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4
src/eynollah/cli/__init__.py
View file

@ -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

9
src/eynollah/cli/cli.py
View file

@ -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,
)

13
src/eynollah/cli/cli_binarize.py
View file

@ -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,

13
src/eynollah/cli/cli_enhance.py
View file

@ -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,

32
src/eynollah/cli/cli_extract_images.py
View file

@ -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",
"-ep/-noep",
"--enable-plotting",
"-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",
"-ib/-irgb",
"--input_binary",
"-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",
"-ipe/-epi",
"--ignore_page_extraction",
"-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(

8
src/eynollah/cli/cli_layout.py
View file

@ -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,

26
src/eynollah/cli/cli_ocr.py
View file

@ -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,

9
src/eynollah/cli/cli_readingorder.py
View file

@ -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,

7
src/eynollah/extract_images.py
View file

@ -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)")

1
src/eynollah/eynollah.py
View file

@ -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)

13
src/eynollah/eynollah_imports.py
View file

@ -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']

238
src/eynollah/eynollah_ocr.py
View file

@ -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 eynollah.utils.font import get_font
from eynollah.utils.xml import etree_namespace_for_element_tag
try:
import torch
except ImportError:
torch = None
from ocrd_utils import polygon_from_points, xywh_from_polygon
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
self.b_s = 2 if batch_size is None and tr_ocr else 8 if batch_size is None else batch_size
if tr_ocr:
self.model_zoo.load_models('trocr_processor')
self.model_zoo.load_models(['ocr', 'tr'])
self.model_zoo.get('ocr').to(self.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_models('ocr')
self.model_zoo.load_models('num_to_char')
self.model_zoo.load_models('characters')
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,
@ -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
indexer_b_s = 0
for n_region, region in enumerate(page_tree.getroot().iter('{%s}TextRegion' % page_ns)):
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'):
for child_textregion in nn:
if child_textregion.tag.endswith("TextLine"):
coords = line.find('{%s}Coords' % page_ns)
if coords is None:
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:
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])
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)
img_poly_on_img = np.copy(img)
mask_poly = np.zeros(img.shape)
mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
mask_poly = mask_poly[y:y+h, x:x+w, :]
img_crop = img_poly_on_img[y:y+h, x:x+w, :]
img_crop[mask_poly==0] = 255
self.logger.debug("processing %d lines for '%s'",
len(cropped_lines), nn.attrib['id'])
if h2w_ratio > 0.1:
if h > 0.1 * w:
cropped_lines.append(resize_image(img_crop,
tr_ocr_input_height_and_width,
tr_ocr_input_height_and_width) )
cropped_lines_meging_indexing.append(0)
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
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],
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
else:
cropped_lines.append(img_crop)
cropped_lines_meging_indexing.append(0)
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
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
self.logger.debug("processing %d lines for %d regions",
len(cropped_lines), len(set(cropped_lines_region_indexer)))
for imgs in batched(cropped_lines, self.b_s):
pixel_values = self.model_zoo.get('trocr_processor')(
imgs, return_tensors="pt").pixel_values
output = self.model_zoo.get('ocr').generate(
pixel_values.to(self.device),
# beam search instead of greedy decoding:
num_beams=4,
# also return probability
output_scores=True,
return_dict_in_generate=True)
if output.sequences_scores is not None:
# log-prob averaged over length
conf = output.sequences_scores.exp().clamp(0.0, 1.0).tolist()
else:
conf = [1.0] * len(output.sequences)
text = self.model_zoo.get('trocr_processor').batch_decode(
output.sequences,
skip_special_tokens=True,
clean_up_tokenization_spaces=False)
extracted_confs.extend(conf)
extracted_texts.extend(text)
del cropped_lines
gc.collect()
extracted_texts_merged = [extracted_texts[ind]
if cropped_lines_meging_indexing[ind]==0
else extracted_texts[ind]+" "+extracted_texts[ind+1]
if cropped_lines_meging_indexing[ind]==1
else None
for ind in range(len(cropped_lines_meging_indexing))]
extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
#print(extracted_texts_merged, len(extracted_texts_merged))
if cropped_lines_meging_indexing[ind] == 0
else extracted_texts[ind] + " " + extracted_texts[ind + 1]
for ind in range(len(cropped_lines_meging_indexing))
if cropped_lines_meging_indexing[ind] >= 0]
extracted_confs_merged = [extracted_confs[ind]
if cropped_lines_meging_indexing[ind] == 0
else 0.5 * (extracted_confs[ind] + extracted_confs[ind + 1])
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(

24
src/eynollah/mb_ro_on_layout.py
View file

@ -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
import tensorflow as tf
from .eynollah import Eynollah
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,
*,
model_zoo: EynollahModelZoo,
logger : Optional[logging.Logger] = None,
device: str = '',
):
self.logger = logger or logging.getLogger('eynollah.mbreorder')
self.model_zoo = model_zoo
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.model_zoo.load_model('reading_order')
self.setup_models(device=device)
def setup_models(self, device=''):
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)

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src/eynollah/model_zoo/.nfs00000002feddea7d00000031
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158
src/eynollah/model_zoo/model_zoo.py
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@ -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(
@ -82,24 +85,42 @@ 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
load_args = [model_category]
model_category, model_variant = load_args, ""
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]
load_kwargs = {}
model_category, model_variant = load_args
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':
ret[model_category] = Predictor(self.logger, self)
ret[model_category].load_model(*load_args, **load_kwargs, device=device)
if model_category == 'ocr':
model = self._load_ocr_model(variant=model_variant, 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
@ -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'))
@ -166,28 +205,16 @@ 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():
# prefer SavedModel over HDF5 format if it exists
model_path = Path(model_path.stem)
if model_category == 'ocr':
model = self._load_ocr_model(variant=model_variant)
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
if model_path.is_dir() and not (model_path / "keras_metadata.pb").exists():
# short-cut to avoid warning for exported models
raise ValueError()
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.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)
@ -196,35 +223,58 @@ class EynollahModelZoo:
model = wrap_layout_model_patched(model)
model._name = model_category + '_patched'
else:
model.jit_compile = True
model.make_predict_function()
# 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
from tensorflow.keras.models import load_model
ocr_model_dir = self.model_path('ocr', variant)
model_dir = self.model_path('ocr', variant)
if variant == 'tr':
from transformers import VisionEncoderDecoderModel
ret = VisionEncoderDecoderModel.from_pretrained(ocr_model_dir)
assert isinstance(ret, VisionEncoderDecoderModel)
return ret
import torch
model = VisionEncoderDecoderModel.from_pretrained(model_dir)
assert isinstance(model, VisionEncoderDecoderModel)
device0 = torch.device('cpu')
if not device and torch.cuda.is_available():
device = 'GPU' # try
if device and ':' in device:
for spec in device.split(','):
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:
ocr_model = load_model(ocr_model_dir, compile=False)
assert isinstance(ocr_model, KerasModel)
return KerasModel(
ocr_model.get_layer(name="image").input, # type: ignore
ocr_model.get_layer(name="dense2").output, # type: ignore
)
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()):
if isinstance(self._loaded[needle], Predictor):
self._loaded[needle].shutdown()
del self._loaded[needle]

6
src/eynollah/ocrd_cli.py
View file

@ -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):

7
src/eynollah/predictor.py
View file

@ -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()

7
src/eynollah/training/models.py
View file

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

7
src/eynollah/training/reload-models-v0.8.mk
View file

@ -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 \

9
src/eynollah/training/train.py
View file

@ -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)

92
src/eynollah/utils/contour.py
View file

@ -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)

4
src/eynollah/utils/rotate.py
View file

@ -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)

14
src/eynollah/utils/utils_ocr.py
View file

@ -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

3
tests/cli_tests/conftest.py
View file

@ -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)

9
tests/cli_tests/test_layout.py
View file

@ -6,11 +6,12 @@ from ocrd_models.constants import NAMESPACES as NS
"options",
[
[], # defaults
#["--allow_scaling", "--curved-line"],
["--allow_scaling", "--curved-line", "--full-layout"],
["--allow_scaling", "--curved-line", "--full-layout", "--reading_order_machine_based"],
#["--curved-line"],
["--curved-line", "--full-layout"],
["--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(

4
tests/cli_tests/test_ocr.py
View file

@ -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

4
tests/test_model_zoo.py
View file

@ -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: