add multi lang ocr (PaddlePaddle#1702)

Author: neonhuang

Diff for: README.md

@@ -231,3 +231,4 @@ We welcome you to contribute code to PaddleHub, and thank you for your feedback.
 * Many thanks to [zl1271](https://github.com/zl1271) for fixing serving docs typo
 * Many thanks to [AK391](https://github.com/AK391) for adding the webdemo of UGATIT and deoldify models in Hugging Face spaces
 * Many thanks to [itegel](https://github.com/itegel) for fixing quick start docs typo
+* Many thanks to [AK391](https://github.com/AK391) for adding the webdemo of Photo2Cartoon model in Hugging Face spaces

Diff for: README_ch.md

@@ -247,3 +247,4 @@ print(results)
 * 非常感谢[zl1271](https://github.com/zl1271)修复了serving文档中的错别字
 * 非常感谢[AK391](https://github.com/AK391)在Hugging Face spaces中添加了UGATIT和deoldify模型的web demo
 * 非常感谢[itegel](https://github.com/itegel)修复了快速开始文档中的错别字
+* 非常感谢[AK391](https://github.com/AK391)在Hugging Face spaces中添加了Photo2Cartoon模型的web demo

Diff for: docs/docs_en/visualization.md

@@ -50,6 +50,8 @@
 
 **UGATIT Selfie2anime Huggingface Web Demo**: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/U-GAT-IT-selfie2anime)
 
+**Photo2Cartoon Huggingface Web Demo**: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/photo2cartoon)
+
 
 ### Object Detection
 - Pedestrian detection, vehicle detection, and more industrial-grade ultra-large-scale pretrained models are provided.

Diff for: modules/image/text_recognition/arabic_ocr_db_crnn_mobile/README.md

@@ -0,0 +1,173 @@
+# arabic_ocr_db_crnn_mobile
+
+|模型名称|arabic_ocr_db_crnn_mobile|
+| :--- | :---: |
+|类别|图像-文字识别|
+|网络|Differentiable Binarization+CRNN|
+|数据集|icdar2015数据集|
+|是否支持Fine-tuning|否|
+|最新更新日期|2021-12-2|
+|数据指标|-|
+
+
+## 一、模型基本信息
+
+- ### 模型介绍
+
+  - arabic_ocr_db_crnn_mobile Module用于识别图片当中的阿拉伯文字，包括阿拉伯文、波斯文、维吾尔文。其基于multi_languages_ocr_db_crnn检测得到的文本框，继续识别文本框中的阿拉伯文文字。最终识别文字算法采用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是一个识别阿拉伯文的轻量级OCR模型，支持直接预测。
+
+  - 更多详情参考：
+    - [Real-time Scene Text Detection with Differentiable Binarization](https://arxiv.org/pdf/1911.08947.pdf)
+    - [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)
+
+
+
+## 二、安装
+
+- ### 1、环境依赖  
+
+  - PaddlePaddle >= 2.0.2  
+
+  - Python >= 3.6  
+
+  - PaddleOCR >= 2.0.1   | [如何安装PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/quickstart.md#1)
+
+  - PaddleHub >= 2.0.0   | [如何安装paddlehub](../../../../docs/docs_ch/get_start/installation.rst)
+
+  - Paddle2Onnx >= 0.9.0   | [如何安装paddle2onnx](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/README_zh.md)
+
+  - shapely
+
+  - pyclipper
+
+  - ```shell
+    $ pip3.6 install "paddleocr==2.3.0.2"
+    $ pip3.6 install shapely -i https://pypi.tuna.tsinghua.edu.cn/simple
+    $ pip3.6 install pyclipper -i https://pypi.tuna.tsinghua.edu.cn/simple
+    ```
+  - **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。**  
+
+- ### 2、安装
+
+  - ```shell
+    $ hub install arabic_ocr_db_crnn_mobile
+    ```
+  - 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
+ | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
+
+
+
+## 三、模型API预测
+
+- ### 1、命令行预测
+
+  - ```shell
+    $ hub run arabic_ocr_db_crnn_mobile --input_path "/PATH/TO/IMAGE"
+    $ hub run arabic_ocr_db_crnn_mobile --input_path "/PATH/TO/IMAGE" --det True --rec True --use_angle_cls True  --box_thresh 0.7 --angle_classification_thresh 0.8 --visualization True
+    ```
+  - 通过命令行方式实现文字识别模型的调用，更多请见 [PaddleHub命令行指令](../../../../docs/docs_ch/tutorial/cmd_usage.rst)
+
+- ### 2、代码示例
+
+  - ```python
+    import paddlehub as hub
+    import cv2
+
+    ocr = hub.Module(name="arabic_ocr_db_crnn_mobile", enable_mkldnn=True)       # mkldnn加速仅在CPU下有效
+    result = ocr.recognize_text(images=[cv2.imread('/PATH/TO/IMAGE')])
+
+    # or
+    # result = ocr.recognize_text(paths=['/PATH/TO/IMAGE'])
+    ```
+
+- ### 3、API
+
+  - ```python
+    def __init__(self,
+                 det=True,
+                 rec=True,
+                 use_angle_cls=False,
+                 enable_mkldnn=False,  
+                 use_gpu=False,
+                 box_thresh=0.6,
+                 angle_classification_thresh=0.9)
+    ```
+
+    - 构造ArabicOCRDBCRNNMobile对象
+
+    - **参数**
+      - det(bool): 是否开启文字检测。默认为True。
+      - rec(bool): 是否开启文字识别。默认为True。
+      - use_angle_cls(bool): 是否开启方向分类, 用于设置使用方向分类器识别180度旋转文字。默认为False。
+      - enable_mkldnn(bool): 是否开启mkldnn加速CPU计算。该参数仅在CPU运行下设置有效。默认为False。
+      - 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；
+      - angle_classification_thresh(float): 文本方向分类置信度的阈值
+
+
+  - ```python
+    def recognize_text(images=[],
+                       paths=[],
+                       output_dir='ocr_result',
+                       visualization=False)
+    ```
+
+    - 预测API，检测输入图片中的所有文本的位置和识别文本结果。
+
+    - **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；
+      - output\_dir (str): 图片的保存路径，默认设为 ocr\_result；
+      - visualization (bool): 是否将识别结果保存为图片文件, 仅有检测开启时有效, 默认为False；
+
+    - **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(list): 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\[\]
+          - orientation(str): 分类的方向，仅在只有方向分类开启时输出
+          - score(float): 分类的得分，仅在只有方向分类开启时输出
+        - save_path (str, optional): 识别结果的保存路径，如不保存图片则save_path为''
+
+
+## 四、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 arabic_ocr_db_crnn_mobile
+    ```
+
+  - 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/arabic_ocr_db_crnn_mobile"
+    r = requests.post(url=url, headers=headers, data=json.dumps(data))
+
+    # 打印预测结果
+    print(r.json()["results"])
+    ```

Diff for: modules/image/text_recognition/arabic_ocr_db_crnn_mobile/__init__.py

@@ -0,0 +1,87 @@
+import paddlehub as hub
+from paddleocr.ppocr.utils.logging import get_logger
+from paddleocr.tools.infer.utility import base64_to_cv2
+from paddlehub.module.module import moduleinfo, runnable, serving
+
+
+@moduleinfo(
+    name="arabic_ocr_db_crnn_mobile",
+    version="1.1.0",
+    summary="ocr service",
+    author="PaddlePaddle",
+    type="cv/text_recognition")
+class ArabicOCRDBCRNNMobile:
+    def __init__(self,
+                 det=True,
+                 rec=True,
+                 use_angle_cls=False,
+                 enable_mkldnn=False,
+                 use_gpu=False,
+                 box_thresh=0.6,
+                 angle_classification_thresh=0.9):
+        """
+        initialize with the necessary elements
+        Args:
+            det(bool): Whether to use text detector.
+            rec(bool): Whether to use text recognizer.
+            use_angle_cls(bool): Whether to use text orientation classifier.
+            enable_mkldnn(bool): Whether to enable mkldnn.
+            use_gpu (bool): Whether to use gpu.
+            box_thresh(float): the threshold of the detected text box's confidence
+            angle_classification_thresh(float): the threshold of the angle classification confidence
+        """
+        self.logger = get_logger()
+        self.model = hub.Module(
+            name="multi_languages_ocr_db_crnn",
+            lang="arabic",
+            det=det,
+            rec=rec,
+            use_angle_cls=use_angle_cls,
+            enable_mkldnn=enable_mkldnn,
+            use_gpu=use_gpu,
+            box_thresh=box_thresh,
+            angle_classification_thresh=angle_classification_thresh)
+        self.model.name = self.name
+
+    def recognize_text(self, images=[], paths=[], output_dir='ocr_result', visualization=False):
+        """
+        Get the text 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
+            output_dir (str): The directory to store output images.
+            visualization (bool): Whether to save image or not.
+        Returns:
+            res (list): The result of text detection box and save path of images.
+        """
+        all_results = self.model.recognize_text(
+            images=images, paths=paths, output_dir=output_dir, visualization=visualization)
+        return all_results
+
+    @serving
+    def serving_method(self, images, **kwargs):
+        """
+        Run as a service.
+        """
+        images_decode = [base64_to_cv2(image) for image in images]
+        results = self.recognize_text(images_decode, **kwargs)
+        return results
+
+    @runnable
+    def run_cmd(self, argvs):
+        """
+        Run as a command
+        """
+        results = self.model.run_cmd(argvs)
+        return results
+
+    def export_onnx_model(self, dirname: str, input_shape_dict=None, opset_version=10):
+        '''
+        Export the model to ONNX format.
+
+        Args:
+            dirname(str): The directory to save the onnx model.
+            input_shape_dict: dictionary ``{ input_name: input_value }, eg. {'x': [-1, 3, -1, -1]}``
+            opset_version(int): operator set
+        '''
+        self.model.export_onnx_model(dirname=dirname, input_shape_dict=input_shape_dict, opset_version=opset_version)

Diff for: modules/image/text_recognition/arabic_ocr_db_crnn_mobile/module.py

@@ -0,0 +1,4 @@
+paddleocr>=2.3.0.2
+paddle2onnx>=0.9.0
+shapely
+pyclipper