# from typing import List
# from langchain.document_loaders.unstructured import UnstructuredFileLoader
# import cv2
# from PIL import Image
# import numpy as np
# from configs import PDF_OCR_THRESHOLD
# from document_loaders.ocr import get_ocr
# import tqdm


# 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"

#                 img_list = page.get_image_info(xrefs=True)
#                 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) < PDF_OCR_THRESHOLD[0]
#                             or (bbox[3] - bbox[1]) / (page.rect.height) < 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)

#                 # 更新进度
#                 b_unit.update(1)
#             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)