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提交 76320bf0 编写于 作者: littletomatodonkey's avatar littletomatodonkey
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Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dev/add_thread_pred

上级 e19bedf5 824ceca6
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configs/det/det_r50_vd_sast_icdar15.yml
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......@@ -20,6 +20,7 @@ Global:
infer_img:
save_res_path: ./output/sast_r50_vd_ic15/predicts_sast.txt
Architecture:
model_type: det
algorithm: SAST
......
configs/e2e/e2e_r50_vd_pg.yml 0 → 100644
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Global:
use_gpu: True
epoch_num: 600
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/pgnet_r50_vd_totaltext/
save_epoch_step: 10
# evaluation is run every 0 iterationss after the 1000th iteration
eval_batch_step: [ 0, 1000 ]
# 1. If pretrained_model is saved in static mode, such as classification pretrained model
# from static branch, load_static_weights must be set as True.
# 2. If you want to finetune the pretrained models we provide in the docs,
# you should set load_static_weights as False.
load_static_weights: False
cal_metric_during_train: False
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img:
valid_set: totaltext # two mode: totaltext valid curved words, partvgg valid non-curved words
save_res_path: ./output/pgnet_r50_vd_totaltext/predicts_pgnet.txt
character_dict_path: ppocr/utils/ic15_dict.txt
character_type: EN
max_text_length: 50 # the max length in seq
max_text_nums: 30 # the max seq nums in a pic
tcl_len: 64
Architecture:
model_type: e2e
algorithm: PGNet
Transform:
Backbone:
name: ResNet
layers: 50
Neck:
name: PGFPN
Head:
name: PGHead
Loss:
name: PGLoss
tcl_bs: 64
max_text_length: 50 # the same as Global: max_text_length
max_text_nums: 30 # the same as Global：max_text_nums
pad_num: 36 # the length of dict for pad
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: PGPostProcess
score_thresh: 0.5
Metric:
name: E2EMetric
character_dict_path: ppocr/utils/ic15_dict.txt
main_indicator: f_score_e2e
Train:
dataset:
name: PGDataSet
label_file_list: [.././train_data/total_text/train/]
ratio_list: [1.0]
data_format: icdar #two data format: icdar/textnet
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- PGProcessTrain:
batch_size: 14 # same as loader: batch_size_per_card
min_crop_size: 24
min_text_size: 4
max_text_size: 512
- KeepKeys:
keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
loader:
shuffle: True
drop_last: True
batch_size_per_card: 14
num_workers: 16
Eval:
dataset:
name: PGDataSet
data_dir: ./train_data/
label_file_list: [./train_data/total_text/test/]
transforms:
- DecodeImage: # load image
img_mode: RGB
channel_first: False
- E2ELabelEncode:
- E2EResizeForTest:
max_side_len: 768
- NormalizeImage:
scale: 1./255.
mean: [ 0.485, 0.456, 0.406 ]
std: [ 0.229, 0.224, 0.225 ]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags' ]
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
\ No newline at end of file
doc/doc_ch/inference.md
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......@@ -13,6 +13,7 @@ inference 模型（`paddle.jit.save`保存的模型）
- [检测模型转inference模型](#检测模型转inference模型)
- [识别模型转inference模型](#识别模型转inference模型)
- [方向分类模型转inference模型](#方向分类模型转inference模型)
- [端到端模型转inference模型](#端到端模型转inference模型)
- [二、文本检测模型推理](#文本检测模型推理)
- [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
......@@ -27,10 +28,13 @@ inference 模型（`paddle.jit.save`保存的模型）
- [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
- [5. 多语言模型的推理](#多语言模型的推理)
- [四、方向分类模型推理](#方向识别模型推理)
- [四、端到端模型推理](#端到端模型推理)
- [1. PGNet端到端模型推理](#PGNet端到端模型推理)
- [五、方向分类模型推理](#方向识别模型推理)
- [1. 方向分类模型推理](#方向分类模型推理)
- [五、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
- [六、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
- [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
- [2. 其他模型推理](#其他模型推理)
......@@ -118,6 +122,32 @@ python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_mo
├── inference.pdiparams.info # 分类inference模型的参数信息，可忽略
└── inference.pdmodel # 分类inference模型的program文件
```
<a name="端到端模型转inference模型"></a>
### 端到端模型转inference模型
下载端到端模型：
```
wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
```
端到端模型转inference模型与检测的方式相同，如下：
```
# -c 后面设置训练算法的yml配置文件
# -o 配置可选参数
# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
# Global.load_static_weights 参数需要设置为 False。
# Global.save_inference_dir参数设置转换的模型将保存的地址。
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/e2e/
```
转换成功后，在目录下有三个文件：
```
/inference/e2e/
├── inference.pdiparams # 分类inference模型的参数文件
├── inference.pdiparams.info # 分类inference模型的参数信息，可忽略
└── inference.pdmodel # 分类inference模型的program文件
```
<a name="文本检测模型推理"></a>
## 二、文本检测模型推理
......@@ -332,8 +362,38 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
```
<a name="端到端模型推理"></a>
## 四、端到端模型推理
端到端模型推理，默认使用PGNet模型的配置参数。当不使用PGNet模型时，在推理时，需要通过传入相应的参数进行算法适配，细节参考下文。
<a name="PGNet端到端模型推理"></a>
### 1. PGNet端到端模型推理
#### (1). 四边形文本检测模型（ICDAR2015）
首先将PGNet端到端训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar))，可以使用如下命令进行转换：
```
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/iter_epoch_450 Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
```
**PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`**，可以执行如下命令：
```
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=False
```
可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
![](../imgs_results/e2e_res_img_10_pgnet.jpg)
#### (2). 弯曲文本检测模型（Total-Text）
和四边形文本检测模型共用一个推理模型
**PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`，同时，还需要增加参数`--e2e_pgnet_polygon=True`，**可以执行如下命令：
```
python3.7 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
```
可视化文本端到端结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
![](../imgs_results/e2e_res_img623_pgnet.jpg)
<a name="方向分类模型推理"></a>
## 四、方向分类模型推理
## 五、方向分类模型推理
下面将介绍方向分类模型推理。
......@@ -358,7 +418,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
```
<a name="文本检测、方向分类和文字识别串联推理"></a>
## 五、文本检测、方向分类和文字识别串联推理
## 六、文本检测、方向分类和文字识别串联推理
<a name="超轻量中文OCR模型推理"></a>
### 1. 超轻量中文OCR模型推理
......
doc/doc_ch/multi_languages.md 0 → 100644
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# 多语言模型
**近期更新**
- 2021.4.9 支持**80种**语言的检测和识别
- 2021.4.9 支持**轻量高精度**英文模型检测识别
- [1 安装](#安装)
- [1.1 paddle 安装](#paddle安装)
- [1.2 paddleocr package 安装](#paddleocr_package_安装)
- [2 快速使用](#快速使用)
- [2.1 命令行运行](#命令行运行)
- [2.1.1 整图预测](#bash_检测+识别)
- [2.1.2 识别预测](#bash_识别)
- [2.1.3 检测预测](#bash_检测)
- [2.2 python 脚本运行](#python_脚本运行)
- [2.2.1 整图预测](#python_检测+识别)
- [2.2.2 识别预测](#python_识别)
- [2.2.3 检测预测](#python_检测)
- [3 自定义训练](#自定义训练)
- [4 支持语种及缩写](#语种缩写)
<a name="安装"></a>
## 1 安装
<a name="paddle安装"></a>
### 1.1 paddle 安装
```
# cpu
pip install paddlepaddle
# gpu
pip instll paddlepaddle-gpu
```
<a name="paddleocr_package_安装"></a>
### 1.2 paddleocr package 安装
pip 安装
```
pip install "paddleocr>=2.0.4" # 推荐使用2.0.4版本
```
本地构建并安装
```
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
```
<a name="快速使用"></a>
## 2 快速使用
<a name="命令行运行"></a>
### 2.1 命令行运行
查看帮助信息
```
paddleocr -h
```
* 整图预测（检测+识别）
Paddleocr目前支持80个语种，可以通过修改--lang参数进行切换，具体支持的[语种](#语种缩写)可查看表格。
``` bash
paddleocr --image_dir doc/imgs/japan_2.jpg --lang=japan
```
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs/japan_2.jpg)
结果是一个list，每个item包含了文本框，文字和识别置信度
```text
[[[671.0, 60.0], [847.0, 63.0], [847.0, 104.0], [671.0, 102.0]], ('もちもち', 0.9993342)]
[[[394.0, 82.0], [536.0, 77.0], [538.0, 127.0], [396.0, 132.0]], ('天然の', 0.9919842)]
[[[880.0, 89.0], [1014.0, 93.0], [1013.0, 127.0], [879.0, 124.0]], ('とろっと', 0.9976762)]
[[[1067.0, 101.0], [1294.0, 101.0], [1294.0, 138.0], [1067.0, 138.0]], ('後味のよい', 0.9988712)]
......
```
* 识别预测
```bash
paddleocr --image_dir doc/imgs_words/japan/1.jpg --det false --lang=japan
```
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_words/japan/1.jpg)
结果是一个tuple，返回识别结果和识别置信度
```text
('したがって', 0.99965394)
```
* 检测预测
```
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
```
[[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]]
......
```
<a name="python_脚本运行"></a>
### 2.2 python 脚本运行
ppocr 也支持在python脚本中运行，便于嵌入到您自己的代码中：
* 整图预测（检测+识别）
```
from paddleocr import PaddleOCR, draw_ocr
# 同样也是通过修改 lang 参数切换语种
ocr = PaddleOCR(lang="korean") # 首次执行会自动下载模型文件
img_path = 'doc/imgs/korean_1.jpg '
result = ocr.ocr(img_path)
# 打印检测框和识别结果
for line in result:
print(line)
# 可视化
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]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/path/to/PaddleOCR/doc/korean.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
```
结果可视化:
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_results/korean.jpg)
* 识别预测
```
from paddleocr import PaddleOCR
ocr = PaddleOCR(lang="german")
img_path = 'PaddleOCR/doc/imgs_words/german/1.jpg'
result = ocr.ocr(img_path, det=False, cls=True)
for line in result:
print(line)
```
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_words/german/1.jpg)
结果是一个tuple，只包含识别结果和识别置信度
```
('leider auch jetzt', 0.97538936)
```
* 检测预测
```python
from paddleocr import PaddleOCR, draw_ocr
ocr = PaddleOCR() # need to run only once to download and load model into memory
img_path = 'PaddleOCR/doc/imgs_en/img_12.jpg'
result = ocr.ocr(img_path, rec=False)
for line in result:
print(line)
# 显示结果
from PIL import Image
image = Image.open(img_path).convert('RGB')
im_show = draw_ocr(image, result, txts=None, scores=None, font_path='/path/to/PaddleOCR/doc/fonts/simfang.ttf')
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 ：
![](https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.0/doc/imgs_results/whl/12_det.jpg)
ppocr 还支持方向分类， 更多使用方式请参考：[whl包使用说明](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.0/doc/doc_ch/whl.md)。
<a name="自定义训练"></a>
## 3 自定义训练
ppocr 支持使用自己的数据进行自定义训练或finetune, 其中识别模型可以参考 [法语配置文件](../../configs/rec/multi_language/rec_french_lite_train.yml)
修改训练数据路径、字典等参数。
具体数据准备、训练过程可参考：[文本检测](../doc_ch/detection.md)、[文本识别](../doc_ch/recognition.md)，更多功能如预测部署、
数据标注等功能可以阅读完整的[文档教程](../../README_ch.md)。
<a name="语种缩写"></a>
## 4 支持语种及缩写
| 语种 | 描述 | 缩写 |
| --- | --- | --- |
|中文|chinese and english|ch|
|英文|english|en|
|法文|french|fr|
|德文|german|german|
|日文|japan|japan|
|韩文|korean|korean|
|中文繁体|chinese traditional |ch_tra|
|意大利文| Italian |it|
|西班牙文|Spanish |es|
|葡萄牙文| Portuguese|pt|
|俄罗斯文|Russia|ru|
|阿拉伯文|Arabic|ar|
|印地文|Hindi|hi|
|维吾尔|Uyghur|ug|
|波斯文|Persian|fa|
|乌尔都文|Urdu|ur|
|塞尔维亚文（latin)| Serbian(latin) |rs_latin|
|欧西坦文|Occitan |oc|
|马拉地文|Marathi|mr|
|尼泊尔文|Nepali|ne|
|塞尔维亚文（cyrillic)|Serbian(cyrillic)|rs_cyrillic|
|保加利亚文|Bulgarian |bg|
|乌克兰文|Ukranian|uk|
|白俄罗斯文|Belarusian|be|
|泰卢固文|Telugu |te|
|卡纳达文|Kannada |kn|
|泰米尔文|Tamil |ta|
|南非荷兰文 |Afrikaans |af|
|阿塞拜疆文 |Azerbaijani |az|
|波斯尼亚文|Bosnian|bs|
|捷克文|Czech|cs|
|威尔士文 |Welsh |cy|
|丹麦文 |Danish|da|
|爱沙尼亚文 |Estonian |et|
|爱尔兰文 |Irish |ga|
|克罗地亚文|Croatian |hr|
|匈牙利文|Hungarian |hu|
|印尼文|Indonesian|id|
|冰岛文 |Icelandic|is|
|库尔德文 |Kurdish|ku|
|立陶宛文|Lithuanian |lt|
|拉脱维亚文 |Latvian |lv|
|毛利文|Maori|mi|
|马来文 |Malay|ms|
|马耳他文 |Maltese |mt|
|荷兰文 |Dutch |nl|
|挪威文 |Norwegian |no|
|波兰文|Polish |pl|
| 罗马尼亚文|Romanian |ro|
| 斯洛伐克文|Slovak |sk|
| 斯洛文尼亚文|Slovenian |sl|
| 阿尔巴尼亚文|Albanian |sq|
| 瑞典文|Swedish |sv|
| 西瓦希里文|Swahili |sw|
| 塔加洛文|Tagalog |tl|
| 土耳其文|Turkish |tr|
| 乌兹别克文|Uzbek |uz|
| 越南文|Vietnamese |vi|
| 蒙古文|Mongolian |mn|
| 阿巴扎文|Abaza |abq|
| 阿迪赫文|Adyghe |ady|
| 卡巴丹文|Kabardian |kbd|
| 阿瓦尔文|Avar |ava|
| 达尔瓦文|Dargwa |dar|
| 因古什文|Ingush |inh|
| 拉克文|Lak |lbe|
| 莱兹甘文|Lezghian |lez|
|塔巴萨兰文 |Tabassaran |tab|
| 比尔哈文|Bihari |bh|
| 迈蒂利文|Maithili |mai|
| 昂加文|Angika |ang|
| 孟加拉文|Bhojpuri |bho|
| 摩揭陀文 |Magahi |mah|
| 那格浦尔文|Nagpur |sck|
| 尼瓦尔文|Newari |new|
| 保加利亚文 |Goan Konkani|gom|
| 沙特阿拉伯文|Saudi Arabia|sa|
doc/doc_ch/pgnet.md 0 → 100644
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# 端对端OCR算法-PGNet
- [一、简介](#简介)
- [二、环境配置](#环境配置)
- [三、快速使用](#快速使用)
- [四、快速训练](#开始训练)
- [五、预测推理](#预测推理)
<a name="简介"></a>
##简介
OCR算法可以分为两阶段算法和端对端的算法。二阶段OCR算法一般分为两个部分，文本检测和文本识别算法，文件检测算法从图像中得到文本行的检测框，然后识别算法去识别文本框中的内容。而端对端OCR算法可以在一个算法中完成文字检测和文字识别，其基本思想是设计一个同时具有检测单元和识别模块的模型，共享其中两者的CNN特征，并联合训练。由于一个算法即可完成文字识别，端对端模型更小，速度更快。
### PGNet算法介绍
近些年来，端对端OCR算法得到了良好的发展，包括MaskTextSpotter系列、TextSnake、TextDragon、PGNet系列等算法。在这些算法中，PGNet算法具备其他算法不具备的优势，包括：
- 设计PGNet loss指导训练，不需要字符级别的标注
- 不需要NMS和ROI相关操作，加速预测
- 提出预测文本行内的阅读顺序模块；
- 提出基于图的修正模块（GRM）来进一步提高模型识别性能
- 精度更高，预测速度更快
PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf)， 算法原理图如下所示：
![](../pgnet_framework.png)
输入图像经过特征提取送入四个分支，分别是：文本边缘偏移量预测TBO模块，文本中心线预测TCL模块，文本方向偏移量预测TDO模块，以及文本字符分类图预测TCC模块。
其中TBO以及TCL的输出经过后处理后可以得到文本的检测结果，TCL、TDO、TCC负责文本识别。
其检测识别效果图如下：
![](../imgs_results/e2e_res_img293_pgnet.png)
![](../imgs_results/e2e_res_img295_pgnet.png)
<a name="环境配置"></a>
##环境配置
请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
*注意：也可以通过 whl 包安装使用PaddleOCR，具体参考[Paddleocr Package使用说明](./whl.md)。*
<a name="快速使用"></a>
##快速使用
### inference模型下载
本节以训练好的端到端模型为例，快速使用模型预测，首先下载训练好的端到端inference模型[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar)
```
mkdir inference && cd inference
# 下载英文端到端模型并解压
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar && tar xf e2e_server_pgnetA_infer.tar
```
* windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下
解压完毕后应有如下文件结构：
```
├── e2e_server_pgnetA_infer
│ ├── inference.pdiparams
│ ├── inference.pdiparams.info
│ └── inference.pdmodel
```
### 单张图像或者图像集合预测
```bash
# 预测image_dir指定的单张图像
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
# 预测image_dir指定的图像集合
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
# 如果想使用CPU进行预测，需设置use_gpu参数为False
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True --use_gpu=False
```
<a name="开始训练"></a>
##开始训练
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。
###数据形式为icdar, 十六点标注数据
解压数据集和下载标注文件后，PaddleOCR/train_data/total_text/train/ 有两个文件夹，分别是：
```
/PaddleOCR/train_data/total_text/train/
|- rgb/ total_text数据集的训练数据
|- gt_0.png
| ...
|- total_text.txt total_text数据集的训练标注
```
提供的标注文件格式如下，中间用"\t"分隔：
```
" 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]]}, {...}]
```
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。**
如果您想在其他数据集上训练，可以按照上述形式构建标注文件。
### 快速启动训练
模型训练一般分两步骤进行，第一步可以选择用合成数据训练，第二步加载第一步训练好的模型训练，这边我们提供了第一步训练好的模型，可以直接加载，从第二步开始训练
[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/train_step1.tar)
```shell
cd PaddleOCR/
下载ResNet50_vd的动态图预训练模型
wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/train_step1.tar
可以得到以下的文件格式
./pretrain_models/train_step1/
└─ best_accuracy.pdopt
└─ best_accuracy.states
└─ best_accuracy.pdparams
```
*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 e2e 模型
python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/train_step1/best_accuracy Global.load_static_weights=False
# 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/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/train_step1/best_accuracy Global.load_static_weights=False
```
上述指令中，通过-c 选择训练使用configs/e2e/e2e_r50_vd_pg.yml配置文件。
有关配置文件的详细解释，请参考[链接](./config.md)。
您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
```shell
python3 tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Optimizer.base_lr=0.0001
```
#### 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/e2e/e2e_r50_vd_pg.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。
<a name="预测推理"></a>
## 预测推理
PaddleOCR计算三个OCR端到端相关的指标，分别是：Precision、Recall、Hmean。
运行如下代码，根据配置文件`e2e_r50_vd_pg.yml`中`save_res_path`指定的测试集检测结果文件，计算评估指标。
评估时设置后处理参数`max_side_len=768`，使用不同数据集、不同模型训练，可调整参数进行优化
训练中模型参数默认保存在`Global.save_model_dir`目录下。在评估指标时，需要设置`Global.checkpoints`指向保存的参数文件。
```shell
python3 tools/eval.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.checkpoints="{path/to/weights}/best_accuracy"
```
### 测试端到端效果
测试单张图像的端到端识别效果
```shell
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
```
测试文件夹下所有图像的端到端识别效果
```shell
python3 tools/infer_e2e.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
```
###转为推理模型
### (1). 四边形文本检测模型（ICDAR2015）
首先将PGNet端到端训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，以英文数据集训练的模型为例[模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar) ，可以使用如下命令进行转换：
```
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar && tar xf en_server_pgnetA.tar
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/iter_epoch_450 Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
```
**PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`**，可以执行如下命令：
```
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=False
```
可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
![](../imgs_results/e2e_res_img_10_pgnet.jpg)
### (2). 弯曲文本检测模型（Total-Text）
对于弯曲文本样例
**PGNet端到端模型推理，需要设置参数`--e2e_algorithm="PGNet"`，同时，还需要增加参数`--e2e_pgnet_polygon=True`，**可以执行如下命令：
```
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
```
可视化文本端到端结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'e2e_res'。结果示例如下：
![](../imgs_results/e2e_res_img623_pgnet.jpg)
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paddleocr.py
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......@@ -66,6 +66,46 @@ model_urls = {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ta_dict.txt'
},
'te': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/te_dict.txt'
},
'ka': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ka_dict.txt'
},
'latin': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/latin_dict.txt'
},
'arabic': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/arabic_dict.txt'
},
'cyrillic': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'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'
}
},
'cls':
......@@ -233,6 +273,29 @@ class PaddleOCR(predict_system.TextSystem):
postprocess_params.__dict__.update(**kwargs)
self.use_angle_cls = postprocess_params.use_angle_cls
lang = postprocess_params.lang
latin_lang = [
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'en', '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)
......
ppocr/data/__init__.py
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......@@ -34,6 +34,7 @@ import paddle.distributed as dist
from ppocr.data.imaug import transform, create_operators
from ppocr.data.simple_dataset import SimpleDataSet
from ppocr.data.lmdb_dataset import LMDBDataSet
from ppocr.data.pgnet_dataset import PGDataSet
__all__ = ['build_dataloader', 'transform', 'create_operators']
......@@ -54,7 +55,7 @@ signal.signal(signal.SIGTERM, term_mp)
def build_dataloader(config, mode, device, logger, seed=None):
config = copy.deepcopy(config)
support_dict = ['SimpleDataSet', 'LMDBDataSet']
support_dict = ['SimpleDataSet', 'LMDBDataSet', 'PGDataSet']
module_name = config[mode]['dataset']['name']
assert module_name in support_dict, Exception(
'DataSet only support {}'.format(support_dict))
......@@ -72,14 +73,14 @@ def build_dataloader(config, mode, device, logger, seed=None):
else:
use_shared_memory = True
if mode == "Train":
#Distribute data to multiple cards
# Distribute data to multiple cards
batch_sampler = DistributedBatchSampler(
dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
drop_last=drop_last)
else:
#Distribute data to single card
# Distribute data to single card
batch_sampler = BatchSampler(
dataset=dataset,
batch_size=batch_size,
......
ppocr/data/imaug/__init__.py
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......@@ -28,6 +28,7 @@ from .label_ops import *
from .east_process import *
from .sast_process import *
from .pg_process import *
def transform(data, ops=None):
......
ppocr/data/imaug/label_ops.py
浏览文件 @ 76320bf0
......@@ -187,6 +187,34 @@ class CTCLabelEncode(BaseRecLabelEncode):
return dict_character
class E2ELabelEncode(BaseRecLabelEncode):
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='EN',
use_space_char=False,
**kwargs):
super(E2ELabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
self.pad_num = len(self.dict) # the length to pad
def __call__(self, data):
text_label_index_list, temp_text = [], []
texts = data['strs']
for text in texts:
text = text.lower()
temp_text = []
for c_ in text:
if c_ in self.dict:
temp_text.append(self.dict[c_])
temp_text = temp_text + [self.pad_num] * (self.max_text_len -
len(temp_text))
text_label_index_list.append(temp_text)
data['strs'] = np.array(text_label_index_list)
return data
class AttnLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """
......
ppocr/data/imaug/operators.py
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......@@ -197,7 +197,6 @@ class DetResizeForTest(object):
sys.exit(0)
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
# return img, np.array([h, w])
return img, [ratio_h, ratio_w]
def resize_image_type2(self, img):
......@@ -206,7 +205,6 @@ class DetResizeForTest(object):
resize_w = w
resize_h = h
# Fix the longer side
if resize_h > resize_w:
ratio = float(self.resize_long) / resize_h
else:
......@@ -223,3 +221,72 @@ class DetResizeForTest(object):
ratio_w = resize_w / float(w)
return img, [ratio_h, ratio_w]
class E2EResizeForTest(object):
def __init__(self, **kwargs):
super(E2EResizeForTest, self).__init__()
self.max_side_len = kwargs['max_side_len']
self.valid_set = kwargs['valid_set']
def __call__(self, data):
img = data['image']
src_h, src_w, _ = img.shape
if self.valid_set == 'totaltext':
im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext(
img, max_side_len=self.max_side_len)
else:
im_resized, (ratio_h, ratio_w) = self.resize_image(
img, max_side_len=self.max_side_len)
data['image'] = im_resized
data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
return data
def resize_image_for_totaltext(self, im, max_side_len=512):
h, w, _ = im.shape
resize_w = w
resize_h = h
ratio = 1.25
if h * ratio > max_side_len:
ratio = float(max_side_len) / resize_h
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
max_stride = 128
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
def resize_image(self, im, max_side_len=512):
"""
resize image to a size multiple of max_stride which is required by the network
:param im: the resized image
:param max_side_len: limit of max image size to avoid out of memory in gpu
:return: the resized image and the resize ratio
"""
h, w, _ = im.shape
resize_w = w
resize_h = h
# Fix the longer side
if resize_h > resize_w:
ratio = float(max_side_len) / resize_h
else:
ratio = float(max_side_len) / resize_w
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
max_stride = 128
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
ppocr/data/imaug/pg_process.py 0 → 100644
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# copyright (c) 2021 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
#
# 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 math
import cv2
import numpy as np
__all__ = ['PGProcessTrain']
class PGProcessTrain(object):
def __init__(self,
character_dict_path,
max_text_length,
max_text_nums,
tcl_len,
batch_size=14,
min_crop_size=24,
min_text_size=4,
max_text_size=512,
**kwargs):
self.tcl_len = tcl_len
self.max_text_length = max_text_length
self.max_text_nums = max_text_nums
self.batch_size = batch_size
self.min_crop_size = min_crop_size
self.min_text_size = min_text_size
self.max_text_size = max_text_size
self.Lexicon_Table = self.get_dict(character_dict_path)
self.pad_num = len(self.Lexicon_Table)
self.img_id = 0
def get_dict(self, character_dict_path):
character_str = ""
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
line = line.decode('utf-8').strip("\n").strip("\r\n")
character_str += line
dict_character = list(character_str)
return dict_character
def quad_area(self, poly):
"""
compute area of a polygon
:param poly:
:return:
"""
edge = [(poly[1][0] - poly[0][0]) * (poly[1][1] + poly[0][1]),
(poly[2][0] - poly[1][0]) * (poly[2][1] + poly[1][1]),
(poly[3][0] - poly[2][0]) * (poly[3][1] + poly[2][1]),
(poly[0][0] - poly[3][0]) * (poly[0][1] + poly[3][1])]
return np.sum(edge) / 2.
def gen_quad_from_poly(self, poly):
"""
Generate min area quad from poly.
"""
point_num = poly.shape[0]
min_area_quad = np.zeros((4, 2), dtype=np.float32)
rect = cv2.minAreaRect(poly.astype(
np.int32)) # (center (x,y), (width, height), angle of rotation)
box = np.array(cv2.boxPoints(rect))
first_point_idx = 0
min_dist = 1e4
for i in range(4):
dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \
np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \
np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \
np.linalg.norm(box[(i + 3) % 4] - poly[-1])
if dist < min_dist:
min_dist = dist
first_point_idx = i
for i in range(4):
min_area_quad[i] = box[(first_point_idx + i) % 4]
return min_area_quad
def check_and_validate_polys(self, polys, tags, xxx_todo_changeme):
"""
check so that the text poly is in the same direction,
and also filter some invalid polygons
:param polys:
:param tags:
:return:
"""
(h, w) = xxx_todo_changeme
if polys.shape[0] == 0:
return polys, np.array([]), np.array([])
polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1)
polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h - 1)
validated_polys = []
validated_tags = []
hv_tags = []
for poly, tag in zip(polys, tags):
quad = self.gen_quad_from_poly(poly)
p_area = self.quad_area(quad)
if abs(p_area) < 1:
print('invalid poly')
continue
if p_area > 0:
if tag == False:
print('poly in wrong direction')
tag = True # reversed cases should be ignore
poly = poly[(0, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2,
1), :]
quad = quad[(0, 3, 2, 1), :]
len_w = np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[3] -
quad[2])
len_h = np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] -
quad[2])
hv_tag = 1
if len_w * 2.0 < len_h:
hv_tag = 0
validated_polys.append(poly)
validated_tags.append(tag)
hv_tags.append(hv_tag)
return np.array(validated_polys), np.array(validated_tags), np.array(
hv_tags)
def crop_area(self,
im,
polys,
tags,
hv_tags,
txts,
crop_background=False,
max_tries=25):
"""
make random crop from the input image
:param im:
:param polys: [b,4,2]
:param tags:
:param crop_background:
:param max_tries: 50 -> 25
:return:
"""
h, w, _ = im.shape
pad_h = h // 10
pad_w = w // 10
h_array = np.zeros((h + pad_h * 2), dtype=np.int32)
w_array = np.zeros((w + pad_w * 2), dtype=np.int32)
for poly in polys:
poly = np.round(poly, decimals=0).astype(np.int32)
minx = np.min(poly[:, 0])
maxx = np.max(poly[:, 0])
w_array[minx + pad_w:maxx + pad_w] = 1
miny = np.min(poly[:, 1])
maxy = np.max(poly[:, 1])
h_array[miny + pad_h:maxy + pad_h] = 1
# ensure the cropped area not across a text
h_axis = np.where(h_array == 0)[0]
w_axis = np.where(w_array == 0)[0]
if len(h_axis) == 0 or len(w_axis) == 0:
return im, polys, tags, hv_tags, txts
for i in range(max_tries):
xx = np.random.choice(w_axis, size=2)
xmin = np.min(xx) - pad_w
xmax = np.max(xx) - pad_w
xmin = np.clip(xmin, 0, w - 1)
xmax = np.clip(xmax, 0, w - 1)
yy = np.random.choice(h_axis, size=2)
ymin = np.min(yy) - pad_h
ymax = np.max(yy) - pad_h
ymin = np.clip(ymin, 0, h - 1)
ymax = np.clip(ymax, 0, h - 1)
if xmax - xmin < self.min_crop_size or \
ymax - ymin < self.min_crop_size:
continue
if polys.shape[0] != 0:
poly_axis_in_area = (polys[:, :, 0] >= xmin) & (polys[:, :, 0] <= xmax) \
& (polys[:, :, 1] >= ymin) & (polys[:, :, 1] <= ymax)
selected_polys = np.where(
np.sum(poly_axis_in_area, axis=1) == 4)[0]
else:
selected_polys = []
if len(selected_polys) == 0:
# no text in this area
if crop_background:
txts_tmp = []
for selected_poly in selected_polys:
txts_tmp.append(txts[selected_poly])
txts = txts_tmp
return im[ymin: ymax + 1, xmin: xmax + 1, :], \
polys[selected_polys], tags[selected_polys], hv_tags[selected_polys], txts
else:
continue
im = im[ymin:ymax + 1, xmin:xmax + 1, :]
polys = polys[selected_polys]
tags = tags[selected_polys]
hv_tags = hv_tags[selected_polys]
txts_tmp = []
for selected_poly in selected_polys:
txts_tmp.append(txts[selected_poly])
txts = txts_tmp
polys[:, :, 0] -= xmin
polys[:, :, 1] -= ymin
return im, polys, tags, hv_tags, txts
return im, polys, tags, hv_tags, txts
def fit_and_gather_tcl_points_v2(self,
min_area_quad,
poly,
max_h,
max_w,
fixed_point_num=64,
img_id=0,
reference_height=3):
"""
Find the center point of poly as key_points, then fit and gather.
"""
key_point_xys = []
point_num = poly.shape[0]
for idx in range(point_num // 2):
center_point = (poly[idx] + poly[point_num - 1 - idx]) / 2.0
key_point_xys.append(center_point)
tmp_image = np.zeros(
shape=(
max_h,
max_w, ), dtype='float32')
cv2.polylines(tmp_image, [np.array(key_point_xys).astype('int32')],
False, 1.0)
ys, xs = np.where(tmp_image > 0)
xy_text = np.array(list(zip(xs, ys)), dtype='float32')
left_center_pt = (
(min_area_quad[0] - min_area_quad[1]) / 2.0).reshape(1, 2)
right_center_pt = (
(min_area_quad[1] - min_area_quad[2]) / 2.0).reshape(1, 2)
proj_unit_vec = (right_center_pt - left_center_pt) / (
np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
proj_unit_vec_tile = np.tile(proj_unit_vec,
(xy_text.shape[0], 1)) # (n, 2)
left_center_pt_tile = np.tile(left_center_pt,
(xy_text.shape[0], 1)) # (n, 2)
xy_text_to_left_center = xy_text - left_center_pt_tile
proj_value = np.sum(xy_text_to_left_center * proj_unit_vec_tile, axis=1)
xy_text = xy_text[np.argsort(proj_value)]
# convert to np and keep the num of point not greater then fixed_point_num
pos_info = np.array(xy_text).reshape(-1, 2)[:, ::-1] # xy-> yx
point_num = len(pos_info)
if point_num > fixed_point_num:
keep_ids = [
int((point_num * 1.0 / fixed_point_num) * x)
for x in range(fixed_point_num)
]
pos_info = pos_info[keep_ids, :]
keep = int(min(len(pos_info), fixed_point_num))
if np.random.rand() < 0.2 and reference_height >= 3:
dl = (np.random.rand(keep) - 0.5) * reference_height * 0.3
random_float = np.array([1, 0]).reshape([1, 2]) * dl.reshape(
[keep, 1])
pos_info += random_float
pos_info[:, 0] = np.clip(pos_info[:, 0], 0, max_h - 1)
pos_info[:, 1] = np.clip(pos_info[:, 1], 0, max_w - 1)
# padding to fixed length
pos_l = np.zeros((self.tcl_len, 3), dtype=np.int32)
pos_l[:, 0] = np.ones((self.tcl_len, )) * img_id
pos_m = np.zeros((self.tcl_len, 1), dtype=np.float32)
pos_l[:keep, 1:] = np.round(pos_info).astype(np.int32)
pos_m[:keep] = 1.0
return pos_l, pos_m
def generate_direction_map(self, poly_quads, n_char, direction_map):
"""
"""
width_list = []
height_list = []
for quad in poly_quads:
quad_w = (np.linalg.norm(quad[0] - quad[1]) +
np.linalg.norm(quad[2] - quad[3])) / 2.0
quad_h = (np.linalg.norm(quad[0] - quad[3]) +
np.linalg.norm(quad[2] - quad[1])) / 2.0
width_list.append(quad_w)
height_list.append(quad_h)
norm_width = max(sum(width_list) / n_char, 1.0)
average_height = max(sum(height_list) / len(height_list), 1.0)
k = 1
for quad in poly_quads:
direct_vector_full = (
(quad[1] + quad[2]) - (quad[0] + quad[3])) / 2.0
direct_vector = direct_vector_full / (
np.linalg.norm(direct_vector_full) + 1e-6) * norm_width
direction_label = tuple(
map(float,
[direct_vector[0], direct_vector[1], 1.0 / average_height]))
cv2.fillPoly(direction_map,
quad.round().astype(np.int32)[np.newaxis, :, :],
direction_label)
k += 1
return direction_map
def calculate_average_height(self, poly_quads):
"""
"""
height_list = []
for quad in poly_quads:
quad_h = (np.linalg.norm(quad[0] - quad[3]) +
np.linalg.norm(quad[2] - quad[1])) / 2.0
height_list.append(quad_h)
average_height = max(sum(height_list) / len(height_list), 1.0)
return average_height
def generate_tcl_ctc_label(self,
h,
w,
polys,
tags,
text_strs,
ds_ratio,
tcl_ratio=0.3,
shrink_ratio_of_width=0.15):
"""
Generate polygon.
"""
score_map_big = np.zeros(
(
h,
w, ), dtype=np.float32)
h, w = int(h * ds_ratio), int(w * ds_ratio)
polys = polys * ds_ratio
score_map = np.zeros(
(
h,
w, ), dtype=np.float32)
score_label_map = np.zeros(
(
h,
w, ), dtype=np.float32)
tbo_map = np.zeros((h, w, 5), dtype=np.float32)
training_mask = np.ones(
(
h,
w, ), dtype=np.float32)
direction_map = np.ones((h, w, 3)) * np.array([0, 0, 1]).reshape(
[1, 1, 3]).astype(np.float32)
label_idx = 0
score_label_map_text_label_list = []
pos_list, pos_mask, label_list = [], [], []
for poly_idx, poly_tag in enumerate(zip(polys, tags)):
poly = poly_tag[0]
tag = poly_tag[1]
# generate min_area_quad
min_area_quad, center_point = self.gen_min_area_quad_from_poly(poly)
min_area_quad_h = 0.5 * (
np.linalg.norm(min_area_quad[0] - min_area_quad[3]) +
np.linalg.norm(min_area_quad[1] - min_area_quad[2]))
min_area_quad_w = 0.5 * (
np.linalg.norm(min_area_quad[0] - min_area_quad[1]) +
np.linalg.norm(min_area_quad[2] - min_area_quad[3]))
if min(min_area_quad_h, min_area_quad_w) < self.min_text_size * ds_ratio \
or min(min_area_quad_h, min_area_quad_w) > self.max_text_size * ds_ratio:
continue
if tag:
cv2.fillPoly(training_mask,
poly.astype(np.int32)[np.newaxis, :, :], 0.15)
else:
text_label = text_strs[poly_idx]
text_label = self.prepare_text_label(text_label,
self.Lexicon_Table)
text_label_index_list = [[self.Lexicon_Table.index(c_)]
for c_ in text_label
if c_ in self.Lexicon_Table]
if len(text_label_index_list) < 1:
continue
tcl_poly = self.poly2tcl(poly, tcl_ratio)
tcl_quads = self.poly2quads(tcl_poly)
poly_quads = self.poly2quads(poly)
stcl_quads, quad_index = self.shrink_poly_along_width(
tcl_quads,
shrink_ratio_of_width=shrink_ratio_of_width,
expand_height_ratio=1.0 / tcl_ratio)
cv2.fillPoly(score_map,
np.round(stcl_quads).astype(np.int32), 1.0)
cv2.fillPoly(score_map_big,
np.round(stcl_quads / ds_ratio).astype(np.int32),
1.0)
for idx, quad in enumerate(stcl_quads):
quad_mask = np.zeros((h, w), dtype=np.float32)
quad_mask = cv2.fillPoly(
quad_mask,
np.round(quad[np.newaxis, :, :]).astype(np.int32), 1.0)
tbo_map = self.gen_quad_tbo(poly_quads[quad_index[idx]],
quad_mask, tbo_map)
# score label map and score_label_map_text_label_list for refine
if label_idx == 0:
text_pos_list_ = [[len(self.Lexicon_Table)], ]
score_label_map_text_label_list.append(text_pos_list_)
label_idx += 1
cv2.fillPoly(score_label_map,
np.round(poly_quads).astype(np.int32), label_idx)
score_label_map_text_label_list.append(text_label_index_list)
# direction info, fix-me
n_char = len(text_label_index_list)
direction_map = self.generate_direction_map(poly_quads, n_char,
direction_map)
# pos info
average_shrink_height = self.calculate_average_height(
stcl_quads)
pos_l, pos_m = self.fit_and_gather_tcl_points_v2(
min_area_quad,
poly,
max_h=h,
max_w=w,
fixed_point_num=64,
img_id=self.img_id,
reference_height=average_shrink_height)
label_l = text_label_index_list
if len(text_label_index_list) < 2:
continue
pos_list.append(pos_l)
pos_mask.append(pos_m)
label_list.append(label_l)
# use big score_map for smooth tcl lines
score_map_big_resized = cv2.resize(
score_map_big, dsize=None, fx=ds_ratio, fy=ds_ratio)
score_map = np.array(score_map_big_resized > 1e-3, dtype='float32')
return score_map, score_label_map, tbo_map, direction_map, training_mask, \
pos_list, pos_mask, label_list, score_label_map_text_label_list
def adjust_point(self, poly):
"""
adjust point order.
"""
point_num = poly.shape[0]
if point_num == 4:
len_1 = np.linalg.norm(poly[0] - poly[1])
len_2 = np.linalg.norm(poly[1] - poly[2])
len_3 = np.linalg.norm(poly[2] - poly[3])
len_4 = np.linalg.norm(poly[3] - poly[0])
if (len_1 + len_3) * 1.5 < (len_2 + len_4):
poly = poly[[1, 2, 3, 0], :]
elif point_num > 4:
vector_1 = poly[0] - poly[1]
vector_2 = poly[1] - poly[2]
cos_theta = np.dot(vector_1, vector_2) / (
np.linalg.norm(vector_1) * np.linalg.norm(vector_2) + 1e-6)
theta = np.arccos(np.round(cos_theta, decimals=4))
if abs(theta) > (70 / 180 * math.pi):
index = list(range(1, point_num)) + [0]
poly = poly[np.array(index), :]
return poly
def gen_min_area_quad_from_poly(self, poly):
"""
Generate min area quad from poly.
"""
point_num = poly.shape[0]
min_area_quad = np.zeros((4, 2), dtype=np.float32)
if point_num == 4:
min_area_quad = poly
center_point = np.sum(poly, axis=0) / 4
else:
rect = cv2.minAreaRect(poly.astype(
np.int32)) # (center (x,y), (width, height), angle of rotation)
center_point = rect[0]
box = np.array(cv2.boxPoints(rect))
first_point_idx = 0
min_dist = 1e4
for i in range(4):
dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \
np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \
np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \
np.linalg.norm(box[(i + 3) % 4] - poly[-1])
if dist < min_dist:
min_dist = dist
first_point_idx = i
for i in range(4):
min_area_quad[i] = box[(first_point_idx + i) % 4]
return min_area_quad, center_point
def shrink_quad_along_width(self,
quad,
begin_width_ratio=0.,
end_width_ratio=1.):
"""
Generate shrink_quad_along_width.
"""
ratio_pair = np.array(
[[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
def shrink_poly_along_width(self,
quads,
shrink_ratio_of_width,
expand_height_ratio=1.0):
"""
shrink poly with given length.
"""
upper_edge_list = []
def get_cut_info(edge_len_list, cut_len):
for idx, edge_len in enumerate(edge_len_list):
cut_len -= edge_len
if cut_len <= 0.000001:
ratio = (cut_len + edge_len_list[idx]) / edge_len_list[idx]
return idx, ratio
for quad in quads:
upper_edge_len = np.linalg.norm(quad[0] - quad[1])
upper_edge_list.append(upper_edge_len)
# length of left edge and right edge.
left_length = np.linalg.norm(quads[0][0] - quads[0][
3]) * expand_height_ratio
right_length = np.linalg.norm(quads[-1][1] - quads[-1][
2]) * expand_height_ratio
shrink_length = min(left_length, right_length,
sum(upper_edge_list)) * shrink_ratio_of_width
# shrinking length
upper_len_left = shrink_length
upper_len_right = sum(upper_edge_list) - shrink_length
left_idx, left_ratio = get_cut_info(upper_edge_list, upper_len_left)
left_quad = self.shrink_quad_along_width(
quads[left_idx], begin_width_ratio=left_ratio, end_width_ratio=1)
right_idx, right_ratio = get_cut_info(upper_edge_list, upper_len_right)
right_quad = self.shrink_quad_along_width(
quads[right_idx], begin_width_ratio=0, end_width_ratio=right_ratio)
out_quad_list = []
if left_idx == right_idx:
out_quad_list.append(
[left_quad[0], right_quad[1], right_quad[2], left_quad[3]])
else:
out_quad_list.append(left_quad)
for idx in range(left_idx + 1, right_idx):
out_quad_list.append(quads[idx])
out_quad_list.append(right_quad)
return np.array(out_quad_list), list(range(left_idx, right_idx + 1))
def prepare_text_label(self, label_str, Lexicon_Table):
"""
Prepare text lablel by given Lexicon_Table.
"""
if len(Lexicon_Table) == 36:
return label_str.lower()
else:
return label_str
def vector_angle(self, A, B):
"""
Calculate the angle between vector AB and x-axis positive direction.
"""
AB = np.array([B[1] - A[1], B[0] - A[0]])
return np.arctan2(*AB)
def theta_line_cross_point(self, theta, point):
"""
Calculate the line through given point and angle in ax + by + c =0 form.
"""
x, y = point
cos = np.cos(theta)
sin = np.sin(theta)
return [sin, -cos, cos * y - sin * x]
def line_cross_two_point(self, A, B):
"""
Calculate the line through given point A and B in ax + by + c =0 form.
"""
angle = self.vector_angle(A, B)
return self.theta_line_cross_point(angle, A)
def average_angle(self, poly):
"""
Calculate the average angle between left and right edge in given poly.
"""
p0, p1, p2, p3 = poly
angle30 = self.vector_angle(p3, p0)
angle21 = self.vector_angle(p2, p1)
return (angle30 + angle21) / 2
def line_cross_point(self, line1, line2):
"""
line1 and line2 in 0=ax+by+c form, compute the cross point of line1 and line2
"""
a1, b1, c1 = line1
a2, b2, c2 = line2
d = a1 * b2 - a2 * b1
if d == 0:
print('Cross point does not exist')
return np.array([0, 0], dtype=np.float32)
else:
x = (b1 * c2 - b2 * c1) / d
y = (a2 * c1 - a1 * c2) / d
return np.array([x, y], dtype=np.float32)
def quad2tcl(self, poly, ratio):
"""
Generate center line by poly clock-wise point. (4, 2)
"""
ratio_pair = np.array(
[[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32)
p0_3 = poly[0] + (poly[3] - poly[0]) * ratio_pair
p1_2 = poly[1] + (poly[2] - poly[1]) * ratio_pair
return np.array([p0_3[0], p1_2[0], p1_2[1], p0_3[1]])
def poly2tcl(self, poly, ratio):
"""
Generate center line by poly clock-wise point.
"""
ratio_pair = np.array(
[[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32)
tcl_poly = np.zeros_like(poly)
point_num = poly.shape[0]
for idx in range(point_num // 2):
point_pair = poly[idx] + (poly[point_num - 1 - idx] - poly[idx]
) * ratio_pair
tcl_poly[idx] = point_pair[0]
tcl_poly[point_num - 1 - idx] = point_pair[1]
return tcl_poly
def gen_quad_tbo(self, quad, tcl_mask, tbo_map):
"""
Generate tbo_map for give quad.
"""
# upper and lower line function: ax + by + c = 0;
up_line = self.line_cross_two_point(quad[0], quad[1])
lower_line = self.line_cross_two_point(quad[3], quad[2])
quad_h = 0.5 * (np.linalg.norm(quad[0] - quad[3]) +
np.linalg.norm(quad[1] - quad[2]))
quad_w = 0.5 * (np.linalg.norm(quad[0] - quad[1]) +
np.linalg.norm(quad[2] - quad[3]))
# average angle of left and right line.
angle = self.average_angle(quad)
xy_in_poly = np.argwhere(tcl_mask == 1)
for y, x in xy_in_poly:
point = (x, y)
line = self.theta_line_cross_point(angle, point)
cross_point_upper = self.line_cross_point(up_line, line)
cross_point_lower = self.line_cross_point(lower_line, line)
##FIX, offset reverse
upper_offset_x, upper_offset_y = cross_point_upper - point
lower_offset_x, lower_offset_y = cross_point_lower - point
tbo_map[y, x, 0] = upper_offset_y
tbo_map[y, x, 1] = upper_offset_x
tbo_map[y, x, 2] = lower_offset_y
tbo_map[y, x, 3] = lower_offset_x
tbo_map[y, x, 4] = 1.0 / max(min(quad_h, quad_w), 1.0) * 2
return tbo_map
def poly2quads(self, poly):
"""
Split poly into quads.
"""
quad_list = []
point_num = poly.shape[0]
# point pair
point_pair_list = []
for idx in range(point_num // 2):
point_pair = [poly[idx], poly[point_num - 1 - idx]]
point_pair_list.append(point_pair)
quad_num = point_num // 2 - 1
for idx in range(quad_num):
# reshape and adjust to clock-wise
quad_list.append((np.array(point_pair_list)[[idx, idx + 1]]
).reshape(4, 2)[[0, 2, 3, 1]])
return np.array(quad_list)
def rotate_im_poly(self, im, text_polys):
"""
rotate image with 90 / 180 / 270 degre
"""
im_w, im_h = im.shape[1], im.shape[0]
dst_im = im.copy()
dst_polys = []
rand_degree_ratio = np.random.rand()
rand_degree_cnt = 1
if rand_degree_ratio > 0.5:
rand_degree_cnt = 3
for i in range(rand_degree_cnt):
dst_im = np.rot90(dst_im)
rot_degree = -90 * rand_degree_cnt
rot_angle = rot_degree * math.pi / 180.0
n_poly = text_polys.shape[0]
cx, cy = 0.5 * im_w, 0.5 * im_h
ncx, ncy = 0.5 * dst_im.shape[1], 0.5 * dst_im.shape[0]
for i in range(n_poly):
wordBB = text_polys[i]
poly = []
for j in range(4): # 16->4
sx, sy = wordBB[j][0], wordBB[j][1]
dx = math.cos(rot_angle) * (sx - cx) - math.sin(rot_angle) * (
sy - cy) + ncx
dy = math.sin(rot_angle) * (sx - cx) + math.cos(rot_angle) * (
sy - cy) + ncy
poly.append([dx, dy])
dst_polys.append(poly)
return dst_im, np.array(dst_polys, dtype=np.float32)
def __call__(self, data):
input_size = 512
im = data['image']
text_polys = data['polys']
text_tags = data['tags']
text_strs = data['strs']
h, w, _ = im.shape
text_polys, text_tags, hv_tags = self.check_and_validate_polys(
text_polys, text_tags, (h, w))
if text_polys.shape[0] <= 0:
return None
# set aspect ratio and keep area fix
asp_scales = np.arange(1.0, 1.55, 0.1)
asp_scale = np.random.choice(asp_scales)
if np.random.rand() < 0.5:
asp_scale = 1.0 / asp_scale
asp_scale = math.sqrt(asp_scale)
asp_wx = asp_scale
asp_hy = 1.0 / asp_scale
im = cv2.resize(im, dsize=None, fx=asp_wx, fy=asp_hy)
text_polys[:, :, 0] *= asp_wx
text_polys[:, :, 1] *= asp_hy
h, w, _ = im.shape
if max(h, w) > 2048:
rd_scale = 2048.0 / max(h, w)
im = cv2.resize(im, dsize=None, fx=rd_scale, fy=rd_scale)
text_polys *= rd_scale
h, w, _ = im.shape
if min(h, w) < 16:
return None
# no background
im, text_polys, text_tags, hv_tags, text_strs = self.crop_area(
im,
text_polys,
text_tags,
hv_tags,
text_strs,
crop_background=False)
if text_polys.shape[0] == 0:
return None
# # continue for all ignore case
if np.sum((text_tags * 1.0)) >= text_tags.size:
return None
new_h, new_w, _ = im.shape
if (new_h is None) or (new_w is None):
return None
# resize image
std_ratio = float(input_size) / max(new_w, new_h)
rand_scales = np.array(
[0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0, 1.0, 1.0, 1.0, 1.0])
rz_scale = std_ratio * np.random.choice(rand_scales)
im = cv2.resize(im, dsize=None, fx=rz_scale, fy=rz_scale)
text_polys[:, :, 0] *= rz_scale
text_polys[:, :, 1] *= rz_scale
# add gaussian blur
if np.random.rand() < 0.1 * 0.5:
ks = np.random.permutation(5)[0] + 1
ks = int(ks / 2) * 2 + 1
im = cv2.GaussianBlur(im, ksize=(ks, ks), sigmaX=0, sigmaY=0)
# add brighter
if np.random.rand() < 0.1 * 0.5:
im = im * (1.0 + np.random.rand() * 0.5)
im = np.clip(im, 0.0, 255.0)
# add darker
if np.random.rand() < 0.1 * 0.5:
im = im * (1.0 - np.random.rand() * 0.5)
im = np.clip(im, 0.0, 255.0)
# Padding the im to [input_size, input_size]
new_h, new_w, _ = im.shape
if min(new_w, new_h) < input_size * 0.5:
return None
im_padded = np.ones((input_size, input_size, 3), dtype=np.float32)
im_padded[:, :, 2] = 0.485 * 255
im_padded[:, :, 1] = 0.456 * 255
im_padded[:, :, 0] = 0.406 * 255
# Random the start position
del_h = input_size - new_h
del_w = input_size - new_w
sh, sw = 0, 0
if del_h > 1:
sh = int(np.random.rand() * del_h)
if del_w > 1:
sw = int(np.random.rand() * del_w)
# Padding
im_padded[sh:sh + new_h, sw:sw + new_w, :] = im.copy()
text_polys[:, :, 0] += sw
text_polys[:, :, 1] += sh
score_map, score_label_map, border_map, direction_map, training_mask, \
pos_list, pos_mask, label_list, score_label_map_text_label = self.generate_tcl_ctc_label(input_size,
input_size,
text_polys,
text_tags,
text_strs, 0.25)
if len(label_list) <= 0: # eliminate negative samples
return None
pos_list_temp = np.zeros([64, 3])
pos_mask_temp = np.zeros([64, 1])
label_list_temp = np.zeros([self.max_text_length, 1]) + self.pad_num
for i, label in enumerate(label_list):
n = len(label)
if n > self.max_text_length:
label_list[i] = label[:self.max_text_length]
continue
while n < self.max_text_length:
label.append([self.pad_num])
n += 1
for i in range(len(label_list)):
label_list[i] = np.array(label_list[i])
if len(pos_list) <= 0 or len(pos_list) > self.max_text_nums:
return None
for __ in range(self.max_text_nums - len(pos_list), 0, -1):
pos_list.append(pos_list_temp)
pos_mask.append(pos_mask_temp)
label_list.append(label_list_temp)
if self.img_id == self.batch_size - 1:
self.img_id = 0
else:
self.img_id += 1
im_padded[:, :, 2] -= 0.485 * 255
im_padded[:, :, 1] -= 0.456 * 255
im_padded[:, :, 0] -= 0.406 * 255
im_padded[:, :, 2] /= (255.0 * 0.229)
im_padded[:, :, 1] /= (255.0 * 0.224)
im_padded[:, :, 0] /= (255.0 * 0.225)
im_padded = im_padded.transpose((2, 0, 1))
images = im_padded[::-1, :, :]
tcl_maps = score_map[np.newaxis, :, :]
tcl_label_maps = score_label_map[np.newaxis, :, :]
border_maps = border_map.transpose((2, 0, 1))
direction_maps = direction_map.transpose((2, 0, 1))
training_masks = training_mask[np.newaxis, :, :]
pos_list = np.array(pos_list)
pos_mask = np.array(pos_mask)
label_list = np.array(label_list)
data['images'] = images
data['tcl_maps'] = tcl_maps
data['tcl_label_maps'] = tcl_label_maps
data['border_maps'] = border_maps
data['direction_maps'] = direction_maps
data['training_masks'] = training_masks
data['label_list'] = label_list
data['pos_list'] = pos_list
data['pos_mask'] = pos_mask
return data
ppocr/data/pgnet_dataset.py 0 → 100644
浏览文件 @ 76320bf0
# copyright (c) 2021 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
#
# 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 numpy as np
import os
from paddle.io import Dataset
from .imaug import transform, create_operators
import random
class PGDataSet(Dataset):
def __init__(self, config, mode, logger, seed=None):
super(PGDataSet, self).__init__()
self.logger = logger
self.seed = seed
self.mode = mode
global_config = config['Global']
dataset_config = config[mode]['dataset']
loader_config = config[mode]['loader']
label_file_list = dataset_config.pop('label_file_list')
data_source_num = len(label_file_list)
ratio_list = dataset_config.get("ratio_list", [1.0])
if isinstance(ratio_list, (float, int)):
ratio_list = [float(ratio_list)] * int(data_source_num)
self.data_format = dataset_config.get('data_format', 'icdar')
assert len(
ratio_list
) == data_source_num, "The length of ratio_list should be the same as the file_list."
self.do_shuffle = loader_config['shuffle']
logger.info("Initialize indexs of datasets:%s" % label_file_list)
self.data_lines = self.get_image_info_list(label_file_list, ratio_list,
self.data_format)
self.data_idx_order_list = list(range(len(self.data_lines)))
if mode.lower() == "train":
self.shuffle_data_random()
self.ops = create_operators(dataset_config['transforms'], global_config)
def shuffle_data_random(self):
if self.do_shuffle:
random.seed(self.seed)
random.shuffle(self.data_lines)
return
def extract_polys(self, poly_txt_path):
"""
Read text_polys, txt_tags, txts from give txt file.
"""
text_polys, txt_tags, txts = [], [], []
with open(poly_txt_path) as f:
for line in f.readlines():
poly_str, txt = line.strip().split('\t')
poly = list(map(float, poly_str.split(',')))
if self.mode.lower() == "eval":
while len(poly) < 100:
poly.append(-1)
text_polys.append(
np.array(
poly, dtype=np.float32).reshape(-1, 2))
txts.append(txt)
txt_tags.append(txt == '###')
return np.array(list(map(np.array, text_polys))), \
np.array(txt_tags, dtype=np.bool), txts
def extract_info_textnet(self, im_fn, img_dir=''):
"""
Extract information from line in textnet format.
"""
info_list = im_fn.split('\t')
img_path = ''
for ext in [
'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'JPG'
]:
if os.path.exists(os.path.join(img_dir, info_list[0] + "." + ext)):
img_path = os.path.join(img_dir, info_list[0] + "." + ext)
break
if img_path == '':
print('Image {0} NOT found in {1}, and it will be ignored.'.format(
info_list[0], img_dir))
nBox = (len(info_list) - 1) // 9
wordBBs, txts, txt_tags = [], [], []
for n in range(0, nBox):
wordBB = list(map(float, info_list[n * 9 + 1:(n + 1) * 9]))
txt = info_list[(n + 1) * 9]
wordBBs.append([[wordBB[0], wordBB[1]], [wordBB[2], wordBB[3]],
[wordBB[4], wordBB[5]], [wordBB[6], wordBB[7]]])
txts.append(txt)
if txt == '###':
txt_tags.append(True)
else:
txt_tags.append(False)
return img_path, np.array(wordBBs, dtype=np.float32), txt_tags, txts
def get_image_info_list(self, file_list, ratio_list, data_format='textnet'):
if isinstance(file_list, str):
file_list = [file_list]
data_lines = []
for idx, data_source in enumerate(file_list):
image_files = []
if data_format == 'icdar':
image_files = [(data_source, x) for x in
os.listdir(os.path.join(data_source, 'rgb'))
if x.split('.')[-1] in [
'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif',
'tiff', 'gif', 'JPG'
]]
elif data_format == 'textnet':
with open(data_source) as f:
image_files = [(data_source, x.strip())
for x in f.readlines()]
else:
print("Unrecognized data format...")
exit(-1)
random.seed(self.seed)
image_files = random.sample(
image_files, round(len(image_files) * ratio_list[idx]))
data_lines.extend(image_files)
return data_lines
def __getitem__(self, idx):
file_idx = self.data_idx_order_list[idx]
data_path, data_line = self.data_lines[file_idx]
try:
if self.data_format == 'icdar':
im_path = os.path.join(data_path, 'rgb', data_line)
if self.mode.lower() == "eval":
poly_path = os.path.join(data_path, 'poly_gt',
data_line.split('.')[0] + '.txt')
else:
poly_path = os.path.join(data_path, 'poly',
data_line.split('.')[0] + '.txt')
text_polys, text_tags, text_strs = self.extract_polys(poly_path)
else:
image_dir = os.path.join(os.path.dirname(data_path), 'image')
im_path, text_polys, text_tags, text_strs = self.extract_info_textnet(
data_line, image_dir)
data = {
'img_path': im_path,
'polys': text_polys,
'tags': text_tags,
'strs': text_strs
}
with open(data['img_path'], 'rb') as f:
img = f.read()
data['image'] = img
outs = transform(data, self.ops)
except Exception as e:
self.logger.error(
"When parsing line {}, error happened with msg: {}".format(
self.data_idx_order_list[idx], e))
outs = None
if outs is None:
return self.__getitem__(np.random.randint(self.__len__()))
return outs
def __len__(self):
return len(self.data_idx_order_list)
ppocr/losses/__init__.py
浏览文件 @ 76320bf0
......@@ -29,10 +29,11 @@ def build_loss(config):
# cls loss
from .cls_loss import ClsLoss
# e2e loss
from .e2e_pg_loss import PGLoss
support_dict = [
'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
'SRNLoss'
]
'SRNLoss', 'PGLoss']
config = copy.deepcopy(config)
module_name = config.pop('name')
......
ppocr/losses/e2e_pg_loss.py 0 → 100644
浏览文件 @ 76320bf0
# copyright (c) 2021 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
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from paddle import nn
import paddle
from .det_basic_loss import DiceLoss
from ppocr.utils.e2e_utils.extract_batchsize import pre_process
class PGLoss(nn.Layer):
def __init__(self,
tcl_bs,
max_text_length,
max_text_nums,
pad_num,
eps=1e-6,
**kwargs):
super(PGLoss, self).__init__()
self.tcl_bs = tcl_bs
self.max_text_nums = max_text_nums
self.max_text_length = max_text_length
self.pad_num = pad_num
self.dice_loss = DiceLoss(eps=eps)
def border_loss(self, f_border, l_border, l_score, l_mask):
l_border_split, l_border_norm = paddle.tensor.split(
l_border, num_or_sections=[4, 1], axis=1)
f_border_split = f_border
b, c, h, w = l_border_norm.shape
l_border_norm_split = paddle.expand(
x=l_border_norm, shape=[b, 4 * c, h, w])
b, c, h, w = l_score.shape
l_border_score = paddle.expand(x=l_score, shape=[b, 4 * c, h, w])
b, c, h, w = l_mask.shape
l_border_mask = paddle.expand(x=l_mask, shape=[b, 4 * c, h, w])
border_diff = l_border_split - f_border_split
abs_border_diff = paddle.abs(border_diff)
border_sign = abs_border_diff < 1.0
border_sign = paddle.cast(border_sign, dtype='float32')
border_sign.stop_gradient = True
border_in_loss = 0.5 * abs_border_diff * abs_border_diff * border_sign + \
(abs_border_diff - 0.5) * (1.0 - border_sign)
border_out_loss = l_border_norm_split * border_in_loss
border_loss = paddle.sum(border_out_loss * l_border_score * l_border_mask) / \
(paddle.sum(l_border_score * l_border_mask) + 1e-5)
return border_loss
def direction_loss(self, f_direction, l_direction, l_score, l_mask):
l_direction_split, l_direction_norm = paddle.tensor.split(
l_direction, num_or_sections=[2, 1], axis=1)
f_direction_split = f_direction
b, c, h, w = l_direction_norm.shape
l_direction_norm_split = paddle.expand(
x=l_direction_norm, shape=[b, 2 * c, h, w])
b, c, h, w = l_score.shape
l_direction_score = paddle.expand(x=l_score, shape=[b, 2 * c, h, w])
b, c, h, w = l_mask.shape
l_direction_mask = paddle.expand(x=l_mask, shape=[b, 2 * c, h, w])
direction_diff = l_direction_split - f_direction_split
abs_direction_diff = paddle.abs(direction_diff)
direction_sign = abs_direction_diff < 1.0
direction_sign = paddle.cast(direction_sign, dtype='float32')
direction_sign.stop_gradient = True
direction_in_loss = 0.5 * abs_direction_diff * abs_direction_diff * direction_sign + \
(abs_direction_diff - 0.5) * (1.0 - direction_sign)
direction_out_loss = l_direction_norm_split * direction_in_loss
direction_loss = paddle.sum(direction_out_loss * l_direction_score * l_direction_mask) / \
(paddle.sum(l_direction_score * l_direction_mask) + 1e-5)
return direction_loss
def ctcloss(self, f_char, tcl_pos, tcl_mask, tcl_label, label_t):
f_char = paddle.transpose(f_char, [0, 2, 3, 1])
tcl_pos = paddle.reshape(tcl_pos, [-1, 3])
tcl_pos = paddle.cast(tcl_pos, dtype=int)
f_tcl_char = paddle.gather_nd(f_char, tcl_pos)
f_tcl_char = paddle.reshape(f_tcl_char,
[-1, 64, 37]) # len(Lexicon_Table)+1
f_tcl_char_fg, f_tcl_char_bg = paddle.split(f_tcl_char, [36, 1], axis=2)
f_tcl_char_bg = f_tcl_char_bg * tcl_mask + (1.0 - tcl_mask) * 20.0
b, c, l = tcl_mask.shape
tcl_mask_fg = paddle.expand(x=tcl_mask, shape=[b, c, 36 * l])
tcl_mask_fg.stop_gradient = True
f_tcl_char_fg = f_tcl_char_fg * tcl_mask_fg + (1.0 - tcl_mask_fg) * (
-20.0)
f_tcl_char_mask = paddle.concat([f_tcl_char_fg, f_tcl_char_bg], axis=2)
f_tcl_char_ld = paddle.transpose(f_tcl_char_mask, (1, 0, 2))
N, B, _ = f_tcl_char_ld.shape
input_lengths = paddle.to_tensor([N] * B, dtype='int64')
cost = paddle.nn.functional.ctc_loss(
log_probs=f_tcl_char_ld,
labels=tcl_label,
input_lengths=input_lengths,
label_lengths=label_t,
blank=self.pad_num,
reduction='none')
cost = cost.mean()
return cost
def forward(self, predicts, labels):
images, tcl_maps, tcl_label_maps, border_maps \
, direction_maps, training_masks, label_list, pos_list, pos_mask = labels
# for all the batch_size
pos_list, pos_mask, label_list, label_t = pre_process(
label_list, pos_list, pos_mask, self.max_text_length,
self.max_text_nums, self.pad_num, self.tcl_bs)
f_score, f_border, f_direction, f_char = predicts['f_score'], predicts['f_border'], predicts['f_direction'], \
predicts['f_char']
score_loss = self.dice_loss(f_score, tcl_maps, training_masks)
border_loss = self.border_loss(f_border, border_maps, tcl_maps,
training_masks)
direction_loss = self.direction_loss(f_direction, direction_maps,
tcl_maps, training_masks)
ctc_loss = self.ctcloss(f_char, pos_list, pos_mask, label_list, label_t)
loss_all = score_loss + border_loss + direction_loss + 5 * ctc_loss
losses = {
'loss': loss_all,
"score_loss": score_loss,
"border_loss": border_loss,
"direction_loss": direction_loss,
"ctc_loss": ctc_loss
}
return losses
ppocr/metrics/__init__.py
浏览文件 @ 76320bf0
......@@ -26,8 +26,9 @@ def build_metric(config):
from .det_metric import DetMetric
from .rec_metric import RecMetric
from .cls_metric import ClsMetric
from .e2e_metric import E2EMetric
support_dict = ['DetMetric', 'RecMetric', 'ClsMetric']
support_dict = ['DetMetric', 'RecMetric', 'ClsMetric', 'E2EMetric']
config = copy.deepcopy(config)
module_name = config.pop('name')
......
ppocr/metrics/e2e_metric.py 0 → 100644
浏览文件 @ 76320bf0
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__all__ = ['E2EMetric']
from ppocr.utils.e2e_metric.Deteval import get_socre, combine_results
from ppocr.utils.e2e_utils.extract_textpoint import get_dict
class E2EMetric(object):
def __init__(self,
character_dict_path,
main_indicator='f_score_e2e',
**kwargs):
self.label_list = get_dict(character_dict_path)
self.max_index = len(self.label_list)
self.main_indicator = main_indicator
self.reset()
def __call__(self, preds, batch, **kwargs):
temp_gt_polyons_batch = batch[2]
temp_gt_strs_batch = batch[3]
ignore_tags_batch = batch[4]
gt_polyons_batch = []
gt_strs_batch = []
temp_gt_polyons_batch = temp_gt_polyons_batch[0].tolist()
for temp_list in temp_gt_polyons_batch:
t = []
for index in temp_list:
if index[0] != -1 and index[1] != -1:
t.append(index)
gt_polyons_batch.append(t)
temp_gt_strs_batch = temp_gt_strs_batch[0].tolist()
for temp_list in temp_gt_strs_batch:
t = ""
for index in temp_list:
if index < self.max_index:
t += self.label_list[index]
gt_strs_batch.append(t)
for pred, gt_polyons, gt_strs, ignore_tags in zip(
[preds], [gt_polyons_batch], [gt_strs_batch], ignore_tags_batch):
# prepare gt
gt_info_list = [{
'points': gt_polyon,
'text': gt_str,
'ignore': ignore_tag
} for gt_polyon, gt_str, ignore_tag in
zip(gt_polyons, gt_strs, ignore_tags)]
# prepare det
e2e_info_list = [{
'points': det_polyon,
'text': pred_str
} for det_polyon, pred_str in zip(pred['points'], pred['strs'])]
result = get_socre(gt_info_list, e2e_info_list)
self.results.append(result)
def get_metric(self):
metircs = combine_results(self.results)
self.reset()
return metircs
def reset(self):
self.results = [] # clear results
ppocr/metrics/eval_det_iou.py
浏览文件 @ 76320bf0
......@@ -200,7 +200,8 @@ class DetectionIoUEvaluator(object):
methodPrecision = 0 if numGlobalCareDet == 0 else float(
matchedSum) / numGlobalCareDet
methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \
methodRecall * methodPrecision / (methodRecall + methodPrecision)
methodRecall * methodPrecision / (
methodRecall + methodPrecision)
# print(methodRecall, methodPrecision, methodHmean)
# sys.exit(-1)
methodMetrics = {
......
ppocr/modeling/backbones/__init__.py
浏览文件 @ 76320bf0
......@@ -26,6 +26,9 @@ def build_backbone(config, model_type):
from .rec_resnet_vd import ResNet
from .rec_resnet_fpn import ResNetFPN
support_dict = ['MobileNetV3', 'ResNet', 'ResNetFPN']
elif model_type == 'e2e':
from .e2e_resnet_vd_pg import ResNet
support_dict = ['ResNet']
else:
raise NotImplementedError
......
ppocr/modeling/backbones/e2e_resnet_vd_pg.py 0 → 100644
浏览文件 @ 76320bf0
# copyright (c) 2021 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
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
__all__ = ["ResNet"]
class ConvBNLayer(nn.Layer):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
name=None, ):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self._batch_norm = nn.BatchNorm(
out_channels,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class BottleneckBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
name=None):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
act='relu',
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
act='relu',
name=name + "_branch2b")
self.conv2 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels * 4,
kernel_size=1,
act=None,
name=name + "_branch2c")
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels * 4,
kernel_size=1,
stride=stride,
is_vd_mode=False if if_first else True,
name=name + "_branch1")
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv2)
y = F.relu(y)
return y
class BasicBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
name=None):
super(BasicBlock, self).__init__()
self.stride = stride
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
act='relu',
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
act=None,
name=name + "_branch2b")
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=1,
is_vd_mode=False if if_first else True,
name=name + "_branch1")
self.shortcut = shortcut
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = paddle.add(x=short, y=conv1)
y = F.relu(y)
return y
class ResNet(nn.Layer):
def __init__(self, in_channels=3, layers=50, **kwargs):
super(ResNet, self).__init__()
self.layers = layers
supported_layers = [18, 34, 50, 101, 152, 200]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(
supported_layers, layers)
if layers == 18:
depth = [2, 2, 2, 2]
elif layers == 34 or layers == 50:
# depth = [3, 4, 6, 3]
depth = [3, 4, 6, 3, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
elif layers == 200:
depth = [3, 12, 48, 3]
num_channels = [64, 256, 512, 1024,
2048] if layers >= 50 else [64, 64, 128, 256]
num_filters = [64, 128, 256, 512, 512]
self.conv1_1 = ConvBNLayer(
in_channels=in_channels,
out_channels=64,
kernel_size=7,
stride=2,
act='relu',
name="conv1_1")
self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.stages = []
self.out_channels = [3, 64]
# num_filters = [64, 128, 256, 512, 512]
if layers >= 50:
for block in range(len(depth)):
block_list = []
shortcut = False
for i in range(depth[block]):
if layers in [101, 152] and block == 2:
if i == 0:
conv_name = "res" + str(block + 2) + "a"
else:
conv_name = "res" + str(block + 2) + "b" + str(i)
else:
conv_name = "res" + str(block + 2) + chr(97 + i)
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block] * 4,
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name))
shortcut = True
block_list.append(bottleneck_block)
self.out_channels.append(num_filters[block] * 4)
self.stages.append(nn.Sequential(*block_list))
else:
for block in range(len(depth)):
block_list = []
shortcut = False
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
basic_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BasicBlock(
in_channels=num_channels[block]
if i == 0 else num_filters[block],
out_channels=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name))
shortcut = True
block_list.append(basic_block)
self.out_channels.append(num_filters[block])
self.stages.append(nn.Sequential(*block_list))
def forward(self, inputs):
out = [inputs]
y = self.conv1_1(inputs)
out.append(y)
y = self.pool2d_max(y)
for block in self.stages:
y = block(y)
out.append(y)
return out
ppocr/modeling/heads/__init__.py
浏览文件 @ 76320bf0
......@@ -20,6 +20,7 @@ def build_head(config):
from .det_db_head import DBHead
from .det_east_head import EASTHead
from .det_sast_head import SASTHead
from .e2e_pg_head import PGHead
# rec head
from .rec_ctc_head import CTCHead
......@@ -30,8 +31,8 @@ def build_head(config):
from .cls_head import ClsHead
support_dict = [
'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
'SRNHead'
]
'SRNHead', 'PGHead']
module_name = config.pop('name')
assert module_name in support_dict, Exception('head only support {}'.format(
......
ppocr/modeling/heads/e2e_pg_head.py 0 → 100644
浏览文件 @ 76320bf0
# copyright (c) 2021 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
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import nn
import paddle.nn.functional as F
from paddle import ParamAttr
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
groups=1,
if_act=True,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
self.if_act = if_act
self.act = act
self.conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(name=name + '_weights'),
bias_attr=False)
self.bn = nn.BatchNorm(
num_channels=out_channels,
act=act,
param_attr=ParamAttr(name="bn_" + name + "_scale"),
bias_attr=ParamAttr(name="bn_" + name + "_offset"),
moving_mean_name="bn_" + name + "_mean",
moving_variance_name="bn_" + name + "_variance",
use_global_stats=False)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class PGHead(nn.Layer):
"""
"""
def __init__(self, in_channels, **kwargs):
super(PGHead, self).__init__()
self.conv_f_score1 = ConvBNLayer(
in_channels=in_channels,
out_channels=64,
kernel_size=1,
stride=1,
padding=0,
act='relu',
name="conv_f_score{}".format(1))
self.conv_f_score2 = ConvBNLayer(
in_channels=64,
out_channels=64,
kernel_size=3,
stride=1,
padding=1,
act='relu',
name="conv_f_score{}".format(2))
self.conv_f_score3 = ConvBNLayer(
in_channels=64,
out_channels=128,
kernel_size=1,
stride=1,
padding=0,
act='relu',
name="conv_f_score{}".format(3))
self.conv1 = nn.Conv2D(
in_channels=128,
out_channels=1,
kernel_size=3,
stride=1,
padding=1,
groups=1,
weight_attr=ParamAttr(name="conv_f_score{}".format(4)),
bias_attr=False)
self.conv_f_boder1 = ConvBNLayer(
in_channels=in_channels,
out_channels=64,
kernel_size=1,
stride=1,
padding=0,
act='relu',
name="conv_f_boder{}".format(1))
self.conv_f_boder2 = ConvBNLayer(
in_channels=64,
out_channels=64,
kernel_size=3,
stride=1,
padding=1,
act='relu',
name="conv_f_boder{}".format(2))
self.conv_f_boder3 = ConvBNLayer(
in_channels=64,
out_channels=128,
kernel_size=1,
stride=1,
padding=0,
act='relu',
name="conv_f_boder{}".format(3))
self.conv2 = nn.Conv2D(
in_channels=128,
out_channels=4,
kernel_size=3,
stride=1,
padding=1,
groups=1,
weight_attr=ParamAttr(name="conv_f_boder{}".format(4)),
bias_attr=False)
self.conv_f_char1 = ConvBNLayer(
in_channels=in_channels,
out_channels=128,
kernel_size=1,
stride=1,
padding=0,
act='relu',
name="conv_f_char{}".format(1))
self.conv_f_char2 = ConvBNLayer(
in_channels=128,
out_channels=128,
kernel_size=3,
stride=1,
padding=1,
act='relu',
name="conv_f_char{}".format(2))
self.conv_f_char3 = ConvBNLayer(
in_channels=128,
out_channels=256,
kernel_size=1,
stride=1,
padding=0,
act='relu',
name="conv_f_char{}".format(3))
self.conv_f_char4 = ConvBNLayer(
in_channels=256,
out_channels=256,
kernel_size=3,
stride=1,
padding=1,
act='relu',
name="conv_f_char{}".format(4))
self.conv_f_char5 = ConvBNLayer(
in_channels=256,
out_channels=256,
kernel_size=1,
stride=1,
padding=0,
act='relu',
name="conv_f_char{}".format(5))
self.conv3 = nn.Conv2D(
in_channels=256,
out_channels=37,
kernel_size=3,
stride=1,
padding=1,
groups=1,
weight_attr=ParamAttr(name="conv_f_char{}".format(6)),
bias_attr=False)
self.conv_f_direc1 = ConvBNLayer(
in_channels=in_channels,
out_channels=64,
kernel_size=1,
stride=1,
padding=0,
act='relu',
name="conv_f_direc{}".format(1))
self.conv_f_direc2 = ConvBNLayer(
in_channels=64,
out_channels=64,
kernel_size=3,
stride=1,
padding=1,
act='relu',
name="conv_f_direc{}".format(2))
self.conv_f_direc3 = ConvBNLayer(
in_channels=64,
out_channels=128,
kernel_size=1,
stride=1,
padding=0,
act='relu',
name="conv_f_direc{}".format(3))
self.conv4 = nn.Conv2D(
in_channels=128,
out_channels=2,
kernel_size=3,
stride=1,
padding=1,
groups=1,
weight_attr=ParamAttr(name="conv_f_direc{}".format(4)),
bias_attr=False)
def forward(self, x):
f_score = self.conv_f_score1(x)
f_score = self.conv_f_score2(f_score)
f_score = self.conv_f_score3(f_score)
f_score = self.conv1(f_score)
f_score = F.sigmoid(f_score)
# f_border
f_border = self.conv_f_boder1(x)
f_border = self.conv_f_boder2(f_border)
f_border = self.conv_f_boder3(f_border)
f_border = self.conv2(f_border)
f_char = self.conv_f_char1(x)
f_char = self.conv_f_char2(f_char)
f_char = self.conv_f_char3(f_char)
f_char = self.conv_f_char4(f_char)
f_char = self.conv_f_char5(f_char)
f_char = self.conv3(f_char)
f_direction = self.conv_f_direc1(x)
f_direction = self.conv_f_direc2(f_direction)
f_direction = self.conv_f_direc3(f_direction)
f_direction = self.conv4(f_direction)
predicts = {}
predicts['f_score'] = f_score
predicts['f_border'] = f_border
predicts['f_char'] = f_char
predicts['f_direction'] = f_direction
return predicts
ppocr/modeling/necks/__init__.py
浏览文件 @ 76320bf0
......@@ -14,12 +14,14 @@
__all__ = ['build_neck']
def build_neck(config):
from .db_fpn import DBFPN
from .east_fpn import EASTFPN
from .sast_fpn import SASTFPN
from .rnn import SequenceEncoder
support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder']
from .pg_fpn import PGFPN
support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN']
module_name = config.pop('name')
assert module_name in support_dict, Exception('neck only support {}'.format(
......
ppocr/modeling/necks/pg_fpn.py 0 → 100644
浏览文件 @ 76320bf0
# copyright (c) 2021 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
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import nn
import paddle.nn.functional as F
from paddle import ParamAttr
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
self._conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
self._batch_norm = nn.BatchNorm(
out_channels,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance',
use_global_stats=False)
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
class DeConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size=4,
stride=2,
padding=1,
groups=1,
if_act=True,
act=None,
name=None):
super(DeConvBNLayer, self).__init__()
self.if_act = if_act
self.act = act
self.deconv = nn.Conv2DTranspose(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(name=name + '_weights'),
bias_attr=False)
self.bn = nn.BatchNorm(
num_channels=out_channels,
act=act,
param_attr=ParamAttr(name="bn_" + name + "_scale"),
bias_attr=ParamAttr(name="bn_" + name + "_offset"),
moving_mean_name="bn_" + name + "_mean",
moving_variance_name="bn_" + name + "_variance",
use_global_stats=False)
def forward(self, x):
x = self.deconv(x)
x = self.bn(x)
return x
class PGFPN(nn.Layer):
def __init__(self, in_channels, **kwargs):
super(PGFPN, self).__init__()
num_inputs = [2048, 2048, 1024, 512, 256]
num_outputs = [256, 256, 192, 192, 128]
self.out_channels = 128
self.conv_bn_layer_1 = ConvBNLayer(
in_channels=3,
out_channels=32,
kernel_size=3,
stride=1,
act=None,
name='FPN_d1')
self.conv_bn_layer_2 = ConvBNLayer(
in_channels=64,
out_channels=64,
kernel_size=3,
stride=1,
act=None,
name='FPN_d2')
self.conv_bn_layer_3 = ConvBNLayer(
in_channels=256,
out_channels=128,
kernel_size=3,
stride=1,
act=None,
name='FPN_d3')
self.conv_bn_layer_4 = ConvBNLayer(
in_channels=32,
out_channels=64,
kernel_size=3,
stride=2,
act=None,
name='FPN_d4')
self.conv_bn_layer_5 = ConvBNLayer(
in_channels=64,
out_channels=64,
kernel_size=3,
stride=1,
act='relu',
name='FPN_d5')
self.conv_bn_layer_6 = ConvBNLayer(
in_channels=64,
out_channels=128,
kernel_size=3,
stride=2,
act=None,
name='FPN_d6')
self.conv_bn_layer_7 = ConvBNLayer(
in_channels=128,
out_channels=128,
kernel_size=3,
stride=1,
act='relu',
name='FPN_d7')
self.conv_bn_layer_8 = ConvBNLayer(
in_channels=128,
out_channels=128,
kernel_size=1,
stride=1,
act=None,
name='FPN_d8')
self.conv_h0 = ConvBNLayer(
in_channels=num_inputs[0],
out_channels=num_outputs[0],
kernel_size=1,
stride=1,
act=None,
name="conv_h{}".format(0))
self.conv_h1 = ConvBNLayer(
in_channels=num_inputs[1],
out_channels=num_outputs[1],
kernel_size=1,
stride=1,
act=None,
name="conv_h{}".format(1))
self.conv_h2 = ConvBNLayer(
in_channels=num_inputs[2],
out_channels=num_outputs[2],
kernel_size=1,
stride=1,
act=None,
name="conv_h{}".format(2))
self.conv_h3 = ConvBNLayer(
in_channels=num_inputs[3],
out_channels=num_outputs[3],
kernel_size=1,
stride=1,
act=None,
name="conv_h{}".format(3))
self.conv_h4 = ConvBNLayer(
in_channels=num_inputs[4],
out_channels=num_outputs[4],
kernel_size=1,
stride=1,
act=None,
name="conv_h{}".format(4))
self.dconv0 = DeConvBNLayer(
in_channels=num_outputs[0],
out_channels=num_outputs[0 + 1],
name="dconv_{}".format(0))
self.dconv1 = DeConvBNLayer(
in_channels=num_outputs[1],
out_channels=num_outputs[1 + 1],
act=None,
name="dconv_{}".format(1))
self.dconv2 = DeConvBNLayer(
in_channels=num_outputs[2],
out_channels=num_outputs[2 + 1],
act=None,
name="dconv_{}".format(2))
self.dconv3 = DeConvBNLayer(
in_channels=num_outputs[3],
out_channels=num_outputs[3 + 1],
act=None,
name="dconv_{}".format(3))
self.conv_g1 = ConvBNLayer(
in_channels=num_outputs[1],
out_channels=num_outputs[1],
kernel_size=3,
stride=1,
act='relu',
name="conv_g{}".format(1))
self.conv_g2 = ConvBNLayer(
in_channels=num_outputs[2],
out_channels=num_outputs[2],
kernel_size=3,
stride=1,
act='relu',
name="conv_g{}".format(2))
self.conv_g3 = ConvBNLayer(
in_channels=num_outputs[3],
out_channels=num_outputs[3],
kernel_size=3,
stride=1,
act='relu',
name="conv_g{}".format(3))
self.conv_g4 = ConvBNLayer(
in_channels=num_outputs[4],
out_channels=num_outputs[4],
kernel_size=3,
stride=1,
act='relu',
name="conv_g{}".format(4))
self.convf = ConvBNLayer(
in_channels=num_outputs[4],
out_channels=num_outputs[4],
kernel_size=1,
stride=1,
act=None,
name="conv_f{}".format(4))
def forward(self, x):
c0, c1, c2, c3, c4, c5, c6 = x
# FPN_Down_Fusion
f = [c0, c1, c2]
g = [None, None, None]
h = [None, None, None]
h[0] = self.conv_bn_layer_1(f[0])
h[1] = self.conv_bn_layer_2(f[1])
h[2] = self.conv_bn_layer_3(f[2])
g[0] = self.conv_bn_layer_4(h[0])
g[1] = paddle.add(g[0], h[1])
g[1] = F.relu(g[1])
g[1] = self.conv_bn_layer_5(g[1])
g[1] = self.conv_bn_layer_6(g[1])
g[2] = paddle.add(g[1], h[2])
g[2] = F.relu(g[2])
g[2] = self.conv_bn_layer_7(g[2])
f_down = self.conv_bn_layer_8(g[2])
# FPN UP Fusion
f1 = [c6, c5, c4, c3, c2]
g = [None, None, None, None, None]
h = [None, None, None, None, None]
h[0] = self.conv_h0(f1[0])
h[1] = self.conv_h1(f1[1])
h[2] = self.conv_h2(f1[2])
h[3] = self.conv_h3(f1[3])
h[4] = self.conv_h4(f1[4])
g[0] = self.dconv0(h[0])
g[1] = paddle.add(g[0], h[1])
g[1] = F.relu(g[1])
g[1] = self.conv_g1(g[1])
g[1] = self.dconv1(g[1])
g[2] = paddle.add(g[1], h[2])
g[2] = F.relu(g[2])
g[2] = self.conv_g2(g[2])
g[2] = self.dconv2(g[2])
g[3] = paddle.add(g[2], h[3])
g[3] = F.relu(g[3])
g[3] = self.conv_g3(g[3])
g[3] = self.dconv3(g[3])
g[4] = paddle.add(x=g[3], y=h[4])
g[4] = F.relu(g[4])
g[4] = self.conv_g4(g[4])
f_up = self.convf(g[4])
f_common = paddle.add(f_down, f_up)
f_common = F.relu(f_common)
return f_common
ppocr/postprocess/__init__.py
浏览文件 @ 76320bf0
......@@ -28,10 +28,11 @@ def build_post_process(config, global_config=None):
from .sast_postprocess import SASTPostProcess
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode
from .cls_postprocess import ClsPostProcess
from .pg_postprocess import PGPostProcess
support_dict = [
'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode'
'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess'
]
config = copy.deepcopy(config)
......
ppocr/postprocess/pg_postprocess.py 0 → 100644
浏览文件 @ 76320bf0
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
__dir__ = os.path.dirname(__file__)
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..'))
from ppocr.utils.e2e_utils.extract_textpoint import *
from ppocr.utils.e2e_utils.visual import *
import paddle
class PGPostProcess(object):
"""
The post process for PGNet.
"""
def __init__(self, character_dict_path, valid_set, score_thresh, **kwargs):
self.Lexicon_Table = get_dict(character_dict_path)
self.valid_set = valid_set
self.score_thresh = score_thresh
# c++ la-nms is faster, but only support python 3.5
self.is_python35 = False
if sys.version_info.major == 3 and sys.version_info.minor == 5:
self.is_python35 = True
def __call__(self, outs_dict, shape_list):
p_score = outs_dict['f_score']
p_border = outs_dict['f_border']
p_char = outs_dict['f_char']
p_direction = outs_dict['f_direction']
if isinstance(p_score, paddle.Tensor):
p_score = p_score[0].numpy()
p_border = p_border[0].numpy()
p_direction = p_direction[0].numpy()
p_char = p_char[0].numpy()
else:
p_score = p_score[0]
p_border = p_border[0]
p_direction = p_direction[0]
p_char = p_char[0]
src_h, src_w, ratio_h, ratio_w = shape_list[0]
is_curved = self.valid_set == "totaltext"
instance_yxs_list = generate_pivot_list(
p_score,
p_char,
p_direction,
score_thresh=self.score_thresh,
is_backbone=True,
is_curved=is_curved)
p_char = paddle.to_tensor(np.expand_dims(p_char, axis=0))
char_seq_idx_set = []
for i in range(len(instance_yxs_list)):
gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
feature_seq = paddle.gather_nd(f_char_map, gather_info_lod)
feature_seq = np.expand_dims(feature_seq.numpy(), axis=0)
feature_len = [len(feature_seq[0])]
featyre_seq = paddle.to_tensor(feature_seq)
feature_len = np.array([feature_len]).astype(np.int64)
length = paddle.to_tensor(feature_len)
seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
input=featyre_seq, blank=36, input_length=length)
seq_pred_str = seq_pred[0].numpy().tolist()[0]
seq_len = seq_pred[1].numpy()[0][0]
temp_t = []
for c in seq_pred_str[:seq_len]:
temp_t.append(c)
char_seq_idx_set.append(temp_t)
seq_strs = []
for char_idx_set in char_seq_idx_set:
pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
seq_strs.append(pr_str)
poly_list = []
keep_str_list = []
all_point_list = []
all_point_pair_list = []
for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
if len(yx_center_line) == 1:
yx_center_line.append(yx_center_line[-1])
offset_expand = 1.0
if self.valid_set == 'totaltext':
offset_expand = 1.2
point_pair_list = []
for batch_id, y, x in yx_center_line:
offset = p_border[:, y, x].reshape(2, 2)
if offset_expand != 1.0:
offset_length = np.linalg.norm(
offset, axis=1, keepdims=True)
expand_length = np.clip(
offset_length * (offset_expand - 1),
a_min=0.5,
a_max=3.0)
offset_detal = offset / offset_length * expand_length
offset = offset + offset_detal
ori_yx = np.array([y, x], dtype=np.float32)
point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
[ratio_w, ratio_h]).reshape(-1, 2)
point_pair_list.append(point_pair)
all_point_list.append([
int(round(x * 4.0 / ratio_w)),
int(round(y * 4.0 / ratio_h))
])
all_point_pair_list.append(point_pair.round().astype(np.int32)
.tolist())
detected_poly, pair_length_info = point_pair2poly(point_pair_list)
detected_poly = expand_poly_along_width(
detected_poly, shrink_ratio_of_width=0.2)
detected_poly[:, 0] = np.clip(
detected_poly[:, 0], a_min=0, a_max=src_w)
detected_poly[:, 1] = np.clip(
detected_poly[:, 1], a_min=0, a_max=src_h)
if len(keep_str) < 2:
continue
keep_str_list.append(keep_str)
if self.valid_set == 'partvgg':
middle_point = len(detected_poly) // 2
detected_poly = detected_poly[
[0, middle_point - 1, middle_point, -1], :]
poly_list.append(detected_poly)
elif self.valid_set == 'totaltext':
poly_list.append(detected_poly)
else:
print('--> Not supported format.')
exit(-1)
data = {
'points': poly_list,
'strs': keep_str_list,
}
return data
ppocr/postprocess/sast_postprocess.py
浏览文件 @ 76320bf0
......@@ -18,6 +18,7 @@ from __future__ import print_function
import os
import sys
__dir__ = os.path.dirname(__file__)
sys.path.append(__dir__)
sys.path.append(os.path.join(__dir__, '..'))
......@@ -67,11 +68,15 @@ class SASTPostProcess(object):
point_list[point_num - 1 - idx] = point_pair[1]
return np.array(point_list).reshape(-1, 2)
def shrink_quad_along_width(self, quad, begin_width_ratio=0., end_width_ratio=1.):
def shrink_quad_along_width(self,
quad,
begin_width_ratio=0.,
end_width_ratio=1.):
"""
Generate shrink_quad_along_width.
"""
ratio_pair = np.array([[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
ratio_pair = np.array(
[[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
......@@ -81,16 +86,23 @@ class SASTPostProcess(object):
expand poly along width.
"""
point_num = poly.shape[0]
left_quad = np.array([poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
left_quad = np.array(
[poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
(np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio, 1.0)
right_quad = np.array([poly[point_num // 2 - 2], poly[point_num // 2 - 1],
poly[point_num // 2], poly[point_num // 2 + 1]], dtype=np.float32)
left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio,
1.0)
right_quad = np.array(
[
poly[point_num // 2 - 2], poly[point_num // 2 - 1],
poly[point_num // 2], poly[point_num // 2 + 1]
],
dtype=np.float32)
right_ratio = 1.0 + \
shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
(np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0, right_ratio)
right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0,
right_ratio)
poly[0] = left_quad_expand[0]
poly[-1] = left_quad_expand[-1]
poly[point_num // 2 - 1] = right_quad_expand[1]
......@@ -121,12 +133,10 @@ class SASTPostProcess(object):
"""
compute area of a quad.
"""
edge = [
(quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
edge = [(quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
(quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
(quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
(quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])
]
(quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])]
return np.sum(edge) / 2.
def nms(self, dets):
......@@ -157,7 +167,8 @@ class SASTPostProcess(object):
m = quads.shape[0]
gt_tc = np.mean(quads, axis=1) # (m, 2)
pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :], (1, m, 1)) # (n, m, 2)
pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :],
(1, m, 1)) # (n, m, 2)
gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1)) # (n, m, 2)
dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2) # (n, m)
xy_text_assign = np.argmin(dist_mat, axis=1) + 1 # (n,)
......@@ -169,26 +180,47 @@ class SASTPostProcess(object):
"""
Estimate sample points number.
"""
eh = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])) / 2.0
ew = (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) / 2.0
eh = (np.linalg.norm(quad[0] - quad[3]) +
np.linalg.norm(quad[1] - quad[2])) / 2.0
ew = (np.linalg.norm(quad[0] - quad[1]) +
np.linalg.norm(quad[2] - quad[3])) / 2.0
dense_sample_pts_num = max(2, int(ew))
dense_xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, dense_sample_pts_num,
endpoint=True, dtype=np.float32).astype(np.int32)]
dense_xy_center_line_diff = dense_xy_center_line[1:] - dense_xy_center_line[:-1]
estimate_arc_len = np.sum(np.linalg.norm(dense_xy_center_line_diff, axis=1))
dense_xy_center_line = xy_text[np.linspace(
0,
xy_text.shape[0] - 1,
dense_sample_pts_num,
endpoint=True,
dtype=np.float32).astype(np.int32)]
dense_xy_center_line_diff = dense_xy_center_line[
1:] - dense_xy_center_line[:-1]
estimate_arc_len = np.sum(
np.linalg.norm(
dense_xy_center_line_diff, axis=1))
sample_pts_num = max(2, int(estimate_arc_len / eh))
return sample_pts_num
def detect_sast(self, tcl_map, tvo_map, tbo_map, tco_map, ratio_w, ratio_h, src_w, src_h,
shrink_ratio_of_width=0.3, tcl_map_thresh=0.5, offset_expand=1.0, out_strid=4.0):
def detect_sast(self,
tcl_map,
tvo_map,
tbo_map,
tco_map,
ratio_w,
ratio_h,
src_w,
src_h,
shrink_ratio_of_width=0.3,
tcl_map_thresh=0.5,
offset_expand=1.0,
out_strid=4.0):
"""
first resize the tcl_map, tvo_map and tbo_map to the input_size, then restore the polys
"""
# restore quad
scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh, tvo_map)
scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh,
tvo_map)
dets = np.hstack((quads, scores)).astype(np.float32, copy=False)
dets = self.nms(dets)
if dets.shape[0] == 0:
......@@ -202,7 +234,8 @@ class SASTPostProcess(object):
# instance segmentation
# instance_count, instance_label_map = cv2.connectedComponents(tcl_map.astype(np.uint8), connectivity=8)
instance_count, instance_label_map = self.cluster_by_quads_tco(tcl_map, tcl_map_thresh, quads, tco_map)
instance_count, instance_label_map = self.cluster_by_quads_tco(
tcl_map, tcl_map_thresh, quads, tco_map)
# restore single poly with tcl instance.
poly_list = []
......@@ -214,8 +247,8 @@ class SASTPostProcess(object):
continue
#
len1 = float(np.linalg.norm(quad[0] -quad[1]))
len2 = float(np.linalg.norm(quad[1] -quad[2]))
len1 = float(np.linalg.norm(quad[0] - quad[1]))
len2 = float(np.linalg.norm(quad[1] - quad[2]))
min_len = min(len1, len2)
if min_len < 3:
continue
......@@ -231,9 +264,11 @@ class SASTPostProcess(object):
continue
# sort xy_text
left_center_pt = np.array([[(quad[0, 0] + quad[-1, 0]) / 2.0,
left_center_pt = np.array(
[[(quad[0, 0] + quad[-1, 0]) / 2.0,
(quad[0, 1] + quad[-1, 1]) / 2.0]]) # (1, 2)
right_center_pt = np.array([[(quad[1, 0] + quad[2, 0]) / 2.0,
right_center_pt = np.array(
[[(quad[1, 0] + quad[2, 0]) / 2.0,
(quad[1, 1] + quad[2, 1]) / 2.0]]) # (1, 2)
proj_unit_vec = (right_center_pt - left_center_pt) / \
(np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
......@@ -245,28 +280,40 @@ class SASTPostProcess(object):
sample_pts_num = self.estimate_sample_pts_num(quad, xy_text)
else:
sample_pts_num = self.sample_pts_num
xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, sample_pts_num,
endpoint=True, dtype=np.float32).astype(np.int32)]
xy_center_line = xy_text[np.linspace(
0,
xy_text.shape[0] - 1,
sample_pts_num,
endpoint=True,
dtype=np.float32).astype(np.int32)]
point_pair_list = []
for x, y in xy_center_line:
# get corresponding offset
offset = tbo_map[y, x, :].reshape(2, 2)
if offset_expand != 1.0:
offset_length = np.linalg.norm(offset, axis=1, keepdims=True)
expand_length = np.clip(offset_length * (offset_expand - 1), a_min=0.5, a_max=3.0)
offset_length = np.linalg.norm(
offset, axis=1, keepdims=True)
expand_length = np.clip(
offset_length * (offset_expand - 1),
a_min=0.5,
a_max=3.0)
offset_detal = offset / offset_length * expand_length
offset = offset + offset_detal
# original point
ori_yx = np.array([y, x], dtype=np.float32)
point_pair = (ori_yx + offset)[:, ::-1]* out_strid / np.array([ratio_w, ratio_h]).reshape(-1, 2)
point_pair = (ori_yx + offset)[:, ::-1] * out_strid / np.array(
[ratio_w, ratio_h]).reshape(-1, 2)
point_pair_list.append(point_pair)
# ndarry: (x, 2), expand poly along width
detected_poly = self.point_pair2poly(point_pair_list)
detected_poly = self.expand_poly_along_width(detected_poly, shrink_ratio_of_width)
detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w)
detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h)
detected_poly = self.expand_poly_along_width(detected_poly,
shrink_ratio_of_width)
detected_poly[:, 0] = np.clip(
detected_poly[:, 0], a_min=0, a_max=src_w)
detected_poly[:, 1] = np.clip(
detected_poly[:, 1], a_min=0, a_max=src_h)
poly_list.append(detected_poly)
return poly_list
......@@ -285,16 +332,24 @@ class SASTPostProcess(object):
img_num = len(shape_list)
poly_lists = []
for ino in range(img_num):
p_score = score_list[ino].transpose((1,2,0))
p_border = border_list[ino].transpose((1,2,0))
p_tvo = tvo_list[ino].transpose((1,2,0))
p_tco = tco_list[ino].transpose((1,2,0))
p_score = score_list[ino].transpose((1, 2, 0))
p_border = border_list[ino].transpose((1, 2, 0))
p_tvo = tvo_list[ino].transpose((1, 2, 0))
p_tco = tco_list[ino].transpose((1, 2, 0))
src_h, src_w, ratio_h, ratio_w = shape_list[ino]
poly_list = self.detect_sast(p_score, p_tvo, p_border, p_tco, ratio_w, ratio_h, src_w, src_h,
poly_list = self.detect_sast(
p_score,
p_tvo,
p_border,
p_tco,
ratio_w,
ratio_h,
src_w,
src_h,
shrink_ratio_of_width=self.shrink_ratio_of_width,
tcl_map_thresh=self.tcl_map_thresh, offset_expand=self.expand_scale)
tcl_map_thresh=self.tcl_map_thresh,
offset_expand=self.expand_scale)
poly_lists.append({'points': np.array(poly_list)})
return poly_lists
ppocr/utils/dict/arabic_dict.txt 0 → 100644
浏览文件 @ 76320bf0
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ppocr/utils/dict/devanagari_dict.txt 0 → 100644
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ppocr/utils/e2e_metric/Deteval.py 0 → 100755
浏览文件 @ 76320bf0
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ppocr.utils.e2e_metric.polygon_fast import iod, area_of_intersection, area
def get_socre(gt_dict, pred_dict):
allInputs = 1
def input_reading_mod(pred_dict):
"""This helper reads input from txt files"""
det = []
n = len(pred_dict)
for i in range(n):
points = pred_dict[i]['points']
text = pred_dict[i]['text']
point = ",".join(map(str, points.reshape(-1, )))
det.append([point, text])
return det
def gt_reading_mod(gt_dict):
"""This helper reads groundtruths from mat files"""
gt = []
n = len(gt_dict)
for i in range(n):
points = gt_dict[i]['points']
h = len(points)
text = gt_dict[i]['text']
xx = [
np.array(
['x:'], dtype='<U2'), 0, np.array(
['y:'], dtype='<U2'), 0, np.array(
['#'], dtype='<U1'), np.array(
['#'], dtype='<U1')
]
t_x, t_y = [], []
for j in range(h):
t_x.append(points[j][0])
t_y.append(points[j][1])
xx[1] = np.array([t_x], dtype='int16')
xx[3] = np.array([t_y], dtype='int16')
if text != "" and "#" not in text:
xx[4] = np.array([text], dtype='U{}'.format(len(text)))
xx[5] = np.array(['c'], dtype='<U1')
gt.append(xx)
return gt
def detection_filtering(detections, groundtruths, threshold=0.5):
for gt_id, gt in enumerate(groundtruths):
if (gt[5] == '#') and (gt[1].shape[1] > 1):
gt_x = list(map(int, np.squeeze(gt[1])))
gt_y = list(map(int, np.squeeze(gt[3])))
for det_id, detection in enumerate(detections):
detection_orig = detection
detection = [float(x) for x in detection[0].split(',')]
detection = list(map(int, detection))
det_x = detection[0::2]
det_y = detection[1::2]
det_gt_iou = iod(det_x, det_y, gt_x, gt_y)
if det_gt_iou > threshold:
detections[det_id] = []
detections[:] = [item for item in detections if item != []]
return detections
def sigma_calculation(det_x, det_y, gt_x, gt_y):
"""
sigma = inter_area / gt_area
"""
return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
area(gt_x, gt_y)), 2)
def tau_calculation(det_x, det_y, gt_x, gt_y):
if area(det_x, det_y) == 0.0:
return 0
return np.round((area_of_intersection(det_x, det_y, gt_x, gt_y) /
area(det_x, det_y)), 2)
##############################Initialization###################################
# global_sigma = []
# global_tau = []
# global_pred_str = []
# global_gt_str = []
###############################################################################
for input_id in range(allInputs):
if (input_id != '.DS_Store') and (input_id != 'Pascal_result.txt') and (
input_id != 'Pascal_result_curved.txt') and (input_id != 'Pascal_result_non_curved.txt') and (
input_id != 'Deteval_result.txt') and (input_id != 'Deteval_result_curved.txt') \
and (input_id != 'Deteval_result_non_curved.txt'):
detections = input_reading_mod(pred_dict)
groundtruths = gt_reading_mod(gt_dict)
detections = detection_filtering(
detections,
groundtruths) # filters detections overlapping with DC area
dc_id = []
for i in range(len(groundtruths)):
if groundtruths[i][5] == '#':
dc_id.append(i)
cnt = 0
for a in dc_id:
num = a - cnt
del groundtruths[num]
cnt += 1
local_sigma_table = np.zeros((len(groundtruths), len(detections)))
local_tau_table = np.zeros((len(groundtruths), len(detections)))
local_pred_str = {}
local_gt_str = {}
for gt_id, gt in enumerate(groundtruths):
if len(detections) > 0:
for det_id, detection in enumerate(detections):
detection_orig = detection
detection = [float(x) for x in detection[0].split(',')]
detection = list(map(int, detection))
pred_seq_str = detection_orig[1].strip()
det_x = detection[0::2]
det_y = detection[1::2]
gt_x = list(map(int, np.squeeze(gt[1])))
gt_y = list(map(int, np.squeeze(gt[3])))
gt_seq_str = str(gt[4].tolist()[0])
local_sigma_table[gt_id, det_id] = sigma_calculation(
det_x, det_y, gt_x, gt_y)
local_tau_table[gt_id, det_id] = tau_calculation(
det_x, det_y, gt_x, gt_y)
local_pred_str[det_id] = pred_seq_str
local_gt_str[gt_id] = gt_seq_str
global_sigma = local_sigma_table
global_tau = local_tau_table
global_pred_str = local_pred_str
global_gt_str = local_gt_str
single_data = {}
single_data['sigma'] = global_sigma
single_data['global_tau'] = global_tau
single_data['global_pred_str'] = global_pred_str
single_data['global_gt_str'] = global_gt_str
return single_data
def combine_results(all_data):
tr = 0.7
tp = 0.6
fsc_k = 0.8
k = 2
global_sigma = []
global_tau = []
global_pred_str = []
global_gt_str = []
for data in all_data:
global_sigma.append(data['sigma'])
global_tau.append(data['global_tau'])
global_pred_str.append(data['global_pred_str'])
global_gt_str.append(data['global_gt_str'])
global_accumulative_recall = 0
global_accumulative_precision = 0
total_num_gt = 0
total_num_det = 0
hit_str_count = 0
hit_count = 0
def one_to_one(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idy):
hit_str_num = 0
for gt_id in range(num_gt):
gt_matching_qualified_sigma_candidates = np.where(
local_sigma_table[gt_id, :] > tr)
gt_matching_num_qualified_sigma_candidates = gt_matching_qualified_sigma_candidates[
0].shape[0]
gt_matching_qualified_tau_candidates = np.where(
local_tau_table[gt_id, :] > tp)
gt_matching_num_qualified_tau_candidates = gt_matching_qualified_tau_candidates[
0].shape[0]
det_matching_qualified_sigma_candidates = np.where(
local_sigma_table[:, gt_matching_qualified_sigma_candidates[0]]
> tr)
det_matching_num_qualified_sigma_candidates = det_matching_qualified_sigma_candidates[
0].shape[0]
det_matching_qualified_tau_candidates = np.where(
local_tau_table[:, gt_matching_qualified_tau_candidates[0]] >
tp)
det_matching_num_qualified_tau_candidates = det_matching_qualified_tau_candidates[
0].shape[0]
if (gt_matching_num_qualified_sigma_candidates == 1) and (gt_matching_num_qualified_tau_candidates == 1) and \
(det_matching_num_qualified_sigma_candidates == 1) and (
det_matching_num_qualified_tau_candidates == 1):
global_accumulative_recall = global_accumulative_recall + 1.0
global_accumulative_precision = global_accumulative_precision + 1.0
local_accumulative_recall = local_accumulative_recall + 1.0
local_accumulative_precision = local_accumulative_precision + 1.0
gt_flag[0, gt_id] = 1
matched_det_id = np.where(local_sigma_table[gt_id, :] > tr)
# recg start
gt_str_cur = global_gt_str[idy][gt_id]
pred_str_cur = global_pred_str[idy][matched_det_id[0].tolist()[
0]]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
# recg end
det_flag[0, matched_det_id] = 1
return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
def one_to_many(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idy):
hit_str_num = 0
for gt_id in range(num_gt):
# skip the following if the groundtruth was matched
if gt_flag[0, gt_id] > 0:
continue
non_zero_in_sigma = np.where(local_sigma_table[gt_id, :] > 0)
num_non_zero_in_sigma = non_zero_in_sigma[0].shape[0]
if num_non_zero_in_sigma >= k:
####search for all detections that overlaps with this groundtruth
qualified_tau_candidates = np.where((local_tau_table[
gt_id, :] >= tp) & (det_flag[0, :] == 0))
num_qualified_tau_candidates = qualified_tau_candidates[
0].shape[0]
if num_qualified_tau_candidates == 1:
if ((local_tau_table[gt_id, qualified_tau_candidates] >= tp)
and
(local_sigma_table[gt_id, qualified_tau_candidates] >=
tr)):
# became an one-to-one case
global_accumulative_recall = global_accumulative_recall + 1.0
global_accumulative_precision = global_accumulative_precision + 1.0
local_accumulative_recall = local_accumulative_recall + 1.0
local_accumulative_precision = local_accumulative_precision + 1.0
gt_flag[0, gt_id] = 1
det_flag[0, qualified_tau_candidates] = 1
# recg start
gt_str_cur = global_gt_str[idy][gt_id]
pred_str_cur = global_pred_str[idy][
qualified_tau_candidates[0].tolist()[0]]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
# recg end
elif (np.sum(local_sigma_table[gt_id, qualified_tau_candidates])
>= tr):
gt_flag[0, gt_id] = 1
det_flag[0, qualified_tau_candidates] = 1
# recg start
gt_str_cur = global_gt_str[idy][gt_id]
pred_str_cur = global_pred_str[idy][
qualified_tau_candidates[0].tolist()[0]]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
# recg end
global_accumulative_recall = global_accumulative_recall + fsc_k
global_accumulative_precision = global_accumulative_precision + num_qualified_tau_candidates * fsc_k
local_accumulative_recall = local_accumulative_recall + fsc_k
local_accumulative_precision = local_accumulative_precision + num_qualified_tau_candidates * fsc_k
return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
def many_to_one(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idy):
hit_str_num = 0
for det_id in range(num_det):
# skip the following if the detection was matched
if det_flag[0, det_id] > 0:
continue
non_zero_in_tau = np.where(local_tau_table[:, det_id] > 0)
num_non_zero_in_tau = non_zero_in_tau[0].shape[0]
if num_non_zero_in_tau >= k:
####search for all detections that overlaps with this groundtruth
qualified_sigma_candidates = np.where((
local_sigma_table[:, det_id] >= tp) & (gt_flag[0, :] == 0))
num_qualified_sigma_candidates = qualified_sigma_candidates[
0].shape[0]
if num_qualified_sigma_candidates == 1:
if ((local_tau_table[qualified_sigma_candidates, det_id] >=
tp) and
(local_sigma_table[qualified_sigma_candidates, det_id]
>= tr)):
# became an one-to-one case
global_accumulative_recall = global_accumulative_recall + 1.0
global_accumulative_precision = global_accumulative_precision + 1.0
local_accumulative_recall = local_accumulative_recall + 1.0
local_accumulative_precision = local_accumulative_precision + 1.0
gt_flag[0, qualified_sigma_candidates] = 1
det_flag[0, det_id] = 1
# recg start
pred_str_cur = global_pred_str[idy][det_id]
gt_len = len(qualified_sigma_candidates[0])
for idx in range(gt_len):
ele_gt_id = qualified_sigma_candidates[0].tolist()[
idx]
if ele_gt_id not in global_gt_str[idy]:
continue
gt_str_cur = global_gt_str[idy][ele_gt_id]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
break
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
break
# recg end
elif (np.sum(local_tau_table[qualified_sigma_candidates,
det_id]) >= tp):
det_flag[0, det_id] = 1
gt_flag[0, qualified_sigma_candidates] = 1
# recg start
pred_str_cur = global_pred_str[idy][det_id]
gt_len = len(qualified_sigma_candidates[0])
for idx in range(gt_len):
ele_gt_id = qualified_sigma_candidates[0].tolist()[idx]
if ele_gt_id not in global_gt_str[idy]:
continue
gt_str_cur = global_gt_str[idy][ele_gt_id]
if pred_str_cur == gt_str_cur:
hit_str_num += 1
break
else:
if pred_str_cur.lower() == gt_str_cur.lower():
hit_str_num += 1
break
# recg end
global_accumulative_recall = global_accumulative_recall + num_qualified_sigma_candidates * fsc_k
global_accumulative_precision = global_accumulative_precision + fsc_k
local_accumulative_recall = local_accumulative_recall + num_qualified_sigma_candidates * fsc_k
local_accumulative_precision = local_accumulative_precision + fsc_k
return local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, gt_flag, det_flag, hit_str_num
for idx in range(len(global_sigma)):
local_sigma_table = np.array(global_sigma[idx])
local_tau_table = global_tau[idx]
num_gt = local_sigma_table.shape[0]
num_det = local_sigma_table.shape[1]
total_num_gt = total_num_gt + num_gt
total_num_det = total_num_det + num_det
local_accumulative_recall = 0
local_accumulative_precision = 0
gt_flag = np.zeros((1, num_gt))
det_flag = np.zeros((1, num_det))
#######first check for one-to-one case##########
local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
gt_flag, det_flag, hit_str_num = one_to_one(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idx)
hit_str_count += hit_str_num
#######then check for one-to-many case##########
local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
gt_flag, det_flag, hit_str_num = one_to_many(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idx)
hit_str_count += hit_str_num
#######then check for many-to-one case##########
local_accumulative_recall, local_accumulative_precision, global_accumulative_recall, global_accumulative_precision, \
gt_flag, det_flag, hit_str_num = many_to_one(local_sigma_table, local_tau_table,
local_accumulative_recall, local_accumulative_precision,
global_accumulative_recall, global_accumulative_precision,
gt_flag, det_flag, idx)
hit_str_count += hit_str_num
try:
recall = global_accumulative_recall / total_num_gt
except ZeroDivisionError:
recall = 0
try:
precision = global_accumulative_precision / total_num_det
except ZeroDivisionError:
precision = 0
try:
f_score = 2 * precision * recall / (precision + recall)
except ZeroDivisionError:
f_score = 0
try:
seqerr = 1 - float(hit_str_count) / global_accumulative_recall
except ZeroDivisionError:
seqerr = 1
try:
recall_e2e = float(hit_str_count) / total_num_gt
except ZeroDivisionError:
recall_e2e = 0
try:
precision_e2e = float(hit_str_count) / total_num_det
except ZeroDivisionError:
precision_e2e = 0
try:
f_score_e2e = 2 * precision_e2e * recall_e2e / (
precision_e2e + recall_e2e)
except ZeroDivisionError:
f_score_e2e = 0
final = {
'total_num_gt': total_num_gt,
'total_num_det': total_num_det,
'global_accumulative_recall': global_accumulative_recall,
'hit_str_count': hit_str_count,
'recall': recall,
'precision': precision,
'f_score': f_score,
'seqerr': seqerr,
'recall_e2e': recall_e2e,
'precision_e2e': precision_e2e,
'f_score_e2e': f_score_e2e
}
return final
ppocr/utils/e2e_metric/polygon_fast.py 0 → 100755
浏览文件 @ 76320bf0
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from shapely.geometry import Polygon
"""
:param det_x: [1, N] Xs of detection's vertices
:param det_y: [1, N] Ys of detection's vertices
:param gt_x: [1, N] Xs of groundtruth's vertices
:param gt_y: [1, N] Ys of groundtruth's vertices
##############
All the calculation of 'AREA' in this script is handled by:
1) First generating a binary mask with the polygon area filled up with 1's
2) Summing up all the 1's
"""
def area(x, y):
polygon = Polygon(np.stack([x, y], axis=1))
return float(polygon.area)
def approx_area_of_intersection(det_x, det_y, gt_x, gt_y):
"""
This helper determine if both polygons are intersecting with each others with an approximation method.
Area of intersection represented by the minimum bounding rectangular [xmin, ymin, xmax, ymax]
"""
det_ymax = np.max(det_y)
det_xmax = np.max(det_x)
det_ymin = np.min(det_y)
det_xmin = np.min(det_x)
gt_ymax = np.max(gt_y)
gt_xmax = np.max(gt_x)
gt_ymin = np.min(gt_y)
gt_xmin = np.min(gt_x)
all_min_ymax = np.minimum(det_ymax, gt_ymax)
all_max_ymin = np.maximum(det_ymin, gt_ymin)
intersect_heights = np.maximum(0.0, (all_min_ymax - all_max_ymin))
all_min_xmax = np.minimum(det_xmax, gt_xmax)
all_max_xmin = np.maximum(det_xmin, gt_xmin)
intersect_widths = np.maximum(0.0, (all_min_xmax - all_max_xmin))
return intersect_heights * intersect_widths
def area_of_intersection(det_x, det_y, gt_x, gt_y):
p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
return float(p1.intersection(p2).area)
def area_of_union(det_x, det_y, gt_x, gt_y):
p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
return float(p1.union(p2).area)
def iou(det_x, det_y, gt_x, gt_y):
return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
area_of_union(det_x, det_y, gt_x, gt_y) + 1.0)
def iod(det_x, det_y, gt_x, gt_y):
"""
This helper determine the fraction of intersection area over detection area
"""
return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
area(det_x, det_y) + 1.0)
ppocr/utils/e2e_utils/extract_batchsize.py 0 → 100644
浏览文件 @ 76320bf0
import paddle
import numpy as np
import copy
def org_tcl_rois(batch_size, pos_lists, pos_masks, label_lists, tcl_bs):
"""
"""
pos_lists_, pos_masks_, label_lists_ = [], [], []
img_bs = batch_size
ngpu = int(batch_size / img_bs)
img_ids = np.array(pos_lists, dtype=np.int32)[:, 0, 0].copy()
pos_lists_split, pos_masks_split, label_lists_split = [], [], []
for i in range(ngpu):
pos_lists_split.append([])
pos_masks_split.append([])
label_lists_split.append([])
for i in range(img_ids.shape[0]):
img_id = img_ids[i]
gpu_id = int(img_id / img_bs)
img_id = img_id % img_bs
pos_list = pos_lists[i].copy()
pos_list[:, 0] = img_id
pos_lists_split[gpu_id].append(pos_list)
pos_masks_split[gpu_id].append(pos_masks[i].copy())
label_lists_split[gpu_id].append(copy.deepcopy(label_lists[i]))
# repeat or delete
for i in range(ngpu):
vp_len = len(pos_lists_split[i])
if vp_len <= tcl_bs:
for j in range(0, tcl_bs - vp_len):
pos_list = pos_lists_split[i][j].copy()
pos_lists_split[i].append(pos_list)
pos_mask = pos_masks_split[i][j].copy()
pos_masks_split[i].append(pos_mask)
label_list = copy.deepcopy(label_lists_split[i][j])
label_lists_split[i].append(label_list)
else:
for j in range(0, vp_len - tcl_bs):
c_len = len(pos_lists_split[i])
pop_id = np.random.permutation(c_len)[0]
pos_lists_split[i].pop(pop_id)
pos_masks_split[i].pop(pop_id)
label_lists_split[i].pop(pop_id)
# merge
for i in range(ngpu):
pos_lists_.extend(pos_lists_split[i])
pos_masks_.extend(pos_masks_split[i])
label_lists_.extend(label_lists_split[i])
return pos_lists_, pos_masks_, label_lists_
def pre_process(label_list, pos_list, pos_mask, max_text_length, max_text_nums,
pad_num, tcl_bs):
label_list = label_list.numpy()
batch, _, _, _ = label_list.shape
pos_list = pos_list.numpy()
pos_mask = pos_mask.numpy()
pos_list_t = []
pos_mask_t = []
label_list_t = []
for i in range(batch):
for j in range(max_text_nums):
if pos_mask[i, j].any():
pos_list_t.append(pos_list[i][j])
pos_mask_t.append(pos_mask[i][j])
label_list_t.append(label_list[i][j])
pos_list, pos_mask, label_list = org_tcl_rois(batch, pos_list_t, pos_mask_t,
label_list_t, tcl_bs)
label = []
tt = [l.tolist() for l in label_list]
for i in range(tcl_bs):
k = 0
for j in range(max_text_length):
if tt[i][j][0] != pad_num:
k += 1
else:
break
label.append(k)
label = paddle.to_tensor(label)
label = paddle.cast(label, dtype='int64')
pos_list = paddle.to_tensor(pos_list)
pos_mask = paddle.to_tensor(pos_mask)
label_list = paddle.squeeze(paddle.to_tensor(label_list), axis=2)
label_list = paddle.cast(label_list, dtype='int32')
return pos_list, pos_mask, label_list, label
ppocr/utils/e2e_utils/extract_textpoint.py 0 → 100644
浏览文件 @ 76320bf0
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains various CTC decoders."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cv2
import math
import numpy as np
from itertools import groupby
from skimage.morphology._skeletonize import thin
def get_dict(character_dict_path):
character_str = ""
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
line = line.decode('utf-8').strip("\n").strip("\r\n")
character_str += line
dict_character = list(character_str)
return dict_character
def softmax(logits):
"""
logits: N x d
"""
max_value = np.max(logits, axis=1, keepdims=True)
exp = np.exp(logits - max_value)
exp_sum = np.sum(exp, axis=1, keepdims=True)
dist = exp / exp_sum
return dist
def get_keep_pos_idxs(labels, remove_blank=None):
"""
Remove duplicate and get pos idxs of keep items.
The value of keep_blank should be [None, 95].
"""
duplicate_len_list = []
keep_pos_idx_list = []
keep_char_idx_list = []
for k, v_ in groupby(labels):
current_len = len(list(v_))
if k != remove_blank:
current_idx = int(sum(duplicate_len_list) + current_len // 2)
keep_pos_idx_list.append(current_idx)
keep_char_idx_list.append(k)
duplicate_len_list.append(current_len)
return keep_char_idx_list, keep_pos_idx_list
def remove_blank(labels, blank=0):
new_labels = [x for x in labels if x != blank]
return new_labels
def insert_blank(labels, blank=0):
new_labels = [blank]
for l in labels:
new_labels += [l, blank]
return new_labels
def ctc_greedy_decoder(probs_seq, blank=95, keep_blank_in_idxs=True):
"""
CTC greedy (best path) decoder.
"""
raw_str = np.argmax(np.array(probs_seq), axis=1)
remove_blank_in_pos = None if keep_blank_in_idxs else blank
dedup_str, keep_idx_list = get_keep_pos_idxs(
raw_str, remove_blank=remove_blank_in_pos)
dst_str = remove_blank(dedup_str, blank=blank)
return dst_str, keep_idx_list
def instance_ctc_greedy_decoder(gather_info,
logits_map,
keep_blank_in_idxs=True):
"""
gather_info: [[x, y], [x, y] ...]
logits_map: H x W X (n_chars + 1)
"""
_, _, C = logits_map.shape
ys, xs = zip(*gather_info)
logits_seq = logits_map[list(ys), list(xs)] # n x 96
probs_seq = softmax(logits_seq)
dst_str, keep_idx_list = ctc_greedy_decoder(
probs_seq, blank=C - 1, keep_blank_in_idxs=keep_blank_in_idxs)
keep_gather_list = [gather_info[idx] for idx in keep_idx_list]
return dst_str, keep_gather_list
def ctc_decoder_for_image(gather_info_list, logits_map,
keep_blank_in_idxs=True):
"""
CTC decoder using multiple processes.
"""
decoder_results = []
for gather_info in gather_info_list:
res = instance_ctc_greedy_decoder(
gather_info, logits_map, keep_blank_in_idxs=keep_blank_in_idxs)
decoder_results.append(res)
return decoder_results
def sort_with_direction(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
def sort_part_with_direction(pos_list, point_direction):
pos_list = np.array(pos_list).reshape(-1, 2)
point_direction = np.array(point_direction).reshape(-1, 2)
average_direction = np.mean(point_direction, axis=0, keepdims=True)
pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
sorted_list = pos_list[np.argsort(pos_proj_leng)].tolist()
sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
return sorted_list, sorted_direction
pos_list = np.array(pos_list).reshape(-1, 2)
point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]] # x, y
point_direction = point_direction[:, ::-1] # x, y -> y, x
sorted_point, sorted_direction = sort_part_with_direction(pos_list,
point_direction)
point_num = len(sorted_point)
if point_num >= 16:
middle_num = point_num // 2
first_part_point = sorted_point[:middle_num]
first_point_direction = sorted_direction[:middle_num]
sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
first_part_point, first_point_direction)
last_part_point = sorted_point[middle_num:]
last_point_direction = sorted_direction[middle_num:]
sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
last_part_point, last_point_direction)
sorted_point = sorted_fist_part_point + sorted_last_part_point
sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
return sorted_point, np.array(sorted_direction)
def add_id(pos_list, image_id=0):
"""
Add id for gather feature, for inference.
"""
new_list = []
for item in pos_list:
new_list.append((image_id, item[0], item[1]))
return new_list
def sort_and_expand_with_direction(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
h, w, _ = f_direction.shape
sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
# expand along
point_num = len(sorted_list)
sub_direction_len = max(point_num // 3, 2)
left_direction = point_direction[:sub_direction_len, :]
right_dirction = point_direction[point_num - sub_direction_len:, :]
left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
left_average_len = np.linalg.norm(left_average_direction)
left_start = np.array(sorted_list[0])
left_step = left_average_direction / (left_average_len + 1e-6)
right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
right_average_len = np.linalg.norm(right_average_direction)
right_step = right_average_direction / (right_average_len + 1e-6)
right_start = np.array(sorted_list[-1])
append_num = max(
int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
left_list = []
right_list = []
for i in range(append_num):
ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
'int32').tolist()
if ly < h and lx < w and (ly, lx) not in left_list:
left_list.append((ly, lx))
ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
'int32').tolist()
if ry < h and rx < w and (ry, rx) not in right_list:
right_list.append((ry, rx))
all_list = left_list[::-1] + sorted_list + right_list
return all_list
def sort_and_expand_with_direction_v2(pos_list, f_direction, binary_tcl_map):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
binary_tcl_map: h x w
"""
h, w, _ = f_direction.shape
sorted_list, point_direction = sort_with_direction(pos_list, f_direction)
# expand along
point_num = len(sorted_list)
sub_direction_len = max(point_num // 3, 2)
left_direction = point_direction[:sub_direction_len, :]
right_dirction = point_direction[point_num - sub_direction_len:, :]
left_average_direction = -np.mean(left_direction, axis=0, keepdims=True)
left_average_len = np.linalg.norm(left_average_direction)
left_start = np.array(sorted_list[0])
left_step = left_average_direction / (left_average_len + 1e-6)
right_average_direction = np.mean(right_dirction, axis=0, keepdims=True)
right_average_len = np.linalg.norm(right_average_direction)
right_step = right_average_direction / (right_average_len + 1e-6)
right_start = np.array(sorted_list[-1])
append_num = max(
int((left_average_len + right_average_len) / 2.0 * 0.15), 1)
max_append_num = 2 * append_num
left_list = []
right_list = []
for i in range(max_append_num):
ly, lx = np.round(left_start + left_step * (i + 1)).flatten().astype(
'int32').tolist()
if ly < h and lx < w and (ly, lx) not in left_list:
if binary_tcl_map[ly, lx] > 0.5:
left_list.append((ly, lx))
else:
break
for i in range(max_append_num):
ry, rx = np.round(right_start + right_step * (i + 1)).flatten().astype(
'int32').tolist()
if ry < h and rx < w and (ry, rx) not in right_list:
if binary_tcl_map[ry, rx] > 0.5:
right_list.append((ry, rx))
else:
break
all_list = left_list[::-1] + sorted_list + right_list
return all_list
def generate_pivot_list_curved(p_score,
p_char_maps,
f_direction,
score_thresh=0.5,
is_expand=True,
is_backbone=False,
image_id=0):
"""
return center point and end point of TCL instance; filter with the char maps;
"""
p_score = p_score[0]
f_direction = f_direction.transpose(1, 2, 0)
p_tcl_map = (p_score > score_thresh) * 1.0
skeleton_map = thin(p_tcl_map)
instance_count, instance_label_map = cv2.connectedComponents(
skeleton_map.astype(np.uint8), connectivity=8)
# get TCL Instance
all_pos_yxs = []
center_pos_yxs = []
end_points_yxs = []
instance_center_pos_yxs = []
if instance_count > 0:
for instance_id in range(1, instance_count):
pos_list = []
ys, xs = np.where(instance_label_map == instance_id)
pos_list = list(zip(ys, xs))
### FIX-ME, eliminate outlier
if len(pos_list) < 3:
continue
if is_expand:
pos_list_sorted = sort_and_expand_with_direction_v2(
pos_list, f_direction, p_tcl_map)
else:
pos_list_sorted, _ = sort_with_direction(pos_list, f_direction)
all_pos_yxs.append(pos_list_sorted)
# use decoder to filter backgroud points.
p_char_maps = p_char_maps.transpose([1, 2, 0])
decode_res = ctc_decoder_for_image(
all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
for decoded_str, keep_yxs_list in decode_res:
if is_backbone:
keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
instance_center_pos_yxs.append(keep_yxs_list_with_id)
else:
end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
center_pos_yxs.extend(keep_yxs_list)
if is_backbone:
return instance_center_pos_yxs
else:
return center_pos_yxs, end_points_yxs
def generate_pivot_list_horizontal(p_score,
p_char_maps,
f_direction,
score_thresh=0.5,
is_backbone=False,
image_id=0):
"""
return center point and end point of TCL instance; filter with the char maps;
"""
p_score = p_score[0]
f_direction = f_direction.transpose(1, 2, 0)
p_tcl_map_bi = (p_score > score_thresh) * 1.0
instance_count, instance_label_map = cv2.connectedComponents(
p_tcl_map_bi.astype(np.uint8), connectivity=8)
# get TCL Instance
all_pos_yxs = []
center_pos_yxs = []
end_points_yxs = []
instance_center_pos_yxs = []
if instance_count > 0:
for instance_id in range(1, instance_count):
pos_list = []
ys, xs = np.where(instance_label_map == instance_id)
pos_list = list(zip(ys, xs))
### FIX-ME, eliminate outlier
if len(pos_list) < 5:
continue
# add rule here
main_direction = extract_main_direction(pos_list,
f_direction) # y x
reference_directin = np.array([0, 1]).reshape([-1, 2]) # y x
is_h_angle = abs(np.sum(
main_direction * reference_directin)) < math.cos(math.pi / 180 *
70)
point_yxs = np.array(pos_list)
max_y, max_x = np.max(point_yxs, axis=0)
min_y, min_x = np.min(point_yxs, axis=0)
is_h_len = (max_y - min_y) < 1.5 * (max_x - min_x)
pos_list_final = []
if is_h_len:
xs = np.unique(xs)
for x in xs:
ys = instance_label_map[:, x].copy().reshape((-1, ))
y = int(np.where(ys == instance_id)[0].mean())
pos_list_final.append((y, x))
else:
ys = np.unique(ys)
for y in ys:
xs = instance_label_map[y, :].copy().reshape((-1, ))
x = int(np.where(xs == instance_id)[0].mean())
pos_list_final.append((y, x))
pos_list_sorted, _ = sort_with_direction(pos_list_final,
f_direction)
all_pos_yxs.append(pos_list_sorted)
# use decoder to filter backgroud points.
p_char_maps = p_char_maps.transpose([1, 2, 0])
decode_res = ctc_decoder_for_image(
all_pos_yxs, logits_map=p_char_maps, keep_blank_in_idxs=True)
for decoded_str, keep_yxs_list in decode_res:
if is_backbone:
keep_yxs_list_with_id = add_id(keep_yxs_list, image_id=image_id)
instance_center_pos_yxs.append(keep_yxs_list_with_id)
else:
end_points_yxs.extend((keep_yxs_list[0], keep_yxs_list[-1]))
center_pos_yxs.extend(keep_yxs_list)
if is_backbone:
return instance_center_pos_yxs
else:
return center_pos_yxs, end_points_yxs
def generate_pivot_list(p_score,
p_char_maps,
f_direction,
score_thresh=0.5,
is_backbone=False,
is_curved=True,
image_id=0):
"""
Warp all the function together.
"""
if is_curved:
return generate_pivot_list_curved(
p_score,
p_char_maps,
f_direction,
score_thresh=score_thresh,
is_expand=True,
is_backbone=is_backbone,
image_id=image_id)
else:
return generate_pivot_list_horizontal(
p_score,
p_char_maps,
f_direction,
score_thresh=score_thresh,
is_backbone=is_backbone,
image_id=image_id)
# for refine module
def extract_main_direction(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
pos_list = np.array(pos_list)
point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]]
point_direction = point_direction[:, ::-1] # x, y -> y, x
average_direction = np.mean(point_direction, axis=0, keepdims=True)
average_direction = average_direction / (
np.linalg.norm(average_direction) + 1e-6)
return average_direction
def sort_by_direction_with_image_id_deprecated(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
"""
pos_list_full = np.array(pos_list).reshape(-1, 3)
pos_list = pos_list_full[:, 1:]
point_direction = f_direction[pos_list[:, 0], pos_list[:, 1]] # x, y
point_direction = point_direction[:, ::-1] # x, y -> y, x
average_direction = np.mean(point_direction, axis=0, keepdims=True)
pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
return sorted_list
def sort_by_direction_with_image_id(pos_list, f_direction):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
def sort_part_with_direction(pos_list_full, point_direction):
pos_list_full = np.array(pos_list_full).reshape(-1, 3)
pos_list = pos_list_full[:, 1:]
point_direction = np.array(point_direction).reshape(-1, 2)
average_direction = np.mean(point_direction, axis=0, keepdims=True)
pos_proj_leng = np.sum(pos_list * average_direction, axis=1)
sorted_list = pos_list_full[np.argsort(pos_proj_leng)].tolist()
sorted_direction = point_direction[np.argsort(pos_proj_leng)].tolist()
return sorted_list, sorted_direction
pos_list = np.array(pos_list).reshape(-1, 3)
point_direction = f_direction[pos_list[:, 1], pos_list[:, 2]] # x, y
point_direction = point_direction[:, ::-1] # x, y -> y, x
sorted_point, sorted_direction = sort_part_with_direction(pos_list,
point_direction)
point_num = len(sorted_point)
if point_num >= 16:
middle_num = point_num // 2
first_part_point = sorted_point[:middle_num]
first_point_direction = sorted_direction[:middle_num]
sorted_fist_part_point, sorted_fist_part_direction = sort_part_with_direction(
first_part_point, first_point_direction)
last_part_point = sorted_point[middle_num:]
last_point_direction = sorted_direction[middle_num:]
sorted_last_part_point, sorted_last_part_direction = sort_part_with_direction(
last_part_point, last_point_direction)
sorted_point = sorted_fist_part_point + sorted_last_part_point
sorted_direction = sorted_fist_part_direction + sorted_last_part_direction
return sorted_point
def generate_pivot_list_tt_inference(p_score,
p_char_maps,
f_direction,
score_thresh=0.5,
is_backbone=False,
is_curved=True,
image_id=0):
"""
return center point and end point of TCL instance; filter with the char maps;
"""
p_score = p_score[0]
f_direction = f_direction.transpose(1, 2, 0)
p_tcl_map = (p_score > score_thresh) * 1.0
skeleton_map = thin(p_tcl_map)
instance_count, instance_label_map = cv2.connectedComponents(
skeleton_map.astype(np.uint8), connectivity=8)
# get TCL Instance
all_pos_yxs = []
if instance_count > 0:
for instance_id in range(1, instance_count):
pos_list = []
ys, xs = np.where(instance_label_map == instance_id)
pos_list = list(zip(ys, xs))
### FIX-ME, eliminate outlier
if len(pos_list) < 3:
continue
pos_list_sorted = sort_and_expand_with_direction_v2(
pos_list, f_direction, p_tcl_map)
pos_list_sorted_with_id = add_id(pos_list_sorted, image_id=image_id)
all_pos_yxs.append(pos_list_sorted_with_id)
return all_pos_yxs
ppocr/utils/e2e_utils/visual.py 0 → 100644
浏览文件 @ 76320bf0
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import cv2
import time
def resize_image(im, max_side_len=512):
"""
resize image to a size multiple of max_stride which is required by the network
:param im: the resized image
:param max_side_len: limit of max image size to avoid out of memory in gpu
:return: the resized image and the resize ratio
"""
h, w, _ = im.shape
resize_w = w
resize_h = h
if resize_h > resize_w:
ratio = float(max_side_len) / resize_h
else:
ratio = float(max_side_len) / resize_w
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
max_stride = 128
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
def resize_image_min(im, max_side_len=512):
"""
"""
h, w, _ = im.shape
resize_w = w
resize_h = h
if resize_h < resize_w:
ratio = float(max_side_len) / resize_h
else:
ratio = float(max_side_len) / resize_w
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
max_stride = 128
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
def resize_image_for_totaltext(im, max_side_len=512):
"""
"""
h, w, _ = im.shape
resize_w = w
resize_h = h
ratio = 1.25
if h * ratio > max_side_len:
ratio = float(max_side_len) / resize_h
resize_h = int(resize_h * ratio)
resize_w = int(resize_w * ratio)
max_stride = 128
resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
im = cv2.resize(im, (int(resize_w), int(resize_h)))
ratio_h = resize_h / float(h)
ratio_w = resize_w / float(w)
return im, (ratio_h, ratio_w)
def point_pair2poly(point_pair_list):
"""
Transfer vertical point_pairs into poly point in clockwise.
"""
pair_length_list = []
for point_pair in point_pair_list:
pair_length = np.linalg.norm(point_pair[0] - point_pair[1])
pair_length_list.append(pair_length)
pair_length_list = np.array(pair_length_list)
pair_info = (pair_length_list.max(), pair_length_list.min(),
pair_length_list.mean())
point_num = len(point_pair_list) * 2
point_list = [0] * point_num
for idx, point_pair in enumerate(point_pair_list):
point_list[idx] = point_pair[0]
point_list[point_num - 1 - idx] = point_pair[1]
return np.array(point_list).reshape(-1, 2), pair_info
def shrink_quad_along_width(quad, begin_width_ratio=0., end_width_ratio=1.):
"""
Generate shrink_quad_along_width.
"""
ratio_pair = np.array(
[[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
def expand_poly_along_width(poly, shrink_ratio_of_width=0.3):
"""
expand poly along width.
"""
point_num = poly.shape[0]
left_quad = np.array(
[poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
(np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
left_quad_expand = shrink_quad_along_width(left_quad, left_ratio, 1.0)
right_quad = np.array(
[
poly[point_num // 2 - 2], poly[point_num // 2 - 1],
poly[point_num // 2], poly[point_num // 2 + 1]
],
dtype=np.float32)
right_ratio = 1.0 + \
shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
(np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
right_quad_expand = shrink_quad_along_width(right_quad, 0.0, right_ratio)
poly[0] = left_quad_expand[0]
poly[-1] = left_quad_expand[-1]
poly[point_num // 2 - 1] = right_quad_expand[1]
poly[point_num // 2] = right_quad_expand[2]
return poly
def norm2(x, axis=None):
if axis:
return np.sqrt(np.sum(x**2, axis=axis))
return np.sqrt(np.sum(x**2))
def cos(p1, p2):
return (p1 * p2).sum() / (norm2(p1) * norm2(p2))
setup.py
浏览文件 @ 76320bf0
......@@ -32,7 +32,7 @@ setup(
package_dir={'paddleocr': ''},
include_package_data=True,
entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
version='2.0.3',
version='2.0.4',
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',
......
tools/infer/predict_e2e.py 0 → 100755
浏览文件 @ 76320bf0
# 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
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
import numpy as np
import time
import sys
import tools.infer.utility as utility
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.data import create_operators, transform
from ppocr.postprocess import build_post_process
logger = get_logger()
class TextE2E(object):
def __init__(self, args):
self.args = args
self.e2e_algorithm = args.e2e_algorithm
pre_process_list = [{
'E2EResizeForTest': {}
}, {
'NormalizeImage': {
'std': [0.229, 0.224, 0.225],
'mean': [0.485, 0.456, 0.406],
'scale': '1./255.',
'order': 'hwc'
}
}, {
'ToCHWImage': None
}, {
'KeepKeys': {
'keep_keys': ['image', 'shape']
}
}]
postprocess_params = {}
if self.e2e_algorithm == "PGNet":
pre_process_list[0] = {
'E2EResizeForTest': {
'max_side_len': args.e2e_limit_side_len,
'valid_set': 'totaltext'
}
}
postprocess_params['name'] = 'PGPostProcess'
postprocess_params["score_thresh"] = args.e2e_pgnet_score_thresh
postprocess_params["character_dict_path"] = args.e2e_char_dict_path
postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
self.e2e_pgnet_polygon = args.e2e_pgnet_polygon
else:
logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))
sys.exit(0)
self.preprocess_op = create_operators(pre_process_list)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = utility.create_predictor(
args, 'e2e', logger) # paddle.jit.load(args.det_model_dir)
# self.predictor.eval()
def clip_det_res(self, points, img_height, img_width):
for pno in range(points.shape[0]):
points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
return points
def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
img_height, img_width = image_shape[0:2]
dt_boxes_new = []
for box in dt_boxes:
box = self.clip_det_res(box, img_height, img_width)
dt_boxes_new.append(box)
dt_boxes = np.array(dt_boxes_new)
return dt_boxes
def __call__(self, img):
ori_im = img.copy()
data = {'image': img}
data = transform(data, self.preprocess_op)
img, shape_list = data
if img is None:
return None, 0
img = np.expand_dims(img, axis=0)
shape_list = np.expand_dims(shape_list, axis=0)
img = img.copy()
starttime = time.time()
self.input_tensor.copy_from_cpu(img)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = {}
if self.e2e_algorithm == 'PGNet':
preds['f_border'] = outputs[0]
preds['f_char'] = outputs[1]
preds['f_direction'] = outputs[2]
preds['f_score'] = outputs[3]
else:
raise NotImplementedError
post_result = self.postprocess_op(preds, shape_list)
points, strs = post_result['points'], post_result['strs']
dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)
elapse = time.time() - starttime
return dt_boxes, strs, elapse
if __name__ == "__main__":
args = utility.parse_args()
image_file_list = get_image_file_list(args.image_dir)
text_detector = TextE2E(args)
count = 0
total_time = 0
draw_img_save = "./inference_results"
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
for image_file in image_file_list:
img, flag = check_and_read_gif(image_file)
if not flag:
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
points, strs, elapse = text_detector(img)
if count > 0:
total_time += elapse
count += 1
logger.info("Predict time of {}: {}".format(image_file, elapse))
src_im = utility.draw_e2e_res(points, strs, image_file)
img_name_pure = os.path.split(image_file)[-1]
img_path = os.path.join(draw_img_save,
"e2e_res_{}".format(img_name_pure))
cv2.imwrite(img_path, src_im)
logger.info("The visualized image saved in {}".format(img_path))
if count > 1:
logger.info("Avg Time: {}".format(total_time / (count - 1)))
tools/infer/utility.py
浏览文件 @ 76320bf0
......@@ -74,6 +74,19 @@ def parse_args():
"--vis_font_path", type=str, default="./doc/fonts/simfang.ttf")
parser.add_argument("--drop_score", type=float, default=0.5)
# params for e2e
parser.add_argument("--e2e_algorithm", type=str, default='PGNet')
parser.add_argument("--e2e_model_dir", type=str)
parser.add_argument("--e2e_limit_side_len", type=float, default=768)
parser.add_argument("--e2e_limit_type", type=str, default='max')
# PGNet parmas
parser.add_argument("--e2e_pgnet_score_thresh", type=float, default=0.5)
parser.add_argument(
"--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
parser.add_argument("--e2e_pgnet_polygon", type=bool, default=True)
# params for text classifier
parser.add_argument("--use_angle_cls", type=str2bool, default=False)
parser.add_argument("--cls_model_dir", type=str)
......@@ -96,8 +109,10 @@ def create_predictor(args, mode, logger):
model_dir = args.det_model_dir
elif mode == 'cls':
model_dir = args.cls_model_dir
else:
elif mode == 'rec':
model_dir = args.rec_model_dir
else:
model_dir = args.e2e_model_dir
if model_dir is None:
logger.info("not find {} model file path {}".format(mode, model_dir))
......@@ -151,6 +166,22 @@ def create_predictor(args, mode, logger):
return predictor, input_tensor, output_tensors
def draw_e2e_res(dt_boxes, strs, img_path):
src_im = cv2.imread(img_path)
for box, str in zip(dt_boxes, strs):
box = box.astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
cv2.putText(
src_im,
str,
org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
fontFace=cv2.FONT_HERSHEY_COMPLEX,
fontScale=0.7,
color=(0, 255, 0),
thickness=1)
return src_im
def draw_text_det_res(dt_boxes, img_path):
src_im = cv2.imread(img_path)
for box in dt_boxes:
......
tools/infer_e2e.py 0 → 100755
浏览文件 @ 76320bf0
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
import cv2
import json
import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.utility import get_image_file_list
import tools.program as program
def draw_e2e_res(dt_boxes, strs, config, img, img_name):
if len(dt_boxes) > 0:
src_im = img
for box, str in zip(dt_boxes, strs):
box = box.astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
cv2.putText(
src_im,
str,
org=(int(box[0, 0, 0]), int(box[0, 0, 1])),
fontFace=cv2.FONT_HERSHEY_COMPLEX,
fontScale=0.7,
color=(0, 255, 0),
thickness=1)
save_det_path = os.path.dirname(config['Global'][
'save_res_path']) + "/e2e_results/"
if not os.path.exists(save_det_path):
os.makedirs(save_det_path)
save_path = os.path.join(save_det_path, os.path.basename(img_name))
cv2.imwrite(save_path, src_im)
logger.info("The e2e Image saved in {}".format(save_path))
def main():
global_config = config['Global']
# build model
model = build_model(config['Architecture'])
init_model(config, model, logger)
# build post process
post_process_class = build_post_process(config['PostProcess'],
global_config)
# create data ops
transforms = []
for op in config['Eval']['dataset']['transforms']:
op_name = list(op)[0]
if 'Label' in op_name:
continue
elif op_name == 'KeepKeys':
op[op_name]['keep_keys'] = ['image', 'shape']
transforms.append(op)
ops = create_operators(transforms, global_config)
save_res_path = config['Global']['save_res_path']
if not os.path.exists(os.path.dirname(save_res_path)):
os.makedirs(os.path.dirname(save_res_path))
model.eval()
with open(save_res_path, "wb") as fout:
for file in get_image_file_list(config['Global']['infer_img']):
logger.info("infer_img: {}".format(file))
with open(file, 'rb') as f:
img = f.read()
data = {'image': img}
batch = transform(data, ops)
images = np.expand_dims(batch[0], axis=0)
shape_list = np.expand_dims(batch[1], axis=0)
images = paddle.to_tensor(images)
preds = model(images)
post_result = post_process_class(preds, shape_list)
points, strs = post_result['points'], post_result['strs']
# write resule
dt_boxes_json = []
for poly, str in zip(points, strs):
tmp_json = {"transcription": str}
tmp_json['points'] = poly.tolist()
dt_boxes_json.append(tmp_json)
otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
fout.write(otstr.encode())
src_img = cv2.imread(file)
draw_e2e_res(points, strs, config, src_img, file)
logger.info("success!")
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
main()
tools/program.py
浏览文件 @ 76320bf0
......@@ -375,7 +375,8 @@ def preprocess(is_train=False):
alg = config['Architecture']['algorithm']
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet'
]
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
......
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