pdf_extract_kit.py 17.8 KB
Newer Older
1
from loguru import logger
myhloli's avatar
myhloli committed
2
import os
3
import time
4

5
from magic_pdf.libs.Constants import *
6
from magic_pdf.model.model_list import AtomicModel
7
8

os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
myhloli's avatar
myhloli committed
9
10
11
12
13
14
try:
    import cv2
    import yaml
    import argparse
    import numpy as np
    import torch
15
    import torchtext
16

17
18
    if torchtext.__version__ >= "0.18.0":
        torchtext.disable_torchtext_deprecation_warning()
myhloli's avatar
myhloli committed
19
20
21
22
23
24
25
    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
赵小蒙's avatar
update:  
赵小蒙 committed
26

27
28
except ImportError as e:
    logger.exception(e)
29
30
    logger.error(
        'Required dependency not installed, please install by \n'
31
        '"pip install magic-pdf[full] --extra-index-url https://myhloli.github.io/wheels/"')
myhloli's avatar
myhloli committed
32
    exit(1)
赵小蒙's avatar
update:  
赵小蒙 committed
33

34
from magic_pdf.model.pek_sub_modules.layoutlmv3.model_init import Layoutlmv3_Predictor
35
from magic_pdf.model.pek_sub_modules.post_process import latex_rm_whitespace
36
from magic_pdf.model.pek_sub_modules.self_modify import ModifiedPaddleOCR
37
from magic_pdf.model.pek_sub_modules.structeqtable.StructTableModel import StructTableModel
38
39
40
41
42
43
44
45
46
47
48
49
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)
50
    return table_model
51

赵小蒙's avatar
update:  
赵小蒙 committed
52

53
54
55
def mfd_model_init(weight):
    mfd_model = YOLO(weight)
    return mfd_model
赵小蒙's avatar
update:  
赵小蒙 committed
56
57


58
def mfr_model_init(weight_dir, cfg_path, _device_='cpu'):
59
60
    args = argparse.Namespace(cfg_path=cfg_path, options=None)
    cfg = Config(args)
61
    cfg.config.model.pretrained = os.path.join(weight_dir, "pytorch_model.pth")
62
63
64
65
    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)
66
    model = model.to(_device_)
67
    vis_processor = load_processor('formula_image_eval', cfg.config.datasets.formula_rec_eval.vis_processor.eval)
68
69
    mfr_transform = transforms.Compose([vis_processor, ])
    return [model, mfr_transform]
赵小蒙's avatar
update:  
赵小蒙 committed
70
71


72
73
74
75
76
def layout_model_init(weight, config_file, device):
    model = Layoutlmv3_Predictor(weight, config_file, device)
    return model


77
78
79
80
81
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)
82
83
84
    return model


85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
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)
101
            return image
102
103


104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
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"),
140
141
            kwargs.get("det_db_box_thresh"),
            kwargs.get("lang")
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
        )
    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


157
class CustomPEKModel:
158

159
160
161
162
163
164
165
166
167
168
169
170
171
172
    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')
173
        with open(config_path, "r", encoding='utf-8') as f:
174
175
176
177
            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"])
178
        # table config
179
        self.table_config = kwargs.get("table_config", self.configs["config"]["table_config"])
180
        self.apply_table = self.table_config.get("is_table_recog_enable", False)
181
        self.table_max_time = self.table_config.get("max_time", TABLE_MAX_TIME_VALUE)
182
        self.table_model_type = self.table_config.get("model", TABLE_MASTER)
183
        self.apply_ocr = ocr
184
        self.lang = kwargs.get("lang", None)
185
        logger.info(
186
187
            "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
赵小蒙's avatar
update:  
赵小蒙 committed
188
            )
189
190
191
        )
        assert self.apply_layout, "DocAnalysis must contain layout model."
        # 初始化解析方案
192
        self.device = kwargs.get("device", self.configs["config"]["device"])
193
        logger.info("using device: {}".format(self.device))
194
        models_dir = kwargs.get("models_dir", os.path.join(root_dir, "resources", "models"))
195
        logger.info("using models_dir: {}".format(models_dir))
196

197
198
        atom_model_manager = AtomModelSingleton()

199
200
201
        # 初始化公式识别
        if self.apply_formula:
            # 初始化公式检测模型
202
203
204
205
206
            # 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"]))
            )
207
            # 初始化公式解析模型
208
209
            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"))
210
211
212
213
214
215
216
217
            # 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
            )
218
219

        # 初始化layout模型
220
221
222
223
224
225
226
227
228
        # 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")),
229
230
            device=self.device
        )
231
232
        # 初始化ocr
        if self.apply_ocr:
drunkpig's avatar
drunkpig committed
233

234
235
236
237
            # 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,
238
239
                det_db_box_thresh=0.3,
                lang=self.lang
240
            )
241
        # init table model
242
        if self.apply_table:
243
            table_model_dir = self.configs["weights"][self.table_model_type]
244
245
246
247
248
249
250
251
252
            # 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
            )
drunkpig's avatar
drunkpig committed
253

254
        logger.info('DocAnalysis init done!')
赵小蒙's avatar
update:  
赵小蒙 committed
255

256
257
    def __call__(self, image):

258
259
260
        latex_filling_list = []
        mf_image_list = []

261
262
263
264
265
266
        # 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 cost: {layout_cost}")

267
268
        pil_img = Image.fromarray(image)

269
270
271
272
273
274
275
276
277
278
279
280
281
        if self.apply_formula:
            # 公式检测
            mfd_res = self.mfd_model.predict(image, imgsz=1888, conf=0.25, iou=0.45, verbose=True)[0]
            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)
282
283
                # bbox_img = get_croped_image(pil_img, [xmin, ymin, xmax, ymax])
                bbox_img = pil_img.crop((xmin, ymin, xmax, ymax))
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
                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)
                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}")
299

300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
        # 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)

        #  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

myhloli's avatar
myhloli committed
331
        # ocr识别
332
        if self.apply_ocr:
333
            ocr_start = time.time()
334
            # Process each area that requires OCR processing
335
            for res in ocr_res_list:
336
337
338
                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
339
340
341
                adjusted_mfdetrec_res = []
                for mf_res in single_page_mfdetrec_res:
                    mf_xmin, mf_ymin, mf_xmax, mf_ymax = mf_res["bbox"]
342
                    # Adjust the coordinates of the formula area to the coordinates relative to the cropping area
343
344
345
346
                    x0 = mf_xmin - xmin + paste_x
                    y0 = mf_ymin - ymin + paste_y
                    x1 = mf_xmax - xmin + paste_x
                    y1 = mf_ymax - ymin + paste_y
347
                    # Filter formula blocks outside the graph
348
                    if any([x1 < 0, y1 < 0]) or any([x0 > new_width, y0 > new_height]):
349
350
351
352
353
354
                        continue
                    else:
                        adjusted_mfdetrec_res.append({
                            "bbox": [x0, y0, x1, y1],
                        })

355
                # OCR recognition
356
357
                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]
358

359
                # Integration results
360
361
362
363
364
                if ocr_res:
                    for box_ocr_res in ocr_res:
                        p1, p2, p3, p4 = box_ocr_res[0]
                        text, score = box_ocr_res[1]

365
                        # Convert the coordinates back to the original coordinate system
366
367
368
369
370
371
372
373
374
375
376
377
                        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,
                        })

378
379
380
            ocr_cost = round(time.time() - ocr_start, 2)
            logger.info(f"ocr cost: {ocr_cost}")

381
382
        # 表格识别 table recognition
        if self.apply_table:
383
384
385
386
387
            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-----------------")
388
389
                latex_code = None
                html_code = None
390
391
                if self.table_model_type == STRUCT_EQTABLE:
                    with torch.no_grad():
392
                        latex_code = self.table_model.image2latex(new_image)[0]
393
394
                else:
                    html_code = self.table_model.img2html(new_image)
drunkpig's avatar
drunkpig committed
395

396
397
398
399
400
                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
401
402
403
404
405
406
407
408
409
410

                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----------")
                elif html_code:
                    res["html"] = html_code
411
412
413
414
415
                else:
                    logger.warning(f"------------table recognition processing fails----------")
            table_cost = round(time.time() - table_start, 2)
            logger.info(f"table cost: {table_cost}")

416
        return layout_res