \
 all: $(RELOADABLE_MODELS)
 
 $(MODELS_DST)/%: $(MODELS_SRC)/%
-	mkdir -p $@
 	test -e $</config.json || exit 1
-	eynollah-training train --force \
+	{ mkdir -p $@ \
+	&& eynollah-training train --force \
 		with $</config.json \
 		reload_weights=True \
 		continue_training=False \
 		dir_output=$(dir $@) \
 		dir_of_start_model=$< \
+	&& cp $</config.json $@/config.json \
+	|| { rm -rf $@; false; }; } \
 	2>&1 | tee $(notdir $<).log
-	cp $</config.json $@/config.json
 
 compare: 
 	for i in `find $(MODELS_DST) -mindepth 2`;do \

src/eynollah/training/train.py

@@ -562,7 +562,8 @@ def run(_config,
         if reload_weights:
             model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
             dir_save = os.path.join(dir_output, os.path.basename(os.path.normpath(dir_of_start_model)))
-            model.save(dir_save, include_optimizer=False)
+            #model.save(dir_save, include_optimizer=False)
+            model.export(dir_save)
             with open(os.path.join(dir_save, "config.json"), "w") as fp:
                 json.dump(_config, fp)  # encode dict into JSON
             _log.info("reloaded model from %s to %s", dir_of_start_model, dir_save)
@@ -725,7 +726,8 @@ def run(_config,
         if reload_weights:
             model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
             dir_save = os.path.join(dir_output, os.path.basename(os.path.normpath(dir_of_start_model)))
-            model.save(dir_save, include_optimizer=False)
+            #model.save(dir_save, include_optimizer=False)
+            model.export(dir_save)
             with open(os.path.join(dir_save, "config.json"), "w") as fp:
                 json.dump(_config, fp)  # encode dict into JSON
             _log.info("reloaded model from %s to %s", dir_of_start_model, dir_save)
@@ -843,7 +845,8 @@ def run(_config,
         if reload_weights:
             model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
             dir_save = os.path.join(dir_output, os.path.basename(os.path.normpath(dir_of_start_model)))
-            model.save(dir_save, include_optimizer=False)
+            #model.save(dir_save, include_optimizer=False)
+            model.export(dir_save)
             with open(os.path.join(dir_save, "config.json"), "w") as fp:
                 json.dump(_config, fp)  # encode dict into JSON
             _log.info("reloaded model from %s to %s", dir_of_start_model, dir_save)

src/eynollah/utils/contour.py

@@ -11,7 +11,7 @@ from shapely.geometry.polygon import orient
 from shapely import set_precision, affinity
 from shapely.ops import unary_union, nearest_points
 
-from .rotate import rotate_image, rotation_image_new
+from .rotate import rotate_image
 
 def contours_in_same_horizon(cy_main_hor):
     """
@@ -120,94 +120,6 @@ def return_contours_of_interested_region(region_pre_p, label, min_area=0.0002, d
                                                          dilate=dilate)
     return contours_imgs
 
-def do_work_of_contours_in_image(contour, index_r_con, img, slope_first):
-    img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
-    img_copy = cv2.fillPoly(img_copy, pts=[contour], color=1)
-
-    img_copy = rotation_image_new(img_copy, -slope_first)
-    _, thresh = cv2.threshold(img_copy, 0, 255, 0)
-
-    cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-    cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
-    cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
-
-    return cont_int[0], index_r_con
-
-def get_textregion_contours_in_org_image_multi(cnts, img, slope_first, map=map):
-    if not len(cnts):
-        return [], []
-    results = map(partial(do_work_of_contours_in_image,
-                          img=img,
-                          slope_first=slope_first,
-                          ),
-                  cnts, range(len(cnts)))
-    return tuple(zip(*results))
-
-def get_textregion_contours_in_org_image(cnts, img, slope_first):
-    cnts_org = []
-    # print(cnts,'cnts')
-    for i in range(len(cnts)):
-        img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
-        img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=1)
-
-        # plt.imshow(img_copy)
-        # plt.show()
-
-        # print(img.shape,'img')
-        img_copy = rotation_image_new(img_copy, -slope_first)
-        ##print(img_copy.shape,'img_copy')
-        # plt.imshow(img_copy)
-        # plt.show()
-
-        _, thresh = cv2.threshold(img_copy, 0, 255, 0)
-
-        cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-        cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
-        cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
-        # print(np.shape(cont_int[0]))
-        cnts_org.append(cont_int[0])
-
-    return cnts_org
-
-def get_textregion_confidences_old(cnts, img, slope_first):
-    zoom = 3
-    img = cv2.resize(img, (img.shape[1] // zoom,
-                           img.shape[0] // zoom),
-                     interpolation=cv2.INTER_NEAREST)
-    cnts_org = []
-    for cnt in cnts:
-        img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
-        img_copy = cv2.fillPoly(img_copy, pts=[cnt // zoom], color=1)
-
-        img_copy = rotation_image_new(img_copy, -slope_first).astype(np.uint8)
-        _, thresh = cv2.threshold(img_copy, 0, 255, 0)
-
-        cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-        cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
-        cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
-        cnts_org.append(cont_int[0] * zoom)
-
-    return cnts_org
-
-def do_back_rotation_and_get_cnt_back(contour_par, index_r_con, img, slope_first, confidence_matrix):
-    img_copy = np.zeros(img.shape[:2], dtype=np.uint8)
-    img_copy = cv2.fillPoly(img_copy, pts=[contour_par], color=1)
-    confidence_matrix_mapped_with_contour = confidence_matrix * img_copy
-    confidence_contour = np.sum(confidence_matrix_mapped_with_contour) / float(np.sum(img_copy))
-
-    img_copy = rotation_image_new(img_copy, -slope_first).astype(np.uint8)
-    _, thresh = cv2.threshold(img_copy, 0, 255, 0)
-
-    cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-    if len(cont_int)==0:
-        cont_int = [contour_par]
-        confidence_contour = 0
-    else:
-        cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
-        cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
-    return cont_int[0], index_r_con, confidence_contour
-
 def get_region_confidences(cnts, confidence_matrix):
     if not len(cnts):
         return []
@@ -418,7 +330,7 @@ def estimate_skew_contours(contours):
     if not np.any(usable):
         raise ValueError("not enough contours with consistent length")
     if np.count_nonzero(usable) == 1:
-        return angle_in[usable]
+        return angle_in[usable][0]
     # 4. there is no way to distinguish between +90 and -89.9 here,
     # so map to [0,180] when calculating averages, then map back to [-90,90]
     # (we don't want -90 and +89 to average zero, or +1 and +179 to average 90)

src/eynollah/utils/rotate.py

@@ -2,10 +2,6 @@ import math
 import cv2
 
 
-def rotation_image_new(img, thetha):
-    rotated = rotate_image(img, thetha)
-    return rotate_max_area_new(img, rotated, thetha)
-
 def rotate_image(img_patch, slope):
     (h, w) = img_patch.shape[:2]
     center = (w // 2, h // 2)

src/eynollah/utils/utils_ocr.py

@@ -1,9 +1,12 @@
 import math
 import copy
+from itertools import islice
 
 import numpy as np
 import cv2
-import tensorflow as tf
+# avoid module-level import:
+# import tensorflow as tf
+# (wait for tf-keras and logging setup in ModelZoo.load_model)
 from scipy.signal import find_peaks
 from scipy.ndimage import gaussian_filter1d
 from PIL import Image, ImageDraw, ImageFont
@@ -12,6 +15,8 @@ from .resize import resize_image
 
 
 def decode_batch_predictions(pred, num_to_char, max_len = 128):
+    import tensorflow as tf
+
     # input_len is the product of the batch size and the
     # number of time steps.
     input_len = np.ones(pred.shape[0]) * pred.shape[1]
@@ -39,6 +44,8 @@ def decode_batch_predictions(pred, num_to_char, max_len = 128):
     
     
 def distortion_free_resize(image, img_size):
+    import tensorflow as tf
+
     w, h = img_size
     image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True)
 
@@ -502,3 +509,8 @@ def return_rnn_cnn_ocr_of_given_textlines(image,
             ocr_textline_in_textregion.append(text_textline)
         ocr_all_textlines.append(ocr_textline_in_textregion)
     return ocr_all_textlines
+
+def batched(iterable, n):
+    iterator = iter(iterable)
+    while batch := tuple(islice(iterator, n)):
+        yield batch

tests/cli_tests/conftest.py

@@ -1,4 +1,5 @@
 from typing import List
+import os
 import pytest
 import logging
 
@@ -31,6 +32,8 @@ def run_eynollah_ok_and_check_logs(
                 subcommand,
                 *args
         ]
+        if 'EYNOLLAH_OPTIONS' in os.environ:
+            args = os.environ['EYNOLLAH_OPTIONS'].split() + args
         if pytestconfig.getoption('verbose') > 0:
             args = ['-l', 'DEBUG'] + args
         caplog.set_level(logging.INFO)

tests/cli_tests/test_layout.py

@@ -6,11 +6,12 @@ from ocrd_models.constants import NAMESPACES as NS
     "options",
     [
             [], # defaults
-            #["--allow_scaling", "--curved-line"],
+            #["--curved-line"],
-            ["--allow_scaling", "--curved-line", "--full-layout"],
+            ["--curved-line", "--full-layout"],
-            ["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based"],
+            ["--curved-line", "--full-layout", "--reading_order_machine_based"],
             # -ep ...
-            # -eoi ...
+            # --input_binary
+            # --ignore_page_extraction
             # --skip_layout_and_reading_order
     ], ids=str)
 def test_run_eynollah_layout_filename(

tests/cli_tests/test_ocr.py

@@ -30,7 +30,7 @@ def test_run_eynollah_ocr_filename(
             '-o', str(outfile.parent),
         ] + options,
         [
-            # FIXME: ocr has no logging!
+            'output filename:'
         ]
     )
     assert outfile.exists()
@@ -57,7 +57,7 @@ def test_run_eynollah_ocr_directory(
             '-o', str(outdir),
         ],
         [
-            # FIXME: ocr has no logging!
+            'output filename:'
         ]
     )
     assert len(list(outdir.iterdir())) == 2

tests/test_model_zoo.py

@@ -6,10 +6,10 @@ def test_trocr1(
     model_zoo = EynollahModelZoo(model_dir)
     try:
         from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-        model_zoo.load_models('trocr_processor')
+        model_zoo.load_models('trocr_processor',
+                              ('ocr', 'tr'))
         proc = model_zoo.get('trocr_processor')
         assert isinstance(proc, TrOCRProcessor)
-        model_zoo.load_models(['ocr', 'tr'])
         model = model_zoo.get('ocr')
         assert isinstance(model, VisionEncoderDecoderModel)
     except ImportError: