Merge pull request #924 from WenmuZhou/dygraph

Dygraph

paddleocr.py

+# 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 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
+import tarfile
+import requests
+from tqdm import tqdm
+
+from tools.infer import predict_system
+from ppocr.utils.utility import initial_logger
+
+logger = initial_logger()
+from ppocr.utils.utility import check_and_read_gif, get_image_file_list
+
+__all__ = ['PaddleOCR']
+
+model_params = {
+    'det': 'https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar',
+    'rec':
+    'https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_enhance_infer.tar',
+}
+
+SUPPORT_DET_MODEL = ['DB']
+SUPPORT_REC_MODEL = ['CRNN']
+BASE_DIR = os.path.expanduser("~/.paddleocr/")
+
+
+def download_with_progressbar(url, save_path):
+    response = requests.get(url, stream=True)
+    total_size_in_bytes = int(response.headers.get('content-length', 0))
+    block_size = 1024  # 1 Kibibyte
+    progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
+    with open(save_path, 'wb') as file:
+        for data in response.iter_content(block_size):
+            progress_bar.update(len(data))
+            file.write(data)
+    progress_bar.close()
+    if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
+        logger.error("ERROR, something went wrong")
+        sys.exit(0)
+
+
+def maybe_download(model_storage_directory, url):
+    # using custom model
+    if not os.path.exists(os.path.join(
+            model_storage_directory, 'model')) or not os.path.exists(
+                os.path.join(model_storage_directory, 'params')):
+        tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
+        print('download {} to {}'.format(url, tmp_path))
+        os.makedirs(model_storage_directory, exist_ok=True)
+        download_with_progressbar(url, tmp_path)
+        with tarfile.open(tmp_path, 'r') as tarObj:
+            for member in tarObj.getmembers():
+                if "model" in member.name:
+                    filename = 'model'
+                elif "params" in member.name:
+                    filename = 'params'
+                else:
+                    continue
+                file = tarObj.extractfile(member)
+                with open(
+                        os.path.join(model_storage_directory, filename),
+                        'wb') as f:
+                    f.write(file.read())
+        os.remove(tmp_path)
+
+
+def parse_args():
+    import argparse
+
+    def str2bool(v):
+        return v.lower() in ("true", "t", "1")
+
+    parser = argparse.ArgumentParser()
+    # params for prediction engine
+    parser.add_argument("--use_gpu", type=str2bool, default=True)
+    parser.add_argument("--ir_optim", type=str2bool, default=True)
+    parser.add_argument("--use_tensorrt", type=str2bool, default=False)
+    parser.add_argument("--gpu_mem", type=int, default=8000)
+
+    # params for text detector
+    parser.add_argument("--image_dir", type=str)
+    parser.add_argument("--det_algorithm", type=str, default='DB')
+    parser.add_argument("--det_model_dir", type=str, default=None)
+    parser.add_argument("--det_max_side_len", type=float, default=960)
+
+    # DB parmas
+    parser.add_argument("--det_db_thresh", type=float, default=0.3)
+    parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
+    parser.add_argument("--det_db_unclip_ratio", type=float, default=2.0)
+
+    # EAST parmas
+    parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
+    parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
+    parser.add_argument("--det_east_nms_thresh", type=float, default=0.2)
+
+    # params for text recognizer
+    parser.add_argument("--rec_algorithm", type=str, default='CRNN')
+    parser.add_argument("--rec_model_dir", type=str, default=None)
+    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
+    parser.add_argument("--rec_char_type", type=str, default='ch')
+    parser.add_argument("--rec_batch_num", type=int, default=30)
+    parser.add_argument("--max_text_length", type=int, default=25)
+    parser.add_argument(
+        "--rec_char_dict_path",
+        type=str,
+        default="./ppocr/utils/ppocr_keys_v1.txt")
+    parser.add_argument("--use_space_char", type=bool, default=True)
+    parser.add_argument("--enable_mkldnn", type=bool, default=False)
+
+    parser.add_argument("--det", type=str2bool, default=True)
+    parser.add_argument("--rec", type=str2bool, default=True)
+    parser.add_argument("--use_zero_copy_run", type=bool, default=False)
+    return parser.parse_args()
+
+
+class PaddleOCR(predict_system.TextSystem):
+    def __init__(self, **kwargs):
+        """
+        paddleocr package
+        args:
+            **kwargs: other params show in paddleocr --help
+        """
+        postprocess_params = parse_args()
+        postprocess_params.__dict__.update(**kwargs)
+
+        # init model dir
+        if postprocess_params.det_model_dir is None:
+            postprocess_params.det_model_dir = os.path.join(BASE_DIR, 'det')
+        if postprocess_params.rec_model_dir is None:
+            postprocess_params.rec_model_dir = os.path.join(BASE_DIR, 'rec')
+        print(postprocess_params)
+        # download model
+        maybe_download(postprocess_params.det_model_dir, model_params['det'])
+        maybe_download(postprocess_params.rec_model_dir, model_params['rec'])
+
+        if postprocess_params.det_algorithm not in SUPPORT_DET_MODEL:
+            logger.error('det_algorithm must in {}'.format(SUPPORT_DET_MODEL))
+            sys.exit(0)
+        if postprocess_params.rec_algorithm not in SUPPORT_REC_MODEL:
+            logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
+            sys.exit(0)
+
+        postprocess_params.rec_char_dict_path = Path(
+            __file__).parent / postprocess_params.rec_char_dict_path
+
+        # init det_model and rec_model
+        super().__init__(postprocess_params)
+
+    def ocr(self, img, det=True, rec=True):
+        """
+        ocr with paddleocr
+        args：
+            img: img for ocr, support ndarray, img_path and list or ndarray
+            det: use text detection or not, if false, only rec will be exec. default is True
+            rec: use text recognition or not, if false, only det will be exec. default is True
+        """
+        assert isinstance(img, (np.ndarray, list, str))
+        if isinstance(img, str):
+            image_file = img
+            img, flag = check_and_read_gif(image_file)
+            if not flag:
+                img = cv2.imread(image_file)
+            if img is None:
+                logger.error("error in loading image:{}".format(image_file))
+                return None
+        if det and rec:
+            dt_boxes, rec_res = self.__call__(img)
+            return [[box.tolist(), res] for box, res in zip(dt_boxes, rec_res)]
+        elif det and not rec:
+            dt_boxes, elapse = self.text_detector(img)
+            if dt_boxes is None:
+                return None
+            return [box.tolist() for box in dt_boxes]
+        else:
+            if not isinstance(img, list):
+                img = [img]
+            rec_res, elapse = self.text_recognizer(img)
+            return rec_res
+
+
+def main():
+    # for com
+    args = parse_args()
+    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
+    ocr_engine = PaddleOCR()
+    for img_path in image_file_list:
+        print(img_path)
+        result = ocr_engine.ocr(img_path, det=args.det, rec=args.rec)
+        for line in result:
+            print(line)
\ No newline at end of file

ppocr/__init__.py

+# Copyright (c) 2019 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.

ppocr/data/__init__.py

+# 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import os
+import sys
+import numpy as np
+import paddle
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+
+import copy
+from paddle.io import DataLoader, DistributedBatchSampler, BatchSampler
+import paddle.distributed as dist
+
+from ppocr.data.imaug import transform, create_operators
+
+__all__ = ['build_dataloader', 'transform', 'create_operators']
+
+
+def build_dataset(config, global_config):
+    from ppocr.data.dataset import SimpleDataSet, LMDBDateSet
+    support_dict = ['SimpleDataSet', 'LMDBDateSet']
+
+    module_name = config.pop('name')
+    assert module_name in support_dict, Exception(
+        'DataSet only support {}'.format(support_dict))
+
+    dataset = eval(module_name)(config, global_config)
+    return dataset
+
+
+def build_dataloader(config, device, distributed=False, global_config=None):
+    from ppocr.data.dataset import BatchBalancedDataLoader
+
+    config = copy.deepcopy(config)
+    dataset_config = config['dataset']
+
+    _dataset_list = []
+    file_list = dataset_config.pop('file_list')
+    if len(file_list) == 1:
+        ratio_list = [1.0]
+    else:
+        ratio_list = dataset_config.pop('ratio_list')
+    for file in file_list:
+        dataset_config['file_list'] = file
+        _dataset = build_dataset(dataset_config, global_config)
+        _dataset_list.append(_dataset)
+    data_loader = BatchBalancedDataLoader(_dataset_list, ratio_list,
+                                          distributed, device, config['loader'])
+    return data_loader, _dataset.info_dict
+
+
+def test_loader():
+    import time
+    from tools.program import load_config, ArgsParser
+
+    FLAGS = ArgsParser().parse_args()
+    config = load_config(FLAGS.config)
+
+    place = paddle.CPUPlace()
+    paddle.disable_static(place)
+    import time
+
+    data_loader, _ = build_dataloader(
+        config['TRAIN'], place, global_config=config['Global'])
+    start = time.time()
+    print(len(data_loader))
+    for epoch in range(1):
+        print('epoch {} ****************'.format(epoch))
+        for i, batch in enumerate(data_loader):
+            if i > len(data_loader):
+                break
+            t = time.time() - start
+            start = time.time()
+            print('{}, batch : {} ,time {}'.format(i, len(batch[0]), t))
+
+            continue
+            import matplotlib.pyplot as plt
+
+            from matplotlib import pyplot as plt
+            import cv2
+            fig = plt.figure()
+            # # cv2.imwrite('img.jpg',batch[0].numpy()[0].transpose((1,2,0)))
+            # # cv2.imwrite('bmap.jpg',batch[1].numpy()[0])
+            # # cv2.imwrite('bmask.jpg',batch[2].numpy()[0])
+            # # cv2.imwrite('smap.jpg',batch[3].numpy()[0])
+            # # cv2.imwrite('smask.jpg',batch[4].numpy()[0])
+            plt.title('img')
+            plt.imshow(batch[0].numpy()[0].transpose((1, 2, 0)))
+            # plt.figure()
+            # plt.title('bmap')
+            # plt.imshow(batch[1].numpy()[0],cmap='Greys')
+            # plt.figure()
+            # plt.title('bmask')
+            # plt.imshow(batch[2].numpy()[0],cmap='Greys')
+            # plt.figure()
+            # plt.title('smap')
+            # plt.imshow(batch[3].numpy()[0],cmap='Greys')
+            # plt.figure()
+            # plt.title('smask')
+            # plt.imshow(batch[4].numpy()[0],cmap='Greys')
+            # plt.show()
+            # break
+
+
+if __name__ == '__main__':
+    test_loader()

ppocr/data/dataset.py

+# 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 copy
+import numpy as np
+import os
+import lmdb
+import random
+import signal
+import paddle
+from paddle.io import Dataset, DataLoader, DistributedBatchSampler, BatchSampler
+
+from .imaug import transform, create_operators
+from ppocr.utils.logging import get_logger
+
+
+def term_mp(sig_num, frame):
+    """ kill all child processes
+    """
+    pid = os.getpid()
+    pgid = os.getpgid(os.getpid())
+    print("main proc {} exit, kill process group " "{}".format(pid, pgid))
+    os.killpg(pgid, signal.SIGKILL)
+
+
+signal.signal(signal.SIGINT, term_mp)
+signal.signal(signal.SIGTERM, term_mp)
+
+
+class ModeException(Exception):
+    """
+    ModeException
+    """
+
+    def __init__(self, message='', mode=''):
+        message += "\nOnly the following 3 modes are supported: " \
+                   "train, valid, test. Given mode is {}".format(mode)
+        super(ModeException, self).__init__(message)
+
+
+class SampleNumException(Exception):
+    """
+    SampleNumException
+    """
+
+    def __init__(self, message='', sample_num=0, batch_size=1):
+        message += "\nError: The number of the whole data ({}) " \
+                   "is smaller than the batch_size ({}), and drop_last " \
+                   "is turnning on, so nothing  will feed in program, " \
+                   "Terminated now. Please reset batch_size to a smaller " \
+                   "number or feed more data!".format(sample_num, batch_size)
+        super(SampleNumException, self).__init__(message)
+
+
+def get_file_list(file_list, data_dir, delimiter='\t'):
+    """
+    read label list from file and shuffle the list
+
+    Args:
+        params(dict):
+    """
+    if isinstance(file_list, str):
+        file_list = [file_list]
+    data_source_list = []
+    for file in file_list:
+        with open(file) as f:
+            full_lines = [line.strip() for line in f]
+            for line in full_lines:
+                try:
+                    img_path, label = line.split(delimiter)
+                except:
+                    logger = get_logger()
+                    logger.warning('label error in {}'.format(line))
+                img_path = os.path.join(data_dir, img_path)
+                data = {'img_path': img_path, 'label': label}
+                data_source_list.append(data)
+    return data_source_list
+
+
+class LMDBDateSet(Dataset):
+    def __init__(self, config, global_config):
+        super(LMDBDateSet, self).__init__()
+        self.data_list = self.load_lmdb_dataset(
+            config['file_list'], global_config['max_text_length'])
+        random.shuffle(self.data_list)
+
+        self.ops = create_operators(config['transforms'], global_config)
+
+        # for rec
+        character = ''
+        for op in self.ops:
+            if hasattr(op, 'character'):
+                character = getattr(op, 'character')
+
+        self.info_dict = {'character': character}
+
+    def load_lmdb_dataset(self, data_dir, max_text_length):
+        self.env = lmdb.open(
+            data_dir,
+            max_readers=32,
+            readonly=True,
+            lock=False,
+            readahead=False,
+            meminit=False)
+        if not self.env:
+            print('cannot create lmdb from %s' % (data_dir))
+            exit(0)
+
+        filtered_index_list = []
+        with self.env.begin(write=False) as txn:
+            nSamples = int(txn.get('num-samples'.encode()))
+            self.nSamples = nSamples
+            for index in range(self.nSamples):
+                index += 1  # lmdb starts with 1
+                label_key = 'label-%09d'.encode() % index
+                label = txn.get(label_key).decode('utf-8')
+                if len(label) > max_text_length:
+                    # print(f'The length of the label is longer than max_length: length
+                    # {len(label)}, {label} in dataset {self.root}')
+                    continue
+
+                # By default, images containing characters which are not in opt.character are filtered.
+                # You can add [UNK] token to `opt.character` in utils.py instead of this filtering.
+                filtered_index_list.append(index)
+        return filtered_index_list
+
+    def print_lmdb_sets_info(self, lmdb_sets):
+        lmdb_info_strs = []
+        for dataset_idx in range(len(lmdb_sets)):
+            tmp_str = " %s:%d," % (lmdb_sets[dataset_idx]['dirpath'],
+                                   lmdb_sets[dataset_idx]['num_samples'])
+            lmdb_info_strs.append(tmp_str)
+        lmdb_info_strs = ''.join(lmdb_info_strs)
+        logger = get_logger()
+        logger.info("DataSummary:" + lmdb_info_strs)
+        return
+
+    def __getitem__(self, idx):
+        idx = self.data_list[idx]
+        with self.env.begin(write=False) as txn:
+            label_key = 'label-%09d'.encode() % idx
+            label = txn.get(label_key)
+            if label is not None:
+                label = label.decode('utf-8')
+                img_key = 'image-%09d'.encode() % idx
+                imgbuf = txn.get(img_key)
+                data = {'image': imgbuf, 'label': label}
+                outs = transform(data, self.ops)
+            else:
+                outs = None
+            if outs is None:
+                return self.__getitem__(np.random.randint(self.__len__()))
+            return outs
+
+    def __len__(self):
+        return len(self.data_list)
+
+
+class SimpleDataSet(Dataset):
+    def __init__(self, config, global_config):
+        super(SimpleDataSet, self).__init__()
+        delimiter = config.get('delimiter', '\t')
+        self.data_list = get_file_list(config['file_list'], config['data_dir'],
+                                       delimiter)
+        random.shuffle(self.data_list)
+
+        self.ops = create_operators(config['transforms'], global_config)
+
+        # for rec
+        character = ''
+        for op in self.ops:
+            if hasattr(op, 'character'):
+                character = getattr(op, 'character')
+
+        self.info_dict = {'character': character}
+
+    def __getitem__(self, idx):
+        data = copy.deepcopy(self.data_list[idx])
+        with open(data['img_path'], 'rb') as f:
+            img = f.read()
+            data['image'] = img
+        outs = transform(data, self.ops)
+        if outs is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        return outs
+
+    def __len__(self):
+        return len(self.data_list)
+
+
+class BatchBalancedDataLoader(object):
+    def __init__(self,
+                 dataset_list: list,
+                 ratio_list: list,
+                 distributed,
+                 device,
+                 loader_args: dict):
+        """
+        对datasetlist里的dataset按照ratio_list里对应的比例组合，似的每个batch里的数据按按照比例采样的
+        :param dataset_list: 数据集列表
+        :param ratio_list: 比例列表
+        :param loader_args: dataloader的配置
+        """
+        assert sum(ratio_list) == 1 and len(dataset_list) == len(ratio_list)
+
+        self.dataset_len = 0
+        self.data_loader_list = []
+        self.dataloader_iter_list = []
+        all_batch_size = loader_args.pop('batch_size')
+        batch_size_list = list(
+            map(int, [max(1.0, all_batch_size * x) for x in ratio_list]))
+        remain_num = all_batch_size - sum(batch_size_list)
+        batch_size_list[np.argmax(ratio_list)] += remain_num
+
+        for _dataset, _batch_size in zip(dataset_list, batch_size_list):
+            if distributed:
+                batch_sampler_class = DistributedBatchSampler
+            else:
+                batch_sampler_class = BatchSampler
+            batch_sampler = batch_sampler_class(
+                dataset=_dataset,
+                batch_size=_batch_size,
+                shuffle=loader_args['shuffle'],
+                drop_last=loader_args['drop_last'], )
+            _data_loader = DataLoader(
+                dataset=_dataset,
+                batch_sampler=batch_sampler,
+                places=device,
+                num_workers=loader_args['num_workers'],
+                return_list=True, )
+            self.data_loader_list.append(_data_loader)
+            self.dataloader_iter_list.append(iter(_data_loader))
+            self.dataset_len += len(_dataset)
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return min([len(x) for x in self.data_loader_list])
+
+    def __next__(self):
+        batch = []
+        for i, data_loader_iter in enumerate(self.dataloader_iter_list):
+            try:
+                _batch_i = next(data_loader_iter)
+                batch.append(_batch_i)
+            except StopIteration:
+                self.dataloader_iter_list[i] = iter(self.data_loader_list[i])
+                _batch_i = next(self.dataloader_iter_list[i])
+                batch.append(_batch_i)
+            except ValueError:
+                pass
+        if len(batch) > 0:
+            batch_list = []
+            batch_item_size = len(batch[0])
+            for i in range(batch_item_size):
+                cur_item_list = [batch_i[i] for batch_i in batch]
+                batch_list.append(paddle.concat(cur_item_list, axis=0))
+        else:
+            batch_list = batch[0]
+        return batch_list
+
+
+def fill_batch(batch):
+    """
+    2020.09.08： The current paddle version only supports returning data with the same length.
+                Therefore, fill in the batches with inconsistent lengths.
+                this method is currently only useful for text detection
+    """
+    keys = list(range(len(batch[0])))
+    v_max_len_dict = {}
+    for k in keys:
+        v_max_len_dict[k] = max([len(item[k]) for item in batch])
+    for item in batch:
+        length = []
+        for k in keys:
+            v = item[k]
+            length.append(len(v))
+            assert isinstance(v, np.ndarray)
+            if len(v) == v_max_len_dict[k]:
+                continue
+            try:
+                tmp_shape = [v_max_len_dict[k] - len(v)] + list(v[0].shape)
+            except:
+                a = 1
+            tmp_array = np.zeros(tmp_shape, dtype=v[0].dtype)
+            new_array = np.concatenate([v, tmp_array])
+            item[k] = new_array
+        item.append(length)
+    return batch

ppocr/data/imaug/__init__.py

+# 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.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+from .iaa_augment import IaaAugment
+from .make_border_map import MakeBorderMap
+from .make_shrink_map import MakeShrinkMap
+from .random_crop_data import EastRandomCropData, PSERandomCrop
+
+from .rec_img_aug import RecAug, RecResizeImg
+
+from .operators import *
+from .label_ops import *
+
+
+def transform(data, ops=None):
+    """ transform """
+    if ops is None:
+        ops = []
+    for op in ops:
+        data = op(data)
+        if data is None:
+            return None
+    return data
+
+
+def create_operators(op_param_list, global_config=None):
+    """
+    create operators based on the config
+
+    Args:
+        params(list): a dict list, used to create some operators
+    """
+    assert isinstance(op_param_list, list), ('operator config should be a list')
+    ops = []
+    for operator in op_param_list:
+        assert isinstance(operator,
+                          dict) and len(operator) == 1, "yaml format error"
+        op_name = list(operator)[0]
+        param = {} if operator[op_name] is None else operator[op_name]
+        if global_config is not None:
+            param.update(global_config)
+        op = eval(op_name)(**param)
+        ops.append(op)
+    return ops

ppocr/data/imaug/iaa_augment.py

+# 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.
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np
+import imgaug
+import imgaug.augmenters as iaa
+
+
+class AugmenterBuilder(object):
+    def __init__(self):
+        pass
+
+    def build(self, args, root=True):
+        if args is None or len(args) == 0:
+            return None
+        elif isinstance(args, list):
+            if root:
+                sequence = [self.build(value, root=False) for value in args]
+                return iaa.Sequential(sequence)
+            else:
+                return getattr(iaa, args[0])(
+                    *[self.to_tuple_if_list(a) for a in args[1:]])
+        elif isinstance(args, dict):
+            cls = getattr(iaa, args['type'])
+            return cls(**{
+                k: self.to_tuple_if_list(v)
+                for k, v in args['args'].items()
+            })
+        else:
+            raise RuntimeError('unknown augmenter arg: ' + str(args))
+
+    def to_tuple_if_list(self, obj):
+        if isinstance(obj, list):
+            return tuple(obj)
+        return obj
+
+
+class IaaAugment():
+    def __init__(self, augmenter_args=None, **kwargs):
+        if augmenter_args is None:
+            augmenter_args = [{
+                'type': 'Fliplr',
+                'args': {
+                    'p': 0.5
+                }
+            }, {
+                'type': 'Affine',
+                'args': {
+                    'rotate': [-10, 10]
+                }
+            }, {
+                'type': 'Resize',
+                'args': {
+                    'size': [0.5, 3]
+                }
+            }]
+        self.augmenter = AugmenterBuilder().build(augmenter_args)
+
+    def __call__(self, data):
+        image = data['image']
+        shape = image.shape
+
+        if self.augmenter:
+            aug = self.augmenter.to_deterministic()
+            data['image'] = aug.augment_image(image)
+            data = self.may_augment_annotation(aug, data, shape)
+        return data
+
+    def may_augment_annotation(self, aug, data, shape):
+        if aug is None:
+            return data
+
+        line_polys = []
+        for poly in data['polys']:
+            new_poly = self.may_augment_poly(aug, shape, poly)
+            line_polys.append(new_poly)
+        data['polys'] = np.array(line_polys)
+        return data
+
+    def may_augment_poly(self, aug, img_shape, poly):
+        keypoints = [imgaug.Keypoint(p[0], p[1]) for p in poly]
+        keypoints = aug.augment_keypoints(
+            [imgaug.KeypointsOnImage(
+                keypoints, shape=img_shape)])[0].keypoints
+        poly = [(p.x, p.y) for p in keypoints]
+        return poly

ppocr/data/imaug/label_ops.py

+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np
+from ppocr.utils.logging import get_logger
+
+
+class DetLabelEncode(object):
+    def __init__(self, **kwargs):
+        pass
+
+    def __call__(self, data):
+        import json
+        label = data['label']
+        label = json.loads(label)
+        nBox = len(label)
+        boxes, txts, txt_tags = [], [], []
+        for bno in range(0, nBox):
+            box = label[bno]['points']
+            txt = label[bno]['transcription']
+            boxes.append(box)
+            txts.append(txt)
+            if txt in ['*', '###']:
+                txt_tags.append(True)
+            else:
+                txt_tags.append(False)
+        boxes = np.array(boxes, dtype=np.float32)
+        txt_tags = np.array(txt_tags, dtype=np.bool)
+
+        data['polys'] = boxes
+        data['texts'] = txts
+        data['ignore_tags'] = txt_tags
+        return data
+
+    def order_points_clockwise(self, pts):
+        rect = np.zeros((4, 2), dtype="float32")
+        s = pts.sum(axis=1)
+        rect[0] = pts[np.argmin(s)]
+        rect[2] = pts[np.argmax(s)]
+        diff = np.diff(pts, axis=1)
+        rect[1] = pts[np.argmin(diff)]
+        rect[3] = pts[np.argmax(diff)]
+        return rect
+
+
+class BaseRecLabelEncode(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 character_type='ch',
+                 use_space_char=False):
+        support_character_type = ['ch', 'en', 'en_sensitive']
+        assert character_type in support_character_type, "Only {} are supported now but get {}".format(
+            support_character_type, self.character_str)
+
+        self.max_text_len = max_text_length
+        if character_type == "en":
+            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+            dict_character = list(self.character_str)
+        elif character_type == "ch":
+            self.character_str = ""
+            assert character_dict_path is not None, "character_dict_path should not be None when character_type is ch"
+            with open(character_dict_path, "rb") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    line = line.decode('utf-8').strip("\n").strip("\r\n")
+                    self.character_str += line
+            if use_space_char:
+                self.character_str += " "
+            dict_character = list(self.character_str)
+        elif character_type == "en_sensitive":
+            # same with ASTER setting (use 94 char).
+            import string
+            self.character_str = string.printable[:-6]
+            dict_character = list(self.character_str)
+        self.character_type = character_type
+        dict_character = self.add_special_char(dict_character)
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+        self.character = dict_character
+
+    def add_special_char(self, dict_character):
+        return dict_character
+
+    def encode(self, text):
+        """convert text-label into text-index.
+        input:
+            text: text labels of each image. [batch_size]
+
+        output:
+            text: concatenated text index for CTCLoss.
+                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
+            length: length of each text. [batch_size]
+        """
+        if len(text) > self.max_text_len:
+            return None
+        if self.character_type == "en":
+            text = text.lower()
+        text_list = []
+        for char in text:
+            if char not in self.dict:
+                # logger = get_logger()
+                # logger.warning('{} is not in dict'.format(char))
+                continue
+            text_list.append(self.dict[char])
+        if len(text_list) == 0:
+            return None
+        return text_list
+
+    def get_ignored_tokens(self):
+        return [0]  # for ctc blank
+
+
+class CTCLabelEncode(BaseRecLabelEncode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 character_type='ch',
+                 use_space_char=False,
+                 **kwargs):
+        super(CTCLabelEncode,
+              self).__init__(max_text_length, character_dict_path,
+                             character_type, use_space_char)
+
+    def __call__(self, data):
+        text = data['label']
+        text = self.encode(text)
+        if text is None:
+            return None
+        data['length'] = np.array(len(text))
+        text = text + [0] * (self.max_text_len - len(text))
+        data['label'] = np.array(text)
+        return data
+
+    def add_special_char(self, dict_character):
+        dict_character = ['blank'] + dict_character
+        return dict_character
+
+
+class AttnLabelEncode(BaseRecLabelEncode):
+    """ Convert between text-label and text-index """
+
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 character_type='ch',
+                 use_space_char=False,
+                 **kwargs):
+        super(AttnLabelEncode,
+              self).__init__(max_text_length, character_dict_path,
+                             character_type, use_space_char)
+        self.beg_str = "sos"
+        self.end_str = "eos"
+
+    def add_special_char(self, dict_character):
+        dict_character = [self.beg_str, self.end_str] + dict_character
+        return dict_character
+
+    def __call__(self, text):
+        text = self.encode(text)
+        return text
+
+    def get_ignored_tokens(self):
+        beg_idx = self.get_beg_end_flag_idx("beg")
+        end_idx = self.get_beg_end_flag_idx("end")
+        return [beg_idx, end_idx]
+
+    def get_beg_end_flag_idx(self, beg_or_end):
+        if beg_or_end == "beg":
+            idx = np.array(self.dict[self.beg_str])
+        elif beg_or_end == "end":
+            idx = np.array(self.dict[self.end_str])
+        else:
+            assert False, "Unsupport type %s in get_beg_end_flag_idx" \
+                          % beg_or_end
+        return idx

ppocr/data/imaug/make_border_map.py

+# -*- coding:utf-8 -*- 
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np
+import cv2
+
+np.seterr(divide='ignore', invalid='ignore')
+import pyclipper
+from shapely.geometry import Polygon
+import sys
+import warnings
+
+warnings.simplefilter("ignore")
+
+__all__ = ['MakeBorderMap']
+
+
+class MakeBorderMap(object):
+    def __init__(self,
+                 shrink_ratio=0.4,
+                 thresh_min=0.3,
+                 thresh_max=0.7,
+                 **kwargs):
+        self.shrink_ratio = shrink_ratio
+        self.thresh_min = thresh_min
+        self.thresh_max = thresh_max
+
+    def __call__(self, data: dict) -> dict:
+
+        img = data['image']
+        text_polys = data['polys']
+        ignore_tags = data['ignore_tags']
+
+        canvas = np.zeros(img.shape[:2], dtype=np.float32)
+        mask = np.zeros(img.shape[:2], dtype=np.float32)
+
+        for i in range(len(text_polys)):
+            if ignore_tags[i]:
+                continue
+            self.draw_border_map(text_polys[i], canvas, mask=mask)
+        canvas = canvas * (self.thresh_max - self.thresh_min) + self.thresh_min
+
+        data['threshold_map'] = canvas
+        data['threshold_mask'] = mask
+        return data
+
+    def draw_border_map(self, polygon, canvas, mask):
+        polygon = np.array(polygon)
+        assert polygon.ndim == 2
+        assert polygon.shape[1] == 2
+
+        polygon_shape = Polygon(polygon)
+        if polygon_shape.area <= 0:
+            return
+        distance = polygon_shape.area * (
+            1 - np.power(self.shrink_ratio, 2)) / polygon_shape.length
+        subject = [tuple(l) for l in polygon]
+        padding = pyclipper.PyclipperOffset()
+        padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+
+        padded_polygon = np.array(padding.Execute(distance)[0])
+        cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0)
+
+        xmin = padded_polygon[:, 0].min()
+        xmax = padded_polygon[:, 0].max()
+        ymin = padded_polygon[:, 1].min()
+        ymax = padded_polygon[:, 1].max()
+        width = xmax - xmin + 1
+        height = ymax - ymin + 1
+
+        polygon[:, 0] = polygon[:, 0] - xmin
+        polygon[:, 1] = polygon[:, 1] - ymin
+
+        xs = np.broadcast_to(
+            np.linspace(
+                0, width - 1, num=width).reshape(1, width), (height, width))
+        ys = np.broadcast_to(
+            np.linspace(
+                0, height - 1, num=height).reshape(height, 1), (height, width))
+
+        distance_map = np.zeros(
+            (polygon.shape[0], height, width), dtype=np.float32)
+        for i in range(polygon.shape[0]):
+            j = (i + 1) % polygon.shape[0]
+            absolute_distance = self._distance(xs, ys, polygon[i], polygon[j])
+            distance_map[i] = np.clip(absolute_distance / distance, 0, 1)
+        distance_map = distance_map.min(axis=0)
+
+        xmin_valid = min(max(0, xmin), canvas.shape[1] - 1)
+        xmax_valid = min(max(0, xmax), canvas.shape[1] - 1)
+        ymin_valid = min(max(0, ymin), canvas.shape[0] - 1)
+        ymax_valid = min(max(0, ymax), canvas.shape[0] - 1)
+        canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1] = np.fmax(
+            1 - distance_map[ymin_valid - ymin:ymax_valid - ymax + height,
+                             xmin_valid - xmin:xmax_valid - xmax + width],
+            canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1])
+
+    def _distance(self, xs, ys, point_1, point_2):
+        '''
+        compute the distance from point to a line
+        ys: coordinates in the first axis
+        xs: coordinates in the second axis
+        point_1, point_2: (x, y), the end of the line
+        '''
+        height, width = xs.shape[:2]
+        square_distance_1 = np.square(xs - point_1[0]) + np.square(ys - point_1[
+            1])
+        square_distance_2 = np.square(xs - point_2[0]) + np.square(ys - point_2[
+            1])
+        square_distance = np.square(point_1[0] - point_2[0]) + np.square(
+            point_1[1] - point_2[1])
+
+        cosin = (square_distance - square_distance_1 - square_distance_2) / (
+            2 * np.sqrt(square_distance_1 * square_distance_2))
+        square_sin = 1 - np.square(cosin)
+        square_sin = np.nan_to_num(square_sin)
+        result = np.sqrt(square_distance_1 * square_distance_2 * square_sin /
+                         square_distance)
+
+        result[cosin <
+               0] = np.sqrt(np.fmin(square_distance_1, square_distance_2))[cosin
+                                                                           < 0]
+        # self.extend_line(point_1, point_2, result)
+        return result
+
+    def extend_line(self, point_1, point_2, result, shrink_ratio):
+        ex_point_1 = (int(
+            round(point_1[0] + (point_1[0] - point_2[0]) * (1 + shrink_ratio))),
+                      int(
+                          round(point_1[1] + (point_1[1] - point_2[1]) * (
+                              1 + shrink_ratio))))
+        cv2.line(
+            result,
+            tuple(ex_point_1),
+            tuple(point_1),
+            4096.0,
+            1,
+            lineType=cv2.LINE_AA,
+            shift=0)
+        ex_point_2 = (int(
+            round(point_2[0] + (point_2[0] - point_1[0]) * (1 + shrink_ratio))),
+                      int(
+                          round(point_2[1] + (point_2[1] - point_1[1]) * (
+                              1 + shrink_ratio))))
+        cv2.line(
+            result,
+            tuple(ex_point_2),
+            tuple(point_2),
+            4096.0,
+            1,
+            lineType=cv2.LINE_AA,
+            shift=0)
+        return ex_point_1, ex_point_2

ppocr/data/imaug/make_shrink_map.py

+# -*- coding:utf-8 -*- 
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np
+import cv2
+from shapely.geometry import Polygon
+import pyclipper
+
+__all__ = ['MakeShrinkMap']
+
+
+class MakeShrinkMap(object):
+    r'''
+    Making binary mask from detection data with ICDAR format.
+    Typically following the process of class `MakeICDARData`.
+    '''
+
+    def __init__(self, min_text_size=8, shrink_ratio=0.4, **kwargs):
+        self.min_text_size = min_text_size
+        self.shrink_ratio = shrink_ratio
+
+    def __call__(self, data):
+        image = data['image']
+        text_polys = data['polys']
+        ignore_tags = data['ignore_tags']
+
+        h, w = image.shape[:2]
+        text_polys, ignore_tags = self.validate_polygons(text_polys,
+                                                         ignore_tags, h, w)
+        gt = np.zeros((h, w), dtype=np.float32)
+        # gt = np.zeros((1, h, w), dtype=np.float32)
+        mask = np.ones((h, w), dtype=np.float32)
+        for i in range(len(text_polys)):
+            polygon = text_polys[i]
+            height = max(polygon[:, 1]) - min(polygon[:, 1])
+            width = max(polygon[:, 0]) - min(polygon[:, 0])
+            if ignore_tags[i] or min(height, width) < self.min_text_size:
+                cv2.fillPoly(mask,
+                             polygon.astype(np.int32)[np.newaxis, :, :], 0)
+                ignore_tags[i] = True
+            else:
+                polygon_shape = Polygon(polygon)
+                distance = polygon_shape.area * (
+                    1 - np.power(self.shrink_ratio, 2)) / polygon_shape.length
+                subject = [tuple(l) for l in text_polys[i]]
+                padding = pyclipper.PyclipperOffset()
+                padding.AddPath(subject, pyclipper.JT_ROUND,
+                                pyclipper.ET_CLOSEDPOLYGON)
+                shrinked = padding.Execute(-distance)
+                if shrinked == []:
+                    cv2.fillPoly(mask,
+                                 polygon.astype(np.int32)[np.newaxis, :, :], 0)
+                    ignore_tags[i] = True
+                    continue
+                shrinked = np.array(shrinked[0]).reshape(-1, 2)
+                cv2.fillPoly(gt, [shrinked.astype(np.int32)], 1)
+                # cv2.fillPoly(gt[0], [shrinked.astype(np.int32)], 1)
+
+        data['shrink_map'] = gt
+        data['shrink_mask'] = mask
+        return data
+
+    def validate_polygons(self, polygons, ignore_tags, h, w):
+        '''
+        polygons (numpy.array, required): of shape (num_instances, num_points, 2)
+        '''
+        if len(polygons) == 0:
+            return polygons, ignore_tags
+        assert len(polygons) == len(ignore_tags)
+        for polygon in polygons:
+            polygon[:, 0] = np.clip(polygon[:, 0], 0, w - 1)
+            polygon[:, 1] = np.clip(polygon[:, 1], 0, h - 1)
+
+        for i in range(len(polygons)):
+            area = self.polygon_area(polygons[i])
+            if abs(area) < 1:
+                ignore_tags[i] = True
+            if area > 0:
+                polygons[i] = polygons[i][::-1, :]
+        return polygons, ignore_tags
+
+    def polygon_area(self, polygon):
+        # return cv2.contourArea(polygon.astype(np.float32))
+        edge = 0
+        for i in range(polygon.shape[0]):
+            next_index = (i + 1) % polygon.shape[0]
+            edge += (polygon[next_index, 0] - polygon[i, 0]) * (
+                polygon[next_index, 1] - polygon[i, 1])
+
+        return edge / 2.

ppocr/data/imaug/operators.py

+"""
+# 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import sys
+import six
+import cv2
+import numpy as np
+
+
+class DecodeImage(object):
+    """ decode image """
+
+    def __init__(self, img_mode='RGB', channel_first=False, **kwargs):
+        self.img_mode = img_mode
+        self.channel_first = channel_first
+
+    def __call__(self, data):
+        img = data['image']
+        if six.PY2:
+            assert type(img) is str and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        else:
+            assert type(img) is bytes and len(
+                img) > 0, "invalid input 'img' in DecodeImage"
+        img = np.frombuffer(img, dtype='uint8')
+        img = cv2.imdecode(img, 1)
+        if self.img_mode == 'GRAY':
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        elif self.img_mode == 'RGB':
+            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape)
+            img = img[:, :, ::-1]
+
+        if self.channel_first:
+            img = img.transpose((2, 0, 1))
+
+        data['image'] = img
+        return data
+
+
+class NormalizeImage(object):
+    """ normalize image such as substract mean, divide std
+    """
+
+    def __init__(self, scale=None, mean=None, std=None, order='chw', **kwargs):
+        if isinstance(scale, str):
+            scale = eval(scale)
+        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+        mean = mean if mean is not None else [0.485, 0.456, 0.406]
+        std = std if std is not None else [0.229, 0.224, 0.225]
+
+        shape = (3, 1, 1) if order == 'chw' else (1, 1, 3)
+        self.mean = np.array(mean).reshape(shape).astype('float32')
+        self.std = np.array(std).reshape(shape).astype('float32')
+
+    def __call__(self, data):
+        img = data['image']
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+
+        assert isinstance(img,
+                          np.ndarray), "invalid input 'img' in NormalizeImage"
+        data['image'] = (
+            img.astype('float32') * self.scale - self.mean) / self.std
+        return data
+
+
+class ToCHWImage(object):
+    """ convert hwc image to chw image
+    """
+
+    def __init__(self, **kwargs):
+        pass
+
+    def __call__(self, data):
+        img = data['image']
+        from PIL import Image
+        if isinstance(img, Image.Image):
+            img = np.array(img)
+        data['image'] = img.transpose((2, 0, 1))
+        return data
+
+
+class keepKeys(object):
+    def __init__(self, keep_keys, **kwargs):
+        self.keep_keys = keep_keys
+
+    def __call__(self, data):
+        data_list = []
+        for key in self.keep_keys:
+            data_list.append(data[key])
+        return data_list
+
+
+class DetResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(DetResizeForTest, self).__init__()
+        self.resize_type = 0
+        if 'image_shape' in kwargs:
+            self.image_shape = kwargs['image_shape']
+            self.resize_type = 1
+        if 'limit_side_len' in kwargs:
+            self.limit_side_len = kwargs['limit_side_len']
+            self.limit_type = kwargs.get('limit_type', 'min')
+        else:
+            self.limit_side_len = 736
+            self.limit_type = 'min'
+
+    def __call__(self, data):
+        img = data['image']
+
+        if self.resize_type == 0:
+            img, shape = self.resize_image_type0(img)
+        else:
+            img, shape = self.resize_image_type1(img)
+        data['image'] = img
+        data['shape'] = shape
+        return data
+
+    def resize_image_type1(self, img):
+        resize_h, resize_w = self.image_shape
+        ori_h, ori_w = img.shape[:2]  # (h, w, c)
+        img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        return img, np.array([ori_h, ori_w])
+
+    def resize_image_type0(self, img):
+        """
+        resize image to a size multiple of 32 which is required by the network
+        args:
+            img(array): array with shape [h, w, c]
+        return(tuple):
+            img, (ratio_h, ratio_w)
+        """
+        limit_side_len = self.limit_side_len
+        h, w, _ = img.shape
+
+        # limit the max side
+        if self.limit_type == 'max':
+            if max(h, w) > limit_side_len:
+                if h > w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        else:
+            if min(h, w) < limit_side_len:
+                if h < w:
+                    ratio = float(limit_side_len) / h
+                else:
+                    ratio = float(limit_side_len) / w
+            else:
+                ratio = 1.
+        resize_h = int(h * ratio)
+        resize_w = int(w * ratio)
+
+        resize_h = int(round(resize_h / 32) * 32)
+        resize_w = int(round(resize_w / 32) * 32)
+
+        try:
+            if int(resize_w) <= 0 or int(resize_h) <= 0:
+                return None, (None, None)
+            img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        except:
+            print(img.shape, resize_w, resize_h)
+            sys.exit(0)
+        return img, np.array([h, w])

ppocr/data/imaug/random_crop_data.py

+# -*- coding:utf-8 -*- 
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np
+import cv2
+import random
+
+
+def is_poly_in_rect(poly, x, y, w, h):
+    poly = np.array(poly)
+    if poly[:, 0].min() < x or poly[:, 0].max() > x + w:
+        return False
+    if poly[:, 1].min() < y or poly[:, 1].max() > y + h:
+        return False
+    return True
+
+
+def is_poly_outside_rect(poly, x, y, w, h):
+    poly = np.array(poly)
+    if poly[:, 0].max() < x or poly[:, 0].min() > x + w:
+        return True
+    if poly[:, 1].max() < y or poly[:, 1].min() > y + h:
+        return True
+    return False
+
+
+def split_regions(axis):
+    regions = []
+    min_axis = 0
+    for i in range(1, axis.shape[0]):
+        if axis[i] != axis[i - 1] + 1:
+            region = axis[min_axis:i]
+            min_axis = i
+            regions.append(region)
+    return regions
+
+
+def random_select(axis, max_size):
+    xx = np.random.choice(axis, size=2)
+    xmin = np.min(xx)
+    xmax = np.max(xx)
+    xmin = np.clip(xmin, 0, max_size - 1)
+    xmax = np.clip(xmax, 0, max_size - 1)
+    return xmin, xmax
+
+
+def region_wise_random_select(regions, max_size):
+    selected_index = list(np.random.choice(len(regions), 2))
+    selected_values = []
+    for index in selected_index:
+        axis = regions[index]
+        xx = int(np.random.choice(axis, size=1))
+        selected_values.append(xx)
+    xmin = min(selected_values)
+    xmax = max(selected_values)
+    return xmin, xmax
+
+
+def crop_area(im, text_polys, min_crop_side_ratio, max_tries):
+    h, w, _ = im.shape
+    h_array = np.zeros(h, dtype=np.int32)
+    w_array = np.zeros(w, dtype=np.int32)
+    for points in text_polys:
+        points = np.round(points, decimals=0).astype(np.int32)
+        minx = np.min(points[:, 0])
+        maxx = np.max(points[:, 0])
+        w_array[minx:maxx] = 1
+        miny = np.min(points[:, 1])
+        maxy = np.max(points[:, 1])
+        h_array[miny:maxy] = 1
+    # ensure the cropped area not across a text
+    h_axis = np.where(h_array == 0)[0]
+    w_axis = np.where(w_array == 0)[0]
+
+    if len(h_axis) == 0 or len(w_axis) == 0:
+        return 0, 0, w, h
+
+    h_regions = split_regions(h_axis)
+    w_regions = split_regions(w_axis)
+
+    for i in range(max_tries):
+        if len(w_regions) > 1:
+            xmin, xmax = region_wise_random_select(w_regions, w)
+        else:
+            xmin, xmax = random_select(w_axis, w)
+        if len(h_regions) > 1:
+            ymin, ymax = region_wise_random_select(h_regions, h)
+        else:
+            ymin, ymax = random_select(h_axis, h)
+
+        if xmax - xmin < min_crop_side_ratio * w or ymax - ymin < min_crop_side_ratio * h:
+            # area too small
+            continue
+        num_poly_in_rect = 0
+        for poly in text_polys:
+            if not is_poly_outside_rect(poly, xmin, ymin, xmax - xmin,
+                                        ymax - ymin):
+                num_poly_in_rect += 1
+                break
+
+        if num_poly_in_rect > 0:
+            return xmin, ymin, xmax - xmin, ymax - ymin
+
+    return 0, 0, w, h
+
+
+class EastRandomCropData(object):
+    def __init__(self,
+                 size=(640, 640),
+                 max_tries=10,
+                 min_crop_side_ratio=0.1,
+                 keep_ratio=True,
+                 **kwargs):
+        self.size = size
+        self.max_tries = max_tries
+        self.min_crop_side_ratio = min_crop_side_ratio
+        self.keep_ratio = keep_ratio
+
+    def __call__(self, data):
+        img = data['image']
+        text_polys = data['polys']
+        ignore_tags = data['ignore_tags']
+        texts = data['texts']
+        all_care_polys = [
+            text_polys[i] for i, tag in enumerate(ignore_tags) if not tag
+        ]
+        # 计算crop区域
+        crop_x, crop_y, crop_w, crop_h = crop_area(
+            img, all_care_polys, self.min_crop_side_ratio, self.max_tries)
+        # crop 图片 保持比例填充
+        scale_w = self.size[0] / crop_w
+        scale_h = self.size[1] / crop_h
+        scale = min(scale_w, scale_h)
+        h = int(crop_h * scale)
+        w = int(crop_w * scale)
+        if self.keep_ratio:
+            padimg = np.zeros((self.size[1], self.size[0], img.shape[2]),
+                              img.dtype)
+            padimg[:h, :w] = cv2.resize(
+                img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w], (w, h))
+            img = padimg
+        else:
+            img = cv2.resize(
+                img[crop_y:crop_y + crop_h, crop_x:crop_x + crop_w],
+                tuple(self.size))
+        # crop 文本框
+        text_polys_crop = []
+        ignore_tags_crop = []
+        texts_crop = []
+        for poly, text, tag in zip(text_polys, texts, ignore_tags):
+            poly = ((poly - (crop_x, crop_y)) * scale).tolist()
+            if not is_poly_outside_rect(poly, 0, 0, w, h):
+                text_polys_crop.append(poly)
+                ignore_tags_crop.append(tag)
+                texts_crop.append(text)
+        data['image'] = img
+        data['polys'] = np.array(text_polys_crop)
+        data['ignore_tags'] = ignore_tags_crop
+        data['texts'] = texts_crop
+        return data
+
+
+class PSERandomCrop(object):
+    def __init__(self, size, **kwargs):
+        self.size = size
+
+    def __call__(self, data):
+        imgs = data['imgs']
+
+        h, w = imgs[0].shape[0:2]
+        th, tw = self.size
+        if w == tw and h == th:
+            return imgs
+
+        # label中存在文本实例，并且按照概率进行裁剪，使用threshold_label_map控制
+        if np.max(imgs[2]) > 0 and random.random() > 3 / 8:
+            # 文本实例的左上角点
+            tl = np.min(np.where(imgs[2] > 0), axis=1) - self.size
+            tl[tl < 0] = 0
+            # 文本实例的右下角点
+            br = np.max(np.where(imgs[2] > 0), axis=1) - self.size
+            br[br < 0] = 0
+            # 保证选到右下角点时，有足够的距离进行crop
+            br[0] = min(br[0], h - th)
+            br[1] = min(br[1], w - tw)
+
+            for _ in range(50000):
+                i = random.randint(tl[0], br[0])
+                j = random.randint(tl[1], br[1])
+                # 保证shrink_label_map有文本
+                if imgs[1][i:i + th, j:j + tw].sum() <= 0:
+                    continue
+                else:
+                    break
+        else:
+            i = random.randint(0, h - th)
+            j = random.randint(0, w - tw)
+
+        # return i, j, th, tw
+        for idx in range(len(imgs)):
+            if len(imgs[idx].shape) == 3:
+                imgs[idx] = imgs[idx][i:i + th, j:j + tw, :]
+            else:
+                imgs[idx] = imgs[idx][i:i + th, j:j + tw]
+        data['imgs'] = imgs
+        return data

ppocr/data/imaug/rec_img_aug.py

+# 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.
+
+# 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 math
+import cv2
+import numpy as np
+import random
+
+from .text_image_aug import tia_perspective, tia_stretch, tia_distort
+
+
+class RecAug(object):
+    def __init__(self, **kwargsz):
+        pass
+
+    def __call__(self, data):
+        img = data['image']
+        img = warp(img, 10)
+        data['image'] = img
+        return data
+
+
+class RecResizeImg(object):
+    def __init__(self,
+                 image_shape,
+                 infer_mode=False,
+                 character_type='ch',
+                 use_tps=False,
+                 **kwargs):
+        self.image_shape = image_shape
+        self.infer_mode = infer_mode
+        self.character_type = character_type
+        self.use_tps = use_tps
+
+    def __call__(self, data):
+        img = data['image']
+        if self.infer_mode and self.character_type == "ch" and not self.use_tps:
+            norm_img = resize_norm_img_chinese(img, self.image_shape)
+        else:
+            norm_img = resize_norm_img(img, self.image_shape)
+        data['image'] = norm_img
+        return data
+
+
+def resize_norm_img(img, image_shape):
+    imgC, imgH, imgW = image_shape
+    h = img.shape[0]
+    w = img.shape[1]
+    ratio = w / float(h)
+    if math.ceil(imgH * ratio) > imgW:
+        resized_w = imgW
+    else:
+        resized_w = int(math.ceil(imgH * ratio))
+    resized_image = cv2.resize(img, (resized_w, imgH))
+    resized_image = resized_image.astype('float32')
+    if image_shape[0] == 1:
+        resized_image = resized_image / 255
+        resized_image = resized_image[np.newaxis, :]
+    else:
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+    resized_image -= 0.5
+    resized_image /= 0.5
+    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+    padding_im[:, :, 0:resized_w] = resized_image
+    return padding_im
+
+
+def resize_norm_img_chinese(img, image_shape):
+    imgC, imgH, imgW = image_shape
+    # todo: change to 0 and modified image shape
+    max_wh_ratio = 0
+    h, w = img.shape[0], img.shape[1]
+    ratio = w * 1.0 / h
+    max_wh_ratio = max(max_wh_ratio, ratio)
+    imgW = int(32 * max_wh_ratio)
+    if math.ceil(imgH * ratio) > imgW:
+        resized_w = imgW
+    else:
+        resized_w = int(math.ceil(imgH * ratio))
+    resized_image = cv2.resize(img, (resized_w, imgH))
+    resized_image = resized_image.astype('float32')
+    if image_shape[0] == 1:
+        resized_image = resized_image / 255
+        resized_image = resized_image[np.newaxis, :]
+    else:
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+    resized_image -= 0.5
+    resized_image /= 0.5
+    padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+    padding_im[:, :, 0:resized_w] = resized_image
+    return padding_im
+
+
+def flag():
+    """
+    flag
+    """
+    return 1 if random.random() > 0.5000001 else -1
+
+
+def cvtColor(img):
+    """
+    cvtColor
+    """
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    delta = 0.001 * random.random() * flag()
+    hsv[:, :, 2] = hsv[:, :, 2] * (1 + delta)
+    new_img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
+    return new_img
+
+
+def blur(img):
+    """
+    blur
+    """
+    h, w, _ = img.shape
+    if h > 10 and w > 10:
+        return cv2.GaussianBlur(img, (5, 5), 1)
+    else:
+        return img
+
+
+def jitter(img):
+    """
+    jitter
+    """
+    w, h, _ = img.shape
+    if h > 10 and w > 10:
+        thres = min(w, h)
+        s = int(random.random() * thres * 0.01)
+        src_img = img.copy()
+        for i in range(s):
+            img[i:, i:, :] = src_img[:w - i, :h - i, :]
+        return img
+    else:
+        return img
+
+
+def add_gasuss_noise(image, mean=0, var=0.1):
+    """
+    Gasuss noise
+    """
+
+    noise = np.random.normal(mean, var**0.5, image.shape)
+    out = image + 0.5 * noise
+    out = np.clip(out, 0, 255)
+    out = np.uint8(out)
+    return out
+
+
+def get_crop(image):
+    """
+    random crop
+    """
+    h, w, _ = image.shape
+    top_min = 1
+    top_max = 8
+    top_crop = int(random.randint(top_min, top_max))
+    top_crop = min(top_crop, h - 1)
+    crop_img = image.copy()
+    ratio = random.randint(0, 1)
+    if ratio:
+        crop_img = crop_img[top_crop:h, :, :]
+    else:
+        crop_img = crop_img[0:h - top_crop, :, :]
+    return crop_img
+
+
+class Config:
+    """
+    Config
+    """
+
+    def __init__(self, ):
+        self.anglex = random.random() * 30
+        self.angley = random.random() * 15
+        self.anglez = random.random() * 10
+        self.fov = 42
+        self.r = 0
+        self.shearx = random.random() * 0.3
+        self.sheary = random.random() * 0.05
+        self.borderMode = cv2.BORDER_REPLICATE
+
+    def make(self, w, h, ang):
+        """
+        make
+        """
+        self.anglex = random.random() * 5 * flag()
+        self.angley = random.random() * 5 * flag()
+        self.anglez = -1 * random.random() * int(ang) * flag()
+        self.fov = 42
+        self.r = 0
+        self.shearx = 0
+        self.sheary = 0
+        self.borderMode = cv2.BORDER_REPLICATE
+        self.w = w
+        self.h = h
+
+        self.perspective = True
+        self.stretch = True
+        self.distort = True
+
+        self.crop = True
+        self.affine = False
+        self.reverse = True
+        self.noise = True
+        self.jitter = True
+        self.blur = True
+        self.color = True
+
+
+def rad(x):
+    """
+    rad
+    """
+    return x * np.pi / 180
+
+
+def get_warpR(config):
+    """
+    get_warpR
+    """
+    anglex, angley, anglez, fov, w, h, r = \
+        config.anglex, config.angley, config.anglez, config.fov, config.w, config.h, config.r
+    if w > 69 and w < 112:
+        anglex = anglex * 1.5
+
+    z = np.sqrt(w**2 + h**2) / 2 / np.tan(rad(fov / 2))
+    # Homogeneous coordinate transformation matrix
+    rx = np.array([[1, 0, 0, 0],
+                   [0, np.cos(rad(anglex)), -np.sin(rad(anglex)), 0], [
+                       0,
+                       -np.sin(rad(anglex)),
+                       np.cos(rad(anglex)),
+                       0,
+                   ], [0, 0, 0, 1]], np.float32)
+    ry = np.array([[np.cos(rad(angley)), 0, np.sin(rad(angley)), 0],
+                   [0, 1, 0, 0], [
+                       -np.sin(rad(angley)),
+                       0,
+                       np.cos(rad(angley)),
+                       0,
+                   ], [0, 0, 0, 1]], np.float32)
+    rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0, 0],
+                   [-np.sin(rad(anglez)), np.cos(rad(anglez)), 0, 0],
+                   [0, 0, 1, 0], [0, 0, 0, 1]], np.float32)
+    r = rx.dot(ry).dot(rz)
+    # generate 4 points
+    pcenter = np.array([h / 2, w / 2, 0, 0], np.float32)
+    p1 = np.array([0, 0, 0, 0], np.float32) - pcenter
+    p2 = np.array([w, 0, 0, 0], np.float32) - pcenter
+    p3 = np.array([0, h, 0, 0], np.float32) - pcenter
+    p4 = np.array([w, h, 0, 0], np.float32) - pcenter
+    dst1 = r.dot(p1)
+    dst2 = r.dot(p2)
+    dst3 = r.dot(p3)
+    dst4 = r.dot(p4)
+    list_dst = np.array([dst1, dst2, dst3, dst4])
+    org = np.array([[0, 0], [w, 0], [0, h], [w, h]], np.float32)
+    dst = np.zeros((4, 2), np.float32)
+    # Project onto the image plane
+    dst[:, 0] = list_dst[:, 0] * z / (z - list_dst[:, 2]) + pcenter[0]
+    dst[:, 1] = list_dst[:, 1] * z / (z - list_dst[:, 2]) + pcenter[1]
+
+    warpR = cv2.getPerspectiveTransform(org, dst)
+
+    dst1, dst2, dst3, dst4 = dst
+    r1 = int(min(dst1[1], dst2[1]))
+    r2 = int(max(dst3[1], dst4[1]))
+    c1 = int(min(dst1[0], dst3[0]))
+    c2 = int(max(dst2[0], dst4[0]))
+
+    try:
+        ratio = min(1.0 * h / (r2 - r1), 1.0 * w / (c2 - c1))
+
+        dx = -c1
+        dy = -r1
+        T1 = np.float32([[1., 0, dx], [0, 1., dy], [0, 0, 1.0 / ratio]])
+        ret = T1.dot(warpR)
+    except:
+        ratio = 1.0
+        T1 = np.float32([[1., 0, 0], [0, 1., 0], [0, 0, 1.]])
+        ret = T1
+    return ret, (-r1, -c1), ratio, dst
+
+
+def get_warpAffine(config):
+    """
+    get_warpAffine
+    """
+    anglez = config.anglez
+    rz = np.array([[np.cos(rad(anglez)), np.sin(rad(anglez)), 0],
+                   [-np.sin(rad(anglez)), np.cos(rad(anglez)), 0]], np.float32)
+    return rz
+
+
+def warp(img, ang):
+    """
+    warp
+    """
+    h, w, _ = img.shape
+    config = Config()
+    config.make(w, h, ang)
+    new_img = img
+
+    prob = 0.4
+
+    if config.distort:
+        img_height, img_width = img.shape[0:2]
+        if random.random() <= prob and img_height >= 20 and img_width >= 20:
+            new_img = tia_distort(new_img, random.randint(3, 6))
+
+    if config.stretch:
+        img_height, img_width = img.shape[0:2]
+        if random.random() <= prob and img_height >= 20 and img_width >= 20:
+            new_img = tia_stretch(new_img, random.randint(3, 6))
+
+    if config.perspective:
+        if random.random() <= prob:
+            new_img = tia_perspective(new_img)
+
+    if config.crop:
+        img_height, img_width = img.shape[0:2]
+        if random.random() <= prob and img_height >= 20 and img_width >= 20:
+            new_img = get_crop(new_img)
+
+    if config.blur:
+        if random.random() <= prob:
+            new_img = blur(new_img)
+    if config.color:
+        if random.random() <= prob:
+            new_img = cvtColor(new_img)
+    if config.jitter:
+        new_img = jitter(new_img)
+    if config.noise:
+        if random.random() <= prob:
+            new_img = add_gasuss_noise(new_img)
+    if config.reverse:
+        if random.random() <= prob:
+            new_img = 255 - new_img
+    return new_img

ppocr/data/imaug/text_image_aug/__init__.py

+# 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.
+
+from .augment import tia_perspective, tia_distort, tia_stretch
+
+__all__ = ['tia_distort', 'tia_stretch', 'tia_perspective']

ppocr/data/imaug/text_image_aug/augment.py

+# 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 numpy as np
+from .warp_mls import WarpMLS
+
+
+def tia_distort(src, segment=4):
+    img_h, img_w = src.shape[:2]
+
+    cut = img_w // segment
+    thresh = cut // 3
+
+    src_pts = list()
+    dst_pts = list()
+
+    src_pts.append([0, 0])
+    src_pts.append([img_w, 0])
+    src_pts.append([img_w, img_h])
+    src_pts.append([0, img_h])
+
+    dst_pts.append([np.random.randint(thresh), np.random.randint(thresh)])
+    dst_pts.append(
+        [img_w - np.random.randint(thresh), np.random.randint(thresh)])
+    dst_pts.append(
+        [img_w - np.random.randint(thresh), img_h - np.random.randint(thresh)])
+    dst_pts.append(
+        [np.random.randint(thresh), img_h - np.random.randint(thresh)])
+
+    half_thresh = thresh * 0.5
+
+    for cut_idx in np.arange(1, segment, 1):
+        src_pts.append([cut * cut_idx, 0])
+        src_pts.append([cut * cut_idx, img_h])
+        dst_pts.append([
+            cut * cut_idx + np.random.randint(thresh) - half_thresh,
+            np.random.randint(thresh) - half_thresh
+        ])
+        dst_pts.append([
+            cut * cut_idx + np.random.randint(thresh) - half_thresh,
+            img_h + np.random.randint(thresh) - half_thresh
+        ])
+
+    trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
+    dst = trans.generate()
+
+    return dst
+
+
+def tia_stretch(src, segment=4):
+    img_h, img_w = src.shape[:2]
+
+    cut = img_w // segment
+    thresh = cut * 4 // 5
+
+    src_pts = list()
+    dst_pts = list()
+
+    src_pts.append([0, 0])
+    src_pts.append([img_w, 0])
+    src_pts.append([img_w, img_h])
+    src_pts.append([0, img_h])
+
+    dst_pts.append([0, 0])
+    dst_pts.append([img_w, 0])
+    dst_pts.append([img_w, img_h])
+    dst_pts.append([0, img_h])
+
+    half_thresh = thresh * 0.5
+
+    for cut_idx in np.arange(1, segment, 1):
+        move = np.random.randint(thresh) - half_thresh
+        src_pts.append([cut * cut_idx, 0])
+        src_pts.append([cut * cut_idx, img_h])
+        dst_pts.append([cut * cut_idx + move, 0])
+        dst_pts.append([cut * cut_idx + move, img_h])
+
+    trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
+    dst = trans.generate()
+
+    return dst
+
+
+def tia_perspective(src):
+    img_h, img_w = src.shape[:2]
+
+    thresh = img_h // 2
+
+    src_pts = list()
+    dst_pts = list()
+
+    src_pts.append([0, 0])
+    src_pts.append([img_w, 0])
+    src_pts.append([img_w, img_h])
+    src_pts.append([0, img_h])
+
+    dst_pts.append([0, np.random.randint(thresh)])
+    dst_pts.append([img_w, np.random.randint(thresh)])
+    dst_pts.append([img_w, img_h - np.random.randint(thresh)])
+    dst_pts.append([0, img_h - np.random.randint(thresh)])
+
+    trans = WarpMLS(src, src_pts, dst_pts, img_w, img_h)
+    dst = trans.generate()
+
+    return dst
\ No newline at end of file

ppocr/data/imaug/text_image_aug/warp_mls.py

+# 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 numpy as np
+
+
+class WarpMLS:
+    def __init__(self, src, src_pts, dst_pts, dst_w, dst_h, trans_ratio=1.):
+        self.src = src
+        self.src_pts = src_pts
+        self.dst_pts = dst_pts
+        self.pt_count = len(self.dst_pts)
+        self.dst_w = dst_w
+        self.dst_h = dst_h
+        self.trans_ratio = trans_ratio
+        self.grid_size = 100
+        self.rdx = np.zeros((self.dst_h, self.dst_w))
+        self.rdy = np.zeros((self.dst_h, self.dst_w))
+
+    @staticmethod
+    def __bilinear_interp(x, y, v11, v12, v21, v22):
+        return (v11 * (1 - y) + v12 * y) * (1 - x) + (v21 *
+                                                      (1 - y) + v22 * y) * x
+
+    def generate(self):
+        self.calc_delta()
+        return self.gen_img()
+
+    def calc_delta(self):
+        w = np.zeros(self.pt_count, dtype=np.float32)
+
+        if self.pt_count < 2:
+            return
+
+        i = 0
+        while 1:
+            if self.dst_w <= i < self.dst_w + self.grid_size - 1:
+                i = self.dst_w - 1
+            elif i >= self.dst_w:
+                break
+
+            j = 0
+            while 1:
+                if self.dst_h <= j < self.dst_h + self.grid_size - 1:
+                    j = self.dst_h - 1
+                elif j >= self.dst_h:
+                    break
+
+                sw = 0
+                swp = np.zeros(2, dtype=np.float32)
+                swq = np.zeros(2, dtype=np.float32)
+                new_pt = np.zeros(2, dtype=np.float32)
+                cur_pt = np.array([i, j], dtype=np.float32)
+
+                k = 0
+                for k in range(self.pt_count):
+                    if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
+                        break
+
+                    w[k] = 1. / (
+                        (i - self.dst_pts[k][0]) * (i - self.dst_pts[k][0]) +
+                        (j - self.dst_pts[k][1]) * (j - self.dst_pts[k][1]))
+
+                    sw += w[k]
+                    swp = swp + w[k] * np.array(self.dst_pts[k])
+                    swq = swq + w[k] * np.array(self.src_pts[k])
+
+                if k == self.pt_count - 1:
+                    pstar = 1 / sw * swp
+                    qstar = 1 / sw * swq
+
+                    miu_s = 0
+                    for k in range(self.pt_count):
+                        if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
+                            continue
+                        pt_i = self.dst_pts[k] - pstar
+                        miu_s += w[k] * np.sum(pt_i * pt_i)
+
+                    cur_pt -= pstar
+                    cur_pt_j = np.array([-cur_pt[1], cur_pt[0]])
+
+                    for k in range(self.pt_count):
+                        if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
+                            continue
+
+                        pt_i = self.dst_pts[k] - pstar
+                        pt_j = np.array([-pt_i[1], pt_i[0]])
+
+                        tmp_pt = np.zeros(2, dtype=np.float32)
+                        tmp_pt[0] = np.sum(pt_i * cur_pt) * self.src_pts[k][0] - \
+                                    np.sum(pt_j * cur_pt) * self.src_pts[k][1]
+                        tmp_pt[1] = -np.sum(pt_i * cur_pt_j) * self.src_pts[k][0] + \
+                                    np.sum(pt_j * cur_pt_j) * self.src_pts[k][1]
+                        tmp_pt *= (w[k] / miu_s)
+                        new_pt += tmp_pt
+
+                    new_pt += qstar
+                else:
+                    new_pt = self.src_pts[k]
+
+                self.rdx[j, i] = new_pt[0] - i
+                self.rdy[j, i] = new_pt[1] - j
+
+                j += self.grid_size
+            i += self.grid_size
+
+    def gen_img(self):
+        src_h, src_w = self.src.shape[:2]
+        dst = np.zeros_like(self.src, dtype=np.float32)
+
+        for i in np.arange(0, self.dst_h, self.grid_size):
+            for j in np.arange(0, self.dst_w, self.grid_size):
+                ni = i + self.grid_size
+                nj = j + self.grid_size
+                w = h = self.grid_size
+                if ni >= self.dst_h:
+                    ni = self.dst_h - 1
+                    h = ni - i + 1
+                if nj >= self.dst_w:
+                    nj = self.dst_w - 1
+                    w = nj - j + 1
+
+                di = np.reshape(np.arange(h), (-1, 1))
+                dj = np.reshape(np.arange(w), (1, -1))
+                delta_x = self.__bilinear_interp(
+                    di / h, dj / w, self.rdx[i, j], self.rdx[i, nj],
+                    self.rdx[ni, j], self.rdx[ni, nj])
+                delta_y = self.__bilinear_interp(
+                    di / h, dj / w, self.rdy[i, j], self.rdy[i, nj],
+                    self.rdy[ni, j], self.rdy[ni, nj])
+                nx = j + dj + delta_x * self.trans_ratio
+                ny = i + di + delta_y * self.trans_ratio
+                nx = np.clip(nx, 0, src_w - 1)
+                ny = np.clip(ny, 0, src_h - 1)
+                nxi = np.array(np.floor(nx), dtype=np.int32)
+                nyi = np.array(np.floor(ny), dtype=np.int32)
+                nxi1 = np.array(np.ceil(nx), dtype=np.int32)
+                nyi1 = np.array(np.ceil(ny), dtype=np.int32)
+
+                if len(self.src.shape) == 3:
+                    x = np.tile(np.expand_dims(ny - nyi, axis=-1), (1, 1, 3))
+                    y = np.tile(np.expand_dims(nx - nxi, axis=-1), (1, 1, 3))
+                else:
+                    x = ny - nyi
+                    y = nx - nxi
+                dst[i:i + h, j:j + w] = self.__bilinear_interp(
+                    x, y, self.src[nyi, nxi], self.src[nyi, nxi1],
+                    self.src[nyi1, nxi], self.src[nyi1, nxi1])
+
+        dst = np.clip(dst, 0, 255)
+        dst = np.array(dst, dtype=np.uint8)
+
+        return dst
\ No newline at end of file

ppocr/metrics/DetMetric.py

+# 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+__all__ = ['DetMetric']
+
+from .eval_det_iou import DetectionIoUEvaluator
+
+
+class DetMetric(object):
+    def __init__(self, main_indicator='hmean', **kwargs):
+        self.evaluator = DetectionIoUEvaluator()
+        self.main_indicator = main_indicator
+        self.reset()
+
+    def __call__(self, preds, batch, **kwargs):
+        '''
+       batch: a list produced by dataloaders.
+           image: np.ndarray  of shape (N, C, H, W).
+           ratio_list: np.ndarray  of shape(N,2)
+           polygons: np.ndarray  of shape (N, K, 4, 2), the polygons of objective regions.
+           ignore_tags: np.ndarray  of shape (N, K), indicates whether a region is ignorable or not.
+       preds: a list of dict produced by post process
+            points: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
+       '''
+        gt_polyons_batch = batch[2]
+        ignore_tags_batch = batch[3]
+        for pred, gt_polyons, ignore_tags in zip(preds, gt_polyons_batch,
+                                                 ignore_tags_batch):
+            # prepare gt
+            gt_info_list = [{
+                'points': gt_polyon,
+                'text': '',
+                'ignore': ignore_tag
+            } for gt_polyon, ignore_tag in zip(gt_polyons, ignore_tags)]
+            # prepare det
+            det_info_list = [{
+                'points': det_polyon,
+                'text': ''
+            } for det_polyon in pred['points']]
+            result = self.evaluator.evaluate_image(gt_info_list, det_info_list)
+            self.results.append(result)
+
+    def get_metric(self):
+        """
+        return metircs {
+                 'precision': 0,
+                 'recall': 0,
+                 'hmean': 0
+            }
+        """
+
+        metircs = self.evaluator.combine_results(self.results)
+        self.reset()
+        return metircs
+
+    def reset(self):
+        self.results = []  # clear results

ppocr/metrics/RecMetric.py

+# 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 Levenshtein
+
+
+class RecMetric(object):
+    def __init__(self, main_indicator='acc', **kwargs):
+        self.main_indicator = main_indicator
+        self.reset()
+
+    def __call__(self, pred_label, *args, **kwargs):
+        preds, labels = pred_label
+        correct_num = 0
+        all_num = 0
+        norm_edit_dis = 0.0
+        for (pred, pred_conf), (target, _) in zip(preds, labels):
+            norm_edit_dis += Levenshtein.distance(pred, target) / max(
+                len(pred), len(target))
+            if pred == target:
+                correct_num += 1
+            all_num += 1
+            # if all_num < 10 and kwargs.get('show_str', False):
+            #     print('{} -> {}'.format(pred, target))
+        self.correct_num += correct_num
+        self.all_num += all_num
+        self.norm_edit_dis += norm_edit_dis
+        return {
+            'acc': correct_num / all_num,
+            'norm_edit_dis': 1 - norm_edit_dis / all_num
+        }
+
+    def get_metric(self):
+        """
+        return metircs {
+                 'acc': 0,
+                 'norm_edit_dis': 0,
+            }
+        """
+        acc = self.correct_num / self.all_num
+        norm_edit_dis = 1 - self.norm_edit_dis / self.all_num
+        self.reset()
+        return {'acc': acc, 'norm_edit_dis': norm_edit_dis}
+
+    def reset(self):
+        self.correct_num = 0
+        self.all_num = 0
+        self.norm_edit_dis = 0

ppocr/metrics/__init__.py

+# 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import copy
+
+__all__ = ['build_metric']
+
+
+def build_metric(config):
+    from .DetMetric import DetMetric
+    from .RecMetric import RecMetric
+
+    support_dict = ['DetMetric', 'RecMetric']
+
+    config = copy.deepcopy(config)
+    module_name = config.pop('name')
+    assert module_name in support_dict, Exception(
+        'metric only support {}'.format(support_dict))
+    module_class = eval(module_name)(**config)
+    return module_class

ppocr/metrics/eval_det_iou.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+from collections import namedtuple
+import numpy as np
+from shapely.geometry import Polygon
+"""
+reference from :
+https://github.com/MhLiao/DB/blob/3c32b808d4412680310d3d28eeb6a2d5bf1566c5/concern/icdar2015_eval/detection/iou.py#L8
+"""
+
+
+class DetectionIoUEvaluator(object):
+    def __init__(self, iou_constraint=0.5, area_precision_constraint=0.5):
+        self.iou_constraint = iou_constraint
+        self.area_precision_constraint = area_precision_constraint
+
+    def evaluate_image(self, gt, pred):
+        def get_union(pD, pG):
+            return Polygon(pD).union(Polygon(pG)).area
+
+        def get_intersection_over_union(pD, pG):
+            return get_intersection(pD, pG) / get_union(pD, pG)
+
+        def get_intersection(pD, pG):
+            return Polygon(pD).intersection(Polygon(pG)).area
+
+        def compute_ap(confList, matchList, numGtCare):
+            correct = 0
+            AP = 0
+            if len(confList) > 0:
+                confList = np.array(confList)
+                matchList = np.array(matchList)
+                sorted_ind = np.argsort(-confList)
+                confList = confList[sorted_ind]
+                matchList = matchList[sorted_ind]
+                for n in range(len(confList)):
+                    match = matchList[n]
+                    if match:
+                        correct += 1
+                        AP += float(correct) / (n + 1)
+
+                if numGtCare > 0:
+                    AP /= numGtCare
+
+            return AP
+
+        perSampleMetrics = {}
+
+        matchedSum = 0
+
+        Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')
+
+        numGlobalCareGt = 0
+        numGlobalCareDet = 0
+
+        arrGlobalConfidences = []
+        arrGlobalMatches = []
+
+        recall = 0
+        precision = 0
+        hmean = 0
+
+        detMatched = 0
+
+        iouMat = np.empty([1, 1])
+
+        gtPols = []
+        detPols = []
+
+        gtPolPoints = []
+        detPolPoints = []
+
+        # Array of Ground Truth Polygons' keys marked as don't Care
+        gtDontCarePolsNum = []
+        # Array of Detected Polygons' matched with a don't Care GT
+        detDontCarePolsNum = []
+
+        pairs = []
+        detMatchedNums = []
+
+        arrSampleConfidences = []
+        arrSampleMatch = []
+
+        evaluationLog = ""
+
+        # print(len(gt))
+        for n in range(len(gt)):
+            points = gt[n]['points']
+            # transcription = gt[n]['text']
+            dontCare = gt[n]['ignore']
+            #             points = Polygon(points)
+            #             points = points.buffer(0)
+            if not Polygon(points).is_valid or not Polygon(points).is_simple:
+                continue
+
+            gtPol = points
+            gtPols.append(gtPol)
+            gtPolPoints.append(points)
+            if dontCare:
+                gtDontCarePolsNum.append(len(gtPols) - 1)
+
+        evaluationLog += "GT polygons: " + str(len(gtPols)) + (
+            " (" + str(len(gtDontCarePolsNum)) + " don't care)\n"
+            if len(gtDontCarePolsNum) > 0 else "\n")
+
+        for n in range(len(pred)):
+            points = pred[n]['points']
+            #             points = Polygon(points)
+            #             points = points.buffer(0)
+            if not Polygon(points).is_valid or not Polygon(points).is_simple:
+                continue
+
+            detPol = points
+            detPols.append(detPol)
+            detPolPoints.append(points)
+            if len(gtDontCarePolsNum) > 0:
+                for dontCarePol in gtDontCarePolsNum:
+                    dontCarePol = gtPols[dontCarePol]
+                    intersected_area = get_intersection(dontCarePol, detPol)
+                    pdDimensions = Polygon(detPol).area
+                    precision = 0 if pdDimensions == 0 else intersected_area / pdDimensions
+                    if (precision > self.area_precision_constraint):
+                        detDontCarePolsNum.append(len(detPols) - 1)
+                        break
+
+        evaluationLog += "DET polygons: " + str(len(detPols)) + (
+            " (" + str(len(detDontCarePolsNum)) + " don't care)\n"
+            if len(detDontCarePolsNum) > 0 else "\n")
+
+        if len(gtPols) > 0 and len(detPols) > 0:
+            # Calculate IoU and precision matrixs
+            outputShape = [len(gtPols), len(detPols)]
+            iouMat = np.empty(outputShape)
+            gtRectMat = np.zeros(len(gtPols), np.int8)
+            detRectMat = np.zeros(len(detPols), np.int8)
+            for gtNum in range(len(gtPols)):
+                for detNum in range(len(detPols)):
+                    pG = gtPols[gtNum]
+                    pD = detPols[detNum]
+                    iouMat[gtNum, detNum] = get_intersection_over_union(pD, pG)
+
+            for gtNum in range(len(gtPols)):
+                for detNum in range(len(detPols)):
+                    if gtRectMat[gtNum] == 0 and detRectMat[
+                            detNum] == 0 and gtNum not in gtDontCarePolsNum and detNum not in detDontCarePolsNum:
+                        if iouMat[gtNum, detNum] > self.iou_constraint:
+                            gtRectMat[gtNum] = 1
+                            detRectMat[detNum] = 1
+                            detMatched += 1
+                            pairs.append({'gt': gtNum, 'det': detNum})
+                            detMatchedNums.append(detNum)
+                            evaluationLog += "Match GT #" + \
+                                str(gtNum) + " with Det #" + str(detNum) + "\n"
+
+        numGtCare = (len(gtPols) - len(gtDontCarePolsNum))
+        numDetCare = (len(detPols) - len(detDontCarePolsNum))
+        if numGtCare == 0:
+            recall = float(1)
+            precision = float(0) if numDetCare > 0 else float(1)
+        else:
+            recall = float(detMatched) / numGtCare
+            precision = 0 if numDetCare == 0 else float(detMatched) / numDetCare
+
+        hmean = 0 if (precision + recall) == 0 else 2.0 * \
+            precision * recall / (precision + recall)
+
+        matchedSum += detMatched
+        numGlobalCareGt += numGtCare
+        numGlobalCareDet += numDetCare
+
+        perSampleMetrics = {
+            'precision': precision,
+            'recall': recall,
+            'hmean': hmean,
+            'pairs': pairs,
+            'iouMat': [] if len(detPols) > 100 else iouMat.tolist(),
+            'gtPolPoints': gtPolPoints,
+            'detPolPoints': detPolPoints,
+            'gtCare': numGtCare,
+            'detCare': numDetCare,
+            'gtDontCare': gtDontCarePolsNum,
+            'detDontCare': detDontCarePolsNum,
+            'detMatched': detMatched,
+            'evaluationLog': evaluationLog
+        }
+
+        return perSampleMetrics
+
+    def combine_results(self, results):
+        numGlobalCareGt = 0
+        numGlobalCareDet = 0
+        matchedSum = 0
+        for result in results:
+            numGlobalCareGt += result['gtCare']
+            numGlobalCareDet += result['detCare']
+            matchedSum += result['detMatched']
+
+        methodRecall = 0 if numGlobalCareGt == 0 else float(
+            matchedSum) / numGlobalCareGt
+        methodPrecision = 0 if numGlobalCareDet == 0 else float(
+            matchedSum) / numGlobalCareDet
+        methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \
+            methodRecall * methodPrecision / (methodRecall + methodPrecision)
+        # print(methodRecall, methodPrecision, methodHmean)
+        # sys.exit(-1)
+        methodMetrics = {
+            'precision': methodPrecision,
+            'recall': methodRecall,
+            'hmean': methodHmean
+        }
+
+        return methodMetrics
+
+
+if __name__ == '__main__':
+    evaluator = DetectionIoUEvaluator()
+    gts = [[{
+        'points': [(0, 0), (1, 0), (1, 1), (0, 1)],
+        'text': 1234,
+        'ignore': False,
+    }, {
+        'points': [(2, 2), (3, 2), (3, 3), (2, 3)],
+        'text': 5678,
+        'ignore': False,
+    }]]
+    preds = [[{
+        'points': [(0.1, 0.1), (1, 0), (1, 1), (0, 1)],
+        'text': 123,
+        'ignore': False,
+    }]]
+    results = []
+    for gt, pred in zip(gts, preds):
+        results.append(evaluator.evaluate_image(gt, pred))
+    metrics = evaluator.combine_results(results)
+    print(metrics)

ppocr/modeling/__init__.py

+# 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 copy
+from .losses import build_loss
+
+__all__ = ['build_model', 'build_loss']
+
+
+def build_model(config):
+    from .architectures import Model
+
+    config = copy.deepcopy(config)
+    module_class = Model(config)
+    return module_class