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eynollah
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Provide OCR as an option to process a directory of XML files, incorporating layout and text line coordinates.

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pull/140/head
vahidrezanezhad 2 days ago
parent fbeef79d50
commit 92bfac4b41
2 changed files with 459 additions and 11 deletions
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56
src/eynollah/cli.py
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@ -1,7 +1,7 @@
import sys
import click
from ocrd_utils import initLogging, setOverrideLogLevel
from eynollah.eynollah import Eynollah
from eynollah.eynollah import Eynollah, Eynollah_ocr
from eynollah.sbb_binarize import SbbBinarizer
@click.group()
@ -306,5 +306,59 @@ def layout(image, out, dir_in, model, save_images, save_layout, save_deskewed, s
pcgts = eynollah.run()
eynollah.writer.write_pagexml(pcgts)
@main.command()
@click.option(
"--dir_in",
"-di",
help="directory of images",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--out",
"-o",
help="directory to write output xml data",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--dir_xmls",
"-dx",
help="directory of xmls",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--model",
"-m",
help="directory of models",
type=click.Path(exists=True, file_okay=False),
required=True,
)
@click.option(
"--tr_ocr",
"-trocr/-notrocr",
is_flag=True,
help="if this parameter set to true, transformer ocr will be applied, otherwise cnn_rnn model.",
)
@click.option(
"--log_level",
"-l",
type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
help="Override log level globally to this",
)
def ocr(dir_in, out, dir_xmls, model, tr_ocr, log_level):
if log_level:
setOverrideLogLevel(log_level)
initLogging()
eynollah_ocr = Eynollah_ocr(
dir_xmls=dir_xmls,
dir_in=dir_in,
dir_out=out,
dir_models=model,
tr_ocr=tr_ocr,
)
eynollah_ocr.run()
if __name__ == "__main__":
main()

414
src/eynollah/eynollah.py
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@ -41,6 +41,9 @@ import matplotlib.pyplot as plt
# use tf1 compatibility for keras backend
from tensorflow.compat.v1.keras.backend import set_session
from tensorflow.keras import layers
import json
import xml.etree.ElementTree as ET
from tensorflow.keras.layers import StringLookup
from .utils.contour import (
filter_contours_area_of_image,
@ -2188,18 +2191,18 @@ class Eynollah:
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
if not self.dir_in:
prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3, thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
###prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3, thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
##prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, model_textline, n_batch_inference=3)
prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, model_textline, n_batch_inference=3)
#if not thresholding_for_artificial_class_in_light_version:
#if num_col_classifier==1:
#prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
#prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
else:
prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3,thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
##prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3,thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
###prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, self.model_textline, n_batch_inference=3)
prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, self.model_textline, n_batch_inference=3)
#if not thresholding_for_artificial_class_in_light_version:
#if num_col_classifier==1:
#prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
@ -2479,17 +2482,17 @@ class Eynollah:
if num_col_classifier == 1 or num_col_classifier == 2:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
##prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
###prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
else:
prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
##prediction_regions_page = self.do_prediction_new_concept_scatter_nd(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
prediction_regions_page = self.do_prediction_new_concept_scatter_nd(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
##prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
else:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
prediction_regions_org = self.do_prediction_new_concept(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
###prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
###prediction_regions_org = self.do_prediction_new_concept(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
##model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
else:
@ -5610,3 +5613,394 @@ class Eynollah:
if self.dir_in:
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)
print("all Job done in %.1fs", time.time() - t0_tot)
class Eynollah_ocr:
def __init__(
self,
dir_models,
dir_xmls=None,
dir_in=None,
dir_out=None,
tr_ocr=False,
logger=None,
):
self.dir_in = dir_in
self.dir_out = dir_out
self.dir_xmls = dir_xmls
self.dir_models = dir_models
self.tr_ocr = tr_ocr
if tr_ocr:
self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed")
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.model_ocr_dir = dir_models + "/trocr_model_ens_of_3_checkpoints_201124"
self.model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir)
self.model_ocr.to(self.device)
else:
self.model_ocr_dir = dir_models + "/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
model_ocr = load_model(self.model_ocr_dir , compile=False)
self.prediction_model = tf.keras.models.Model(
model_ocr.get_layer(name = "image").input,
model_ocr.get_layer(name = "dense2").output)
with open(os.path.join(self.model_ocr_dir, "characters_org.txt"),"r") as config_file:
characters = json.load(config_file)
AUTOTUNE = tf.data.AUTOTUNE
# Mapping characters to integers.
char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)
# Mapping integers back to original characters.
self.num_to_char = StringLookup(
vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
)
def decode_batch_predictions(self, pred, max_len = 128):
# 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]
# Decode CTC predictions using greedy search.
# decoded is a tuple with 2 elements.
decoded = tf.keras.backend.ctc_decode(pred,
input_length = input_len,
beam_width = 100)
# The outputs are in the first element of the tuple.
# Additionally, the first element is actually a list,
# therefore we take the first element of that list as well.
#print(decoded,'decoded')
decoded = decoded[0][0][:, :max_len]
#print(decoded, decoded.shape,'decoded')
output = []
for d in decoded:
# Convert the predicted indices to the corresponding chars.
d = tf.strings.reduce_join(self.num_to_char(d))
d = d.numpy().decode("utf-8")
output.append(d)
return output
def distortion_free_resize(self, image, img_size):
w, h = img_size
image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True)
# Check tha amount of padding needed to be done.
pad_height = h - tf.shape(image)[0]
pad_width = w - tf.shape(image)[1]
# Only necessary if you want to do same amount of padding on both sides.
if pad_height % 2 != 0:
height = pad_height // 2
pad_height_top = height + 1
pad_height_bottom = height
else:
pad_height_top = pad_height_bottom = pad_height // 2
if pad_width % 2 != 0:
width = pad_width // 2
pad_width_left = width + 1
pad_width_right = width
else:
pad_width_left = pad_width_right = pad_width // 2
image = tf.pad(
image,
paddings=[
[pad_height_top, pad_height_bottom],
[pad_width_left, pad_width_right],
[0, 0],
],
)
image = tf.transpose(image, (1, 0, 2))
image = tf.image.flip_left_right(image)
return image
def return_start_and_end_of_common_text_of_textline_ocr_without_common_section(self, textline_image):
width = np.shape(textline_image)[1]
height = np.shape(textline_image)[0]
common_window = int(0.06*width)
width1 = int ( width/2. - common_window )
width2 = int ( width/2. + common_window )
img_sum = np.sum(textline_image[:,:,0], axis=0)
sum_smoothed = gaussian_filter1d(img_sum, 3)
peaks_real, _ = find_peaks(sum_smoothed, height=0)
if len(peaks_real)>70:
peaks_real = peaks_real[(peaks_real<width2) & (peaks_real>width1)]
arg_max = np.argmax(sum_smoothed[peaks_real])
peaks_final = peaks_real[arg_max]
return peaks_final
else:
return None
def return_textlines_split_if_needed(self, textline_image):
split_point = self.return_start_and_end_of_common_text_of_textline_ocr_without_common_section(textline_image)
if split_point:
image1 = textline_image[:, :split_point,:]# image.crop((0, 0, width2, height))
image2 = textline_image[:, split_point:,:]#image.crop((width1, 0, width, height))
return [image1, image2]
else:
return None
def run(self):
ls_imgs = os.listdir(self.dir_in)
if self.tr_ocr:
b_s = 2
for ind_img in ls_imgs:
t0 = time.time()
file_name = ind_img.split('.')[0]
dir_img = os.path.join(self.dir_in, ind_img)
dir_xml = os.path.join(self.dir_xmls, file_name+'.xml')
out_file_ocr = os.path.join(self.dir_out, file_name+'.xml')
img = cv2.imread(dir_img)
##file_name = Path(dir_xmls).stem
tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding = 'iso-8859-5'))
root1=tree1.getroot()
alltags=[elem.tag for elem in root1.iter()]
link=alltags[0].split('}')[0]+'}'
name_space = alltags[0].split('}')[0]
name_space = name_space.split('{')[1]
region_tags=np.unique([x for x in alltags if x.endswith('TextRegion')])
cropped_lines = []
cropped_lines_region_indexer = []
cropped_lines_meging_indexing = []
indexer_text_region = 0
for nn in root1.iter(region_tags):
for child_textregion in nn:
if child_textregion.tag.endswith("TextLine"):
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)
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
if h2w_ratio > 0.05:
cropped_lines.append(img_crop)
cropped_lines_meging_indexing.append(0)
else:
splited_images = self.return_textlines_split_if_needed(img_crop)
#print(splited_images)
if splited_images:
cropped_lines.append(splited_images[0])
cropped_lines_meging_indexing.append(1)
cropped_lines.append(splited_images[1])
cropped_lines_meging_indexing.append(-1)
else:
cropped_lines.append(img_crop)
cropped_lines_meging_indexing.append(0)
indexer_text_region = indexer_text_region +1
extracted_texts = []
n_iterations = math.ceil(len(cropped_lines) / b_s)
for i in range(n_iterations):
if i==(n_iterations-1):
n_start = i*b_s
imgs = cropped_lines[n_start:]
else:
n_start = i*b_s
n_end = (i+1)*b_s
imgs = cropped_lines[n_start:n_end]
pixel_values_merged = self.processor(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_ocr.generate(pixel_values_merged.to(self.device))
generated_text_merged = self.processor.batch_decode(generated_ids_merged, skip_special_tokens=True)
extracted_texts = extracted_texts + generated_text_merged
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))
unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
#print(len(unique_cropped_lines_region_indexer), 'unique_cropped_lines_region_indexer')
text_by_textregion = []
for ind in unique_cropped_lines_region_indexer:
extracted_texts_merged_un = np.array(extracted_texts_merged)[np.array(cropped_lines_region_indexer)==ind]
text_by_textregion.append(" ".join(extracted_texts_merged_un))
#print(len(text_by_textregion) , indexer_text_region, "text_by_textregion")
#print(time.time() - t0 ,'elapsed time')
indexer = 0
indexer_textregion = 0
for nn in root1.iter(region_tags):
text_subelement_textregion = ET.SubElement(nn, 'TextEquiv')
unicode_textregion = ET.SubElement(text_subelement_textregion, 'Unicode')
has_textline = False
for child_textregion in nn:
if child_textregion.tag.endswith("TextLine"):
text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
unicode_textline = ET.SubElement(text_subelement, 'Unicode')
unicode_textline.text = extracted_texts_merged[indexer]
indexer = indexer + 1
has_textline = True
if has_textline:
unicode_textregion.text = text_by_textregion[indexer_textregion]
indexer_textregion = indexer_textregion + 1
ET.register_namespace("",name_space)
tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf8",default_namespace=None)
#print("Job done in %.1fs", time.time() - t0)
else:
max_len = 512
padding_token = 299
image_width = max_len * 4
image_height = 32
b_s = 8
img_size=(image_width, image_height)
for ind_img in ls_imgs:
t0 = time.time()
file_name = ind_img.split('.')[0]
dir_img = os.path.join(self.dir_in, ind_img)
dir_xml = os.path.join(self.dir_xmls, file_name+'.xml')
out_file_ocr = os.path.join(self.dir_out, file_name+'.xml')
img = cv2.imread(dir_img)
tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding = 'iso-8859-5'))
root1=tree1.getroot()
alltags=[elem.tag for elem in root1.iter()]
link=alltags[0].split('}')[0]+'}'
name_space = alltags[0].split('}')[0]
name_space = name_space.split('{')[1]
region_tags=np.unique([x for x in alltags if x.endswith('TextRegion')])
cropped_lines = []
cropped_lines_region_indexer = []
cropped_lines_meging_indexing = []
tinl = time.time()
indexer_text_region = 0
for nn in root1.iter(region_tags):
for child_textregion in nn:
if child_textregion.tag.endswith("TextLine"):
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)
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
img_crop = tf.reverse(img_crop,axis=[-1])
img_crop = self.distortion_free_resize(img_crop, img_size)
img_crop = tf.cast(img_crop, tf.float32) / 255.0
cropped_lines.append(img_crop)
indexer_text_region = indexer_text_region +1
extracted_texts = []
n_iterations = math.ceil(len(cropped_lines) / b_s)
for i in range(n_iterations):
if i==(n_iterations-1):
n_start = i*b_s
imgs = cropped_lines[n_start:]
imgs = np.array(imgs)
imgs = imgs.reshape(imgs.shape[0], image_width, image_height, 3)
else:
n_start = i*b_s
n_end = (i+1)*b_s
imgs = cropped_lines[n_start:n_end]
imgs = np.array(imgs).reshape(b_s, image_width, image_height, 3)
preds = self.prediction_model.predict(imgs, verbose=0)
pred_texts = self.decode_batch_predictions(preds)
for ib in range(imgs.shape[0]):
pred_texts_ib = pred_texts[ib].strip("[UNK]")
extracted_texts.append(pred_texts_ib)
unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
text_by_textregion = []
for ind in unique_cropped_lines_region_indexer:
extracted_texts_merged_un = np.array(extracted_texts)[np.array(cropped_lines_region_indexer)==ind]
text_by_textregion.append(" ".join(extracted_texts_merged_un))
indexer = 0
indexer_textregion = 0
for nn in root1.iter(region_tags):
text_subelement_textregion = ET.SubElement(nn, 'TextEquiv')
unicode_textregion = ET.SubElement(text_subelement_textregion, 'Unicode')
has_textline = False
for child_textregion in nn:
if child_textregion.tag.endswith("TextLine"):
text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
unicode_textline = ET.SubElement(text_subelement, 'Unicode')
unicode_textline.text = extracted_texts[indexer]
indexer = indexer + 1
has_textline = True
if has_textline:
unicode_textregion.text = text_by_textregion[indexer_textregion]
indexer_textregion = indexer_textregion + 1
ET.register_namespace("",name_space)
tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf8",default_namespace=None)
#print("Job done in %.1fs", time.time() - t0)

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