Merge pull request #297 from MissPenguin/develop

add hub serving

deploy/ocr_hubserving/ocr_det/config.json

+{
+    "modules_info": {
+        "ocr_det": {
+            "init_args": {
+                "version": "1.0.0",
+                "det_model_dir": "./inference/ch_det_mv3_db/",
+                "use_gpu": true
+            },
+            "predict_args": {
+                "visualization": false
+            }
+        }
+    }
+}

deploy/ocr_hubserving/ocr_det/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 tools.infer.utility import draw_boxes, base64_to_cv2
+from tools.infer.predict_det import TextDetector
+
+class Config(object):
+    pass
+
+@moduleinfo(
+    name="ocr_det",
+    version="1.0.0",
+    summary="ocr detection service",
+    author="paddle-dev",
+    author_email="paddle-dev@baidu.com",
+    type="cv/text_recognition")
+class OCRDet(hub.Module):
+    def _initialize(self, 
+                    det_model_dir="",
+                    det_algorithm="DB",
+                    use_gpu=False
+                    ):
+        """
+        initialize with the necessary elements
+        """
+        self.config = Config()
+        self.config.use_gpu = use_gpu
+        if use_gpu:
+            try:
+                _places = os.environ["CUDA_VISIBLE_DEVICES"]
+                int(_places[0])
+                print("use gpu: ", use_gpu)
+                print("CUDA_VISIBLE_DEVICES: ", _places)
+            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.config.ir_optim = True
+        self.config.gpu_mem = 8000
+
+        #params for text detector
+        self.config.det_algorithm = det_algorithm
+        self.config.det_model_dir = det_model_dir
+        # self.config.det_model_dir = "./inference/det/"
+
+        #DB parmas
+        self.config.det_db_thresh =0.3
+        self.config.det_db_box_thresh =0.5
+        self.config.det_db_unclip_ratio =2.0
+
+        #EAST parmas
+        self.config.det_east_score_thresh = 0.8
+        self.config.det_east_cover_thresh = 0.1
+        self.config.det_east_nms_thresh = 0.2
+
+    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 det_text(self,
+                images=[],
+                paths=[],
+                det_max_side_len=960,
+                draw_img_save='ocr_det_result',
+                visualization=False):
+        """
+        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 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."
+        
+        self.config.det_max_side_len = det_max_side_len
+        text_detector = TextDetector(self.config)
+        all_results = []
+        for img in predicted_data:
+            result = {'save_path': ''}
+            if img is None:
+                logger.info("error in loading image")
+                result['data'] = []
+                all_results.append(result)
+                continue
+            dt_boxes, elapse = text_detector(img)
+            print("Predict time : ", elapse)
+            result['data'] = dt_boxes.astype(np.int).tolist()
+
+            if visualization:
+                image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+                draw_img = draw_boxes(image, dt_boxes)
+                draw_img = np.array(draw_img)
+                if not os.path.exists(draw_img_save):
+                    os.makedirs(draw_img_save)
+                saved_name = 'ndarray_{}.jpg'.format(time.time())
+                save_file_path = os.path.join(draw_img_save, saved_name)
+                cv2.imwrite(save_file_path, draw_img[:, :, ::-1])
+                print("The visualized image saved in {}".format(save_file_path))
+                result['save_path'] = save_file_path
+
+            all_results.append(result)
+        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.det_text(images_decode, **kwargs)
+        return results
+
+   
+if __name__ == '__main__':
+    ocr = OCRDet()
+    image_path = [
+        './doc/imgs/11.jpg',
+        './doc/imgs/12.jpg',
+    ]
+    res = ocr.det_text(paths=image_path, visualization=True)
+    print(res)
\ No newline at end of file

deploy/ocr_hubserving/ocr_rec/config.json

+{
+    "modules_info": {
+        "ocr_rec": {
+            "init_args": {
+                "version": "1.0.0",
+                "det_model_dir": "./inference/ch_rec_mv3_crnn/",
+                "use_gpu": true
+            },
+            "predict_args": {
+            }
+        }
+    }
+}

deploy/ocr_hubserving/ocr_rec/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 tools.infer.utility import base64_to_cv2
+from tools.infer.predict_rec import TextRecognizer
+
+class Config(object):
+    pass
+
+@moduleinfo(
+    name="ocr_rec",
+    version="1.0.0",
+    summary="ocr recognition service",
+    author="paddle-dev",
+    author_email="paddle-dev@baidu.com",
+    type="cv/text_recognition")
+class OCRRec(hub.Module):
+    def _initialize(self, 
+                    rec_model_dir="",
+                    rec_algorithm="CRNN",
+                    rec_char_dict_path="./ppocr/utils/ppocr_keys_v1.txt",
+                    rec_batch_num=30,
+                    use_gpu=False
+                    ):
+        """
+        initialize with the necessary elements
+        """
+        self.config = Config()
+        self.config.use_gpu = use_gpu
+        if use_gpu:
+            try:
+                _places = os.environ["CUDA_VISIBLE_DEVICES"]
+                int(_places[0])
+                print("use gpu: ", use_gpu)
+                print("CUDA_VISIBLE_DEVICES: ", _places)
+            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.config.ir_optim = True
+        self.config.gpu_mem = 8000
+
+        #params for text recognizer
+        self.config.rec_algorithm = rec_algorithm
+        self.config.rec_model_dir = rec_model_dir
+        # self.config.rec_model_dir = "./inference/rec/"
+        
+        self.config.rec_image_shape = "3, 32, 320"
+        self.config.rec_char_type = 'ch'
+        self.config.rec_batch_num = rec_batch_num
+        self.config.rec_char_dict_path = rec_char_dict_path
+        self.config.use_space_char = True
+
+    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 rec_text(self,
+                images=[],
+                paths=[]):
+        """
+        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
+        Returns:
+            res (list): The result of text detection box and save path of images.
+        """
+
+        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."
+        
+        text_recognizer = TextRecognizer(self.config)
+        img_list = []
+        for img in predicted_data:
+            if img is None:
+                continue
+            img_list.append(img)
+        try:
+            rec_res, predict_time = text_recognizer(img_list)
+        except Exception as e:
+            print(e)
+            return []
+        return rec_res
+
+    @serving
+    def serving_method(self, images, **kwargs):
+        """
+        Run as a service.
+        """
+        images_decode = [base64_to_cv2(image) for image in images]
+        results = self.det_text(images_decode, **kwargs)
+        return results
+
+   
+if __name__ == '__main__':
+    ocr = OCRRec()
+    image_path = [
+        './doc/imgs_words/ch/word_1.jpg',
+        './doc/imgs_words/ch/word_2.jpg',
+        './doc/imgs_words/ch/word_3.jpg',
+    ]
+    res = ocr.rec_text(paths=image_path)
+    print(res)
\ No newline at end of file

deploy/ocr_hubserving/ocr_system/config.json

+{
+    "modules_info": {
+        "ocr_system": {
+            "init_args": {
+                "version": "1.0.0",
+                "det_model_dir": "./inference/ch_det_mv3_db/",
+                "rec_model_dir": "./inference/ch_rec_mv3_crnn/",
+                "use_gpu": true
+            },
+            "predict_args": {
+                "visualization": false
+            }
+        }
+    }
+}
+

deploy/ocr_hubserving/ocr_system/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 tools.infer.utility import draw_ocr, base64_to_cv2
+from tools.infer.predict_system import TextSystem
+
+
+class Config(object):
+    pass
+
+@moduleinfo(
+    name="ocr_system",
+    version="1.0.0",
+    summary="ocr system service",
+    author="paddle-dev",
+    author_email="paddle-dev@baidu.com",
+    type="cv/text_recognition")
+class OCRSystem(hub.Module):
+    def _initialize(self, 
+                    det_model_dir="",
+                    det_algorithm="DB",
+                    rec_model_dir="",
+                    rec_algorithm="CRNN",
+                    rec_char_dict_path="./ppocr/utils/ppocr_keys_v1.txt",
+                    rec_batch_num=30,
+                    use_gpu=False
+                    ):
+        """
+        initialize with the necessary elements
+        """
+        self.config = Config()
+        self.config.use_gpu = use_gpu
+        if use_gpu:
+            try:
+                _places = os.environ["CUDA_VISIBLE_DEVICES"]
+                int(_places[0])
+                print("use gpu: ", use_gpu)
+                print("CUDA_VISIBLE_DEVICES: ", _places)
+            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.config.ir_optim = True
+        self.config.gpu_mem = 8000
+
+        #params for text detector
+        self.config.det_algorithm = det_algorithm
+        self.config.det_model_dir = det_model_dir
+        # self.config.det_model_dir = "./inference/det/"
+
+        #DB parmas
+        self.config.det_db_thresh =0.3
+        self.config.det_db_box_thresh =0.5
+        self.config.det_db_unclip_ratio =2.0
+
+        #EAST parmas
+        self.config.det_east_score_thresh = 0.8
+        self.config.det_east_cover_thresh = 0.1
+        self.config.det_east_nms_thresh = 0.2
+
+        #params for text recognizer
+        self.config.rec_algorithm = rec_algorithm
+        self.config.rec_model_dir = rec_model_dir
+        # self.config.rec_model_dir = "./inference/rec/"
+
+        self.config.rec_image_shape = "3, 32, 320"
+        self.config.rec_char_type = 'ch'
+        self.config.rec_batch_num = rec_batch_num
+        self.config.rec_char_dict_path = rec_char_dict_path
+        self.config.use_space_char = True
+
+    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 recognize_text(self,
+                       images=[],
+                       paths=[],
+                       det_max_side_len=960,
+                       draw_img_save='ocr_result',
+                       visualization=False,
+                       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 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."
+
+        self.config.det_max_side_len = det_max_side_len
+        text_sys = TextSystem(self.config)
+        cnt = 0
+        all_results = []
+        for img in predicted_data:
+            result = {'save_path': ''}
+            if img is None:
+                logger.info("error in loading image")
+                result['data'] = []
+                all_results.append(result)
+                continue
+            starttime = time.time()
+            dt_boxes, rec_res = text_sys(img)
+            elapse = time.time() - starttime
+            cnt += 1
+            print("Predict time of image %d: %.3fs" % (cnt, elapse))
+            dt_num = len(dt_boxes)
+            rec_res_final = []
+            for dno in range(dt_num):
+                text, score = rec_res[dno]
+                # if the recognized text confidence score is lower than text_thresh, then drop it
+                if score >= text_thresh:
+                    # text_str = "%s, %.3f" % (text, score)
+                    # print(text_str)
+                    rec_res_final.append(
+                        {
+                            'text': text,
+                            'confidence': float(score),
+                            'text_box_position': dt_boxes[dno].astype(np.int).tolist()
+                        }
+                    )
+            result['data'] = rec_res_final
+
+            if visualization:
+                image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+                boxes = dt_boxes
+                txts = [rec_res[i][0] for i in range(len(rec_res))]
+                scores = [rec_res[i][1] for i in range(len(rec_res))]
+
+                draw_img = draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5)
+                if not os.path.exists(draw_img_save):
+                    os.makedirs(draw_img_save)
+                saved_name = 'ndarray_{}.jpg'.format(time.time())
+                save_file_path = os.path.join(draw_img_save, saved_name)
+                cv2.imwrite(save_file_path, draw_img[:, :, ::-1])
+                print("The visualized image saved in {}".format(save_file_path))
+                result['save_path'] = save_file_path
+
+            all_results.append(result)
+        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
+
+   
+if __name__ == '__main__':
+    ocr = OCRSystem()
+    image_path = [
+        './doc/imgs/11.jpg',
+        './doc/imgs/12.jpg',
+    ]
+    res = ocr.recognize_text(paths=image_path, visualization=True)
+    print(res)
\ No newline at end of file

doc/doc_ch/hubserving.md

+# 服务部署
+
+PaddleOCR提供2种服务部署方式：
+- 基于HubServing的部署：已集成到PaddleOCR中（[code](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/hubserving)），按照本教程使用；
+- 基于PaddleServing的部署：详见PaddleServing官网[demo](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/ocr)，后续也将集成到PaddleOCR。  
+
+服务部署目录下包括检测、识别、2阶段串联三种服务包，根据需求选择相应的服务包进行安装和启动。目录如下：
+```
+deploy/hubserving/
+  └─  ocr_det     检测模块服务包
+  └─  ocr_rec     识别模块服务包
+  └─  ocr_system  检测+识别串联服务包
+```
+
+每个服务包下包含3个文件。以2阶段串联服务包为例，目录如下：
+```
+deploy/hubserving/ocr_system/
+  └─  __init__.py    空文件
+  └─  config.json    配置文件，启动服务时作为参数传入
+  └─  module.py      主模块，包含服务的完整逻辑
+```
+
+## 启动服务
+以下步骤以检测+识别2阶段串联服务为例，如果只需要检测服务或识别服务，替换相应文件路径即可。
+### 1. 安装paddlehub
+```pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple```
+
+### 2. 安装服务模块
+PaddleOCR提供3种服务模块，根据需要安装所需模块。如：  
+安装检测服务模块：  
+```hub install deploy/hubserving/ocr_det/```  
+或，安装识别服务模块：  
+```hub install deploy/hubserving/ocr_rec/```  
+或，安装检测+识别串联服务模块：  
+```hub install deploy/hubserving/ocr_system/```  
+
+### 3. 修改配置文件
+在config.json中指定模型路径、是否使用GPU、是否对结果做可视化等参数，如，串联服务ocr_system的配置：
+```python
+{
+    "modules_info": {
+        "ocr_system": {
+            "init_args": {
+                "version": "1.0.0",
+                "det_model_dir": "./inference/det/",
+                "rec_model_dir": "./inference/rec/",
+                "use_gpu": true
+            },
+            "predict_args": {
+                "visualization": false
+            }
+        }
+    }
+}
+```
+其中，模型路径对应的模型为```inference模型```。
+
+### 4. 运行启动命令
+```hub serving start -m ocr_system --config hubserving/ocr_det/config.json```  
+
+这样就完成了一个服务化API的部署，默认端口号为8866。
+
+**NOTE:** 如使用GPU预测(即，config中use_gpu置为true)，则需要在启动服务之前，设置CUDA_VISIBLE_DEVICES环境变量，如：```export CUDA_VISIBLE_DEVICES=0```，否则不用设置。
+
+## 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):
+    return base64.b64encode(image).decode('utf8')
+
+# 发送HTTP请求
+data = {'images':[cv2_to_base64(open("./doc/imgs/11.jpg", 'rb').read())]}
+headers = {"Content-type": "application/json"}
+# url = "http://127.0.0.1:8866/predict/ocr_det"
+# url = "http://127.0.0.1:8866/predict/ocr_rec"
+url = "http://127.0.0.1:8866/predict/ocr_system"
+r = requests.post(url=url, headers=headers, data=json.dumps(data))
+
+# 打印预测结果
+print(r.json()["results"])
+```
+
+你可能需要根据实际情况修改```url```字符串中的端口号和服务模块名称。  
+
+上面所示代码都已写入测试脚本，可直接运行命令：```python tools/test_hubserving.py```
+
+## 自定义修改服务模块
+如果需要修改服务逻辑，你一般需要操作以下步骤：  
+
+1、 停止服务  
+```hub serving stop -m ocr_system```  
+
+2、 到相应的module.py文件中根据实际需求修改代码  
+
+3、 卸载旧服务包  
+```hub uninstall ocr_system```  
+
+4、 安装修改后的新服务包  
+```hub install deploy/hubserving/ocr_system/```  
+

tools/infer/utility.py

@@ -302,6 +302,25 @@ def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
     return np.array(blank_img)
 
 
+def base64_to_cv2(b64str):
+    import base64
+    data = base64.b64decode(b64str.encode('utf8'))
+    data = np.fromstring(data, np.uint8)
+    data = cv2.imdecode(data, cv2.IMREAD_COLOR)
+    return data
+
+
+def draw_boxes(image, boxes, scores=None, drop_score=0.5):
+    if scores is None:
+        scores = [1] * len(boxes)
+    for (box, score) in zip(boxes, scores):
+        if score < drop_score:
+            continue
+        box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64)
+        image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
+    return image
+
+
 if __name__ == '__main__':
     test_img = "./doc/test_v2"
     predict_txt = "./doc/predict.txt"

tools/test_hubserving.py

+#!usr/bin/python
+# -*- coding: utf-8 -*-
+
+import requests
+import json
+import cv2
+import base64
+import time
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode('utf8')
+
+start = time.time()
+# 发送HTTP请求
+data = {'images':[cv2_to_base64(open("./doc/imgs/11.jpg", 'rb').read())]}
+headers = {"Content-type": "application/json"}
+# url = "http://127.0.0.1:8866/predict/ocr_det"
+# url = "http://127.0.0.1:8866/predict/ocr_rec"
+url = "http://127.0.0.1:8866/predict/ocr_system"
+r = requests.post(url=url, headers=headers, data=json.dumps(data))
+end = time.time()
+
+# 打印预测结果
+print(r.json()["results"])
+print("time cost: ", end - start)