qurator-spk/eynollah
1
0
Fork 0
mirror of https://github.com/qurator-spk/eynollah.git synced 2026-05-31 01:59:27 +02:00
Code Issues Projects Releases Wiki Activity

Merge 13f2f81c45 into 2e3f45c300

Browse source
This commit is contained in:
Robert Sachunsky 2026-05-28 16:44:33 +00:00 • committed by GitHub
commit c8a1ec93c4
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
30 changed files with 733 additions and 646 deletions
Download patch file Download diff file Expand all files Collapse all files

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']

274
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:
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
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)
else:
splited_images, _ = return_textlines_split_if_needed(img_crop, None)
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)
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)
else:
cropped_lines.append(img_crop)
cropped_lines_meging_indexing.append(0)
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(

32
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,
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)

313
src/eynollah/model_zoo/model_zoo.py
View file

@ -14,6 +14,19 @@ from .default_specs import DEFAULT_MODEL_SPECS
from .types import AnyModel, T
MODEL_VRAM_LIMITS = {
"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,
}
class EynollahModelZoo:
"""
Wrapper class that handles storage and loading of models for all eynollah runners.
@ -35,7 +48,7 @@ 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 +83,13 @@ 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)
if model_path.with_suffix('.onnx').exists():
# prefer ONNX over SavedModel format if it exists
model_path = model_path.with_suffix('.onnx')
return model_path
def load_models(
@ -82,32 +102,50 @@ 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
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
def load_model(
self,
model_category: str,
model_variant: str = '',
model_path_override: Optional[str] = None,
self,
model_category: str,
model_variant: str = '',
model_path_override: Optional[str] = None,
patched: bool = False,
resized: bool = False,
device: str = '',
@ -115,24 +153,39 @@ class EynollahModelZoo:
"""
Load any model
"""
os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
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.is_dir() and (model_path / "keras_metadata.pb").exists():
# Keras model
model = self._load_keras_model(model_category, model_path, device=device)
elif model_path.is_dir():
# TF-Serving model
model = self._load_serving_model(model_category, model_path, device=device)
elif model_path.suffix == '.onnx':
# ONNX model
model = self._load_onnx_model(model_category, model_path, device=device)
else:
raise ValueError("unknown model type for '%s'" % str(model_path))
model._name = model_category
return model
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 _configure_tf_device(self, model_category, device=''):
from ocrd_utils import tf_disable_interactive_logs
tf_disable_interactive_logs()
import tensorflow as tf
from tensorflow.keras.models import load_model
from ..patch_encoder import (
PatchEncoder,
Patches,
wrap_layout_model_patched,
wrap_layout_model_resized,
)
cuda = False
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 +200,14 @@ 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=MODEL_VRAM_LIMITS[model_category])])
vendor_name = (
tf.config.experimental.get_device_details(device)
.get('device_name', 'unknown'))
@ -155,76 +215,159 @@ class EynollahModelZoo:
self.logger.info("using GPU %s (%s) for model %s",
device.name,
vendor_name,
model_category + (
"_patched" if patched else
"_resized" if resized else ""))
model_category # + (
# "_patched" if patched else
# "_resized" if resized else "")
)
except RuntimeError:
self.logger.exception("cannot configure GPU devices")
if not cuda:
self.logger.warning("no GPU device available")
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)
def _load_keras_model(self, model_category, model_path, device=''):
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.models import load_model
from tensorflow.keras.models import Model as KerasModel
self._configure_tf_device(model_category, device=device)
model = load_model(model_path, compile=False)
# from ..patch_encoder import (
# wrap_layout_model_patched,
# wrap_layout_model_resized,
# )
# 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'
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:
# cnn-rnn-ocr task model may not be in inference mode, yet
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'
model.get_layer(name='ctc_loss')
except ValueError:
pass
else:
model.jit_compile = True
model.make_predict_function()
model = KerasModel(
model.get_layer(name="image").input, # type: ignore
model.get_layer(name="dense2").output, # type: ignore
)
model.make_predict_function()
return model
def get(self, model_category: str) -> Predictor:
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_serving_model(self, model_category, model_path, device=''):
from ocrd_utils import tf_disable_interactive_logs
tf_disable_interactive_logs()
import tensorflow as tf
def _load_ocr_model(self, variant: str) -> AnyModel:
self._configure_tf_device(model_category, device=device)
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)
return model
def _load_onnx_model(self, model_category, model_path, device=''):
import onnxruntime as ort
import numpy as np
providers = ort.get_available_providers()
if device:
if ':' in device:
for spec in device.split(','):
cat, dev = spec.split(':')
if fnmatchcase(model_category, cat):
device = dev
break
if device == 'CPU':
gpu = -1
else:
assert device.startswith('GPU')
gpu = int(device[3:] or "0")
else:
gpu = 0 # try first allowable
# configure and prioritise
if 'CUDAExecutionProvider' in providers:
providers.remove('CUDAExecutionProvider')
if gpu >= 0:
providers = [('CUDAExecutionProvider', {
'device_id': gpu,
# 'arena_extend_strategy': 'kNextPowerOfTwo',
'gpu_mem_limit': MODEL_VRAM_LIMITS[model_category] * 1024 * 1024,
# 'cudnn_conv_algo_search': 'EXHAUSTIVE',
# 'do_copy_in_default_stream': True,
# ...
})] + providers
if 'TensorrtExecutionProvider' in providers:
providers.remove('TensorrtExecutionProvider')
if gpu >= 0:
providers = [('TensorrtExecutionProvider', {
'device_id': gpu,
'trt_max_workspace_size': MODEL_VRAM_LIMITS[model_category] * 1024 * 1024,
# 'trt_fp16_enable': True,
# 'trt_engine_cache_enable': True,
# 'trt_timing_cache_enable': True,
# ...
})] + providers
model = ort.InferenceSession(
model_path,
providers=providers)
# FIXME: notify about selected provider/device
input_name = model.get_inputs()[0].name
output_name = model.get_outputs()[0].name
def predict_onnx(inputs):
# models expect data_type() == 'tensor(float)', but np.float16 is 'tensor(float16)'
# FIXME: do this dynamically (but how to convert .type to np.dtype?)
inputs = inputs.astype(np.float32)
return model.run(
[output_name], {input_name: inputs})[0]
model.predict_on_batch = predict_onnx
model.input_shape = model.get_inputs()[0].shape
return model
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
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
)
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:
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 +380,10 @@ 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 +424,5 @@ class EynollahModelZoo:
"""
if hasattr(self, '_loaded') and getattr(self, '_loaded'):
for needle in list(self._loaded.keys()):
self._loaded[needle].shutdown()
del self._loaded[needle]
if isinstance(self._loaded[needle], Predictor):
self._loaded[needle].shutdown()

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):

8
src/eynollah/patch_encoder.py
View file

@ -6,8 +6,8 @@ from tensorflow.keras import layers, models
class PatchEncoder(layers.Layer):
# 441=21*21 # 14*14 # 28*28
def __init__(self, num_patches=441, projection_dim=64):
super().__init__()
def __init__(self, num_patches=441, projection_dim=64, name='encode_patches'):
super().__init__(name=name)
self.num_patches = num_patches
self.projection_dim = projection_dim
self.projection = layers.Dense(self.projection_dim)
@ -23,8 +23,8 @@ class PatchEncoder(layers.Layer):
**super().get_config())
class Patches(layers.Layer):
def __init__(self, patch_size_x=1, patch_size_y=1):
super().__init__()
def __init__(self, patch_size_x=1, patch_size_y=1, name='extract_patches'):
super().__init__(name=name)
self.patch_size_x = patch_size_x
self.patch_size_y = patch_size_y

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()

11
src/eynollah/training/cli.py
View file

@ -7,17 +7,11 @@ import sys
from .build_model_load_pretrained_weights_and_save import build_model_load_pretrained_weights_and_save
from .generate_gt_for_training import main as generate_gt_cli
from .inference import main as inference_cli
from .train import ex
from .train import train_cli
from .convert import convert_cli
from .extract_line_gt import linegt_cli
from .weights_ensembling import ensemble_cli
@click.command(context_settings=dict(
ignore_unknown_options=True,
))
@click.argument('SACRED_ARGS', nargs=-1, type=click.UNPROCESSED)
def train_cli(sacred_args):
ex.run_commandline([sys.argv[0]] + list(sacred_args))
@click.group('training')
def main():
pass
@ -26,5 +20,6 @@ main.add_command(build_model_load_pretrained_weights_and_save)
main.add_command(generate_gt_cli, 'generate-gt')
main.add_command(inference_cli, 'inference')
main.add_command(train_cli, 'train')
main.add_command(convert_cli, 'convert')
main.add_command(linegt_cli, 'export_textline_images_and_text')
main.add_command(ensemble_cli, 'ensembling')

107
src/eynollah/training/convert.py Normal file
View file

@ -0,0 +1,107 @@
import os
from pathlib import Path
from shutil import copy2
import logging
import click
@click.command(context_settings=dict(
help_option_names=['-h', '--help'],
show_default=True))
@click.option(
"--rebuild",
"-r",
help="build new model from code and then load existing weights (requires input in SavedModel directory format with config.json present)",
is_flag=True
)
@click.option(
"--format",
"-f",
"format_",
help="data format to convert to",
type=click.Choice(["hdf5", "keras", "tf", "tf-serving", "onnx"]),
default="tf"
)
@click.option(
"--in",
"-i",
"in_",
help="path to input model (file in hdf5 / keras format, or directory in tf format)",
required=True,
type=click.Path(exists=True, dir_okay=True)
)
@click.option(
"--out",
"-o",
help="path to output model (file in hdf5 / keras / onnx format, or directory in tf / tf-serving format)",
required=True,
type=click.Path(exists=False, dir_okay=True)
)
def convert_cli(rebuild, format_, in_, out):
"""
convert models for inference
Load model from path, optionally by rebuilding, convert to output format and write model to path.
"""
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()
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.models import Model as KerasModel
model_path = Path(in_)
config_path = model_path / "config.json"
if model_path.is_dir():
assert (model_path / "keras_metadata.pb").exists(), (
"input directory must be Keras model in SavedModel format")
if rebuild:
from .train import ex
from .models import get_model
assert config_path.exists(), (
"rebuilding requires input model in SavedModel format with config.json")
# merge defaults with existing config file
ex.add_config(str(config_path))
# some models deviate between training and inference
ex.add_config(inference=True)
# just retrieve final config (via pseudo-run)
ex.main(lambda: 0)
config = ex.run(options={'--loglevel': 'ERROR'}).config
# use the config to capture the model builder
model = get_model(config, logging.root)
model.load_weights(model_path).assert_existing_objects_matched().expect_partial()
else:
model = load_model(model_path, compile=False)
if isinstance(model, KerasModel):
# cnn-rnn-ocr task deviates between training and inference
try:
model.get_layer(name='ctc_loss')
except ValueError:
pass
else:
model = KerasModel(
model.get_layer(name='image').input,
model.get_layer(name='dense2').output)
if format_ in ["hdf5", "keras", "tf"]:
kwargs = {"save_format": {"hdf5": "h5"}.get(format_, format_)}
if format_ != "keras":
kwargs["include_optimizer"] = False
model.save(out, **kwargs)
elif format_ == "tf-serving":
model.export(out)
elif format_ == "onnx":
import tf2onnx
tf2onnx.convert.from_keras(model, opset=18, output_path=out)
else:
raise ValueError("unknown output format '%s'" % format_)
# copy config.json if possible
if config_path.exists() and format_ in ['tf', 'tf-serving']:
copy2(config_path, Path(out) / config_path.name)

90
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],
img.shape[2],
projection_dim // (patchsize_x * patchsize_y)])
encoded_patches = Reshape((img.shape[1],
img.shape[2],
projection_dim // (patchsize_x * patchsize_y)),
name="reshape_patches")(encoded_patches)
return encoded_patches
def vit_resnet50_unet(num_patches,
@ -423,11 +422,11 @@ def machine_based_reading_order_model(n_classes,input_height=224,input_width=224
return model
def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_seq=None):
input_img = Input(shape=(image_height, image_width, 3), name="image")
def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_len=None, inference=False):
inputs = Input(shape=(image_height, image_width, 3), name="image")
labels = Input(name="label", shape=(None,))
x = Conv2D(64,kernel_size=(3,3),padding="same")(input_img)
x = Conv2D(64,kernel_size=(3,3),padding="same")(inputs)
x = BatchNormalization(name="bn1")(x)
x = Activation("relu", name="relu1")(x)
x = Conv2D(64,kernel_size=(3,3),padding="same")(x)
@ -460,43 +459,92 @@ def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_s
x2d = MaxPooling2D(pool_size=(1,2),strides=(1,2))(x)
x4d = MaxPooling2D(pool_size=(1,2),strides=(1,2))(x2d)
new_shape = (x.shape[1]*x.shape[2], x.shape[3])
new_shape2 = (x2d.shape[1]*x2d.shape[2], x2d.shape[3])
new_shape4 = (x4d.shape[1]*x4d.shape[2], x4d.shape[3])
x = Reshape(target_shape=new_shape, name="reshape")(x)
x2d = Reshape(target_shape=new_shape2, name="reshape2")(x2d)
x4d = Reshape(target_shape=new_shape4, name="reshape4")(x4d)
x = Reshape(new_shape, name="reshape")(x)
x2d = Reshape(new_shape2, name="reshape2")(x2d)
x4d = Reshape(new_shape4, name="reshape4")(x4d)
xrnnorg = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x)
xrnn2d = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x2d)
xrnn4d = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x4d)
xrnn2d = Reshape(target_shape=(1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d)
xrnn4d = Reshape(target_shape=(1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d)
xrnn2d = Reshape((1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d)
xrnn4d = Reshape((1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d)
xrnn2dup = UpSampling2D(size=(1, 2), interpolation="nearest")(xrnn2d)
xrnn4dup = UpSampling2D(size=(1, 4), interpolation="nearest")(xrnn4d)
xrnn2dup = Reshape(target_shape=(xrnn2dup.shape[2], xrnn2dup.shape[3]), name="reshape10")(xrnn2dup)
xrnn4dup = Reshape(target_shape=(xrnn4dup.shape[2], xrnn4dup.shape[3]), name="reshape12")(xrnn4dup)
xrnn2dup = Reshape((xrnn2dup.shape[2], xrnn2dup.shape[3]), name="reshape10")(xrnn2dup)
xrnn4dup = Reshape((xrnn4dup.shape[2], xrnn4dup.shape[3]), name="reshape12")(xrnn4dup)
addition = Add()([xrnnorg, xrnn2dup, xrnn4dup])
addition_rnn = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(addition)
out = Conv1D(max_seq, 1, data_format="channels_first")(addition_rnn)
out = Conv1D(max_len, 1, data_format="channels_first")(addition_rnn)
out = BatchNormalization(name="bn9")(out)
out = Activation("relu", name="relu9")(out)
#out = Conv1D(n_classes, 1, activation='relu', data_format="channels_last")(out)
out = Dense(n_classes, activation="softmax", name="dense2")(out)
if inference:
return Model(inputs, out)
# Add CTC layer for calculating CTC loss at each step.
output = CTCLayer(name="ctc_loss")(labels, out)
out = CTCLayer(name="ctc_loss")(labels, out)
model = Model(inputs=(input_img, labels), outputs=output, name="handwriting_recognizer")
return Model((inputs, labels), out)
return model
def get_model(config, logger):
from sacred.config import create_captured_function
task = config['task']
if task in ["segmentation", "enhancement", "binarization"]:
if config['backbone_type'] == 'nontransformer':
builder = resnet50_unet
else:
num_patches_x, num_patches_y = config['transformer_num_patches_xy']
num_patches = num_patches_x * num_patches_y
if config['transformer_cnn_first']:
builder = vit_resnet50_unet
multiple = 32
else:
builder = vit_resnet50_unet_transformer_before_cnn
multiple = 1
assert config['input_height'] == (
num_patches_y * config['transformer_patchsize_y'] * multiple), (
"transformer_patchsize_y or transformer_num_patches_xy height value error: "
"input_height should be equal to "
"(transformer_num_patches_xy height value * transformer_patchsize_y * %d)" % multiple)
assert config['input_width'] == (
num_patches_x * config['transformer_patchsize_x'] * multiple), (
"transformer_patchsize_x or transformer_num_patches_xy width value error: "
"input_width should be equal to "
"(transformer_num_patches_xy width value * transformer_patchsize_x * %d)" % multiple)
assert 0 == (config['transformer_projection_dim'] %
(config['transformer_patchsize_y'] *
config['transformer_patchsize_x'])), (
"transformer_projection_dim error: "
"The remainder when parameter transformer_projection_dim is divided by "
"(transformer_patchsize_y*transformer_patchsize_x) should be zero")
config['num_patches'] = num_patches
elif task == "cnn-rnn-ocr":
builder = cnn_rnn_ocr_model
elif task=='classification':
builder = resnet50_classifier
elif task=='reading_order':
builder = machine_based_reading_order_model
else:
raise ValueError("unknown model task '%s'" % task)
builder = create_captured_function(builder)
builder.config = config
builder.logger = logger
return builder()

84
src/eynollah/training/reload-models-v0.8.mk
View file

@ -4,38 +4,65 @@ MODELS_SRC = models_eynollah
MODELS_DST = reloaded/models_eynollah
# $(MODELS_DST)/eynollah-binarization_20210425 \
# $(MODELS_DST)/eynollah-column-classifier_20210425 \
# $(MODELS_DST)/eynollah-enhancement_20210425 \
# $(MODELS_DST)/eynollah-main-regions-aug-rotation_20210425 \
# $(MODELS_DST)/eynollah-main-regions-aug-scaling_20210425 \
# $(MODELS_DST)/eynollah-main-regions-ensembled_20210425 \
# $(MODELS_DST)/eynollah-main-regions_20220314 \
# $(MODELS_DST)/eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18 \
# $(MODELS_DST)/eynollah-tables_20210319 \
# $(MODELS_DST)/model_eynollah_ocr_cnnrnn_20250930 \
# eynollah-main-regions-aug-rotation_20210425
# eynollah-main-regions-aug-scaling_20210425
# eynollah-main-regions-ensembled_20210425
# eynollah-main-regions_20220314
# eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18
# eynollah-tables_20210319
RELOADABLE_MODELS = \
$(MODELS_DST)/model_eynollah_page_extraction_20250915 \
$(MODELS_DST)/model_eynollah_reading_order_20250824 \
$(MODELS_DST)/modelens_e_l_all_sp_0_1_2_3_4_171024 \
$(MODELS_DST)/modelens_full_lay_1__4_3_091124 \
$(MODELS_DST)/modelens_table_0t4_201124 \
$(MODELS_DST)/modelens_textline_0_1__2_4_16092024
CURRENT_MODELS :=
CURRENT_MODELS += model_eynollah_page_extraction_20250915
CURRENT_MODELS += model_eynollah_reading_order_20250824
CURRENT_MODELS += modelens_e_l_all_sp_0_1_2_3_4_171024
CURRENT_MODELS += modelens_full_lay_1__4_3_091124
CURRENT_MODELS += modelens_table_0t4_201124
CURRENT_MODELS += modelens_textline_0_1__2_4_16092024
CURRENT_MODELS += model_eynollah_ocr_cnnrnn_20250930
CURRENT_MODELS += eynollah-binarization_20210425
CURRENT_MODELS += eynollah-column-classifier_20210425
CURRENT_MODELS += eynollah-enhancement_20210425
all: $(RELOADABLE_MODELS)
all: tf-serving
tf-serving: $(CURRENT_MODELS:%=$(MODELS_DST)/%)
keras: $(CURRENT_MODELS:%=$(MODELS_DST)/%.keras)
hdf5: $(CURRENT_MODELS:%=$(MODELS_DST)/%.h5)
onnx: $(CURRENT_MODELS:%=$(MODELS_DST)/%.onnx)
$(MODELS_DST)/%: $(MODELS_SRC)/%
mkdir -p $@
test -e $</config.json || exit 1
eynollah-training train --force \
with $</config.json \
reload_weights=True \
continue_training=False \
dir_output=$(dir $@) \
dir_of_start_model=$< \
2>&1 | tee $(notdir $<).log
cp $</config.json $@/config.json
eynollah-training convert \
$(and $(wildcard $</config.json),--rebuild) \
--in $< \
--format tf-serving \
--out $@ \
2>&1 | tee $(notdir $<).tf-serving.log
$(MODELS_DST)/%.keras: $(MODELS_SRC)/%
eynollah-training convert \
$(and $(wildcard $</config.json),--rebuild) \
--in $< \
--format keras \
--out $@ \
2>&1 | tee $(notdir $<).keras.log
$(MODELS_DST)/%.h5: $(MODELS_SRC)/%
eynollah-training convert \
$(and $(wildcard $</config.json),--rebuild) \
--in $< \
--format hdf5 \
--out $@ \
2>&1 | tee $(notdir $<).hdf5.log
$(MODELS_DST)/%.onnx: $(MODELS_SRC)/%
if jq -e '.task == "segmentation" and .backbone_type == "transformer"' $</config.json &>/dev/null; then \
echo skipping $@: vision transformer architecture currently does not work with ONNX; else \
eynollah-training convert \
$(and $(wildcard $</config.json),--rebuild) \
--in $< \
--format onnx \
--out $@ \
2>&1 | tee $(notdir $<).onnx.log; fi
compare:
for i in `find $(MODELS_DST) -mindepth 2`;do \
@ -43,6 +70,5 @@ compare:
du -bs $$n $$i ; \
done
clear:
rm -rf $(MODELS_DST)

155
src/eynollah/training/train.py
View file

@ -2,6 +2,7 @@ import os
import sys
import io
import json
import click
from tqdm import tqdm
import requests
@ -17,7 +18,6 @@ from tensorflow.keras.layers import StringLookup
from tensorflow.keras.utils import image_dataset_from_directory
from tensorflow.keras.backend import one_hot
from sacred import Experiment
from sacred.config import create_captured_function
import numpy as np
import cv2
@ -32,16 +32,9 @@ from .metrics import (
connected_components_loss,
)
from .models import (
PatchEncoder,
Patches,
machine_based_reading_order_model,
resnet50_classifier,
resnet50_unet,
vit_resnet50_unet,
vit_resnet50_unet_transformer_before_cnn,
cnn_rnn_ocr_model,
RESNET50_WEIGHTS_PATH,
RESNET50_WEIGHTS_URL
RESNET50_WEIGHTS_URL,
get_model
)
from .utils import (
generate_arrays_from_folder_reading_order,
@ -355,10 +348,9 @@ def config_params():
dir_output = None # Directory where the augmented training data and the model checkpoints will be saved.
pretraining = False # Set to true to (down)load pretrained weights of ResNet50 encoder.
save_interval = None # frequency for writing model checkpoints (positive integer for number of batches saved under "model_step_{batch:04d}", otherwise epoch saved under "model_{epoch:02d}")
reload_weights = False # Set true to build new model from config, load weights from dir_of_start_model, save under dir_output and exit.
continue_training = False # Whether to continue training an existing model.
dir_of_start_model = '' # Directory of model checkpoint to load to continue training or load weights from. (E.g. if you already trained for 3 epochs, set "dir_of_start_model=dir_output/model_03".)
if continue_training:
dir_of_start_model = '' # Directory of model checkpoint to load to continue training. (E.g. if you already trained for 3 epochs, set "dir_of_start_model=dir_output/model_03".)
index_start = 0 # Epoch counter initial value to continue training. (E.g. if you already trained for 3 epochs, set "index_start=3" to continue naming checkpoints model_04, model_05 etc.)
data_is_provided = False # Whether the preprocessed input data (subdirectories "images" and "labels" in both subdirectories "train" and "eval" of "dir_output") has already been generated (in the first epoch of a previous run).
@ -379,7 +371,6 @@ def run(_config,
weight_decay,
learning_rate,
continue_training,
reload_weights,
save_interval,
augmentation,
# dependent config keys need a default,
@ -477,58 +468,15 @@ def run(_config,
if task == "enhancement":
assert not is_loss_soft_dice, "for enhancement, soft_dice loss does not apply"
assert not weighted_loss, "for enhancement, weighted loss does not apply"
if continue_training:
custom_objects = dict()
if is_loss_soft_dice:
custom_objects.update(soft_dice_loss=soft_dice_loss)
elif weighted_loss:
custom_objects.update(loss=weighted_categorical_crossentropy(weights))
if backbone_type == 'transformer':
custom_objects.update(PatchEncoder=PatchEncoder,
Patches=Patches)
model = load_model(dir_of_start_model, compile=False,
custom_objects=custom_objects)
model = load_model(dir_of_start_model, compile=False)
else:
index_start = 0
if backbone_type == 'nontransformer':
model = resnet50_unet(n_classes,
input_height,
input_width,
task,
weight_decay,
pretraining)
else:
num_patches_x = transformer_num_patches_xy[0]
num_patches_y = transformer_num_patches_xy[1]
num_patches = num_patches_x * num_patches_y
if transformer_cnn_first:
model_builder = vit_resnet50_unet
multiple = 32
else:
model_builder = vit_resnet50_unet_transformer_before_cnn
multiple = 1
assert input_height == (
num_patches_y * transformer_patchsize_y * multiple), (
"transformer_patchsize_y or transformer_num_patches_xy height value error: "
"input_height should be equal to "
"(transformer_num_patches_xy height value * transformer_patchsize_y * %d)" % multiple)
assert input_width == (
num_patches_x * transformer_patchsize_x * multiple), (
"transformer_patchsize_x or transformer_num_patches_xy width value error: "
"input_width should be equal to "
"(transformer_num_patches_xy width value * transformer_patchsize_x * %d)" % multiple)
assert 0 == (transformer_projection_dim %
(transformer_patchsize_y * transformer_patchsize_x)), (
"transformer_projection_dim error: "
"The remainder when parameter transformer_projection_dim is divided by "
"(transformer_patchsize_y*transformer_patchsize_x) should be zero")
model_builder = create_captured_function(model_builder)
model_builder.config = _config
model_builder.logger = _log
model = model_builder(num_patches)
model = get_model(_config, _log)
if dir_of_start_model:
model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
_log.info("reloaded weights from %s", dir_of_start_model)
assert model is not None
#if you want to see the model structure just uncomment model summary.
@ -559,15 +507,6 @@ def run(_config,
optimizer=Adam(learning_rate=learning_rate),
metrics=metrics)
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)
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)
return
if not data_is_provided:
# first create a directory in output for both training and evaluations
# in order to flow data from these directories.
@ -708,10 +647,11 @@ def run(_config,
model = load_model(dir_of_start_model)
else:
index_start = 0
model = cnn_rnn_ocr_model(image_height=input_height,
image_width=input_width,
n_classes=n_classes,
max_seq=max_len)
model = get_model(_config, _log)
if dir_of_start_model:
model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
_log.info("reloaded weights from %s", dir_of_start_model)
#initial_learning_rate = 1e-4
#decay_steps = int (n_epochs * ( len_dataset / n_batch ))
#alpha = 0.01
@ -722,15 +662,6 @@ def run(_config,
#print(model.summary())
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)
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)
return
# todo: use Dataset.map() on Dataset.list_files()
def get_dataset(dir_img, dir_lab):
def gen():
@ -772,25 +703,15 @@ def run(_config,
model = load_model(dir_of_start_model, compile=False)
else:
index_start = 0
model = resnet50_classifier(n_classes,
input_height,
input_width,
weight_decay,
pretraining)
model = get_model(_config, _log)
if dir_of_start_model:
model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
_log.info("reloaded weights from %s", dir_of_start_model)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(learning_rate=0.001), # rs: why not learning_rate?
metrics=['accuracy', F1Score(average='macro', name='f1')])
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)
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)
return
list_classes = list(classification_classes_name.values())
data_args = dict(label_mode="categorical",
class_names=list_classes,
@ -828,11 +749,10 @@ def run(_config,
model = load_model(dir_of_start_model, compile=False)
else:
index_start = 0
model = machine_based_reading_order_model(n_classes,
input_height,
input_width,
weight_decay,
pretraining)
model = get_model(_config, _log)
if dir_of_start_model:
model.load_weights(dir_of_start_model).assert_existing_objects_matched().expect_partial()
_log.info("reloaded weights from %s", dir_of_start_model)
#f1score_tot = [0]
model.compile(loss="binary_crossentropy",
@ -840,15 +760,6 @@ def run(_config,
optimizer=Adam(learning_rate=0.0001), # rs: why not learning_rate?
metrics=['accuracy'])
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)
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)
return
dir_flow_train_imgs = os.path.join(dir_train, 'images')
dir_flow_train_labels = os.path.join(dir_train, 'labels')
@ -881,3 +792,23 @@ def run(_config,
model_dir = os.path.join(dir_out,'model_best')
model.save(model_dir)
'''
@click.command(context_settings=dict(
ignore_unknown_options=True,
))
@click.argument('SACRED_ARGS', nargs=-1, type=click.UNPROCESSED)
def train_cli(sacred_args):
"""
train model on extracted GT
SACRED_ARGS as per CLI interface of Sacred, cf.
https://sacred.readthedocs.io/en/stable/command_line.html:
\b
To configure the learning task, pass the string `with`,
followed by any number of
- config JSON file paths
- parameter overrides in the form of key=value
(where the later settings will override the former).
"""
ex.run_commandline([sys.argv[0]] + list(sacred_args))

1
src/eynollah/training/weights_ensembling.py
View file

@ -43,6 +43,7 @@ def run_ensembling(model_dirs, out_dir):
@click.option(
"--in",
"-i",
"in_",
help="input directory of checkpoint models to be read",
multiple=True,
required=True,

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: