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This commit enables the export of cropped text line images along with their corresponding texts from a Page-XML file. These exported text line images and texts can be utilized for training a text line-based OCR model.

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vahidrezanezhad 2025-03-20 18:21:44 +01:00
parent c8b8529951
commit d3a4c06e7f
2 changed files with 110 additions and 94 deletions
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16
src/eynollah/cli.py
View file

@ -347,6 +347,18 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
is_flag=True,
help="if this parameter set to true, transformer ocr will be applied, otherwise cnn_rnn model.",
)
@click.option(
"--export_textline_images_and_text",
"-etit/-noetit",
is_flag=True,
help="if this parameter set to true, images and text in xml will be exported into output dir. This files can be used for training a OCR engine.",
)
@click.option(
"--do_not_mask_with_textline_contour",
"-nmtc/-mtc",
is_flag=True,
help="if this parameter set to true, cropped textline images will not be masked with textline contour.",
)
@click.option(
"--log_level",
"-l",
@ -354,7 +366,7 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
help="Override log level globally to this",
)
def ocr(dir_in, out, dir_xmls, model, tr_ocr, log_level):
def ocr(dir_in, out, dir_xmls, model, tr_ocr, export_textline_images_and_text, do_not_mask_with_textline_contour, log_level):
if log_level:
setOverrideLogLevel(log_level)
initLogging()
@ -364,6 +376,8 @@ def ocr(dir_in, out, dir_xmls, model, tr_ocr, log_level):
dir_out=out,
dir_models=model,
tr_ocr=tr_ocr,
export_textline_images_and_text=export_textline_images_and_text,
do_not_mask_with_textline_contour=do_not_mask_with_textline_contour,
)
eynollah_ocr.run()

188
src/eynollah/eynollah.py
View file

@ -4946,6 +4946,8 @@ class Eynollah_ocr:
dir_in=None,
dir_out=None,
tr_ocr=False,
export_textline_images_and_text=False,
do_not_mask_with_textline_contour=False,
logger=None,
):
self.dir_in = dir_in
@ -4953,6 +4955,8 @@ class Eynollah_ocr:
self.dir_xmls = dir_xmls
self.dir_models = dir_models
self.tr_ocr = tr_ocr
self.export_textline_images_and_text = export_textline_images_and_text
self.do_not_mask_with_textline_contour = do_not_mask_with_textline_contour
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")
@ -4961,7 +4965,7 @@ class Eynollah_ocr:
self.model_ocr.to(self.device)
else:
self.model_ocr_dir = dir_models + "/model_1_new_ocrcnn"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
self.model_ocr_dir = dir_models + "/model_3_new_ocrcnn"#"/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(
@ -5107,7 +5111,7 @@ class Eynollah_ocr:
img = cv2.imread(dir_img)
##file_name = Path(dir_xmls).stem
tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding = 'iso-8859-5'))
tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding="utf-8"))
root1=tree1.getroot()
alltags=[elem.tag for elem in root1.iter()]
link=alltags[0].split('}')[0]+'}'
@ -5241,7 +5245,7 @@ class Eynollah_ocr:
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'))
tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding="utf-8"))
root1=tree1.getroot()
alltags=[elem.tag for elem in root1.iter()]
link=alltags[0].split('}')[0]+'}'
@ -5257,15 +5261,16 @@ class Eynollah_ocr:
tinl = time.time()
indexer_text_region = 0
indexer_textlines = 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)
@ -5276,104 +5281,101 @@ class Eynollah_ocr:
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 not self.do_not_mask_with_textline_contour:
img_crop[mask_poly==0] = 255
if h2w_ratio > 0.05:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(0)
else:
splited_images = self.return_textlines_split_if_needed(img_crop)
#print(splited_images)
if splited_images:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[0], image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(1)
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[1], image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(-1)
else:
if not self.export_textline_images_and_text:
if h2w_ratio > 0.05:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(0)
#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)
else:
splited_images = self.return_textlines_split_if_needed(img_crop)
if splited_images:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[0], image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(1)
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[1], image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(-1)
else:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(0)
if self.export_textline_images_and_text:
if child_textlines.tag.endswith("TextEquiv"):
for cheild_text in child_textlines:
if cheild_text.tag.endswith("Unicode"):
textline_text = cheild_text.text
if not textline_text:
pass
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)
indexer_text_region = indexer_text_region +1
cv2.imwrite(os.path.join(self.dir_out, file_name+'_line_'+str(indexer_textlines)+'.png'), img_crop )
indexer_textlines+=1
if not self.export_textline_images_and_text:
indexer_text_region = indexer_text_region +1
extracted_texts = []
if not self.export_textline_images_and_text:
extracted_texts = []
n_iterations = math.ceil(len(cropped_lines) / b_s)
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_height, image_width, 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_height, image_width, 3)
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_height, image_width, 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_height, image_width, 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)
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]
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_merged)[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')
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)
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))]
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
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))
##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_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)
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)