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from typing import Protocol
import mmcv
import numpy as np
from src.htr_pipeline.inferencer import Inferencer
from src.htr_pipeline.utils.helper import timer_func
from src.htr_pipeline.utils.parser_xml import XmlParser
from src.htr_pipeline.utils.pipeline_inferencer import PipelineInferencer
from src.htr_pipeline.utils.preprocess_img import Preprocess
from src.htr_pipeline.utils.process_segmask import SegMaskHelper
from src.htr_pipeline.utils.visualize_xml import XmlViz
from src.htr_pipeline.utils.xml_helper import XMLHelper
class Pipeline:
def __init__(self, inferencer: Inferencer) -> None:
self.inferencer = inferencer
self.preprocess_img = Preprocess()
self.pipeline_inferencer = PipelineInferencer(SegMaskHelper(), XMLHelper())
@timer_func
def running_htr_pipeline(
self,
input_image: np.ndarray,
pred_score_threshold_regions: float = 0.4,
pred_score_threshold_lines: float = 0.4,
containments_threshold: float = 0.5,
) -> str:
input_image = self.preprocess_img.binarize_img(input_image)
image = mmcv.imread(input_image)
rendered_xml = self.pipeline_inferencer.image_to_page_xml(
image, pred_score_threshold_regions, pred_score_threshold_lines, containments_threshold, self.inferencer
)
return rendered_xml
@timer_func
def visualize_xml(self, input_image: np.ndarray) -> np.ndarray:
xml_viz = XmlViz()
bin_input_image = self.preprocess_img.binarize_img(input_image)
xml_image, text_polygon_dict = xml_viz.visualize_xml(bin_input_image)
return xml_image, text_polygon_dict
@timer_func
def parse_xml_to_txt(self) -> None:
xml_visualizer_and_parser = XmlParser()
xml_visualizer_and_parser.xml_to_txt()
class PipelineInterface(Protocol):
def __init__(self, inferencer: Inferencer) -> None:
...
def running_htr_pipeline(
self,
input_image: np.ndarray,
pred_score_threshold_regions: float = 0.4,
pred_score_threshold_lines: float = 0.4,
containments_threshold: float = 0.5,
) -> str:
...
def visualize_xml(self, input_image: np.ndarray) -> np.ndarray:
...
def parse_xml_to_txt(self) -> None:
...
if __name__ == "__main__":
prediction_model = Inferencer()
pipeline = Pipeline(prediction_model)
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