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import os
import gradio as gr
import pandas as pd
from src.htr_pipeline.inferencer import Inferencer, InferencerInterface
from src.htr_pipeline.pipeline import Pipeline, PipelineInterface
class SingletonModelLoader:
_instance = None
def __new__(cls, *args, **kwargs):
if not cls._instance:
cls._instance = super(SingletonModelLoader, cls).__new__(cls, *args, **kwargs)
return cls._instance
def __init__(self):
self.inferencer = Inferencer(local_run=True)
self.pipeline = Pipeline(self.inferencer)
# fast track
class FastTrack:
def __init__(self, model_loader):
self.pipeline: PipelineInterface = model_loader.pipeline
def segment_to_xml(self, image, radio_button_choices):
xml_xml = "page_xml.xml"
xml_txt = "page_txt.txt"
if os.path.exists(f"./{xml_xml}"):
os.remove(f"./{xml_xml}")
rendered_xml = self.pipeline.running_htr_pipeline(image)
with open(xml_xml, "w") as f:
f.write(rendered_xml)
xml_img = self.visualize_xml_and_return_txt(image, xml_txt)
if len(radio_button_choices) < 2:
if radio_button_choices[0] == "Txt":
returned_file_extension = xml_txt
else:
returned_file_extension = xml_xml
else:
returned_file_extension = [xml_txt, xml_xml]
return xml_img, returned_file_extension, gr.update(visible=True)
def segment_to_xml_api(self, image):
rendered_xml = self.pipeline.running_htr_pipeline(image)
return rendered_xml
def visualize_xml_and_return_txt(self, img, xml_txt):
xml_img = self.pipeline.visualize_xml(img)
if os.path.exists(f"./{xml_txt}"):
os.remove(f"./{xml_txt}")
self.pipeline.parse_xml_to_txt()
return xml_img
# Custom track
class CustomTrack:
def __init__(self, model_loader):
self.inferencer: InferencerInterface = model_loader.inferencer
def region_segment(self, image, pred_score_threshold, containments_treshold):
predicted_regions, regions_cropped_ordered, _, _ = self.inferencer.predict_regions(
image, pred_score_threshold, containments_treshold
)
return predicted_regions, regions_cropped_ordered, gr.update(visible=False), gr.update(visible=True)
def line_segment(self, image, pred_score_threshold, containments_threshold):
predicted_lines, lines_cropped_ordered, _ = self.inferencer.predict_lines(
image, pred_score_threshold, containments_threshold
)
return (
predicted_lines,
image,
lines_cropped_ordered,
lines_cropped_ordered, #
lines_cropped_ordered, # temp_gallery
gr.update(visible=True),
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=True),
)
def transcribe_text(self, df, images):
transcription_temp_list_with_score = []
mapping_dict = {}
for image in images:
transcribed_text, prediction_score_from_htr = self.inferencer.transcribe(image)
transcription_temp_list_with_score.append((transcribed_text, prediction_score_from_htr))
df_trans_explore = pd.DataFrame(
transcription_temp_list_with_score, columns=["Transcribed text", "HTR prediction score"]
)
mapping_dict[transcribed_text] = image
yield df_trans_explore[["Transcribed text"]], df_trans_explore, mapping_dict, gr.update(
visible=False
), gr.update(visible=True), gr.update(visible=False)
def get_select_index_image(self, images_from_gallery, evt: gr.SelectData):
return images_from_gallery[evt.index]["name"]
def get_select_index_df(self, transcribed_text_df_finish, mapping_dict, evt: gr.SelectData):
df_list = transcribed_text_df_finish["Transcribed text"].tolist()
key_text = df_list[evt.index[0]]
sorted_image = mapping_dict[key_text]
new_first = [sorted_image]
new_list = [img for txt, img in mapping_dict.items() if txt != key_text]
new_first.extend(new_list)
return new_first
def download_df_to_txt(self, transcribed_df):
text_in_list = transcribed_df["Transcribed text"].tolist()
file_name = "./transcribed_text.txt"
text_file = open(file_name, "w")
for text in text_in_list:
text_file.write(text + "\n")
text_file.close()
return file_name, gr.update(visible=True)
# def transcribe_text_another_model(self, df, images):
# transcription_temp_list = []
# for image in images:
# transcribed_text = inferencer.transcribe_different_model(image)
# transcription_temp_list.append(transcribed_text)
# df_trans = pd.DataFrame(transcription_temp_list, columns=["Transcribed_text"])
# yield df_trans, df_trans, gr.update(visible=False)
if __name__ == "__main__":
pass
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