MinerU/magic_pdf/model/pdf_extract_kit.py

from loguru import logger
import os
import time
from pathlib import Path
import shutil
from magic_pdf.libs.Constants import *
from magic_pdf.libs.clean_memory import clean_memory
from magic_pdf.model.model_list import AtomicModel

os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
os.environ['YOLO_VERBOSE'] = 'False'  # disable yolo logger
try:
    import cv2
    import yaml
    import argparse
    import numpy as np
    import torch
    import torchtext

    if torchtext.__version__ >= "0.18.0":
        torchtext.disable_torchtext_deprecation_warning()
    from PIL import Image
    from torchvision import transforms
    from torch.utils.data import Dataset, DataLoader
    from ultralytics import YOLO
    from unimernet.common.config import Config
    import unimernet.tasks as tasks
    from unimernet.processors import load_processor

except ImportError as e:
    logger.exception(e)
    logger.error(
        'Required dependency not installed, please install by \n'
        '"pip install magic-pdf[full] --extra-index-url https://myhloli.github.io/wheels/"')
    exit(1)

from magic_pdf.model.pek_sub_modules.layoutlmv3.model_init import Layoutlmv3_Predictor
from magic_pdf.model.pek_sub_modules.post_process import latex_rm_whitespace
from magic_pdf.model.pek_sub_modules.self_modify import ModifiedPaddleOCR
from magic_pdf.model.pek_sub_modules.structeqtable.StructTableModel import StructTableModel
from magic_pdf.model.ppTableModel import ppTableModel


def table_model_init(table_model_type, model_path, max_time, _device_='cpu'):
    if table_model_type == STRUCT_EQTABLE:
        table_model = StructTableModel(model_path, max_time=max_time, device=_device_)
    else:
        config = {
            "model_dir": model_path,
            "device": _device_
        }
        table_model = ppTableModel(config)
    return table_model


def mfd_model_init(weight):
    mfd_model = YOLO(weight)
    return mfd_model


def mfr_model_init(weight_dir, cfg_path, _device_='cpu'):
    args = argparse.Namespace(cfg_path=cfg_path, options=None)
    cfg = Config(args)
    cfg.config.model.pretrained = os.path.join(weight_dir, "pytorch_model.pth")
    cfg.config.model.model_config.model_name = weight_dir
    cfg.config.model.tokenizer_config.path = weight_dir
    task = tasks.setup_task(cfg)
    model = task.build_model(cfg)
    model.to(_device_)
    model.eval()
    vis_processor = load_processor('formula_image_eval', cfg.config.datasets.formula_rec_eval.vis_processor.eval)
    mfr_transform = transforms.Compose([vis_processor, ])
    return [model, mfr_transform]


def layout_model_init(weight, config_file, device):
    model = Layoutlmv3_Predictor(weight, config_file, device)
    return model


def ocr_model_init(show_log: bool = False, det_db_box_thresh=0.3, lang=None):
    if lang is not None:
        model = ModifiedPaddleOCR(show_log=show_log, det_db_box_thresh=det_db_box_thresh, lang=lang)
    else:
        model = ModifiedPaddleOCR(show_log=show_log, det_db_box_thresh=det_db_box_thresh)
    return model


class MathDataset(Dataset):
    def __init__(self, image_paths, transform=None):
        self.image_paths = image_paths
        self.transform = transform

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        # if not pil image, then convert to pil image
        if isinstance(self.image_paths[idx], str):
            raw_image = Image.open(self.image_paths[idx])
        else:
            raw_image = self.image_paths[idx]
        if self.transform:
            image = self.transform(raw_image)
            return image


class AtomModelSingleton:
    _instance = None
    _models = {}

    def __new__(cls, *args, **kwargs):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance

    def get_atom_model(self, atom_model_name: str, **kwargs):
        if atom_model_name not in self._models:
            self._models[atom_model_name] = atom_model_init(model_name=atom_model_name, **kwargs)
        return self._models[atom_model_name]


def atom_model_init(model_name: str, **kwargs):

    if model_name == AtomicModel.Layout:
        atom_model = layout_model_init(
            kwargs.get("layout_weights"),
            kwargs.get("layout_config_file"),
            kwargs.get("device")
        )
    elif model_name == AtomicModel.MFD:
        atom_model = mfd_model_init(
            kwargs.get("mfd_weights")
        )
    elif model_name == AtomicModel.MFR:
        atom_model = mfr_model_init(
            kwargs.get("mfr_weight_dir"),
            kwargs.get("mfr_cfg_path"),
            kwargs.get("device")
        )
    elif model_name == AtomicModel.OCR:
        atom_model = ocr_model_init(
            kwargs.get("ocr_show_log"),
            kwargs.get("det_db_box_thresh"),
            kwargs.get("lang")
        )
    elif model_name == AtomicModel.Table:
        atom_model = table_model_init(
            kwargs.get("table_model_type"),
            kwargs.get("table_model_path"),
            kwargs.get("table_max_time"),
            kwargs.get("device")
        )
    else:
        logger.error("model name not allow")
        exit(1)

    return atom_model


#  Unified crop img logic
def crop_img(input_res, input_pil_img, crop_paste_x=0, crop_paste_y=0):
    crop_xmin, crop_ymin = int(input_res['poly'][0]), int(input_res['poly'][1])
    crop_xmax, crop_ymax = int(input_res['poly'][4]), int(input_res['poly'][5])
    # Create a white background with an additional width and height of 50
    crop_new_width = crop_xmax - crop_xmin + crop_paste_x * 2
    crop_new_height = crop_ymax - crop_ymin + crop_paste_y * 2
    return_image = Image.new('RGB', (crop_new_width, crop_new_height), 'white')

    # Crop image
    crop_box = (crop_xmin, crop_ymin, crop_xmax, crop_ymax)
    cropped_img = input_pil_img.crop(crop_box)
    return_image.paste(cropped_img, (crop_paste_x, crop_paste_y))
    return_list = [crop_paste_x, crop_paste_y, crop_xmin, crop_ymin, crop_xmax, crop_ymax, crop_new_width, crop_new_height]
    return return_image, return_list


class CustomPEKModel:

    def __init__(self, ocr: bool = False, show_log: bool = False, **kwargs):
        """
        ======== model init ========
        """
        # 获取当前文件（即 pdf_extract_kit.py）的绝对路径
        current_file_path = os.path.abspath(__file__)
        # 获取当前文件所在的目录(model)
        current_dir = os.path.dirname(current_file_path)
        # 上一级目录(magic_pdf)
        root_dir = os.path.dirname(current_dir)
        # model_config目录
        model_config_dir = os.path.join(root_dir, 'resources', 'model_config')
        # 构建 model_configs.yaml 文件的完整路径
        config_path = os.path.join(model_config_dir, 'model_configs.yaml')
        with open(config_path, "r", encoding='utf-8') as f:
            self.configs = yaml.load(f, Loader=yaml.FullLoader)
        # 初始化解析配置
        self.apply_layout = kwargs.get("apply_layout", self.configs["config"]["layout"])
        self.apply_formula = kwargs.get("apply_formula", self.configs["config"]["formula"])
        # table config
        self.table_config = kwargs.get("table_config", self.configs["config"]["table_config"])
        self.apply_table = self.table_config.get("is_table_recog_enable", False)
        self.table_max_time = self.table_config.get("max_time", TABLE_MAX_TIME_VALUE)
        self.table_model_type = self.table_config.get("model", TABLE_MASTER)
        self.apply_ocr = ocr
        self.lang = kwargs.get("lang", None)
        logger.info(
            "DocAnalysis init, this may take some times. apply_layout: {}, apply_formula: {}, apply_ocr: {}, apply_table: {}, lang: {}".format(
                self.apply_layout, self.apply_formula, self.apply_ocr, self.apply_table, self.lang
            )
        )
        assert self.apply_layout, "DocAnalysis must contain layout model."
        # 初始化解析方案
        self.device = kwargs.get("device", self.configs["config"]["device"])
        logger.info("using device: {}".format(self.device))
        models_dir = kwargs.get("models_dir", os.path.join(root_dir, "resources", "models"))
        logger.info("using models_dir: {}".format(models_dir))

        atom_model_manager = AtomModelSingleton()

        # 初始化公式识别
        if self.apply_formula:
            # 初始化公式检测模型
            # self.mfd_model = mfd_model_init(str(os.path.join(models_dir, self.configs["weights"]["mfd"])))
            self.mfd_model = atom_model_manager.get_atom_model(
                atom_model_name=AtomicModel.MFD,
                mfd_weights=str(os.path.join(models_dir, self.configs["weights"]["mfd"]))
            )
            # 初始化公式解析模型
            mfr_weight_dir = str(os.path.join(models_dir, self.configs["weights"]["mfr"]))
            mfr_cfg_path = str(os.path.join(model_config_dir, "UniMERNet", "demo.yaml"))
            # self.mfr_model, mfr_vis_processors = mfr_model_init(mfr_weight_dir, mfr_cfg_path, _device_=self.device)
            # self.mfr_transform = transforms.Compose([mfr_vis_processors, ])
            self.mfr_model, self.mfr_transform = atom_model_manager.get_atom_model(
                atom_model_name=AtomicModel.MFR,
                mfr_weight_dir=mfr_weight_dir,
                mfr_cfg_path=mfr_cfg_path,
                device=self.device
            )

        # 初始化layout模型
        # self.layout_model = Layoutlmv3_Predictor(
        #     str(os.path.join(models_dir, self.configs['weights']['layout'])),
        #     str(os.path.join(model_config_dir, "layoutlmv3", "layoutlmv3_base_inference.yaml")),
        #     device=self.device
        # )
        self.layout_model = atom_model_manager.get_atom_model(
            atom_model_name=AtomicModel.Layout,
            layout_weights=str(os.path.join(models_dir, self.configs['weights']['layout'])),
            layout_config_file=str(os.path.join(model_config_dir, "layoutlmv3", "layoutlmv3_base_inference.yaml")),
            device=self.device
        )
        # 初始化ocr
        if self.apply_ocr:

            # self.ocr_model = ModifiedPaddleOCR(show_log=show_log, det_db_box_thresh=0.3)
            self.ocr_model = atom_model_manager.get_atom_model(
                atom_model_name=AtomicModel.OCR,
                ocr_show_log=show_log,
                det_db_box_thresh=0.3,
                lang=self.lang
            )
        # init table model
        if self.apply_table:
            table_model_dir = self.configs["weights"][self.table_model_type]
            # self.table_model = table_model_init(self.table_model_type, str(os.path.join(models_dir, table_model_dir)),
            #                                     max_time=self.table_max_time, _device_=self.device)
            self.table_model = atom_model_manager.get_atom_model(
                atom_model_name=AtomicModel.Table,
                table_model_type=self.table_model_type,
                table_model_path=str(os.path.join(models_dir, table_model_dir)),
                table_max_time=self.table_max_time,
                device=self.device
            )

            home_directory = Path.home()
            det_source = os.path.join(models_dir, table_model_dir, DETECT_MODEL_DIR)
            rec_source = os.path.join(models_dir, table_model_dir, REC_MODEL_DIR)
            det_dest_dir = os.path.join(home_directory, PP_DET_DIRECTORY)
            rec_dest_dir = os.path.join(home_directory, PP_REC_DIRECTORY)

            if not os.path.exists(det_dest_dir):
                shutil.copytree(det_source, det_dest_dir)
            if not os.path.exists(rec_dest_dir):
                shutil.copytree(rec_source, rec_dest_dir)

        logger.info('DocAnalysis init done!')

    def __call__(self, image):

        page_start = time.time()

        latex_filling_list = []
        mf_image_list = []

        # layout检测
        layout_start = time.time()
        layout_res = self.layout_model(image, ignore_catids=[])
        layout_cost = round(time.time() - layout_start, 2)
        logger.info(f"layout detection time: {layout_cost}")

        pil_img = Image.fromarray(image)

        if self.apply_formula:
            # 公式检测
            mfd_start = time.time()
            mfd_res = self.mfd_model.predict(image, imgsz=1888, conf=0.25, iou=0.45, verbose=True)[0]
            logger.info(f"mfd time: {round(time.time() - mfd_start, 2)}")
            for xyxy, conf, cla in zip(mfd_res.boxes.xyxy.cpu(), mfd_res.boxes.conf.cpu(), mfd_res.boxes.cls.cpu()):
                xmin, ymin, xmax, ymax = [int(p.item()) for p in xyxy]
                new_item = {
                    'category_id': 13 + int(cla.item()),
                    'poly': [xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax],
                    'score': round(float(conf.item()), 2),
                    'latex': '',
                }
                layout_res.append(new_item)
                latex_filling_list.append(new_item)
                # bbox_img = get_croped_image(pil_img, [xmin, ymin, xmax, ymax])
                bbox_img = pil_img.crop((xmin, ymin, xmax, ymax))
                mf_image_list.append(bbox_img)

            # 公式识别
            mfr_start = time.time()
            dataset = MathDataset(mf_image_list, transform=self.mfr_transform)
            dataloader = DataLoader(dataset, batch_size=64, num_workers=0)
            mfr_res = []
            for mf_img in dataloader:
                mf_img = mf_img.to(self.device)
                with torch.no_grad():
                    output = self.mfr_model.generate({'image': mf_img})
                mfr_res.extend(output['pred_str'])
            for res, latex in zip(latex_filling_list, mfr_res):
                res['latex'] = latex_rm_whitespace(latex)
            mfr_cost = round(time.time() - mfr_start, 2)
            logger.info(f"formula nums: {len(mf_image_list)}, mfr time: {mfr_cost}")

        # Select regions for OCR / formula regions / table regions
        ocr_res_list = []
        table_res_list = []
        single_page_mfdetrec_res = []
        for res in layout_res:
            if int(res['category_id']) in [13, 14]:
                single_page_mfdetrec_res.append({
                    "bbox": [int(res['poly'][0]), int(res['poly'][1]),
                             int(res['poly'][4]), int(res['poly'][5])],
                })
            elif int(res['category_id']) in [0, 1, 2, 4, 6, 7]:
                ocr_res_list.append(res)
            elif int(res['category_id']) in [5]:
                table_res_list.append(res)

        if torch.cuda.is_available():
            properties = torch.cuda.get_device_properties(self.device)
            total_memory = properties.total_memory / (1024 ** 3)  # 将字节转换为 GB
            if total_memory <= 10:
                gc_start = time.time()
                clean_memory()
                gc_time = round(time.time() - gc_start, 2)
                logger.info(f"gc time: {gc_time}")

        # ocr识别
        if self.apply_ocr:
            ocr_start = time.time()
            # Process each area that requires OCR processing
            for res in ocr_res_list:
                new_image, useful_list = crop_img(res, pil_img, crop_paste_x=50, crop_paste_y=50)
                paste_x, paste_y, xmin, ymin, xmax, ymax, new_width, new_height = useful_list
                # Adjust the coordinates of the formula area
                adjusted_mfdetrec_res = []
                for mf_res in single_page_mfdetrec_res:
                    mf_xmin, mf_ymin, mf_xmax, mf_ymax = mf_res["bbox"]
                    # Adjust the coordinates of the formula area to the coordinates relative to the cropping area
                    x0 = mf_xmin - xmin + paste_x
                    y0 = mf_ymin - ymin + paste_y
                    x1 = mf_xmax - xmin + paste_x
                    y1 = mf_ymax - ymin + paste_y
                    # Filter formula blocks outside the graph
                    if any([x1 < 0, y1 < 0]) or any([x0 > new_width, y0 > new_height]):
                        continue
                    else:
                        adjusted_mfdetrec_res.append({
                            "bbox": [x0, y0, x1, y1],
                        })

                # OCR recognition
                new_image = cv2.cvtColor(np.asarray(new_image), cv2.COLOR_RGB2BGR)
                ocr_res = self.ocr_model.ocr(new_image, mfd_res=adjusted_mfdetrec_res)[0]

                # Integration results
                if ocr_res:
                    for box_ocr_res in ocr_res:
                        p1, p2, p3, p4 = box_ocr_res[0]
                        text, score = box_ocr_res[1]

                        # Convert the coordinates back to the original coordinate system
                        p1 = [p1[0] - paste_x + xmin, p1[1] - paste_y + ymin]
                        p2 = [p2[0] - paste_x + xmin, p2[1] - paste_y + ymin]
                        p3 = [p3[0] - paste_x + xmin, p3[1] - paste_y + ymin]
                        p4 = [p4[0] - paste_x + xmin, p4[1] - paste_y + ymin]

                        layout_res.append({
                            'category_id': 15,
                            'poly': p1 + p2 + p3 + p4,
                            'score': round(score, 2),
                            'text': text,
                        })

            ocr_cost = round(time.time() - ocr_start, 2)
            logger.info(f"ocr time: {ocr_cost}")

        # 表格识别 table recognition
        if self.apply_table:
            table_start = time.time()
            for res in table_res_list:
                new_image, _ = crop_img(res, pil_img)
                single_table_start_time = time.time()
                # logger.info("------------------table recognition processing begins-----------------")
                latex_code = None
                html_code = None
                if self.table_model_type == STRUCT_EQTABLE:
                    with torch.no_grad():
                        latex_code = self.table_model.image2latex(new_image)[0]
                else:
                    html_code = self.table_model.img2html(new_image)

                run_time = time.time() - single_table_start_time
                # logger.info(f"------------table recognition processing ends within {run_time}s-----")
                if run_time > self.table_max_time:
                    logger.warning(f"------------table recognition processing exceeds max time {self.table_max_time}s----------")
                # 判断是否返回正常

                if latex_code:
                    expected_ending = latex_code.strip().endswith('end{tabular}') or latex_code.strip().endswith(
                        'end{table}')
                    if expected_ending:
                        res["latex"] = latex_code
                    else:
                        logger.warning(f"table recognition processing fails, not found expected LaTeX table end")
                elif html_code:
                    res["html"] = html_code
                else:
                    logger.warning(f"table recognition processing fails, not get latex or html return")
            logger.info(f"table time: {round(time.time() - table_start, 2)}")

        logger.info(f"-----page total time: {round(time.time() - page_start, 2)}-----")

        return layout_res