基于Aidlux平台的智能版面分析

from layout_engine import *
# cap = cvs.VideoCapture()

if __name__ == "__main__":

    print("----------------------------- 相关配置 --------------------------------")
    # 加载检测和识别模型
    OCR_model = OcrEngine()
    layout_model = predictor.load_layout_model()
    print("-->模型加载成功")

    # 输入的PDF路径
    pdf_path = "inputs/paper1.pdf"
    pdf_name = pdf_path.split("/")[-1].split(".pdf")[0]

    print("----------------------------- PDF转图片 --------------------------")
    # 获取当前请求时间
    ti = time.localtime()
    date = f"{ti[0]}_{ti[1]}_{ti[2]}"
    uid = uuid.uuid4().hex[:10]

    # 需要储存图片的目录
    imagePath = f"outputs/pdf/{ti[0]}_{ti[1]}_{ti[2]}_{ti[3]}_{ti[4]}_{ti[5]}_{uid}"
    os.makedirs(imagePath, exist_ok=True)
    pyMuPDF_fitz(pdf_path, imagePath)

    # 创建一个doc文档，用于后续填充内容
    doc = docx.Document()
    default_section = doc.sections[0]
    default_section.page_width = Cm(21)
    default_section.page_height = Cm(30)

    pdf_image_path_list = os.listdir(imagePath)
    # os.listdir的数字从小到大排序
    pdf_image_path_list.sort(key=lambda x: int(x[:-4]))
    img_num = 0
    for pdf_image in tqdm.tqdm(pdf_image_path_list):
        print("----------------------------- 版面检测--------------------------")
        pdf_image_path = os.path.join(imagePath, pdf_image)
        im_cv2 = cv2.imread(pdf_image_path)
        im_b64 = np2base64(im_cv2)
        layout_result,results = predictor.layout_predict(layout_model, im_b64)
        results = results[0].plot()

        # 填充图像、表格、页眉、页脚区域为白色，避免文本OCR的干扰
        im_cv2_plot = im_cv2.copy()
        for item in layout_result:
            points = item.values()
            for point in points:
                im_cv2_plot = cv2.rectangle(im_cv2_plot, (point[0], point[1]), (point[2], point[3]), (255, 255, 255),
                                            -1)

        print("----------------------------- 文本检测和识别--------------------------")
        img_draw, result_list = OCR_model.text_predict(im_cv2_plot, 960)  # 文本检测和识别
        # 将绘制后的图片从BGR格式转换为RGB格式
        img_draw_PIL = Image.fromarray(cv2.cvtColor(results, cv2.COLOR_BGR2RGB))
        ocr_result = []
        for result in result_list:
            ocr_dict = {}
            box, text = result[0].tolist(), result[1]
            box_xy = [box[0][0], box[0][1], box[2][0], box[2][1]]
            ocr_dict[text] = box_xy
            ocr_result.append(ocr_dict)
            img_draw_PIL = cv2ImgAddText(img_draw_PIL, text, box[0][0], box[0][1])
        img_draw_cv = cv2.cvtColor(np.asarray(img_draw_PIL), cv2.COLOR_RGB2BGR)
        # cvs.imshow(img_draw_cv)
        cv2.imwrite(f"outputs/plot/{img_num}.jpg",img_draw_cv)
        img_num = img_num + 1

        print("----------------------------- 写入Word--------------------------")
        # 图片和文本行按照y轴方向进行排序（单栏适用，多栏请先做好分栏操作）
        final_result = ocr_result + layout_result
        final_result_sort = sorted(final_result, key=lambda x: x[list(x.keys())[0]][1])

        for item in final_result_sort:
            keys_list = item.keys()
            for key in keys_list:
                # 对图片和表格进行处理：裁剪-->保存-->写入Word文档
                if key in ["Figure", "Table"]:
                    points = item[key]
                    crop_img = im_cv2[points[1]:points[3], points[0]:points[2]]
                    uid = uuid.uuid4().hex[:10]
                    name = f"{ti[0]}_{ti[1]}_{ti[2]}_{ti[3]}_{ti[4]}_{ti[5]}_{uid}"
                    crop_img_path = f"outputs/crop/{name}.jpg"
                    cv2.imwrite(crop_img_path, crop_img)
                    doc.add_picture(crop_img_path, width=Cm(11))

                # 对页眉和页脚不做写入操作，跳过
                elif key in ["Header", "Footer"]:
                    continue

                # 对其他情况（Text正文部分）：保存并设置字体和大小
                else:
                    paragraph = doc.add_paragraph()
                    run = paragraph.add_run(key)
                    font = run.font
                    font.name = 'Times New Roman'
                    font.size = docx.shared.Pt(11)

    # 保存文档
    word_name = f"{pdf_name}_{ti[0]}_{ti[1]}_{ti[2]}_{ti[3]}_{ti[4]}_{ti[5]}_{uid}"
    word_path = f'outputs/words/{word_name}.docx'
    doc.save(word_path)
    print("Done!")