Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into test_v7
This commit is contained in:
LDOUBLEV
commit
dad8397fd3
|
|
@ -2,7 +2,8 @@ include LICENSE
|
|||
include README.md
|
||||
|
||||
recursive-include ppocr/utils *.txt utility.py logging.py network.py
|
||||
recursive-include ppocr/data/ *.py
|
||||
recursive-include ppocr/data *.py
|
||||
recursive-include ppocr/postprocess *.py
|
||||
recursive-include tools/infer *.py
|
||||
recursive-include ppocr/utils/e2e_utils/ *.py
|
||||
recursive-include ppocr/utils/e2e_utils *.py
|
||||
recursive-include ppstructure *.py
|
||||
|
|
@ -11,7 +11,8 @@
|
|||
# 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 paddleocr
|
||||
from .paddleocr import *
|
||||
|
||||
__all__ = ['PaddleOCR', 'draw_ocr']
|
||||
from .paddleocr import PaddleOCR
|
||||
from .tools.infer.utility import draw_ocr
|
||||
__version__ = paddleocr.VERSION
|
||||
__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res','download_with_progressbar']
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ PaddleOCR在Windows 平台下基于`Visual Studio 2019 Community` 进行了测
|
|||
|
||||
### Step1: 下载PaddlePaddle C++ 预测库 fluid_inference
|
||||
|
||||
PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/windows_cpp_inference.html)
|
||||
PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#windows)
|
||||
|
||||
解压后`D:\projects\fluid_inference`目录包含内容为：
|
||||
```
|
||||
|
|
@ -93,3 +93,5 @@ cd D:\projects\PaddleOCR\deploy\cpp_infer\out\build\x64-Release
|
|||
|
||||
### 注意
|
||||
* 在Windows下的终端中执行文件exe时，可能会发生乱码的现象，此时需要在终端中输入`CHCP 65001`，将终端的编码方式由GBK编码(默认)改为UTF-8编码，更加具体的解释可以参考这篇博客：[https://blog.csdn.net/qq_35038153/article/details/78430359](https://blog.csdn.net/qq_35038153/article/details/78430359)。
|
||||
|
||||
* 编译时，如果报错`错误：C1083 无法打开包括文件:"dirent.h":No such file or directory`，可以参考该[文档](https://blog.csdn.net/Dora_blank/article/details/117740837#41_C1083_direnthNo_such_file_or_directory_54)，新建`dirent.h`文件，并添加到`VC++`的包含目录中。
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ PaddleOCR模型部署。
|
|||
* 首先需要从opencv官网上下载在Linux环境下源码编译的包，以opencv3.4.7为例，下载命令如下。
|
||||
|
||||
```
|
||||
cd deploy/cpp_infer
|
||||
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
|
||||
tar -xf 3.4.7.tar.gz
|
||||
```
|
||||
|
|
@ -184,7 +185,7 @@ cmake .. \
|
|||
make -j
|
||||
```
|
||||
|
||||
`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
|
||||
`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。**注意**：以上路径都写绝对路径，不要写相对路径。
|
||||
|
||||
|
||||
* 编译完成之后，会在`build`文件夹下生成一个名为`ocr_system`的可执行文件。
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ PaddleOCR model deployment.
|
|||
* First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows.
|
||||
|
||||
```
|
||||
cd deploy/cpp_infer
|
||||
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
|
||||
tar -xf 3.4.7.tar.gz
|
||||
```
|
||||
|
|
|
|||
|
|
@ -29,6 +29,7 @@ deploy/hubserving/ocr_system/
|
|||
### 1. 准备环境
|
||||
```shell
|
||||
# 安装paddlehub
|
||||
# paddlehub 需要 python>3.6.2
|
||||
pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -30,6 +30,7 @@ The following steps take the 2-stage series service as an example. If only the d
|
|||
### 1. Prepare the environment
|
||||
```shell
|
||||
# Install paddlehub
|
||||
# python>3.6.2 is required bt paddlehub
|
||||
pip3 install paddlehub==2.1.0 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -18,9 +18,9 @@ PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支
|
|||
|
||||
```
|
||||
# 将官网下载的标签文件转换为 train_icdar2015_label.txt
|
||||
python gen_label.py --mode="det" --root_path="icdar_c4_train_imgs/" \
|
||||
--input_path="ch4_training_localization_transcription_gt" \
|
||||
--output_label="train_icdar2015_label.txt"
|
||||
python gen_label.py --mode="det" --root_path="/path/to/icdar_c4_train_imgs/" \
|
||||
--input_path="/path/to/ch4_training_localization_transcription_gt" \
|
||||
--output_label="/path/to/train_icdar2015_label.txt"
|
||||
```
|
||||
|
||||
解压数据集和下载标注文件后，PaddleOCR/train_data/ 有两个文件夹和两个文件，分别是：
|
||||
|
|
|
|||
|
|
@ -147,12 +147,12 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_m
|
|||
|
||||
如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以设置det_limit_side_len 为想要的值，比如1216：
|
||||
```
|
||||
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
|
||||
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
|
||||
```
|
||||
|
||||
如果想使用CPU进行预测，执行命令如下
|
||||
```
|
||||
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
|
||||
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
|
||||
```
|
||||
|
||||
<a name="DB文本检测模型推理"></a>
|
||||
|
|
@ -221,7 +221,7 @@ python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Gl
|
|||
|
||||
```
|
||||
|
||||
**SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`，同时，还需要增加参数`--det_sast_polygon=True`，**可以执行如下命令：
|
||||
SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`，同时，还需要增加参数`--det_sast_polygon=True`，可以执行如下命令：
|
||||
```
|
||||
python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
|
||||
```
|
||||
|
|
|
|||
|
|
@ -330,6 +330,8 @@ PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi
|
|||
|
||||
```
|
||||
|
||||
意大利文由拉丁字母组成，因此执行完命令后会得到名为 rec_latin_lite_train.yml 的配置文件。
|
||||
|
||||
2. 手动修改配置文件
|
||||
|
||||
您也可以手动修改模版中的以下几个字段:
|
||||
|
|
|
|||
|
|
@ -5,26 +5,32 @@
|
|||
### 1.1 安装whl包
|
||||
|
||||
pip安装
|
||||
|
||||
```bash
|
||||
pip install "paddleocr>=2.0.1" # 推荐使用2.0.1+版本
|
||||
```
|
||||
|
||||
本地构建并安装
|
||||
|
||||
```bash
|
||||
python3 setup.py bdist_wheel
|
||||
pip3 install dist/paddleocr-x.x.x-py3-none-any.whl # x.x.x是paddleocr的版本号
|
||||
```
|
||||
|
||||
## 2 使用
|
||||
|
||||
### 2.1 代码使用
|
||||
|
||||
paddleocr whl包会自动下载ppocr轻量级模型作为默认模型，可以根据第3节**自定义模型**进行自定义更换。
|
||||
|
||||
* 检测+方向分类器+识别全流程
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
|
||||
# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
|
||||
# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
img_path = 'PaddleOCR/doc/imgs/11.jpg'
|
||||
result = ocr.ocr(img_path, cls=True)
|
||||
for line in result:
|
||||
|
|
@ -32,6 +38,7 @@ for line in result:
|
|||
|
||||
# 显示结果
|
||||
from PIL import Image
|
||||
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
|
|
@ -40,31 +47,36 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
|
|||
im_show = Image.fromarray(im_show)
|
||||
im_show.save('result.jpg')
|
||||
```
|
||||
|
||||
结果是一个list，每个item包含了文本框，文字和识别置信度
|
||||
|
||||
```bash
|
||||
[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
|
||||
[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
|
||||
[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
|
||||
......
|
||||
```
|
||||
|
||||
结果可视化
|
||||
|
||||
<div align="center">
|
||||
<img src="../imgs_results/whl/11_det_rec.jpg" width="800">
|
||||
</div>
|
||||
|
||||
|
||||
* 检测+识别
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
ocr = PaddleOCR() # need to run only once to download and load model into memory
|
||||
|
||||
ocr = PaddleOCR() # need to run only once to download and load model into memory
|
||||
img_path = 'PaddleOCR/doc/imgs/11.jpg'
|
||||
result = ocr.ocr(img_path,cls=False)
|
||||
result = ocr.ocr(img_path, cls=False)
|
||||
for line in result:
|
||||
print(line)
|
||||
|
||||
# 显示结果
|
||||
from PIL import Image
|
||||
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
|
|
@ -73,38 +85,46 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
|
|||
im_show = Image.fromarray(im_show)
|
||||
im_show.save('result.jpg')
|
||||
```
|
||||
|
||||
结果是一个list，每个item包含了文本框，文字和识别置信度
|
||||
|
||||
```bash
|
||||
[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
|
||||
[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
|
||||
[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
|
||||
......
|
||||
```
|
||||
|
||||
结果可视化
|
||||
|
||||
<div align="center">
|
||||
<img src="../imgs_results/whl/11_det_rec.jpg" width="800">
|
||||
</div>
|
||||
|
||||
|
||||
* 方向分类器+识别
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR
|
||||
ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
|
||||
|
||||
ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
|
||||
img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
|
||||
result = ocr.ocr(img_path, det=False, cls=True)
|
||||
for line in result:
|
||||
print(line)
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含识别结果和识别置信度
|
||||
|
||||
```bash
|
||||
['韩国小馆', 0.9907421]
|
||||
```
|
||||
|
||||
* 单独执行检测
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
ocr = PaddleOCR() # need to run only once to download and load model into memory
|
||||
|
||||
ocr = PaddleOCR() # need to run only once to download and load model into memory
|
||||
img_path = 'PaddleOCR/doc/imgs/11.jpg'
|
||||
result = ocr.ocr(img_path, rec=False)
|
||||
for line in result:
|
||||
|
|
@ -118,13 +138,16 @@ im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/Pa
|
|||
im_show = Image.fromarray(im_show)
|
||||
im_show.save('result.jpg')
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含文本框
|
||||
|
||||
```bash
|
||||
[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
|
||||
[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
|
||||
[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
|
||||
......
|
||||
```
|
||||
|
||||
结果可视化
|
||||
|
||||
|
||||
|
|
@ -133,29 +156,37 @@ im_show.save('result.jpg')
|
|||
</div>
|
||||
|
||||
* 单独执行识别
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR
|
||||
ocr = PaddleOCR() # need to run only once to download and load model into memory
|
||||
|
||||
ocr = PaddleOCR() # need to run only once to download and load model into memory
|
||||
img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
|
||||
result = ocr.ocr(img_path, det=False)
|
||||
for line in result:
|
||||
print(line)
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含识别结果和识别置信度
|
||||
|
||||
```bash
|
||||
['韩国小馆', 0.9907421]
|
||||
```
|
||||
|
||||
* 单独执行方向分类器
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR
|
||||
ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
|
||||
|
||||
ocr = PaddleOCR(use_angle_cls=True) # need to run only once to download and load model into memory
|
||||
img_path = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
|
||||
result = ocr.ocr(img_path, det=False, rec=False, cls=True)
|
||||
for line in result:
|
||||
print(line)
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含分类结果和分类置信度
|
||||
|
||||
```bash
|
||||
['0', 0.9999924]
|
||||
```
|
||||
|
|
@ -163,15 +194,19 @@ for line in result:
|
|||
### 2.2 通过命令行使用
|
||||
|
||||
查看帮助信息
|
||||
|
||||
```bash
|
||||
paddleocr -h
|
||||
```
|
||||
|
||||
* 检测+方向分类器+识别全流程
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
|
||||
```
|
||||
|
||||
结果是一个list，每个item包含了文本框，文字和识别置信度
|
||||
|
||||
```bash
|
||||
[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
|
||||
[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
|
||||
|
|
@ -180,10 +215,13 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --use_angle_cls true
|
|||
```
|
||||
|
||||
* 检测+识别
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
|
||||
```
|
||||
|
||||
结果是一个list，每个item包含了文本框，文字和识别置信度
|
||||
|
||||
```bash
|
||||
[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
|
||||
[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
|
||||
|
|
@ -192,20 +230,25 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg
|
|||
```
|
||||
|
||||
* 方向分类器+识别
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含识别结果和识别置信度
|
||||
|
||||
```bash
|
||||
['韩国小馆', 0.9907421]
|
||||
```
|
||||
|
||||
* 单独执行检测
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含文本框
|
||||
|
||||
```bash
|
||||
[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
|
||||
[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
|
||||
|
|
@ -214,34 +257,42 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --rec false
|
|||
```
|
||||
|
||||
* 单独执行识别
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含识别结果和识别置信度
|
||||
|
||||
```bash
|
||||
['韩国小馆', 0.9907421]
|
||||
```
|
||||
|
||||
* 单独执行方向分类器
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --use_angle_cls true --det false --rec false
|
||||
```
|
||||
|
||||
结果是一个list，每个item只包含分类结果和分类置信度
|
||||
|
||||
```bash
|
||||
['0', 0.9999924]
|
||||
```
|
||||
|
||||
## 3 自定义模型
|
||||
当内置模型无法满足需求时，需要使用到自己训练的模型。
|
||||
首先，参照[inference.md](./inference.md) 第一节转换将检测、分类和识别模型转换为inference模型，然后按照如下方式使用
|
||||
|
||||
当内置模型无法满足需求时，需要使用到自己训练的模型。 首先，参照[inference.md](./inference.md) 第一节转换将检测、分类和识别模型转换为inference模型，然后按照如下方式使用
|
||||
|
||||
### 3.1 代码使用
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
|
||||
# 模型路径下必须含有model和params文件
|
||||
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}', rec_model_dir='{your_rec_model_dir}', rec_char_dict_path='{your_rec_char_dict_path}', cls_model_dir='{your_cls_model_dir}', use_angle_cls=True)
|
||||
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}', rec_model_dir='{your_rec_model_dir}',
|
||||
rec_char_dict_path='{your_rec_char_dict_path}', cls_model_dir='{your_cls_model_dir}',
|
||||
use_angle_cls=True)
|
||||
img_path = 'PaddleOCR/doc/imgs/11.jpg'
|
||||
result = ocr.ocr(img_path, cls=True)
|
||||
for line in result:
|
||||
|
|
@ -249,6 +300,7 @@ for line in result:
|
|||
|
||||
# 显示结果
|
||||
from PIL import Image
|
||||
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
|
|
@ -269,11 +321,13 @@ paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg --det_model_dir {your_det_model_
|
|||
### 4.1 网络图片
|
||||
|
||||
- 代码使用
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
from paddleocr import PaddleOCR, draw_ocr, download_with_progressbar
|
||||
|
||||
# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
|
||||
# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
img_path = 'http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg'
|
||||
result = ocr.ocr(img_path, cls=True)
|
||||
for line in result:
|
||||
|
|
@ -281,7 +335,9 @@ for line in result:
|
|||
|
||||
# 显示结果
|
||||
from PIL import Image
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
|
||||
download_with_progressbar(img_path, 'tmp.jpg')
|
||||
image = Image.open('tmp.jpg').convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
scores = [line[1][1] for line in result]
|
||||
|
|
@ -289,18 +345,24 @@ im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc
|
|||
im_show = Image.fromarray(im_show)
|
||||
im_show.save('result.jpg')
|
||||
```
|
||||
|
||||
- 命令行模式
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-fypvuqf1838418.jpg --use_angle_cls=true
|
||||
```
|
||||
|
||||
### 4.2 numpy数组
|
||||
|
||||
仅通过代码使用时支持numpy数组作为输入
|
||||
|
||||
```python
|
||||
import cv2
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
|
||||
# Paddleocr目前支持中英文、英文、法语、德语、韩语、日语，可以通过修改lang参数进行切换
|
||||
# 参数依次为`ch`, `en`, `french`, `german`, `korean`, `japan`。
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
img_path = 'PaddleOCR/doc/imgs/11.jpg'
|
||||
img = cv2.imread(img_path)
|
||||
# img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY), 如果你自己训练的模型支持灰度图，可以将这句话的注释取消
|
||||
|
|
@ -310,6 +372,7 @@ for line in result:
|
|||
|
||||
# 显示结果
|
||||
from PIL import Image
|
||||
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
|
|
@ -356,3 +419,4 @@ im_show.save('result.jpg')
|
|||
| rec | 前向时是否启动识别 | TRUE |
|
||||
| cls | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类) | FALSE |
|
||||
| show_log | 是否打印det和rec等信息 | FALSE |
|
||||
| type | 执行ocr或者表格结构化, 值可选['ocr','structure'] | ocr |
|
||||
|
|
|
|||
|
|
@ -154,12 +154,12 @@ Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest si
|
|||
|
||||
If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216:
|
||||
```
|
||||
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/22.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
|
||||
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
|
||||
```
|
||||
|
||||
If you want to use the CPU for prediction, execute the command as follows
|
||||
```
|
||||
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/22.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
|
||||
python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
|
||||
```
|
||||
|
||||
<a name="DB_DETECTION"></a>
|
||||
|
|
@ -230,7 +230,7 @@ First, convert the model saved in the SAST text detection training process into
|
|||
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.save_inference_dir=./inference/det_sast_tt
|
||||
```
|
||||
|
||||
**For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command:
|
||||
For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`, run the following command:
|
||||
|
||||
```
|
||||
python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
|
||||
|
|
|
|||
|
|
@ -329,6 +329,7 @@ There are two ways to create the required configuration file:：
|
|||
...
|
||||
|
||||
```
|
||||
Italian is made up of Latin letters, so after executing the command, you will get the rec_latin_lite_train.yml.
|
||||
|
||||
2. Manually modify the configuration file
|
||||
|
||||
|
|
|
|||
|
|
@ -305,7 +305,8 @@ paddleocr --image_dir http://n.sinaimg.cn/ent/transform/w630h933/20171222/o111-f
|
|||
Support numpy array as input only when used by code
|
||||
|
||||
```python
|
||||
from paddleocr import PaddleOCR, draw_ocr
|
||||
import cv2
|
||||
from paddleocr import PaddleOCR, draw_ocr, download_with_progressbar
|
||||
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
|
||||
img_path = 'PaddleOCR/doc/imgs/11.jpg'
|
||||
img = cv2.imread(img_path)
|
||||
|
|
@ -316,7 +317,9 @@ for line in result:
|
|||
|
||||
# show result
|
||||
from PIL import Image
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
|
||||
download_with_progressbar(img_path, 'tmp.jpg')
|
||||
image = Image.open('tmp.jpg').convert('RGB')
|
||||
boxes = [line[0] for line in result]
|
||||
txts = [line[1][0] for line in result]
|
||||
scores = [line[1][1] for line in result]
|
||||
|
|
@ -362,5 +365,5 @@ im_show.save('result.jpg')
|
|||
| det | Enable detction when `ppocr.ocr` func exec | TRUE |
|
||||
| rec | Enable recognition when `ppocr.ocr` func exec | TRUE |
|
||||
| cls | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction) | FALSE |
|
||||
| show_log | Whether to print log in det and rec
|
||||
| FALSE |
|
||||
| show_log | Whether to print log in det and rec | FALSE |
|
||||
| type | Perform ocr or table structuring, the value is selected in ['ocr','structure'] | ocr |
|
||||
BIN
doc/joinus.PNG
BIN
doc/joinus.PNG
Binary file not shown.
|
Before Width: | Height: | Size: 189 KiB After Width: | Height: | Size: 212 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 55 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 672 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 1.5 MiB |
Binary file not shown.
|
Before Width: | Height: | Size: 116 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 1.4 MiB |
Binary file not shown.
|
After Width: | Height: | Size: 2.5 MiB |
Binary file not shown.
|
Before Width: | Height: | Size: 386 KiB After Width: | Height: | Size: 521 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 388 KiB After Width: | Height: | Size: 146 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 552 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 26 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 416 KiB |
188
paddleocr.py
188
paddleocr.py
|
|
@ -29,16 +29,19 @@ from ppocr.utils.logging import get_logger
|
|||
logger = get_logger()
|
||||
from ppocr.utils.utility import check_and_read_gif, get_image_file_list
|
||||
from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url
|
||||
from tools.infer.utility import draw_ocr, init_args, str2bool
|
||||
from tools.infer.utility import draw_ocr, str2bool
|
||||
from ppstructure.utility import init_args, draw_structure_result
|
||||
from ppstructure.predict_system import OCRSystem, save_structure_res
|
||||
|
||||
__all__ = ['PaddleOCR']
|
||||
__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res','download_with_progressbar']
|
||||
|
||||
model_urls = {
|
||||
'det': {
|
||||
'ch':
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
|
||||
'en':
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar'
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
|
||||
'structure': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
|
||||
},
|
||||
'rec': {
|
||||
'ch': {
|
||||
|
|
@ -110,14 +113,21 @@ model_urls = {
|
|||
'url':
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
|
||||
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
|
||||
},
|
||||
'structure': {
|
||||
'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
|
||||
'dict_path': 'ppocr/utils/dict/table_dict.txt'
|
||||
}
|
||||
},
|
||||
'cls':
|
||||
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar'
|
||||
'cls': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
|
||||
'table': {
|
||||
'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
|
||||
'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
|
||||
}
|
||||
}
|
||||
|
||||
SUPPORT_DET_MODEL = ['DB']
|
||||
VERSION = '2.1'
|
||||
VERSION = '2.2'
|
||||
SUPPORT_REC_MODEL = ['CRNN']
|
||||
BASE_DIR = os.path.expanduser("~/.paddleocr/")
|
||||
|
||||
|
|
@ -129,9 +139,10 @@ def parse_args(mMain=True):
|
|||
parser.add_argument("--lang", type=str, default='ch')
|
||||
parser.add_argument("--det", type=str2bool, default=True)
|
||||
parser.add_argument("--rec", type=str2bool, default=True)
|
||||
parser.add_argument("--type", type=str, default='ocr')
|
||||
|
||||
for action in parser._actions:
|
||||
if action.dest == 'rec_char_dict_path':
|
||||
if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
|
||||
action.default = None
|
||||
if mMain:
|
||||
return parser.parse_args()
|
||||
|
|
@ -142,6 +153,42 @@ def parse_args(mMain=True):
|
|||
return argparse.Namespace(**inference_args_dict)
|
||||
|
||||
|
||||
def parse_lang(lang):
|
||||
latin_lang = [
|
||||
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
|
||||
'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
|
||||
'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk',
|
||||
'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
|
||||
]
|
||||
arabic_lang = ['ar', 'fa', 'ug', 'ur']
|
||||
cyrillic_lang = [
|
||||
'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd',
|
||||
'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
|
||||
]
|
||||
devanagari_lang = [
|
||||
'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new',
|
||||
'gom', 'sa', 'bgc'
|
||||
]
|
||||
if lang in latin_lang:
|
||||
lang = "latin"
|
||||
elif lang in arabic_lang:
|
||||
lang = "arabic"
|
||||
elif lang in cyrillic_lang:
|
||||
lang = "cyrillic"
|
||||
elif lang in devanagari_lang:
|
||||
lang = "devanagari"
|
||||
assert lang in model_urls[
|
||||
'rec'], 'param lang must in {}, but got {}'.format(
|
||||
model_urls['rec'].keys(), lang)
|
||||
if lang == "ch":
|
||||
det_lang = "ch"
|
||||
elif lang == 'structure':
|
||||
det_lang = 'structure'
|
||||
else:
|
||||
det_lang = "en"
|
||||
return lang, det_lang
|
||||
|
||||
|
||||
class PaddleOCR(predict_system.TextSystem):
|
||||
def __init__(self, **kwargs):
|
||||
"""
|
||||
|
|
@ -154,52 +201,17 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
if not params.show_log:
|
||||
logger.setLevel(logging.INFO)
|
||||
self.use_angle_cls = params.use_angle_cls
|
||||
lang = params.lang
|
||||
latin_lang = [
|
||||
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
|
||||
'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
|
||||
'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk',
|
||||
'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
|
||||
]
|
||||
arabic_lang = ['ar', 'fa', 'ug', 'ur']
|
||||
cyrillic_lang = [
|
||||
'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd',
|
||||
'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
|
||||
]
|
||||
devanagari_lang = [
|
||||
'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new',
|
||||
'gom', 'sa', 'bgc'
|
||||
]
|
||||
if lang in latin_lang:
|
||||
lang = "latin"
|
||||
elif lang in arabic_lang:
|
||||
lang = "arabic"
|
||||
elif lang in cyrillic_lang:
|
||||
lang = "cyrillic"
|
||||
elif lang in devanagari_lang:
|
||||
lang = "devanagari"
|
||||
assert lang in model_urls[
|
||||
'rec'], 'param lang must in {}, but got {}'.format(
|
||||
model_urls['rec'].keys(), lang)
|
||||
if lang == "ch":
|
||||
det_lang = "ch"
|
||||
else:
|
||||
det_lang = "en"
|
||||
use_inner_dict = False
|
||||
if params.rec_char_dict_path is None:
|
||||
use_inner_dict = True
|
||||
params.rec_char_dict_path = model_urls['rec'][lang][
|
||||
'dict_path']
|
||||
lang, det_lang = parse_lang(params.lang)
|
||||
|
||||
# init model dir
|
||||
params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'det', det_lang),
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
|
||||
model_urls['det'][det_lang])
|
||||
params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'rec', lang),
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
|
||||
model_urls['rec'][lang]['url'])
|
||||
params.cls_model_dir, cls_url = confirm_model_dir_url(params.cls_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'cls'),
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
|
||||
model_urls['cls'])
|
||||
# download model
|
||||
maybe_download(params.det_model_dir, det_url)
|
||||
|
|
@ -212,9 +224,9 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
if params.rec_algorithm not in SUPPORT_REC_MODEL:
|
||||
logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
|
||||
sys.exit(0)
|
||||
if use_inner_dict:
|
||||
params.rec_char_dict_path = str(
|
||||
Path(__file__).parent / params.rec_char_dict_path)
|
||||
|
||||
if params.rec_char_dict_path is None:
|
||||
params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
|
||||
|
||||
print(params)
|
||||
# init det_model and rec_model
|
||||
|
|
@ -272,6 +284,59 @@ class PaddleOCR(predict_system.TextSystem):
|
|||
return rec_res
|
||||
|
||||
|
||||
class PPStructure(OCRSystem):
|
||||
def __init__(self, **kwargs):
|
||||
params = parse_args(mMain=False)
|
||||
params.__dict__.update(**kwargs)
|
||||
if not params.show_log:
|
||||
logger.setLevel(logging.INFO)
|
||||
lang, det_lang = parse_lang(params.lang)
|
||||
|
||||
# init model dir
|
||||
params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
|
||||
model_urls['det'][det_lang])
|
||||
params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
|
||||
model_urls['rec'][lang]['url'])
|
||||
params.table_model_dir, table_url = confirm_model_dir_url(params.table_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),
|
||||
model_urls['table']['url'])
|
||||
# download model
|
||||
maybe_download(params.det_model_dir, det_url)
|
||||
maybe_download(params.rec_model_dir, rec_url)
|
||||
maybe_download(params.table_model_dir, table_url)
|
||||
|
||||
if params.rec_char_dict_path is None:
|
||||
params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
|
||||
if params.table_char_dict_path is None:
|
||||
params.table_char_dict_path = str(Path(__file__).parent / model_urls['table']['dict_path'])
|
||||
|
||||
print(params)
|
||||
super().__init__(params)
|
||||
|
||||
def __call__(self, img):
|
||||
if isinstance(img, str):
|
||||
# download net image
|
||||
if img.startswith('http'):
|
||||
download_with_progressbar(img, 'tmp.jpg')
|
||||
img = 'tmp.jpg'
|
||||
image_file = img
|
||||
img, flag = check_and_read_gif(image_file)
|
||||
if not flag:
|
||||
with open(image_file, 'rb') as f:
|
||||
np_arr = np.frombuffer(f.read(), dtype=np.uint8)
|
||||
img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
|
||||
if img is None:
|
||||
logger.error("error in loading image:{}".format(image_file))
|
||||
return None
|
||||
if isinstance(img, np.ndarray) and len(img.shape) == 2:
|
||||
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
||||
|
||||
res = super().__call__(img)
|
||||
return res
|
||||
|
||||
|
||||
def main():
|
||||
# for cmd
|
||||
args = parse_args(mMain=True)
|
||||
|
|
@ -284,14 +349,29 @@ def main():
|
|||
if len(image_file_list) == 0:
|
||||
logger.error('no images find in {}'.format(args.image_dir))
|
||||
return
|
||||
if args.type == 'ocr':
|
||||
engine = PaddleOCR(**(args.__dict__))
|
||||
elif args.type == 'structure':
|
||||
engine = PPStructure(**(args.__dict__))
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
ocr_engine = PaddleOCR(**(args.__dict__))
|
||||
for img_path in image_file_list:
|
||||
img_name = os.path.basename(img_path).split('.')[0]
|
||||
logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
|
||||
result = ocr_engine.ocr(img_path,
|
||||
if args.type == 'ocr':
|
||||
result = engine.ocr(img_path,
|
||||
det=args.det,
|
||||
rec=args.rec,
|
||||
cls=args.use_angle_cls)
|
||||
if result is not None:
|
||||
for line in result:
|
||||
logger.info(line)
|
||||
if result is not None:
|
||||
for line in result:
|
||||
logger.info(line)
|
||||
elif args.type == 'structure':
|
||||
result = engine(img_path)
|
||||
save_structure_res(result, args.output, img_name)
|
||||
|
||||
for item in result:
|
||||
item.pop('img')
|
||||
logger.info(item)
|
||||
|
||||
|
|
|
|||
|
|
@ -19,6 +19,7 @@ from __future__ import unicode_literals
|
|||
|
||||
import numpy as np
|
||||
import string
|
||||
import json
|
||||
|
||||
|
||||
class ClsLabelEncode(object):
|
||||
|
|
@ -39,7 +40,6 @@ class DetLabelEncode(object):
|
|||
pass
|
||||
|
||||
def __call__(self, data):
|
||||
import json
|
||||
label = data['label']
|
||||
label = json.loads(label)
|
||||
nBox = len(label)
|
||||
|
|
@ -53,6 +53,8 @@ class DetLabelEncode(object):
|
|||
txt_tags.append(True)
|
||||
else:
|
||||
txt_tags.append(False)
|
||||
if len(boxes) == 0:
|
||||
return None
|
||||
boxes = self.expand_points_num(boxes)
|
||||
boxes = np.array(boxes, dtype=np.float32)
|
||||
txt_tags = np.array(txt_tags, dtype=np.bool)
|
||||
|
|
@ -352,19 +354,22 @@ class SRNLabelEncode(BaseRecLabelEncode):
|
|||
% beg_or_end
|
||||
return idx
|
||||
|
||||
|
||||
class TableLabelEncode(object):
|
||||
""" Convert between text-label and text-index """
|
||||
def __init__(self,
|
||||
max_text_length,
|
||||
max_elem_length,
|
||||
max_cell_num,
|
||||
character_dict_path,
|
||||
span_weight = 1.0,
|
||||
**kwargs):
|
||||
|
||||
def __init__(self,
|
||||
max_text_length,
|
||||
max_elem_length,
|
||||
max_cell_num,
|
||||
character_dict_path,
|
||||
span_weight=1.0,
|
||||
**kwargs):
|
||||
self.max_text_length = max_text_length
|
||||
self.max_elem_length = max_elem_length
|
||||
self.max_cell_num = max_cell_num
|
||||
list_character, list_elem = self.load_char_elem_dict(character_dict_path)
|
||||
list_character, list_elem = self.load_char_elem_dict(
|
||||
character_dict_path)
|
||||
list_character = self.add_special_char(list_character)
|
||||
list_elem = self.add_special_char(list_elem)
|
||||
self.dict_character = {}
|
||||
|
|
@ -374,36 +379,37 @@ class TableLabelEncode(object):
|
|||
for i, elem in enumerate(list_elem):
|
||||
self.dict_elem[elem] = i
|
||||
self.span_weight = span_weight
|
||||
|
||||
|
||||
def load_char_elem_dict(self, character_dict_path):
|
||||
list_character = []
|
||||
list_elem = []
|
||||
with open(character_dict_path, "rb") as fin:
|
||||
lines = fin.readlines()
|
||||
substr = lines[0].decode('utf-8').strip("\n").split("\t")
|
||||
substr = lines[0].decode('utf-8').strip("\r\n").split("\t")
|
||||
character_num = int(substr[0])
|
||||
elem_num = int(substr[1])
|
||||
for cno in range(1, 1+character_num):
|
||||
character = lines[cno].decode('utf-8').strip("\n")
|
||||
|
||||
for cno in range(1, 1 + character_num):
|
||||
character = lines[cno].decode('utf-8').strip("\r\n")
|
||||
list_character.append(character)
|
||||
for eno in range(1+character_num, 1+character_num+elem_num):
|
||||
elem = lines[eno].decode('utf-8').strip("\n")
|
||||
for eno in range(1 + character_num, 1 + character_num + elem_num):
|
||||
elem = lines[eno].decode('utf-8').strip("\r\n")
|
||||
list_elem.append(elem)
|
||||
return list_character, list_elem
|
||||
|
||||
|
||||
def add_special_char(self, list_character):
|
||||
self.beg_str = "sos"
|
||||
self.end_str = "eos"
|
||||
list_character = [self.beg_str] + list_character + [self.end_str]
|
||||
return list_character
|
||||
|
||||
|
||||
def get_span_idx_list(self):
|
||||
span_idx_list = []
|
||||
for elem in self.dict_elem:
|
||||
if 'span' in elem:
|
||||
span_idx_list.append(self.dict_elem[elem])
|
||||
return span_idx_list
|
||||
|
||||
|
||||
def __call__(self, data):
|
||||
cells = data['cells']
|
||||
structure = data['structure']['tokens']
|
||||
|
|
@ -412,18 +418,22 @@ class TableLabelEncode(object):
|
|||
return None
|
||||
elem_num = len(structure)
|
||||
structure = [0] + structure + [len(self.dict_elem) - 1]
|
||||
structure = structure + [0] * (self.max_elem_length + 2 - len(structure))
|
||||
structure = structure + [0] * (self.max_elem_length + 2 - len(structure)
|
||||
)
|
||||
structure = np.array(structure)
|
||||
data['structure'] = structure
|
||||
elem_char_idx1 = self.dict_elem['<td>']
|
||||
elem_char_idx2 = self.dict_elem['<td']
|
||||
span_idx_list = self.get_span_idx_list()
|
||||
td_idx_list = np.logical_or(structure == elem_char_idx1, structure == elem_char_idx2)
|
||||
td_idx_list = np.logical_or(structure == elem_char_idx1,
|
||||
structure == elem_char_idx2)
|
||||
td_idx_list = np.where(td_idx_list)[0]
|
||||
|
||||
structure_mask = np.ones((self.max_elem_length + 2, 1), dtype=np.float32)
|
||||
|
||||
structure_mask = np.ones(
|
||||
(self.max_elem_length + 2, 1), dtype=np.float32)
|
||||
bbox_list = np.zeros((self.max_elem_length + 2, 4), dtype=np.float32)
|
||||
bbox_list_mask = np.zeros((self.max_elem_length + 2, 1), dtype=np.float32)
|
||||
bbox_list_mask = np.zeros(
|
||||
(self.max_elem_length + 2, 1), dtype=np.float32)
|
||||
img_height, img_width, img_ch = data['image'].shape
|
||||
if len(span_idx_list) > 0:
|
||||
span_weight = len(td_idx_list) * 1.0 / len(span_idx_list)
|
||||
|
|
@ -450,9 +460,11 @@ class TableLabelEncode(object):
|
|||
char_end_idx = self.get_beg_end_flag_idx('end', 'char')
|
||||
elem_beg_idx = self.get_beg_end_flag_idx('beg', 'elem')
|
||||
elem_end_idx = self.get_beg_end_flag_idx('end', 'elem')
|
||||
data['sp_tokens'] = np.array([char_beg_idx, char_end_idx, elem_beg_idx,
|
||||
elem_end_idx, elem_char_idx1, elem_char_idx2, self.max_text_length,
|
||||
self.max_elem_length, self.max_cell_num, elem_num])
|
||||
data['sp_tokens'] = np.array([
|
||||
char_beg_idx, char_end_idx, elem_beg_idx, elem_end_idx,
|
||||
elem_char_idx1, elem_char_idx2, self.max_text_length,
|
||||
self.max_elem_length, self.max_cell_num, elem_num
|
||||
])
|
||||
return data
|
||||
|
||||
def encode(self, text, char_or_elem):
|
||||
|
|
@ -504,9 +516,8 @@ class TableLabelEncode(object):
|
|||
idx = np.array(self.dict_elem[self.end_str])
|
||||
else:
|
||||
assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \
|
||||
% beg_or_end
|
||||
% beg_or_end
|
||||
else:
|
||||
assert False, "Unsupport type %s in char_or_elem" \
|
||||
% char_or_elem
|
||||
% char_or_elem
|
||||
return idx
|
||||
|
||||
|
|
@ -14,7 +14,6 @@
|
|||
import numpy as np
|
||||
import os
|
||||
import random
|
||||
import traceback
|
||||
from paddle.io import Dataset
|
||||
|
||||
from .imaug import transform, create_operators
|
||||
|
|
@ -105,7 +104,7 @@ class SimpleDataSet(Dataset):
|
|||
data_line = self.data_lines[file_idx]
|
||||
try:
|
||||
data_line = data_line.decode('utf-8')
|
||||
substr = data_line.strip("\n").strip("\r").split(self.delimiter)
|
||||
substr = data_line.strip("\n").split(self.delimiter)
|
||||
file_name = substr[0]
|
||||
label = substr[1]
|
||||
img_path = os.path.join(self.data_dir, file_name)
|
||||
|
|
@ -117,11 +116,10 @@ class SimpleDataSet(Dataset):
|
|||
data['image'] = img
|
||||
data['ext_data'] = self.get_ext_data()
|
||||
outs = transform(data, self.ops)
|
||||
except:
|
||||
error_meg = traceback.format_exc()
|
||||
except Exception as e:
|
||||
self.logger.error(
|
||||
"When parsing line {}, error happened with msg: {}".format(
|
||||
data_line, error_meg))
|
||||
data_line, e))
|
||||
outs = None
|
||||
if outs is None:
|
||||
# during evaluation, we should fix the idx to get same results for many times of evaluation.
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@ class DistillationModel(nn.Layer):
|
|||
pretrained = model_config.pop("pretrained")
|
||||
model = BaseModel(model_config)
|
||||
if pretrained is not None:
|
||||
model = load_pretrained_params(model, pretrained)
|
||||
load_pretrained_params(model, pretrained)
|
||||
if freeze_params:
|
||||
for param in model.parameters():
|
||||
param.trainable = False
|
||||
|
|
|
|||
|
|
@ -346,14 +346,14 @@ class TableLabelDecode(object):
|
|||
list_elem = []
|
||||
with open(character_dict_path, "rb") as fin:
|
||||
lines = fin.readlines()
|
||||
substr = lines[0].decode('utf-8').strip("\n").split("\t")
|
||||
substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split("\t")
|
||||
character_num = int(substr[0])
|
||||
elem_num = int(substr[1])
|
||||
for cno in range(1, 1 + character_num):
|
||||
character = lines[cno].decode('utf-8').strip("\n")
|
||||
character = lines[cno].decode('utf-8').strip("\n").strip("\r\n")
|
||||
list_character.append(character)
|
||||
for eno in range(1 + character_num, 1 + character_num + elem_num):
|
||||
elem = lines[eno].decode('utf-8').strip("\n")
|
||||
elem = lines[eno].decode('utf-8').strip("\n").strip("\r\n")
|
||||
list_elem.append(elem)
|
||||
return list_character, list_elem
|
||||
|
||||
|
|
|
|||
|
|
@ -1,16 +1,16 @@
|
|||
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
||||
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
#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
|
||||
# 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.
|
||||
# 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 argparse
|
||||
import json
|
||||
|
|
@ -31,7 +31,9 @@ def gen_det_label(root_path, input_dir, out_label):
|
|||
for label_file in os.listdir(input_dir):
|
||||
img_path = root_path + label_file[3:-4] + ".jpg"
|
||||
label = []
|
||||
with open(os.path.join(input_dir, label_file), 'r') as f:
|
||||
with open(
|
||||
os.path.join(input_dir, label_file), 'r',
|
||||
encoding='utf-8-sig') as f:
|
||||
for line in f.readlines():
|
||||
tmp = line.strip("\n\r").replace("\xef\xbb\xbf",
|
||||
"").split(',')
|
||||
|
|
|
|||
|
|
@ -0,0 +1,189 @@
|
|||
English | [简体中文](README_ch.md)
|
||||
|
||||
# PP-Structure
|
||||
|
||||
PP-Structure is an OCR toolkit that can be used for complex documents analysis. The main features are as follows:
|
||||
- Support the layout analysis of documents, divide the documents into 5 types of areas **text, title, table, image and list** (conjunction with Layout-Parser)
|
||||
- Support to extract the texts from the text, title, picture and list areas (used in conjunction with PP-OCR)
|
||||
- Support to extract excel files from the table areas
|
||||
- Support python whl package and command line usage, easy to use
|
||||
- Support custom training for layout analysis and table structure tasks
|
||||
|
||||
## 1. Visualization
|
||||
|
||||
<img src="../doc/table/ppstructure.GIF" width="100%"/>
|
||||
|
||||
|
||||
|
||||
## 2. Installation
|
||||
|
||||
### 2.1 Install requirements
|
||||
|
||||
- **（1) Install PaddlePaddle**
|
||||
|
||||
```bash
|
||||
pip3 install --upgrade pip
|
||||
|
||||
# GPU
|
||||
python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/simple
|
||||
|
||||
# CPU
|
||||
python3 -m pip install paddlepaddle==2.1.1 -i https://mirror.baidu.com/pypi/simple
|
||||
|
||||
# For more，refer[Installation](https://www.paddlepaddle.org.cn/install/quick)。
|
||||
```
|
||||
|
||||
- **(2) Install Layout-Parser**
|
||||
|
||||
```bash
|
||||
pip3 install -U premailer paddleocr https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
### 2.2 Install PaddleOCR（including PP-OCR and PP-Structure）
|
||||
|
||||
- **（1) PIP install PaddleOCR whl package（inference only）**
|
||||
|
||||
```bash
|
||||
pip install "paddleocr>=2.2"
|
||||
```
|
||||
|
||||
- **（2) Clone PaddleOCR（Inference+training）**
|
||||
|
||||
```bash
|
||||
git clone https://github.com/PaddlePaddle/PaddleOCR
|
||||
```
|
||||
|
||||
|
||||
## 3. Quick Start
|
||||
|
||||
### 3.1 Use by command line
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir=../doc/table/1.png --type=structure
|
||||
```
|
||||
|
||||
### 3.2 Use by python API
|
||||
|
||||
```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'
|
||||
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')
|
||||
```
|
||||
### 3.3 Returned results format
|
||||
The returned results of PP-Structure is a list composed of a dict, an example is as follows
|
||||
|
||||
```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)])
|
||||
}
|
||||
]
|
||||
```
|
||||
The description of each field in dict is as follows
|
||||
|
||||
| Parameter | Description |
|
||||
| --------------- | -------------|
|
||||
|type|Type of image area|
|
||||
|bbox|The coordinates of the image area in the original image, respectively [left upper x, left upper y, right bottom x, right bottom y]|
|
||||
|res|OCR or table recognition result of image area。<br> Table: HTML string of the table; <br> OCR: A tuple containing the detection coordinates and recognition results of each single line of text|
|
||||
|
||||
|
||||
### 3.4 Parameter description：
|
||||
|
||||
| Parameter | Description | Default value |
|
||||
| --------------- | ---------------------------------------- | ------------------------------------------- |
|
||||
| output | The path where excel and recognition results are saved | ./output/table |
|
||||
| table_max_len | The long side of the image is resized in table structure model | 488 |
|
||||
| table_model_dir | inference model path of table structure model | None |
|
||||
| table_char_type | dict path of table structure model | ../ppocr/utils/dict/table_structure_dict.tx |
|
||||
|
||||
Most of the parameters are consistent with the paddleocr whl package, see [doc of whl](../doc/doc_en/whl_en.md)
|
||||
|
||||
After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
|
||||
|
||||
## 4. PP-Structure Pipeline
|
||||
|
||||
the process is as follows
|
||||
|
||||
|
||||
In PP-Structure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, including **text, title, image, list and table** 5 categories. For the first 4 types of areas, directly use the PP-OCR to complete the text detection and recognition. The table area will be converted to an excel file of the same table style via Table OCR.
|
||||
|
||||
### 4.1 LayoutParser
|
||||
|
||||
Layout analysis divides the document data into regions, including the use of Python scripts for layout analysis tools, extraction of special category detection boxes, performance indicators, and custom training layout analysis models. For details, please refer to [document](layout/README_en.md).
|
||||
|
||||
### 4.2 Table Recognition
|
||||
|
||||
Table Recognition converts table image into excel documents, which include the detection and recognition of table text and the prediction of table structure and cell coordinates. For detailed, please refer to [document](table/README.md)
|
||||
|
||||
## 5. Prediction by inference engine
|
||||
|
||||
Use the following commands to complete the inference.
|
||||
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
|
||||
# download model
|
||||
mkdir inference && cd inference
|
||||
# Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
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
|
||||
# Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
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
|
||||
# Download the table structure model of the ultra-lightweight Chinese OCR model and uncompress it
|
||||
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 --rec_char_type=ch --output=../output/table --vis_font_path=../doc/fonts/simfang.ttf
|
||||
```
|
||||
After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel and figure area will be cropped and saved, the excel and image file name will be the coordinates of the table in the image.
|
||||
|
||||
**Model List**
|
||||
|
||||
|
||||
|model name|description|config|model size|download|
|
||||
| --- | --- | --- | --- | --- |
|
||||
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction for English table scenarios|[table_mv3.yml](../configs/table/table_mv3.yml)|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
|
||||
|
||||
**Model List**
|
||||
|
||||
LayoutParser model
|
||||
|
||||
|model name|description|download|
|
||||
| --- | --- | --- |
|
||||
| ppyolov2_r50vd_dcn_365e_publaynet | The layout analysis model trained on the PubLayNet data set can be divided into 5 types of areas **text, title, table, picture and list** | [PubLayNet](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar) |
|
||||
| ppyolov2_r50vd_dcn_365e_tableBank_word | The layout analysis model trained on the TableBank Word dataset can only detect tables | [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 | The layout analysis model trained on the TableBank Latex dataset can only detect tables | [TableBank Latex](https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_tableBank_latex.tar) |
|
||||
|
||||
OCR and table recognition model
|
||||
|
||||
|model name|description|model size|download|
|
||||
| --- | --- | --- | --- |
|
||||
|ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|2.6M|[inference model](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 pruned and quantized lightweight model, supporting Chinese, English and number recognition|6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_det|Text detection of English table scenes trained on PubLayNet dataset|4.7M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_rec|Text recognition of English table scene trained on PubLayNet dataset|6.9M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar) |
|
||||
|en_ppocr_mobile_v2.0_table_structure|Table structure prediction of English table scene trained on PubLayNet dataset|18.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar) |
|
||||
|
||||
If you need to use other models, you can download the model in [model_list](../doc/doc_en/models_list_en.md) or use your own trained model to configure it to the three fields of `det_model_dir`, `rec_model_dir`, `table_model_dir` .
|
||||
|
|
@ -0,0 +1,188 @@
|
|||
[English](README.md) | 简体中文
|
||||
|
||||
# PP-Structure
|
||||
|
||||
PP-Structure是一个可用于复杂文档结构分析和处理的OCR工具包，主要特性如下：
|
||||
- 支持对图片形式的文档进行版面分析，可以划分**文字、标题、表格、图片以及列表**5类区域（与Layout-Parser联合使用）
|
||||
- 支持文字、标题、图片以及列表区域提取为文字字段（与PP-OCR联合使用）
|
||||
- 支持表格区域进行结构化分析，最终结果输出Excel文件
|
||||
- 支持python whl包和命令行两种方式，简单易用
|
||||
- 支持版面分析和表格结构化两类任务自定义训练
|
||||
|
||||
## 1. 效果展示
|
||||
|
||||
<img src="../doc/table/ppstructure.GIF" width="100%"/>
|
||||
|
||||
|
||||
|
||||
## 2. 安装
|
||||
|
||||
### 2.1 安装依赖
|
||||
|
||||
- **（1) 安装PaddlePaddle**
|
||||
|
||||
```bash
|
||||
pip3 install --upgrade pip
|
||||
|
||||
# GPU安装
|
||||
python3 -m pip install paddlepaddle-gpu==2.1.1 -i https://mirror.baidu.com/pypi/simple
|
||||
|
||||
# CPU安装
|
||||
python3 -m pip install paddlepaddle==2.1.1 -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。
|
||||
```
|
||||
|
||||
- **(2) 安装 Layout-Parser**
|
||||
|
||||
```bash
|
||||
pip3 install -U premailer paddleocr https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
### 2.2 安装PaddleOCR（包含PP-OCR和PP-Structure）
|
||||
|
||||
- **（1) PIP快速安装PaddleOCR whl包（仅预测）**
|
||||
|
||||
```bash
|
||||
pip install "paddleocr>=2.2" # 推荐使用2.2+版本
|
||||
```
|
||||
|
||||
- **（2) 完整克隆PaddleOCR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. PP-Structure 快速开始
|
||||
|
||||
### 3.1 命令行使用（默认参数，极简）
|
||||
|
||||
```bash
|
||||
paddleocr --image_dir=../doc/table/1.png --type=structure
|
||||
```
|
||||
|
||||
### 3.2 Python脚本使用（自定义参数，灵活）
|
||||
|
||||
```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')
|
||||
```
|
||||
|
||||
### 3.3 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
|
||||
|
||||
```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|
|
||||
|
||||
|
||||
### 3.4 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.tx |
|
||||
|
||||
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。
|
||||
|
||||
|
||||
## 4. PP-Structure Pipeline介绍
|
||||
|
||||
|
||||
|
||||
在PP-Structure中，图片会先经由Layout-Parser进行版面分析，在版面分析中，会对图片里的区域进行分类，包括**文字、标题、图片、列表和表格**5类。对于前4类区域，直接使用PP-OCR完成对应区域文字检测与识别。对于表格类区域，经过表格结构化处理后，表格图片转换为相同表格样式的Excel文件。
|
||||
|
||||
### 4.1 版面分析
|
||||
|
||||
版面分析对文档数据进行区域分类，其中包括版面分析工具的Python脚本使用、提取指定类别检测框、性能指标以及自定义训练版面分析模型，详细内容可以参考[文档](layout/README_ch.md)。
|
||||
|
||||
### 4.2 表格识别
|
||||
|
||||
表格识别将表格图片转换为excel文档，其中包含对于表格文本的检测和识别以及对于表格结构和单元格坐标的预测，详细说明参考[文档](table/README_ch.md)
|
||||
|
||||
## 5. 预测引擎推理（与whl包效果相同）
|
||||
|
||||
使用如下命令即可完成预测引擎的推理
|
||||
|
||||
```python
|
||||
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 --rec_char_type=ch --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。
|
||||
|
||||
**Model List**
|
||||
|
||||
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) |
|
||||
|
||||
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) |
|
||||
|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) |
|
||||
|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) |
|
||||
|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) |
|
||||
|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) |
|
||||
|
||||
如需要使用其他模型，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`,`table_model_dir`三个字段即可。
|
||||
|
|
@ -0,0 +1,141 @@
|
|||
English | [简体中文](README_ch.md)
|
||||
|
||||
|
||||
# Getting Started
|
||||
|
||||
[1. Install whl package](#Install)
|
||||
|
||||
[2. Quick Start](#QuickStart)
|
||||
|
||||
[3. PostProcess](#PostProcess)
|
||||
|
||||
[4. Results](#Results)
|
||||
|
||||
[5. Training](#Training)
|
||||
|
||||
<a name="Install"></a>
|
||||
|
||||
## 1. Install whl package
|
||||
```bash
|
||||
wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
pip install -U layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
<a name="QuickStart"></a>
|
||||
|
||||
## 2. Quick Start
|
||||
|
||||
Use LayoutParser to identify the layout of a document:
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import layoutparser as lp
|
||||
image = cv2.imread("doc/table/layout.jpg")
|
||||
image = image[..., ::-1]
|
||||
|
||||
# load model
|
||||
model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
|
||||
threshold=0.5,
|
||||
label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
|
||||
enforce_cpu=False,
|
||||
enable_mkldnn=True)
|
||||
# detect
|
||||
layout = model.detect(image)
|
||||
|
||||
# show result
|
||||
show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True)
|
||||
show_img.show()
|
||||
```
|
||||
|
||||
The following figure shows the result, with different colored detection boxes representing different categories and displaying specific categories in the upper left corner of the box with `show_element_type`
|
||||
|
||||
<div align="center">
|
||||
<img src="../../doc/table/result_all.jpg" width = "600" />
|
||||
</div>
|
||||
`PaddleDetectionLayoutModel`parameters are described as follows:
|
||||
|
||||
| parameter | description | default | remark |
|
||||
| :------------: | :------------------------------------------------------: | :---------: | :----------------------------------------------------------: |
|
||||
| config_path | model config path | None | Specify config_ path will automatically download the model (only for the first time,the model will exist and will not be downloaded again) |
|
||||
| model_path | model path | None | local model path, config_ path and model_ path must be set to one, cannot be none at the same time |
|
||||
| threshold | threshold of prediction score | 0.5 | \ |
|
||||
| input_shape | picture size of reshape | [3,640,640] | \ |
|
||||
| batch_size | testing batch size | 1 | \ |
|
||||
| label_map | category mapping table | None | Setting config_ path, it can be none, and the label is automatically obtained according to the dataset name_ map |
|
||||
| enforce_cpu | whether to use CPU | False | False to use GPU, and True to force the use of CPU |
|
||||
| enforce_mkldnn | whether mkldnn acceleration is enabled in CPU prediction | True | \ |
|
||||
| thread_num | the number of CPU threads | 10 | \ |
|
||||
|
||||
The following model configurations and label maps are currently supported, which you can use by modifying '--config_path' and '--label_map' to detect different types of content:
|
||||
|
||||
| dataset | config_path | label_map |
|
||||
| ------------------------------------------------------------ | ------------------------------------------------------------ | --------------------------------------------------------- |
|
||||
| [TableBank](https://doc-analysis.github.io/tablebank-page/index.html) word | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_word/config | {0:"Table"} |
|
||||
| TableBank latex | lp://TableBank/ppyolov2_r50vd_dcn_365e_tableBank_latex/config | {0:"Table"} |
|
||||
| [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) | lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config | {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"} |
|
||||
|
||||
* TableBank word and TableBank latex are trained on datasets of word documents and latex documents respectively;
|
||||
* Download TableBank dataset contains both word and latex。
|
||||
|
||||
<a name="PostProcess"></a>
|
||||
|
||||
## 3. PostProcess
|
||||
|
||||
Layout parser contains multiple categories, if you only want to get the detection box for a specific category (such as the "Text" category), you can use the following code:
|
||||
|
||||
```python
|
||||
# follow the above code
|
||||
# filter areas for a specific text type
|
||||
text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
|
||||
figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
|
||||
|
||||
# text areas may be detected within the image area, delete these areas
|
||||
text_blocks = lp.Layout([b for b in text_blocks \
|
||||
if not any(b.is_in(b_fig) for b_fig in figure_blocks)])
|
||||
|
||||
# sort text areas and assign ID
|
||||
h, w = image.shape[:2]
|
||||
|
||||
left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)
|
||||
|
||||
left_blocks = text_blocks.filter_by(left_interval, center=True)
|
||||
left_blocks.sort(key = lambda b:b.coordinates[1])
|
||||
|
||||
right_blocks = [b for b in text_blocks if b not in left_blocks]
|
||||
right_blocks.sort(key = lambda b:b.coordinates[1])
|
||||
|
||||
# the two lists are merged and the indexes are added in order
|
||||
text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])
|
||||
|
||||
# display result
|
||||
show_img = lp.draw_box(image, text_blocks,
|
||||
box_width=3,
|
||||
show_element_id=True)
|
||||
show_img.show()
|
||||
```
|
||||
|
||||
Displays results with only the "Text" category：
|
||||
|
||||
<div align="center">
|
||||
<img src="../../doc/table/result_text.jpg" width = "600" />
|
||||
</div>
|
||||
<a name="Results"></a>
|
||||
|
||||
## 4. Results
|
||||
|
||||
| Dataset | mAP | CPU time cost | GPU time cost |
|
||||
| --------- | ---- | ------------- | ------------- |
|
||||
| PubLayNet | 93.6 | 1713.7ms | 66.6ms |
|
||||
| TableBank | 96.2 | 1968.4ms | 65.1ms |
|
||||
|
||||
**Envrionment：**
|
||||
|
||||
**CPU：** Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core
|
||||
|
||||
**GPU：** a single NVIDIA Tesla P40
|
||||
|
||||
<a name="Training"></a>
|
||||
|
||||
## 5. Training
|
||||
|
||||
The above model is based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). If you want to train your own layout parser model，please refer to：[train_layoutparser_model](train_layoutparser_model.md)
|
||||
|
|
@ -1,17 +1,22 @@
|
|||
[English](README.md) | 简体中文
|
||||
|
||||
# 版面分析使用说明
|
||||
|
||||
* [1. 安装whl包](#安装whl包)
|
||||
* [2. 使用](#使用)
|
||||
* [3. 后处理](#后处理)
|
||||
* [4. 指标](#指标)
|
||||
* [5. 训练版面分析模型](#训练版面分析模型)
|
||||
[1. 安装whl包](#安装whl包)
|
||||
|
||||
[2. 使用](#使用)
|
||||
|
||||
[3. 后处理](#后处理)
|
||||
|
||||
[4. 指标](#指标)
|
||||
|
||||
[5. 训练版面分析模型](#训练版面分析模型)
|
||||
|
||||
<a name="安装whl包"></a>
|
||||
|
||||
## 1. 安装whl包
|
||||
```bash
|
||||
wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
pip install -U layoutparser-0.0.0-py3-none-any.whl
|
||||
pip install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
<a name="使用"></a>
|
||||
|
|
@ -21,21 +26,23 @@ pip install -U layoutparser-0.0.0-py3-none-any.whl
|
|||
使用layoutparser识别给定文档的布局：
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import layoutparser as lp
|
||||
image = cv2.imread("imags/paper-image.jpg")
|
||||
image = cv2.imread("doc/table/layout.jpg")
|
||||
image = image[..., ::-1]
|
||||
|
||||
# 加载模型
|
||||
model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
|
||||
model = lp.PaddleDetectionLayoutModel(config_path="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
|
||||
threshold=0.5,
|
||||
label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},
|
||||
enforce_cpu=False,
|
||||
enforce_cpu=False,
|
||||
enable_mkldnn=True)
|
||||
# 检测
|
||||
layout = model.detect(image)
|
||||
|
||||
# 显示结果
|
||||
lp.draw_box(image, layout, box_width=3, show_element_type=True)
|
||||
show_img = lp.draw_box(image, layout, box_width=3, show_element_type=True)
|
||||
show_img.show()
|
||||
```
|
||||
|
||||
下图展示了结果，不同颜色的检测框表示不同的类别，并通过`show_element_type`在框的左上角显示具体类别：
|
||||
|
|
@ -67,7 +74,7 @@ lp.draw_box(image, layout, box_width=3, show_element_type=True)
|
|||
| [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) | lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config | {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"} |
|
||||
|
||||
* TableBank word和TableBank latex分别在word文档、latex文档数据集训练；
|
||||
* 下载TableBank数据集同时包含word和latex。
|
||||
* 下载的TableBank数据集里同时包含word和latex。
|
||||
|
||||
<a name="后处理"></a>
|
||||
|
||||
|
|
@ -76,6 +83,7 @@ lp.draw_box(image, layout, box_width=3, show_element_type=True)
|
|||
版面分析检测包含多个类别，如果只想获取指定类别(如"Text"类别)的检测框、可以使用下述代码：
|
||||
|
||||
```python
|
||||
# 接上面代码
|
||||
# 首先过滤特定文本类型的区域
|
||||
text_blocks = lp.Layout([b for b in layout if b.type=='Text'])
|
||||
figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
|
||||
|
|
@ -84,7 +92,7 @@ figure_blocks = lp.Layout([b for b in layout if b.type=='Figure'])
|
|||
text_blocks = lp.Layout([b for b in text_blocks \
|
||||
if not any(b.is_in(b_fig) for b_fig in figure_blocks)])
|
||||
|
||||
# 对文本区域排序并分配id
|
||||
# 对文本区域排序并分配id
|
||||
h, w = image.shape[:2]
|
||||
|
||||
left_interval = lp.Interval(0, w/2*1.05, axis='x').put_on_canvas(image)
|
||||
|
|
@ -99,9 +107,10 @@ right_blocks.sort(key = lambda b:b.coordinates[1])
|
|||
text_blocks = lp.Layout([b.set(id = idx) for idx, b in enumerate(left_blocks + right_blocks)])
|
||||
|
||||
# 显示结果
|
||||
lp.draw_box(image, text_blocks,
|
||||
box_width=3,
|
||||
show_img = lp.draw_box(image, text_blocks,
|
||||
box_width=3,
|
||||
show_element_id=True)
|
||||
show_img.show()
|
||||
```
|
||||
|
||||
显示只有"Text"类别的结果：
|
||||
|
|
@ -119,15 +128,14 @@ lp.draw_box(image, text_blocks,
|
|||
| PubLayNet | 93.6 | 1713.7ms | 66.6ms |
|
||||
| TableBank | 96.2 | 1968.4ms | 65.1ms |
|
||||
|
||||
**Envrionment：**
|
||||
**Envrionment：**
|
||||
|
||||
**CPU：** Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core
|
||||
**CPU：** Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20GHz，24core
|
||||
|
||||
**GPU：** a single NVIDIA Tesla P40
|
||||
**GPU：** a single NVIDIA Tesla P40
|
||||
|
||||
<a name="训练版面分析模型"></a>
|
||||
|
||||
## 5. 训练版面分析模型
|
||||
|
||||
上述模型基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) 训练，如果您想训练自己的版面分析模型，请参考：[train_layoutparser_model](train_layoutparser_model.md)
|
||||
|
||||
上述模型基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) 训练，如果您想训练自己的版面分析模型，请参考：[train_layoutparser_model](train_layoutparser_model_ch.md)
|
||||
|
|
@ -0,0 +1,203 @@
|
|||
# Training layout-parse
|
||||
|
||||
[1. Installation](#Installation)
|
||||
|
||||
[1.1 Requirements](#Requirements)
|
||||
|
||||
[1.2 Install PaddleDetection](#Install PaddleDetection)
|
||||
|
||||
[2. Data preparation](#Data preparation)
|
||||
|
||||
[3. Configuration](#Configuration)
|
||||
|
||||
[4. Training](#Training)
|
||||
|
||||
[5. Prediction](#Prediction)
|
||||
|
||||
[6. Deployment](#Deployment)
|
||||
|
||||
[6.1 Export model](#Export model)
|
||||
|
||||
[6.2 Inference](#Inference)
|
||||
|
||||
<a name="Installation"></a>
|
||||
|
||||
## 1. Installation
|
||||
|
||||
<a name="Requirements"></a>
|
||||
|
||||
### 1.1 Requirements
|
||||
|
||||
- PaddlePaddle 2.1
|
||||
- OS 64 bit
|
||||
- Python 3(3.5.1+/3.6/3.7/3.8/3.9)，64 bit
|
||||
- pip/pip3(9.0.1+), 64 bit
|
||||
- CUDA >= 10.1
|
||||
- cuDNN >= 7.6
|
||||
|
||||
<a name="Install PaddleDetection"></a>
|
||||
|
||||
### 1.2 Install PaddleDetection
|
||||
|
||||
```bash
|
||||
# Clone PaddleDetection repository
|
||||
cd <path/to/clone/PaddleDetection>
|
||||
git clone https://github.com/PaddlePaddle/PaddleDetection.git
|
||||
|
||||
cd PaddleDetection
|
||||
# Install other dependencies
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
For more installation tutorials, please refer to： [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
|
||||
|
||||
<a name="Data preparation"></a>
|
||||
|
||||
## 2. Data preparation
|
||||
|
||||
Download the [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) dataset
|
||||
|
||||
```bash
|
||||
cd PaddleDetection/dataset/
|
||||
mkdir publaynet
|
||||
# execute the command，download PubLayNet
|
||||
wget -O publaynet.tar.gz https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz?_ga=2.104193024.1076900768.1622560733-649911202.1622560733
|
||||
# unpack
|
||||
tar -xvf publaynet.tar.gz
|
||||
```
|
||||
|
||||
PubLayNet directory structure after decompressing ：
|
||||
|
||||
| File or Folder | Description | num |
|
||||
| :------------- | :----------------------------------------------- | ------- |
|
||||
| `train/` | Images in the training subset | 335,703 |
|
||||
| `val/` | Images in the validation subset | 11,245 |
|
||||
| `test/` | Images in the testing subset | 11,405 |
|
||||
| `train.json` | Annotations for training images | 1 |
|
||||
| `val.json` | Annotations for validation images | 1 |
|
||||
| `LICENSE.txt` | Plaintext version of the CDLA-Permissive license | 1 |
|
||||
| `README.txt` | Text file with the file names and description | 1 |
|
||||
|
||||
For other datasets，please refer to [the PrepareDataSet]((https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md) )
|
||||
|
||||
<a name="Configuration"></a>
|
||||
|
||||
## 3. Configuration
|
||||
|
||||
We use the `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml` configuration for training，the configuration file is as follows
|
||||
|
||||
```bash
|
||||
_BASE_: [
|
||||
'../datasets/coco_detection.yml',
|
||||
'../runtime.yml',
|
||||
'./_base_/ppyolov2_r50vd_dcn.yml',
|
||||
'./_base_/optimizer_365e.yml',
|
||||
'./_base_/ppyolov2_reader.yml',
|
||||
]
|
||||
|
||||
snapshot_epoch: 8
|
||||
weights: output/ppyolov2_r50vd_dcn_365e_coco/model_final
|
||||
```
|
||||
The `ppyolov2_r50vd_dcn_365e_coco.yml` configuration depends on other configuration files, in this case:
|
||||
|
||||
- coco_detection.yml：mainly explains the path of training data and verification data
|
||||
|
||||
- runtime.yml：mainly describes the common parameters, such as whether to use the GPU and how many epoch to save model etc.
|
||||
|
||||
- optimizer_365e.yml：mainly explains the learning rate and optimizer configuration
|
||||
|
||||
- ppyolov2_r50vd_dcn.yml：mainly describes the model and the network
|
||||
|
||||
- ppyolov2_reader.yml：mainly describes the configuration of data readers, such as batch size and number of concurrent loading child processes, and also includes post preprocessing, such as resize and data augmention etc.
|
||||
|
||||
|
||||
Modify the preceding files, such as the dataset path and batch size etc.
|
||||
|
||||
<a name="Training"></a>
|
||||
|
||||
## 4. Training
|
||||
|
||||
PaddleDetection provides single-card/multi-card training mode to meet various training needs of users:
|
||||
|
||||
* GPU single card training
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0 #Don't need to run this command on Windows and Mac
|
||||
python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml
|
||||
```
|
||||
|
||||
* GPU multi-card training
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval
|
||||
```
|
||||
|
||||
--eval: training while verifying
|
||||
|
||||
* Model recovery training
|
||||
|
||||
During the daily training, if training is interrupted due to some reasons, you can use the -r command to resume the training:
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval -r output/ppyolov2_r50vd_dcn_365e_coco/10000
|
||||
```
|
||||
|
||||
Note: If you encounter "`Out of memory error`" , try reducing `batch_size` in the `ppyolov2_reader.yml` file
|
||||
|
||||
prediction<a name="Prediction"></a>
|
||||
|
||||
## 5. Prediction
|
||||
|
||||
Set parameters and use PaddleDetection to predict：
|
||||
|
||||
```bash
|
||||
export CUDA_VISIBLE_DEVICES=0
|
||||
python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer_img=images/paper-image.jpg --output_dir=infer_output/ --draw_threshold=0.5 -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final --use_vdl=Ture
|
||||
```
|
||||
|
||||
`--draw_threshold` is an optional parameter. According to the calculation of [NMS](https://ieeexplore.ieee.org/document/1699659), different threshold will produce different results, ` keep_top_k ` represent the maximum amount of output target, the default value is 10. You can set different value according to your own actual situation。
|
||||
|
||||
<a name="Deployment"></a>
|
||||
|
||||
## 6. Deployment
|
||||
|
||||
Use your trained model in Layout Parser
|
||||
|
||||
<a name="Export model"></a>
|
||||
|
||||
### 6.1 Export model
|
||||
|
||||
n the process of model training, the model file saved contains the process of forward prediction and back propagation. In the actual industrial deployment, there is no need for back propagation. Therefore, the model should be translated into the model format required by the deployment. The `tools/export_model.py` script is provided in PaddleDetection to export the model.
|
||||
|
||||
The exported model name defaults to `model.*`, Layout Parser's code model is `inference.*`, So change [PaddleDetection/ppdet/engine/trainer. Py ](https://github.com/PaddlePaddle/PaddleDetection/blob/b87a1ea86fa18ce69e44a17ad1b49c1326f19ff9/ppdet/engine/trainer.py# L512) (click on the link to see the detailed line of code), change 'model' to 'inference'.
|
||||
|
||||
Execute the script to export model:
|
||||
|
||||
```bash
|
||||
python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --output_dir=./inference -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final.pdparams
|
||||
```
|
||||
|
||||
The prediction model is exported to `inference/ppyolov2_r50vd_dcn_365e_coco` ,including:`infer_cfg.yml`(prediction not required), `inference.pdiparams`, `inference.pdiparams.info`,`inference.pdmodel`
|
||||
|
||||
More model export tutorials, please refer to：[EXPORT_MODEL](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/deploy/EXPORT_MODEL.md)
|
||||
|
||||
<a name="Inference"></a>
|
||||
|
||||
### 6.2 Inference
|
||||
|
||||
`model_path` represent the trained model path, and layoutparser is used to predict:
|
||||
|
||||
```bash
|
||||
import layoutparser as lp
|
||||
model = lp.PaddleDetectionLayoutModel(model_path="inference/ppyolov2_r50vd_dcn_365e_coco", threshold=0.5,label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},enforce_cpu=True,enable_mkldnn=True)
|
||||
```
|
||||
|
||||
|
||||
|
||||
***
|
||||
|
||||
More PaddleDetection training tutorials，please reference：[PaddleDetection Training](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)
|
||||
|
||||
***
|
||||
|
|
@ -1,15 +1,24 @@
|
|||
# 训练版面分析
|
||||
|
||||
* [1. 安装](#安装)
|
||||
* [1.1 环境要求](#环境要求)
|
||||
* [1.2 安装PaddleDetection](#安装PaddleDetection)
|
||||
* [2. 准备数据](#准备数据)
|
||||
* [3. 配置文件改动和说明](#配置文件改动和说明)
|
||||
* [4. PaddleDetection训练](#训练)
|
||||
* [5. PaddleDetection预测](#预测)
|
||||
* [6. 预测部署](#预测部署)
|
||||
* [6.1 模型导出](#模型导出)
|
||||
* [6.2 layout parser预测](#layout_parser预测)
|
||||
[1. 安装](#安装)
|
||||
|
||||
[1.1 环境要求](#环境要求)
|
||||
|
||||
[1.2 安装PaddleDetection](#安装PaddleDetection)
|
||||
|
||||
[2. 准备数据](#准备数据)
|
||||
|
||||
[3. 配置文件改动和说明](#配置文件改动和说明)
|
||||
|
||||
[4. PaddleDetection训练](#训练)
|
||||
|
||||
[5. PaddleDetection预测](#预测)
|
||||
|
||||
[6. 预测部署](#预测部署)
|
||||
|
||||
[6.1 模型导出](#模型导出)
|
||||
|
||||
[6.2 layout parser预测](#layout_parser预测)
|
||||
|
||||
<a name="安装"></a>
|
||||
|
||||
|
|
@ -64,10 +73,10 @@ tar -xvf publaynet.tar.gz
|
|||
| `train/` | Images in the training subset | 335,703 |
|
||||
| `val/` | Images in the validation subset | 11,245 |
|
||||
| `test/` | Images in the testing subset | 11,405 |
|
||||
| `train.json` | Annotations for training images | |
|
||||
| `val.json` | Annotations for validation images | |
|
||||
| `LICENSE.txt` | Plaintext version of the CDLA-Permissive license | |
|
||||
| `README.txt` | Text file with the file names and description | |
|
||||
| `train.json` | Annotations for training images | 1 |
|
||||
| `val.json` | Annotations for validation images | 1 |
|
||||
| `LICENSE.txt` | Plaintext version of the CDLA-Permissive license | 1 |
|
||||
| `README.txt` | Text file with the file names and description | 1 |
|
||||
|
||||
如果使用其它数据集，请参考[准备训练数据](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/PrepareDataSet.md)
|
||||
|
||||
|
|
@ -77,23 +86,30 @@ tar -xvf publaynet.tar.gz
|
|||
|
||||
我们使用 `configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml`配置进行训练，配置文件摘要如下：
|
||||
|
||||
<div align='center'>
|
||||
<img src='../../doc/table/PaddleDetection_config.png' width='600px'/>
|
||||
</div>
|
||||
|
||||
从上图看到 `ppyolov2_r50vd_dcn_365e_coco.yml` 配置需要依赖其他的配置文件，在该例子中需要依赖:
|
||||
```bash
|
||||
_BASE_: [
|
||||
'../datasets/coco_detection.yml',
|
||||
'../runtime.yml',
|
||||
'./_base_/ppyolov2_r50vd_dcn.yml',
|
||||
'./_base_/optimizer_365e.yml',
|
||||
'./_base_/ppyolov2_reader.yml',
|
||||
]
|
||||
|
||||
snapshot_epoch: 8
|
||||
weights: output/ppyolov2_r50vd_dcn_365e_coco/model_final
|
||||
```
|
||||
coco_detection.yml：主要说明了训练数据和验证数据的路径
|
||||
从中可以看到 `ppyolov2_r50vd_dcn_365e_coco.yml` 配置需要依赖其他的配置文件，在该例子中需要依赖:
|
||||
|
||||
runtime.yml：主要说明了公共的运行参数，比如是否使用GPU、每多少个epoch存储checkpoint等
|
||||
- coco_detection.yml：主要说明了训练数据和验证数据的路径
|
||||
|
||||
optimizer_365e.yml：主要说明了学习率和优化器的配置
|
||||
- runtime.yml：主要说明了公共的运行参数，比如是否使用GPU、每多少个epoch存储checkpoint等
|
||||
|
||||
ppyolov2_r50vd_dcn.yml：主要说明模型和主干网络的情况
|
||||
- optimizer_365e.yml：主要说明了学习率和优化器的配置
|
||||
|
||||
- ppyolov2_r50vd_dcn.yml：主要说明模型和主干网络的情况
|
||||
|
||||
- ppyolov2_reader.yml：主要说明数据读取器配置，如batch size，并发加载子进程数等，同时包含读取后预处理操作，如resize、数据增强等等
|
||||
|
||||
ppyolov2_reader.yml：主要说明数据读取器配置，如batch size，并发加载子进程数等，同时包含读取后预处理操作，如resize、数据增强等等
|
||||
```
|
||||
|
||||
根据实际情况，修改上述文件，比如数据集路径、batch size等。
|
||||
|
||||
|
|
@ -147,7 +163,7 @@ python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer
|
|||
|
||||
## 6. 预测部署
|
||||
|
||||
在layout parser中使用自己训练好的模型，
|
||||
在layout parser中使用自己训练好的模型。
|
||||
|
||||
<a name="模型导出"></a>
|
||||
|
||||
|
|
@ -185,4 +201,3 @@ model = lp.PaddleDetectionLayoutModel(model_path="inference/ppyolov2_r50vd_dcn_3
|
|||
更多PaddleDetection训练教程，请参考：[PaddleDetection训练](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/GETTING_STARTED_cn.md)
|
||||
|
||||
***
|
||||
|
||||
|
|
@ -26,26 +26,33 @@ import numpy as np
|
|||
import time
|
||||
import logging
|
||||
|
||||
import layoutparser as lp
|
||||
|
||||
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
|
||||
from ppocr.utils.logging import get_logger
|
||||
from tools.infer.predict_system import TextSystem
|
||||
from test1.table.predict_table import TableSystem, to_excel
|
||||
from test1.utility import parse_args, draw_result
|
||||
from ppstructure.table.predict_table import TableSystem, to_excel
|
||||
from ppstructure.utility import parse_args, draw_structure_result
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
class OCRSystem(object):
|
||||
def __init__(self, args):
|
||||
args.det_limit_type = 'resize_long'
|
||||
import layoutparser as lp
|
||||
# args.det_limit_type = 'resize_long'
|
||||
args.drop_score = 0
|
||||
if not args.show_log:
|
||||
logger.setLevel(logging.INFO)
|
||||
self.text_system = TextSystem(args)
|
||||
self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer)
|
||||
self.table_layout = lp.PaddleDetectionLayoutModel("lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
|
||||
|
||||
config_path = None
|
||||
model_path = None
|
||||
if os.path.isdir(args.layout_path_model):
|
||||
model_path = args.layout_path_model
|
||||
else:
|
||||
config_path = args.layout_path_model
|
||||
self.table_layout = lp.PaddleDetectionLayoutModel(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
|
||||
|
|
@ -65,24 +72,35 @@ class OCRSystem(object):
|
|||
filter_boxes, filter_rec_res = self.text_system(roi_img)
|
||||
filter_boxes = [x + [x1, y1] for x in filter_boxes]
|
||||
filter_boxes = [x.reshape(-1).tolist() for x in filter_boxes]
|
||||
|
||||
res = (filter_boxes, filter_rec_res)
|
||||
res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'res': res})
|
||||
# remove style char
|
||||
style_token = ['<strike>', '<strike>', '<sup>', '</sub>', '<b>', '</b>', '<sub>', '</sup>',
|
||||
'<overline>', '</overline>', '<underline>', '</underline>', '<i>', '</i>']
|
||||
filter_rec_res_tmp = []
|
||||
for rec_res in filter_rec_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})
|
||||
return res_list
|
||||
|
||||
|
||||
def save_res(res, save_folder, img_name):
|
||||
def save_structure_res(res, save_folder, img_name):
|
||||
excel_save_folder = os.path.join(save_folder, img_name)
|
||||
os.makedirs(excel_save_folder, exist_ok=True)
|
||||
# save res
|
||||
for region in res:
|
||||
if region['type'] == 'Table':
|
||||
excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox']))
|
||||
to_excel(region['res'], excel_path)
|
||||
elif region['type'] == 'Figure':
|
||||
pass
|
||||
else:
|
||||
with open(os.path.join(excel_save_folder, 'res.txt'), 'a', encoding='utf8') as f:
|
||||
with open(os.path.join(excel_save_folder, 'res.txt'), 'w', encoding='utf8') as f:
|
||||
for region in res:
|
||||
if region['type'] == 'Table':
|
||||
excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox']))
|
||||
to_excel(region['res'], excel_path)
|
||||
if region['type'] == 'Figure':
|
||||
roi_img = region['img']
|
||||
img_path = os.path.join(excel_save_folder, '{}.jpg'.format(region['bbox']))
|
||||
cv2.imwrite(img_path, roi_img)
|
||||
else:
|
||||
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))
|
||||
|
||||
|
|
@ -108,8 +126,8 @@ def main(args):
|
|||
continue
|
||||
starttime = time.time()
|
||||
res = structure_sys(img)
|
||||
save_res(res, save_folder, img_name)
|
||||
draw_img = draw_result(img, res, args.vis_font_path)
|
||||
save_structure_res(res, save_folder, img_name)
|
||||
draw_img = draw_structure_result(img, res, args.vis_font_path)
|
||||
cv2.imwrite(os.path.join(save_folder, img_name, 'show.jpg'), draw_img)
|
||||
logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
|
||||
elapse = time.time() - starttime
|
||||
|
|
@ -0,0 +1,116 @@
|
|||
# Table Recognition
|
||||
|
||||
## 1. pipeline
|
||||
The table recognition mainly contains three models
|
||||
1. Single line text detection-DB
|
||||
2. Single line text recognition-CRNN
|
||||
3. Table structure and cell coordinate prediction-RARE
|
||||
|
||||
The table recognition flow chart is as follows
|
||||
|
||||
|
||||
|
||||
1. The coordinates of single-line text is detected by DB model, and then sends it to the recognition model to get the recognition result.
|
||||
2. The table structure and cell coordinates is predicted by RARE model.
|
||||
3. The recognition result of the cell is combined by the coordinates, recognition result of the single line and the coordinates of the cell.
|
||||
4. The cell recognition result and the table structure together construct the html string of the table.
|
||||
|
||||
## 2. Performance
|
||||
We evaluated the algorithm on the PubTabNet<sup>[1]</sup> eval dataset, and the performance is as follows:
|
||||
|
||||
|
||||
|Method|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
|
||||
| --- | --- |
|
||||
| EDD<sup>[2]</sup> | 88.3 |
|
||||
| Ours | 93.32 |
|
||||
|
||||
## 3. How to use
|
||||
|
||||
### 3.1 quick start
|
||||
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
|
||||
# download model
|
||||
mkdir inference && cd inference
|
||||
# Download the detection model of the ultra-lightweight table English OCR model and unzip it
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar
|
||||
# Download the recognition model of the ultra-lightweight table English OCR model and unzip it
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar && tar xf en_ppocr_mobile_v2.0_table_rec_infer.tar
|
||||
# Download the ultra-lightweight English table inch model and unzip it
|
||||
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 ..
|
||||
# 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/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --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`.
|
||||
|
||||
After running, the excel sheet of each picture will be saved in the directory specified by the output field
|
||||
|
||||
### 3.2 Train
|
||||
|
||||
In this chapter, we only introduce the training of the table structure model, For model training of [text detection](../../doc/doc_en/detection_en.md) and [text recognition](../../doc/doc_en/recognition_en.md), please refer to the corresponding documents
|
||||
|
||||
#### data preparation
|
||||
The training data uses public data set [PubTabNet](https://arxiv.org/abs/1911.10683 ), Can be downloaded from the official [website](https://github.com/ibm-aur-nlp/PubTabNet) 。The PubTabNet data set contains about 500,000 images, as well as annotations in html format。
|
||||
|
||||
#### Start training
|
||||
*If you are installing the cpu version of paddle, please modify the `use_gpu` field in the configuration file to false*
|
||||
```shell
|
||||
# single GPU training
|
||||
python3 tools/train.py -c configs/table/table_mv3.yml
|
||||
# multi-GPU training
|
||||
# Set the GPU ID used by the '--gpus' parameter.
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/table_mv3.yml
|
||||
```
|
||||
|
||||
In the above instruction, use `-c` to select the training to use the `configs/table/table_mv3.yml` configuration file.
|
||||
For a detailed explanation of the configuration file, please refer to [config](../../doc/doc_en/config_en.md).
|
||||
|
||||
#### load trained model and continue training
|
||||
|
||||
If you expect to load trained model and continue the training again, you can specify the parameter `Global.checkpoints` as the model path to be loaded.
|
||||
|
||||
```shell
|
||||
python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./your/trained/model
|
||||
```
|
||||
|
||||
**Note**: The priority of `Global.checkpoints` is higher than that of `Global.pretrain_weights`, that is, when two parameters are specified at the same time, the model specified by `Global.checkpoints` will be loaded first. If the model path specified by `Global.checkpoints` is wrong, the one specified by `Global.pretrain_weights` will be loaded.
|
||||
|
||||
### 3.3 Eval
|
||||
|
||||
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
|
||||
{"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>"],
|
||||
[[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"]]
|
||||
]}
|
||||
```
|
||||
In gt json, the key is the image name, the value is the corresponding gt, and gt is a list composed of four items, and each item is
|
||||
1. HTML string list of table structure
|
||||
2. The coordinates of each cell (not including the empty text in the cell)
|
||||
3. The text information in each cell (not including the empty text in the cell)
|
||||
|
||||
Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output.
|
||||
```python
|
||||
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
|
||||
```
|
||||
|
||||
If the PubLatNet eval dataset is used, it will be output
|
||||
```bash
|
||||
teds: 93.32
|
||||
```
|
||||
|
||||
### 3.4 Inference
|
||||
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
python3 table/predict_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 --output ../output/table
|
||||
```
|
||||
After running, the excel sheet of each picture will be saved in the directory specified by the output field
|
||||
|
||||
Reference
|
||||
1. https://github.com/ibm-aur-nlp/PubTabNet
|
||||
2. https://arxiv.org/pdf/1911.10683
|
||||
|
|
@ -0,0 +1,113 @@
|
|||
# 表格识别
|
||||
|
||||
## 1. 表格识别 pipeline
|
||||
表格识别主要包含三个模型
|
||||
1. 单行文本检测-DB
|
||||
2. 单行文本识别-CRNN
|
||||
3. 表格结构和cell坐标预测-RARE
|
||||
|
||||
具体流程图如下
|
||||
|
||||
|
||||
|
||||
流程说明:
|
||||
|
||||
1. 图片由单行文字检测模型检测到单行文字的坐标，然后送入识别模型拿到识别结果。
|
||||
2. 图片由表格结构和cell坐标预测模型拿到表格的结构信息和单元格的坐标信息。
|
||||
3. 由单行文字的坐标、识别结果和单元格的坐标一起组合出单元格的识别结果。
|
||||
4. 单元格的识别结果和表格结构一起构造表格的html字符串。
|
||||
|
||||
## 2. 性能
|
||||
我们在 PubTabNet<sup>[1]</sup> 评估数据集上对算法进行了评估，性能如下
|
||||
|
||||
|
||||
|算法|[TEDS(Tree-Edit-Distance-based Similarity)](https://github.com/ibm-aur-nlp/PubTabNet/tree/master/src)|
|
||||
| --- | --- |
|
||||
| EDD<sup>[2]</sup> | 88.3 |
|
||||
| Ours | 93.32 |
|
||||
|
||||
## 3. 使用
|
||||
|
||||
### 3.1 快速开始
|
||||
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
|
||||
# 下载模型
|
||||
mkdir inference && cd inference
|
||||
# 下载超轻量级表格英文OCR模型的检测模型并解压
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar && tar xf en_ppocr_mobile_v2.0_table_det_infer.tar
|
||||
# 下载超轻量级表格英文OCR模型的识别模型并解压
|
||||
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar && tar xf en_ppocr_mobile_v2.0_table_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 ..
|
||||
# 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/ppocr_keys_v1.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=ch --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
|
||||
|
||||
note: 上述模型是在 PubLayNet 数据集上训练的表格识别模型，仅支持英文扫描场景，如需识别其他场景需要自己训练模型后替换 `det_model_dir`,`rec_model_dir`,`table_model_dir`三个字段即可。
|
||||
|
||||
### 3.2 训练
|
||||
在这一章节中，我们仅介绍表格结构模型的训练，[文字检测](../../doc/doc_ch/detection.md)和[文字识别](../../doc/doc_ch/recognition.md)的模型训练请参考对应的文档。
|
||||
|
||||
#### 数据准备
|
||||
训练数据使用公开数据集PubTabNet ([论文](https://arxiv.org/abs/1911.10683)，[下载地址](https://github.com/ibm-aur-nlp/PubTabNet))。PubTabNet数据集包含约50万张表格数据的图像，以及图像对应的html格式的注释。
|
||||
|
||||
#### 启动训练
|
||||
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
|
||||
```shell
|
||||
# 单机单卡训练
|
||||
python3 tools/train.py -c configs/table/table_mv3.yml
|
||||
# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/table_mv3.yml
|
||||
```
|
||||
|
||||
上述指令中，通过-c 选择训练使用configs/table/table_mv3.yml配置文件。有关配置文件的详细解释，请参考[链接](../../doc/doc_ch/config.md)。
|
||||
|
||||
#### 断点训练
|
||||
|
||||
如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
|
||||
```shell
|
||||
python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./your/trained/model
|
||||
```
|
||||
|
||||
**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
|
||||
|
||||
|
||||
### 3.3 评估
|
||||
|
||||
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
|
||||
{"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>"],
|
||||
[[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"]]
|
||||
]}
|
||||
```
|
||||
json 中，key为图片名，value为对应的gt，gt是一个由三个item组成的list，每个item分别为
|
||||
1. 表格结构的html字符串list
|
||||
2. 每个cell的坐标 (不包括cell里文字为空的)
|
||||
3. 每个cell里的文字信息 (不包括cell里文字为空的)
|
||||
|
||||
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
|
||||
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
|
||||
```
|
||||
如使用PubLatNet评估数据集，将会输出
|
||||
```bash
|
||||
teds: 93.32
|
||||
```
|
||||
|
||||
### 3.4 预测
|
||||
|
||||
```python
|
||||
cd PaddleOCR/ppstructure
|
||||
python3 table/predict_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 --output ../output/table
|
||||
```
|
||||
|
||||
Reference
|
||||
1. https://github.com/ibm-aur-nlp/PubTabNet
|
||||
2. https://arxiv.org/pdf/1911.10683
|
||||
|
|
@ -11,7 +11,3 @@
|
|||
# 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.
|
||||
|
||||
from .paddlestructure import PaddleStructure, draw_result, to_excel
|
||||
|
||||
__all__ = ['PaddleStructure', 'draw_result', 'to_excel']
|
||||
|
|
@ -20,9 +20,9 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
|
|||
import cv2
|
||||
import json
|
||||
from tqdm import tqdm
|
||||
from test1.table.table_metric import TEDS
|
||||
from test1.table.predict_table import TableSystem
|
||||
from test1.utility import init_args
|
||||
from ppstructure.table.table_metric import TEDS
|
||||
from ppstructure.table.predict_table import TableSystem
|
||||
from ppstructure.utility import init_args
|
||||
from ppocr.utils.logging import get_logger
|
||||
|
||||
logger = get_logger()
|
||||
|
|
@ -46,20 +46,20 @@ def main(gt_path, img_root, args):
|
|||
pred_html = text_sys(img)
|
||||
pred_htmls.append(pred_html)
|
||||
|
||||
gt_structures, gt_bboxes, gt_contents, contents_with_block = jsons_gt[img_name]
|
||||
gt_html, gt = get_gt_html(gt_structures, contents_with_block)
|
||||
gt_structures, gt_bboxes, gt_contents = jsons_gt[img_name]
|
||||
gt_html, gt = get_gt_html(gt_structures, gt_contents)
|
||||
gt_htmls.append(gt_html)
|
||||
scores = teds.batch_evaluate_html(gt_htmls, pred_htmls)
|
||||
logger.info('teds:', sum(scores) / len(scores))
|
||||
|
||||
|
||||
def get_gt_html(gt_structures, contents_with_block):
|
||||
def get_gt_html(gt_structures, gt_contents):
|
||||
end_html = []
|
||||
td_index = 0
|
||||
for tag in gt_structures:
|
||||
if '</td>' in tag:
|
||||
if contents_with_block[td_index] != []:
|
||||
end_html.extend(contents_with_block[td_index])
|
||||
if gt_contents[td_index] != []:
|
||||
end_html.extend(gt_contents[td_index])
|
||||
end_html.append(tag)
|
||||
td_index += 1
|
||||
else:
|
||||
|
|
@ -22,17 +22,14 @@ os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
|
|||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import math
|
||||
import time
|
||||
import traceback
|
||||
import paddle
|
||||
|
||||
import tools.infer.utility as utility
|
||||
from ppocr.data import create_operators, transform
|
||||
from ppocr.postprocess import build_post_process
|
||||
from ppocr.utils.logging import get_logger
|
||||
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
|
||||
from test1.utility import parse_args
|
||||
from ppstructure.utility import parse_args
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
|
@ -30,9 +30,9 @@ import tools.infer.predict_rec as predict_rec
|
|||
import tools.infer.predict_det as predict_det
|
||||
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
|
||||
from ppocr.utils.logging import get_logger
|
||||
from test1.table.matcher import distance, compute_iou
|
||||
from test1.utility import parse_args
|
||||
import test1.table.predict_structure as predict_strture
|
||||
from ppstructure.table.matcher import distance, compute_iou
|
||||
from ppstructure.utility import parse_args
|
||||
import ppstructure.table.predict_structure as predict_strture
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
|
@ -27,7 +27,7 @@ def init_args():
|
|||
parser.add_argument("--table_model_dir", type=str)
|
||||
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("--layout_path_model", type=str, default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
|
||||
return parser
|
||||
|
||||
|
||||
|
|
@ -36,15 +36,13 @@ def parse_args():
|
|||
return parser.parse_args()
|
||||
|
||||
|
||||
def draw_result(image, result, font_path):
|
||||
def draw_structure_result(image, result, font_path):
|
||||
if isinstance(image, np.ndarray):
|
||||
image = Image.fromarray(image)
|
||||
boxes, txts, scores = [], [], []
|
||||
for region in result:
|
||||
if region['type'] == 'Table':
|
||||
pass
|
||||
elif region['type'] == 'Figure':
|
||||
pass
|
||||
else:
|
||||
for box, rec_res in zip(region['res'][0], region['res'][1]):
|
||||
boxes.append(np.array(box).reshape(-1, 2))
|
||||
3
setup.py
3
setup.py
|
|
@ -14,6 +14,7 @@
|
|||
|
||||
from setuptools import setup
|
||||
from io import open
|
||||
from paddleocr import VERSION
|
||||
|
||||
with open('requirements.txt', encoding="utf-8-sig") as f:
|
||||
requirements = f.readlines()
|
||||
|
|
@ -32,7 +33,7 @@ setup(
|
|||
package_dir={'paddleocr': ''},
|
||||
include_package_data=True,
|
||||
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
|
||||
version='2.0.6',
|
||||
version=VERSION,
|
||||
install_requires=requirements,
|
||||
license='Apache License 2.0',
|
||||
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
|
||||
|
|
|
|||
|
|
@ -1,9 +0,0 @@
|
|||
include LICENSE
|
||||
include README.md
|
||||
|
||||
recursive-include ppocr/utils *.txt utility.py logging.py network.py
|
||||
recursive-include ppocr/data/ *.py
|
||||
recursive-include ppocr/postprocess *.py
|
||||
recursive-include tools/infer *.py
|
||||
recursive-include test1 *.py
|
||||
|
||||
86
test1/api.md
86
test1/api.md
|
|
@ -1,86 +0,0 @@
|
|||
# PaddleStructure
|
||||
|
||||
install layoutparser
|
||||
```sh
|
||||
wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
pip3 install layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
## 1. Introduction to pipeline
|
||||
|
||||
PaddleStructure is a toolkit for complex layout text OCR, the process is as follows
|
||||
|
||||
|
||||
|
||||
In PaddleStructure, the image will be analyzed by layoutparser first. In the layout analysis, the area in the image will be classified, and the OCR process will be carried out according to the category.
|
||||
|
||||
Currently layoutparser will output five categories:
|
||||
1. Text
|
||||
2. Title
|
||||
3. Figure
|
||||
4. List
|
||||
5. Table
|
||||
|
||||
Types 1-4 follow the traditional OCR process, and 5 follow the Table OCR process.
|
||||
|
||||
## 2. LayoutParser
|
||||
|
||||
|
||||
## 3. Table OCR
|
||||
|
||||
[doc](table/README.md)
|
||||
|
||||
## 4. Predictive by inference engine
|
||||
|
||||
Use the following commands to complete the inference
|
||||
```python
|
||||
python3 table/predict_system.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 --output ../output/table
|
||||
```
|
||||
After running, each image will have a directory with the same name under the directory specified in the output field. Each table in the picture will be stored as an excel, and the excel file name will be the coordinates of the table in the image.
|
||||
|
||||
## 5. PaddleStructure whl package introduction
|
||||
|
||||
### 5.1 Use
|
||||
|
||||
5.1.1 Use by code
|
||||
```python
|
||||
import os
|
||||
import cv2
|
||||
from paddlestructure import PaddleStructure,draw_result,save_res
|
||||
|
||||
table_engine = PaddleStructure(show_log=True)
|
||||
|
||||
save_folder = './output/table'
|
||||
img_path = '../doc/table/1.png'
|
||||
img = cv2.imread(img_path)
|
||||
result = table_engine(img)
|
||||
save_res(result, save_folder,os.path.basename(img_path).split('.')[0])
|
||||
|
||||
for line in result:
|
||||
print(line)
|
||||
|
||||
from PIL import Image
|
||||
|
||||
font_path = 'path/tp/PaddleOCR/doc/fonts/simfang.ttf'
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
im_show = draw_result(image, result,font_path=font_path)
|
||||
im_show = Image.fromarray(im_show)
|
||||
im_show.save('result.jpg')
|
||||
```
|
||||
|
||||
5.1.2 Use by command line
|
||||
```bash
|
||||
paddlestructure --image_dir=../doc/table/1.png
|
||||
```
|
||||
|
||||
### Parameter Description
|
||||
Most of the parameters are consistent with the paddleocr whl package, see [whl package documentation](../doc/doc_ch/whl.md)
|
||||
|
||||
| Parameter | Description | Default |
|
||||
|------------------------|------------------------------------------------------|------------------|
|
||||
| output | The path where excel and recognition results are saved | ./output/table |
|
||||
| structure_max_len | When the table structure model predicts, the long side of the image is resized | 488 |
|
||||
| structure_model_dir | Table structure inference model path | None |
|
||||
| structure_char_type | Dictionary path used by table structure model | ../ppocr/utils/dict/table_structure_dict.tx |
|
||||
|
||||
|
||||
|
|
@ -1,86 +0,0 @@
|
|||
# PaddleStructure
|
||||
|
||||
安装layoutparser
|
||||
```sh
|
||||
wget https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
|
||||
pip3 install layoutparser-0.0.0-py3-none-any.whl
|
||||
```
|
||||
|
||||
## 1. pipeline介绍
|
||||
|
||||
PaddleStructure 是一个用于复杂板式文字OCR的工具包，流程如下
|
||||
|
||||
|
||||
在PaddleStructure中，图片会先经由layoutparser进行版面分析，在版面分析中，会对图片里的区域进行分类，根据根据类别进行对于的ocr流程。
|
||||
|
||||
目前layoutparser会输出五个类别:
|
||||
1. Text
|
||||
2. Title
|
||||
3. Figure
|
||||
4. List
|
||||
5. Table
|
||||
|
||||
1-4类走传统的OCR流程，5走表格的OCR流程。
|
||||
|
||||
## 2. LayoutParser
|
||||
|
||||
[文档](layout/README.md)
|
||||
|
||||
## 3. Table OCR
|
||||
|
||||
[文档](table/README_ch.md)
|
||||
|
||||
## 4. 预测引擎推理
|
||||
|
||||
使用如下命令即可完成预测引擎的推理
|
||||
```python
|
||||
python3 table/predict_system.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 --output ../output/table
|
||||
```
|
||||
运行完成后，每张图片会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，excel文件名为表格在图片里的坐标。
|
||||
|
||||
## 5. PaddleStructure whl包介绍
|
||||
|
||||
### 5.1 使用
|
||||
|
||||
5.1.1 代码使用
|
||||
```python
|
||||
import os
|
||||
import cv2
|
||||
from paddlestructure import PaddleStructure,draw_result,save_res
|
||||
|
||||
table_engine = PaddleStructure(show_log=True)
|
||||
|
||||
save_folder = './output/table'
|
||||
img_path = '../doc/table/1.png'
|
||||
img = cv2.imread(img_path)
|
||||
result = table_engine(img)
|
||||
save_res(result, save_folder,os.path.basename(img_path).split('.')[0])
|
||||
|
||||
for line in result:
|
||||
print(line)
|
||||
|
||||
from PIL import Image
|
||||
|
||||
font_path = 'path/tp/PaddleOCR/doc/fonts/simfang.ttf'
|
||||
image = Image.open(img_path).convert('RGB')
|
||||
im_show = draw_result(image, result,font_path=font_path)
|
||||
im_show = Image.fromarray(im_show)
|
||||
im_show.save('result.jpg')
|
||||
```
|
||||
|
||||
5.1.2 命令行使用
|
||||
```bash
|
||||
paddlestructure --image_dir=../doc/table/1.png
|
||||
```
|
||||
|
||||
### 参数说明
|
||||
大部分参数和paddleocr whl包保持一致，见 [whl包文档](../doc/doc_ch/whl.md)
|
||||
|
||||
| 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.tx |
|
||||
|
||||
|
||||
|
|
@ -1,148 +0,0 @@
|
|||
# Copyright (c) 2020 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 logging
|
||||
import os
|
||||
import sys
|
||||
|
||||
__dir__ = os.path.dirname(__file__)
|
||||
sys.path.append(__dir__)
|
||||
sys.path.append(os.path.join(__dir__, '..'))
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
|
||||
from ppocr.utils.logging import get_logger
|
||||
from test1.predict_system import OCRSystem, save_res
|
||||
from test1.table.predict_table import to_excel
|
||||
from test1.utility import init_args, draw_result
|
||||
|
||||
logger = get_logger()
|
||||
from ppocr.utils.utility import check_and_read_gif, get_image_file_list
|
||||
from ppocr.utils.network import maybe_download, download_with_progressbar, confirm_model_dir_url, is_link
|
||||
|
||||
__all__ = ['PaddleStructure', 'draw_result', 'save_res']
|
||||
|
||||
VERSION = '2.1'
|
||||
BASE_DIR = os.path.expanduser("~/.paddlestructure/")
|
||||
|
||||
model_urls = {
|
||||
'det': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar',
|
||||
'rec': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
|
||||
'table': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar'
|
||||
|
||||
}
|
||||
|
||||
|
||||
def parse_args(mMain=True):
|
||||
import argparse
|
||||
parser = init_args()
|
||||
parser.add_help = mMain
|
||||
|
||||
for action in parser._actions:
|
||||
if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
|
||||
action.default = None
|
||||
if mMain:
|
||||
return parser.parse_args()
|
||||
else:
|
||||
inference_args_dict = {}
|
||||
for action in parser._actions:
|
||||
inference_args_dict[action.dest] = action.default
|
||||
return argparse.Namespace(**inference_args_dict)
|
||||
|
||||
|
||||
class PaddleStructure(OCRSystem):
|
||||
def __init__(self, **kwargs):
|
||||
params = parse_args(mMain=False)
|
||||
params.__dict__.update(**kwargs)
|
||||
if not params.show_log:
|
||||
logger.setLevel(logging.INFO)
|
||||
params.use_angle_cls = False
|
||||
# init model dir
|
||||
params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'det'),
|
||||
model_urls['det'])
|
||||
params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'rec'),
|
||||
model_urls['rec'])
|
||||
params.table_model_dir, table_url = confirm_model_dir_url(params.table_model_dir,
|
||||
os.path.join(BASE_DIR, VERSION, 'table'),
|
||||
model_urls['table'])
|
||||
# download model
|
||||
maybe_download(params.det_model_dir, det_url)
|
||||
maybe_download(params.rec_model_dir, rec_url)
|
||||
maybe_download(params.table_model_dir, table_url)
|
||||
|
||||
if params.rec_char_dict_path is None:
|
||||
params.rec_char_type = 'EN'
|
||||
if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')):
|
||||
params.rec_char_dict_path = str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')
|
||||
else:
|
||||
params.rec_char_dict_path = str(Path(__file__).parent.parent / 'ppocr/utils/dict/table_dict.txt')
|
||||
if params.table_char_dict_path is None:
|
||||
if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')):
|
||||
params.table_char_dict_path = str(
|
||||
Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')
|
||||
else:
|
||||
params.table_char_dict_path = str(
|
||||
Path(__file__).parent.parent / 'ppocr/utils/dict/table_structure_dict.txt')
|
||||
|
||||
print(params)
|
||||
super().__init__(params)
|
||||
|
||||
def __call__(self, img):
|
||||
if isinstance(img, str):
|
||||
# download net image
|
||||
if img.startswith('http'):
|
||||
download_with_progressbar(img, 'tmp.jpg')
|
||||
img = 'tmp.jpg'
|
||||
image_file = img
|
||||
img, flag = check_and_read_gif(image_file)
|
||||
if not flag:
|
||||
with open(image_file, 'rb') as f:
|
||||
np_arr = np.frombuffer(f.read(), dtype=np.uint8)
|
||||
img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
|
||||
if img is None:
|
||||
logger.error("error in loading image:{}".format(image_file))
|
||||
return None
|
||||
if isinstance(img, np.ndarray) and len(img.shape) == 2:
|
||||
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
|
||||
|
||||
res = super().__call__(img)
|
||||
return res
|
||||
|
||||
|
||||
def main():
|
||||
# for cmd
|
||||
args = parse_args(mMain=True)
|
||||
image_dir = args.image_dir
|
||||
save_folder = args.output
|
||||
if image_dir.startswith('http'):
|
||||
download_with_progressbar(image_dir, 'tmp.jpg')
|
||||
image_file_list = ['tmp.jpg']
|
||||
else:
|
||||
image_file_list = get_image_file_list(args.image_dir)
|
||||
if len(image_file_list) == 0:
|
||||
logger.error('no images find in {}'.format(args.image_dir))
|
||||
return
|
||||
|
||||
structure_engine = PaddleStructure(**(args.__dict__))
|
||||
for img_path in image_file_list:
|
||||
img_name = os.path.basename(img_path).split('.')[0]
|
||||
logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
|
||||
result = structure_engine(img_path)
|
||||
for item in result:
|
||||
logger.info(item['res'])
|
||||
save_res(result, save_folder, img_name)
|
||||
logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
|
||||
|
|
@ -1,70 +0,0 @@
|
|||
# Copyright (c) 2020 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
|
||||
|
||||
from setuptools import setup
|
||||
from io import open
|
||||
import shutil
|
||||
|
||||
with open('../requirements.txt', encoding="utf-8-sig") as f:
|
||||
requirements = f.readlines()
|
||||
requirements.append('tqdm')
|
||||
|
||||
|
||||
def readme():
|
||||
with open('api_ch.md', encoding="utf-8-sig") as f:
|
||||
README = f.read()
|
||||
return README
|
||||
|
||||
|
||||
shutil.copytree('./table', './test1/table')
|
||||
shutil.copyfile('./predict_system.py', './test1/predict_system.py')
|
||||
shutil.copyfile('./utility.py', './test1/utility.py')
|
||||
shutil.copytree('../ppocr', './ppocr')
|
||||
shutil.copytree('../tools', './tools')
|
||||
shutil.copyfile('../LICENSE', './LICENSE')
|
||||
|
||||
setup(
|
||||
name='paddlestructure',
|
||||
packages=['paddlestructure'],
|
||||
package_dir={'paddlestructure': ''},
|
||||
include_package_data=True,
|
||||
entry_points={"console_scripts": ["paddlestructure= paddlestructure.paddlestructure:main"]},
|
||||
version='1.0',
|
||||
install_requires=requirements,
|
||||
license='Apache License 2.0',
|
||||
description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
|
||||
long_description=readme(),
|
||||
long_description_content_type='text/markdown',
|
||||
url='https://github.com/PaddlePaddle/PaddleOCR',
|
||||
download_url='https://github.com/PaddlePaddle/PaddleOCR.git',
|
||||
keywords=[
|
||||
'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition'
|
||||
],
|
||||
classifiers=[
|
||||
'Intended Audience :: Developers', 'Operating System :: OS Independent',
|
||||
'Natural Language :: Chinese (Simplified)',
|
||||
'Programming Language :: Python :: 3',
|
||||
'Programming Language :: Python :: 3.2',
|
||||
'Programming Language :: Python :: 3.3',
|
||||
'Programming Language :: Python :: 3.4',
|
||||
'Programming Language :: Python :: 3.5',
|
||||
'Programming Language :: Python :: 3.6',
|
||||
'Programming Language :: Python :: 3.7', 'Topic :: Utilities'
|
||||
], )
|
||||
|
||||
shutil.rmtree('ppocr')
|
||||
shutil.rmtree('tools')
|
||||
shutil.rmtree('test1')
|
||||
os.remove('LICENSE')
|
||||
|
|
@ -1,49 +0,0 @@
|
|||
# Table structure and content prediction
|
||||
|
||||
## 1. pipeline
|
||||
The ocr of the table mainly contains three models
|
||||
1. Single line text detection-DB
|
||||
2. Single line text recognition-CRNN
|
||||
3. Table structure and cell coordinate prediction-RARE
|
||||
|
||||
The table ocr flow chart is as follows
|
||||
|
||||
|
||||
|
||||
1. The coordinates of single-line text is detected by DB model, and then sends it to the recognition model to get the recognition result.
|
||||
2. The table structure and cell coordinates is predicted by RARE model.
|
||||
3. The recognition result of the cell is combined by the coordinates, recognition result of the single line and the coordinates of the cell.
|
||||
4. The cell recognition result and the table structure together construct the html string of the table.
|
||||
|
||||
## 2. How to use
|
||||
|
||||
|
||||
### 2.1 Train
|
||||
TBD
|
||||
|
||||
### 2.2 Eval
|
||||
First cd to the PaddleOCR/ppstructure directory
|
||||
|
||||
The table uses TEDS (Tree-Edit-Distance-based Similarity) 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
|
||||
{"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>"], [[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"]]]}
|
||||
```
|
||||
In gt json, the key is the image name, the value is the corresponding gt, and gt is a list composed of four items, and each item is
|
||||
1. HTML string list of table structure
|
||||
2. The coordinates of each cell (not including the empty text in the cell)
|
||||
3. The text information in each cell (not including the empty text in the cell)
|
||||
4. The text information in each cell (including the empty text in the cell)
|
||||
|
||||
Use the following command to evaluate. After the evaluation is completed, the teds indicator will be output.
|
||||
```python
|
||||
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
|
||||
```
|
||||
|
||||
|
||||
### 2.3 Inference
|
||||
First cd to the PaddleOCR/ppstructure directory
|
||||
|
||||
```python
|
||||
python3 table/predict_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 --output ../output/table
|
||||
```
|
||||
After running, the excel sheet of each picture will be saved in the directory specified by the output field
|
||||
|
|
@ -1,71 +0,0 @@
|
|||
# 表格结构和内容预测
|
||||
|
||||
## 1. pipeline
|
||||
表格的ocr主要包含三个模型
|
||||
1. 单行文本检测-DB
|
||||
2. 单行文本识别-CRNN
|
||||
3. 表格结构和cell坐标预测-RARE
|
||||
|
||||
具体流程图如下
|
||||
|
||||
|
||||
|
||||
1. 图片由单行文字检测检测模型到单行文字的坐标，然后送入识别模型拿到识别结果。
|
||||
2. 图片由表格结构和cell坐标预测模型拿到表格的结构信息和单元格的坐标信息。
|
||||
3. 由单行文字的坐标、识别结果和单元格的坐标一起组合出单元格的识别结果。
|
||||
4. 单元格的识别结果和表格结构一起构造表格的html字符串。
|
||||
|
||||
## 2. 使用
|
||||
|
||||
|
||||
### 2.1 训练
|
||||
#### 数据准备
|
||||
训练数据使用公开数据集[PubTabNet](https://arxiv.org/abs/1911.10683)，可以从[官网](https://github.com/ibm-aur-nlp/PubTabNet)下载。PubTabNet数据集包含约50万张表格数据的图像，以及图像对应的html格式的注释。
|
||||
|
||||
#### 启动训练
|
||||
*如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
|
||||
```shell
|
||||
# 单机单卡训练
|
||||
python3 tools/train.py -c configs/table/table_mv3.yml
|
||||
# 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
|
||||
python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/table/table_mv3.yml
|
||||
```
|
||||
|
||||
上述指令中，通过-c 选择训练使用configs/table/table_mv3.yml配置文件。有关配置文件的详细解释，请参考[链接](./config.md)。
|
||||
|
||||
#### 断点训练
|
||||
|
||||
如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
|
||||
```shell
|
||||
python3 tools/train.py -c configs/table/table_mv3.yml -o Global.checkpoints=./your/trained/model
|
||||
```
|
||||
|
||||
**注意**：`Global.checkpoints`的优先级高于`Global.pretrain_weights`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrain_weights`指定的模型。
|
||||
|
||||
|
||||
### 2.2 评估
|
||||
先cd到PaddleOCR/ppstructure目录下
|
||||
|
||||
表格使用 TEDS(Tree-Edit-Distance-based Similarity) 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
|
||||
{"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>"], [[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"]]]}
|
||||
```
|
||||
json 中，key为图片名，value为对于的gt，gt是一个由四个item组成的list，每个item分别为
|
||||
1. 表格结构的html字符串list
|
||||
2. 每个cell的坐标 (不包括cell里文字为空的)
|
||||
3. 每个cell里的文字信息 (不包括cell里文字为空的)
|
||||
4. 每个cell里的文字信息 (包括cell里文字为空的)
|
||||
|
||||
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
|
||||
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
|
||||
```
|
||||
|
||||
|
||||
### 2.3 预测
|
||||
先cd到PaddleOCR/ppstructure目录下
|
||||
|
||||
```python
|
||||
python3 table/predict_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 --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
|
||||
|
|
@ -112,7 +112,6 @@ class TextClassifier(object):
|
|||
if '180' in label and score > self.cls_thresh:
|
||||
img_list[indices[beg_img_no + rno]] = cv2.rotate(
|
||||
img_list[indices[beg_img_no + rno]], 1)
|
||||
elapse = time.time() - starttime
|
||||
return img_list, cls_res, elapse
|
||||
|
||||
|
||||
|
|
@ -146,7 +145,6 @@ def main(args):
|
|||
cls_res[ino]))
|
||||
logger.info(
|
||||
"The predict time about text angle classify module is as follows: ")
|
||||
text_classifier.cls_times.info(average=False)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -24,8 +24,6 @@ from paddle import inference
|
|||
import time
|
||||
from ppocr.utils.logging import get_logger
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
def str2bool(v):
|
||||
return v.lower() in ("true", "t", "1")
|
||||
|
|
@ -50,8 +48,8 @@ def init_args():
|
|||
|
||||
# DB parmas
|
||||
parser.add_argument("--det_db_thresh", type=float, default=0.3)
|
||||
parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
|
||||
parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
|
||||
parser.add_argument("--det_db_box_thresh", type=float, default=0.6)
|
||||
parser.add_argument("--det_db_unclip_ratio", type=float, default=1.5)
|
||||
parser.add_argument("--max_batch_size", type=int, default=10)
|
||||
parser.add_argument("--use_dilation", type=bool, default=False)
|
||||
parser.add_argument("--det_db_score_mode", type=str, default="fast")
|
||||
|
|
@ -171,45 +169,45 @@ def create_predictor(args, mode, logger):
|
|||
if mode == "det":
|
||||
min_input_shape = {
|
||||
"x": [1, 3, 50, 50],
|
||||
"conv2d_92.tmp_0": [1, 96, 20, 20],
|
||||
"conv2d_91.tmp_0": [1, 96, 10, 10],
|
||||
"conv2d_92.tmp_0": [1, 120, 20, 20],
|
||||
"conv2d_91.tmp_0": [1, 24, 10, 10],
|
||||
"conv2d_59.tmp_0": [1, 96, 20, 20],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 96, 10, 10],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 96, 20, 20],
|
||||
"conv2d_124.tmp_0": [1, 96, 20, 20],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 24, 20, 20],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 24, 20, 20],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 24, 20, 20],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
|
||||
"conv2d_124.tmp_0": [1, 256, 20, 20],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
|
||||
"elementwise_add_7": [1, 56, 2, 2],
|
||||
"nearest_interp_v2_0.tmp_0": [1, 96, 2, 2]
|
||||
"nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
|
||||
}
|
||||
max_input_shape = {
|
||||
"x": [1, 3, 2000, 2000],
|
||||
"conv2d_92.tmp_0": [1, 96, 400, 400],
|
||||
"conv2d_91.tmp_0": [1, 96, 200, 200],
|
||||
"conv2d_92.tmp_0": [1, 120, 400, 400],
|
||||
"conv2d_91.tmp_0": [1, 24, 200, 200],
|
||||
"conv2d_59.tmp_0": [1, 96, 400, 400],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 96, 200, 200],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
|
||||
"conv2d_124.tmp_0": [1, 256, 400, 400],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 96, 400, 400],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 24, 400, 400],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 24, 400, 400],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 24, 400, 400],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
|
||||
"elementwise_add_7": [1, 56, 400, 400],
|
||||
"nearest_interp_v2_0.tmp_0": [1, 96, 400, 400]
|
||||
"nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
|
||||
}
|
||||
opt_input_shape = {
|
||||
"x": [1, 3, 640, 640],
|
||||
"conv2d_92.tmp_0": [1, 96, 160, 160],
|
||||
"conv2d_91.tmp_0": [1, 96, 80, 80],
|
||||
"conv2d_92.tmp_0": [1, 120, 160, 160],
|
||||
"conv2d_91.tmp_0": [1, 24, 80, 80],
|
||||
"conv2d_59.tmp_0": [1, 96, 160, 160],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 96, 80, 80],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 96, 160, 160],
|
||||
"nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
|
||||
"nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
|
||||
"conv2d_124.tmp_0": [1, 256, 160, 160],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 24, 160, 160],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 24, 160, 160],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 24, 160, 160],
|
||||
"nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
|
||||
"nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
|
||||
"nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
|
||||
"elementwise_add_7": [1, 56, 40, 40],
|
||||
"nearest_interp_v2_0.tmp_0": [1, 96, 40, 40]
|
||||
"nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
|
||||
}
|
||||
elif mode == "rec":
|
||||
min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
|
||||
|
|
|
|||
Loading…
Reference in New Issue