Add ocr modules (#611)

* add ocr modules (chinese_ocr_db_rcnn & chinese_text_detection_db)

hub_module/modules/image/text_recognition/chinese_ocr_db_rcnn/README.md

+## 概述
+
+chinese_ocr_db_rcnn Module用于识别图片当中的汉字。其基于[chinese_text_detection_db Module](https://www.paddlepaddle.org.cn/hubdetail?name=chinese_text_detection_db&en_category=TextRecognition)检测得到的文本框，继续识别文本框中的中文文字。识别文字算法采用CRNN（Convolutional Recurrent Neural Network）即卷积递归神经网络。其是DCNN和RNN的组合，专门用于识别图像中的序列式对象。与CTC loss配合使用，进行文字识别，可以直接从文本词级或行级的标注中学习，不需要详细的字符级的标注。该Module支持直接预测。
+
+
+<p align="center">
+<img src="https://bj.bcebos.com/paddlehub/model/image/ocr/rcnn.png" hspace='10'/> <br />
+</p>
+
+更多详情参考[An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition](https://arxiv.org/pdf/1507.05717.pdf)
+
+## 命令行预测
+
+```shell
+$ hub run chinese_ocr_db_rcnn --input_path "/PATH/TO/IMAGE"
+```
+
+## API
+
+```python
+def recognize_text(images=[],
+                    paths=[],
+                    use_gpu=False,
+                    output_dir='ocr_result',
+                    visualization=False,
+                    box_thresh=0.5,
+                    text_thresh=0.5)
+```
+
+预测API，检测输入图片中的所有中文文本的位置。
+
+**参数**
+
+* paths (list\[str\]): 图片的路径；
+* images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，BGR格式；
+* use\_gpu (bool): 是否使用 GPU；**若使用GPU，请先设置CUDA_VISIBLE_DEVICES环境变量**
+* box\_thresh (float): 检测文本框置信度的阈值；
+* text\_thresh (float): 识别中文文本置信度的阈值；
+* visualization (bool): 是否将识别结果保存为图片文件；
+* output\_dir (str): 图片的保存路径，默认设为 detection\_result；
+
+**返回**
+
+* res (list\[dict\]): 识别结果的列表，列表中每一个元素为 dict，各字段为：
+    * data (list\[dict\]): 识别文本结果，列表中每一个元素为 dict，各字段为：
+        * text(str): 识别得到的文本
+        * confidence(float): 识别文本结果置信度
+        * text_box_position(numpy.ndarray): 文本框在原图中的像素坐标，4*2的矩阵，依次表示文本框左下、右下、右上、左上顶点的坐标
+      如果无识别结果则data为\[\]
+    * save_path (str, optional): 识别结果的保存路径，如不保存图片则save_path为''
+
+### 代码示例
+
+```python
+import paddlehub as hub
+import cv2
+
+ocr = hub.Module(name="chinese_ocr_db_rcnn")
+result = ocr.recognize_text(images=[cv2.imread('/PATH/TO/IMAGE')])
+
+# or
+# result = ocr.recognize_text(paths=['/PATH/TO/IMAGE'])
+```
+
+* 样例结果示例
+
+<p align="center">
+<img src="https://bj.bcebos.com/paddlehub/model/image/ocr/ocr_res.jpg" hspace='10'/> <br />
+</p>
+
+## 服务部署
+
+PaddleHub Serving 可以部署一个目标检测的在线服务。
+
+### 第一步：启动PaddleHub Serving
+
+运行启动命令：
+```shell
+$ hub serving start -m chinese_ocr_db_rcnn
+```
+
+这样就完成了一个目标检测的服务化API的部署，默认端口号为8866。
+
+**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。
+
+### 第二步：发送预测请求
+
+配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果
+
+```python
+import requests
+import json
+import cv2
+import base64
+
+def cv2_to_base64(image):
+    data = cv2.imencode('.jpg', image)[1]
+    return base64.b64encode(data.tostring()).decode('utf8')
+
+# 发送HTTP请求
+data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]}
+headers = {"Content-type": "application/json"}
+url = "http://127.0.0.1:8866/predict/chinese_ocr_db_rcnn"
+r = requests.post(url=url, headers=headers, data=json.dumps(data))
+
+# 打印预测结果
+print(r.json()["results"])
+```
+
+## 查看代码
+
+https://github.com/PaddlePaddle/PaddleOCR
+
+### 依赖
+
+paddlepaddle >= 1.7.2
+
+paddlehub >= 1.6.0
+
+
+## 更新历史
+
+* 1.0.0
+
+  初始发布

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+繡
+枱
+β
+綉
+á
+獅
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+™
+麵
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+ò
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+濱
+濃
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+铪
+桼
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+㐂
+垚
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+揸
+捲
+鐘
+滏
+𣇉
+爍
+輪
+燜
+鴻
+鮮
+動
+鹞
+鷗
+丄
+慶
+鉌
+翥
+飮
+腸
+⇋
+漁
+覺
+來
+熘
+昴
+翏
+鲱
+圧
+鄉
+萭
+頔
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+г
+貭
+類
+聯
+幛
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+珣
+䝉
+扙
+嵐
+銷
+處
+ㄱ
+語
+誘
+苝
+歸
+儀
+燒
+楿
+內
+粢
+葒
+奧
+麥
+礻
+滿
+蠔
+穵
+瞭
+態
+鱬
+榞
+硂
+鄭
+黃
+煙
+祐
+奓
+逺
+＊
+瑄
+獲
+聞
+薦
+讀
+這
+樣
+決
+問
+啟
+們
+執
+説
+轉
+單
+隨
+唘
+帶
+倉
+庫
+還
+贈
+尙
+皺
+■
+餅
+產
+○
+∈
+報
+狀
+楓
+賠
+琯
+嗮
+禮
+｀
+傳
+＞
+≤
+嗞
+Φ
+≥
+換
+咭
+∣
+↓
+曬
+ε
+応
+寫
+″
+終
+様
+純
+費
+療
+聨
+凍
+壐
+郵
+ü
+黒
+∫
+製
+塊
+調
+軽
+確
+撃
+級
+馴
+Ⅲ
+涇
+繹
+數
+碼
+證
+狒
+処
+劑
+＜
+晧
+賀
+衆
+］
+櫥
+兩
+陰
+絶
+對
+鯉
+憶
+◎
+ｐ
+ｅ
+Ｙ
+蕒
+煖
+頓
+測
+試
+鼽
+僑
+碩
+妝
+帯
+≈
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+舖
+權
+喫
+倆
+ˋ
+該
+悅
+ā
+俫
+．
+ｆ
+ｓ
+ｂ
+ｍ
+ｋ
+ｇ
+ｕ
+ｊ
+貼
+淨
+濕
+針
+適
+備
+ｌ
+／
+給
+謢
+強
+觸
+衛
+與
+⊙
+＄
+緯
+變
+⑴
+⑵
+⑶
+㎏
+殺
+∩
+幚
+─
+價
+▲
+離
+ú
+ó
+飄
+烏
+関
+閟
+﹝
+﹞
+邏
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+屆
+層
+▼
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+錄
+熳
+ē
+艦
+吋
+錶
+辧
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+顯
+④
+禦
+販
+気
+対
+枰
+閩
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+瞓
+貊
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+△
+眞
+墊
+Ω
+獻
+褲
+縫
+緑
+亜
+鉅
+餠
+｛
+｝
+◆
+蘆
+薈
+█
+◇
+溫
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+凖
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+П
+舊
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+圍
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+ǐ
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+ǒ
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+醬
+籤
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+據
+à
+ɡ
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+彐
+┌
+婦
+獸
+鲩
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+ī
+蒟
+蒻
+齊
+袆
+腦
+寧
+凈
+妳
+煥
+詢
+偽
+謹
+啫
+鯽
+騷
+鱸
+損
+傷
+鎻
+髮
+買
+冏
+儥
+両
+﹢
+∞
+載
+喰
+ｚ
+羙
+悵
+燙
+曉
+員
+組
+徹
+艷
+痠
+鋼
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+縮
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+嚒
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+鱻
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+浥
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+軎
+²
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+醜
+刪
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+賜
+噁
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+彌
+葚
+濛
+請
+溇
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+訪
+獴
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+資
+縤
+陣
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+韻
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+總
+衖
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+籃
+駿
+苼
+瘋
+昽
+紡
+驊
+腎
+﹗
+響
+杋
+剛
+嚴
+禪
+歓
+槍
+傘
+檸
+檫
+炣
+勢
+鏜
+鎢
+銑
+尐
+減
+奪
+惡
+θ
+僮
+婭
+臘
+ū
+ì
+殻
+鉄
+∑
+蛲
+焼
+緖
+續
+紹
+懮

hub_module/modules/image/text_recognition/chinese_ocr_db_rcnn/character.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 numpy as np
+import string
+
+
+class CharacterOps(object):
+    """ Convert between text-label and text-index """
+
+    def __init__(self, config):
+        self.character_type = config['character_type']
+        self.loss_type = config['loss_type']
+        if self.character_type == "en":
+            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+            dict_character = list(self.character_str)
+        elif self.character_type == "ch":
+            character_dict_path = config['character_dict_path']
+            self.character_str = ""
+            with open(character_dict_path, "rb") as fin:
+                lines = fin.readlines()
+                for line in lines:
+                    line = line.decode('utf-8').strip("\n")
+                    self.character_str += line
+            dict_character = list(self.character_str)
+        elif self.character_type == "en_sensitive":
+            # same with ASTER setting (use 94 char).
+            self.character_str = string.printable[:-6]
+            dict_character = list(self.character_str)
+        else:
+            self.character_str = None
+        assert self.character_str is not None, \
+            "Nonsupport type of the character: {}".format(self.character_str)
+        self.beg_str = "sos"
+        self.end_str = "eos"
+        if self.loss_type == "attention":
+            dict_character = [self.beg_str, self.end_str] + dict_character
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+        self.character = 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 self.character_type == "en":
+            text = text.lower()
+
+        text_list = []
+        for char in text:
+            if char not in self.dict:
+                continue
+            text_list.append(self.dict[char])
+        text = np.array(text_list)
+        return text
+
+    def decode(self, text_index, is_remove_duplicate=False):
+        """ convert text-index into text-label. """
+        char_list = []
+        char_num = self.get_char_num()
+
+        if self.loss_type == "attention":
+            beg_idx = self.get_beg_end_flag_idx("beg")
+            end_idx = self.get_beg_end_flag_idx("end")
+            ignored_tokens = [beg_idx, end_idx]
+        else:
+            ignored_tokens = [char_num]
+
+        for idx in range(len(text_index)):
+            if text_index[idx] in ignored_tokens:
+                continue
+            if is_remove_duplicate:
+                if idx > 0 and text_index[idx - 1] == text_index[idx]:
+                    continue
+            char_list.append(self.character[text_index[idx]])
+        text = ''.join(char_list)
+        return text
+
+    def get_char_num(self):
+        return len(self.character)
+
+    def get_beg_end_flag_idx(self, beg_or_end):
+        if self.loss_type == "attention":
+            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
+        else:
+            err = "error in get_beg_end_flag_idx when using the loss %s"\
+                % (self.loss_type)
+            assert False, err
+
+
+def cal_predicts_accuracy(char_ops,
+                          preds,
+                          preds_lod,
+                          labels,
+                          labels_lod,
+                          is_remove_duplicate=False):
+    acc_num = 0
+    img_num = 0
+    for ino in range(len(labels_lod) - 1):
+        beg_no = preds_lod[ino]
+        end_no = preds_lod[ino + 1]
+        preds_text = preds[beg_no:end_no].reshape(-1)
+        preds_text = char_ops.decode(preds_text, is_remove_duplicate)
+
+        beg_no = labels_lod[ino]
+        end_no = labels_lod[ino + 1]
+        labels_text = labels[beg_no:end_no].reshape(-1)
+        labels_text = char_ops.decode(labels_text, is_remove_duplicate)
+        img_num += 1
+
+        if preds_text == labels_text:
+            acc_num += 1
+    acc = acc_num * 1.0 / img_num
+    return acc, acc_num, img_num
+
+
+def convert_rec_attention_infer_res(preds):
+    img_num = preds.shape[0]
+    target_lod = [0]
+    convert_ids = []
+    for ino in range(img_num):
+        end_pos = np.where(preds[ino, :] == 1)[0]
+        if len(end_pos) <= 1:
+            text_list = preds[ino, 1:]
+        else:
+            text_list = preds[ino, 1:end_pos[1]]
+        target_lod.append(target_lod[ino] + len(text_list))
+        convert_ids = convert_ids + list(text_list)
+    convert_ids = np.array(convert_ids)
+    convert_ids = convert_ids.reshape((-1, 1))
+    return convert_ids, target_lod
+
+
+def convert_rec_label_to_lod(ori_labels):
+    img_num = len(ori_labels)
+    target_lod = [0]
+    convert_ids = []
+    for ino in range(img_num):
+        target_lod.append(target_lod[ino] + len(ori_labels[ino]))
+        convert_ids = convert_ids + list(ori_labels[ino])
+    convert_ids = np.array(convert_ids)
+    convert_ids = convert_ids.reshape((-1, 1))
+    return convert_ids, target_lod

hub_module/modules/image/text_recognition/chinese_ocr_db_rcnn/module.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import ast
+import copy
+import math
+import os
+import time
+
+from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
+from paddlehub.common.logger import logger
+from paddlehub.module.module import moduleinfo, runnable, serving
+from PIL import Image
+import cv2
+import numpy as np
+import paddle.fluid as fluid
+import paddlehub as hub
+
+from chinese_ocr_db_rcnn.character import CharacterOps
+from chinese_ocr_db_rcnn.utils import draw_ocr, get_image_ext, sorted_boxes
+
+
+@moduleinfo(
+    name="chinese_ocr_db_rcnn",
+    version="1.0.0",
+    summary=
+    "The module can recognize the chinese texts in an image. Firstly, it will detect the text box positions based on the differentiable_binarization_chn module. Then it recognizes the chinese texts. ",
+    author="paddle-dev",
+    author_email="paddle-dev@baidu.com",
+    type="cv/text_recognition")
+class ChineseOCRDBRCNN(hub.Module):
+    def _initialize(self, text_detector_module=None):
+        """
+        initialize with the necessary elements
+        """
+        self.character_dict_path = os.path.join(self.directory, 'assets',
+                                                'ppocr_keys_v1.txt')
+        char_ops_params = {
+            'character_type': 'ch',
+            'character_dict_path': self.character_dict_path,
+            'loss_type': 'ctc'
+        }
+        self.char_ops = CharacterOps(char_ops_params)
+        self.rec_image_shape = [3, 32, 320]
+        self._text_detector_module = text_detector_module
+        self.font_file = os.path.join(self.directory, 'assets', 'simfang.ttf')
+        self.pretrained_model_path = os.path.join(self.directory,
+                                                  'inference_model')
+        self._set_config()
+
+    def _set_config(self):
+        """
+        predictor config setting
+        """
+        model_file_path = os.path.join(self.pretrained_model_path, 'model')
+        params_file_path = os.path.join(self.pretrained_model_path, 'params')
+
+        config = AnalysisConfig(model_file_path, params_file_path)
+        try:
+            _places = os.environ["CUDA_VISIBLE_DEVICES"]
+            int(_places[0])
+            use_gpu = True
+        except:
+            use_gpu = False
+
+        if use_gpu:
+            config.enable_use_gpu(8000, 0)
+        else:
+            config.disable_gpu()
+
+        config.disable_glog_info()
+
+        # use zero copy
+        config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
+        config.switch_use_feed_fetch_ops(False)
+        self.predictor = create_paddle_predictor(config)
+        input_names = self.predictor.get_input_names()
+        self.input_tensor = self.predictor.get_input_tensor(input_names[0])
+        output_names = self.predictor.get_output_names()
+        self.output_tensors = []
+        for output_name in output_names:
+            output_tensor = self.predictor.get_output_tensor(output_name)
+            self.output_tensors.append(output_tensor)
+
+    @property
+    def text_detector_module(self):
+        """
+        text detect module
+        """
+        if not self._text_detector_module:
+            self._text_detector_module = hub.Module(
+                name='chinese_text_detection_db')
+        return self._text_detector_module
+
+    def read_images(self, paths=[]):
+        images = []
+        for img_path in paths:
+            assert os.path.isfile(
+                img_path), "The {} isn't a valid file.".format(img_path)
+            img = cv2.imread(img_path)
+            if img is None:
+                logger.info("error in loading image:{}".format(img_path))
+                continue
+            images.append(img)
+        return images
+
+    def get_rotate_crop_image(self, img, points):
+        img_height, img_width = img.shape[0:2]
+        left = int(np.min(points[:, 0]))
+        right = int(np.max(points[:, 0]))
+        top = int(np.min(points[:, 1]))
+        bottom = int(np.max(points[:, 1]))
+        img_crop = img[top:bottom, left:right, :].copy()
+        points[:, 0] = points[:, 0] - left
+        points[:, 1] = points[:, 1] - top
+        img_crop_width = int(np.linalg.norm(points[0] - points[1]))
+        img_crop_height = int(np.linalg.norm(points[0] - points[3]))
+        pts_std = np.float32([[0, 0], [img_crop_width, 0],\
+            [img_crop_width, img_crop_height], [0, img_crop_height]])
+        M = cv2.getPerspectiveTransform(points, pts_std)
+        dst_img = cv2.warpPerspective(
+            img_crop,
+            M, (img_crop_width, img_crop_height),
+            borderMode=cv2.BORDER_REPLICATE)
+        dst_img_height, dst_img_width = dst_img.shape[0:2]
+        if dst_img_height * 1.0 / dst_img_width >= 1.5:
+            dst_img = np.rot90(dst_img)
+        return dst_img
+
+    def resize_norm_img(self, img, max_wh_ratio):
+        imgC, imgH, imgW = self.rec_image_shape
+        imgW = int(32 * max_wh_ratio)
+        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')
+        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
+
+    @serving
+    def recognize_text(self,
+                       images=[],
+                       paths=[],
+                       use_gpu=False,
+                       output_dir='ocr_result',
+                       visualization=False,
+                       box_thresh=0.5,
+                       text_thresh=0.5):
+        """
+        Get the chinese texts in the predicted images.
+        Args:
+            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
+            paths (list[str]): The paths of images. If paths not images
+            use_gpu (bool): Whether to use gpu.
+            batch_size(int): the program deals once with one
+            output_dir (str): The directory to store output images.
+            visualization (bool): Whether to save image or not.
+            box_thresh(float): the threshold of the detected text box's confidence
+            text_thresh(float): the threshold of the recognize chinese texts' confidence
+        Returns:
+            res (list): The result of chinese texts and save path of images.
+        """
+        if use_gpu:
+            try:
+                _places = os.environ["CUDA_VISIBLE_DEVICES"]
+                int(_places[0])
+            except:
+                raise RuntimeError(
+                    "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
+                )
+
+        self.use_gpu = use_gpu
+
+        if images != [] and isinstance(images, list) and paths == []:
+            predicted_data = images
+        elif images == [] and isinstance(paths, list) and paths != []:
+            predicted_data = self.read_images(paths)
+        else:
+            raise TypeError("The input data is inconsistent with expectations.")
+
+        assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
+
+        detection_results = self.text_detector_module.detect_text(
+            images=predicted_data, use_gpu=self.use_gpu, box_thresh=box_thresh)
+        boxes = [item['data'] for item in detection_results]
+        all_results = []
+        for index, img_boxes in enumerate(boxes):
+            original_image = predicted_data[index].copy()
+            result = {'save_path': ''}
+            if img_boxes is None:
+                result['data'] = []
+            else:
+                img_crop_list = []
+                boxes = sorted_boxes(img_boxes)
+                for num_box in range(len(boxes)):
+                    tmp_box = copy.deepcopy(boxes[num_box])
+                    img_crop = self.get_rotate_crop_image(
+                        original_image, tmp_box)
+                    img_crop_list.append(img_crop)
+
+                rec_results = self._recognize_text(img_crop_list)
+                # if the recognized text confidence score is lower than text_thresh, then drop it
+                rec_res_final = []
+                for index, res in enumerate(rec_results):
+                    text, score = res
+                    if score >= text_thresh:
+                        rec_res_final.append({
+                            'text': text,
+                            'confidence': score,
+                            'text_box_position': boxes[index]
+                        })
+                result['data'] = rec_res_final
+
+                if visualization and result['data']:
+                    result['save_path'] = self.save_result_image(
+                        original_image, boxes, rec_results, output_dir,
+                        text_thresh)
+            all_results.append(result)
+
+        return all_results
+
+    def save_result_image(self,
+                          original_image,
+                          detection_boxes,
+                          rec_results,
+                          output_dir='ocr_result',
+                          text_thresh=0.5):
+        image = Image.fromarray(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))
+        txts = [item[0] for item in rec_results]
+        scores = [item[1] for item in rec_results]
+        draw_img = draw_ocr(
+            image,
+            detection_boxes,
+            txts,
+            scores,
+            font_file=self.font_file,
+            draw_txt=True,
+            drop_score=text_thresh)
+
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+        ext = get_image_ext(original_image)
+        saved_name = 'ndarray_{}{}'.format(time.time(), ext)
+        save_file_path = os.path.join(output_dir, saved_name)
+        cv2.imwrite(save_file_path, draw_img[:, :, ::-1])
+        return save_file_path
+
+    def _recognize_text(self, image_list):
+        img_num = len(image_list)
+        batch_num = 30
+        rec_res = []
+        predict_time = 0
+        for beg_img_no in range(0, img_num, batch_num):
+            end_img_no = min(img_num, beg_img_no + batch_num)
+            norm_img_batch = []
+            max_wh_ratio = 0
+            for ino in range(beg_img_no, end_img_no):
+                h, w = image_list[ino].shape[0:2]
+                wh_ratio = w / h
+                max_wh_ratio = max(max_wh_ratio, wh_ratio)
+            for ino in range(beg_img_no, end_img_no):
+                norm_img = self.resize_norm_img(image_list[ino], max_wh_ratio)
+                norm_img = norm_img[np.newaxis, :]
+                norm_img_batch.append(norm_img)
+            norm_img_batch = np.concatenate(norm_img_batch)
+            norm_img_batch = norm_img_batch.copy()
+            self.input_tensor.copy_from_cpu(norm_img_batch)
+            self.predictor.zero_copy_run()
+            rec_idx_batch = self.output_tensors[0].copy_to_cpu()
+            rec_idx_lod = self.output_tensors[0].lod()[0]
+            predict_batch = self.output_tensors[1].copy_to_cpu()
+            predict_lod = self.output_tensors[1].lod()[0]
+
+            for rno in range(len(rec_idx_lod) - 1):
+                beg = rec_idx_lod[rno]
+                end = rec_idx_lod[rno + 1]
+                rec_idx_tmp = rec_idx_batch[beg:end, 0]
+                preds_text = self.char_ops.decode(rec_idx_tmp)
+                beg = predict_lod[rno]
+                end = predict_lod[rno + 1]
+                probs = predict_batch[beg:end, :]
+                ind = np.argmax(probs, axis=1)
+                blank = probs.shape[1]
+                valid_ind = np.where(ind != (blank - 1))[0]
+                score = np.mean(probs[valid_ind, ind[valid_ind]])
+                rec_res.append([preds_text, score])
+
+        return rec_res
+
+    def save_inference_model(self,
+                             dirname,
+                             model_filename=None,
+                             params_filename=None,
+                             combined=True):
+        detector_dir = os.path.join(dirname, 'text_detector')
+        recognizer_dir = os.path.join(dirname, 'text_recognizer')
+        self._save_detector_model(detector_dir, model_filename, params_filename,
+                                  combined)
+        self._save_recognizer_model(recognizer_dir, model_filename,
+                                    params_filename, combined)
+        logger.info("The inference model has been saved in the path {}".format(
+            os.path.realpath(dirname)))
+
+    def _save_detector_model(self,
+                             dirname,
+                             model_filename=None,
+                             params_filename=None,
+                             combined=True):
+        self.text_detector_module.save_inference_model(
+            dirname, model_filename, params_filename, combined)
+
+    def _save_recognizer_model(self,
+                               dirname,
+                               model_filename=None,
+                               params_filename=None,
+                               combined=True):
+        if combined:
+            model_filename = "__model__" if not model_filename else model_filename
+            params_filename = "__params__" if not params_filename else params_filename
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+
+        model_file_path = os.path.join(self.pretrained_model_path, 'model')
+        params_file_path = os.path.join(self.pretrained_model_path, 'params')
+        program, feeded_var_names, target_vars = fluid.io.load_inference_model(
+            dirname=self.pretrained_model_path,
+            model_filename=model_file_path,
+            params_filename=params_file_path,
+            executor=exe)
+
+        fluid.io.save_inference_model(
+            dirname=dirname,
+            main_program=program,
+            executor=exe,
+            feeded_var_names=feeded_var_names,
+            target_vars=target_vars,
+            model_filename=model_filename,
+            params_filename=params_filename)
+
+    @runnable
+    def run_cmd(self, argvs):
+        """
+        Run as a command
+        """
+        self.parser = argparse.ArgumentParser(
+            description="Run the chinese_ocr_db_rcnn module.",
+            prog='hub run chinese_ocr_db_rcnn',
+            usage='%(prog)s',
+            add_help=True)
+
+        self.arg_input_group = self.parser.add_argument_group(
+            title="Input options", description="Input data. Required")
+        self.arg_config_group = self.parser.add_argument_group(
+            title="Config options",
+            description=
+            "Run configuration for controlling module behavior, not required.")
+
+        self.add_module_config_arg()
+        self.add_module_input_arg()
+
+        args = self.parser.parse_args(argvs)
+        results = self.recognize_texts(
+            paths=[args.input_path],
+            use_gpu=args.use_gpu,
+            output_dir=args.output_dir,
+            visualization=args.visualization)
+        return results
+
+    def add_module_config_arg(self):
+        """
+        Add the command config options
+        """
+        self.arg_config_group.add_argument(
+            '--use_gpu',
+            type=ast.literal_eval,
+            default=False,
+            help="whether use GPU or not")
+        self.arg_config_group.add_argument(
+            '--output_dir',
+            type=str,
+            default='ocr_result',
+            help="The directory to save output images.")
+        self.arg_config_group.add_argument(
+            '--visualization',
+            type=ast.literal_eval,
+            default=False,
+            help="whether to save output as images.")
+
+    def add_module_input_arg(self):
+        """
+        Add the command input options
+        """
+        self.arg_input_group.add_argument(
+            '--input_path', type=str, default=None, help="diretory to image")
+
+
+if __name__ == '__main__':
+    ocr = ChineseOCRDBRCNN()
+    image_path = [
+        '../doc/imgs/11.jpg', '../doc/imgs/12.jpg', '../test_image.jpg'
+    ]
+    res = ocr.recognize_text(paths=image_path, visualization=True)
+    ocr.save_inference_model('save')
+    print(res)

hub_module/modules/image/text_recognition/chinese_ocr_db_rcnn/utils.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from PIL import Image, ImageDraw, ImageFont
+import cv2
+import numpy as np
+
+
+def draw_ocr(image,
+             boxes,
+             txts,
+             scores,
+             font_file,
+             draw_txt=True,
+             drop_score=0.5):
+    img = image.copy()
+    draw = ImageDraw.Draw(img)
+    if scores is None:
+        scores = [1] * len(boxes)
+    for (box, score) in zip(boxes, scores):
+        if score < drop_score:
+            continue
+        draw.line([(box[0][0], box[0][1]), (box[1][0], box[1][1])], fill='red')
+        draw.line([(box[1][0], box[1][1]), (box[2][0], box[2][1])], fill='red')
+        draw.line([(box[2][0], box[2][1]), (box[3][0], box[3][1])], fill='red')
+        draw.line([(box[3][0], box[3][1]), (box[0][0], box[0][1])], fill='red')
+        draw.line([(box[0][0] - 1, box[0][1] + 1),
+                   (box[1][0] - 1, box[1][1] + 1)],
+                  fill='red')
+        draw.line([(box[1][0] - 1, box[1][1] + 1),
+                   (box[2][0] - 1, box[2][1] + 1)],
+                  fill='red')
+        draw.line([(box[2][0] - 1, box[2][1] + 1),
+                   (box[3][0] - 1, box[3][1] + 1)],
+                  fill='red')
+        draw.line([(box[3][0] - 1, box[3][1] + 1),
+                   (box[0][0] - 1, box[0][1] + 1)],
+                  fill='red')
+
+    if draw_txt:
+        txt_color = (0, 0, 0)
+        img = np.array(resize_img(img))
+        _h = img.shape[0]
+        blank_img = np.ones(shape=[_h, 600], dtype=np.int8) * 255
+        blank_img = Image.fromarray(blank_img).convert("RGB")
+        draw_txt = ImageDraw.Draw(blank_img)
+
+        font_size = 20
+        gap = 20
+        title = "index           text           score"
+        font = ImageFont.truetype(font_file, font_size, encoding="utf-8")
+
+        draw_txt.text((20, 0), title, txt_color, font=font)
+        count = 0
+        for idx, txt in enumerate(txts):
+            if scores[idx] < drop_score:
+                continue
+            font = ImageFont.truetype(font_file, font_size, encoding="utf-8")
+            new_txt = str(count) + ':  ' + txt + '    ' + str(scores[count])
+            draw_txt.text((20, gap * (count + 1)),
+                          new_txt,
+                          txt_color,
+                          font=font)
+            count += 1
+        img = np.concatenate([np.array(img), np.array(blank_img)], axis=1)
+    return img
+
+
+def resize_img(img, input_size=600):
+    img = np.array(img)
+    im_shape = img.shape
+    im_size_min = np.min(im_shape[0:2])
+    im_size_max = np.max(im_shape[0:2])
+    im_scale = float(input_size) / float(im_size_max)
+    im = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
+    return im
+
+
+def get_image_ext(image):
+    if image.shape[2] == 4:
+        return ".png"
+    return ".jpg"
+
+
+def sorted_boxes(dt_boxes):
+    """
+    Sort text boxes in order from top to bottom, left to right
+    args:
+        dt_boxes(array):detected text boxes with shape [4, 2]
+    return:
+        sorted boxes(array) with shape [4, 2]
+    """
+    num_boxes = dt_boxes.shape[0]
+    sorted_boxes = sorted(dt_boxes, key=lambda x: x[0][1])
+    _boxes = list(sorted_boxes)
+
+    for i in range(num_boxes - 1):
+        if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \
+            (_boxes[i + 1][0][0] < _boxes[i][0][0]):
+            tmp = _boxes[i]
+            _boxes[i] = _boxes[i + 1]
+            _boxes[i + 1] = tmp
+    return _boxes

hub_module/modules/image/text_recognition/chinese_text_detection_db/README.md

+## 概述
+
+Differentiable Binarization（简称DB）是一种基于分割的文本检测算法。在各种文本检测算法中，基于分割的检测算法可以更好地处理弯曲等不规则形状文本，因此往往能取得更好的检测效果。但分割法后处理步骤中将分割结果转化为检测框的流程复杂，耗时严重。DB将二值化阈值加入训练中学习，可以获得更准确的检测边界，从而简化后处理流程。该Module支持直接预测。
+
+<p align="center">
+<img src="https://bj.bcebos.com/paddlehub/model/image/ocr/db_algo.png" hspace='10'/> <br />
+</p>
+
+更多详情参考[Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/pdf/1911.08947.pdf)
+
+
+## 命令行预测
+
+```shell
+$ hub run chinese_text_detection_db --input_path "/PATH/TO/IMAGE"
+```
+
+**该Module依赖于第三方库shapely和pyclipper，使用该Module之前，请先安装shapely和pyclipper。**
+
+## API
+
+```python
+def detect_text(paths=[],
+                images=[],
+                use_gpu=False,
+                output_dir='detection_result',
+                box_thresh=0.5,
+                visualization=False)
+```
+
+预测API，检测输入图片中的所有中文文本的位置。
+
+**参数**
+
+* paths (list\[str\]): 图片的路径；
+* images (list\[numpy.ndarray\]): 图片数据，ndarray.shape 为 \[H, W, C\]，BGR格式；
+* use\_gpu (bool): 是否使用 GPU；**若使用GPU，请先设置CUDA_VISIBLE_DEVICES环境变量**
+* box\_thresh (float): 检测文本框置信度的阈值；
+* visualization (bool): 是否将识别结果保存为图片文件；
+* output\_dir (str): 图片的保存路径，默认设为 detection\_result；
+
+**返回**
+
+* res (list\[dict\]): 识别结果的列表，列表中每一个元素为 dict，各字段为：
+    * data (list): 检测文本框结果，numpy.ndarray，文本框在原图中的像素坐标，4*2的矩阵，依次表示文本框左下、右下、右上、左上顶点的坐标
+    * save_path (str): 识别结果的保存路径, 如不保存图片则save_path为''
+
+### 代码示例
+
+```python
+import paddlehub as hub
+import cv2
+
+text_detector = hub.Module(name="chinese_text_detection_db")
+result = text_detector.detect_text(images=[cv2.imread('/PATH/TO/IMAGE')])
+
+# or
+# result =text_detector.detect_text(paths=['/PATH/TO/IMAGE'])
+```
+
+
+## 服务部署
+
+PaddleHub Serving 可以部署一个目标检测的在线服务。
+
+### 第一步：启动PaddleHub Serving
+
+运行启动命令：
+```shell
+$ hub serving start -m chinese_text_detection_db
+```
+
+这样就完成了一个目标检测的服务化API的部署，默认端口号为8866。
+
+**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。
+
+### 第二步：发送预测请求
+
+配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果
+
+```python
+import requests
+import json
+import cv2
+import base64
+
+def cv2_to_base64(image):
+    data = cv2.imencode('.jpg', image)[1]
+    return base64.b64encode(data.tostring()).decode('utf8')
+
+# 发送HTTP请求
+data = {'images':[cv2_to_base64(cv2.imread("/PATH/TO/IMAGE"))]}
+headers = {"Content-type": "application/json"}
+url = "http://127.0.0.1:8866/predict/chinese_text_detection_db"
+r = requests.post(url=url, headers=headers, data=json.dumps(data))
+
+# 打印预测结果
+print(r.json()["results"])
+```
+
+## 查看代码
+
+https://github.com/PaddlePaddle/PaddleOCR
+
+## 依赖
+
+paddlepaddle >= 1.7.2
+
+paddlehub >= 1.6.0
+
+shapely
+
+pyclipper
+
+## 更新历史
+
+* 1.0.0
+
+  初始发布

hub_module/modules/image/text_recognition/chinese_text_detection_db/module.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import ast
+import math
+import os
+import time
+
+from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
+from paddlehub.common.logger import logger
+from paddlehub.module.module import moduleinfo, runnable, serving
+from PIL import Image
+import cv2
+import numpy as np
+import paddle.fluid as fluid
+import paddlehub as hub
+
+from chinese_text_detection_db.processor import DBPreProcess, DBPostProcess, draw_boxes, get_image_ext
+
+
+@moduleinfo(
+    name="chinese_text_detection_db",
+    version="1.0.0",
+    summary=
+    "The module aims to detect chinese text position in the image, which is based on differentiable_binarization algorithm.",
+    author="paddle-dev",
+    author_email="paddle-dev@baidu.com",
+    type="cv/text_recognition")
+class ChineseTextDetectionDB(hub.Module):
+    def _initialize(self):
+        """
+        initialize with the necessary elements
+        """
+        self.check_requirements()
+        self.pretrained_model_path = os.path.join(self.directory,
+                                                  'inference_model')
+        self._set_config()
+
+    def _set_config(self):
+        """
+        predictor config setting
+        """
+        model_file_path = os.path.join(self.pretrained_model_path, 'model')
+        params_file_path = os.path.join(self.pretrained_model_path, 'params')
+
+        config = AnalysisConfig(model_file_path, params_file_path)
+        try:
+            _places = os.environ["CUDA_VISIBLE_DEVICES"]
+            int(_places[0])
+            use_gpu = True
+        except:
+            use_gpu = False
+
+        if use_gpu:
+            config.enable_use_gpu(8000, 0)
+        else:
+            config.disable_gpu()
+
+        config.disable_glog_info()
+
+        # use zero copy
+        config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
+        config.switch_use_feed_fetch_ops(False)
+        self.predictor = create_paddle_predictor(config)
+        input_names = self.predictor.get_input_names()
+        self.input_tensor = self.predictor.get_input_tensor(input_names[0])
+        output_names = self.predictor.get_output_names()
+        self.output_tensors = []
+        for output_name in output_names:
+            output_tensor = self.predictor.get_output_tensor(output_name)
+            self.output_tensors.append(output_tensor)
+
+    def check_requirements(self):
+        try:
+            import shapely, pyclipper
+        except:
+            logger.error(
+                'This module requires the shapely, pyclipper tools. The running enviroment does not meet the requirments. Please install the two packages.'
+            )
+            exit()
+
+    def read_images(self, paths=[]):
+        images = []
+        for img_path in paths:
+            assert os.path.isfile(
+                img_path), "The {} isn't a valid file.".format(img_path)
+            img = cv2.imread(img_path)
+            if img is None:
+                logger.info("error in loading image:{}".format(img_path))
+                continue
+            images.append(img)
+        return images
+
+    def filter_tag_det_res(self, dt_boxes, image_shape):
+        img_height, img_width = image_shape[0:2]
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.order_points_clockwise(box)
+            left = int(np.min(box[:, 0]))
+            right = int(np.max(box[:, 0]))
+            top = int(np.min(box[:, 1]))
+            bottom = int(np.max(box[:, 1]))
+            bbox_height = bottom - top
+            bbox_width = right - left
+            diffh = math.fabs(box[0, 1] - box[1, 1])
+            diffw = math.fabs(box[0, 0] - box[3, 0])
+            rect_width = int(np.linalg.norm(box[0] - box[1]))
+            rect_height = int(np.linalg.norm(box[0] - box[3]))
+            if rect_width <= 10 or rect_height <= 10:
+                continue
+            dt_boxes_new.append(box)
+        dt_boxes = np.array(dt_boxes_new)
+        return dt_boxes
+
+    def order_points_clockwise(self, pts):
+        """
+        reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
+        # sort the points based on their x-coordinates
+        """
+        xSorted = pts[np.argsort(pts[:, 0]), :]
+
+        # grab the left-most and right-most points from the sorted
+        # x-roodinate points
+        leftMost = xSorted[:2, :]
+        rightMost = xSorted[2:, :]
+
+        # now, sort the left-most coordinates according to their
+        # y-coordinates so we can grab the top-left and bottom-left
+        # points, respectively
+        leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+        (tl, bl) = leftMost
+
+        rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
+        (tr, br) = rightMost
+
+        rect = np.array([tl, tr, br, bl], dtype="float32")
+        return rect
+
+    @serving
+    def detect_text(self,
+                    images=[],
+                    paths=[],
+                    use_gpu=False,
+                    output_dir='detection_result',
+                    visualization=False,
+                    box_thresh=0.5):
+        """
+        Get the text box in the predicted images.
+        Args:
+            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
+            paths (list[str]): The paths of images. If paths not images
+            use_gpu (bool): Whether to use gpu. Default false.
+            output_dir (str): The directory to store output images.
+            visualization (bool): Whether to save image or not.
+            box_thresh(float): the threshold of the detected text box's confidence
+        Returns:
+            res (list): The result of text detection box and save path of images.
+        """
+        if use_gpu:
+            try:
+                _places = os.environ["CUDA_VISIBLE_DEVICES"]
+                int(_places[0])
+            except:
+                raise RuntimeError(
+                    "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
+                )
+
+        if images != [] and isinstance(images, list) and paths == []:
+            predicted_data = images
+        elif images == [] and isinstance(paths, list) and paths != []:
+            predicted_data = self.read_images(paths)
+        else:
+            raise TypeError("The input data is inconsistent with expectations.")
+
+        assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
+
+        preprocessor = DBPreProcess()
+        postprocessor = DBPostProcess(box_thresh)
+
+        all_imgs = []
+        all_ratios = []
+        all_results = []
+        for original_image in predicted_data:
+            im, ratio_list = preprocessor(original_image)
+            res = {'save_path': ''}
+            if im is None:
+                res['data'] = []
+
+            else:
+                im = im.copy()
+                starttime = time.time()
+                self.input_tensor.copy_from_cpu(im)
+                self.predictor.zero_copy_run()
+                data_out = self.output_tensors[0].copy_to_cpu()
+                dt_boxes_list = postprocessor(data_out, [ratio_list])
+                boxes = self.filter_tag_det_res(dt_boxes_list[0],
+                                                original_image.shape)
+                res['data'] = boxes
+
+                all_imgs.append(im)
+                all_ratios.append(ratio_list)
+                if visualization:
+                    img = Image.fromarray(
+                        cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))
+                    draw_img = draw_boxes(img, boxes)
+                    draw_img = np.array(draw_img)
+                    if not os.path.exists(output_dir):
+                        os.makedirs(output_dir)
+                    ext = get_image_ext(original_image)
+                    saved_name = 'ndarray_{}{}'.format(time.time(), ext)
+                    cv2.imwrite(
+                        os.path.join(output_dir, saved_name),
+                        draw_img[:, :, ::-1])
+                    res['save_path'] = os.path.join(output_dir, saved_name)
+
+            all_results.append(res)
+
+        return all_results
+
+    def save_inference_model(self,
+                             dirname,
+                             model_filename=None,
+                             params_filename=None,
+                             combined=True):
+        if combined:
+            model_filename = "__model__" if not model_filename else model_filename
+            params_filename = "__params__" if not params_filename else params_filename
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+
+        model_file_path = os.path.join(self.pretrained_model_path, 'model')
+        params_file_path = os.path.join(self.pretrained_model_path, 'params')
+        program, feeded_var_names, target_vars = fluid.io.load_inference_model(
+            dirname=self.pretrained_model_path,
+            model_filename=model_file_path,
+            params_filename=params_file_path,
+            executor=exe)
+
+        fluid.io.save_inference_model(
+            dirname=dirname,
+            main_program=program,
+            executor=exe,
+            feeded_var_names=feeded_var_names,
+            target_vars=target_vars,
+            model_filename=model_filename,
+            params_filename=params_filename)
+
+    @runnable
+    def run_cmd(self, argvs):
+        """
+        Run as a command
+        """
+        self.parser = argparse.ArgumentParser(
+            description="Run the chinese_text_detection_db module.",
+            prog='hub run chinese_text_detection_db',
+            usage='%(prog)s',
+            add_help=True)
+
+        self.arg_input_group = self.parser.add_argument_group(
+            title="Input options", description="Input data. Required")
+        self.arg_config_group = self.parser.add_argument_group(
+            title="Config options",
+            description=
+            "Run configuration for controlling module behavior, not required.")
+
+        self.add_module_config_arg()
+        self.add_module_input_arg()
+
+        args = self.parser.parse_args(argvs)
+        results = self.detect_text(
+            paths=[args.input_path],
+            use_gpu=args.use_gpu,
+            output_dir=args.output_dir,
+            visualization=args.visualization)
+        return results
+
+    def add_module_config_arg(self):
+        """
+        Add the command config options
+        """
+        self.arg_config_group.add_argument(
+            '--use_gpu',
+            type=ast.literal_eval,
+            default=False,
+            help="whether use GPU or not")
+        self.arg_config_group.add_argument(
+            '--output_dir',
+            type=str,
+            default='detection_result',
+            help="The directory to save output images.")
+        self.arg_config_group.add_argument(
+            '--visualization',
+            type=ast.literal_eval,
+            default=False,
+            help="whether to save output as images.")
+
+    def add_module_input_arg(self):
+        """
+        Add the command input options
+        """
+        self.arg_input_group.add_argument(
+            '--input_path', type=str, default=None, help="diretory to image")
+
+
+if __name__ == '__main__':
+    db = ChineseTextDetectionDB()
+    image_path = ['../doc/imgs/11.jpg', '../doc/imgs/12.jpg']
+    res = db.detect_text(paths=image_path, visualization=True)
+    db.save_inference_model('save')
+    print(res)

hub_module/modules/image/text_recognition/chinese_text_detection_db/processor.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+
+from PIL import Image, ImageDraw, ImageFont
+from shapely.geometry import Polygon
+import cv2
+import numpy as np
+import pyclipper
+
+
+class DBPreProcess(object):
+    def __init__(self, max_side_len=960):
+        self.max_side_len = max_side_len
+
+    def resize_image_type(self, im):
+        """
+        resize image to a size multiple of 32 which is required by the network
+        """
+        h, w, _ = im.shape
+
+        resize_w = w
+        resize_h = h
+
+        # limit the max side
+        if max(resize_h, resize_w) > self.max_side_len:
+            if resize_h > resize_w:
+                ratio = float(self.max_side_len) / resize_h
+            else:
+                ratio = float(self.max_side_len) / resize_w
+        else:
+            ratio = 1.
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+        if resize_h % 32 == 0:
+            resize_h = resize_h
+        elif resize_h // 32 <= 1:
+            resize_h = 32
+        else:
+            resize_h = (resize_h // 32 - 1) * 32
+        if resize_w % 32 == 0:
+            resize_w = resize_w
+        elif resize_w // 32 <= 1:
+            resize_w = 32
+        else:
+            resize_w = (resize_w // 32 - 1) * 32
+        try:
+            if int(resize_w) <= 0 or int(resize_h) <= 0:
+                return None, (None, None)
+            im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        except:
+            print(im.shape, resize_w, resize_h)
+            sys.exit(0)
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return im, (ratio_h, ratio_w)
+
+    def normalize(self, im):
+        img_mean = [0.485, 0.456, 0.406]
+        img_std = [0.229, 0.224, 0.225]
+        im = im.astype(np.float32, copy=False)
+        im = im / 255
+        im -= img_mean
+        im /= img_std
+        channel_swap = (2, 0, 1)
+        im = im.transpose(channel_swap)
+        return im
+
+    def __call__(self, im):
+        im, (ratio_h, ratio_w) = self.resize_image_type(im)
+        im = self.normalize(im)
+        im = im[np.newaxis, :]
+        return [im, (ratio_h, ratio_w)]
+
+
+class DBPostProcess(object):
+    """
+    The post process for Differentiable Binarization (DB).
+    """
+
+    def __init__(self, thresh=0.3, box_thresh=0.5, max_candidates=1000):
+        self.thresh = thresh
+        self.box_thresh = box_thresh
+        self.max_candidates = max_candidates
+        self.min_size = 3
+
+    def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+        '''
+        _bitmap: single map with shape (1, H, W),
+                whose values are binarized as {0, 1}
+        '''
+
+        bitmap = _bitmap
+        height, width = bitmap.shape
+
+        outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
+                                cv2.CHAIN_APPROX_SIMPLE)
+        if len(outs) == 3:
+            img, contours, _ = outs[0], outs[1], outs[2]
+        elif len(outs) == 2:
+            contours, _ = outs[0], outs[1]
+
+        num_contours = min(len(contours), self.max_candidates)
+        boxes = np.zeros((num_contours, 4, 2), dtype=np.int16)
+        scores = np.zeros((num_contours, ), dtype=np.float32)
+
+        for index in range(num_contours):
+            contour = contours[index]
+            points, sside = self.get_mini_boxes(contour)
+            if sside < self.min_size:
+                continue
+            points = np.array(points)
+            score = self.box_score_fast(pred, points.reshape(-1, 2))
+            if self.box_thresh > score:
+                continue
+
+            box = self.unclip(points).reshape(-1, 1, 2)
+            box, sside = self.get_mini_boxes(box)
+            if sside < self.min_size + 2:
+                continue
+            box = np.array(box)
+            if not isinstance(dest_width, int):
+                dest_width = dest_width.item()
+                dest_height = dest_height.item()
+
+            box[:, 0] = np.clip(
+                np.round(box[:, 0] / width * dest_width), 0, dest_width)
+            box[:, 1] = np.clip(
+                np.round(box[:, 1] / height * dest_height), 0, dest_height)
+            boxes[index, :, :] = box.astype(np.int16)
+            scores[index] = score
+        return boxes, scores
+
+    def unclip(self, box, unclip_ratio=2.0):
+        poly = Polygon(box)
+        distance = poly.area * unclip_ratio / poly.length
+        offset = pyclipper.PyclipperOffset()
+        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+        expanded = np.array(offset.Execute(distance))
+        return expanded
+
+    def get_mini_boxes(self, contour):
+        bounding_box = cv2.minAreaRect(contour)
+        points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
+
+        index_1, index_2, index_3, index_4 = 0, 1, 2, 3
+        if points[1][1] > points[0][1]:
+            index_1 = 0
+            index_4 = 1
+        else:
+            index_1 = 1
+            index_4 = 0
+        if points[3][1] > points[2][1]:
+            index_2 = 2
+            index_3 = 3
+        else:
+            index_2 = 3
+            index_3 = 2
+
+        box = [
+            points[index_1], points[index_2], points[index_3], points[index_4]
+        ]
+        return box, min(bounding_box[1])
+
+    def box_score_fast(self, bitmap, _box):
+        h, w = bitmap.shape[:2]
+        box = _box.copy()
+        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+        box[:, 0] = box[:, 0] - xmin
+        box[:, 1] = box[:, 1] - ymin
+        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]
+
+    def __call__(self, predictions, ratio_list):
+        pred = predictions[:, 0, :, :]
+        segmentation = pred > self.thresh
+
+        boxes_batch = []
+        for batch_index in range(pred.shape[0]):
+            height, width = pred.shape[-2:]
+            tmp_boxes, tmp_scores = self.boxes_from_bitmap(
+                pred[batch_index], segmentation[batch_index], width, height)
+
+            boxes = []
+            for k in range(len(tmp_boxes)):
+                if tmp_scores[k] > self.box_thresh:
+                    boxes.append(tmp_boxes[k])
+            if len(boxes) > 0:
+                boxes = np.array(boxes)
+
+                ratio_h, ratio_w = ratio_list[batch_index]
+                boxes[:, :, 0] = boxes[:, :, 0] / ratio_w
+                boxes[:, :, 1] = boxes[:, :, 1] / ratio_h
+
+            boxes_batch.append(boxes)
+        return boxes_batch
+
+
+def draw_boxes(image, boxes, scores=None, drop_score=0.5):
+    img = image.copy()
+    draw = ImageDraw.Draw(img)
+    if scores is None:
+        scores = [1] * len(boxes)
+    for (box, score) in zip(boxes, scores):
+        if score < drop_score:
+            continue
+        draw.line([(box[0][0], box[0][1]), (box[1][0], box[1][1])], fill='red')
+        draw.line([(box[1][0], box[1][1]), (box[2][0], box[2][1])], fill='red')
+        draw.line([(box[2][0], box[2][1]), (box[3][0], box[3][1])], fill='red')
+        draw.line([(box[3][0], box[3][1]), (box[0][0], box[0][1])], fill='red')
+        draw.line([(box[0][0] - 1, box[0][1] + 1),
+                   (box[1][0] - 1, box[1][1] + 1)],
+                  fill='red')
+        draw.line([(box[1][0] - 1, box[1][1] + 1),
+                   (box[2][0] - 1, box[2][1] + 1)],
+                  fill='red')
+        draw.line([(box[2][0] - 1, box[2][1] + 1),
+                   (box[3][0] - 1, box[3][1] + 1)],
+                  fill='red')
+        draw.line([(box[3][0] - 1, box[3][1] + 1),
+                   (box[0][0] - 1, box[0][1] + 1)],
+                  fill='red')
+    return img
+
+
+def get_image_ext(image):
+    if image.shape[2] == 4:
+        return ".png"
+    return ".jpg"

hub_module/scripts/configs/chinese_ocr_db_rcnn.yml

+name: chinese_ocr_db_rcnn
+dir: "modules/image/text_recognition/chinese_ocr_db_rcnn"
+exclude:
+  - README.md
+
+resources:
+  -
+    url: https://bj.bcebos.com/paddlehub/model/image/ocr/chinese_ocr_db_rcnn_infer_model.tar.gz
+    dest: .
+    uncompress: True

hub_module/scripts/configs/chinese_text_detection_db.yml

+name: chinese_text_detection_db
+dir: "modules/image/text_recognition/chinese_text_detection_db"
+exclude:
+  - README.md
+
+resources:
+  -
+    url: https://bj.bcebos.com/paddlehub/model/image/ocr/chinese_text_detection_db_infer_model.tar.gz
+    dest: .
+    uncompress: True

hub_module/tests/unittests/test_chinese_ocr_db_rcnn.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
+from unittest import TestCase, main
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import cv2
+import paddlehub as hub
+
+
+class ChineseOCRDBRCNNTestCase(TestCase):
+    def setUp(self):
+        self.module = hub.Module(name='chinese_ocr_db_rcnn')
+        self.test_images = [
+            "../image_dataset/text_recognition/11.jpg",
+            "../image_dataset/text_recognition/test_image.jpg"
+        ]
+
+    def test_detect_text(self):
+        results_1 = self.module.recognize_text(
+            paths=self.test_images, use_gpu=True)
+        results_2 = self.module.recognize_text(
+            paths=self.test_images, use_gpu=False)
+
+        test_images = [cv2.imread(img) for img in self.test_images]
+        results_3 = self.module.recognize_text(
+            images=test_images, use_gpu=False)
+        for i, res in enumerate(results_1):
+            self.assertEqual(res['save_path'], '')
+
+            for j, item in enumerate(res['data']):
+                self.assertEqual(item['confidence'],
+                                 results_2[i]['data'][j]['confidence'])
+                self.assertEqual(item['confidence'],
+                                 results_3[i]['data'][j]['confidence'])
+                self.assertEqual(item['text'], results_2[i]['data'][j]['text'])
+                self.assertEqual(item['text'], results_3[i]['data'][j]['text'])
+                self.assertEqual(
+                    (item['text_box_position'].all() == results_2[i]['data'][j]
+                     ['text_box_position'].all()), True)
+                self.assertEqual(
+                    (item['text_box_position'].all() == results_3[i]['data'][j]
+                     ['text_box_position'].all()), True)
+
+
+if __name__ == '__main__':
+    main()

hub_module/tests/unittests/test_chinese_text_detection_db.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
+from unittest import TestCase, main
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+import cv2
+import paddlehub as hub
+
+
+class ChineseTextDetectionDBTestCase(TestCase):
+    def setUp(self):
+        self.module = hub.Module(name='chinese_text_detection_db')
+        self.test_images = [
+            "../image_dataset/text_recognition/11.jpg",
+            "../image_dataset/text_recognition/test_image.jpg"
+        ]
+
+    def test_detect_text(self):
+        results_1 = self.module.detect_text(
+            paths=self.test_images, use_gpu=True)
+        results_2 = self.module.detect_text(
+            paths=self.test_images, use_gpu=False)
+
+        test_images = [cv2.imread(img) for img in self.test_images]
+        results_3 = self.module.detect_text(images=test_images, use_gpu=False)
+        for index, res in enumerate(results_1):
+            self.assertEqual(res['save_path'], '')
+            self.assertEqual(
+                (res['data'].all() == results_2[index]['data'].all()), True)
+            self.assertEqual(
+                (res['data'].all() == results_3[index]['data'].all()), True)
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -4,7 +4,7 @@ yapf == 0.26.0
 six >= 1.10.0
 flask >= 1.1.0
 flake8
-visualdl == 2.0.0a0
+visualdl >= 2.0.0b
 cma >= 2.7.0
 sentencepiece
 colorlog