Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into rfl_branch
zhiminzhang0830
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# 智能运营：通用中文表格识别
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- [1. 背景介绍](#1-背景介绍)
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- [2. 中文表格识别](#2-中文表格识别)
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- [2.1 环境准备](#21-环境准备)
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- [2.2 准备数据集](#22-准备数据集)
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- [2.2.1 划分训练测试集](#221-划分训练测试集)
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- [2.2.2 查看数据集](#222-查看数据集)
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- [2.3 训练](#23-训练)
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- [2.4 验证](#24-验证)
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- [2.5 训练引擎推理](#25-训练引擎推理)
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- [2.6 模型导出](#26-模型导出)
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- [2.7 预测引擎推理](#27-预测引擎推理)
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- [2.8 表格识别](#28-表格识别)
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- [3. 表格属性识别](#3-表格属性识别)
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- [3.1 代码、环境、数据准备](#31-代码环境数据准备)
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- [3.1.1 代码准备](#311-代码准备)
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- [3.1.2 环境准备](#312-环境准备)
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- [3.1.3 数据准备](#313-数据准备)
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- [3.2 表格属性识别训练](#32-表格属性识别训练)
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- [3.3 表格属性识别推理和部署](#33-表格属性识别推理和部署)
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- [3.3.1 模型转换](#331-模型转换)
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- [3.3.2 模型推理](#332-模型推理)
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## 1. 背景介绍
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中文表格识别在金融行业有着广泛的应用，如保险理赔、财报分析和信息录入等领域。当前，金融行业的表格识别主要以手动录入为主，开发一种自动表格识别成为丞待解决的问题。
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在金融行业中，表格图像主要有清单类的单元格密集型表格，申请表类的大单元格表格，拍照表格和倾斜表格四种主要形式。
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当前的表格识别算法不能很好的处理这些场景下的表格图像。在本例中，我们使用PP-Structurev2最新发布的表格识别模型SLANet来演示如何进行中文表格是识别。同时，为了方便作业流程，我们使用表格属性识别模型对表格图像的属性进行识别，对表格的难易程度进行判断，加快人工进行校对速度。
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本项目AI Studio链接：https://aistudio.baidu.com/aistudio/projectdetail/4588067
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## 2. 中文表格识别
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### 2.1 环境准备
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```python
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# 下载PaddleOCR代码
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! git clone -b dygraph https://gitee.com/paddlepaddle/PaddleOCR
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```
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```python
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# 安装PaddleOCR环境
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! pip install -r PaddleOCR/requirements.txt --force-reinstall
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! pip install protobuf==3.19
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```
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### 2.2 准备数据集
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本例中使用的数据集采用表格[生成工具](https://github.com/WenmuZhou/TableGeneration)制作。
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使用如下命令对数据集进行解压，并查看数据集大小
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```python
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! cd data/data165849 && tar -xf table_gen_dataset.tar && cd -
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! wc -l data/data165849/table_gen_dataset/gt.txt
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```
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#### 2.2.1 划分训练测试集
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使用下述命令将数据集划分为训练集和测试集, 这里将90%划分为训练集，10%划分为测试集
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```python
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import random
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with open('/home/aistudio/data/data165849/table_gen_dataset/gt.txt') as f:
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lines = f.readlines()
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random.shuffle(lines)
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train_len = int(len(lines)*0.9)
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train_list = lines[:train_len]
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val_list = lines[train_len:]
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# 保存结果
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with open('/home/aistudio/train.txt','w',encoding='utf-8') as f:
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f.writelines(train_list)
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with open('/home/aistudio/val.txt','w',encoding='utf-8') as f:
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f.writelines(val_list)
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```
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划分完成后，数据集信息如下
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|类型|数量|图片地址|标注文件路径|
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|---|---|---|---|
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|训练集|18000|/home/aistudio/data/data165849/table_gen_dataset|/home/aistudio/train.txt|
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|测试集|2000|/home/aistudio/data/data165849/table_gen_dataset|/home/aistudio/val.txt|
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#### 2.2.2 查看数据集
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```python
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import cv2
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import os, json
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import numpy as np
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from matplotlib import pyplot as plt
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%matplotlib inline
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def parse_line(data_dir, line):
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data_line = line.strip("\n")
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info = json.loads(data_line)
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file_name = info['filename']
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cells = info['html']['cells'].copy()
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structure = info['html']['structure']['tokens'].copy()
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img_path = os.path.join(data_dir, file_name)
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if not os.path.exists(img_path):
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print(img_path)
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return None
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data = {
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'img_path': img_path,
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'cells': cells,
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'structure': structure,
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'file_name': file_name
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}
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return data
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def draw_bbox(img_path, points, color=(255, 0, 0), thickness=2):
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if isinstance(img_path, str):
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img_path = cv2.imread(img_path)
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img_path = img_path.copy()
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for point in points:
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cv2.polylines(img_path, [point.astype(int)], True, color, thickness)
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return img_path
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def rebuild_html(data):
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html_code = data['structure']
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cells = data['cells']
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to_insert = [i for i, tag in enumerate(html_code) if tag in ('<td>', '>')]
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for i, cell in zip(to_insert[::-1], cells[::-1]):
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if cell['tokens']:
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text = ''.join(cell['tokens'])
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# skip empty text
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sp_char_list = ['<b>', '</b>', '\u2028', ' ', '<i>', '</i>']
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text_remove_style = skip_char(text, sp_char_list)
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if len(text_remove_style) == 0:
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continue
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html_code.insert(i + 1, text)
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html_code = ''.join(html_code)
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return html_code
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def skip_char(text, sp_char_list):
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"""
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skip empty cell
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@param text: text in cell
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@param sp_char_list: style char and special code
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@return:
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"""
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for sp_char in sp_char_list:
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text = text.replace(sp_char, '')
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return text
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save_dir = '/home/aistudio/vis'
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os.makedirs(save_dir, exist_ok=True)
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image_dir = '/home/aistudio/data/data165849/'
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html_str = '<table border="1">'
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# 解析标注信息并还原html表格
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data = parse_line(image_dir, val_list[0])
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img = cv2.imread(data['img_path'])
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img_name = ''.join(os.path.basename(data['file_name']).split('.')[:-1])
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img_save_name = os.path.join(save_dir, img_name)
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boxes = [np.array(x['bbox']) for x in data['cells']]
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show_img = draw_bbox(data['img_path'], boxes)
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cv2.imwrite(img_save_name + '_show.jpg', show_img)
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html = rebuild_html(data)
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html_str += html
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html_str += '</table>'
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# 显示标注的html字符串
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from IPython.core.display import display, HTML
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display(HTML(html_str))
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# 显示单元格坐标
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plt.figure(figsize=(15,15))
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plt.imshow(show_img)
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plt.show()
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```
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### 2.3 训练
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这里选用PP-Structurev2中的表格识别模型[SLANet](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/configs/table/SLANet.yml)
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SLANet是PP-Structurev2全新推出的表格识别模型，相比PP-Structurev1中TableRec-RARE，在速度不变的情况下精度提升4.7%。TEDS提升2%
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|算法|Acc|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|Speed|
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| --- | --- | --- | ---|
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| EDD<sup>[2]</sup> |x| 88.3% |x|
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| TableRec-RARE(ours) | 71.73%| 93.88% |779ms|
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| SLANet(ours) | 76.31%| 95.89%|766ms|
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进行训练之前先使用如下命令下载预训练模型
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```python
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# 进入PaddleOCR工作目录
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os.chdir('/home/aistudio/PaddleOCR')
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# 下载英文预训练模型
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! wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/en_ppstructure_mobile_v2.0_SLANet_train.tar --no-check-certificate
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! cd ./pretrain_models/ && tar xf en_ppstructure_mobile_v2.0_SLANet_train.tar && cd ../
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```
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使用如下命令即可启动训练，需要修改的配置有
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|字段|修改值|含义|
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|---|---|---|
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|Global.pretrained_model|./pretrain_models/en_ppstructure_mobile_v2.0_SLANet_train/best_accuracy.pdparams|指向英文表格预训练模型地址|
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|Global.eval_batch_step|562|模型多少step评估一次，一般设置为一个epoch总的step数|
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|Optimizer.lr.name|Const|学习率衰减器 |
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|Optimizer.lr.learning_rate|0.0005|学习率设为之前的0.05倍 |
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|Train.dataset.data_dir|/home/aistudio/data/data165849|指向训练集图片存放目录 |
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|Train.dataset.label_file_list|/home/aistudio/data/data165849/table_gen_dataset/train.txt|指向训练集标注文件 |
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|Train.loader.batch_size_per_card|32|训练时每张卡的batch_size |
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|Train.loader.num_workers|1|训练集多进程数据读取的进程数，在aistudio中需要设为1 |
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|Eval.dataset.data_dir|/home/aistudio/data/data165849|指向测试集图片存放目录 |
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|Eval.dataset.label_file_list|/home/aistudio/data/data165849/table_gen_dataset/val.txt|指向测试集标注文件 |
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|Eval.loader.batch_size_per_card|32|测试时每张卡的batch_size |
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|Eval.loader.num_workers|1|测试集多进程数据读取的进程数，在aistudio中需要设为1 |
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已经修改好的配置存储在 `/home/aistudio/SLANet_ch.yml`
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```python
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import os
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os.chdir('/home/aistudio/PaddleOCR')
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! python3 tools/train.py -c /home/aistudio/SLANet_ch.yml
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```
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大约在7个epoch后达到最高精度 97.49%
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### 2.4 验证
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训练完成后，可使用如下命令在测试集上评估最优模型的精度
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```python
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! python3 tools/eval.py -c /home/aistudio/SLANet_ch.yml -o Global.checkpoints=/home/aistudio/PaddleOCR/output/SLANet_ch/best_accuracy.pdparams
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```
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### 2.5 训练引擎推理
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使用如下命令可使用训练引擎对单张图片进行推理
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```python
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import os;os.chdir('/home/aistudio/PaddleOCR')
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! python3 tools/infer_table.py -c /home/aistudio/SLANet_ch.yml -o Global.checkpoints=/home/aistudio/PaddleOCR/output/SLANet_ch/best_accuracy.pdparams Global.infer_img=/home/aistudio/data/data165849/table_gen_dataset/img/no_border_18298_G7XZH93DDCMATGJQ8RW2.jpg
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```
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```python
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import cv2
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from matplotlib import pyplot as plt
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%matplotlib inline
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# 显示原图
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show_img = cv2.imread('/home/aistudio/data/data165849/table_gen_dataset/img/no_border_18298_G7XZH93DDCMATGJQ8RW2.jpg')
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plt.figure(figsize=(15,15))
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plt.imshow(show_img)
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plt.show()
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# 显示预测的单元格
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show_img = cv2.imread('/home/aistudio/PaddleOCR/output/infer/no_border_18298_G7XZH93DDCMATGJQ8RW2.jpg')
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plt.figure(figsize=(15,15))
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plt.imshow(show_img)
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plt.show()
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```
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### 2.6 模型导出
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使用如下命令可将模型导出为inference模型
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```python
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! python3 tools/export_model.py -c /home/aistudio/SLANet_ch.yml -o Global.checkpoints=/home/aistudio/PaddleOCR/output/SLANet_ch/best_accuracy.pdparams Global.save_inference_dir=/home/aistudio/SLANet_ch/infer
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```
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### 2.7 预测引擎推理
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使用如下命令可使用预测引擎对单张图片进行推理
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```python
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os.chdir('/home/aistudio/PaddleOCR/ppstructure')
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! python3 table/predict_structure.py \
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--table_model_dir=/home/aistudio/SLANet_ch/infer \
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--table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
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--image_dir=/home/aistudio/data/data165849/table_gen_dataset/img/no_border_18298_G7XZH93DDCMATGJQ8RW2.jpg \
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--output=../output/inference
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```
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```python
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# 显示原图
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show_img = cv2.imread('/home/aistudio/data/data165849/table_gen_dataset/img/no_border_18298_G7XZH93DDCMATGJQ8RW2.jpg')
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plt.figure(figsize=(15,15))
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plt.imshow(show_img)
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plt.show()
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# 显示预测的单元格
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show_img = cv2.imread('/home/aistudio/PaddleOCR/output/inference/no_border_18298_G7XZH93DDCMATGJQ8RW2.jpg')
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plt.figure(figsize=(15,15))
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plt.imshow(show_img)
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plt.show()
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```
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### 2.8 表格识别
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在表格结构模型训练完成后，可结合OCR检测识别模型，对表格内容进行识别。
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首先下载PP-OCRv3文字检测识别模型
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```python
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# 下载PP-OCRv3文本检测识别模型并解压
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! wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_slim_infer.tar --no-check-certificate
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! wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_slim_infer.tar --no-check-certificate
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! cd ./inference/ && tar xf ch_PP-OCRv3_det_slim_infer.tar && tar xf ch_PP-OCRv3_rec_slim_infer.tar && cd ../
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```
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模型下载完成后，使用如下命令进行表格识别
|
||||
|
||||
|
||||
```python
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import os;os.chdir('/home/aistudio/PaddleOCR/ppstructure')
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! python3 table/predict_table.py \
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--det_model_dir=inference/ch_PP-OCRv3_det_slim_infer \
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--rec_model_dir=inference/ch_PP-OCRv3_rec_slim_infer \
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--table_model_dir=/home/aistudio/SLANet_ch/infer \
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--rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt \
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--table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
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--image_dir=/home/aistudio/data/data165849/table_gen_dataset/img/no_border_18298_G7XZH93DDCMATGJQ8RW2.jpg \
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--output=../output/table
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```
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|
||||
|
||||
```python
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||||
# 显示原图
|
||||
show_img = cv2.imread('/home/aistudio/data/data165849/table_gen_dataset/img/no_border_18298_G7XZH93DDCMATGJQ8RW2.jpg')
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plt.figure(figsize=(15,15))
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plt.imshow(show_img)
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plt.show()
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# 显示预测结果
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from IPython.core.display import display, HTML
|
||||
display(HTML('<html><body><table><tr><td colspan="5">alleadersh</td><td rowspan="2">不贰过，推</td><td rowspan="2">从自己参与浙江数</td><td rowspan="2">。另一方</td></tr><tr><td>AnSha</td><td>自己越</td><td>共商共建工作协商</td><td>w.east </td><td>抓好改革试点任务</td></tr><tr><td>Edime</td><td>ImisesElec</td><td>怀天下”。</td><td></td><td>22.26 </td><td>31.61</td><td>4.30 </td><td>794.94</td></tr><tr><td rowspan="2">ip</td><td> Profundi</td><td>：2019年12月1</td><td>Horspro</td><td>444.48</td><td>2.41 </td><td>87</td><td>679.98</td></tr><tr><td> iehaiTrain</td><td>组长蒋蕊</td><td>Toafterdec</td><td>203.43</td><td>23.54 </td><td>4</td><td>4266.62</td></tr><tr><td>Tyint </td><td> roudlyRol</td><td>谢您的好意，我知道</td><td>ErChows</td><td></td><td>48.90</td><td>1031</td><td>6</td></tr><tr><td>NaFlint</td><td></td><td>一辈的</td><td>aterreclam</td><td>7823.86</td><td>9829.23</td><td>7.96 </td><td> 3068</td></tr><tr><td>家上下游企业，5</td><td>Tr</td><td>景象。当地球上的我们</td><td>Urelaw</td><td>799.62</td><td>354.96</td><td>12.98</td><td>33 </td></tr><tr><td>赛事（</td><td> uestCh</td><td>复制的业务模式并</td><td>Listicjust</td><td>9.23</td><td></td><td>92</td><td>53.22</td></tr><tr><td> Ca</td><td> Iskole</td><td>扶贫"之名引导</td><td> Papua </td><td>7191.90</td><td>1.65</td><td>3.62</td><td>48</td></tr><tr><td rowspan="2">避讳</td><td>ir</td><td>但由于</td><td>Fficeof</td><td>0.22</td><td>6.37</td><td>7.17</td><td>3397.75</td></tr><tr><td>ndaTurk</td><td>百处遗址</td><td>gMa</td><td>1288.34</td><td>2053.66</td><td>2.29</td><td>885.45</td></tr></table></body></html>'))
|
||||
```
|
||||
|
||||
## 3. 表格属性识别
|
||||
### 3.1 代码、环境、数据准备
|
||||
#### 3.1.1 代码准备
|
||||
首先，我们需要准备训练表格属性的代码，PaddleClas集成了PULC方案，该方案可以快速获得一个在CPU上用时2ms的属性识别模型。PaddleClas代码可以clone下载得到。获取方式如下：
|
||||
|
||||
|
||||
|
||||
```python
|
||||
! git clone -b develop https://gitee.com/paddlepaddle/PaddleClas
|
||||
```
|
||||
|
||||
#### 3.1.2 环境准备
|
||||
其次，我们需要安装训练PaddleClas相关的依赖包
|
||||
|
||||
|
||||
```python
|
||||
! pip install -r PaddleClas/requirements.txt --force-reinstall
|
||||
! pip install protobuf==3.20.0
|
||||
```
|
||||
|
||||
|
||||
#### 3.1.3 数据准备
|
||||
|
||||
最后，准备训练数据。在这里，我们一共定义了表格的6个属性，分别是表格来源、表格数量、表格颜色、表格清晰度、表格有无干扰、表格角度。其可视化如下：
|
||||
|
||||
|
||||
|
||||
这里，我们提供了一个表格属性的demo子集，可以快速迭代体验。下载方式如下：
|
||||
|
||||
|
||||
```python
|
||||
%cd PaddleClas/dataset
|
||||
!wget https://paddleclas.bj.bcebos.com/data/PULC/table_attribute.tar
|
||||
!tar -xf table_attribute.tar
|
||||
%cd ../PaddleClas/dataset
|
||||
%cd ../
|
||||
```
|
||||
|
||||
### 3.2 表格属性识别训练
|
||||
表格属性训练整体pipelinie如下：
|
||||
|
||||
|
||||
|
||||
1.训练过程中，图片经过预处理之后，送入到骨干网络之中，骨干网络将抽取表格图片的特征，最终该特征连接输出的FC层，FC层经过Sigmoid激活函数后和真实标签做交叉熵损失函数，优化器通过对该损失函数做梯度下降来更新骨干网络的参数，经过多轮训练后，骨干网络的参数可以对为止图片做很好的预测；
|
||||
|
||||
2.推理过程中，图片经过预处理之后，送入到骨干网络之中，骨干网络加载学习好的权重后对该表格图片做出预测，预测的结果为一个6维向量，该向量中的每个元素反映了每个属性对应的概率值，通过对该值进一步卡阈值之后，得到最终的输出，最终的输出描述了该表格的6个属性。
|
||||
|
||||
当准备好相关的数据之后，可以一键启动表格属性的训练，训练代码如下：
|
||||
|
||||
|
||||
```python
|
||||
|
||||
!python tools/train.py -c ./ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml -o Global.device=cpu -o Global.epochs=10
|
||||
```
|
||||
|
||||
### 3.3 表格属性识别推理和部署
|
||||
#### 3.3.1 模型转换
|
||||
当训练好模型之后，需要将模型转换为推理模型进行部署。转换脚本如下：
|
||||
|
||||
|
||||
```python
|
||||
!python tools/export_model.py -c ppcls/configs/PULC/table_attribute/PPLCNet_x1_0.yaml -o Global.pretrained_model=output/PPLCNet_x1_0/best_model
|
||||
```
|
||||
|
||||
执行以上命令之后，会在当前目录上生成`inference`文件夹，该文件夹中保存了当前精度最高的推理模型。
|
||||
|
||||
#### 3.3.2 模型推理
|
||||
安装推理需要的paddleclas包, 此时需要通过下载安装paddleclas的develop的whl包
|
||||
|
||||
|
||||
|
||||
```python
|
||||
!pip install https://paddleclas.bj.bcebos.com/whl/paddleclas-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
进入`deploy`目录下即可对模型进行推理
|
||||
|
||||
|
||||
```python
|
||||
%cd deploy/
|
||||
```
|
||||
|
||||
推理命令如下：
|
||||
|
||||
|
||||
```python
|
||||
!python python/predict_cls.py -c configs/PULC/table_attribute/inference_table_attribute.yaml -o Global.inference_model_dir="../inference" -o Global.infer_imgs="../dataset/table_attribute/Table_val/val_9.jpg"
|
||||
!python python/predict_cls.py -c configs/PULC/table_attribute/inference_table_attribute.yaml -o Global.inference_model_dir="../inference" -o Global.infer_imgs="../dataset/table_attribute/Table_val/val_3253.jpg"
|
||||
```
|
||||
|
||||
推理的表格图片：
|
||||
|
||||
|
||||
|
||||
预测结果如下：
|
||||
```
|
||||
val_9.jpg: {'attributes': ['Scanned', 'Little', 'Black-and-White', 'Clear', 'Without-Obstacles', 'Horizontal'], 'output': [1, 1, 1, 1, 1, 1]}
|
||||
```
|
||||
|
||||
|
||||
推理的表格图片：
|
||||
|
||||
|
||||
|
||||
预测结果如下：
|
||||
```
|
||||
val_3253.jpg: {'attributes': ['Photo', 'Little', 'Black-and-White', 'Blurry', 'Without-Obstacles', 'Tilted'], 'output': [0, 1, 1, 0, 1, 0]}
|
||||
```
|
||||
|
||||
对比两张图片可以发现，第一张图片比较清晰，表格属性的结果也偏向于比较容易识别，我们可以更相信表格识别的结果，第二张图片比较模糊，且存在倾斜现象，表格识别可能存在错误，需要我们人工进一步校验。通过表格的属性识别能力，可以进一步将“人工”和“智能”很好的结合起来，为表格识别能力的落地的精度提供保障。
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -12,7 +12,7 @@ Global:
|
|||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: ./inference/rec_inference
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path: ppocr/utils/dict90.txt
|
||||
max_text_length: &max_text_length 40
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ Global:
|
|||
checkpoints:
|
||||
save_inference_dir:
|
||||
use_visualdl: False
|
||||
infer_img: doc/imgs_words/ch/word_1.jpg
|
||||
infer_img: doc/imgs_words_en/word_10.png
|
||||
# for data or label process
|
||||
character_dict_path: ./ppocr/utils/dict/spin_dict.txt
|
||||
max_text_length: 25
|
||||
|
|
|
|||
|
|
@ -43,7 +43,6 @@ Architecture:
|
|||
Head:
|
||||
name: TableAttentionHead
|
||||
hidden_size: 256
|
||||
loc_type: 2
|
||||
max_text_length: *max_text_length
|
||||
loc_reg_num: &loc_reg_num 4
|
||||
|
||||
|
|
|
|||
|
|
@ -101,11 +101,10 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
|
|||
|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) |
|
||||
|ABINet|Resnet45| 90.75% | rec_r45_abinet | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
|
||||
|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) |
|
||||
|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon |
|
||||
|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | coming soon |
|
||||
|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar) |
|
||||
|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | [训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar)|
|
||||
|RFL|ResNetRFL| 88.63% | rec_resnet_rfl_att | [训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar) |
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
|
||||
## 2. 端到端算法
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ Zhang
|
|||
|
||||
|模型|骨干网络|配置文件|Acc|下载链接|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|RobustScanner|ResNet31|[rec_r31_robustscanner.yml](../../configs/rec/rec_r31_robustscanner.yml)|87.77%|coming soon|
|
||||
|RobustScanner|ResNet31|[rec_r31_robustscanner.yml](../../configs/rec/rec_r31_robustscanner.yml)|87.77%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar)|
|
||||
|
||||
注：除了使用MJSynth和SynthText两个文字识别数据集外，还加入了[SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg)数据（提取码：627x），和部分真实数据，具体数据细节可以参考论文。
|
||||
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ SPIN收录于AAAI2020。主要用于OCR识别任务。在任意形状文本识
|
|||
|
||||
|模型|骨干网络|配置文件|Acc|下载链接|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|coming soon|
|
||||
|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|[训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar)|
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@
|
|||
| 参数名称 | 类型 | 默认值 | 含义 |
|
||||
| :--: | :--: | :--: | :--: |
|
||||
| image_dir | str | 无，必须显式指定 | 图像或者文件夹路径 |
|
||||
| page_num | int | 0 | 当输入类型为pdf文件时有效，指定预测前面page_num页，默认预测所有页 |
|
||||
| vis_font_path | str | "./doc/fonts/simfang.ttf" | 用于可视化的字体路径 |
|
||||
| drop_score | float | 0.5 | 识别得分小于该值的结果会被丢弃，不会作为返回结果 |
|
||||
| use_pdserving | bool | False | 是否使用Paddle Serving进行预测 |
|
||||
|
|
|
|||
|
|
@ -98,8 +98,8 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
|
|||
|ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) |
|
||||
|ABINet|Resnet45| 90.75% | rec_r45_abinet | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
|
||||
|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) |
|
||||
|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon |
|
||||
|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | coming soon |
|
||||
|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar) |
|
||||
|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | [trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar)|
|
||||
|RFL|ResNetRFL| 88.63% | rec_resnet_rfl_att | [trained model](https://paddleocr.bj.bcebos.com/contribution/rec_resnet_rfl.tar) |
|
||||
|
||||
<a name="2"></a>
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval
|
|||
|
||||
|Model|Backbone|config|Acc|Download link|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|RobustScanner|ResNet31|[rec_r31_robustscanner.yml](../../configs/rec/rec_r31_robustscanner.yml)|87.77%|coming soon|
|
||||
|RobustScanner|ResNet31|[rec_r31_robustscanner.yml](../../configs/rec/rec_r31_robustscanner.yml)|87.77%|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar)|
|
||||
|
||||
Note:In addition to using the two text recognition datasets MJSynth and SynthText, [SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg) data (extraction code: 627x), and some real data are used in training, the specific data details can refer to the paper.
|
||||
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval
|
|||
|
||||
|Model|Backbone|config|Acc|Download link|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|coming soon|
|
||||
|SPIN|ResNet32|[rec_r32_gaspin_bilstm_att.yml](../../configs/rec/rec_r32_gaspin_bilstm_att.yml)|90.0%|[trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar) |
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ When using PaddleOCR for model inference, you can customize the modification par
|
|||
| parameters | type | default | implication |
|
||||
| :--: | :--: | :--: | :--: |
|
||||
| image_dir | str | None, must be specified explicitly | Image or folder path |
|
||||
| page_num | int | 0 | Valid when the input type is pdf file, specify to predict the previous page_num pages, all pages are predicted by default |
|
||||
| vis_font_path | str | "./doc/fonts/simfang.ttf" | font path for visualization |
|
||||
| drop_score | float | 0.5 | Results with a recognition score less than this value will be discarded and will not be returned as results |
|
||||
| use_pdserving | bool | False | Whether to use Paddle Serving for prediction |
|
||||
|
|
|
|||
|
|
@ -480,10 +480,11 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
params.rec_image_shape = "3, 48, 320"
|
||||
else:
|
||||
params.rec_image_shape = "3, 32, 320"
|
||||
# download model
|
||||
maybe_download(params.det_model_dir, det_url)
|
||||
maybe_download(params.rec_model_dir, rec_url)
|
||||
maybe_download(params.cls_model_dir, cls_url)
|
||||
# download model if using paddle infer
|
||||
if not params.use_onnx:
|
||||
maybe_download(params.det_model_dir, det_url)
|
||||
maybe_download(params.rec_model_dir, rec_url)
|
||||
maybe_download(params.cls_model_dir, cls_url)
|
||||
|
||||
if params.det_algorithm not in SUPPORT_DET_MODEL:
|
||||
logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ from __future__ import absolute_import
|
|||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import math
|
||||
import paddle
|
||||
import paddle.nn as nn
|
||||
from paddle import ParamAttr
|
||||
|
|
@ -42,7 +43,6 @@ class TableAttentionHead(nn.Layer):
|
|||
def __init__(self,
|
||||
in_channels,
|
||||
hidden_size,
|
||||
loc_type,
|
||||
in_max_len=488,
|
||||
max_text_length=800,
|
||||
out_channels=30,
|
||||
|
|
@ -57,20 +57,16 @@ class TableAttentionHead(nn.Layer):
|
|||
self.structure_attention_cell = AttentionGRUCell(
|
||||
self.input_size, hidden_size, self.out_channels, use_gru=False)
|
||||
self.structure_generator = nn.Linear(hidden_size, self.out_channels)
|
||||
self.loc_type = loc_type
|
||||
self.in_max_len = in_max_len
|
||||
|
||||
if self.loc_type == 1:
|
||||
self.loc_generator = nn.Linear(hidden_size, 4)
|
||||
if self.in_max_len == 640:
|
||||
self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1)
|
||||
elif self.in_max_len == 800:
|
||||
self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1)
|
||||
else:
|
||||
if self.in_max_len == 640:
|
||||
self.loc_fea_trans = nn.Linear(400, self.max_text_length + 1)
|
||||
elif self.in_max_len == 800:
|
||||
self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1)
|
||||
else:
|
||||
self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1)
|
||||
self.loc_generator = nn.Linear(self.input_size + hidden_size,
|
||||
loc_reg_num)
|
||||
self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1)
|
||||
self.loc_generator = nn.Linear(self.input_size + hidden_size,
|
||||
loc_reg_num)
|
||||
|
||||
def _char_to_onehot(self, input_char, onehot_dim):
|
||||
input_ont_hot = F.one_hot(input_char, onehot_dim)
|
||||
|
|
@ -80,16 +76,13 @@ class TableAttentionHead(nn.Layer):
|
|||
# if and else branch are both needed when you want to assign a variable
|
||||
# if you modify the var in just one branch, then the modification will not work.
|
||||
fea = inputs[-1]
|
||||
if len(fea.shape) == 3:
|
||||
pass
|
||||
else:
|
||||
last_shape = int(np.prod(fea.shape[2:])) # gry added
|
||||
fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape])
|
||||
fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
|
||||
last_shape = int(np.prod(fea.shape[2:])) # gry added
|
||||
fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape])
|
||||
fea = fea.transpose([0, 2, 1]) # (NTC)(batch, width, channels)
|
||||
batch_size = fea.shape[0]
|
||||
|
||||
hidden = paddle.zeros((batch_size, self.hidden_size))
|
||||
output_hiddens = []
|
||||
output_hiddens = paddle.zeros((batch_size, self.max_text_length + 1, self.hidden_size))
|
||||
if self.training and targets is not None:
|
||||
structure = targets[0]
|
||||
for i in range(self.max_text_length + 1):
|
||||
|
|
@ -97,7 +90,8 @@ class TableAttentionHead(nn.Layer):
|
|||
structure[:, i], onehot_dim=self.out_channels)
|
||||
(outputs, hidden), alpha = self.structure_attention_cell(
|
||||
hidden, fea, elem_onehots)
|
||||
output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
|
||||
output_hiddens[:, i, :] = outputs
|
||||
# output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
|
||||
output = paddle.concat(output_hiddens, axis=1)
|
||||
structure_probs = self.structure_generator(output)
|
||||
if self.loc_type == 1:
|
||||
|
|
@ -118,30 +112,25 @@ class TableAttentionHead(nn.Layer):
|
|||
outputs = None
|
||||
alpha = None
|
||||
max_text_length = paddle.to_tensor(self.max_text_length)
|
||||
i = 0
|
||||
while i < max_text_length + 1:
|
||||
for i in range(max_text_length + 1):
|
||||
elem_onehots = self._char_to_onehot(
|
||||
temp_elem, onehot_dim=self.out_channels)
|
||||
(outputs, hidden), alpha = self.structure_attention_cell(
|
||||
hidden, fea, elem_onehots)
|
||||
output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
|
||||
output_hiddens[:, i, :] = outputs
|
||||
# output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
|
||||
structure_probs_step = self.structure_generator(outputs)
|
||||
temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
|
||||
i += 1
|
||||
|
||||
output = paddle.concat(output_hiddens, axis=1)
|
||||
output = output_hiddens
|
||||
structure_probs = self.structure_generator(output)
|
||||
structure_probs = F.softmax(structure_probs)
|
||||
if self.loc_type == 1:
|
||||
loc_preds = self.loc_generator(output)
|
||||
loc_preds = F.sigmoid(loc_preds)
|
||||
else:
|
||||
loc_fea = fea.transpose([0, 2, 1])
|
||||
loc_fea = self.loc_fea_trans(loc_fea)
|
||||
loc_fea = loc_fea.transpose([0, 2, 1])
|
||||
loc_concat = paddle.concat([output, loc_fea], axis=2)
|
||||
loc_preds = self.loc_generator(loc_concat)
|
||||
loc_preds = F.sigmoid(loc_preds)
|
||||
loc_fea = fea.transpose([0, 2, 1])
|
||||
loc_fea = self.loc_fea_trans(loc_fea)
|
||||
loc_fea = loc_fea.transpose([0, 2, 1])
|
||||
loc_concat = paddle.concat([output, loc_fea], axis=2)
|
||||
loc_preds = self.loc_generator(loc_concat)
|
||||
loc_preds = F.sigmoid(loc_preds)
|
||||
return {'structure_probs': structure_probs, 'loc_preds': loc_preds}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ python3 table/eval_table.py \
|
|||
--det_model_dir=path/to/det_model_dir \
|
||||
--rec_model_dir=path/to/rec_model_dir \
|
||||
--table_model_dir=path/to/table_model_dir \
|
||||
--image_dir=../doc/table/1.png \
|
||||
--image_dir=docs/table/table.jpg \
|
||||
--rec_char_dict_path=../ppocr/utils/dict/table_dict.txt \
|
||||
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
|
||||
--det_limit_side_len=736 \
|
||||
|
|
@ -145,6 +145,7 @@ python3 table/eval_table.py \
|
|||
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
|
||||
--det_limit_side_len=736 \
|
||||
--det_limit_type=min \
|
||||
--rec_image_shape=3,32,320 \
|
||||
--gt_path=path/to/gt.txt
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -118,7 +118,7 @@ python3 table/eval_table.py \
|
|||
--det_model_dir=path/to/det_model_dir \
|
||||
--rec_model_dir=path/to/rec_model_dir \
|
||||
--table_model_dir=path/to/table_model_dir \
|
||||
--image_dir=../doc/table/1.png \
|
||||
--image_dir=docs/table/table.jpg \
|
||||
--rec_char_dict_path=../ppocr/utils/dict/table_dict.txt \
|
||||
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
|
||||
--det_limit_side_len=736 \
|
||||
|
|
@ -149,6 +149,7 @@ python3 table/eval_table.py \
|
|||
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
|
||||
--det_limit_side_len=736 \
|
||||
--det_limit_type=min \
|
||||
--rec_image_shape=3,32,320 \
|
||||
--gt_path=path/to/gt.txt
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -15,3 +15,4 @@ premailer
|
|||
openpyxl
|
||||
attrdict
|
||||
Polygon3
|
||||
PyMuPDF==1.18.7
|
||||
|
|
|
|||
|
|
@ -0,0 +1,17 @@
|
|||
===========================paddle2onnx_params===========================
|
||||
model_name:en_table_structure
|
||||
python:python3.7
|
||||
2onnx: paddle2onnx
|
||||
--det_model_dir:./inference/en_ppocr_mobile_v2.0_table_structure_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--det_save_file:./inference/en_ppocr_mobile_v2.0_table_structure_infer/model.onnx
|
||||
--rec_model_dir:
|
||||
--rec_save_file:
|
||||
--opset_version:10
|
||||
--enable_onnx_checker:True
|
||||
inference:ppstructure/table/predict_structure.py --table_char_dict_path=./ppocr/utils/dict/table_structure_dict.txt
|
||||
--use_gpu:True|False
|
||||
--det_model_dir:
|
||||
--rec_model_dir:
|
||||
--image_dir:./ppstructure/docs/table/table.jpg
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:layoutxlm_ser
|
||||
python:python3.7
|
||||
gpu_list:192.168.0.1,192.168.0.2;0,1
|
||||
Global.use_gpu:True
|
||||
Global.auto_cast:fp32
|
||||
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=17
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=4|whole_train_whole_infer=8
|
||||
Architecture.Backbone.checkpoints:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:ppstructure/docs/kie/input/zh_val_42.jpg
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c test_tipc/configs/layoutxlm_ser/ser_layoutxlm_xfund_zh.yml -o
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Architecture.Backbone.checkpoints:
|
||||
norm_export:tools/export_model.py -c test_tipc/configs/layoutxlm_ser/ser_layoutxlm_xfund_zh.yml -o
|
||||
quant_export:
|
||||
fpgm_export:
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
##
|
||||
infer_model:null
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:ppstructure/kie/predict_kie_token_ser.py --kie_algorithm=LayoutXLM --ser_dict_path=train_data/XFUND/class_list_xfun.txt --output=output
|
||||
--use_gpu:False
|
||||
--enable_mkldnn:False
|
||||
--cpu_threads:6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False
|
||||
--precision:fp32
|
||||
--ser_model_dir:
|
||||
--image_dir:./ppstructure/docs/kie/input/zh_val_42.jpg
|
||||
null:null
|
||||
--benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:layoutxlm_ser
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
Global.use_gpu:True|True
|
||||
Global.auto_cast:amp
|
||||
Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=17
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=4|whole_train_whole_infer=8
|
||||
Architecture.Backbone.checkpoints:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:ppstructure/docs/kie/input/zh_val_42.jpg
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c test_tipc/configs/layoutxlm_ser/ser_layoutxlm_xfund_zh.yml -o Global.print_batch_step=1 Global.eval_batch_step=[1000,1000] Train.loader.shuffle=false
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Architecture.Backbone.checkpoints:
|
||||
norm_export:tools/export_model.py -c test_tipc/configs/layoutxlm_ser/ser_layoutxlm_xfund_zh.yml -o
|
||||
quant_export:
|
||||
fpgm_export:
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
##
|
||||
infer_model:null
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:ppstructure/kie/predict_kie_token_ser.py --kie_algorithm=LayoutXLM --ser_dict_path=train_data/XFUND/class_list_xfun.txt --output=output
|
||||
--use_gpu:True|False
|
||||
--enable_mkldnn:False
|
||||
--cpu_threads:6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False
|
||||
--precision:fp32
|
||||
--ser_model_dir:
|
||||
--image_dir:./ppstructure/docs/kie/input/zh_val_42.jpg
|
||||
null:null
|
||||
--benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
===========================paddle2onnx_params===========================
|
||||
model_name:slanet
|
||||
python:python3.7
|
||||
2onnx: paddle2onnx
|
||||
--det_model_dir:./inference/ch_ppstructure_mobile_v2.0_SLANet_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--det_save_file:./inference/ch_ppstructure_mobile_v2.0_SLANet_infer/model.onnx
|
||||
--rec_model_dir:
|
||||
--rec_save_file:
|
||||
--opset_version:10
|
||||
--enable_onnx_checker:True
|
||||
inference:ppstructure/table/predict_structure.py --table_char_dict_path=./ppocr/utils/dict/table_structure_dict_ch.txt
|
||||
--use_gpu:True|False
|
||||
--det_model_dir:
|
||||
--rec_model_dir:
|
||||
--image_dir:./ppstructure/docs/table/table.jpg
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:slanet
|
||||
python:python3.7
|
||||
gpu_list:192.168.0.1,192.168.0.2;0,1
|
||||
Global.use_gpu:True
|
||||
Global.auto_cast:fp32
|
||||
Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=50
|
||||
Global.save_model_dir:./output/
|
||||
Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128
|
||||
Global.pretrained_model:./pretrain_models/en_ppstructure_mobile_v2.0_SLANet_train/best_accuracy
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./ppstructure/docs/table/table.jpg
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c test_tipc/configs/slanet/SLANet.yml -o
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:null
|
||||
null:null
|
||||
##
|
||||
===========================infer_params===========================
|
||||
Global.save_inference_dir:./output/
|
||||
Global.checkpoints:
|
||||
norm_export:tools/export_model.py -c test_tipc/configs/slanet/SLANet.yml -o
|
||||
quant_export:
|
||||
fpgm_export:
|
||||
distill_export:null
|
||||
export1:null
|
||||
export2:null
|
||||
##
|
||||
infer_model:./inference/en_ppstructure_mobile_v2.0_SLANet_train
|
||||
infer_export:null
|
||||
infer_quant:False
|
||||
inference:ppstructure/table/predict_table.py --det_model_dir=./inference/en_ppocr_mobile_v2.0_table_det_infer --rec_model_dir=./inference/en_ppocr_mobile_v2.0_table_rec_infer --rec_char_dict_path=./ppocr/utils/dict/table_dict.txt --table_char_dict_path=./ppocr/utils/dict/table_structure_dict.txt --image_dir=./ppstructure/docs/table/table.jpg --det_limit_side_len=736 --det_limit_type=min --output ./output/table
|
||||
--use_gpu:False
|
||||
--enable_mkldnn:False
|
||||
--cpu_threads:6
|
||||
--rec_batch_num:1
|
||||
--use_tensorrt:False
|
||||
--precision:fp32
|
||||
--table_model_dir:
|
||||
--image_dir:./ppstructure/docs/table/table.jpg
|
||||
null:null
|
||||
--benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,488,488]}]
|
||||
|
|
@ -700,10 +700,18 @@ if [ ${MODE} = "cpp_infer" ];then
|
|||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar --no-check-certificate
|
||||
cd ./inference && tar xf ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../
|
||||
elif [[ ${model_name} =~ "en_table_structure" ]];then
|
||||
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar --no-check-certificate
|
||||
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar --no-check-certificate
|
||||
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar --no-check-certificate
|
||||
cd ./inference/ && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar && cd ../
|
||||
|
||||
cd ./inference/ && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar
|
||||
if [ ${model_name} == "en_table_structure" ]; then
|
||||
wget -nc https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar --no-check-certificate
|
||||
tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
|
||||
elif [ ${model_name} == "en_table_structure_PACT" ]; then
|
||||
wget -nc https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_slim_infer.tar --no-check-certificate
|
||||
tar xf en_ppocr_mobile_v2.0_table_structure_slim_infer.tar
|
||||
fi
|
||||
cd ../
|
||||
elif [[ ${model_name} =~ "slanet" ]];then
|
||||
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar --no-check-certificate
|
||||
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar --no-check-certificate
|
||||
|
|
@ -791,6 +799,12 @@ if [ ${MODE} = "paddle2onnx_infer" ];then
|
|||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar --no-check-certificate
|
||||
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar --no-check-certificate
|
||||
cd ./inference && tar xf ch_PP-OCRv3_det_infer.tar && tar xf ch_PP-OCRv3_rec_infer.tar && cd ../
|
||||
elif [[ ${model_name} =~ "slanet" ]];then
|
||||
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/ppstructure/models/slanet/ch_ppstructure_mobile_v2.0_SLANet_infer.tar --no-check-certificate
|
||||
cd ./inference/ && tar xf ch_ppstructure_mobile_v2.0_SLANet_infer.tar && cd ../
|
||||
elif [[ ${model_name} =~ "en_table_structure" ]];then
|
||||
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar --no-check-certificate
|
||||
cd ./inference/ && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar && cd ../
|
||||
fi
|
||||
|
||||
# wget data
|
||||
|
|
|
|||
|
|
@ -105,6 +105,19 @@ function func_paddle2onnx(){
|
|||
eval $trans_model_cmd
|
||||
last_status=${PIPESTATUS[0]}
|
||||
status_check $last_status "${trans_model_cmd}" "${status_log}" "${model_name}" "${trans_rec_log}"
|
||||
elif [ ${model_name} = "slanet" ] || [ ${model_name} = "en_table_structure" ]; then
|
||||
# trans det
|
||||
set_dirname=$(func_set_params "--model_dir" "${det_infer_model_dir_value}")
|
||||
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
|
||||
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
|
||||
set_save_model=$(func_set_params "--save_file" "${det_save_file_value}")
|
||||
set_opset_version=$(func_set_params "${opset_version_key}" "${opset_version_value}")
|
||||
set_enable_onnx_checker=$(func_set_params "${enable_onnx_checker_key}" "${enable_onnx_checker_value}")
|
||||
trans_det_log="${LOG_PATH}/trans_model_det.log"
|
||||
trans_model_cmd="${padlle2onnx_cmd} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_save_model} ${set_opset_version} ${set_enable_onnx_checker} --enable_dev_version=True > ${trans_det_log} 2>&1 "
|
||||
eval $trans_model_cmd
|
||||
last_status=${PIPESTATUS[0]}
|
||||
status_check $last_status "${trans_model_cmd}" "${status_log}" "${model_name}" "${trans_det_log}"
|
||||
fi
|
||||
|
||||
# python inference
|
||||
|
|
@ -117,7 +130,7 @@ function func_paddle2onnx(){
|
|||
set_det_model_dir=$(func_set_params "${det_model_key}" "${det_save_file_value}")
|
||||
set_rec_model_dir=$(func_set_params "${rec_model_key}" "${rec_save_file_value}")
|
||||
infer_model_cmd="${python} ${inference_py} ${set_gpu} ${set_img_dir} ${set_det_model_dir} ${set_rec_model_dir} --use_onnx=True > ${_save_log_path} 2>&1 "
|
||||
elif [[ ${model_name} =~ "det" ]]; then
|
||||
elif [[ ${model_name} =~ "det" ]] || [ ${model_name} = "slanet" ] || [ ${model_name} = "en_table_structure" ]; then
|
||||
set_det_model_dir=$(func_set_params "${det_model_key}" "${det_save_file_value}")
|
||||
infer_model_cmd="${python} ${inference_py} ${set_gpu} ${set_img_dir} ${set_det_model_dir} --use_onnx=True > ${_save_log_path} 2>&1 "
|
||||
elif [[ ${model_name} =~ "rec" ]]; then
|
||||
|
|
@ -136,7 +149,7 @@ function func_paddle2onnx(){
|
|||
set_det_model_dir=$(func_set_params "${det_model_key}" "${det_save_file_value}")
|
||||
set_rec_model_dir=$(func_set_params "${rec_model_key}" "${rec_save_file_value}")
|
||||
infer_model_cmd="${python} ${inference_py} ${set_gpu} ${set_img_dir} ${set_det_model_dir} ${set_rec_model_dir} --use_onnx=True > ${_save_log_path} 2>&1 "
|
||||
elif [[ ${model_name} =~ "det" ]]; then
|
||||
elif [[ ${model_name} =~ "det" ]]|| [ ${model_name} = "slanet" ] || [ ${model_name} = "en_table_structure" ]; then
|
||||
set_det_model_dir=$(func_set_params "${det_model_key}" "${det_save_file_value}")
|
||||
infer_model_cmd="${python} ${inference_py} ${set_gpu} ${set_img_dir} ${set_det_model_dir} --use_onnx=True > ${_save_log_path} 2>&1 "
|
||||
elif [[ ${model_name} =~ "rec" ]]; then
|
||||
|
|
|
|||
|
|
@ -282,44 +282,67 @@ if __name__ == "__main__":
|
|||
args = utility.parse_args()
|
||||
image_file_list = get_image_file_list(args.image_dir)
|
||||
text_detector = TextDetector(args)
|
||||
count = 0
|
||||
total_time = 0
|
||||
draw_img_save = "./inference_results"
|
||||
draw_img_save_dir = args.draw_img_save_dir
|
||||
os.makedirs(draw_img_save_dir, exist_ok=True)
|
||||
|
||||
if args.warmup:
|
||||
img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
|
||||
for i in range(2):
|
||||
res = text_detector(img)
|
||||
|
||||
if not os.path.exists(draw_img_save):
|
||||
os.makedirs(draw_img_save)
|
||||
save_results = []
|
||||
for image_file in image_file_list:
|
||||
img, flag, _ = check_and_read(image_file)
|
||||
if not flag:
|
||||
for idx, image_file in enumerate(image_file_list):
|
||||
img, flag_gif, flag_pdf = check_and_read(image_file)
|
||||
if not flag_gif and not flag_pdf:
|
||||
img = cv2.imread(image_file)
|
||||
if img is None:
|
||||
logger.info("error in loading image:{}".format(image_file))
|
||||
continue
|
||||
st = time.time()
|
||||
dt_boxes, _ = text_detector(img)
|
||||
elapse = time.time() - st
|
||||
if count > 0:
|
||||
if not flag_pdf:
|
||||
if img is None:
|
||||
logger.debug("error in loading image:{}".format(image_file))
|
||||
continue
|
||||
imgs = [img]
|
||||
else:
|
||||
page_num = args.page_num
|
||||
if page_num > len(img) or page_num == 0:
|
||||
page_num = len(img)
|
||||
imgs = img[:page_num]
|
||||
for index, img in enumerate(imgs):
|
||||
st = time.time()
|
||||
dt_boxes, _ = text_detector(img)
|
||||
elapse = time.time() - st
|
||||
total_time += elapse
|
||||
count += 1
|
||||
save_pred = os.path.basename(image_file) + "\t" + str(
|
||||
json.dumps([x.tolist() for x in dt_boxes])) + "\n"
|
||||
save_results.append(save_pred)
|
||||
logger.info(save_pred)
|
||||
logger.info("The predict time of {}: {}".format(image_file, elapse))
|
||||
src_im = utility.draw_text_det_res(dt_boxes, image_file)
|
||||
img_name_pure = os.path.split(image_file)[-1]
|
||||
img_path = os.path.join(draw_img_save,
|
||||
"det_res_{}".format(img_name_pure))
|
||||
cv2.imwrite(img_path, src_im)
|
||||
logger.info("The visualized image saved in {}".format(img_path))
|
||||
if len(imgs) > 1:
|
||||
save_pred = os.path.basename(image_file) + '_' + str(
|
||||
index) + "\t" + str(
|
||||
json.dumps([x.tolist() for x in dt_boxes])) + "\n"
|
||||
else:
|
||||
save_pred = os.path.basename(image_file) + "\t" + str(
|
||||
json.dumps([x.tolist() for x in dt_boxes])) + "\n"
|
||||
save_results.append(save_pred)
|
||||
logger.info(save_pred)
|
||||
if len(imgs) > 1:
|
||||
logger.info("{}_{} The predict time of {}: {}".format(
|
||||
idx, index, image_file, elapse))
|
||||
else:
|
||||
logger.info("{} The predict time of {}: {}".format(
|
||||
idx, image_file, elapse))
|
||||
|
||||
with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f:
|
||||
src_im = utility.draw_text_det_res(dt_boxes, img)
|
||||
|
||||
if flag_gif:
|
||||
save_file = image_file[:-3] + "png"
|
||||
elif flag_pdf:
|
||||
save_file = image_file.replace('.pdf',
|
||||
'_' + str(index) + '.png')
|
||||
else:
|
||||
save_file = image_file
|
||||
img_path = os.path.join(
|
||||
draw_img_save_dir,
|
||||
"det_res_{}".format(os.path.basename(save_file)))
|
||||
cv2.imwrite(img_path, src_im)
|
||||
logger.info("The visualized image saved in {}".format(img_path))
|
||||
|
||||
with open(os.path.join(draw_img_save_dir, "det_results.txt"), 'w') as f:
|
||||
f.writelines(save_results)
|
||||
f.close()
|
||||
if args.benchmark:
|
||||
|
|
|
|||
|
|
@ -159,50 +159,75 @@ def main(args):
|
|||
count = 0
|
||||
for idx, image_file in enumerate(image_file_list):
|
||||
|
||||
img, flag, _ = check_and_read(image_file)
|
||||
if not flag:
|
||||
img, flag_gif, flag_pdf = check_and_read(image_file)
|
||||
if not flag_gif and not flag_pdf:
|
||||
img = cv2.imread(image_file)
|
||||
if img is None:
|
||||
logger.debug("error in loading image:{}".format(image_file))
|
||||
continue
|
||||
starttime = time.time()
|
||||
dt_boxes, rec_res, time_dict = text_sys(img)
|
||||
elapse = time.time() - starttime
|
||||
total_time += elapse
|
||||
if not flag_pdf:
|
||||
if img is None:
|
||||
logger.debug("error in loading image:{}".format(image_file))
|
||||
continue
|
||||
imgs = [img]
|
||||
else:
|
||||
page_num = args.page_num
|
||||
if page_num > len(img) or page_num == 0:
|
||||
page_num = len(img)
|
||||
imgs = img[:page_num]
|
||||
for index, img in enumerate(imgs):
|
||||
starttime = time.time()
|
||||
dt_boxes, rec_res, time_dict = text_sys(img)
|
||||
elapse = time.time() - starttime
|
||||
total_time += elapse
|
||||
if len(imgs) > 1:
|
||||
logger.debug(
|
||||
str(idx) + '_' + str(index) + " Predict time of %s: %.3fs"
|
||||
% (image_file, elapse))
|
||||
else:
|
||||
logger.debug(
|
||||
str(idx) + " Predict time of %s: %.3fs" % (image_file,
|
||||
elapse))
|
||||
for text, score in rec_res:
|
||||
logger.debug("{}, {:.3f}".format(text, score))
|
||||
|
||||
logger.debug(
|
||||
str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse))
|
||||
for text, score in rec_res:
|
||||
logger.debug("{}, {:.3f}".format(text, score))
|
||||
res = [{
|
||||
"transcription": rec_res[i][0],
|
||||
"points": np.array(dt_boxes[i]).astype(np.int32).tolist(),
|
||||
} for i in range(len(dt_boxes))]
|
||||
if len(imgs) > 1:
|
||||
save_pred = os.path.basename(image_file) + '_' + str(
|
||||
index) + "\t" + json.dumps(
|
||||
res, ensure_ascii=False) + "\n"
|
||||
else:
|
||||
save_pred = os.path.basename(image_file) + "\t" + json.dumps(
|
||||
res, ensure_ascii=False) + "\n"
|
||||
save_results.append(save_pred)
|
||||
|
||||
res = [{
|
||||
"transcription": rec_res[idx][0],
|
||||
"points": np.array(dt_boxes[idx]).astype(np.int32).tolist(),
|
||||
} for idx in range(len(dt_boxes))]
|
||||
save_pred = os.path.basename(image_file) + "\t" + json.dumps(
|
||||
res, ensure_ascii=False) + "\n"
|
||||
save_results.append(save_pred)
|
||||
if is_visualize:
|
||||
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
|
||||
boxes = dt_boxes
|
||||
txts = [rec_res[i][0] for i in range(len(rec_res))]
|
||||
scores = [rec_res[i][1] for i in range(len(rec_res))]
|
||||
|
||||
if is_visualize:
|
||||
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
|
||||
boxes = dt_boxes
|
||||
txts = [rec_res[i][0] for i in range(len(rec_res))]
|
||||
scores = [rec_res[i][1] for i in range(len(rec_res))]
|
||||
|
||||
draw_img = draw_ocr_box_txt(
|
||||
image,
|
||||
boxes,
|
||||
txts,
|
||||
scores,
|
||||
drop_score=drop_score,
|
||||
font_path=font_path)
|
||||
if flag:
|
||||
image_file = image_file[:-3] + "png"
|
||||
cv2.imwrite(
|
||||
os.path.join(draw_img_save_dir, os.path.basename(image_file)),
|
||||
draw_img[:, :, ::-1])
|
||||
logger.debug("The visualized image saved in {}".format(
|
||||
os.path.join(draw_img_save_dir, os.path.basename(image_file))))
|
||||
draw_img = draw_ocr_box_txt(
|
||||
image,
|
||||
boxes,
|
||||
txts,
|
||||
scores,
|
||||
drop_score=drop_score,
|
||||
font_path=font_path)
|
||||
if flag_gif:
|
||||
save_file = image_file[:-3] + "png"
|
||||
elif flag_pdf:
|
||||
save_file = image_file.replace('.pdf',
|
||||
'_' + str(index) + '.png')
|
||||
else:
|
||||
save_file = image_file
|
||||
cv2.imwrite(
|
||||
os.path.join(draw_img_save_dir,
|
||||
os.path.basename(save_file)),
|
||||
draw_img[:, :, ::-1])
|
||||
logger.debug("The visualized image saved in {}".format(
|
||||
os.path.join(draw_img_save_dir, os.path.basename(
|
||||
save_file))))
|
||||
|
||||
logger.info("The predict total time is {}".format(time.time() - _st))
|
||||
if args.benchmark:
|
||||
|
|
|
|||
|
|
@ -45,6 +45,7 @@ def init_args():
|
|||
|
||||
# params for text detector
|
||||
parser.add_argument("--image_dir", type=str)
|
||||
parser.add_argument("--page_num", type=int, default=0)
|
||||
parser.add_argument("--det_algorithm", type=str, default='DB')
|
||||
parser.add_argument("--det_model_dir", type=str)
|
||||
parser.add_argument("--det_limit_side_len", type=float, default=960)
|
||||
|
|
@ -337,12 +338,11 @@ def draw_e2e_res(dt_boxes, strs, img_path):
|
|||
return src_im
|
||||
|
||||
|
||||
def draw_text_det_res(dt_boxes, img_path):
|
||||
src_im = cv2.imread(img_path)
|
||||
def draw_text_det_res(dt_boxes, img):
|
||||
for box in dt_boxes:
|
||||
box = np.array(box).astype(np.int32).reshape(-1, 2)
|
||||
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
|
||||
return src_im
|
||||
cv2.polylines(img, [box], True, color=(255, 255, 0), thickness=2)
|
||||
return img
|
||||
|
||||
|
||||
def resize_img(img, input_size=600):
|
||||
|
|
|
|||
Loading…
Reference in New Issue