update inference for east & sast

doc/doc_ch/inference.md

@@ -173,7 +173,7 @@ 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>
 ### 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
@@ -186,7 +186,7 @@ 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：
 
-(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。
 
@@ -194,7 +194,7 @@ 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>
 ### 4. SAST文本检测模型推理
 #### (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
 
@@ -205,10 +205,10 @@ 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：
 
-(coming soon)
+![](../imgs_results/det_res_img_10_sast.jpg)
 
 #### (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
@@ -221,7 +221,7 @@ 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：
 
-(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。
 

doc/doc_en/inference_en.md

@@ -179,7 +179,7 @@ The visualized text detection results are saved to the `./inference_results` fol
 <a name="EAST_DETECTION"></a>
 ### 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
@@ -192,7 +192,7 @@ 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:
 
-(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.
 
@@ -200,7 +200,7 @@ The visualized text detection results are saved to the `./inference_results` fol
 <a name="SAST_DETECTION"></a>
 ### 4. SAST TEXT DETECTION MODEL INFERENCE
 #### (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
@@ -214,10 +214,10 @@ 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:
 
-(coming soon)
+![](../imgs_results/det_res_img_10_sast.jpg)
 
 #### (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
@@ -231,7 +231,7 @@ 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:
 
-(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.
 

ppocr/postprocess/db_postprocess.py

@@ -132,7 +132,8 @@ class DBPostProcess(object):
         cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
         return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
 
-    def __call__(self, pred, shape_list):
+    def __call__(self, outs_dict, shape_list):
+        pred = outs_dict['maps']
         if isinstance(pred, paddle.Tensor):
             pred = pred.numpy()
         pred = pred[:, 0, :, :]

ppocr/postprocess/east_postprocess.py

@@ -19,12 +19,10 @@ from __future__ import print_function
 import numpy as np
 from .locality_aware_nms import nms_locality
 import cv2
+import paddle
 
 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__, '..')))
 
 
 class EASTPostProcess(object):
@@ -113,11 +111,14 @@ class EASTPostProcess(object):
     def __call__(self, outs_dict, shape_list):
         score_list = outs_dict['f_score']
         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)
         dt_boxes_list = []
         for ino in range(img_num):
-            score = score_list[ino].numpy()
-            geo = geo_list[ino].numpy()
+            score = score_list[ino]
+            geo = geo_list[ino]
             boxes = self.detect(
                 score_map=score,
                 geo_map=geo,

ppocr/postprocess/sast_postprocess.py

@@ -24,7 +24,7 @@ sys.path.append(os.path.join(__dir__, '..'))
 
 import numpy as np
 from .locality_aware_nms import nms_locality
-# import lanms
+import paddle
 import cv2
 import time
 
@@ -276,14 +276,19 @@ class SASTPostProcess(object):
         border_list = outs_dict['f_border']
         tvo_list = outs_dict['f_tvo']
         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)
         poly_lists = []
         for ino in range(img_num):
-            p_score = score_list[ino].transpose((1,2,0)).numpy()
-            p_border = border_list[ino].transpose((1,2,0)).numpy()
-            p_tvo = tvo_list[ino].transpose((1,2,0)).numpy()
-            p_tco = tco_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))
+            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, 

tools/infer/predict_det.py

@@ -37,8 +37,6 @@ class TextDetector(object):
     def __init__(self, args):
         self.det_algorithm = args.det_algorithm
         self.use_zero_copy_run = args.use_zero_copy_run
-        postprocess_params = {}
-        if self.det_algorithm == "DB":
         pre_process_list = [{
             'DetResizeForTest': {
                 'limit_side_len': args.det_limit_side_len,
@@ -58,12 +56,32 @@ class TextDetector(object):
                 'keep_keys': ['image', 'shape']
             }
         }]
+        postprocess_params = {}
+        if self.det_algorithm == "DB":
             postprocess_params['name'] = 'DBPostProcess'
             postprocess_params["thresh"] = args.det_db_thresh
             postprocess_params["box_thresh"] = args.det_db_box_thresh
             postprocess_params["max_candidates"] = 1000
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
             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:
             logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
             sys.exit(0)
@@ -149,11 +167,24 @@ class TextDetector(object):
         for output_tensor in self.output_tensors:
             output = output_tensor.copy_to_cpu()
             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['maps'] = outputs[0]
+
         post_result = self.postprocess_op(preds, shape_list)
         dt_boxes = post_result[0]['points']
+        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
         return dt_boxes, elapse