update inference for east & sast
This commit is contained in:
MissPenguin
parent
1f926f5f01
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3f64d27b71
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@ -173,7 +173,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
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<a name="EAST文本检测模型推理"></a>
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### 3. EAST文本检测模型推理
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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：
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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：
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```
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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
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@ -186,7 +186,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img
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```
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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：
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(coming soon)
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**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。
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@ -194,7 +194,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img
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<a name="SAST文本检测模型推理"></a>
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### 4. SAST文本检测模型推理
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#### (1). 四边形文本检测模型（ICDAR2015）
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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：
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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：
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```
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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
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@ -205,10 +205,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
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```
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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：
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(coming soon)
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#### (2). 弯曲文本检测模型（Total-Text）
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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：
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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：
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```
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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
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@ -221,7 +221,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
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```
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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：
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(coming soon)
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**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。
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@ -179,7 +179,7 @@ The visualized text detection results are saved to the `./inference_results` fol
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<a name="EAST_DETECTION"></a>
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### 3. EAST TEXT DETECTION MODEL INFERENCE
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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:
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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:
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```
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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
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@ -192,7 +192,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
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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:
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(coming soon)
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**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.
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@ -200,7 +200,7 @@ The visualized text detection results are saved to the `./inference_results` fol
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<a name="SAST_DETECTION"></a>
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### 4. SAST TEXT DETECTION MODEL INFERENCE
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#### (1). Quadrangle text detection model (ICDAR2015)
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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:
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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:
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```
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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
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@ -214,10 +214,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
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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:
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(coming soon)
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#### (2). Curved text detection model (Total-Text)
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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:
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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:
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```
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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
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@ -231,7 +231,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
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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:
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(coming soon)
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**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.
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@ -132,7 +132,8 @@ class DBPostProcess(object):
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cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
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return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
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def __call__(self, pred, shape_list):
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def __call__(self, outs_dict, shape_list):
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pred = outs_dict['maps']
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if isinstance(pred, paddle.Tensor):
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pred = pred.numpy()
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pred = pred[:, 0, :, :]
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@ -19,12 +19,10 @@ from __future__ import print_function
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import numpy as np
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from .locality_aware_nms import nms_locality
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import cv2
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import paddle
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import os
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import sys
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# __dir__ = os.path.dirname(os.path.abspath(__file__))
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# sys.path.append(__dir__)
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# sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
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class EASTPostProcess(object):
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@ -113,11 +111,14 @@ class EASTPostProcess(object):
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def __call__(self, outs_dict, shape_list):
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score_list = outs_dict['f_score']
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geo_list = outs_dict['f_geo']
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if isinstance(score_list, paddle.Tensor):
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score_list = score_list.numpy()
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geo_list = geo_list.numpy()
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img_num = len(shape_list)
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dt_boxes_list = []
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for ino in range(img_num):
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score = score_list[ino].numpy()
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geo = geo_list[ino].numpy()
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score = score_list[ino]
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geo = geo_list[ino]
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boxes = self.detect(
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score_map=score,
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geo_map=geo,
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@ -24,7 +24,7 @@ sys.path.append(os.path.join(__dir__, '..'))
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import numpy as np
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from .locality_aware_nms import nms_locality
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# import lanms
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import paddle
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import cv2
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import time
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@ -276,14 +276,19 @@ class SASTPostProcess(object):
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border_list = outs_dict['f_border']
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tvo_list = outs_dict['f_tvo']
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tco_list = outs_dict['f_tco']
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if isinstance(score_list, paddle.Tensor):
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score_list = score_list.numpy()
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border_list = border_list.numpy()
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tvo_list = tvo_list.numpy()
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tco_list = tco_list.numpy()
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img_num = len(shape_list)
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poly_lists = []
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for ino in range(img_num):
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p_score = score_list[ino].transpose((1,2,0)).numpy()
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p_border = border_list[ino].transpose((1,2,0)).numpy()
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p_tvo = tvo_list[ino].transpose((1,2,0)).numpy()
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p_tco = tco_list[ino].transpose((1,2,0)).numpy()
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p_score = score_list[ino].transpose((1,2,0))
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p_border = border_list[ino].transpose((1,2,0))
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p_tvo = tvo_list[ino].transpose((1,2,0))
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p_tco = tco_list[ino].transpose((1,2,0))
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src_h, src_w, ratio_h, ratio_w = shape_list[ino]
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poly_list = self.detect_sast(p_score, p_tvo, p_border, p_tco, ratio_w, ratio_h, src_w, src_h,
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@ -37,33 +37,51 @@ class TextDetector(object):
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def __init__(self, args):
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self.det_algorithm = args.det_algorithm
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self.use_zero_copy_run = args.use_zero_copy_run
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pre_process_list = [{
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'DetResizeForTest': {
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'limit_side_len': args.det_limit_side_len,
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'limit_type': args.det_limit_type
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}
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}, {
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'NormalizeImage': {
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'std': [0.229, 0.224, 0.225],
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'mean': [0.485, 0.456, 0.406],
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'scale': '1./255.',
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'order': 'hwc'
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}
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}, {
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'ToCHWImage': None
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}, {
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'KeepKeys': {
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'keep_keys': ['image', 'shape']
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}
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}]
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postprocess_params = {}
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if self.det_algorithm == "DB":
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pre_process_list = [{
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'DetResizeForTest': {
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'limit_side_len': args.det_limit_side_len,
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'limit_type': args.det_limit_type
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}
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}, {
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'NormalizeImage': {
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'std': [0.229, 0.224, 0.225],
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'mean': [0.485, 0.456, 0.406],
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'scale': '1./255.',
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'order': 'hwc'
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}
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}, {
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'ToCHWImage': None
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}, {
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'KeepKeys': {
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'keep_keys': ['image', 'shape']
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}
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}]
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postprocess_params['name'] = 'DBPostProcess'
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postprocess_params["thresh"] = args.det_db_thresh
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postprocess_params["box_thresh"] = args.det_db_box_thresh
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postprocess_params["max_candidates"] = 1000
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postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
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postprocess_params["use_dilation"] = True
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elif self.det_algorithm == "EAST":
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postprocess_params['name'] = 'EASTPostProcess'
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postprocess_params["score_thresh"] = args.det_east_score_thresh
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postprocess_params["cover_thresh"] = args.det_east_cover_thresh
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postprocess_params["nms_thresh"] = args.det_east_nms_thresh
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elif self.det_algorithm == "SAST":
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postprocess_params['name'] = 'SASTPostProcess'
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postprocess_params["score_thresh"] = args.det_sast_score_thresh
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postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
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self.det_sast_polygon = args.det_sast_polygon
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if self.det_sast_polygon:
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postprocess_params["sample_pts_num"] = 6
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postprocess_params["expand_scale"] = 1.2
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postprocess_params["shrink_ratio_of_width"] = 0.2
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else:
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postprocess_params["sample_pts_num"] = 2
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postprocess_params["expand_scale"] = 1.0
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postprocess_params["shrink_ratio_of_width"] = 0.3
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else:
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logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
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sys.exit(0)
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@ -149,12 +167,25 @@ class TextDetector(object):
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for output_tensor in self.output_tensors:
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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preds = outputs[0]
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# preds = self.predictor(img)
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preds = {}
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if self.det_algorithm == "EAST":
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preds['f_geo'] = outputs[0]
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preds['f_score'] = outputs[1]
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elif self.det_algorithm == 'SAST':
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preds['f_border'] = outputs[0]
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preds['f_score'] = outputs[1]
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preds['f_tco'] = outputs[2]
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preds['f_tvo'] = outputs[3]
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else:
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preds['maps'] = outputs[0]
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post_result = self.postprocess_op(preds, shape_list)
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dt_boxes = post_result[0]['points']
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dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
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if self.det_algorithm == "SAST" and self.det_sast_polygon:
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dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
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else:
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dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
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elapse = time.time() - starttime
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return dt_boxes, elapse
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