Langchain-Chatchat/libs/chatchat-server/chatchat/server/file_rag/document_loaders/mypdfloader.py

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2024-12-20 16:04:03 +08:00
from typing import List
import cv2
import numpy as np
import tqdm
from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
from PIL import Image
import re
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from chatchat.settings import Settings
from chatchat.server.file_rag.document_loaders.ocr import get_ocr
from chatchat.utils import build_logger
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logger = build_logger()
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class RapidOCRPDFLoader(UnstructuredFileLoader):
def _get_elements(self) -> List:
def rotate_img(img, angle):
"""
img --image
angle --rotation angle
return--rotated img
"""
h, w = img.shape[:2]
rotate_center = (w / 2, h / 2)
# 获取旋转矩阵
# 参数1为旋转中心点;
# 参数2为旋转角度,正值-逆时针旋转;负值-顺时针旋转
# 参数3为各向同性的比例因子,1.0原图2.0变成原来的2倍0.5变成原来的0.5倍
M = cv2.getRotationMatrix2D(rotate_center, angle, 1.0)
# 计算图像新边界
new_w = int(h * np.abs(M[0, 1]) + w * np.abs(M[0, 0]))
new_h = int(h * np.abs(M[0, 0]) + w * np.abs(M[0, 1]))
# 调整旋转矩阵以考虑平移
M[0, 2] += (new_w - w) / 2
M[1, 2] += (new_h - h) / 2
rotated_img = cv2.warpAffine(img, M, (new_w, new_h))
return rotated_img
def pdf2text(filepath):
import fitz # pyMuPDF里面的fitz包不要与pip install fitz混淆
import numpy as np
ocr = get_ocr()
doc = fitz.open(filepath)
resp = ""
b_unit = tqdm.tqdm(
total=doc.page_count, desc="RapidOCRPDFLoader context page index: 0"
)
for i, page in enumerate(doc):
b_unit.set_description(
"RapidOCRPDFLoader context page index: {}".format(i)
)
b_unit.refresh()
text = page.get_text("")
# resp += text + "\n"
text_lines = text.strip().split("\n")
logger.info(f"****page:{i+1}****,文字内容:{text_lines}")
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img_list = page.get_image_info(xrefs=True)
ocr_result = []
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for img in img_list:
if xref := img.get("xref"):
bbox = img["bbox"]
# 检查图片尺寸是否超过设定的阈值
if (bbox[2] - bbox[0]) / (page.rect.width) < Settings.kb_settings.PDF_OCR_THRESHOLD[
0
] or (bbox[3] - bbox[1]) / (
page.rect.height
) < Settings.kb_settings.PDF_OCR_THRESHOLD[1]:
continue
pix = fitz.Pixmap(doc, xref)
samples = pix.samples
if int(page.rotation) != 0: # 如果Page有旋转角度则旋转图片
img_array = np.frombuffer(
pix.samples, dtype=np.uint8
).reshape(pix.height, pix.width, -1)
tmp_img = Image.fromarray(img_array)
ori_img = cv2.cvtColor(np.array(tmp_img), cv2.COLOR_RGB2BGR)
rot_img = rotate_img(img=ori_img, angle=360 - page.rotation)
img_array = cv2.cvtColor(rot_img, cv2.COLOR_RGB2BGR)
else:
img_array = np.frombuffer(
pix.samples, dtype=np.uint8
).reshape(pix.height, pix.width, -1)
result, _ = ocr(img_array)
if result:
ocr_result = [line[1] for line in result]
resp += "\n".join(ocr_result)
if (len(ocr_result)>0):
resp += "\n".join(ocr_result)
else:
if text_lines:
# 假设页码在最后一行
if text_lines[-1].isdigit():
text = "\n".join(text_lines[:-1])
print(f"******去除了页码")
resp += text + "\n"
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# 更新进度
b_unit.update(1)
resp = re.sub(r'((?<!.)\d+(?!\.|[a-zA-Z0-9]))', r"\1 ", resp)
resp = re.sub(r'((?<!.)[a-zA-Z0-9]+\s*\.\s*[a-zA-Z0-9]+(?!\.|[a-zA-Z0-9]))', r"\1 ", resp)
resp = re.sub(r'((?<!.)[a-zA-Z0-9]+\s*\.\s*[a-zA-Z0-9]+\s*\.\s*[a-zA-Z0-9]+(?!\.|[a-zA-Z0-9]))', r"\1 ", resp)
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return resp
text = pdf2text(self.file_path)
from unstructured.partition.text import partition_text
return partition_text(text=text, **self.unstructured_kwargs)
if __name__ == "__main__":
loader = RapidOCRPDFLoader(file_path="/Users/tonysong/Desktop/test.pdf")
docs = loader.load()
print(docs)