mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-26 23:34:13 +01:00
move Eynollah_ocr to separate module
This commit is contained in:
kba
parent
a850ef39ea
commit
48d1198d24
3 changed files with 963 additions and 963 deletions
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@ -1,11 +1,11 @@
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from dataclasses import dataclass
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import sys
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import os
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import click
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import logging
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from typing import Tuple, List
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from ocrd_utils import initLogging, getLevelName, getLogger
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from eynollah.eynollah import Eynollah, Eynollah_ocr
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from eynollah.eynollah import Eynollah
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from eynollah.eynollah_ocr import Eynollah_ocr
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from eynollah.sbb_binarize import SbbBinarizer
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from eynollah.image_enhancer import Enhancer
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from eynollah.mb_ro_on_layout import machine_based_reading_order_on_layout
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@ -4739,964 +4739,3 @@ class Eynollah:
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return pcgts
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class Eynollah_ocr:
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def __init__(
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self,
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dir_models,
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model_name=None,
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dir_xmls=None,
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tr_ocr=False,
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batch_size=None,
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export_textline_images_and_text=False,
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do_not_mask_with_textline_contour=False,
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pref_of_dataset=None,
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min_conf_value_of_textline_text : Optional[float]=None,
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logger=None,
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):
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self.model_name = model_name
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self.tr_ocr = tr_ocr
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self.export_textline_images_and_text = export_textline_images_and_text
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self.do_not_mask_with_textline_contour = do_not_mask_with_textline_contour
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self.pref_of_dataset = pref_of_dataset
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self.logger = logger if logger else getLogger('eynollah')
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if not export_textline_images_and_text:
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if min_conf_value_of_textline_text:
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self.min_conf_value_of_textline_text = float(min_conf_value_of_textline_text)
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else:
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self.min_conf_value_of_textline_text = 0.3
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if tr_ocr:
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self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed")
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self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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if self.model_name:
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self.model_ocr_dir = self.model_name
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else:
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self.model_ocr_dir = dir_models + "/model_eynollah_ocr_trocr_20250919"
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self.model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir)
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self.model_ocr.to(self.device)
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if not batch_size:
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self.b_s = 2
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else:
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self.b_s = int(batch_size)
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else:
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if self.model_name:
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self.model_ocr_dir = self.model_name
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else:
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self.model_ocr_dir = dir_models + "/model_eynollah_ocr_cnnrnn_20250930"
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model_ocr = load_model(self.model_ocr_dir , compile=False)
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self.prediction_model = tf.keras.models.Model(
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model_ocr.get_layer(name = "image").input,
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model_ocr.get_layer(name = "dense2").output)
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if not batch_size:
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self.b_s = 8
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else:
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self.b_s = int(batch_size)
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with open(os.path.join(self.model_ocr_dir, "characters_org.txt"),"r") as config_file:
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characters = json.load(config_file)
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AUTOTUNE = tf.data.AUTOTUNE
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# Mapping characters to integers.
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char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)
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# Mapping integers back to original characters.
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self.num_to_char = StringLookup(
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vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
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)
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self.end_character = len(characters) + 2
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def run(self, overwrite: bool = False,
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dir_in: Optional[str] = None,
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dir_in_bin: Optional[str] = None,
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image_filename: Optional[str] = None,
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dir_xmls: Optional[str] = None,
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dir_out_image_text: Optional[str] = None,
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dir_out: Optional[str] = None,
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):
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if dir_in:
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ls_imgs = [os.path.join(dir_in, image_filename)
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for image_filename in filter(is_image_filename,
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os.listdir(dir_in))]
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else:
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ls_imgs = [image_filename]
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if self.tr_ocr:
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tr_ocr_input_height_and_width = 384
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for dir_img in ls_imgs:
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file_name = Path(dir_img).stem
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dir_xml = os.path.join(dir_xmls, file_name+'.xml')
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out_file_ocr = os.path.join(dir_out, file_name+'.xml')
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if os.path.exists(out_file_ocr):
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if overwrite:
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self.logger.warning("will overwrite existing output file '%s'", out_file_ocr)
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else:
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self.logger.warning("will skip input for existing output file '%s'", out_file_ocr)
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continue
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img = cv2.imread(dir_img)
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if dir_out_image_text:
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out_image_with_text = os.path.join(dir_out_image_text, file_name+'.png')
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image_text = Image.new("RGB", (img.shape[1], img.shape[0]), "white")
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draw = ImageDraw.Draw(image_text)
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total_bb_coordinates = []
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##file_name = Path(dir_xmls).stem
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tree1 = ET.parse(dir_xml, parser = ET.XMLParser(encoding="utf-8"))
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root1=tree1.getroot()
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alltags=[elem.tag for elem in root1.iter()]
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link=alltags[0].split('}')[0]+'}'
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name_space = alltags[0].split('}')[0]
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name_space = name_space.split('{')[1]
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region_tags=np.unique([x for x in alltags if x.endswith('TextRegion')])
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cropped_lines = []
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cropped_lines_region_indexer = []
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cropped_lines_meging_indexing = []
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extracted_texts = []
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indexer_text_region = 0
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indexer_b_s = 0
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for nn in root1.iter(region_tags):
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for child_textregion in nn:
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if child_textregion.tag.endswith("TextLine"):
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for child_textlines in child_textregion:
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if child_textlines.tag.endswith("Coords"):
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cropped_lines_region_indexer.append(indexer_text_region)
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p_h=child_textlines.attrib['points'].split(' ')
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textline_coords = np.array( [ [int(x.split(',')[0]),
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int(x.split(',')[1]) ]
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for x in p_h] )
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x,y,w,h = cv2.boundingRect(textline_coords)
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if dir_out_image_text:
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total_bb_coordinates.append([x,y,w,h])
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h2w_ratio = h/float(w)
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img_poly_on_img = np.copy(img)
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mask_poly = np.zeros(img.shape)
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mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
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mask_poly = mask_poly[y:y+h, x:x+w, :]
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img_crop = img_poly_on_img[y:y+h, x:x+w, :]
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img_crop[mask_poly==0] = 255
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self.logger.debug("processing %d lines for '%s'",
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len(cropped_lines), nn.attrib['id'])
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if h2w_ratio > 0.1:
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cropped_lines.append(resize_image(img_crop,
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tr_ocr_input_height_and_width,
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tr_ocr_input_height_and_width) )
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cropped_lines_meging_indexing.append(0)
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indexer_b_s+=1
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if indexer_b_s==self.b_s:
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imgs = cropped_lines[:]
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cropped_lines = []
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indexer_b_s = 0
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pixel_values_merged = self.processor(imgs, return_tensors="pt").pixel_values
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generated_ids_merged = self.model_ocr.generate(
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pixel_values_merged.to(self.device))
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generated_text_merged = self.processor.batch_decode(
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generated_ids_merged, skip_special_tokens=True)
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extracted_texts = extracted_texts + generated_text_merged
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else:
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splited_images, _ = return_textlines_split_if_needed(img_crop, None)
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#print(splited_images)
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if splited_images:
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cropped_lines.append(resize_image(splited_images[0],
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tr_ocr_input_height_and_width,
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tr_ocr_input_height_and_width))
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cropped_lines_meging_indexing.append(1)
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indexer_b_s+=1
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if indexer_b_s==self.b_s:
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imgs = cropped_lines[:]
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cropped_lines = []
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indexer_b_s = 0
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pixel_values_merged = self.processor(imgs, return_tensors="pt").pixel_values
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generated_ids_merged = self.model_ocr.generate(
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pixel_values_merged.to(self.device))
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generated_text_merged = self.processor.batch_decode(
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generated_ids_merged, skip_special_tokens=True)
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extracted_texts = extracted_texts + generated_text_merged
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cropped_lines.append(resize_image(splited_images[1],
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tr_ocr_input_height_and_width,
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tr_ocr_input_height_and_width))
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cropped_lines_meging_indexing.append(-1)
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indexer_b_s+=1
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if indexer_b_s==self.b_s:
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imgs = cropped_lines[:]
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cropped_lines = []
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indexer_b_s = 0
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pixel_values_merged = self.processor(imgs, return_tensors="pt").pixel_values
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generated_ids_merged = self.model_ocr.generate(
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pixel_values_merged.to(self.device))
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generated_text_merged = self.processor.batch_decode(
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generated_ids_merged, skip_special_tokens=True)
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extracted_texts = extracted_texts + generated_text_merged
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else:
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cropped_lines.append(img_crop)
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cropped_lines_meging_indexing.append(0)
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indexer_b_s+=1
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if indexer_b_s==self.b_s:
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imgs = cropped_lines[:]
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cropped_lines = []
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indexer_b_s = 0
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pixel_values_merged = self.processor(imgs, return_tensors="pt").pixel_values
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generated_ids_merged = self.model_ocr.generate(
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pixel_values_merged.to(self.device))
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generated_text_merged = self.processor.batch_decode(
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generated_ids_merged, skip_special_tokens=True)
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extracted_texts = extracted_texts + generated_text_merged
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indexer_text_region = indexer_text_region +1
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if indexer_b_s!=0:
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imgs = cropped_lines[:]
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cropped_lines = []
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indexer_b_s = 0
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pixel_values_merged = self.processor(imgs, return_tensors="pt").pixel_values
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generated_ids_merged = self.model_ocr.generate(pixel_values_merged.to(self.device))
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generated_text_merged = self.processor.batch_decode(generated_ids_merged, skip_special_tokens=True)
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extracted_texts = extracted_texts + generated_text_merged
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####extracted_texts = []
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####n_iterations = math.ceil(len(cropped_lines) / self.b_s)
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####for i in range(n_iterations):
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####if i==(n_iterations-1):
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####n_start = i*self.b_s
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####imgs = cropped_lines[n_start:]
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####else:
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####n_start = i*self.b_s
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####n_end = (i+1)*self.b_s
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####imgs = cropped_lines[n_start:n_end]
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####pixel_values_merged = self.processor(imgs, return_tensors="pt").pixel_values
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####generated_ids_merged = self.model_ocr.generate(
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#### pixel_values_merged.to(self.device))
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####generated_text_merged = self.processor.batch_decode(
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#### generated_ids_merged, skip_special_tokens=True)
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####extracted_texts = extracted_texts + generated_text_merged
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del cropped_lines
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gc.collect()
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extracted_texts_merged = [extracted_texts[ind]
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if cropped_lines_meging_indexing[ind]==0
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else extracted_texts[ind]+" "+extracted_texts[ind+1]
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if cropped_lines_meging_indexing[ind]==1
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else None
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for ind in range(len(cropped_lines_meging_indexing))]
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extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
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#print(extracted_texts_merged, len(extracted_texts_merged))
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unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
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if dir_out_image_text:
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#font_path = "Charis-7.000/Charis-Regular.ttf" # Make sure this file exists!
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font = importlib_resources.files(__package__) / "Charis-Regular.ttf"
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with importlib_resources.as_file(font) as font:
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font = ImageFont.truetype(font=font, size=40)
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for indexer_text, bb_ind in enumerate(total_bb_coordinates):
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x_bb = bb_ind[0]
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y_bb = bb_ind[1]
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w_bb = bb_ind[2]
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h_bb = bb_ind[3]
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font = fit_text_single_line(draw, extracted_texts_merged[indexer_text],
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font.path, w_bb, int(h_bb*0.4) )
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##draw.rectangle([x_bb, y_bb, x_bb + w_bb, y_bb + h_bb], outline="red", width=2)
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text_bbox = draw.textbbox((0, 0), extracted_texts_merged[indexer_text], font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1]
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text_x = x_bb + (w_bb - text_width) // 2 # Center horizontally
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text_y = y_bb + (h_bb - text_height) // 2 # Center vertically
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# Draw the text
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draw.text((text_x, text_y), extracted_texts_merged[indexer_text], fill="black", font=font)
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image_text.save(out_image_with_text)
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#print(len(unique_cropped_lines_region_indexer), 'unique_cropped_lines_region_indexer')
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#######text_by_textregion = []
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#######for ind in unique_cropped_lines_region_indexer:
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#######ind = np.array(cropped_lines_region_indexer)==ind
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#######extracted_texts_merged_un = np.array(extracted_texts_merged)[ind]
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#######text_by_textregion.append(" ".join(extracted_texts_merged_un))
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text_by_textregion = []
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for ind in unique_cropped_lines_region_indexer:
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ind = np.array(cropped_lines_region_indexer) == ind
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extracted_texts_merged_un = np.array(extracted_texts_merged)[ind]
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if len(extracted_texts_merged_un)>1:
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text_by_textregion_ind = ""
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next_glue = ""
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for indt in range(len(extracted_texts_merged_un)):
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if (extracted_texts_merged_un[indt].endswith('⸗') or
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extracted_texts_merged_un[indt].endswith('-') or
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extracted_texts_merged_un[indt].endswith('¬')):
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text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt][:-1]
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next_glue = ""
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else:
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text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt]
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next_glue = " "
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text_by_textregion.append(text_by_textregion_ind)
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else:
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text_by_textregion.append(" ".join(extracted_texts_merged_un))
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indexer = 0
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indexer_textregion = 0
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for nn in root1.iter(region_tags):
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#id_textregion = nn.attrib['id']
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#id_textregions.append(id_textregion)
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#textregions_by_existing_ids.append(text_by_textregion[indexer_textregion])
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is_textregion_text = False
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for childtest in nn:
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if childtest.tag.endswith("TextEquiv"):
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is_textregion_text = True
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if not is_textregion_text:
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text_subelement_textregion = ET.SubElement(nn, 'TextEquiv')
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unicode_textregion = ET.SubElement(text_subelement_textregion, 'Unicode')
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has_textline = False
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for child_textregion in nn:
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if child_textregion.tag.endswith("TextLine"):
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is_textline_text = False
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for childtest2 in child_textregion:
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if childtest2.tag.endswith("TextEquiv"):
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is_textline_text = True
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if not is_textline_text:
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text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
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##text_subelement.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
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unicode_textline = ET.SubElement(text_subelement, 'Unicode')
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unicode_textline.text = extracted_texts_merged[indexer]
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else:
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for childtest3 in child_textregion:
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if childtest3.tag.endswith("TextEquiv"):
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for child_uc in childtest3:
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if child_uc.tag.endswith("Unicode"):
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##childtest3.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
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child_uc.text = extracted_texts_merged[indexer]
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indexer = indexer + 1
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has_textline = True
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if has_textline:
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if is_textregion_text:
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for child4 in nn:
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if child4.tag.endswith("TextEquiv"):
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for childtr_uc in child4:
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if childtr_uc.tag.endswith("Unicode"):
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childtr_uc.text = text_by_textregion[indexer_textregion]
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else:
|
||||
unicode_textregion.text = text_by_textregion[indexer_textregion]
|
||||
indexer_textregion = indexer_textregion + 1
|
||||
|
||||
###sample_order = [(id_to_order[tid], text)
|
||||
### for tid, text in zip(id_textregions, textregions_by_existing_ids)
|
||||
### if tid in id_to_order]
|
||||
|
||||
##ordered_texts_sample = [text for _, text in sorted(sample_order)]
|
||||
##tot_page_text = ' '.join(ordered_texts_sample)
|
||||
|
||||
##for page_element in root1.iter(link+'Page'):
|
||||
##text_page = ET.SubElement(page_element, 'TextEquiv')
|
||||
##unicode_textpage = ET.SubElement(text_page, 'Unicode')
|
||||
##unicode_textpage.text = tot_page_text
|
||||
|
||||
ET.register_namespace("",name_space)
|
||||
tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf-8",default_namespace=None)
|
||||
else:
|
||||
###max_len = 280#512#280#512
|
||||
###padding_token = 1500#299#1500#299
|
||||
image_width = 512#max_len * 4
|
||||
image_height = 32
|
||||
|
||||
|
||||
img_size=(image_width, image_height)
|
||||
|
||||
for dir_img in ls_imgs:
|
||||
file_name = Path(dir_img).stem
|
||||
dir_xml = os.path.join(dir_xmls, file_name+'.xml')
|
||||
out_file_ocr = os.path.join(dir_out, file_name+'.xml')
|
||||
|
||||
if os.path.exists(out_file_ocr):
|
||||
if overwrite:
|
||||
self.logger.warning("will overwrite existing output file '%s'", out_file_ocr)
|
||||
else:
|
||||
self.logger.warning("will skip input for existing output file '%s'", out_file_ocr)
|
||||
continue
|
||||
|
||||
img = cv2.imread(dir_img)
|
||||
if dir_in_bin is not None:
|
||||
cropped_lines_bin = []
|
||||
dir_img_bin = os.path.join(dir_in_bin, file_name+'.png')
|
||||
img_bin = cv2.imread(dir_img_bin)
|
||||
|
||||
if dir_out_image_text:
|
||||
out_image_with_text = os.path.join(dir_out_image_text, file_name+'.png')
|
||||
image_text = Image.new("RGB", (img.shape[1], img.shape[0]), "white")
|
||||
draw = ImageDraw.Draw(image_text)
|
||||
total_bb_coordinates = []
|
||||
|
||||
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]+'}'
|
||||
|
||||
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_ver_index = []
|
||||
cropped_lines_region_indexer = []
|
||||
cropped_lines_meging_indexing = []
|
||||
|
||||
tinl = time.time()
|
||||
indexer_text_region = 0
|
||||
indexer_textlines = 0
|
||||
for nn in root1.iter(region_tags):
|
||||
try:
|
||||
type_textregion = nn.attrib['type']
|
||||
except:
|
||||
type_textregion = 'paragraph'
|
||||
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)
|
||||
|
||||
angle_radians = math.atan2(h, w)
|
||||
# Convert to degrees
|
||||
angle_degrees = math.degrees(angle_radians)
|
||||
if type_textregion=='drop-capital':
|
||||
angle_degrees = 0
|
||||
|
||||
if dir_out_image_text:
|
||||
total_bb_coordinates.append([x,y,w,h])
|
||||
|
||||
w_scaled = w * image_height/float(h)
|
||||
|
||||
img_poly_on_img = np.copy(img)
|
||||
if dir_in_bin is not None:
|
||||
img_poly_on_img_bin = np.copy(img_bin)
|
||||
img_crop_bin = img_poly_on_img_bin[y:y+h, x:x+w, :]
|
||||
|
||||
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, :]
|
||||
|
||||
if self.export_textline_images_and_text:
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop[mask_poly==0] = 255
|
||||
|
||||
else:
|
||||
# print(file_name, angle_degrees, w*h,
|
||||
# mask_poly[:,:,0].sum(),
|
||||
# mask_poly[:,:,0].sum() /float(w*h) ,
|
||||
# 'didi')
|
||||
|
||||
if angle_degrees > 3:
|
||||
better_des_slope = get_orientation_moments(textline_coords)
|
||||
|
||||
img_crop = rotate_image_with_padding(img_crop, better_des_slope)
|
||||
if dir_in_bin is not None:
|
||||
img_crop_bin = rotate_image_with_padding(img_crop_bin, better_des_slope)
|
||||
|
||||
mask_poly = rotate_image_with_padding(mask_poly, better_des_slope)
|
||||
mask_poly = mask_poly.astype('uint8')
|
||||
|
||||
#new bounding box
|
||||
x_n, y_n, w_n, h_n = get_contours_and_bounding_boxes(mask_poly[:,:,0])
|
||||
|
||||
mask_poly = mask_poly[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
img_crop = img_crop[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop[mask_poly==0] = 255
|
||||
if dir_in_bin is not None:
|
||||
img_crop_bin = img_crop_bin[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop_bin[mask_poly==0] = 255
|
||||
|
||||
if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.50 and w_scaled > 90:
|
||||
if dir_in_bin is not None:
|
||||
img_crop, img_crop_bin = \
|
||||
break_curved_line_into_small_pieces_and_then_merge(
|
||||
img_crop, mask_poly, img_crop_bin)
|
||||
else:
|
||||
img_crop, _ = \
|
||||
break_curved_line_into_small_pieces_and_then_merge(
|
||||
img_crop, mask_poly)
|
||||
|
||||
else:
|
||||
better_des_slope = 0
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop[mask_poly==0] = 255
|
||||
if dir_in_bin is not None:
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop_bin[mask_poly==0] = 255
|
||||
if type_textregion=='drop-capital':
|
||||
pass
|
||||
else:
|
||||
if mask_poly[:,:,0].sum() /float(w*h) < 0.50 and w_scaled > 90:
|
||||
if dir_in_bin is not None:
|
||||
img_crop, img_crop_bin = \
|
||||
break_curved_line_into_small_pieces_and_then_merge(
|
||||
img_crop, mask_poly, img_crop_bin)
|
||||
else:
|
||||
img_crop, _ = \
|
||||
break_curved_line_into_small_pieces_and_then_merge(
|
||||
img_crop, mask_poly)
|
||||
|
||||
if not self.export_textline_images_and_text:
|
||||
if w_scaled < 750:#1.5*image_width:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
img_crop, image_height, image_width)
|
||||
cropped_lines.append(img_fin)
|
||||
if abs(better_des_slope) > 45:
|
||||
cropped_lines_ver_index.append(1)
|
||||
else:
|
||||
cropped_lines_ver_index.append(0)
|
||||
|
||||
cropped_lines_meging_indexing.append(0)
|
||||
if dir_in_bin is not None:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
img_crop_bin, image_height, image_width)
|
||||
cropped_lines_bin.append(img_fin)
|
||||
else:
|
||||
splited_images, splited_images_bin = return_textlines_split_if_needed(
|
||||
img_crop, img_crop_bin if dir_in_bin is not None else None)
|
||||
if splited_images:
|
||||
img_fin = 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)
|
||||
|
||||
if abs(better_des_slope) > 45:
|
||||
cropped_lines_ver_index.append(1)
|
||||
else:
|
||||
cropped_lines_ver_index.append(0)
|
||||
|
||||
img_fin = 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)
|
||||
|
||||
if abs(better_des_slope) > 45:
|
||||
cropped_lines_ver_index.append(1)
|
||||
else:
|
||||
cropped_lines_ver_index.append(0)
|
||||
|
||||
if dir_in_bin is not None:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
splited_images_bin[0], image_height, image_width)
|
||||
cropped_lines_bin.append(img_fin)
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
splited_images_bin[1], image_height, image_width)
|
||||
cropped_lines_bin.append(img_fin)
|
||||
|
||||
else:
|
||||
img_fin = 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 abs(better_des_slope) > 45:
|
||||
cropped_lines_ver_index.append(1)
|
||||
else:
|
||||
cropped_lines_ver_index.append(0)
|
||||
|
||||
if dir_in_bin is not None:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
img_crop_bin, image_height, image_width)
|
||||
cropped_lines_bin.append(img_fin)
|
||||
|
||||
if self.export_textline_images_and_text:
|
||||
if img_crop.shape[0]==0 or img_crop.shape[1]==0:
|
||||
pass
|
||||
else:
|
||||
if child_textlines.tag.endswith("TextEquiv"):
|
||||
for cheild_text in child_textlines:
|
||||
if cheild_text.tag.endswith("Unicode"):
|
||||
textline_text = cheild_text.text
|
||||
if textline_text:
|
||||
base_name = os.path.join(
|
||||
dir_out, file_name + '_line_' + str(indexer_textlines))
|
||||
if self.pref_of_dataset:
|
||||
base_name += '_' + self.pref_of_dataset
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
base_name += '_masked'
|
||||
|
||||
with open(base_name + '.txt', 'w') as text_file:
|
||||
text_file.write(textline_text)
|
||||
cv2.imwrite(base_name + '.png', img_crop)
|
||||
indexer_textlines+=1
|
||||
|
||||
if not self.export_textline_images_and_text:
|
||||
indexer_text_region = indexer_text_region +1
|
||||
|
||||
if not self.export_textline_images_and_text:
|
||||
extracted_texts = []
|
||||
extracted_conf_value = []
|
||||
|
||||
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:]
|
||||
imgs = np.array(imgs)
|
||||
imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3)
|
||||
|
||||
ver_imgs = np.array( cropped_lines_ver_index[n_start:] )
|
||||
indices_ver = np.where(ver_imgs == 1)[0]
|
||||
|
||||
#print(indices_ver, 'indices_ver')
|
||||
if len(indices_ver)>0:
|
||||
imgs_ver_flipped = imgs[indices_ver, : ,: ,:]
|
||||
imgs_ver_flipped = imgs_ver_flipped[:,::-1,::-1,:]
|
||||
#print(imgs_ver_flipped, 'imgs_ver_flipped')
|
||||
|
||||
else:
|
||||
imgs_ver_flipped = None
|
||||
|
||||
if dir_in_bin is not None:
|
||||
imgs_bin = cropped_lines_bin[n_start:]
|
||||
imgs_bin = np.array(imgs_bin)
|
||||
imgs_bin = imgs_bin.reshape(imgs_bin.shape[0], image_height, image_width, 3)
|
||||
|
||||
if len(indices_ver)>0:
|
||||
imgs_bin_ver_flipped = imgs_bin[indices_ver, : ,: ,:]
|
||||
imgs_bin_ver_flipped = imgs_bin_ver_flipped[:,::-1,::-1,:]
|
||||
#print(imgs_ver_flipped, 'imgs_ver_flipped')
|
||||
|
||||
else:
|
||||
imgs_bin_ver_flipped = None
|
||||
else:
|
||||
n_start = i*self.b_s
|
||||
n_end = (i+1)*self.b_s
|
||||
imgs = cropped_lines[n_start:n_end]
|
||||
imgs = np.array(imgs).reshape(self.b_s, image_height, image_width, 3)
|
||||
|
||||
ver_imgs = np.array( cropped_lines_ver_index[n_start:n_end] )
|
||||
indices_ver = np.where(ver_imgs == 1)[0]
|
||||
#print(indices_ver, 'indices_ver')
|
||||
|
||||
if len(indices_ver)>0:
|
||||
imgs_ver_flipped = imgs[indices_ver, : ,: ,:]
|
||||
imgs_ver_flipped = imgs_ver_flipped[:,::-1,::-1,:]
|
||||
#print(imgs_ver_flipped, 'imgs_ver_flipped')
|
||||
else:
|
||||
imgs_ver_flipped = None
|
||||
|
||||
|
||||
if dir_in_bin is not None:
|
||||
imgs_bin = cropped_lines_bin[n_start:n_end]
|
||||
imgs_bin = np.array(imgs_bin).reshape(self.b_s, image_height, image_width, 3)
|
||||
|
||||
|
||||
if len(indices_ver)>0:
|
||||
imgs_bin_ver_flipped = imgs_bin[indices_ver, : ,: ,:]
|
||||
imgs_bin_ver_flipped = imgs_bin_ver_flipped[:,::-1,::-1,:]
|
||||
#print(imgs_ver_flipped, 'imgs_ver_flipped')
|
||||
else:
|
||||
imgs_bin_ver_flipped = None
|
||||
|
||||
|
||||
self.logger.debug("processing next %d lines", len(imgs))
|
||||
preds = self.prediction_model.predict(imgs, verbose=0)
|
||||
|
||||
if len(indices_ver)>0:
|
||||
preds_flipped = self.prediction_model.predict(imgs_ver_flipped, verbose=0)
|
||||
preds_max_fliped = np.max(preds_flipped, axis=2 )
|
||||
preds_max_args_flipped = np.argmax(preds_flipped, axis=2 )
|
||||
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
|
||||
masked_means_flipped = \
|
||||
np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool_flipped, axis=1)
|
||||
masked_means_flipped[np.isnan(masked_means_flipped)] = 0
|
||||
|
||||
preds_max = np.max(preds, axis=2 )
|
||||
preds_max_args = np.argmax(preds, axis=2 )
|
||||
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
|
||||
|
||||
masked_means = \
|
||||
np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool, axis=1)
|
||||
masked_means[np.isnan(masked_means)] = 0
|
||||
|
||||
masked_means_ver = masked_means[indices_ver]
|
||||
#print(masked_means_ver, 'pred_max_not_unk')
|
||||
|
||||
indices_where_flipped_conf_value_is_higher = \
|
||||
np.where(masked_means_flipped > masked_means_ver)[0]
|
||||
|
||||
#print(indices_where_flipped_conf_value_is_higher, 'indices_where_flipped_conf_value_is_higher')
|
||||
if len(indices_where_flipped_conf_value_is_higher)>0:
|
||||
indices_to_be_replaced = indices_ver[indices_where_flipped_conf_value_is_higher]
|
||||
preds[indices_to_be_replaced,:,:] = \
|
||||
preds_flipped[indices_where_flipped_conf_value_is_higher, :, :]
|
||||
if dir_in_bin is not None:
|
||||
preds_bin = self.prediction_model.predict(imgs_bin, verbose=0)
|
||||
|
||||
if len(indices_ver)>0:
|
||||
preds_flipped = self.prediction_model.predict(imgs_bin_ver_flipped, verbose=0)
|
||||
preds_max_fliped = np.max(preds_flipped, axis=2 )
|
||||
preds_max_args_flipped = np.argmax(preds_flipped, axis=2 )
|
||||
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
|
||||
masked_means_flipped = \
|
||||
np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool_flipped, axis=1)
|
||||
masked_means_flipped[np.isnan(masked_means_flipped)] = 0
|
||||
|
||||
preds_max = np.max(preds, axis=2 )
|
||||
preds_max_args = np.argmax(preds, axis=2 )
|
||||
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
|
||||
|
||||
masked_means = \
|
||||
np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool, axis=1)
|
||||
masked_means[np.isnan(masked_means)] = 0
|
||||
|
||||
masked_means_ver = masked_means[indices_ver]
|
||||
#print(masked_means_ver, 'pred_max_not_unk')
|
||||
|
||||
indices_where_flipped_conf_value_is_higher = \
|
||||
np.where(masked_means_flipped > masked_means_ver)[0]
|
||||
|
||||
#print(indices_where_flipped_conf_value_is_higher, 'indices_where_flipped_conf_value_is_higher')
|
||||
if len(indices_where_flipped_conf_value_is_higher)>0:
|
||||
indices_to_be_replaced = indices_ver[indices_where_flipped_conf_value_is_higher]
|
||||
preds_bin[indices_to_be_replaced,:,:] = \
|
||||
preds_flipped[indices_where_flipped_conf_value_is_higher, :, :]
|
||||
|
||||
preds = (preds + preds_bin) / 2.
|
||||
|
||||
pred_texts = decode_batch_predictions(preds, self.num_to_char)
|
||||
|
||||
preds_max = np.max(preds, axis=2 )
|
||||
preds_max_args = np.argmax(preds, axis=2 )
|
||||
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
|
||||
masked_means = \
|
||||
np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool, axis=1)
|
||||
|
||||
for ib in range(imgs.shape[0]):
|
||||
pred_texts_ib = pred_texts[ib].replace("[UNK]", "")
|
||||
if masked_means[ib] >= self.min_conf_value_of_textline_text:
|
||||
extracted_texts.append(pred_texts_ib)
|
||||
extracted_conf_value.append(masked_means[ib])
|
||||
else:
|
||||
extracted_texts.append("")
|
||||
extracted_conf_value.append(0)
|
||||
del cropped_lines
|
||||
if dir_in_bin is not None:
|
||||
del cropped_lines_bin
|
||||
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_conf_value_merged = [extracted_conf_value[ind]
|
||||
if cropped_lines_meging_indexing[ind]==0
|
||||
else (extracted_conf_value[ind]+extracted_conf_value[ind+1])/2.
|
||||
if cropped_lines_meging_indexing[ind]==1
|
||||
else None
|
||||
for ind in range(len(cropped_lines_meging_indexing))]
|
||||
|
||||
extracted_conf_value_merged = [extracted_conf_value_merged[ind_cfm]
|
||||
for ind_cfm in range(len(extracted_texts_merged))
|
||||
if extracted_texts_merged[ind_cfm] is not None]
|
||||
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)
|
||||
|
||||
if dir_out_image_text:
|
||||
#font_path = "Charis-7.000/Charis-Regular.ttf" # Make sure this file exists!
|
||||
font = importlib_resources.files(__package__) / "Charis-Regular.ttf"
|
||||
with importlib_resources.as_file(font) as font:
|
||||
font = ImageFont.truetype(font=font, size=40)
|
||||
|
||||
for indexer_text, bb_ind in enumerate(total_bb_coordinates):
|
||||
x_bb = bb_ind[0]
|
||||
y_bb = bb_ind[1]
|
||||
w_bb = bb_ind[2]
|
||||
h_bb = bb_ind[3]
|
||||
|
||||
font = fit_text_single_line(draw, extracted_texts_merged[indexer_text],
|
||||
font.path, w_bb, int(h_bb*0.4) )
|
||||
|
||||
##draw.rectangle([x_bb, y_bb, x_bb + w_bb, y_bb + h_bb], outline="red", width=2)
|
||||
|
||||
text_bbox = draw.textbbox((0, 0), extracted_texts_merged[indexer_text], font=font)
|
||||
text_width = text_bbox[2] - text_bbox[0]
|
||||
text_height = text_bbox[3] - text_bbox[1]
|
||||
|
||||
text_x = x_bb + (w_bb - text_width) // 2 # Center horizontally
|
||||
text_y = y_bb + (h_bb - text_height) // 2 # Center vertically
|
||||
|
||||
# Draw the text
|
||||
draw.text((text_x, text_y), extracted_texts_merged[indexer_text], fill="black", font=font)
|
||||
image_text.save(out_image_with_text)
|
||||
|
||||
text_by_textregion = []
|
||||
for ind in unique_cropped_lines_region_indexer:
|
||||
ind = np.array(cropped_lines_region_indexer)==ind
|
||||
extracted_texts_merged_un = np.array(extracted_texts_merged)[ind]
|
||||
if len(extracted_texts_merged_un)>1:
|
||||
text_by_textregion_ind = ""
|
||||
next_glue = ""
|
||||
for indt in range(len(extracted_texts_merged_un)):
|
||||
if (extracted_texts_merged_un[indt].endswith('⸗') or
|
||||
extracted_texts_merged_un[indt].endswith('-') or
|
||||
extracted_texts_merged_un[indt].endswith('¬')):
|
||||
text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt][:-1]
|
||||
next_glue = ""
|
||||
else:
|
||||
text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt]
|
||||
next_glue = " "
|
||||
text_by_textregion.append(text_by_textregion_ind)
|
||||
else:
|
||||
text_by_textregion.append(" ".join(extracted_texts_merged_un))
|
||||
#print(text_by_textregion, 'text_by_textregiontext_by_textregiontext_by_textregiontext_by_textregiontext_by_textregion')
|
||||
|
||||
###index_tot_regions = []
|
||||
###tot_region_ref = []
|
||||
|
||||
###for jj in root1.iter(link+'RegionRefIndexed'):
|
||||
###index_tot_regions.append(jj.attrib['index'])
|
||||
###tot_region_ref.append(jj.attrib['regionRef'])
|
||||
|
||||
###id_to_order = {tid: ro for tid, ro in zip(tot_region_ref, index_tot_regions)}
|
||||
|
||||
#id_textregions = []
|
||||
#textregions_by_existing_ids = []
|
||||
indexer = 0
|
||||
indexer_textregion = 0
|
||||
for nn in root1.iter(region_tags):
|
||||
#id_textregion = nn.attrib['id']
|
||||
#id_textregions.append(id_textregion)
|
||||
#textregions_by_existing_ids.append(text_by_textregion[indexer_textregion])
|
||||
|
||||
is_textregion_text = False
|
||||
for childtest in nn:
|
||||
if childtest.tag.endswith("TextEquiv"):
|
||||
is_textregion_text = True
|
||||
|
||||
if not is_textregion_text:
|
||||
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"):
|
||||
|
||||
is_textline_text = False
|
||||
for childtest2 in child_textregion:
|
||||
if childtest2.tag.endswith("TextEquiv"):
|
||||
is_textline_text = True
|
||||
|
||||
|
||||
if not is_textline_text:
|
||||
text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
|
||||
text_subelement.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
|
||||
unicode_textline = ET.SubElement(text_subelement, 'Unicode')
|
||||
unicode_textline.text = extracted_texts_merged[indexer]
|
||||
else:
|
||||
for childtest3 in child_textregion:
|
||||
if childtest3.tag.endswith("TextEquiv"):
|
||||
for child_uc in childtest3:
|
||||
if child_uc.tag.endswith("Unicode"):
|
||||
childtest3.set('conf',
|
||||
f"{extracted_conf_value_merged[indexer]:.2f}")
|
||||
child_uc.text = extracted_texts_merged[indexer]
|
||||
|
||||
indexer = indexer + 1
|
||||
has_textline = True
|
||||
if has_textline:
|
||||
if is_textregion_text:
|
||||
for child4 in nn:
|
||||
if child4.tag.endswith("TextEquiv"):
|
||||
for childtr_uc in child4:
|
||||
if childtr_uc.tag.endswith("Unicode"):
|
||||
childtr_uc.text = text_by_textregion[indexer_textregion]
|
||||
else:
|
||||
unicode_textregion.text = text_by_textregion[indexer_textregion]
|
||||
indexer_textregion = indexer_textregion + 1
|
||||
|
||||
###sample_order = [(id_to_order[tid], text)
|
||||
### for tid, text in zip(id_textregions, textregions_by_existing_ids)
|
||||
### if tid in id_to_order]
|
||||
|
||||
##ordered_texts_sample = [text for _, text in sorted(sample_order)]
|
||||
##tot_page_text = ' '.join(ordered_texts_sample)
|
||||
|
||||
##for page_element in root1.iter(link+'Page'):
|
||||
##text_page = ET.SubElement(page_element, 'TextEquiv')
|
||||
##unicode_textpage = ET.SubElement(text_page, 'Unicode')
|
||||
##unicode_textpage.text = tot_page_text
|
||||
|
||||
ET.register_namespace("",name_space)
|
||||
tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf-8",default_namespace=None)
|
||||
#print("Job done in %.1fs", time.time() - t0)
|
||||
|
|
|
|||
961
src/eynollah/eynollah_ocr.py
Normal file
961
src/eynollah/eynollah_ocr.py
Normal file
|
|
@ -0,0 +1,961 @@
|
|||
class Eynollah_ocr:
|
||||
def __init__(
|
||||
self,
|
||||
dir_models,
|
||||
model_name=None,
|
||||
dir_xmls=None,
|
||||
tr_ocr=False,
|
||||
batch_size=None,
|
||||
export_textline_images_and_text=False,
|
||||
do_not_mask_with_textline_contour=False,
|
||||
pref_of_dataset=None,
|
||||
min_conf_value_of_textline_text : Optional[float]=None,
|
||||
logger=None,
|
||||
):
|
||||
self.model_name = model_name
|
||||
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
|
||||
self.pref_of_dataset = pref_of_dataset
|
||||
self.logger = logger if logger else getLogger('eynollah')
|
||||
|
||||
if not export_textline_images_and_text:
|
||||
if min_conf_value_of_textline_text:
|
||||
self.min_conf_value_of_textline_text = float(min_conf_value_of_textline_text)
|
||||
else:
|
||||
self.min_conf_value_of_textline_text = 0.3
|
||||
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")
|
||||
if self.model_name:
|
||||
self.model_ocr_dir = self.model_name
|
||||
else:
|
||||
self.model_ocr_dir = dir_models + "/model_eynollah_ocr_trocr_20250919"
|
||||
self.model_ocr = VisionEncoderDecoderModel.from_pretrained(self.model_ocr_dir)
|
||||
self.model_ocr.to(self.device)
|
||||
if not batch_size:
|
||||
self.b_s = 2
|
||||
else:
|
||||
self.b_s = int(batch_size)
|
||||
|
||||
else:
|
||||
if self.model_name:
|
||||
self.model_ocr_dir = self.model_name
|
||||
else:
|
||||
self.model_ocr_dir = dir_models + "/model_eynollah_ocr_cnnrnn_20250930"
|
||||
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)
|
||||
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:
|
||||
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
|
||||
)
|
||||
self.end_character = len(characters) + 2
|
||||
|
||||
def run(self, overwrite: bool = False,
|
||||
dir_in: Optional[str] = None,
|
||||
dir_in_bin: Optional[str] = None,
|
||||
image_filename: Optional[str] = None,
|
||||
dir_xmls: Optional[str] = None,
|
||||
dir_out_image_text: Optional[str] = None,
|
||||
dir_out: Optional[str] = None,
|
||||
):
|
||||
if dir_in:
|
||||
ls_imgs = [os.path.join(dir_in, image_filename)
|
||||
for image_filename in filter(is_image_filename,
|
||||
os.listdir(dir_in))]
|
||||
else:
|
||||
ls_imgs = [image_filename]
|
||||
|
||||
if self.tr_ocr:
|
||||
tr_ocr_input_height_and_width = 384
|
||||
for dir_img in ls_imgs:
|
||||
file_name = Path(dir_img).stem
|
||||
dir_xml = os.path.join(dir_xmls, file_name+'.xml')
|
||||
out_file_ocr = os.path.join(dir_out, file_name+'.xml')
|
||||
|
||||
if os.path.exists(out_file_ocr):
|
||||
if overwrite:
|
||||
self.logger.warning("will overwrite existing output file '%s'", out_file_ocr)
|
||||
else:
|
||||
self.logger.warning("will skip input for existing output file '%s'", out_file_ocr)
|
||||
continue
|
||||
|
||||
img = cv2.imread(dir_img)
|
||||
|
||||
if dir_out_image_text:
|
||||
out_image_with_text = os.path.join(dir_out_image_text, file_name+'.png')
|
||||
image_text = Image.new("RGB", (img.shape[1], img.shape[0]), "white")
|
||||
draw = ImageDraw.Draw(image_text)
|
||||
total_bb_coordinates = []
|
||||
|
||||
##file_name = Path(dir_xmls).stem
|
||||
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]+'}'
|
||||
|
||||
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 = []
|
||||
|
||||
extracted_texts = []
|
||||
|
||||
indexer_text_region = 0
|
||||
indexer_b_s = 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)
|
||||
|
||||
if dir_out_image_text:
|
||||
total_bb_coordinates.append([x,y,w,h])
|
||||
|
||||
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.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
|
||||
|
||||
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.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
|
||||
|
||||
|
||||
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.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
|
||||
|
||||
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.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
|
||||
|
||||
|
||||
|
||||
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.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 = []
|
||||
####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.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
|
||||
|
||||
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))
|
||||
|
||||
unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
|
||||
|
||||
if dir_out_image_text:
|
||||
|
||||
#font_path = "Charis-7.000/Charis-Regular.ttf" # Make sure this file exists!
|
||||
font = importlib_resources.files(__package__) / "Charis-Regular.ttf"
|
||||
with importlib_resources.as_file(font) as font:
|
||||
font = ImageFont.truetype(font=font, size=40)
|
||||
|
||||
for indexer_text, bb_ind in enumerate(total_bb_coordinates):
|
||||
|
||||
|
||||
x_bb = bb_ind[0]
|
||||
y_bb = bb_ind[1]
|
||||
w_bb = bb_ind[2]
|
||||
h_bb = bb_ind[3]
|
||||
|
||||
font = fit_text_single_line(draw, extracted_texts_merged[indexer_text],
|
||||
font.path, w_bb, int(h_bb*0.4) )
|
||||
|
||||
##draw.rectangle([x_bb, y_bb, x_bb + w_bb, y_bb + h_bb], outline="red", width=2)
|
||||
|
||||
text_bbox = draw.textbbox((0, 0), extracted_texts_merged[indexer_text], font=font)
|
||||
text_width = text_bbox[2] - text_bbox[0]
|
||||
text_height = text_bbox[3] - text_bbox[1]
|
||||
|
||||
text_x = x_bb + (w_bb - text_width) // 2 # Center horizontally
|
||||
text_y = y_bb + (h_bb - text_height) // 2 # Center vertically
|
||||
|
||||
# Draw the text
|
||||
draw.text((text_x, text_y), extracted_texts_merged[indexer_text], fill="black", font=font)
|
||||
image_text.save(out_image_with_text)
|
||||
|
||||
#print(len(unique_cropped_lines_region_indexer), 'unique_cropped_lines_region_indexer')
|
||||
#######text_by_textregion = []
|
||||
#######for ind in unique_cropped_lines_region_indexer:
|
||||
#######ind = np.array(cropped_lines_region_indexer)==ind
|
||||
#######extracted_texts_merged_un = np.array(extracted_texts_merged)[ind]
|
||||
#######text_by_textregion.append(" ".join(extracted_texts_merged_un))
|
||||
|
||||
text_by_textregion = []
|
||||
for ind in unique_cropped_lines_region_indexer:
|
||||
ind = np.array(cropped_lines_region_indexer) == ind
|
||||
extracted_texts_merged_un = np.array(extracted_texts_merged)[ind]
|
||||
if len(extracted_texts_merged_un)>1:
|
||||
text_by_textregion_ind = ""
|
||||
next_glue = ""
|
||||
for indt in range(len(extracted_texts_merged_un)):
|
||||
if (extracted_texts_merged_un[indt].endswith('⸗') or
|
||||
extracted_texts_merged_un[indt].endswith('-') or
|
||||
extracted_texts_merged_un[indt].endswith('¬')):
|
||||
text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt][:-1]
|
||||
next_glue = ""
|
||||
else:
|
||||
text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt]
|
||||
next_glue = " "
|
||||
text_by_textregion.append(text_by_textregion_ind)
|
||||
else:
|
||||
text_by_textregion.append(" ".join(extracted_texts_merged_un))
|
||||
|
||||
|
||||
indexer = 0
|
||||
indexer_textregion = 0
|
||||
for nn in root1.iter(region_tags):
|
||||
#id_textregion = nn.attrib['id']
|
||||
#id_textregions.append(id_textregion)
|
||||
#textregions_by_existing_ids.append(text_by_textregion[indexer_textregion])
|
||||
|
||||
is_textregion_text = False
|
||||
for childtest in nn:
|
||||
if childtest.tag.endswith("TextEquiv"):
|
||||
is_textregion_text = True
|
||||
|
||||
if not is_textregion_text:
|
||||
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"):
|
||||
|
||||
is_textline_text = False
|
||||
for childtest2 in child_textregion:
|
||||
if childtest2.tag.endswith("TextEquiv"):
|
||||
is_textline_text = True
|
||||
|
||||
|
||||
if not is_textline_text:
|
||||
text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
|
||||
##text_subelement.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
|
||||
unicode_textline = ET.SubElement(text_subelement, 'Unicode')
|
||||
unicode_textline.text = extracted_texts_merged[indexer]
|
||||
else:
|
||||
for childtest3 in child_textregion:
|
||||
if childtest3.tag.endswith("TextEquiv"):
|
||||
for child_uc in childtest3:
|
||||
if child_uc.tag.endswith("Unicode"):
|
||||
##childtest3.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
|
||||
child_uc.text = extracted_texts_merged[indexer]
|
||||
|
||||
indexer = indexer + 1
|
||||
has_textline = True
|
||||
if has_textline:
|
||||
if is_textregion_text:
|
||||
for child4 in nn:
|
||||
if child4.tag.endswith("TextEquiv"):
|
||||
for childtr_uc in child4:
|
||||
if childtr_uc.tag.endswith("Unicode"):
|
||||
childtr_uc.text = text_by_textregion[indexer_textregion]
|
||||
else:
|
||||
unicode_textregion.text = text_by_textregion[indexer_textregion]
|
||||
indexer_textregion = indexer_textregion + 1
|
||||
|
||||
###sample_order = [(id_to_order[tid], text)
|
||||
### for tid, text in zip(id_textregions, textregions_by_existing_ids)
|
||||
### if tid in id_to_order]
|
||||
|
||||
##ordered_texts_sample = [text for _, text in sorted(sample_order)]
|
||||
##tot_page_text = ' '.join(ordered_texts_sample)
|
||||
|
||||
##for page_element in root1.iter(link+'Page'):
|
||||
##text_page = ET.SubElement(page_element, 'TextEquiv')
|
||||
##unicode_textpage = ET.SubElement(text_page, 'Unicode')
|
||||
##unicode_textpage.text = tot_page_text
|
||||
|
||||
ET.register_namespace("",name_space)
|
||||
tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf-8",default_namespace=None)
|
||||
else:
|
||||
###max_len = 280#512#280#512
|
||||
###padding_token = 1500#299#1500#299
|
||||
image_width = 512#max_len * 4
|
||||
image_height = 32
|
||||
|
||||
|
||||
img_size=(image_width, image_height)
|
||||
|
||||
for dir_img in ls_imgs:
|
||||
file_name = Path(dir_img).stem
|
||||
dir_xml = os.path.join(dir_xmls, file_name+'.xml')
|
||||
out_file_ocr = os.path.join(dir_out, file_name+'.xml')
|
||||
|
||||
if os.path.exists(out_file_ocr):
|
||||
if overwrite:
|
||||
self.logger.warning("will overwrite existing output file '%s'", out_file_ocr)
|
||||
else:
|
||||
self.logger.warning("will skip input for existing output file '%s'", out_file_ocr)
|
||||
continue
|
||||
|
||||
img = cv2.imread(dir_img)
|
||||
if dir_in_bin is not None:
|
||||
cropped_lines_bin = []
|
||||
dir_img_bin = os.path.join(dir_in_bin, file_name+'.png')
|
||||
img_bin = cv2.imread(dir_img_bin)
|
||||
|
||||
if dir_out_image_text:
|
||||
out_image_with_text = os.path.join(dir_out_image_text, file_name+'.png')
|
||||
image_text = Image.new("RGB", (img.shape[1], img.shape[0]), "white")
|
||||
draw = ImageDraw.Draw(image_text)
|
||||
total_bb_coordinates = []
|
||||
|
||||
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]+'}'
|
||||
|
||||
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_ver_index = []
|
||||
cropped_lines_region_indexer = []
|
||||
cropped_lines_meging_indexing = []
|
||||
|
||||
tinl = time.time()
|
||||
indexer_text_region = 0
|
||||
indexer_textlines = 0
|
||||
for nn in root1.iter(region_tags):
|
||||
try:
|
||||
type_textregion = nn.attrib['type']
|
||||
except:
|
||||
type_textregion = 'paragraph'
|
||||
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)
|
||||
|
||||
angle_radians = math.atan2(h, w)
|
||||
# Convert to degrees
|
||||
angle_degrees = math.degrees(angle_radians)
|
||||
if type_textregion=='drop-capital':
|
||||
angle_degrees = 0
|
||||
|
||||
if dir_out_image_text:
|
||||
total_bb_coordinates.append([x,y,w,h])
|
||||
|
||||
w_scaled = w * image_height/float(h)
|
||||
|
||||
img_poly_on_img = np.copy(img)
|
||||
if dir_in_bin is not None:
|
||||
img_poly_on_img_bin = np.copy(img_bin)
|
||||
img_crop_bin = img_poly_on_img_bin[y:y+h, x:x+w, :]
|
||||
|
||||
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, :]
|
||||
|
||||
if self.export_textline_images_and_text:
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop[mask_poly==0] = 255
|
||||
|
||||
else:
|
||||
# print(file_name, angle_degrees, w*h,
|
||||
# mask_poly[:,:,0].sum(),
|
||||
# mask_poly[:,:,0].sum() /float(w*h) ,
|
||||
# 'didi')
|
||||
|
||||
if angle_degrees > 3:
|
||||
better_des_slope = get_orientation_moments(textline_coords)
|
||||
|
||||
img_crop = rotate_image_with_padding(img_crop, better_des_slope)
|
||||
if dir_in_bin is not None:
|
||||
img_crop_bin = rotate_image_with_padding(img_crop_bin, better_des_slope)
|
||||
|
||||
mask_poly = rotate_image_with_padding(mask_poly, better_des_slope)
|
||||
mask_poly = mask_poly.astype('uint8')
|
||||
|
||||
#new bounding box
|
||||
x_n, y_n, w_n, h_n = get_contours_and_bounding_boxes(mask_poly[:,:,0])
|
||||
|
||||
mask_poly = mask_poly[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
img_crop = img_crop[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop[mask_poly==0] = 255
|
||||
if dir_in_bin is not None:
|
||||
img_crop_bin = img_crop_bin[y_n:y_n+h_n, x_n:x_n+w_n, :]
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop_bin[mask_poly==0] = 255
|
||||
|
||||
if mask_poly[:,:,0].sum() /float(w_n*h_n) < 0.50 and w_scaled > 90:
|
||||
if dir_in_bin is not None:
|
||||
img_crop, img_crop_bin = \
|
||||
break_curved_line_into_small_pieces_and_then_merge(
|
||||
img_crop, mask_poly, img_crop_bin)
|
||||
else:
|
||||
img_crop, _ = \
|
||||
break_curved_line_into_small_pieces_and_then_merge(
|
||||
img_crop, mask_poly)
|
||||
|
||||
else:
|
||||
better_des_slope = 0
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop[mask_poly==0] = 255
|
||||
if dir_in_bin is not None:
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
img_crop_bin[mask_poly==0] = 255
|
||||
if type_textregion=='drop-capital':
|
||||
pass
|
||||
else:
|
||||
if mask_poly[:,:,0].sum() /float(w*h) < 0.50 and w_scaled > 90:
|
||||
if dir_in_bin is not None:
|
||||
img_crop, img_crop_bin = \
|
||||
break_curved_line_into_small_pieces_and_then_merge(
|
||||
img_crop, mask_poly, img_crop_bin)
|
||||
else:
|
||||
img_crop, _ = \
|
||||
break_curved_line_into_small_pieces_and_then_merge(
|
||||
img_crop, mask_poly)
|
||||
|
||||
if not self.export_textline_images_and_text:
|
||||
if w_scaled < 750:#1.5*image_width:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
img_crop, image_height, image_width)
|
||||
cropped_lines.append(img_fin)
|
||||
if abs(better_des_slope) > 45:
|
||||
cropped_lines_ver_index.append(1)
|
||||
else:
|
||||
cropped_lines_ver_index.append(0)
|
||||
|
||||
cropped_lines_meging_indexing.append(0)
|
||||
if dir_in_bin is not None:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
img_crop_bin, image_height, image_width)
|
||||
cropped_lines_bin.append(img_fin)
|
||||
else:
|
||||
splited_images, splited_images_bin = return_textlines_split_if_needed(
|
||||
img_crop, img_crop_bin if dir_in_bin is not None else None)
|
||||
if splited_images:
|
||||
img_fin = 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)
|
||||
|
||||
if abs(better_des_slope) > 45:
|
||||
cropped_lines_ver_index.append(1)
|
||||
else:
|
||||
cropped_lines_ver_index.append(0)
|
||||
|
||||
img_fin = 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)
|
||||
|
||||
if abs(better_des_slope) > 45:
|
||||
cropped_lines_ver_index.append(1)
|
||||
else:
|
||||
cropped_lines_ver_index.append(0)
|
||||
|
||||
if dir_in_bin is not None:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
splited_images_bin[0], image_height, image_width)
|
||||
cropped_lines_bin.append(img_fin)
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
splited_images_bin[1], image_height, image_width)
|
||||
cropped_lines_bin.append(img_fin)
|
||||
|
||||
else:
|
||||
img_fin = 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 abs(better_des_slope) > 45:
|
||||
cropped_lines_ver_index.append(1)
|
||||
else:
|
||||
cropped_lines_ver_index.append(0)
|
||||
|
||||
if dir_in_bin is not None:
|
||||
img_fin = preprocess_and_resize_image_for_ocrcnn_model(
|
||||
img_crop_bin, image_height, image_width)
|
||||
cropped_lines_bin.append(img_fin)
|
||||
|
||||
if self.export_textline_images_and_text:
|
||||
if img_crop.shape[0]==0 or img_crop.shape[1]==0:
|
||||
pass
|
||||
else:
|
||||
if child_textlines.tag.endswith("TextEquiv"):
|
||||
for cheild_text in child_textlines:
|
||||
if cheild_text.tag.endswith("Unicode"):
|
||||
textline_text = cheild_text.text
|
||||
if textline_text:
|
||||
base_name = os.path.join(
|
||||
dir_out, file_name + '_line_' + str(indexer_textlines))
|
||||
if self.pref_of_dataset:
|
||||
base_name += '_' + self.pref_of_dataset
|
||||
if not self.do_not_mask_with_textline_contour:
|
||||
base_name += '_masked'
|
||||
|
||||
with open(base_name + '.txt', 'w') as text_file:
|
||||
text_file.write(textline_text)
|
||||
cv2.imwrite(base_name + '.png', img_crop)
|
||||
indexer_textlines+=1
|
||||
|
||||
if not self.export_textline_images_and_text:
|
||||
indexer_text_region = indexer_text_region +1
|
||||
|
||||
if not self.export_textline_images_and_text:
|
||||
extracted_texts = []
|
||||
extracted_conf_value = []
|
||||
|
||||
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:]
|
||||
imgs = np.array(imgs)
|
||||
imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3)
|
||||
|
||||
ver_imgs = np.array( cropped_lines_ver_index[n_start:] )
|
||||
indices_ver = np.where(ver_imgs == 1)[0]
|
||||
|
||||
#print(indices_ver, 'indices_ver')
|
||||
if len(indices_ver)>0:
|
||||
imgs_ver_flipped = imgs[indices_ver, : ,: ,:]
|
||||
imgs_ver_flipped = imgs_ver_flipped[:,::-1,::-1,:]
|
||||
#print(imgs_ver_flipped, 'imgs_ver_flipped')
|
||||
|
||||
else:
|
||||
imgs_ver_flipped = None
|
||||
|
||||
if dir_in_bin is not None:
|
||||
imgs_bin = cropped_lines_bin[n_start:]
|
||||
imgs_bin = np.array(imgs_bin)
|
||||
imgs_bin = imgs_bin.reshape(imgs_bin.shape[0], image_height, image_width, 3)
|
||||
|
||||
if len(indices_ver)>0:
|
||||
imgs_bin_ver_flipped = imgs_bin[indices_ver, : ,: ,:]
|
||||
imgs_bin_ver_flipped = imgs_bin_ver_flipped[:,::-1,::-1,:]
|
||||
#print(imgs_ver_flipped, 'imgs_ver_flipped')
|
||||
|
||||
else:
|
||||
imgs_bin_ver_flipped = None
|
||||
else:
|
||||
n_start = i*self.b_s
|
||||
n_end = (i+1)*self.b_s
|
||||
imgs = cropped_lines[n_start:n_end]
|
||||
imgs = np.array(imgs).reshape(self.b_s, image_height, image_width, 3)
|
||||
|
||||
ver_imgs = np.array( cropped_lines_ver_index[n_start:n_end] )
|
||||
indices_ver = np.where(ver_imgs == 1)[0]
|
||||
#print(indices_ver, 'indices_ver')
|
||||
|
||||
if len(indices_ver)>0:
|
||||
imgs_ver_flipped = imgs[indices_ver, : ,: ,:]
|
||||
imgs_ver_flipped = imgs_ver_flipped[:,::-1,::-1,:]
|
||||
#print(imgs_ver_flipped, 'imgs_ver_flipped')
|
||||
else:
|
||||
imgs_ver_flipped = None
|
||||
|
||||
|
||||
if dir_in_bin is not None:
|
||||
imgs_bin = cropped_lines_bin[n_start:n_end]
|
||||
imgs_bin = np.array(imgs_bin).reshape(self.b_s, image_height, image_width, 3)
|
||||
|
||||
|
||||
if len(indices_ver)>0:
|
||||
imgs_bin_ver_flipped = imgs_bin[indices_ver, : ,: ,:]
|
||||
imgs_bin_ver_flipped = imgs_bin_ver_flipped[:,::-1,::-1,:]
|
||||
#print(imgs_ver_flipped, 'imgs_ver_flipped')
|
||||
else:
|
||||
imgs_bin_ver_flipped = None
|
||||
|
||||
|
||||
self.logger.debug("processing next %d lines", len(imgs))
|
||||
preds = self.prediction_model.predict(imgs, verbose=0)
|
||||
|
||||
if len(indices_ver)>0:
|
||||
preds_flipped = self.prediction_model.predict(imgs_ver_flipped, verbose=0)
|
||||
preds_max_fliped = np.max(preds_flipped, axis=2 )
|
||||
preds_max_args_flipped = np.argmax(preds_flipped, axis=2 )
|
||||
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
|
||||
masked_means_flipped = \
|
||||
np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool_flipped, axis=1)
|
||||
masked_means_flipped[np.isnan(masked_means_flipped)] = 0
|
||||
|
||||
preds_max = np.max(preds, axis=2 )
|
||||
preds_max_args = np.argmax(preds, axis=2 )
|
||||
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
|
||||
|
||||
masked_means = \
|
||||
np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool, axis=1)
|
||||
masked_means[np.isnan(masked_means)] = 0
|
||||
|
||||
masked_means_ver = masked_means[indices_ver]
|
||||
#print(masked_means_ver, 'pred_max_not_unk')
|
||||
|
||||
indices_where_flipped_conf_value_is_higher = \
|
||||
np.where(masked_means_flipped > masked_means_ver)[0]
|
||||
|
||||
#print(indices_where_flipped_conf_value_is_higher, 'indices_where_flipped_conf_value_is_higher')
|
||||
if len(indices_where_flipped_conf_value_is_higher)>0:
|
||||
indices_to_be_replaced = indices_ver[indices_where_flipped_conf_value_is_higher]
|
||||
preds[indices_to_be_replaced,:,:] = \
|
||||
preds_flipped[indices_where_flipped_conf_value_is_higher, :, :]
|
||||
if dir_in_bin is not None:
|
||||
preds_bin = self.prediction_model.predict(imgs_bin, verbose=0)
|
||||
|
||||
if len(indices_ver)>0:
|
||||
preds_flipped = self.prediction_model.predict(imgs_bin_ver_flipped, verbose=0)
|
||||
preds_max_fliped = np.max(preds_flipped, axis=2 )
|
||||
preds_max_args_flipped = np.argmax(preds_flipped, axis=2 )
|
||||
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
|
||||
masked_means_flipped = \
|
||||
np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool_flipped, axis=1)
|
||||
masked_means_flipped[np.isnan(masked_means_flipped)] = 0
|
||||
|
||||
preds_max = np.max(preds, axis=2 )
|
||||
preds_max_args = np.argmax(preds, axis=2 )
|
||||
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
|
||||
|
||||
masked_means = \
|
||||
np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool, axis=1)
|
||||
masked_means[np.isnan(masked_means)] = 0
|
||||
|
||||
masked_means_ver = masked_means[indices_ver]
|
||||
#print(masked_means_ver, 'pred_max_not_unk')
|
||||
|
||||
indices_where_flipped_conf_value_is_higher = \
|
||||
np.where(masked_means_flipped > masked_means_ver)[0]
|
||||
|
||||
#print(indices_where_flipped_conf_value_is_higher, 'indices_where_flipped_conf_value_is_higher')
|
||||
if len(indices_where_flipped_conf_value_is_higher)>0:
|
||||
indices_to_be_replaced = indices_ver[indices_where_flipped_conf_value_is_higher]
|
||||
preds_bin[indices_to_be_replaced,:,:] = \
|
||||
preds_flipped[indices_where_flipped_conf_value_is_higher, :, :]
|
||||
|
||||
preds = (preds + preds_bin) / 2.
|
||||
|
||||
pred_texts = decode_batch_predictions(preds, self.num_to_char)
|
||||
|
||||
preds_max = np.max(preds, axis=2 )
|
||||
preds_max_args = np.argmax(preds, axis=2 )
|
||||
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
|
||||
masked_means = \
|
||||
np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / \
|
||||
np.sum(pred_max_not_unk_mask_bool, axis=1)
|
||||
|
||||
for ib in range(imgs.shape[0]):
|
||||
pred_texts_ib = pred_texts[ib].replace("[UNK]", "")
|
||||
if masked_means[ib] >= self.min_conf_value_of_textline_text:
|
||||
extracted_texts.append(pred_texts_ib)
|
||||
extracted_conf_value.append(masked_means[ib])
|
||||
else:
|
||||
extracted_texts.append("")
|
||||
extracted_conf_value.append(0)
|
||||
del cropped_lines
|
||||
if dir_in_bin is not None:
|
||||
del cropped_lines_bin
|
||||
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_conf_value_merged = [extracted_conf_value[ind]
|
||||
if cropped_lines_meging_indexing[ind]==0
|
||||
else (extracted_conf_value[ind]+extracted_conf_value[ind+1])/2.
|
||||
if cropped_lines_meging_indexing[ind]==1
|
||||
else None
|
||||
for ind in range(len(cropped_lines_meging_indexing))]
|
||||
|
||||
extracted_conf_value_merged = [extracted_conf_value_merged[ind_cfm]
|
||||
for ind_cfm in range(len(extracted_texts_merged))
|
||||
if extracted_texts_merged[ind_cfm] is not None]
|
||||
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)
|
||||
|
||||
if dir_out_image_text:
|
||||
#font_path = "Charis-7.000/Charis-Regular.ttf" # Make sure this file exists!
|
||||
font = importlib_resources.files(__package__) / "Charis-Regular.ttf"
|
||||
with importlib_resources.as_file(font) as font:
|
||||
font = ImageFont.truetype(font=font, size=40)
|
||||
|
||||
for indexer_text, bb_ind in enumerate(total_bb_coordinates):
|
||||
x_bb = bb_ind[0]
|
||||
y_bb = bb_ind[1]
|
||||
w_bb = bb_ind[2]
|
||||
h_bb = bb_ind[3]
|
||||
|
||||
font = fit_text_single_line(draw, extracted_texts_merged[indexer_text],
|
||||
font.path, w_bb, int(h_bb*0.4) )
|
||||
|
||||
##draw.rectangle([x_bb, y_bb, x_bb + w_bb, y_bb + h_bb], outline="red", width=2)
|
||||
|
||||
text_bbox = draw.textbbox((0, 0), extracted_texts_merged[indexer_text], font=font)
|
||||
text_width = text_bbox[2] - text_bbox[0]
|
||||
text_height = text_bbox[3] - text_bbox[1]
|
||||
|
||||
text_x = x_bb + (w_bb - text_width) // 2 # Center horizontally
|
||||
text_y = y_bb + (h_bb - text_height) // 2 # Center vertically
|
||||
|
||||
# Draw the text
|
||||
draw.text((text_x, text_y), extracted_texts_merged[indexer_text], fill="black", font=font)
|
||||
image_text.save(out_image_with_text)
|
||||
|
||||
text_by_textregion = []
|
||||
for ind in unique_cropped_lines_region_indexer:
|
||||
ind = np.array(cropped_lines_region_indexer)==ind
|
||||
extracted_texts_merged_un = np.array(extracted_texts_merged)[ind]
|
||||
if len(extracted_texts_merged_un)>1:
|
||||
text_by_textregion_ind = ""
|
||||
next_glue = ""
|
||||
for indt in range(len(extracted_texts_merged_un)):
|
||||
if (extracted_texts_merged_un[indt].endswith('⸗') or
|
||||
extracted_texts_merged_un[indt].endswith('-') or
|
||||
extracted_texts_merged_un[indt].endswith('¬')):
|
||||
text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt][:-1]
|
||||
next_glue = ""
|
||||
else:
|
||||
text_by_textregion_ind += next_glue + extracted_texts_merged_un[indt]
|
||||
next_glue = " "
|
||||
text_by_textregion.append(text_by_textregion_ind)
|
||||
else:
|
||||
text_by_textregion.append(" ".join(extracted_texts_merged_un))
|
||||
#print(text_by_textregion, 'text_by_textregiontext_by_textregiontext_by_textregiontext_by_textregiontext_by_textregion')
|
||||
|
||||
###index_tot_regions = []
|
||||
###tot_region_ref = []
|
||||
|
||||
###for jj in root1.iter(link+'RegionRefIndexed'):
|
||||
###index_tot_regions.append(jj.attrib['index'])
|
||||
###tot_region_ref.append(jj.attrib['regionRef'])
|
||||
|
||||
###id_to_order = {tid: ro for tid, ro in zip(tot_region_ref, index_tot_regions)}
|
||||
|
||||
#id_textregions = []
|
||||
#textregions_by_existing_ids = []
|
||||
indexer = 0
|
||||
indexer_textregion = 0
|
||||
for nn in root1.iter(region_tags):
|
||||
#id_textregion = nn.attrib['id']
|
||||
#id_textregions.append(id_textregion)
|
||||
#textregions_by_existing_ids.append(text_by_textregion[indexer_textregion])
|
||||
|
||||
is_textregion_text = False
|
||||
for childtest in nn:
|
||||
if childtest.tag.endswith("TextEquiv"):
|
||||
is_textregion_text = True
|
||||
|
||||
if not is_textregion_text:
|
||||
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"):
|
||||
|
||||
is_textline_text = False
|
||||
for childtest2 in child_textregion:
|
||||
if childtest2.tag.endswith("TextEquiv"):
|
||||
is_textline_text = True
|
||||
|
||||
|
||||
if not is_textline_text:
|
||||
text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
|
||||
text_subelement.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
|
||||
unicode_textline = ET.SubElement(text_subelement, 'Unicode')
|
||||
unicode_textline.text = extracted_texts_merged[indexer]
|
||||
else:
|
||||
for childtest3 in child_textregion:
|
||||
if childtest3.tag.endswith("TextEquiv"):
|
||||
for child_uc in childtest3:
|
||||
if child_uc.tag.endswith("Unicode"):
|
||||
childtest3.set('conf',
|
||||
f"{extracted_conf_value_merged[indexer]:.2f}")
|
||||
child_uc.text = extracted_texts_merged[indexer]
|
||||
|
||||
indexer = indexer + 1
|
||||
has_textline = True
|
||||
if has_textline:
|
||||
if is_textregion_text:
|
||||
for child4 in nn:
|
||||
if child4.tag.endswith("TextEquiv"):
|
||||
for childtr_uc in child4:
|
||||
if childtr_uc.tag.endswith("Unicode"):
|
||||
childtr_uc.text = text_by_textregion[indexer_textregion]
|
||||
else:
|
||||
unicode_textregion.text = text_by_textregion[indexer_textregion]
|
||||
indexer_textregion = indexer_textregion + 1
|
||||
|
||||
###sample_order = [(id_to_order[tid], text)
|
||||
### for tid, text in zip(id_textregions, textregions_by_existing_ids)
|
||||
### if tid in id_to_order]
|
||||
|
||||
##ordered_texts_sample = [text for _, text in sorted(sample_order)]
|
||||
##tot_page_text = ' '.join(ordered_texts_sample)
|
||||
|
||||
##for page_element in root1.iter(link+'Page'):
|
||||
##text_page = ET.SubElement(page_element, 'TextEquiv')
|
||||
##unicode_textpage = ET.SubElement(text_page, 'Unicode')
|
||||
##unicode_textpage.text = tot_page_text
|
||||
|
||||
ET.register_namespace("",name_space)
|
||||
tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf-8",default_namespace=None)
|
||||
#print("Job done in %.1fs", time.time() - t0)
|
||||
Loading…
Add table
Add a link
Reference in a new issue