Merge pull request #7859 from WenmuZhou/tipc_2

add re to ppstructure system

paddleocr.py

@@ -567,6 +567,7 @@ class PPStructure(StructureSystem):
         assert params.structure_version in SUPPORT_STRUCTURE_MODEL_VERSION, "structure_version must in {}, but get {}".format(
             SUPPORT_STRUCTURE_MODEL_VERSION, params.structure_version)
         params.use_gpu = check_gpu(params.use_gpu)
+        params.mode = 'structure'
 
         if not params.show_log:
             logger.setLevel(logging.INFO)

ppstructure/docs/inference.md

 # 基于Python预测引擎推理
 
-- [1. 版面信息抽取](#1)
-  - [1.1 版面分析+表格识别](#1.1)
-  - [1.2 版面分析](#1.2)
-  - [1.3 表格识别](#1.3)
-- [2. 关键信息抽取](#2)
+- [1. 版面信息抽取](#1-版面信息抽取)
+  - [1.1 版面分析+表格识别](#11-版面分析表格识别)
+  - [1.2 版面分析](#12-版面分析)
+  - [1.3 表格识别](#13-表格识别)
+- [2. 关键信息抽取](#2-关键信息抽取)
+  - [2.1 SER](#21-ser)
+  - [2.2 RE+SER](#22-reser)
 
 <a name="1"></a>
 ## 1. 版面信息抽取
@@ -70,6 +72,8 @@ python3 predict_system.py --det_model_dir=inference/ch_PP-OCRv3_det_infer \
 <a name="2"></a>
 ## 2. 关键信息抽取
 
+### 2.1 SER
+
 ```bash
 cd ppstructure
 
@@ -77,13 +81,38 @@ mkdir inference && cd inference
 # 下载SER XFUND 模型并解压
 wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar && tar -xf ser_vi_layoutxlm_xfund_infer.tar
 cd ..
-python3 kie/predict_kie_token_ser.py \
+python3 predict_system.py \
   --kie_algorithm=LayoutXLM \
-  --ser_model_dir=../inference/ser_vi_layoutxlm_xfund_infer \
+  --ser_model_dir=./inference/ser_vi_layoutxlm_xfund_infer \
   --image_dir=./docs/kie/input/zh_val_42.jpg \
   --ser_dict_path=../ppocr/utils/dict/kie_dict/xfund_class_list.txt \
   --vis_font_path=../doc/fonts/simfang.ttf \
-  --ocr_order_method="tb-yx"
+  --ocr_order_method="tb-yx" \
+  --mode=kie
 ```
 
 运行完成后，每张图片会在`output`字段指定的目录下的`kie`目录下存放可视化之后的图片，图片名和输入图片名一致。
+
+### 2.2 RE+SER
+
+```bash
+cd ppstructure
+
+mkdir inference && cd inference
+# 下载RE SER XFUND 模型并解压
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar && tar -xf ser_vi_layoutxlm_xfund_infer.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_infer.tar && tar -xf re_vi_layoutxlm_xfund_infer.tar
+cd ..
+
+python3 predict_system.py \
+  --kie_algorithm=LayoutXLM \
+  --re_model_dir=./inference/re_vi_layoutxlm_xfund_infer \
+  --ser_model_dir=./inference/ser_vi_layoutxlm_xfund_infer \
+  --image_dir=./docs/kie/input/zh_val_42.jpg \
+  --ser_dict_path=../ppocr/utils/dict/kie_dict/xfund_class_list.txt \
+  --vis_font_path=../doc/fonts/simfang.ttf \
+  --ocr_order_method="tb-yx" \
+  --mode=kie
+```
+
+运行完成后，每张图片会在`output`字段指定的目录下的`kie`目录下有一个同名目录，目录中存放可视化图片和预测结果。

ppstructure/docs/inference_en.md

 # Python Inference
 
-- [1. Layout Structured Analysis](#1)
-  - [1.1 layout analysis + table recognition](#1.1)
-  - [1.2 layout analysis](#1.2)
-  - [1.3 table recognition](#1.3)
-- [2. Key Information Extraction](#2)
+- [1. Layout Structured Analysis](#1-layout-structured-analysis)
+  - [1.1 layout analysis + table recognition](#11-layout-analysis--table-recognition)
+  - [1.2 layout analysis](#12-layout-analysis)
+  - [1.3 table recognition](#13-table-recognition)
+- [2. Key Information Extraction](#2-key-information-extraction)
+  - [2.1 SER](#21-ser)
+  - [2.2 RE+SER](#22-reser)
 
 <a name="1"></a>
 ## 1. Layout Structured Analysis
@@ -72,6 +74,7 @@ After the operation is completed, each image will have a directory with the same
 <a name="2"></a>
 ## 2. Key Information Extraction
 
+### 2.1 SER
 ```bash
 cd ppstructure
 
@@ -79,13 +82,39 @@ mkdir inference && cd inference
 # download model
 wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar && tar -xf ser_vi_layoutxlm_xfund_infer.tar
 cd ..
-python3 kie/predict_kie_token_ser.py \
+python3 predict_system.py \
   --kie_algorithm=LayoutXLM \
-  --ser_model_dir=../inference/ser_vi_layoutxlm_xfund_infer \
+  --ser_model_dir=./inference/ser_vi_layoutxlm_xfund_infer \
   --image_dir=./docs/kie/input/zh_val_42.jpg \
   --ser_dict_path=../ppocr/utils/dict/kie_dict/xfund_class_list.txt \
   --vis_font_path=../doc/fonts/simfang.ttf \
-  --ocr_order_method="tb-yx"
+  --ocr_order_method="tb-yx" \
+  --mode=kie
 ```
 
 After the operation is completed, each image will store the visualized image in the `kie` directory under the directory specified by the `output` field, and the image name is the same as the input image name.
+
+
+### 2.2 RE+SER
+
+```bash
+cd ppstructure
+
+mkdir inference && cd inference
+# download model
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/ser_vi_layoutxlm_xfund_infer.tar && tar -xf ser_vi_layoutxlm_xfund_infer.tar
+wget https://paddleocr.bj.bcebos.com/ppstructure/models/vi_layoutxlm/re_vi_layoutxlm_xfund_infer.tar && tar -xf re_vi_layoutxlm_xfund_infer.tar
+cd ..
+
+python3 predict_system.py \
+  --kie_algorithm=LayoutXLM \
+  --re_model_dir=./inference/re_vi_layoutxlm_xfund_infer \
+  --ser_model_dir=./inference/ser_vi_layoutxlm_xfund_infer \
+  --image_dir=./docs/kie/input/zh_val_42.jpg \
+  --ser_dict_path=../ppocr/utils/dict/kie_dict/xfund_class_list.txt \
+  --vis_font_path=../doc/fonts/simfang.ttf \
+  --ocr_order_method="tb-yx" \
+  --mode=kie
+```
+
+After the operation is completed, each image will have a directory with the same name in the `kie` directory under the directory specified by the `output` field, where the visual images and prediction results are stored.

ppstructure/kie/predict_kie_token_ser_re.py

@@ -29,13 +29,11 @@ import tools.infer.utility as utility
 from tools.infer_kie_token_ser_re import make_input
 from ppocr.postprocess import build_post_process
 from ppocr.utils.logging import get_logger
-from ppocr.utils.visual import draw_re_results
+from ppocr.utils.visual import draw_ser_results, draw_re_results
 from ppocr.utils.utility import get_image_file_list, check_and_read
 from ppstructure.utility import parse_args
 from ppstructure.kie.predict_kie_token_ser import SerPredictor
 
-from paddleocr import PaddleOCR
-
 logger = get_logger()
 
 
@@ -43,15 +41,20 @@ class SerRePredictor(object):
     def __init__(self, args):
         self.use_visual_backbone = args.use_visual_backbone
         self.ser_engine = SerPredictor(args)
-
-        postprocess_params = {'name': 'VQAReTokenLayoutLMPostProcess'}
-        self.postprocess_op = build_post_process(postprocess_params)
-        self.predictor, self.input_tensor, self.output_tensors, self.config = \
-            utility.create_predictor(args, 're', logger)
+        if args.re_model_dir is not None:
+            postprocess_params = {'name': 'VQAReTokenLayoutLMPostProcess'}
+            self.postprocess_op = build_post_process(postprocess_params)
+            self.predictor, self.input_tensor, self.output_tensors, self.config = \
+                utility.create_predictor(args, 're', logger)
+        else:
+            self.predictor = None
 
     def __call__(self, img):
         starttime = time.time()
-        ser_results, ser_inputs, _ = self.ser_engine(img)
+        ser_results, ser_inputs, ser_elapse = self.ser_engine(img)
+        if self.predictor is None:
+            return ser_results, ser_elapse
+
         re_input, entity_idx_dict_batch = make_input(ser_inputs, ser_results)
         if self.use_visual_backbone == False:
             re_input.pop(4)
@@ -79,7 +82,7 @@ class SerRePredictor(object):
 
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
-    ser_predictor = SerRePredictor(args)
+    ser_re_predictor = SerRePredictor(args)
     count = 0
     total_time = 0
 
@@ -95,7 +98,7 @@ def main(args):
             if img is None:
                 logger.info("error in loading image:{}".format(image_file))
                 continue
-            re_res, elapse = ser_predictor(img)
+            re_res, elapse = ser_re_predictor(img)
             re_res = re_res[0]
 
             res_str = '{}\t{}\n'.format(
@@ -105,14 +108,20 @@ def main(args):
                         "ocr_info": re_res,
                     }, ensure_ascii=False))
             f_w.write(res_str)
-
-            img_res = draw_re_results(
-                image_file, re_res, font_path=args.vis_font_path)
-
-            img_save_path = os.path.join(
-                args.output,
-                os.path.splitext(os.path.basename(image_file))[0] +
-                "_ser_re.jpg")
+            if ser_re_predictor.predictor is not None:
+                img_res = draw_re_results(
+                    image_file, re_res, font_path=args.vis_font_path)
+                img_save_path = os.path.join(
+                    args.output,
+                    os.path.splitext(os.path.basename(image_file))[0] +
+                    "_ser_re.jpg")
+            else:
+                img_res = draw_ser_results(
+                    image_file, re_res, font_path=args.vis_font_path)
+                img_save_path = os.path.join(
+                    args.output,
+                    os.path.splitext(os.path.basename(image_file))[0] +
+                    "_ser.jpg")
 
             cv2.imwrite(img_save_path, img_res)
             logger.info("save vis result to {}".format(img_save_path))

ppstructure/predict_system.py

@@ -30,6 +30,7 @@ from copy import deepcopy
 
 from ppocr.utils.utility import get_image_file_list, check_and_read
 from ppocr.utils.logging import get_logger
+from ppocr.utils.visual import draw_ser_results, draw_re_results
 from tools.infer.predict_system import TextSystem
 from ppstructure.layout.predict_layout import LayoutPredictor
 from ppstructure.table.predict_table import TableSystem, to_excel
@@ -75,7 +76,8 @@ class StructureSystem(object):
                     self.table_system = TableSystem(args)
 
         elif self.mode == 'kie':
-            raise NotImplementedError
+            from ppstructure.kie.predict_kie_token_ser_re import SerRePredictor
+            self.kie_predictor = SerRePredictor(args)
 
     def __call__(self, img, return_ocr_result_in_table=False, img_idx=0):
         time_dict = {
@@ -176,7 +178,10 @@ class StructureSystem(object):
             time_dict['all'] = end - start
             return res_list, time_dict
         elif self.mode == 'kie':
-            raise NotImplementedError
+            re_res, elapse = self.kie_predictor(img)
+            time_dict['kie'] = elapse
+            time_dict['all'] = elapse
+            return re_res[0], time_dict
         return None, None
 
 
@@ -235,15 +240,32 @@ def main(args):
         all_res = []
         for index, img in enumerate(imgs):
             res, time_dict = structure_sys(img, img_idx=index)
+            img_save_path = os.path.join(save_folder, img_name,
+                                         'show_{}.jpg'.format(index))
+            os.makedirs(os.path.join(save_folder, img_name), exist_ok=True)
             if structure_sys.mode == 'structure' and res != []:
-                save_structure_res(res, save_folder, img_name, index)
                 draw_img = draw_structure_result(img, res, args.vis_font_path)
-                img_save_path = os.path.join(save_folder, img_name,
-                                             'show_{}.jpg'.format(index))
+                save_structure_res(res, save_folder, img_name, index)
             elif structure_sys.mode == 'kie':
-                raise NotImplementedError
-                # draw_img = draw_ser_results(img, res, args.vis_font_path)
-                # img_save_path = os.path.join(save_folder, img_name + '.jpg')
+                if structure_sys.kie_predictor.predictor is not None:
+                    draw_img = draw_re_results(
+                        img, res, font_path=args.vis_font_path)
+                else:
+                    draw_img = draw_ser_results(
+                        img, res, font_path=args.vis_font_path)
+
+                with open(
+                        os.path.join(save_folder, img_name,
+                                     'res_{}_kie.txt'.format(index)),
+                        'w',
+                        encoding='utf8') as f:
+                    res_str = '{}\t{}\n'.format(
+                        image_file,
+                        json.dumps(
+                            {
+                                "ocr_info": res
+                            }, ensure_ascii=False))
+                    f.write(res_str)
             if res != []:
                 cv2.imwrite(img_save_path, draw_img)
                 logger.info('result save to {}'.format(img_save_path))

ppstructure/utility.py

@@ -11,9 +11,9 @@
 # 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 random
 import ast
-from PIL import Image
+from PIL import Image, ImageDraw, ImageFont
 import numpy as np
 from tools.infer.utility import draw_ocr_box_txt, str2bool, init_args as infer_args
 
@@ -64,6 +64,7 @@ def init_args():
     parser.add_argument(
         "--mode",
         type=str,
+        choices=['structure', 'kie'],
         default='structure',
         help='structure and kie is supported')
     parser.add_argument(