未验证 提交 11f5faf4 编写于 作者: J JetHong 提交者: GitHub

add total text (#2608)

Co-authored-by: NMissPenguin <lichenxia1991@163.com>
Co-authored-by: Nlittletomatodonkey <2120160898@bit.edu.cn>
上级 00228775
...@@ -72,7 +72,7 @@ Train: ...@@ -72,7 +72,7 @@ Train:
dataset: dataset:
name: PGDataSet name: PGDataSet
data_dir: ./train_data/total_text/train data_dir: ./train_data/total_text/train
label_file_list: [./train_data/total_text/train/total_text.txt] label_file_list: [./train_data/total_text/train/train.txt]
ratio_list: [1.0] ratio_list: [1.0]
transforms: transforms:
- DecodeImage: # load image - DecodeImage: # load image
...@@ -96,7 +96,7 @@ Eval: ...@@ -96,7 +96,7 @@ Eval:
dataset: dataset:
name: PGDataSet name: PGDataSet
data_dir: ./train_data/total_text/test data_dir: ./train_data/total_text/test
label_file_list: [./train_data/total_text/test/total_text.txt] label_file_list: [./train_data/total_text/test/test.txt]
transforms: transforms:
- DecodeImage: # load image - DecodeImage: # load image
img_mode: RGB img_mode: RGB
......
...@@ -83,19 +83,19 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im ...@@ -83,19 +83,19 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
本节以totaltext数据集为例,介绍PaddleOCR中端到端模型的训练、评估与测试。 本节以totaltext数据集为例,介绍PaddleOCR中端到端模型的训练、评估与测试。
### 准备数据 ### 准备数据
下载解压[totaltext](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Dataset/README.md) 数据集到PaddleOCR/train_data/目录,数据集组织结构: 下载解压[totaltext](https://paddleocr.bj.bcebos.com/dataset/total_text.tar) 数据集到PaddleOCR/train_data/目录,数据集组织结构:
``` ```
/PaddleOCR/train_data/total_text/train/ /PaddleOCR/train_data/total_text/train/
|- rgb/ # total_text数据集的训练数据 |- rgb/ # total_text数据集的训练数据
|- gt_0.png |- img11.jpg
| ... | ...
|- total_text.txt # total_text数据集的训练标注 |- train.txt # total_text数据集的训练标注
``` ```
total_text.txt标注文件格式如下,文件名和标注信息中间用"\t"分隔: total_text.txt标注文件格式如下,文件名和标注信息中间用"\t"分隔:
``` ```
" 图像文件名 json.dumps编码的图像标注信息" " 图像文件名 json.dumps编码的图像标注信息"
rgb/gt_0.png [{"transcription": "EST", "points": [[1004.0,689.0],[1019.0,698.0],[1034.0,708.0],[1049.0,718.0],[1064.0,728.0],[1079.0,738.0],[1095.0,748.0],[1094.0,774.0],[1079.0,765.0],[1065.0,756.0],[1050.0,747.0],[1036.0,738.0],[1021.0,729.0],[1007.0,721.0]]}, {...}] rgb/img11.jpg [{"transcription": "ASRAMA", "points": [[214.0, 325.0], [235.0, 308.0], [259.0, 296.0], [286.0, 291.0], [313.0, 295.0], [338.0, 305.0], [362.0, 320.0], [349.0, 347.0], [330.0, 337.0], [310.0, 329.0], [290.0, 324.0], [269.0, 328.0], [249.0, 336.0], [231.0, 346.0]]}, {...}]
``` ```
json.dumps编码前的图像标注信息是包含多个字典的list,字典中的 `points` 表示文本框的四个点的坐标(x, y),从左上角的点开始顺时针排列。 json.dumps编码前的图像标注信息是包含多个字典的list,字典中的 `points` 表示文本框的四个点的坐标(x, y),从左上角的点开始顺时针排列。
`transcription` 表示当前文本框的文字,**当其内容为“###”时,表示该文本框无效,在训练时会跳过。** `transcription` 表示当前文本框的文字,**当其内容为“###”时,表示该文本框无效,在训练时会跳过。**
......
...@@ -76,19 +76,19 @@ The visualized end-to-end results are saved to the `./inference_results` folder ...@@ -76,19 +76,19 @@ The visualized end-to-end results are saved to the `./inference_results` folder
This section takes the totaltext dataset as an example to introduce the training, evaluation and testing of the end-to-end model in PaddleOCR. This section takes the totaltext dataset as an example to introduce the training, evaluation and testing of the end-to-end model in PaddleOCR.
### Data Preparation ### Data Preparation
Download and unzip [totaltext](https://github.com/cs-chan/Total-Text-Dataset/blob/master/Dataset/README.md) dataset to PaddleOCR/train_data/, dataset organization structure is as follow: Download and unzip [totaltext](https://paddleocr.bj.bcebos.com/dataset/total_text.tar) dataset to PaddleOCR/train_data/, dataset organization structure is as follow:
``` ```
/PaddleOCR/train_data/total_text/train/ /PaddleOCR/train_data/total_text/train/
|- rgb/ # total_text training data of dataset |- rgb/ # total_text training data of dataset
|- gt_0.png |- img11.png
| ... | ...
|- total_text.txt # total_text training annotation of dataset |- train.txt # total_text training annotation of dataset
``` ```
total_text.txt: the format of dimension file is as follows,the file name and annotation information are separated by "\t": total_text.txt: the format of dimension file is as follows,the file name and annotation information are separated by "\t":
``` ```
" Image file name Image annotation information encoded by json.dumps" " Image file name Image annotation information encoded by json.dumps"
rgb/gt_0.png [{"transcription": "EST", "points": [[1004.0,689.0],[1019.0,698.0],[1034.0,708.0],[1049.0,718.0],[1064.0,728.0],[1079.0,738.0],[1095.0,748.0],[1094.0,774.0],[1079.0,765.0],[1065.0,756.0],[1050.0,747.0],[1036.0,738.0],[1021.0,729.0],[1007.0,721.0]]}, {...}] rgb/img11.jpg [{"transcription": "ASRAMA", "points": [[214.0, 325.0], [235.0, 308.0], [259.0, 296.0], [286.0, 291.0], [313.0, 295.0], [338.0, 305.0], [362.0, 320.0], [349.0, 347.0], [330.0, 337.0], [310.0, 329.0], [290.0, 324.0], [269.0, 328.0], [249.0, 336.0], [231.0, 346.0]]}, {...}]
``` ```
The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries. The image annotation after **json.dumps()** encoding is a list containing multiple dictionaries.
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