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ppstructure/docs/installation.md 0 → 100644
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# 快速安装
## 1. PaddlePaddle 和 PaddleOCR
可参考[PaddleOCR安装文档](../../doc/doc_ch/installation.md)
## 2. 安装其他依赖
### 2.1 版面分析所需 Layout-Parser
Layout-Parser 可通过如下命令安装
```bash
pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
```
### 2.2 VQA所需依赖
* paddleocr
```bash
pip3 install paddleocr
```
* PaddleNLP
```bash
git clone https://github.com/PaddlePaddle/PaddleNLP -b develop
cd PaddleNLP
pip3 install -e .
```
ppstructure/docs/model_list.md 0 → 100644
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# Model List
## 1. LayoutParser 模型
|模型名称|模型简介|下载地址|
| --- | --- | --- |
| ppyolov2_r50vd_dcn_365e_publaynet | PubLayNet 数据集训练的版面分析模型，可以划分**文字、标题、表格、图片以及列表**5类区域 | [PubLayNet](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) |
| ppyolov2_r50vd_dcn_365e_tableBank_word | TableBank Word 数据集训练的版面分析模型，只能检测表格 | [TableBank Word](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_word.tar) |
| ppyolov2_r50vd_dcn_365e_tableBank_latex | TableBank Latex 数据集训练的版面分析模型，只能检测表格 | [TableBank Latex](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) |
## 2. OCR和表格识别模型
|模型名称|模型简介|推理模型大小|下载地址|
| --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
|en_ppocr_mobile_v2.0_table_det|PubLayNet数据集训练的英文表格场景的文字检测|4.7M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_det_train.tar) |
|en_ppocr_mobile_v2.0_table_rec|PubLayNet数据集训练的英文表格场景的文字识别|6.9M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_rec_train.tar) |
|en_ppocr_mobile_v2.0_table_structure|PubLayNet数据集训练的英文表格场景的表格结构预测|18.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/table/en_ppocr_mobile_v2.0_table_structure_train.tar) |
如需要使用其他OCR模型，可以在 [model_list](../../doc/doc_ch/models_list.md) 下载模型或者使用自己训练好的模型配置到`det_model_dir`,`rec_model_dir`两个字段即可。
## 3. VQA模型
|模型名称|模型简介|推理模型大小|下载地址|
| --- | --- | --- | --- |
|PP-Layout_v1.0_ser_pretrained|基于LayoutXLM在xfun中文数据集上训练的SER模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar) |
|PP-Layout_v1.0_re_pretrained|基于LayoutXLM在xfun中文数据集上训练的RE模型|1.4G|[推理模型 coming soon]() / [训练模型](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar) |
ppstructure/docs/quickstart.md 0 → 100644
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# PP-Structure 快速开始
* [1. 安装PaddleOCR whl包](#1)
* [2. 便捷使用](#2)
+ [2.1 命令行使用](#21)
+ [2.2 Python脚本使用](#22)
+ [2.3 返回结果说明](#23)
+ [2.4 参数说明](#24)
* [3. Python脚本使用](#3)
<a name="1"></a>
## 1. 安装依赖包
```bash
pip install "paddleocr>=2.3.0.2" # 推荐使用2.3.0.2+版本
pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
# 安装 PaddleNLP
git clone https://github.com/PaddlePaddle/PaddleNLP -b develop
cd PaddleNLP
pip3 install -e .
```
<a name="2"></a>
## 2. 便捷使用
<a name="21"></a>
### 2.1 命令行使用
* 版面分析+表格识别
```bash
paddleocr --image_dir=../doc/table/1.png --type=structure
```
* VQA
coming soon
<a name="22"></a>
### 2.2 Python脚本使用
* 版面分析+表格识别
```python
import os
import cv2
from paddleocr import PPStructure,draw_structure_result,save_structure_res
table_engine = PPStructure(show_log=True)
save_folder = './output/table'
img_path = '../doc/table/1.png'
img = cv2.imread(img_path)
result = table_engine(img)
save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
for line in result:
line.pop('img')
print(line)
from PIL import Image
font_path = '../doc/fonts/simfang.ttf' # PaddleOCR下提供字体包
image = Image.open(img_path).convert('RGB')
im_show = draw_structure_result(image, result,font_path=font_path)
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
* VQA
comming soon
<a name="23"></a>
### 2.3 返回结果说明
PP-Structure的返回结果为一个dict组成的list，示例如下
* 版面分析+表格识别
```shell
[
{ 'type': 'Text',
'bbox': [34, 432, 345, 462],
'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
[('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent ', 0.465441)])
}
]
```
dict 里各个字段说明如下
| 字段 | 说明 |
| --------------- | -------------|
|type|图片区域的类型|
|bbox|图片区域的在原图的坐标，分别[左上角x，左上角y，右下角x，右下角y]|
|res|图片区域的OCR或表格识别结果。<br> 表格: 表格的HTML字符串; <br> OCR: 一个包含各个单行文字的检测坐标和识别结果的元组|
* VQA
comming soon
<a name="24"></a>
### 2.4 参数说明
| 字段 | 说明 | 默认值 |
| --------------- | ---------------------------------------- | ------------------------------------------- |
| output | excel和识别结果保存的地址 | ./output/table |
| table_max_len | 表格结构模型预测时，图像的长边resize尺度 | 488 |
| table_model_dir | 表格结构模型 inference 模型地址 | None |
| table_char_type | 表格结构模型所用字典地址 | ../ppocr/utils/dict/table_structure_dict.txt |
| model_name_or_path | VQA SER模型地址 | None |
| max_seq_length | VQA SER模型最大支持token长度 | 512 |
| label_map_path | VQA SER 标签文件地址 | ./vqa/labels/labels_ser.txt |
| mode | pipeline预测模式，structure: 版面分析+表格识别; vqa: ser文档信息抽取 | structure |
大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
运行完成后，每张图片会在`output`字段指定的目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
<a name="3"></a>
## 3. Python脚本使用
* 版面分析+表格识别
```bash
cd ppstructure
# 下载模型
mkdir inference && cd inference
# 下载超轻量级中文OCR模型的检测模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
# 下载超轻量级中文OCR模型的识别模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
# 下载超轻量级英文表格英寸模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd ..
python3 predict_system.py --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \
--rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \
--table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer \
--image_dir=../doc/table/1.png \
--rec_char_dict_path=../ppocr/utils/ppocr_keys_v1.txt \
--table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt \
--output=../output/table \
--vis_font_path=../doc/fonts/simfang.ttf
```
运行完成后，每张图片会在`output`字段指定的目录下的`talbe`目录下有一个同名目录，图片里的每个表格会存储为一个excel，图片区域会被裁剪之后保存下来，excel文件和图片名名为表格在图片里的坐标。
* VQA
```bash
cd ppstructure
# 下载模型
mkdir inference && cd inference
# 下载SER xfun 模型并解压
wget https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar && tar xf PP-Layout_v1.0_ser_pretrained.tar
cd ..
python3 predict_system.py --model_name_or_path=vqa/PP-Layout_v1.0_ser_pretrained/ \
--mode=vqa \
--image_dir=vqa/images/input/zh_val_0.jpg \
--vis_font_path=../doc/fonts/simfang.ttf
```
运行完成后，每张图片会在`output`字段指定的目录下的`vqa`目录下存放可视化之后的图片，图片名和输入图片名一致。
ppstructure/predict_system.py
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...@@ -30,6 +30,7 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif ...@@ -30,6 +30,7 @@ from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.utils.logging import get_logger from ppocr.utils.logging import get_logger
from tools.infer.predict_system import TextSystem from tools.infer.predict_system import TextSystem
from ppstructure.table.predict_table import TableSystem, to_excel from ppstructure.table.predict_table import TableSystem, to_excel
from ppstructure.vqa.infer_ser_e2e import SerPredictor, draw_ser_results
from ppstructure.utility import parse_args, draw_structure_result from ppstructure.utility import parse_args, draw_structure_result
logger = get_logger() logger = get_logger()
...@@ -37,53 +38,75 @@ logger = get_logger() ...@@ -37,53 +38,75 @@ logger = get_logger()
class OCRSystem(object): class OCRSystem(object):
def __init__(self, args): def __init__(self, args):
import layoutparser as lp self.mode = args.mode
# args.det_limit_type = 'resize_long' if self.mode == 'structure':
args.drop_score = 0 import layoutparser as lp
if not args.show_log: # args.det_limit_type = 'resize_long'
logger.setLevel(logging.INFO) args.drop_score = 0
self.text_system = TextSystem(args) if not args.show_log:
self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer) logger.setLevel(logging.INFO)
self.text_system = TextSystem(args)
config_path = None self.table_system = TableSystem(args,
model_path = None self.text_system.text_detector,
if os.path.isdir(args.layout_path_model): self.text_system.text_recognizer)
model_path = args.layout_path_model
else: config_path = None
config_path = args.layout_path_model model_path = None
self.table_layout = lp.PaddleDetectionLayoutModel(config_path=config_path, if os.path.isdir(args.layout_path_model):
model_path=model_path, model_path = args.layout_path_model
threshold=0.5, enable_mkldnn=args.enable_mkldnn, else:
enforce_cpu=not args.use_gpu, thread_num=args.cpu_threads) config_path = args.layout_path_model
self.use_angle_cls = args.use_angle_cls self.table_layout = lp.PaddleDetectionLayoutModel(
self.drop_score = args.drop_score config_path=config_path,
model_path=model_path,
threshold=0.5,
enable_mkldnn=args.enable_mkldnn,
enforce_cpu=not args.use_gpu,
thread_num=args.cpu_threads)
self.use_angle_cls = args.use_angle_cls
self.drop_score = args.drop_score
elif self.mode == 'vqa':
self.vqa_engine = SerPredictor(args)
def __call__(self, img): def __call__(self, img):
ori_im = img.copy() if self.mode == 'structure':
layout_res = self.table_layout.detect(img[..., ::-1]) ori_im = img.copy()
res_list = [] layout_res = self.table_layout.detect(img[..., ::-1])
for region in layout_res: res_list = []
x1, y1, x2, y2 = region.coordinates for region in layout_res:
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) x1, y1, x2, y2 = region.coordinates
roi_img = ori_im[y1:y2, x1:x2, :] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
if region.type == 'Table': roi_img = ori_im[y1:y2, x1:x2, :]
res = self.table_system(roi_img) if region.type == 'Table':
else: res = self.table_system(roi_img)
filter_boxes, filter_rec_res = self.text_system(roi_img) else:
filter_boxes = [x + [x1, y1] for x in filter_boxes] filter_boxes, filter_rec_res = self.text_system(roi_img)
filter_boxes = [x.reshape(-1).tolist() for x in filter_boxes] filter_boxes = [x + [x1, y1] for x in filter_boxes]
# remove style char filter_boxes = [
style_token = ['<strike>', '<strike>', '<sup>', '</sub>', '<b>', '</b>', '<sub>', '</sup>', x.reshape(-1).tolist() for x in filter_boxes
'<overline>', '</overline>', '<underline>', '</underline>', '<i>', '</i>'] ]
filter_rec_res_tmp = [] # remove style char
for rec_res in filter_rec_res: style_token = [
rec_str, rec_conf = rec_res '<strike>', '<strike>', '<sup>', '</sub>', '<b>',
for token in style_token: '</b>', '<sub>', '</sup>', '<overline>', '</overline>',
if token in rec_str: '<underline>', '</underline>', '<i>', '</i>'
rec_str = rec_str.replace(token, '') ]
filter_rec_res_tmp.append((rec_str, rec_conf)) filter_rec_res_tmp = []
res = (filter_boxes, filter_rec_res_tmp) for rec_res in filter_rec_res:
res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'img': roi_img, 'res': res}) rec_str, rec_conf = rec_res
for token in style_token:
if token in rec_str:
rec_str = rec_str.replace(token, '')
filter_rec_res_tmp.append((rec_str, rec_conf))
res = (filter_boxes, filter_rec_res_tmp)
res_list.append({
'type': region.type,
'bbox': [x1, y1, x2, y2],
'img': roi_img,
'res': res
})
elif self.mode == 'vqa':
res_list, _ = self.vqa_engine(img)
return res_list return res_list
...@@ -91,29 +114,35 @@ def save_structure_res(res, save_folder, img_name): ...@@ -91,29 +114,35 @@ def save_structure_res(res, save_folder, img_name):
excel_save_folder = os.path.join(save_folder, img_name) excel_save_folder = os.path.join(save_folder, img_name)
os.makedirs(excel_save_folder, exist_ok=True) os.makedirs(excel_save_folder, exist_ok=True)
# save res # save res
with open(os.path.join(excel_save_folder, 'res.txt'), 'w', encoding='utf8') as f: with open(
os.path.join(excel_save_folder, 'res.txt'), 'w',
encoding='utf8') as f:
for region in res: for region in res:
if region['type'] == 'Table': if region['type'] == 'Table':
excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox'])) excel_path = os.path.join(excel_save_folder,
'{}.xlsx'.format(region['bbox']))
to_excel(region['res'], excel_path) to_excel(region['res'], excel_path)
if region['type'] == 'Figure': if region['type'] == 'Figure':
roi_img = region['img'] roi_img = region['img']
img_path = os.path.join(excel_save_folder, '{}.jpg'.format(region['bbox'])) img_path = os.path.join(excel_save_folder,
'{}.jpg'.format(region['bbox']))
cv2.imwrite(img_path, roi_img) cv2.imwrite(img_path, roi_img)
else: else:
for box, rec_res in zip(region['res'][0], region['res'][1]): for box, rec_res in zip(region['res'][0], region['res'][1]):
f.write('{}\t{}\n'.format(np.array(box).reshape(-1).tolist(), rec_res)) f.write('{}\t{}\n'.format(
np.array(box).reshape(-1).tolist(), rec_res))
def main(args): def main(args):
image_file_list = get_image_file_list(args.image_dir) image_file_list = get_image_file_list(args.image_dir)
image_file_list = image_file_list image_file_list = image_file_list
image_file_list = image_file_list[args.process_id::args.total_process_num] image_file_list = image_file_list[args.process_id::args.total_process_num]
save_folder = args.output
os.makedirs(save_folder, exist_ok=True)
structure_sys = OCRSystem(args) structure_sys = OCRSystem(args)
img_num = len(image_file_list) img_num = len(image_file_list)
save_folder = os.path.join(args.output, structure_sys.mode)
os.makedirs(save_folder, exist_ok=True)
for i, image_file in enumerate(image_file_list): for i, image_file in enumerate(image_file_list):
logger.info("[{}/{}] {}".format(i, img_num, image_file)) logger.info("[{}/{}] {}".format(i, img_num, image_file))
img, flag = check_and_read_gif(image_file) img, flag = check_and_read_gif(image_file)
...@@ -126,10 +155,16 @@ def main(args): ...@@ -126,10 +155,16 @@ def main(args):
continue continue
starttime = time.time() starttime = time.time()
res = structure_sys(img) res = structure_sys(img)
save_structure_res(res, save_folder, img_name)
draw_img = draw_structure_result(img, res, args.vis_font_path) if structure_sys.mode == 'structure':
cv2.imwrite(os.path.join(save_folder, img_name, 'show.jpg'), draw_img) save_structure_res(res, save_folder, img_name)
logger.info('result save to {}'.format(os.path.join(save_folder, img_name))) draw_img = draw_structure_result(img, res, args.vis_font_path)
img_save_path = os.path.join(save_folder, img_name, 'show.jpg')
elif structure_sys.mode == 'vqa':
draw_img = draw_ser_results(img, res, args.vis_font_path)
img_save_path = os.path.join(save_folder, img_name + '.jpg')
cv2.imwrite(img_save_path, draw_img)
logger.info('result save to {}'.format(img_save_path))
elapse = time.time() - starttime elapse = time.time() - starttime
logger.info("Predict time : {:.3f}s".format(elapse)) logger.info("Predict time : {:.3f}s".format(elapse))
......
ppstructure/table/README.md
View file @ 5e4d1891
...@@ -20,9 +20,9 @@ We evaluated the algorithm on the PubTabNet<sup>[1]</sup> eval dataset, and the ...@@ -20,9 +20,9 @@ We evaluated the algorithm on the PubTabNet<sup>[1]</sup> eval dataset, and the
|Method|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)| |Method|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
| --- | --- | | --- | --- |
| EDD<sup>[2]</sup> | 88.3 | | EDD<sup>[2]</sup> | 88.3 |
| Ours | 93.32 | | Ours | 93.32 |
## 3. How to use ## 3. How to use
...@@ -41,7 +41,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab ...@@ -41,7 +41,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd .. cd ..
# run # run
python3 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 --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table python3 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 --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_dict_path=../ppocr/utils/dict/en_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
``` ```
Note: The above model is trained on the PubLayNet dataset and only supports English scanning scenarios. If you need to identify other scenarios, you need to train the model yourself and replace the three fields `det_model_dir`, `rec_model_dir`, `table_model_dir`. Note: The above model is trained on the PubLayNet dataset and only supports English scanning scenarios. If you need to identify other scenarios, you need to train the model yourself and replace the three fields `det_model_dir`, `rec_model_dir`, `table_model_dir`.
...@@ -82,8 +82,8 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo ...@@ -82,8 +82,8 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
The table uses [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows: The table uses [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) as the evaluation metric of the model. Before the model evaluation, the three models in the pipeline need to be exported as inference models (we have provided them), and the gt for evaluation needs to be prepared. Examples of gt are as follows:
```json ```json
{"PMC4289340_004_00.png": [ {"PMC4289340_004_00.png": [
["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"], ["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"],
[[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
[["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]] [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]
]} ]}
``` ```
...@@ -95,7 +95,7 @@ In gt json, the key is the image name, the value is the corresponding gt, and gt ...@@ -95,7 +95,7 @@ In gt json, the key is the image name, the value is the corresponding gt, and gt
Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output. Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output.
```python ```python
cd PaddleOCR/ppstructure cd PaddleOCR/ppstructure
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 --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json 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 --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 --det_limit_type=min --gt_path=path/to/gt.json
``` ```
If the PubLatNet eval dataset is used, it will be output If the PubLatNet eval dataset is used, it will be output
...@@ -113,4 +113,4 @@ After running, the excel sheet of each picture will be saved in the directory sp ...@@ -113,4 +113,4 @@ After running, the excel sheet of each picture will be saved in the directory sp
Reference Reference
1. https://github.com/ibm-aur-nlp/PubTabNet 1. https://github.com/ibm-aur-nlp/PubTabNet
2. https://arxiv.org/pdf/1911.10683 2. https://arxiv.org/pdf/1911.10683
\ No newline at end of file
ppstructure/table/README_ch.md
View file @ 5e4d1891
...@@ -34,9 +34,9 @@ ...@@ -34,9 +34,9 @@
|算法|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)| |算法|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
| --- | --- | | --- | --- |
| EDD<sup>[2]</sup> | 88.3 | | EDD<sup>[2]</sup> | 88.3 |
| Ours | 93.32 | | Ours | 93.32 |
<a name="3"></a> <a name="3"></a>
## 3. 使用 ## 3. 使用
...@@ -56,7 +56,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab ...@@ -56,7 +56,7 @@ wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_tab
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar && tar xf en_ppocr_mobile_v2.0_table_structure_infer.tar
cd .. cd ..
# 执行预测 # 执行预测
python3 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 --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table python3 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 --table_model_dir=inference/en_ppocr_mobile_v2.0_table_structure_infer --image_dir=../doc/table/table.jpg --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_dict_path=../ppocr/utils/dict/en_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table
``` ```
运行完成后，每张图片的excel表格会保存到output字段指定的目录下 运行完成后，每张图片的excel表格会保存到output字段指定的目录下
...@@ -94,8 +94,8 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo ...@@ -94,8 +94,8 @@ python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./yo
表格使用 [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下: 表格使用 [TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src) 作为模型的评估指标。在进行模型评估之前，需要将pipeline中的三个模型分别导出为inference模型(我们已经提供好)，还需要准备评估的gt， gt示例如下:
```json ```json
{"PMC4289340_004_00.png": [ {"PMC4289340_004_00.png": [
["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"], ["<html>", "<body>", "<table>", "<thead>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</thead>", "<tbody>", "<tr>", "<td>", "</td>", "<td>", "</td>", "<td>", "</td>", "</tr>", "</tbody>", "</table>", "</body>", "</html>"],
[[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]], [[1, 4, 29, 13], [137, 4, 161, 13], [215, 4, 236, 13], [1, 17, 30, 27], [137, 17, 147, 27], [215, 17, 225, 27]],
[["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]] [["<b>", "F", "e", "a", "t", "u", "r", "e", "</b>"], ["<b>", "G", "b", "3", " ", "+", "</b>"], ["<b>", "G", "b", "3", " ", "-", "</b>"], ["<b>", "P", "a", "t", "i", "e", "n", "t", "s", "</b>"], ["6", "2"], ["4", "5"]]
]} ]}
``` ```
...@@ -107,7 +107,7 @@ json 中，key为图片名，value为对应的gt，gt是一个由三个item组 ...@@ -107,7 +107,7 @@ json 中，key为图片名，value为对应的gt，gt是一个由三个item组
准备完成后使用如下命令进行评估，评估完成后会输出teds指标。 准备完成后使用如下命令进行评估，评估完成后会输出teds指标。
```python ```python
cd PaddleOCR/ppstructure cd PaddleOCR/ppstructure
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 --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json 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 --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 --det_limit_type=min --gt_path=path/to/gt.json
``` ```
如使用PubLatNet评估数据集，将会输出 如使用PubLatNet评估数据集，将会输出
```bash ```bash
...@@ -123,4 +123,4 @@ python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model ...@@ -123,4 +123,4 @@ python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model
Reference Reference
1. https://github.com/ibm-aur-nlp/PubTabNet 1. https://github.com/ibm-aur-nlp/PubTabNet
2. https://arxiv.org/pdf/1911.10683 2. https://arxiv.org/pdf/1911.10683
\ No newline at end of file
ppstructure/utility.py
View file @ 5e4d1891
...@@ -21,13 +21,31 @@ def init_args(): ...@@ -21,13 +21,31 @@ def init_args():
parser = infer_args() parser = infer_args()
# params for output # params for output
parser.add_argument("--output", type=str, default='./output/table') parser.add_argument("--output", type=str, default='./output')
# params for table structure # params for table structure
parser.add_argument("--table_max_len", type=int, default=488) parser.add_argument("--table_max_len", type=int, default=488)
parser.add_argument("--table_model_dir", type=str) parser.add_argument("--table_model_dir", type=str)
parser.add_argument("--table_char_type", type=str, default='en') parser.add_argument("--table_char_type", type=str, default='en')
parser.add_argument("--table_char_dict_path", type=str, default="../ppocr/utils/dict/table_structure_dict.txt") parser.add_argument(
parser.add_argument("--layout_path_model", type=str, default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config") "--table_char_dict_path",
type=str,
default="../ppocr/utils/dict/table_structure_dict.txt")
parser.add_argument(
"--layout_path_model",
type=str,
default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
# params for ser
parser.add_argument("--model_name_or_path", type=str)
parser.add_argument("--max_seq_length", type=int, default=512)
parser.add_argument(
"--label_map_path", type=str, default='./vqa/labels/labels_ser.txt')
parser.add_argument(
"--mode",
type=str,
default='structure',
help='structure and vqa is supported')
return parser return parser
...@@ -48,5 +66,6 @@ def draw_structure_result(image, result, font_path): ...@@ -48,5 +66,6 @@ def draw_structure_result(image, result, font_path):
boxes.append(np.array(box).reshape(-1, 2)) boxes.append(np.array(box).reshape(-1, 2))
txts.append(rec_res[0]) txts.append(rec_res[0])
scores.append(rec_res[1]) scores.append(rec_res[1])
im_show = draw_ocr_box_txt(image, boxes, txts, scores, font_path=font_path,drop_score=0) im_show = draw_ocr_box_txt(
return im_show image, boxes, txts, scores, font_path=font_path, drop_score=0)
\ No newline at end of file return im_show
ppstructure/vqa/README.md 0 → 100644
View file @ 5e4d1891
# 文档视觉问答（DOC-VQA）
VQA指视觉问答，主要针对图像内容进行提问和回答,DOC-VQA是VQA任务中的一种，DOC-VQA主要针对文本图像的文字内容提出问题。
PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进行开发。
主要特性如下：
- 集成[LayoutXLM](https://arxiv.org/pdf/2104.08836.pdf)模型以及PP-OCR预测引擎。
- 支持基于多模态方法的语义实体识别 (Semantic Entity Recognition, SER) 以及关系抽取 (Relation Extraction, RE) 任务。基于 SER 任务，可以完成对图像中的文本识别与分类；基于 RE 任务，可以完成对图象中的文本内容的关系提取，如判断问题对(pair)。
- 支持SER任务和RE任务的自定义训练。
- 支持OCR+SER的端到端系统预测与评估。
- 支持OCR+SER+RE的端到端系统预测。
本项目是 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/pdf/2104.08836.pdf) 在 Paddle 2.2上的开源实现，
包含了在 [XFUND数据集](https://github.com/doc-analysis/XFUND) 上的微调代码。
## 1 性能
我们在 [XFUN](https://github.com/doc-analysis/XFUND) 评估数据集上对算法进行了评估，性能如下
|任务| f1 | 模型下载地址|
|:---:|:---:| :---:|
|SER|0.9056| [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_ser_pretrained.tar)|
|RE|0.7113| [链接](https://paddleocr.bj.bcebos.com/pplayout/PP-Layout_v1.0_re_pretrained.tar)|
## 2. 效果演示
**注意：** 测试图片来源于XFUN数据集。
### 2.1 SER
![](./images/result_ser/zh_val_0_ser.jpg) | ![](./images/result_ser/zh_val_42_ser.jpg)
---|---
图中不同颜色的框表示不同的类别，对于XFUN数据集，有`QUESTION`, `ANSWER`, `HEADER` 3种类别
* 深紫色：HEADER
* 浅紫色：QUESTION
* 军绿色：ANSWER
在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
### 2.2 RE
![](./images/result_re/zh_val_21_re.jpg) | ![](./images/result_re/zh_val_40_re.jpg)
---|---
图中红色框表示问题，蓝色框表示答案，问题和答案之间使用绿色线连接。在OCR检测框的左上方也标出了对应的类别和OCR识别结果。
## 3. 安装
### 3.1 安装依赖
- **（1) 安装PaddlePaddle**
```bash
pip3 install --upgrade pip
# GPU安装
python3 -m pip install paddlepaddle-gpu==2.2 -i https://mirror.baidu.com/pypi/simple
# CPU安装
python3 -m pip install paddlepaddle==2.2 -i https://mirror.baidu.com/pypi/simple
```
更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
### 3.2 安装PaddleOCR（包含 PP-OCR 和 VQA ）
- **（1）pip快速安装PaddleOCR whl包（仅预测）**
```bash
pip install paddleocr
```
- **（2）下载VQA源码（预测+训练）**
```bash
【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
# 如果因为网络问题无法pull成功，也可选择使用码云上的托管：
git clone https://gitee.com/paddlepaddle/PaddleOCR
# 注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
```
- **（3）安装PaddleNLP**
```bash
# 需要使用PaddleNLP最新的代码版本进行安装
git clone https://github.com/PaddlePaddle/PaddleNLP -b develop
cd PaddleNLP
pip install -e .
```
- **（4）安装VQA的`requirements`**
```bash
cd ppstructure/vqa
pip install -r requirements.txt
```
## 4. 使用
### 4.1 数据和预训练模型准备
处理好的XFUN中文数据集下载地址：[https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。
下载并解压该数据集，解压后将数据集放置在当前目录下。
```shell
wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar
```
如果希望转换XFUN中其他语言的数据集，可以参考[XFUN数据转换脚本](helper/trans_xfun_data.py)。
如果希望直接体验预测过程，可以下载我们提供的预训练模型，跳过训练过程，直接预测即可。
### 4.2 SER任务
* 启动训练
```shell
python3.7 train_ser.py \
--model_name_or_path "layoutxlm-base-uncased" \
--train_data_dir "XFUND/zh_train/image" \
--train_label_path "XFUND/zh_train/xfun_normalize_train.json" \
--eval_data_dir "XFUND/zh_val/image" \
--eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
--num_train_epochs 200 \
--eval_steps 10 \
--save_steps 500 \
--output_dir "./output/ser/" \
--learning_rate 5e-5 \
--warmup_steps 50 \
--evaluate_during_training \
--seed 2048
```
最终会打印出`precision`, `recall`, `f1`等指标，模型和训练日志会保存在`./output/ser/`文件夹中。
* 使用评估集合中提供的OCR识别结果进行预测
```shell
export CUDA_VISIBLE_DEVICES=0
python3.7 infer_ser.py \
--model_name_or_path "./PP-Layout_v1.0_ser_pretrained/" \
--output_dir "output_res/" \
--infer_imgs "XFUND/zh_val/image/" \
--ocr_json_path "XFUND/zh_val/xfun_normalize_val.json"
```
最终会在`output_res`目录下保存预测结果可视化图像以及预测结果文本文件，文件名为`infer_results.txt`。
* 使用`OCR引擎 + SER`串联结果
```shell
export CUDA_VISIBLE_DEVICES=0
python3.7 infer_ser_e2e.py \
--model_name_or_path "./output/PP-Layout_v1.0_ser_pretrained/" \
--max_seq_length 512 \
--output_dir "output_res_e2e/" \
--infer_imgs "images/input/zh_val_0.jpg"
```
* 对`OCR引擎 + SER`预测系统进行端到端评估
```shell
export CUDA_VISIBLE_DEVICES=0
python3.7 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt
```
### 3.3 RE任务
* 启动训练
```shell
python3 train_re.py \
--model_name_or_path "layoutxlm-base-uncased" \
--train_data_dir "XFUND/zh_train/image" \
--train_label_path "XFUND/zh_train/xfun_normalize_train.json" \
--eval_data_dir "XFUND/zh_val/image" \
--eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
--label_map_path 'labels/labels_ser.txt' \
--num_train_epochs 2 \
--eval_steps 10 \
--save_steps 500 \
--output_dir "output/re/" \
--learning_rate 5e-5 \
--warmup_steps 50 \
--per_gpu_train_batch_size 8 \
--per_gpu_eval_batch_size 8 \
--evaluate_during_training \
--seed 2048
```
最终会打印出`precision`, `recall`, `f1`等指标，模型和训练日志会保存在`./output/re/`文件夹中。
* 使用评估集合中提供的OCR识别结果进行预测
```shell
export CUDA_VISIBLE_DEVICES=0
python3 infer_re.py \
--model_name_or_path "./PP-Layout_v1.0_re_pretrained/" \
--max_seq_length 512 \
--eval_data_dir "XFUND/zh_val/image" \
--eval_label_path "XFUND/zh_val/xfun_normalize_val.json" \
--label_map_path 'labels/labels_ser.txt' \
--output_dir "output_res" \
--per_gpu_eval_batch_size 1 \
--seed 2048
```
最终会在`output_res`目录下保存预测结果可视化图像以及预测结果文本文件，文件名为`infer_results.txt`。
* 使用`OCR引擎 + SER + RE`串联结果
```shell
export CUDA_VISIBLE_DEVICES=0
# python3.7 infer_ser_re_e2e.py \
--model_name_or_path "./PP-Layout_v1.0_ser_pretrained/" \
--re_model_name_or_path "./PP-Layout_v1.0_re_pretrained/" \
--max_seq_length 512 \
--output_dir "output_ser_re_e2e_train/" \
--infer_imgs "images/input/zh_val_21.jpg"
```
## 参考链接
- LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf
- microsoft/unilm/layoutxlm, https://github.com/microsoft/unilm/tree/master/layoutxlm
- XFUND dataset, https://github.com/doc-analysis/XFUND
ppstructure/vqa/data_collator.py 0 → 100644
View file @ 5e4d1891
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import numbers
import numpy as np
class DataCollator:
"""
data batch
"""
def __call__(self, batch):
data_dict = {}
to_tensor_keys = []
for sample in batch:
for k, v in sample.items():
if k not in data_dict:
data_dict[k] = []
if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)):
if k not in to_tensor_keys:
to_tensor_keys.append(k)
data_dict[k].append(v)
for k in to_tensor_keys:
data_dict[k] = paddle.to_tensor(data_dict[k])
return data_dict
ppstructure/vqa/helper/eval_with_label_end2end.py 0 → 100644
View file @ 5e4d1891
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
import sys
# import Polygon
import shapely
from shapely.geometry import Polygon
import numpy as np
from collections import defaultdict
import operator
import editdistance
import argparse
import json
import copy
def parse_ser_results_fp(fp, fp_type="gt", ignore_background=True):
# img/zh_val_0.jpg {
# "height": 3508,
# "width": 2480,
# "ocr_info": [
# {"text": "Maribyrnong", "label": "other", "bbox": [1958, 144, 2184, 198]},
# {"text": "CITYCOUNCIL", "label": "other", "bbox": [2052, 183, 2171, 214]},
# ]
assert fp_type in ["gt", "pred"]
key = "label" if fp_type == "gt" else "pred"
res_dict = dict()
with open(fp, "r") as fin:
lines = fin.readlines()
for _, line in enumerate(lines):
img_path, info = line.strip().split("\t")
# get key
image_name = os.path.basename(img_path)
res_dict[image_name] = []
# get infos
json_info = json.loads(info)
for single_ocr_info in json_info["ocr_info"]:
label = single_ocr_info[key].upper()
if label in ["O", "OTHERS", "OTHER"]:
label = "O"
if ignore_background and label == "O":
continue
single_ocr_info["label"] = label
res_dict[image_name].append(copy.deepcopy(single_ocr_info))
return res_dict
def polygon_from_str(polygon_points):
"""
Create a shapely polygon object from gt or dt line.
"""
polygon_points = np.array(polygon_points).reshape(4, 2)
polygon = Polygon(polygon_points).convex_hull
return polygon
def polygon_iou(poly1, poly2):
"""
Intersection over union between two shapely polygons.
"""
if not poly1.intersects(
poly2): # this test is fast and can accelerate calculation
iou = 0
else:
try:
inter_area = poly1.intersection(poly2).area
union_area = poly1.area + poly2.area - inter_area
iou = float(inter_area) / union_area
except shapely.geos.TopologicalError:
# except Exception as e:
# print(e)
print('shapely.geos.TopologicalError occured, iou set to 0')
iou = 0
return iou
def ed(args, str1, str2):
if args.ignore_space:
str1 = str1.replace(" ", "")
str2 = str2.replace(" ", "")
if args.ignore_case:
str1 = str1.lower()
str2 = str2.lower()
return editdistance.eval(str1, str2)
def convert_bbox_to_polygon(bbox):
"""
bbox : [x1, y1, x2, y2]
output: [[x1, y1], [x2, y2], [x3, y3], [x4, y4]]
"""
xmin, ymin, xmax, ymax = bbox
poly = [[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]]
return poly
def eval_e2e(args):
# gt
gt_results = parse_ser_results_fp(args.gt_json_path, "gt",
args.ignore_background)
# pred
dt_results = parse_ser_results_fp(args.pred_json_path, "pred",
args.ignore_background)
assert set(gt_results.keys()) == set(dt_results.keys())
iou_thresh = args.iou_thres
num_gt_chars = 0
gt_count = 0
dt_count = 0
hit = 0
ed_sum = 0
for img_name in gt_results:
gt_info = gt_results[img_name]
gt_count += len(gt_info)
dt_info = dt_results[img_name]
dt_count += len(dt_info)
dt_match = [False] * len(dt_info)
gt_match = [False] * len(gt_info)
all_ious = defaultdict(tuple)
# gt: {text, label, bbox or poly}
for index_gt, gt in enumerate(gt_info):
if "poly" not in gt:
gt["poly"] = convert_bbox_to_polygon(gt["bbox"])
gt_poly = polygon_from_str(gt["poly"])
for index_dt, dt in enumerate(dt_info):
if "poly" not in dt:
dt["poly"] = convert_bbox_to_polygon(dt["bbox"])
dt_poly = polygon_from_str(dt["poly"])
iou = polygon_iou(dt_poly, gt_poly)
if iou >= iou_thresh:
all_ious[(index_gt, index_dt)] = iou
sorted_ious = sorted(
all_ious.items(), key=operator.itemgetter(1), reverse=True)
sorted_gt_dt_pairs = [item[0] for item in sorted_ious]
# matched gt and dt
for gt_dt_pair in sorted_gt_dt_pairs:
index_gt, index_dt = gt_dt_pair
if gt_match[index_gt] == False and dt_match[index_dt] == False:
gt_match[index_gt] = True
dt_match[index_dt] = True
# ocr rec results
gt_text = gt_info[index_gt]["text"]
dt_text = dt_info[index_dt]["text"]
# ser results
gt_label = gt_info[index_gt]["label"]
dt_label = dt_info[index_dt]["pred"]
if True: # ignore_masks[index_gt] == '0':
ed_sum += ed(args, gt_text, dt_text)
num_gt_chars += len(gt_text)
if gt_text == dt_text:
if args.ignore_ser_prediction or gt_label == dt_label:
hit += 1
# unmatched dt
for tindex, dt_match_flag in enumerate(dt_match):
if dt_match_flag == False:
dt_text = dt_info[tindex]["text"]
gt_text = ""
ed_sum += ed(args, dt_text, gt_text)
# unmatched gt
for tindex, gt_match_flag in enumerate(gt_match):
if gt_match_flag == False:
dt_text = ""
gt_text = gt_info[tindex]["text"]
ed_sum += ed(args, gt_text, dt_text)
num_gt_chars += len(gt_text)
eps = 1e-9
print("config: ", args)
print('hit, dt_count, gt_count', hit, dt_count, gt_count)
precision = hit / (dt_count + eps)
recall = hit / (gt_count + eps)
fmeasure = 2.0 * precision * recall / (precision + recall + eps)
avg_edit_dist_img = ed_sum / len(gt_results)
avg_edit_dist_field = ed_sum / (gt_count + eps)
character_acc = 1 - ed_sum / (num_gt_chars + eps)
print('character_acc: %.2f' % (character_acc * 100) + "%")
print('avg_edit_dist_field: %.2f' % (avg_edit_dist_field))
print('avg_edit_dist_img: %.2f' % (avg_edit_dist_img))
print('precision: %.2f' % (precision * 100) + "%")
print('recall: %.2f' % (recall * 100) + "%")
print('fmeasure: %.2f' % (fmeasure * 100) + "%")
return
def parse_args():
"""
"""
def str2bool(v):
return v.lower() in ("true", "t", "1")
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--gt_json_path",
default=None,
type=str,
required=True, )
parser.add_argument(
"--pred_json_path",
default=None,
type=str,
required=True, )
parser.add_argument("--iou_thres", default=0.5, type=float)
parser.add_argument(
"--ignore_case",
default=False,
type=str2bool,
help="whether to do lower case for the strs")
parser.add_argument(
"--ignore_space",
default=True,
type=str2bool,
help="whether to ignore space")
parser.add_argument(
"--ignore_background",
default=True,
type=str2bool,
help="whether to ignore other label")
parser.add_argument(
"--ignore_ser_prediction",
default=False,
type=str2bool,
help="whether to ignore ocr pred results")
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
eval_e2e(args)
ppstructure/vqa/helper/trans_xfun_data.py 0 → 100644
View file @ 5e4d1891
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
def transfer_xfun_data(json_path=None, output_file=None):
with open(json_path, "r") as fin:
lines = fin.readlines()
json_info = json.loads(lines[0])
documents = json_info["documents"]
label_info = {}
with open(output_file, "w") as fout:
for idx, document in enumerate(documents):
img_info = document["img"]
document = document["document"]
image_path = img_info["fname"]
label_info["height"] = img_info["height"]
label_info["width"] = img_info["width"]
label_info["ocr_info"] = []
for doc in document:
label_info["ocr_info"].append({
"text": doc["text"],
"label": doc["label"],
"bbox": doc["box"],
"id": doc["id"],
"linking": doc["linking"],
"words": doc["words"]
})
fout.write(image_path + "\t" + json.dumps(
label_info, ensure_ascii=False) + "\n")
print("===ok====")
transfer_xfun_data("./xfun/zh.val.json", "./xfun_normalize_val.json")
ppstructure/vqa/infer_re.py 0 → 100644
View file @ 5e4d1891
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
import random
import cv2
import matplotlib.pyplot as plt
import numpy as np
import paddle
from paddlenlp.transformers import LayoutXLMTokenizer, LayoutXLMModel, LayoutXLMForRelationExtraction
from xfun import XFUNDataset
from utils import parse_args, get_bio_label_maps, draw_re_results
from data_collator import DataCollator
from ppocr.utils.logging import get_logger
def infer(args):
os.makedirs(args.output_dir, exist_ok=True)
logger = get_logger()
label2id_map, id2label_map = get_bio_label_maps(args.label_map_path)
pad_token_label_id = paddle.nn.CrossEntropyLoss().ignore_index
tokenizer = LayoutXLMTokenizer.from_pretrained(args.model_name_or_path)
model = LayoutXLMForRelationExtraction.from_pretrained(
args.model_name_or_path)
eval_dataset = XFUNDataset(
tokenizer,
data_dir=args.eval_data_dir,
label_path=args.eval_label_path,
label2id_map=label2id_map,
img_size=(224, 224),
max_seq_len=args.max_seq_length,
pad_token_label_id=pad_token_label_id,
contains_re=True,
add_special_ids=False,
return_attention_mask=True,
load_mode='all')
eval_dataloader = paddle.io.DataLoader(
eval_dataset,
batch_size=args.per_gpu_eval_batch_size,
num_workers=8,
shuffle=False,
collate_fn=DataCollator())
# 读取gt的oct数据
ocr_info_list = load_ocr(args.eval_data_dir, args.eval_label_path)
for idx, batch in enumerate(eval_dataloader):
logger.info("[Infer] process: {}/{}".format(idx, len(eval_dataloader)))
with paddle.no_grad():
outputs = model(**batch)
pred_relations = outputs['pred_relations']
ocr_info = ocr_info_list[idx]
image_path = ocr_info['image_path']
ocr_info = ocr_info['ocr_info']
# 根据entity里的信息，做token解码后去过滤不要的ocr_info
ocr_info = filter_bg_by_txt(ocr_info, batch, tokenizer)
# 进行 relations 到 ocr信息的转换
result = []
used_tail_id = []
for relations in pred_relations:
for relation in relations:
if relation['tail_id'] in used_tail_id:
continue
if relation['head_id'] not in ocr_info or relation[
'tail_id'] not in ocr_info:
continue
used_tail_id.append(relation['tail_id'])
ocr_info_head = ocr_info[relation['head_id']]
ocr_info_tail = ocr_info[relation['tail_id']]
result.append((ocr_info_head, ocr_info_tail))
img = cv2.imread(image_path)
img_show = draw_re_results(img, result)
save_path = os.path.join(args.output_dir, os.path.basename(image_path))
cv2.imwrite(save_path, img_show)
def load_ocr(img_folder, json_path):
import json
d = []
with open(json_path, "r") as fin:
lines = fin.readlines()
for line in lines:
image_name, info_str = line.split("\t")
info_dict = json.loads(info_str)
info_dict['image_path'] = os.path.join(img_folder, image_name)
d.append(info_dict)
return d
def filter_bg_by_txt(ocr_info, batch, tokenizer):
entities = batch['entities'][0]
input_ids = batch['input_ids'][0]
new_info_dict = {}
for i in range(len(entities['start'])):
entitie_head = entities['start'][i]
entitie_tail = entities['end'][i]
word_input_ids = input_ids[entitie_head:entitie_tail].numpy().tolist()
txt = tokenizer.convert_ids_to_tokens(word_input_ids)
txt = tokenizer.convert_tokens_to_string(txt)
for i, info in enumerate(ocr_info):
if info['text'] == txt:
new_info_dict[i] = info
return new_info_dict
def post_process(pred_relations, ocr_info, img):
result = []
for relations in pred_relations:
for relation in relations:
ocr_info_head = ocr_info[relation['head_id']]
ocr_info_tail = ocr_info[relation['tail_id']]
result.append((ocr_info_head, ocr_info_tail))
return result
def draw_re(result, image_path, output_folder):
img = cv2.imread(image_path)
from matplotlib import pyplot as plt
for ocr_info_head, ocr_info_tail in result:
cv2.rectangle(
img,
tuple(ocr_info_head['bbox'][:2]),
tuple(ocr_info_head['bbox'][2:]), (255, 0, 0),
thickness=2)
cv2.rectangle(
img,
tuple(ocr_info_tail['bbox'][:2]),
tuple(ocr_info_tail['bbox'][2:]), (0, 0, 255),
thickness=2)
center_p1 = [(ocr_info_head['bbox'][0] + ocr_info_head['bbox'][2]) // 2,
(ocr_info_head['bbox'][1] + ocr_info_head['bbox'][3]) // 2]
center_p2 = [(ocr_info_tail['bbox'][0] + ocr_info_tail['bbox'][2]) // 2,
(ocr_info_tail['bbox'][1] + ocr_info_tail['bbox'][3]) // 2]
cv2.line(
img, tuple(center_p1), tuple(center_p2), (0, 255, 0), thickness=2)
plt.imshow(img)
plt.savefig(
os.path.join(output_folder, os.path.basename(image_path)), dpi=600)
# plt.show()
if __name__ == "__main__":
args = parse_args()
infer(args)
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