init commit for paddlestructure

MANIFEST.in

@@ -1,7 +1,7 @@
 include LICENSE.txt
 include README.md
 
-recursive-include ppocr/utils *.txt utility.py logging.py
+recursive-include ppocr/utils *.txt utility.py logging.py network.py
 recursive-include ppocr/data/ *.py
 recursive-include ppocr/postprocess *.py
 recursive-include tools/infer *.py

paddleocr.py

@@ -19,6 +19,7 @@ __dir__ = os.path.dirname(__file__)
 sys.path.append(os.path.join(__dir__, ''))
 
 import cv2
+import logging
 import numpy as np
 from pathlib import Path
 
@@ -150,6 +151,8 @@ class PaddleOCR(predict_system.TextSystem):
         """
         params = parse_args(mMain=False)
         params.__dict__.update(**kwargs)
+        if params.show_log:
+            logger.setLevel(logging.DEBUG)
         self.use_angle_cls = params.use_angle_cls
         lang = params.lang
         latin_lang = [

ppocr/utils/dict/table_dict.txt

@@ -33,8 +33,7 @@ D
 Π
 H
 ║
-</
+</strike>
->
 L
 Φ
 Χ

ppstructure/MANIFEST.in

@@ -0,0 +1,9 @@
+include LICENSE.txt
+include README.md
+
+recursive-include ppocr/utils *.txt utility.py logging.py network.py
+recursive-include ppocr/data/ *.py
+recursive-include ppocr/postprocess *.py
+recursive-include tools/infer *.py
+recursive-include table *.py
+recursive-include ppstructure *.py

ppstructure/paddlestructure.py

@@ -0,0 +1,161 @@
+# 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 logging
+import os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(os.path.join(__dir__, ''))
+
+
+import cv2
+import numpy as np
+from pathlib import Path
+
+from ppocr.utils.logging import get_logger
+from predict_system import OCRSystem, save_res
+from utility import init_args
+
+logger = get_logger()
+from ppocr.utils.utility import check_and_read_gif, get_image_file_list
+from ppocr.utils.network import maybe_download, download_with_progressbar
+
+__all__ = ['PaddleStructure']
+
+VERSION = '2.1'
+BASE_DIR = os.path.expanduser("~/.paddlestructure/")
+
+model_urls = {
+    'det': {
+        'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
+    },
+    'rec': {
+        'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
+    },
+    'structure': {
+        'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
+    },
+}
+
+
+def parse_args(mMain=True):
+    import argparse
+    parser = init_args()
+    parser.add_help = mMain
+
+    for action in parser._actions:
+        if action.dest in ['rec_char_dict_path', 'structure_char_dict_path']:
+            action.default = None
+    if mMain:
+        return parser.parse_args()
+    else:
+        inference_args_dict = {}
+        for action in parser._actions:
+            inference_args_dict[action.dest] = action.default
+        return argparse.Namespace(**inference_args_dict)
+
+
+class PaddleStructure(OCRSystem):
+    def __init__(self, **kwargs):
+        params = parse_args(mMain=False)
+        params.__dict__.update(**kwargs)
+        if params.show_log:
+            logger.setLevel(logging.DEBUG)
+        params.use_angle_cls = False
+        # init model dir
+        if params.det_model_dir is None:
+            params.det_model_dir = os.path.join(BASE_DIR, VERSION, 'det')
+        if params.rec_model_dir is None:
+            params.rec_model_dir = os.path.join(BASE_DIR, VERSION, 'rec')
+        if params.structure_model_dir is None:
+            params.structure_model_dir = os.path.join(BASE_DIR, VERSION, 'structure')
+        # download model
+        maybe_download(params.det_model_dir, model_urls['det'])
+        maybe_download(params.det_model_dir, model_urls['rec'])
+        maybe_download(params.det_model_dir, model_urls['structure'])
+
+        if params.rec_char_dict_path is None:
+            params.rec_char_type = 'EN'
+            if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')):
+                params.rec_char_dict_path = str(Path(__file__).parent / 'ppocr/utils/dict/table_dict.txt')
+            else:
+                params.rec_char_dict_path = str(Path(__file__).parent.parent / 'ppocr/utils/dict/table_dict.txt')
+        if params.structure_char_dict_path is None:
+            if os.path.exists(str(Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')):
+                params.structure_char_dict_path = str(Path(__file__).parent / 'ppocr/utils/dict/table_structure_dict.txt')
+            else:
+                params.structure_char_dict_path = str(Path(__file__).parent.parent / 'ppocr/utils/dict/table_structure_dict.txt')
+
+        print(params)
+        super().__init__(params)
+
+    def __call__(self, img):
+        if isinstance(img, str):
+            # download net image
+            if img.startswith('http'):
+                download_with_progressbar(img, 'tmp.jpg')
+                img = 'tmp.jpg'
+            image_file = img
+            img, flag = check_and_read_gif(image_file)
+            if not flag:
+                with open(image_file, 'rb') as f:
+                    np_arr = np.frombuffer(f.read(), dtype=np.uint8)
+                    img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+            if img is None:
+                logger.error("error in loading image:{}".format(image_file))
+                return None
+        if isinstance(img, np.ndarray) and len(img.shape) == 2:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+        res = super().__call__(img)
+        return res
+
+
+def main():
+    # for cmd
+    args = parse_args(mMain=True)
+    image_dir = args.image_dir
+    save_folder = args.output
+    if image_dir.startswith('http'):
+        download_with_progressbar(image_dir, 'tmp.jpg')
+        image_file_list = ['tmp.jpg']
+    else:
+        image_file_list = get_image_file_list(args.image_dir)
+    if len(image_file_list) == 0:
+        logger.error('no images find in {}'.format(args.image_dir))
+        return
+
+    structure_engine = PaddleStructure(**(args.__dict__))
+    for img_path in image_file_list:
+        img_name = os.path.basename(img_path).split('.')[0]
+        logger.info('{}{}{}'.format('*' * 10, img_path, '*' * 10))
+        result = structure_engine(img_path)
+        save_res(result, args.output, os.path.basename(img_path).split('.')[0])
+        for item in result:
+            logger.info(item['res'])
+        save_res(result, save_folder, img_name)
+        logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
+
+
+
+if __name__ == '__main__':
+    table_engine = PaddleStructure(det_model_dir='../inference/table/ch_ppocr_mobile_v2.0_table_det_infer',
+                                   rec_model_dir='../inference/table/ch_ppocr_mobile_v2.0_table_rec_infer',
+                                   structure_model_dir='../inference/table/ch_ppocr_mobile_v2.0_table_structure_infer',
+                                   output='/Users/zhoujun20/Desktop/工作相关/table/table_pr/PaddleOCR/output/table',
+                                   show_log=True)
+    img = cv2.imread('/Users/zhoujun20/Desktop/工作相关/table/table_pr/PaddleOCR/ppstructure/test_imgs/table_1.png')
+    result = table_engine(img)
+    for line in result:
+        print(line)

ppstructure/predict_system.py

@@ -18,97 +18,93 @@ import subprocess
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
 
 os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
 import cv2
-import copy
 import numpy as np
 import time
-import tools.infer.utility as utility
+
-from tools.infer.predict_system import TextSystem
+import layoutparser as lp
-from ppstructure.table.predict_table import TableSystem, to_excel
+
-from ppstructure.layout.predict_layout import LayoutDetector
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
+from tools.infer.predict_system import TextSystem
+from ppstructure.table.predict_table import TableSystem, to_excel
+from ppstructure.utility import parse_args
 
 logger = get_logger()
 
 
-def parse_args():
+class OCRSystem(object):
-    parser = utility.init_args()
-
-    # params for output
-    parser.add_argument("--table_output", type=str, default='output/table')
-    # params for table structure
-    parser.add_argument("--table_max_len", type=int, default=488)
-    parser.add_argument("--table_max_text_length", type=int, default=100)
-    parser.add_argument("--table_max_elem_length", type=int, default=800)
-    parser.add_argument("--table_max_cell_num", type=int, default=500)
-    parser.add_argument("--table_model_dir", type=str)
-    parser.add_argument("--table_char_type", type=str, default='en')
-    parser.add_argument("--table_char_dict_path", type=str, default="./ppocr/utils/dict/table_structure_dict.txt")
-
-    # params for layout detector
-    parser.add_argument("--layout_model_dir", type=str)
-    return parser.parse_args()
-
-
-class OCRSystem():
     def __init__(self, args):
         self.text_system = TextSystem(args)
-        self.table_system = TableSystem(args)
+        self.table_system = TableSystem(args, self.text_system.text_detector, self.text_system.text_recognizer)
-        self.table_layout = LayoutDetector(args)
+        self.table_layout = lp.PaddleDetectionLayoutModel("lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config",
+                                                          threshold=0.5, enable_mkldnn=args.enable_mkldnn,
+                                                          enforce_cpu=not args.use_gpu)
         self.use_angle_cls = args.use_angle_cls
         self.drop_score = args.drop_score
 
     def __call__(self, img):
         ori_im = img.copy()
-        layout_res = self.table_layout(copy.deepcopy(img))
+        layout_res = self.table_layout.detect(img[..., ::-1])
+        res_list = []
         for region in layout_res:
-            x1, y1, x2, y2 = region['bbox']
+            x1, y1, x2, y2 = region.coordinates
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
             roi_img = ori_im[y1:y2, x1:x2, :]
-            if region['label'] == 'table':
+            if region.type == 'Table':
-                res = self.text_system(roi_img)
+                res = self.table_system(roi_img)
+            elif region.type == 'Figure':
+                continue
             else:
-                res = self.text_system(roi_img)
+                filter_boxes, filter_rec_res = self.text_system(roi_img)
-            region['res'] = res
+                filter_boxes = [x.reshape(-1).tolist() for x in filter_boxes]
-        return layout_res
+                res = (filter_boxes, filter_rec_res)
+            res_list.append({'type': region.type, 'bbox': [x1, y1, x2, y2], 'res': res})
+        return res_list
+
+
+def save_res(res, save_folder, img_name):
+    excel_save_folder = os.path.join(save_folder, img_name)
+    os.makedirs(excel_save_folder, exist_ok=True)
+    # save res
+    for region in res:
+        if region['type'] == 'Table':
+            excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox']))
+            to_excel(region['res'], excel_path)
+        elif region['type'] == 'Figure':
+            pass
+        else:
+            with open(os.path.join(excel_save_folder, 'res.txt'), 'a', encoding='utf8') as f:
+                for box, rec_res in zip(*region['res']):
+                    f.write('{}\t{}\n'.format(np.array(box).reshape(-1).tolist(), rec_res))
 
 
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
+    image_file_list = image_file_list
     image_file_list = image_file_list[args.process_id::args.total_process_num]
-    save_folder = args.table_output
+    save_folder = args.output
     os.makedirs(save_folder, exist_ok=True)
 
-    text_sys = OCRSystem(args)
+    structure_sys = OCRSystem(args)
     img_num = len(image_file_list)
     for i, image_file in enumerate(image_file_list):
         logger.info("[{}/{}] {}".format(i, img_num, image_file))
         img, flag = check_and_read_gif(image_file)
         img_name = os.path.basename(image_file).split('.')[0]
-        # excel_path = os.path.join(excel_save_folder, + '.xlsx')
+
         if not flag:
             img = cv2.imread(image_file)
         if img is None:
-            logger.info("error in loading image:{}".format(image_file))
+            logger.error("error in loading image:{}".format(image_file))
             continue
         starttime = time.time()
-        res = text_sys(img)
+        res = structure_sys(img)
-
+        save_res(res, save_folder, img_name)
-        excel_save_folder = os.path.join(save_folder, img_name)
+        logger.info('result save to {}'.format(os.path.join(save_folder, img_name)))
-        os.makedirs(excel_save_folder, exist_ok=True)
-        # save res
-        for region in res:
-            if region['label'] == 'table':
-                excel_path = os.path.join(excel_save_folder, '{}.xlsx'.format(region['bbox']))
-                to_excel(region['res'], excel_path)
-            else:
-                with open(os.path.join(excel_save_folder, 'res.txt'),'a',encoding='utf8') as f:
-                    for box, rec_res in zip(*region['res']):
-                        f.write('{}\t{}\n'.format(np.array(box).reshape(-1).tolist(), rec_res))
-        logger.info(res)
         elapse = time.time() - starttime
         logger.info("Predict time : {:.3f}s".format(elapse))
 

ppstructure/setup.py

@@ -0,0 +1,65 @@
+# 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 setuptools import setup
+from io import open
+import shutil
+
+with open('../requirements.txt', encoding="utf-8-sig") as f:
+    requirements = f.readlines()
+    requirements.append('tqdm')
+    requirements.append('layoutparser')
+
+
+def readme():
+    with open('README_ch.md', encoding="utf-8-sig") as f:
+        README = f.read()
+    return README
+
+shutil.copytree('../ppocr','./ppocr')
+shutil.copytree('../tools','./tools')
+shutil.copytree('../ppstructure','./ppstructure')
+
+setup(
+    name='paddlestructure',
+    packages=['paddlestructure'],
+    package_dir={'paddlestructure': ''},
+    include_package_data=True,
+    entry_points={"console_scripts": ["paddlestructure= paddlestructure.paddlestructure:main"]},
+    version='2.0.6',
+    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',
+    long_description=readme(),
+    long_description_content_type='text/markdown',
+    url='https://github.com/PaddlePaddle/PaddleOCR',
+    download_url='https://github.com/PaddlePaddle/PaddleOCR.git',
+    keywords=[
+        'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition'
+    ],
+    classifiers=[
+        'Intended Audience :: Developers', 'Operating System :: OS Independent',
+        'Natural Language :: Chinese (Simplified)',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.2',
+        'Programming Language :: Python :: 3.3',
+        'Programming Language :: Python :: 3.4',
+        'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7', 'Topic :: Utilities'
+    ], )
+
+shutil.rmtree('ppocr')
+shutil.rmtree('tools')
+shutil.rmtree('ppstructure')

ppstructure/table/README_ch.md

@@ -0,0 +1,15 @@
+# 表格结构和内容预测
+
+先cd到PaddleOCR/ppstructure目录下
+
+预测
+```python
+python3 table/predict_table.py --det_model_dir=../inference/db --rec_model_dir=../inference/rec_mv3_large1.0/infer --table_model_dir=../inference/explite3/infer --image_dir=../table/imgs/PMC3006023_004_00.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --table_output ../output/table
+```
+运行完成后，每张图片的excel表格会保存到table_output字段指定的目录下
+
+eval
+
+```python
+python3 table/eval_table.py --det_model_dir=../inference/db --rec_model_dir=../inference/rec_mv3_large1.0/infer --table_model_dir=../inference/explite3/infer --image_dir=../table/imgs --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --gt_path=path/to/gt.json
+```

ppstructure/table/eval_table.py

@@ -15,16 +15,21 @@ 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__, '..')))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
 
 import cv2
 import json
 from tqdm import tqdm
 from ppstructure.table.table_metric import TEDS
 from ppstructure.table.predict_table import TableSystem
-from ppstructure.predict_system import parse_args
+from ppstructure.utility import init_args
 
 
+def parse_args():
+    parser = init_args()
+    parser.add_argument("--gt_path", type=str)
+    return parser.parse_args()
+
 def main(gt_path, img_root, args):
     teds = TEDS(n_jobs=16)
 
@@ -33,6 +38,8 @@ def main(gt_path, img_root, args):
     pred_htmls = []
     gt_htmls = []
     for img_name in tqdm(jsons_gt):
+        if img_name != 'PMC1064865_002_00.png':
+            continue
         # 读取信息
         img = cv2.imread(os.path.join(img_root,img_name))
         pred_html = text_sys(img)
@@ -61,6 +68,4 @@ def get_gt_html(gt_structures, contents_with_block):
 
 if __name__ == '__main__':
     args = parse_args()
-    gt_path = 'table/match_code/f_gt_bbox.json'
+    main(args.gt_path,args.image_dir, args)
-    img_root = 'table/imgs'
-    main(gt_path,img_root, args)

ppstructure/table/matcher.py

@@ -194,21 +194,3 @@ def matcher_structure(gt_bboxes, pred_bboxes_rows, pred_bboxes):
             matched[index].append(i)
         pre_bbox = gt_box
     return matched
-
-
-def main():
-    detect_bboxes = json.load(open('./f_detecion_bbox.json'))
-    gt_bboxes = json.load(open('./f_gt_bbox.json'))
-    all_node = 0
-    matched_right = 0
-    key = 'PMC4796501_003_00.png'
-    print(key)
-    gt_bbox = gt_bboxes[key]
-    pred_bbox = detect_bboxes[key]
-    matched = matcher(gt_bbox, pred_bbox)
-    print(matched)
-    
-
-if __name__ == "__main__":
-    main()
-

ppstructure/table/predict_structure.py

@@ -40,7 +40,7 @@ class TableStructurer(object):
     def __init__(self, args):
         pre_process_list = [{
             'ResizeTableImage': {
-                'max_len': args.table_max_len
+                'max_len': args.structure_max_len
             }
         }, {
             'NormalizeImage': {
@@ -60,17 +60,17 @@ class TableStructurer(object):
         }]
         postprocess_params = {
             'name': 'TableLabelDecode',
-            "character_type": args.table_char_type,
+            "character_type": args.structure_char_type,
-            "character_dict_path": args.table_char_dict_path,
+            "character_dict_path": args.structure_char_dict_path,
-            "max_text_length": args.table_max_text_length,
+            "max_text_length": args.structure_max_text_length,
-            "max_elem_length": args.table_max_elem_length,
+            "max_elem_length": args.structure_max_elem_length,
-            "max_cell_num": args.table_max_cell_num
+            "max_cell_num": args.structure_max_cell_num
         }
 
         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, 'table', logger)
+            utility.create_predictor(args, 'structure', logger)
 
     def __call__(self, img):
         ori_im = img.copy()

ppstructure/table/predict_table.py

@@ -18,6 +18,7 @@ import subprocess
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
 sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
 
 os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
@@ -25,13 +26,13 @@ import cv2
 import copy
 import numpy as np
 import time
-import tools.infer.utility as utility
 import tools.infer.predict_rec as predict_rec
 import tools.infer.predict_det as predict_det
 import ppstructure.table.predict_structure as predict_strture
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
-from ppstructure.table.matcher import distance, compute_iou
+from matcher import distance, compute_iou
+from ppstructure.utility import parse_args
 
 logger = get_logger()
 
@@ -52,12 +53,10 @@ def expand(pix, det_box, shape):
 
 
 class TableSystem(object):
-    def __init__(self, args):
+    def __init__(self, args, text_detector=None, text_recognizer=None):
-        self.text_detector = predict_det.TextDetector(args)
+        self.text_detector = predict_det.TextDetector(args) if text_detector is None else text_detector
-        self.text_recognizer = predict_rec.TextRecognizer(args)
+        self.text_recognizer = predict_rec.TextRecognizer(args) if text_recognizer is None else text_recognizer
         self.table_structurer = predict_strture.TableStructurer(args)
-        self.use_angle_cls = args.use_angle_cls
-        self.drop_score = args.drop_score
 
     def __call__(self, img):
         ori_im = img.copy()
@@ -75,8 +74,8 @@ class TableSystem(object):
             r_boxes.append(box)
         dt_boxes = np.array(r_boxes)
 
-        # logger.info("dt_boxes num : {}, elapse : {}".format(
+        logger.debug("dt_boxes num : {}, elapse : {}".format(
-        #     len(dt_boxes), elapse))
+            len(dt_boxes), elapse))
         if dt_boxes is None:
             return None, None
         img_crop_list = []
@@ -87,8 +86,8 @@ class TableSystem(object):
             text_rect = ori_im[int(y0):int(y1), int(x0):int(x1), :]
             img_crop_list.append(text_rect)
         rec_res, elapse = self.text_recognizer(img_crop_list)
-        # logger.info("rec_res num  : {}, elapse : {}".format(
+        logger.debug("rec_res num  : {}, elapse : {}".format(
-        #     len(rec_res), elapse))
+            len(rec_res), elapse))
 
         pred_html, pred = self.rebuild_table(structure_res, dt_boxes, rec_res)
         return pred_html
@@ -172,6 +171,7 @@ def sorted_boxes(dt_boxes):
             _boxes[i + 1] = tmp
     return _boxes
 
+
 def to_excel(html_table, excel_path):
     from tablepyxl import tablepyxl
     tablepyxl.document_to_xl(html_table, excel_path)
@@ -180,19 +180,18 @@ def to_excel(html_table, excel_path):
 def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     image_file_list = image_file_list[args.process_id::args.total_process_num]
-    excel_save_folder = 'output/table'
+    os.makedirs(args.output, exist_ok=True)
-    os.makedirs(excel_save_folder, exist_ok=True)
 
     text_sys = TableSystem(args)
     img_num = len(image_file_list)
     for i, image_file in enumerate(image_file_list):
         logger.info("[{}/{}] {}".format(i, img_num, image_file))
         img, flag = check_and_read_gif(image_file)
-        excel_path = os.path.join(excel_save_folder, os.path.basename(image_file).split('.')[0] + '.xlsx')
+        excel_path = os.path.join(args.table_output, os.path.basename(image_file).split('.')[0] + '.xlsx')
         if not flag:
             img = cv2.imread(image_file)
         if img is None:
-            logger.info("error in loading image:{}".format(image_file))
+            logger.error("error in loading image:{}".format(image_file))
             continue
         starttime = time.time()
         pred_html = text_sys(img)
@@ -205,7 +204,7 @@ def main(args):
 
 
 if __name__ == "__main__":
-    args = utility.parse_args()
+    args = parse_args()
     if args.use_mp:
         p_list = []
         total_process_num = args.total_process_num

ppstructure/utility.py

@@ -0,0 +1,40 @@
+# copyright (c) 2020 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 logging
+
+from tools.infer.utility import str2bool, init_args as infer_args
+
+
+def init_args():
+    parser = infer_args()
+
+    # params for output
+    parser.add_argument("--output", type=str, default='./output/table')
+    # params for table structure
+    parser.add_argument("--structure_max_len", type=int, default=488)
+    parser.add_argument("--structure_max_text_length", type=int, default=100)
+    parser.add_argument("--structure_max_elem_length", type=int, default=800)
+    parser.add_argument("--structure_max_cell_num", type=int, default=500)
+    parser.add_argument("--structure_model_dir", type=str)
+    parser.add_argument("--structure_char_type", type=str, default='en')
+    parser.add_argument("--structure_char_dict_path", type=str, default="../ppocr/utils/dict/table_structure_dict.txt")
+
+    # params for layout detector
+    parser.add_argument("--layout_model_dir", type=str)
+    return parser
+
+
+def parse_args():
+    parser = init_args()
+    return parser.parse_args()

tools/infer/predict_system.py

@@ -88,7 +88,7 @@ class TextSystem(object):
     def __call__(self, img, cls=True):
         ori_im = img.copy()
         dt_boxes, elapse = self.text_detector(img)
-        logger.info("dt_boxes num : {}, elapse : {}".format(
+        logger.debug("dt_boxes num : {}, elapse : {}".format(
             len(dt_boxes), elapse))
         if dt_boxes is None:
             return None, None
@@ -103,11 +103,11 @@ class TextSystem(object):
         if self.use_angle_cls and cls:
             img_crop_list, angle_list, elapse = self.text_classifier(
                 img_crop_list)
-            logger.info("cls num  : {}, elapse : {}".format(
+            logger.debug("cls num  : {}, elapse : {}".format(
                 len(img_crop_list), elapse))
 
         rec_res, elapse = self.text_recognizer(img_crop_list)
-        logger.info("rec_res num  : {}, elapse : {}".format(
+        logger.debug("rec_res num  : {}, elapse : {}".format(
             len(rec_res), elapse))
         # self.print_draw_crop_rec_res(img_crop_list, rec_res)
         filter_boxes, filter_rec_res = [], []
@@ -152,7 +152,7 @@ def main(args):
         if not flag:
             img = cv2.imread(image_file)
         if img is None:
-            logger.info("error in loading image:{}".format(image_file))
+            logger.error("error in loading image:{}".format(image_file))
             continue
         starttime = time.time()
         dt_boxes, rec_res = text_sys(img)

tools/infer/utility.py

@@ -109,7 +109,7 @@ def init_args():
     parser.add_argument("--use_mp", type=str2bool, default=False)
     parser.add_argument("--total_process_num", type=int, default=1)
     parser.add_argument("--process_id", type=int, default=0)
-
+    parser.add_argument("--show_log", type=str2bool, default=True)
     return parser
 
 
@@ -125,8 +125,8 @@ def create_predictor(args, mode, logger):
         model_dir = args.cls_model_dir
     elif mode == 'rec':
         model_dir = args.rec_model_dir
-    elif mode == 'table':
+    elif mode == 'structure':
-        model_dir = args.table_model_dir
+        model_dir = args.structure_model_dir
     else:
         model_dir = args.e2e_model_dir
 
@@ -246,7 +246,8 @@ def create_predictor(args, mode, logger):
 
     config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
     config.switch_use_feed_fetch_ops(False)
-    if mode == 'table':
+    config.switch_ir_optim(True)
+    if mode == 'structure':
         config.switch_ir_optim(False)
     # create predictor
     predictor = inference.create_predictor(config)