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"""Loader that loads image files.""" |
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from typing import List |
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from langchain.document_loaders.unstructured import UnstructuredFileLoader |
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from paddleocr import PaddleOCR |
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import os |
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import nltk |
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class UnstructuredPaddleImageLoader(UnstructuredFileLoader): |
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"""Loader that uses unstructured to load image files, such as PNGs and JPGs.""" |
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def _get_elements(self) -> List: |
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def image_ocr_txt(filepath, dir_path="tmp_files"): |
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full_dir_path = os.path.join(os.path.dirname(filepath), dir_path) |
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if not os.path.exists(full_dir_path): |
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os.makedirs(full_dir_path) |
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filename = os.path.split(filepath)[-1] |
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ocr = PaddleOCR(use_angle_cls=True, lang="ch", use_gpu=False, show_log=False) |
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result = ocr.ocr(img=filepath) |
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ocr_result = [i[1][0] for line in result for i in line] |
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txt_file_path = os.path.join(full_dir_path, "%s.txt" % (filename)) |
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with open(txt_file_path, 'w', encoding='utf-8') as fout: |
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fout.write("\n".join(ocr_result)) |
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return txt_file_path |
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txt_file_path = image_ocr_txt(self.file_path) |
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from unstructured.partition.text import partition_text |
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return partition_text(filename=txt_file_path, **self.unstructured_kwargs) |
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if __name__ == "__main__": |
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import sys |
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sys.path.append(os.path.dirname(os.path.dirname(__file__))) |
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from configs.model_config import NLTK_DATA_PATH |
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nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path |
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filepath = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base", "samples", "content", "test.jpg") |
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loader = UnstructuredPaddleImageLoader(filepath, mode="elements") |
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docs = loader.load() |
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for doc in docs: |
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print(doc) |
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