未验证 提交 80fc4f59 编写于 作者: M MissPenguin 提交者: GitHub

Merge pull request #1438 from MissPenguin/dygraph

update inference for east & sast
...@@ -17,17 +17,17 @@ PaddleOCR开源的文本检测算法列表: ...@@ -17,17 +17,17 @@ PaddleOCR开源的文本检测算法列表:
|模型|骨干网络|precision|recall|Hmean|下载链接| |模型|骨干网络|precision|recall|Hmean|下载链接|
| --- | --- | --- | --- | --- | --- | | --- | --- | --- | --- | --- | --- |
|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[下载链接 (coming soon)](link)| |EAST|ResNet50_vd|88.76%|81.36%|84.90%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
|EAST|MobileNetV3|81.67%|79.83%|80.74%|[下载链接 (coming soon)](coming soon)| |EAST|MobileNetV3|78.24%|79.15%|78.69%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
|DB|ResNet50_vd|83.79%|80.65%|82.19%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)| |DB|ResNet50_vd|86.41%|78.72%|82.38%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
|DB|MobileNetV3|75.92%|73.18%|74.53%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)| |DB|MobileNetV3|77.29%|73.08%|75.12%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[下载链接 (coming soon)](link)| |SAST|ResNet50_vd|91.83%|81.80%|86.52%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar))|
在Total-text文本检测公开数据集上,算法效果如下: 在Total-text文本检测公开数据集上,算法效果如下:
|模型|骨干网络|precision|recall|Hmean|下载链接| |模型|骨干网络|precision|recall|Hmean|下载链接|
| --- | --- | --- | --- | --- | --- | | --- | --- | --- | --- | --- | --- |
|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[下载链接 (coming soon)](link)| |SAST|ResNet50_vd|89.05%|76.80%|82.47%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
**说明:** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载:[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi) **说明:** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载:[百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
...@@ -47,13 +47,10 @@ PaddleOCR基于动态图开源的文本识别算法列表: ...@@ -47,13 +47,10 @@ PaddleOCR基于动态图开源的文本识别算法列表:
参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程,使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法效果如下: 参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程,使用MJSynth和SynthText两个文字识别数据集训练,在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估,算法效果如下:
|模型|骨干网络|Avg Accuracy|模型存储命名|下载链接| |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
| --- | --- | --- | --- | --- | |-|-|-|-|-|
|Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
|Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)| |Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)| |Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
|CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)| |CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接 (coming soon )]()| |CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[下载链接 (coming soon )]()|
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md) PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)
...@@ -180,7 +180,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_ ...@@ -180,7 +180,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
<a name="EAST文本检测模型推理"></a> <a name="EAST文本检测模型推理"></a>
### 3. EAST文本检测模型推理 ### 3. EAST文本检测模型推理
首先将EAST文本检测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 (coming soon)](link) ),可以使用如下命令进行转换: 首先将EAST文本检测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/en/det_r50_vd_east_v2.0_train.tar) ),可以使用如下命令进行转换:
``` ```
python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
...@@ -193,7 +193,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img ...@@ -193,7 +193,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img
``` ```
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'det_res'。结果示例如下: 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'det_res'。结果示例如下:
(coming soon) ![](../imgs_results/det_res_img_10_east.jpg)
**注意**:本代码库中,EAST后处理Locality-Aware NMS有python和c++两种版本,c++版速度明显快于python版。由于c++版本nms编译版本问题,只有python3.5环境下会调用c++版nms,其他情况将调用python版nms。 **注意**:本代码库中,EAST后处理Locality-Aware NMS有python和c++两种版本,c++版速度明显快于python版。由于c++版本nms编译版本问题,只有python3.5环境下会调用c++版nms,其他情况将调用python版nms。
...@@ -201,7 +201,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img ...@@ -201,7 +201,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img
<a name="SAST文本检测模型推理"></a> <a name="SAST文本检测模型推理"></a>
### 4. SAST文本检测模型推理 ### 4. SAST文本检测模型推理
#### (1). 四边形文本检测模型(ICDAR2015) #### (1). 四边形文本检测模型(ICDAR2015)
首先将SAST文本检测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(coming soon)](link)),可以使用如下命令进行转换: 首先将SAST文本检测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/en/det_r50_vd_sast_icdar15_v2.0_train.tar)),可以使用如下命令进行转换:
``` ```
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15 python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
...@@ -212,10 +212,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img ...@@ -212,10 +212,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
``` ```
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'det_res'。结果示例如下: 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'det_res'。结果示例如下:
(coming soon) ![](../imgs_results/det_res_img_10_sast.jpg)
#### (2). 弯曲文本检测模型(Total-Text) #### (2). 弯曲文本检测模型(Total-Text)
首先将SAST文本检测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Total-Text英文数据集训练的模型为例([模型下载地址(coming soon)](link)),可以使用如下命令进行转换: 首先将SAST文本检测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Total-Text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/en/det_r50_vd_sast_totaltext_v2.0_train.tar)),可以使用如下命令进行转换:
``` ```
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
...@@ -228,7 +228,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img ...@@ -228,7 +228,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
``` ```
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'det_res'。结果示例如下: 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'det_res'。结果示例如下:
(coming soon) ![](../imgs_results/det_res_img623_sast.jpg)
**注意**:本代码库中,SAST后处理Locality-Aware NMS有python和c++两种版本,c++版速度明显快于python版。由于c++版本nms编译版本问题,只有python3.5环境下会调用c++版nms,其他情况将调用python版nms。 **注意**:本代码库中,SAST后处理Locality-Aware NMS有python和c++两种版本,c++版速度明显快于python版。由于c++版本nms编译版本问题,只有python3.5环境下会调用c++版nms,其他情况将调用python版nms。
......
...@@ -19,17 +19,17 @@ On the ICDAR2015 dataset, the text detection result is as follows: ...@@ -19,17 +19,17 @@ On the ICDAR2015 dataset, the text detection result is as follows:
|Model|Backbone|precision|recall|Hmean|Download link| |Model|Backbone|precision|recall|Hmean|Download link|
| --- | --- | --- | --- | --- | --- | | --- | --- | --- | --- | --- | --- |
|EAST|ResNet50_vd|88.18%|85.51%|86.82%|[download link (coming soon)](link)| |EAST|ResNet50_vd|88.76%|81.36%|84.90%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
|EAST|MobileNetV3|81.67%|79.83%|80.74%|[download link (coming soon)](coming soon)| |EAST|MobileNetV3|78.24%|79.15%|78.69%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
|DB|ResNet50_vd|83.79%|80.65%|82.19%|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)| |DB|ResNet50_vd|86.41%|78.72%|82.38%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
|DB|MobileNetV3|75.92%|73.18%|74.53%|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)| |DB|MobileNetV3|77.29%|73.08%|75.12%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
|SAST|ResNet50_vd|92.18%|82.96%|87.33%|[download link (coming soon)](link)| |SAST|ResNet50_vd|91.83%|81.80%|86.52%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar))|
On Total-Text dataset, the text detection result is as follows: On Total-Text dataset, the text detection result is as follows:
|Model|Backbone|precision|recall|Hmean|Download link| |Model|Backbone|precision|recall|Hmean|Download link|
| --- | --- | --- | --- | --- | --- | | --- | --- | --- | --- | --- | --- |
|SAST|ResNet50_vd|88.74%|79.80%|84.03%|[download link (coming soon)](link)| |SAST|ResNet50_vd|89.05%|76.80%|82.47%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
**Note:** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi). **Note:** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
...@@ -48,14 +48,10 @@ PaddleOCR open-source text recognition algorithms list: ...@@ -48,14 +48,10 @@ PaddleOCR open-source text recognition algorithms list:
Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow: Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
|Model|Backbone|Avg Accuracy|Module combination|Download link| |Model|Backbone|Avg Accuracy|Module combination|Download link|
| --- | --- | --- | --- | --- | |-|-|-|-|-|
|Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)| |Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
|Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)| |Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)| |CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
|CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)| |CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
|STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[download link (coming soon )]()|
|STAR-Net|Resnet34_vd|83.93%|rec_r34_vd_tps_bilstm_ctc|[download link (coming soon )]()|
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md) Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./doc/doc_en/recognition_en.md)
文件模式从 100644 更改为 100755
...@@ -187,7 +187,7 @@ The visualized text detection results are saved to the `./inference_results` fol ...@@ -187,7 +187,7 @@ The visualized text detection results are saved to the `./inference_results` fol
<a name="EAST_DETECTION"></a> <a name="EAST_DETECTION"></a>
### 3. EAST TEXT DETECTION MODEL INFERENCE ### 3. EAST TEXT DETECTION MODEL INFERENCE
First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link (coming soon)](link)), you can use the following command to convert: First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)), you can use the following command to convert:
``` ```
python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
...@@ -200,7 +200,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_ ...@@ -200,7 +200,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows: The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
(coming soon) ![](../imgs_results/det_res_img_10_east.jpg)
**Note**: EAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases. **Note**: EAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.
...@@ -208,7 +208,7 @@ The visualized text detection results are saved to the `./inference_results` fol ...@@ -208,7 +208,7 @@ The visualized text detection results are saved to the `./inference_results` fol
<a name="SAST_DETECTION"></a> <a name="SAST_DETECTION"></a>
### 4. SAST TEXT DETECTION MODEL INFERENCE ### 4. SAST TEXT DETECTION MODEL INFERENCE
#### (1). Quadrangle text detection model (ICDAR2015) #### (1). Quadrangle text detection model (ICDAR2015)
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link (coming soon)](link)), you can use the following command to convert: First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)), you can use the following command to convert:
``` ```
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15 python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
...@@ -222,10 +222,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img ...@@ -222,10 +222,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows: The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
(coming soon) ![](../imgs_results/det_res_img_10_sast.jpg)
#### (2). Curved text detection model (Total-Text) #### (2). Curved text detection model (Total-Text)
First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link (coming soon)](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert: First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)), you can use the following command to convert:
``` ```
python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
...@@ -239,7 +239,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img ...@@ -239,7 +239,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows: The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
(coming soon) ![](../imgs_results/det_res_img623_sast.jpg)
**Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases. **Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.
......
...@@ -19,12 +19,10 @@ from __future__ import print_function ...@@ -19,12 +19,10 @@ from __future__ import print_function
import numpy as np import numpy as np
from .locality_aware_nms import nms_locality from .locality_aware_nms import nms_locality
import cv2 import cv2
import paddle
import os import os
import sys import sys
# __dir__ = os.path.dirname(os.path.abspath(__file__))
# sys.path.append(__dir__)
# sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
class EASTPostProcess(object): class EASTPostProcess(object):
...@@ -113,11 +111,14 @@ class EASTPostProcess(object): ...@@ -113,11 +111,14 @@ class EASTPostProcess(object):
def __call__(self, outs_dict, shape_list): def __call__(self, outs_dict, shape_list):
score_list = outs_dict['f_score'] score_list = outs_dict['f_score']
geo_list = outs_dict['f_geo'] geo_list = outs_dict['f_geo']
if isinstance(score_list, paddle.Tensor):
score_list = score_list.numpy()
geo_list = geo_list.numpy()
img_num = len(shape_list) img_num = len(shape_list)
dt_boxes_list = [] dt_boxes_list = []
for ino in range(img_num): for ino in range(img_num):
score = score_list[ino].numpy() score = score_list[ino]
geo = geo_list[ino].numpy() geo = geo_list[ino]
boxes = self.detect( boxes = self.detect(
score_map=score, score_map=score,
geo_map=geo, geo_map=geo,
......
...@@ -24,7 +24,7 @@ sys.path.append(os.path.join(__dir__, '..')) ...@@ -24,7 +24,7 @@ sys.path.append(os.path.join(__dir__, '..'))
import numpy as np import numpy as np
from .locality_aware_nms import nms_locality from .locality_aware_nms import nms_locality
# import lanms import paddle
import cv2 import cv2
import time import time
...@@ -276,14 +276,19 @@ class SASTPostProcess(object): ...@@ -276,14 +276,19 @@ class SASTPostProcess(object):
border_list = outs_dict['f_border'] border_list = outs_dict['f_border']
tvo_list = outs_dict['f_tvo'] tvo_list = outs_dict['f_tvo']
tco_list = outs_dict['f_tco'] tco_list = outs_dict['f_tco']
if isinstance(score_list, paddle.Tensor):
score_list = score_list.numpy()
border_list = border_list.numpy()
tvo_list = tvo_list.numpy()
tco_list = tco_list.numpy()
img_num = len(shape_list) img_num = len(shape_list)
poly_lists = [] poly_lists = []
for ino in range(img_num): for ino in range(img_num):
p_score = score_list[ino].transpose((1,2,0)).numpy() p_score = score_list[ino].transpose((1,2,0))
p_border = border_list[ino].transpose((1,2,0)).numpy() p_border = border_list[ino].transpose((1,2,0))
p_tvo = tvo_list[ino].transpose((1,2,0)).numpy() p_tvo = tvo_list[ino].transpose((1,2,0))
p_tco = tco_list[ino].transpose((1,2,0)).numpy() p_tco = tco_list[ino].transpose((1,2,0))
src_h, src_w, ratio_h, ratio_w = shape_list[ino] 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,
......
...@@ -37,33 +37,51 @@ class TextDetector(object): ...@@ -37,33 +37,51 @@ class TextDetector(object):
def __init__(self, args): def __init__(self, args):
self.det_algorithm = args.det_algorithm self.det_algorithm = args.det_algorithm
self.use_zero_copy_run = args.use_zero_copy_run self.use_zero_copy_run = args.use_zero_copy_run
pre_process_list = [{
'DetResizeForTest': {
'limit_side_len': args.det_limit_side_len,
'limit_type': args.det_limit_type
}
}, {
'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 = {} postprocess_params = {}
if self.det_algorithm == "DB": if self.det_algorithm == "DB":
pre_process_list = [{
'DetResizeForTest': {
'limit_side_len': args.det_limit_side_len,
'limit_type': args.det_limit_type
}
}, {
'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['name'] = 'DBPostProcess' postprocess_params['name'] = 'DBPostProcess'
postprocess_params["thresh"] = args.det_db_thresh postprocess_params["thresh"] = args.det_db_thresh
postprocess_params["box_thresh"] = args.det_db_box_thresh postprocess_params["box_thresh"] = args.det_db_box_thresh
postprocess_params["max_candidates"] = 1000 postprocess_params["max_candidates"] = 1000
postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
postprocess_params["use_dilation"] = True postprocess_params["use_dilation"] = True
elif self.det_algorithm == "EAST":
postprocess_params['name'] = 'EASTPostProcess'
postprocess_params["score_thresh"] = args.det_east_score_thresh
postprocess_params["cover_thresh"] = args.det_east_cover_thresh
postprocess_params["nms_thresh"] = args.det_east_nms_thresh
elif self.det_algorithm == "SAST":
postprocess_params['name'] = 'SASTPostProcess'
postprocess_params["score_thresh"] = args.det_sast_score_thresh
postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
self.det_sast_polygon = args.det_sast_polygon
if self.det_sast_polygon:
postprocess_params["sample_pts_num"] = 6
postprocess_params["expand_scale"] = 1.2
postprocess_params["shrink_ratio_of_width"] = 0.2
else:
postprocess_params["sample_pts_num"] = 2
postprocess_params["expand_scale"] = 1.0
postprocess_params["shrink_ratio_of_width"] = 0.3
else: else:
logger.info("unknown det_algorithm:{}".format(self.det_algorithm)) logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
sys.exit(0) sys.exit(0)
...@@ -149,12 +167,25 @@ class TextDetector(object): ...@@ -149,12 +167,25 @@ class TextDetector(object):
for output_tensor in self.output_tensors: for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu() output = output_tensor.copy_to_cpu()
outputs.append(output) outputs.append(output)
preds = outputs[0]
# preds = self.predictor(img) preds = {}
if self.det_algorithm == "EAST":
preds['f_geo'] = outputs[0]
preds['f_score'] = outputs[1]
elif self.det_algorithm == 'SAST':
preds['f_border'] = outputs[0]
preds['f_score'] = outputs[1]
preds['f_tco'] = outputs[2]
preds['f_tvo'] = outputs[3]
else:
preds = outputs[0]
post_result = self.postprocess_op(preds, shape_list) post_result = self.postprocess_op(preds, shape_list)
dt_boxes = post_result[0]['points'] dt_boxes = post_result[0]['points']
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape) if self.det_algorithm == "SAST" and self.det_sast_polygon:
dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
else:
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
elapse = time.time() - starttime elapse = time.time() - starttime
return dt_boxes, elapse return dt_boxes, elapse
......
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