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# PDF2WORD
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PDF2WORD是PaddleOCR社区开发者[whjdark](https://github.com/whjdark) 基于PP-Structure智能文档分析模型实现的PDF转换Word应用程序，提供可直接安装的exe，方便windows用户运行
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## 1.使用
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### 应用程序
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1. 下载与安装：针对Windows用户，根据[软件下载]()一节下载软件后，运行 `pdf2word.exe` 。若您下载的是lite版本，安装过程中会在线下载环境依赖、模型等必要资源，安装时间较长，请确保网络畅通。serve版本打包了相关依赖，安装时间较短，可按需下载。
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2. 转换：由于PP-Structure根据中英文数据分别进行适配，在转换相应文件时可**根据文档语言进行相应选择**。
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### 脚本运行
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首次运行需要将切换路径到 `/ppstructure/pdf2word` ，然后运行代码
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```
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python pdf2word.py
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```
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## 2.软件下载
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如需获取已打包程序，可以扫描下方二维码，关注公众号填写问卷后，加入PaddleOCR官方交流群免费获取20G OCR学习大礼包，内含OCR场景应用集合（包含数码管、液晶屏、车牌、高精度SVTR模型等7个垂类模型）、《动手学OCR》电子书、课程回放视频、前沿论文等重磅资料
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<div align="center">
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<img src="https://user-images.githubusercontent.com/50011306/186369636-35f2008b-df5a-4784-b1f5-cebebcb2b7a5.jpg" width = "150" height = "150" />
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</div>
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import sys
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import tarfile
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import os
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import time
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import datetime
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import functools
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import cv2
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import platform
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import numpy as np
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from qtpy.QtWidgets import QApplication, QWidget, QPushButton, QProgressBar, \
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QGridLayout, QMessageBox, QLabel, QFileDialog
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from qtpy.QtCore import Signal, QThread, QObject
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from qtpy.QtGui import QImage, QPixmap, QIcon
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file = os.path.dirname(os.path.abspath(__file__))
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root = os.path.abspath(os.path.join(file, '../../'))
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sys.path.append(file)
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sys.path.insert(0, root)
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from ppstructure.predict_system import StructureSystem, save_structure_res
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from ppstructure.utility import parse_args, draw_structure_result
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from ppocr.utils.network import download_with_progressbar
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from ppstructure.recovery.recovery_to_doc import sorted_layout_boxes, convert_info_docx
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# from ScreenShotWidget import ScreenShotWidget
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__APPNAME__ = "pdf2word"
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__VERSION__ = "0.1.1"
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URLs_EN = {
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# 下载超英文轻量级PP-OCRv3模型的检测模型并解压
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"en_PP-OCRv3_det_infer": "https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar",
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# 下载英文轻量级PP-OCRv3模型的识别模型并解压
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"en_PP-OCRv3_rec_infer": "https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar",
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# 下载超轻量级英文表格英文模型并解压
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"en_ppstructure_mobile_v2.0_SLANet_infer": "https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar",
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# 英文版面分析模型
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"picodet_lcnet_x1_0_fgd_layout_infer": "https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_infer.tar",
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}
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DICT_EN = {
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"rec_char_dict_path": "en_dict.txt",
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"layout_dict_path": "layout_publaynet_dict.txt",
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}
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URLs_CN = {
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# 下载超中文轻量级PP-OCRv3模型的检测模型并解压
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"cn_PP-OCRv3_det_infer": "https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar",
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# 下载中文轻量级PP-OCRv3模型的识别模型并解压
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"cn_PP-OCRv3_rec_infer": "https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar",
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# 下载超轻量级英文表格英文模型并解压
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"cn_ppstructure_mobile_v2.0_SLANet_infer": "https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_infer.tar",
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# 中文版面分析模型
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"picodet_lcnet_x1_0_fgd_layout_cdla_infer": "https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_0_fgd_layout_cdla_infer.tar",
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}
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DICT_CN = {
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"rec_char_dict_path": "ppocr_keys_v1.txt",
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"layout_dict_path": "layout_cdla_dict.txt",
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}
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def QImageToCvMat(incomingImage) -> np.array:
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'''
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Converts a QImage into an opencv MAT format
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'''
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incomingImage = incomingImage.convertToFormat(QImage.Format.Format_RGBA8888)
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width = incomingImage.width()
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height = incomingImage.height()
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ptr = incomingImage.bits()
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ptr.setsize(height * width * 4)
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arr = np.frombuffer(ptr, np.uint8).reshape((height, width, 4))
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return arr
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def readImage(image_file) -> list:
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if os.path.basename(image_file)[-3:] in ['pdf']:
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import fitz
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from PIL import Image
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imgs = []
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with fitz.open(image_file) as pdf:
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for pg in range(0, pdf.pageCount):
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page = pdf[pg]
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mat = fitz.Matrix(2, 2)
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pm = page.getPixmap(matrix=mat, alpha=False)
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# if width or height > 2000 pixels, don't enlarge the image
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if pm.width > 2000 or pm.height > 2000:
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pm = page.getPixmap(matrix=fitz.Matrix(1, 1), alpha=False)
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img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
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img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
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imgs.append(img)
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else:
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img = cv2.imread(image_file, cv2.IMREAD_COLOR)
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if img is not None:
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imgs = [img]
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return imgs
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class Worker(QThread):
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progressBarValue = Signal(int)
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endsignal = Signal()
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loopFlag = True
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def __init__(self, predictors, save_pdf, vis_font_path):
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super(Worker, self).__init__()
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self.predictors = predictors
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self.save_pdf = save_pdf
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self.vis_font_path = vis_font_path
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self.lang = 'EN'
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self.imagePaths = []
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self.outputDir = None
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self.setStackSize(1024*1024)
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def setImagePath(self, imagePaths):
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self.imagePaths = imagePaths
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def setLang(self, lang):
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self.lang = lang
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def setOutputDir(self, outputDir):
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self.outputDir = outputDir
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def predictAndSave(self, imgs, img_name):
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all_res = []
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for index, img in enumerate(imgs):
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res, time_dict = self.predictors[self.lang](img)
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# save output
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save_structure_res(res, self.outputDir, img_name)
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draw_img = draw_structure_result(img, res, self.vis_font_path)
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img_save_path = os.path.join(self.outputDir, img_name, 'show_{}.jpg'.format(index))
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if res != []:
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cv2.imwrite(img_save_path, draw_img)
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# recovery
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h, w, _ = img.shape
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res = sorted_layout_boxes(res, w)
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all_res += res
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try:
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convert_info_docx(img, all_res, self.outputDir, img_name, self.save_pdf)
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except Exception as ex:
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print(self,
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"error in layout recovery image:{}, err msg: {}".format(
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img_name, ex))
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print('result save to {}'.format(self.outputDir))
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def run(self):
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try:
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findex = 0
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os.makedirs(self.outputDir, exist_ok=True)
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for i, image_file in enumerate(self.imagePaths):
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if self.loopFlag == True:
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imgs = readImage(image_file)
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if len(imgs) == 0:
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continue
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img_name = os.path.basename(image_file).split('.')[0]
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os.makedirs(os.path.join(self.outputDir, img_name), exist_ok=True)
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self.predictAndSave(imgs, img_name)
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findex += 1
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self.progressBarValue.emit(findex)
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else:
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break
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self.endsignal.emit()
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self.exec()
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except Exception as e:
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print(e)
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raise
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class APP_Image2Doc(QWidget):
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def __init__(self):
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super().__init__()
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self.setFixedHeight(90)
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self.setFixedWidth(400)
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# settings
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self.imagePaths = []
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# self.screenShotWg = ScreenShotWidget()
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self.screenShot = None
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self.save_pdf = False
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self.output_dir = None
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self.vis_font_path = os.path.join(root,
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"doc", "fonts", "simfang.ttf")
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# ProgressBar
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self.pb = QProgressBar()
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self.pb.setRange(0, 100)
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self.pb.setValue(0)
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# 初始化界面
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self.setupUi()
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# 下载模型
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self.downloadModels(URLs_EN)
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self.downloadModels(URLs_CN)
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# 初始化模型
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predictors = {
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'EN': self.initPredictor('EN'),
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'CN': self.initPredictor('CN'),
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}
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# 设置工作进程
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self._thread = Worker(predictors, self.save_pdf, self.vis_font_path)
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self._thread.progressBarValue.connect(self.handleProgressBarSingal)
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self._thread.endsignal.connect(self.handleEndsignalSignal)
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self._thread.finished.connect(QObject.deleteLater)
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self.time_start = 0 # save start time
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def setupUi(self):
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self.setObjectName("MainWindow")
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self.setWindowTitle(__APPNAME__ + " " + __VERSION__)
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layout = QGridLayout()
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self.openFileButton = QPushButton("打开文件")
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self.openFileButton.setIcon(QIcon(QPixmap("./icons/folder-plus.png")))
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layout.addWidget(self.openFileButton, 0, 0, 1, 1)
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self.openFileButton.clicked.connect(self.handleOpenFileSignal)
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# screenShotButton = QPushButton("截图识别")
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# layout.addWidget(screenShotButton, 0, 1, 1, 1)
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# screenShotButton.clicked.connect(self.screenShotSlot)
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# screenShotButton.setEnabled(False) # temporarily disenble
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self.startCNButton = QPushButton("中文转换")
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self.startCNButton.setIcon(QIcon(QPixmap("./icons/chinese.png")))
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layout.addWidget(self.startCNButton, 0, 1, 1, 1)
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self.startCNButton.clicked.connect(
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functools.partial(self.handleStartSignal, 'CN'))
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self.startENButton = QPushButton("英文转换")
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self.startENButton.setIcon(QIcon(QPixmap("./icons/english.png")))
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layout.addWidget(self.startENButton, 0, 2, 1, 1)
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self.startENButton.clicked.connect(
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functools.partial(self.handleStartSignal, 'EN'))
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self.showResultButton = QPushButton("显示结果")
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self.showResultButton.setIcon(QIcon(QPixmap("./icons/folder-open.png")))
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layout.addWidget(self.showResultButton, 0, 3, 1, 1)
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self.showResultButton.clicked.connect(self.handleShowResultSignal)
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# ProgressBar
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layout.addWidget(self.pb, 2, 0, 1, 4)
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# time estimate label
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self.timeEstLabel = QLabel(
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("Time Left: --"))
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layout.addWidget(self.timeEstLabel, 3, 0, 1, 4)
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self.setLayout(layout)
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def downloadModels(self, URLs):
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# using custom model
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tar_file_name_list = [
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'inference.pdiparams',
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'inference.pdiparams.info',
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'inference.pdmodel',
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'model.pdiparams',
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'model.pdiparams.info',
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'model.pdmodel'
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]
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model_path = os.path.join(root, 'inference')
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os.makedirs(model_path, exist_ok=True)
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# download and unzip models
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for name in URLs.keys():
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url = URLs[name]
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print("Try downloading file: {}".format(url))
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tarname = url.split('/')[-1]
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tarpath = os.path.join(model_path, tarname)
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if os.path.exists(tarpath):
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print("File have already exist. skip")
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else:
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try:
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download_with_progressbar(url, tarpath)
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except Exception as e:
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print("Error occurred when downloading file, error message:")
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print(e)
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# unzip model tar
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try:
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with tarfile.open(tarpath, 'r') as tarObj:
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storage_dir = os.path.join(model_path, name)
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os.makedirs(storage_dir, exist_ok=True)
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for member in tarObj.getmembers():
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filename = None
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for tar_file_name in tar_file_name_list:
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if tar_file_name in member.name:
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filename = tar_file_name
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if filename is None:
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continue
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file = tarObj.extractfile(member)
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with open(
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os.path.join(storage_dir, filename),
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'wb') as f:
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f.write(file.read())
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except Exception as e:
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print("Error occurred when unziping file, error message:")
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print(e)
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def initPredictor(self, lang='EN'):
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# init predictor args
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args = parse_args()
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args.table_max_len = 488
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args.ocr = True
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args.recovery = True
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args.save_pdf = self.save_pdf
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args.table_char_dict_path = os.path.join(root,
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"ppocr", "utils", "dict", "table_structure_dict.txt")
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if lang == 'EN':
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args.det_model_dir = os.path.join(root, # 此处从这里找到模型存放位置
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"inference", "en_PP-OCRv3_det_infer")
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args.rec_model_dir = os.path.join(root,
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"inference", "en_PP-OCRv3_rec_infer")
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args.table_model_dir = os.path.join(root,
|
||||
"inference", "en_ppstructure_mobile_v2.0_SLANet_infer")
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args.output = os.path.join(root, "output") # 结果保存路径
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args.layout_model_dir = os.path.join(root,
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"inference", "picodet_lcnet_x1_0_fgd_layout_infer")
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lang_dict = DICT_EN
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elif lang == 'CN':
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args.det_model_dir = os.path.join(root, # 此处从这里找到模型存放位置
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||||
"inference", "cn_PP-OCRv3_det_infer")
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args.rec_model_dir = os.path.join(root,
|
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"inference", "cn_PP-OCRv3_rec_infer")
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args.table_model_dir = os.path.join(root,
|
||||
"inference", "cn_ppstructure_mobile_v2.0_SLANet_infer")
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args.output = os.path.join(root, "output") # 结果保存路径
|
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args.layout_model_dir = os.path.join(root,
|
||||
"inference", "picodet_lcnet_x1_0_fgd_layout_cdla_infer")
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lang_dict = DICT_CN
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else:
|
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raise ValueError("Unsupported language")
|
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args.rec_char_dict_path = os.path.join(root,
|
||||
"ppocr", "utils",
|
||||
lang_dict['rec_char_dict_path'])
|
||||
args.layout_dict_path = os.path.join(root,
|
||||
"ppocr", "utils", "dict", "layout_dict",
|
||||
lang_dict['layout_dict_path'])
|
||||
# init predictor
|
||||
return StructureSystem(args)
|
||||
|
||||
def handleOpenFileSignal(self):
|
||||
'''
|
||||
可以多选图像文件
|
||||
'''
|
||||
selectedFiles = QFileDialog.getOpenFileNames(self,
|
||||
"多文件选择", "/", "图片文件 (*.png *.jpeg *.jpg *.bmp *.pdf)")[0]
|
||||
if len(selectedFiles) > 0:
|
||||
self.imagePaths = selectedFiles
|
||||
self.screenShot = None # discard screenshot temp image
|
||||
self.pb.setRange(0, len(self.imagePaths))
|
||||
self.pb.setValue(0)
|
||||
|
||||
# def screenShotSlot(self):
|
||||
# '''
|
||||
# 选定图像文件和截图的转换过程只能同时进行一个
|
||||
# 截图只能同时转换一个
|
||||
# '''
|
||||
# self.screenShotWg.start()
|
||||
# if self.screenShotWg.captureImage:
|
||||
# self.screenShot = self.screenShotWg.captureImage
|
||||
# self.imagePaths.clear() # discard openfile temp list
|
||||
# self.pb.setRange(0, 1)
|
||||
# self.pb.setValue(0)
|
||||
|
||||
def handleStartSignal(self, lang):
|
||||
if self.screenShot: # for screenShot
|
||||
img_name = 'screenshot_' + time.strftime("%Y%m%d%H%M%S", time.localtime())
|
||||
image = QImageToCvMat(self.screenShot)
|
||||
self.predictAndSave(image, img_name, lang)
|
||||
# update Progress Bar
|
||||
self.pb.setValue(1)
|
||||
QMessageBox.information(self,
|
||||
u'Information', "文档提取完成")
|
||||
elif len(self.imagePaths) > 0 : # for image file selection
|
||||
# Must set image path list and language before start
|
||||
self.output_dir = os.path.join(
|
||||
os.path.dirname(self.imagePaths[0]), "output") # output_dir shold be same as imagepath
|
||||
self._thread.setOutputDir(self.output_dir)
|
||||
self._thread.setImagePath(self.imagePaths)
|
||||
self._thread.setLang(lang)
|
||||
# disenble buttons
|
||||
self.openFileButton.setEnabled(False)
|
||||
self.startCNButton.setEnabled(False)
|
||||
self.startENButton.setEnabled(False)
|
||||
# 启动工作进程
|
||||
self._thread.start()
|
||||
self.time_start = time.time() # log start time
|
||||
QMessageBox.information(self,
|
||||
u'Information', "开始转换")
|
||||
else:
|
||||
QMessageBox.warning(self,
|
||||
u'Information', "请选择要识别的文件或截图")
|
||||
|
||||
def handleShowResultSignal(self):
|
||||
if self.output_dir is None:
|
||||
return
|
||||
if os.path.exists(self.output_dir):
|
||||
if platform.system() == 'Windows':
|
||||
os.startfile(self.output_dir)
|
||||
else:
|
||||
os.system('open ' + os.path.normpath(self.output_dir))
|
||||
else:
|
||||
QMessageBox.information(self,
|
||||
u'Information', "输出文件不存在")
|
||||
|
||||
def handleProgressBarSingal(self, i):
|
||||
self.pb.setValue(i)
|
||||
# calculate time left of recognition
|
||||
lenbar = self.pb.maximum()
|
||||
avg_time = (time.time() - self.time_start) / i # Use average time to prevent time fluctuations
|
||||
time_left = str(datetime.timedelta(seconds=avg_time * (lenbar - i))).split(".")[0] # Remove microseconds
|
||||
self.timeEstLabel.setText(f"Time Left: {time_left}") # show time left
|
||||
|
||||
def handleEndsignalSignal(self):
|
||||
# enble buttons
|
||||
self.openFileButton.setEnabled(True)
|
||||
self.startCNButton.setEnabled(True)
|
||||
self.startENButton.setEnabled(True)
|
||||
QMessageBox.information(self, u'Information', "转换结束")
|
||||
|
||||
|
||||
def main():
|
||||
app = QApplication(sys.argv)
|
||||
|
||||
window = APP_Image2Doc() # 创建对象
|
||||
window.show() # 全屏显示窗口
|
||||
|
||||
QApplication.processEvents()
|
||||
sys.exit(app.exec())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue