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let to add dataset abbrevation to extracted textline images and text

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vahidrezanezhad 2025-05-03 02:59:16 +02:00
parent 8c8fa461bb
commit 5d447abcc4
2 changed files with 71 additions and 37 deletions
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17
src/eynollah/cli.py
View file

@ -342,7 +342,6 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
"-m", "-m",
help="directory of models", help="directory of models",
type=click.Path(exists=True, file_okay=False), type=click.Path(exists=True, file_okay=False),
required=True,
) )
@click.option( @click.option(
"--tr_ocr", "--tr_ocr",
@ -379,6 +378,11 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
"-bs", "-bs",
help="number of inference batch size. Default b_s for trocr and cnn_rnn models are 2 and 8 respectively", help="number of inference batch size. Default b_s for trocr and cnn_rnn models are 2 and 8 respectively",
) )
@click.option(
"--dataset_abbrevation",
"-ds_pref",
help="in the case of extracting textline and text from a xml GT file user can add an abbrevation of dataset name to generated dataset",
)
@click.option( @click.option(
"--log_level", "--log_level",
"-l", "-l",
@ -386,10 +390,18 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
help="Override log level globally to this", help="Override log level globally to this",
) )
def ocr(dir_in, dir_in_bin, out, dir_xmls, dir_out_image_text, model, tr_ocr, export_textline_images_and_text, do_not_mask_with_textline_contour, draw_texts_on_image, prediction_with_both_of_rgb_and_bin, batch_size, log_level): def ocr(dir_in, dir_in_bin, out, dir_xmls, dir_out_image_text, model, tr_ocr, export_textline_images_and_text, do_not_mask_with_textline_contour, draw_texts_on_image, prediction_with_both_of_rgb_and_bin, batch_size, dataset_abbrevation, log_level):
initLogging() initLogging()
if log_level: if log_level:
getLogger('eynollah').setLevel(getLevelName(log_level)) getLogger('eynollah').setLevel(getLevelName(log_level))
assert not export_textline_images_and_text or not tr_ocr, "Exporting textline and text -etit can not be set alongside transformer ocr -tr_ocr"
assert not export_textline_images_and_text or not model, "Exporting textline and text -etit can not be set alongside model -m"
assert not export_textline_images_and_text or not batch_size, "Exporting textline and text -etit can not be set alongside batch size -bs"
assert not export_textline_images_and_text or not dir_in_bin, "Exporting textline and text -etit can not be set alongside directory of bin images -dib"
assert not export_textline_images_and_text or not dir_out_image_text, "Exporting textline and text -etit can not be set alongside directory of images with predicted text -doit"
assert not export_textline_images_and_text or not draw_texts_on_image, "Exporting textline and text -etit can not be set alongside draw text on image -dtoi"
assert not export_textline_images_and_text or not prediction_with_both_of_rgb_and_bin, "Exporting textline and text -etit can not be set alongside prediction with both rgb and bin -brb"
eynollah_ocr = Eynollah_ocr( eynollah_ocr = Eynollah_ocr(
dir_xmls=dir_xmls, dir_xmls=dir_xmls,
dir_out_image_text=dir_out_image_text, dir_out_image_text=dir_out_image_text,
@ -403,6 +415,7 @@ def ocr(dir_in, dir_in_bin, out, dir_xmls, dir_out_image_text, model, tr_ocr, ex
draw_texts_on_image=draw_texts_on_image, draw_texts_on_image=draw_texts_on_image,
prediction_with_both_of_rgb_and_bin=prediction_with_both_of_rgb_and_bin, prediction_with_both_of_rgb_and_bin=prediction_with_both_of_rgb_and_bin,
batch_size=batch_size, batch_size=batch_size,
pref_of_dataset=dataset_abbrevation,
) )
eynollah_ocr.run() eynollah_ocr.run()

85
src/eynollah/eynollah.py
View file

@ -4877,6 +4877,7 @@ class Eynollah_ocr:
do_not_mask_with_textline_contour=False, do_not_mask_with_textline_contour=False,
draw_texts_on_image=False, draw_texts_on_image=False,
prediction_with_both_of_rgb_and_bin=False, prediction_with_both_of_rgb_and_bin=False,
pref_of_dataset = None,
logger=None, logger=None,
): ):
self.dir_in = dir_in self.dir_in = dir_in
@ -4890,43 +4891,45 @@ class Eynollah_ocr:
self.draw_texts_on_image = draw_texts_on_image self.draw_texts_on_image = draw_texts_on_image
self.dir_out_image_text = dir_out_image_text self.dir_out_image_text = dir_out_image_text
self.prediction_with_both_of_rgb_and_bin = prediction_with_both_of_rgb_and_bin self.prediction_with_both_of_rgb_and_bin = prediction_with_both_of_rgb_and_bin
if tr_ocr: self.pref_of_dataset = pref_of_dataset
self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed") if not export_textline_images_and_text:
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") if tr_ocr:
self.model_ocr_dir = dir_models + "/trocr_model_ens_of_3_checkpoints_201124" self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed")
self.model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir) self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.model_ocr.to(self.device) self.model_ocr_dir = dir_models + "/trocr_model_ens_of_3_checkpoints_201124"
if not batch_size: self.model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir)
self.b_s = 2 self.model_ocr.to(self.device)
if not batch_size:
self.b_s = 2
else:
self.b_s = int(batch_size)
else: else:
self.b_s = int(batch_size) self.model_ocr_dir = dir_models + "/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
model_ocr = load_model(self.model_ocr_dir , compile=False)
else: self.prediction_model = tf.keras.models.Model(
self.model_ocr_dir = dir_models + "/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn" model_ocr.get_layer(name = "image").input,
model_ocr = load_model(self.model_ocr_dir , compile=False) model_ocr.get_layer(name = "dense2").output)
if not batch_size:
self.prediction_model = tf.keras.models.Model( self.b_s = 8
model_ocr.get_layer(name = "image").input, else:
model_ocr.get_layer(name = "dense2").output) self.b_s = int(batch_size)
if not batch_size:
self.b_s = 8
else:
self.b_s = int(batch_size)
with open(os.path.join(self.model_ocr_dir, "characters_org.txt"),"r") as config_file: with open(os.path.join(self.model_ocr_dir, "characters_org.txt"),"r") as config_file:
characters = json.load(config_file) characters = json.load(config_file)
AUTOTUNE = tf.data.AUTOTUNE AUTOTUNE = tf.data.AUTOTUNE
# Mapping characters to integers. # Mapping characters to integers.
char_to_num = StringLookup(vocabulary=list(characters), mask_token=None) char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)
# Mapping integers back to original characters. # Mapping integers back to original characters.
self.num_to_char = StringLookup( self.num_to_char = StringLookup(
vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
) )
def decode_batch_predictions(self, pred, max_len = 128): def decode_batch_predictions(self, pred, max_len = 128):
@ -5365,10 +5368,28 @@ class Eynollah_ocr:
if cheild_text.tag.endswith("Unicode"): if cheild_text.tag.endswith("Unicode"):
textline_text = cheild_text.text textline_text = cheild_text.text
if textline_text: if textline_text:
with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.txt'), 'w') as text_file: if self.do_not_mask_with_textline_contour:
text_file.write(textline_text) if self.pref_of_dataset:
with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'.txt'), 'w') as text_file:
text_file.write(textline_text)
cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.png'), img_crop ) cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'.png'), img_crop )
else:
with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.txt'), 'w') as text_file:
text_file.write(textline_text)
cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.png'), img_crop )
else:
if self.pref_of_dataset:
with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'_masked.txt'), 'w') as text_file:
text_file.write(textline_text)
cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_'+self.pref_of_dataset+'_masked.png'), img_crop )
else:
with open(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_masked.txt'), 'w') as text_file:
text_file.write(textline_text)
cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'_masked.png'), img_crop )
indexer_textlines+=1 indexer_textlines+=1