update readme, return bbox

deploy/pdserving/README.md

@@ -31,8 +31,6 @@ PaddleOCR operating environment and Paddle Serving operating environment are nee
 
 1. Please prepare PaddleOCR operating environment reference [link](../../doc/doc_ch/installation.md).
 
-   Download the corresponding paddle whl package according to the environment, it is recommended to install version 2.2.2
-
 2. The steps of PaddleServing operating environment prepare are as follows:
 
 
@@ -191,6 +189,15 @@ The recognition model is the same.
     ```
 ## C++ Serving
 
+Service deployment based on python obviously has the advantage of convenient secondary development. However, the real application often needs to pursue better performance. PaddleServing also provides a more performant C++ deployment version.
+
+The C++ service deployment is the same as python in the environment setup and data preparation stages, the difference is when the service is started and the client sends requests.
+
+| Language | Speed ​​| Secondary development | Do you need to compile |
+|-----|-----|---------|------------|
+| C++ | fast | Slightly difficult | Single model prediction does not need to be compiled, multi-model concatenation needs to be compiled |
+| python | general | easy | single-model/multi-model no compilation required |
+
 1. Compile Serving
 
    To improve predictive performance, C++ services also provide multiple model concatenation services. Unlike Python Pipeline services, multiple model concatenation requires the pre - and post-model processing code to be written on the server side, so local recompilation is required to generate serving. Specific may refer to the official document: [how to compile Serving](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Compile_EN.md)
@@ -198,12 +205,28 @@ The recognition model is the same.
 2. Run the following command to start the service.
     ```
     # Start the service and save the running log in log.txt
-    python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralRecOp --port 9293 &>log.txt &
+    python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
     ```
     After the service is successfully started, a log similar to the following will be printed in log.txt
     ![](./imgs/start_server.png)
 
 3. Send service request
+
+   Due to the need for pre and post-processing in the C++Server part, in order to speed up the input to the C++Server is only the base64 encoded string of the picture, it needs to be manually modified
+   Change the feed_type field and shape field in ppocrv2_det_client/serving_client_conf.prototxt to the following:
+
+   ```
+    feed_var {
+    name: "x"
+    alias_name: "x"
+    is_lod_tensor: false
+    feed_type: 20
+    shape: 1
+    }
+   ```
+   
+   start the client:
+
     ```
     python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
     ```

deploy/pdserving/README_CN.md

@@ -6,6 +6,8 @@ PaddleOCR提供2种服务部署方式：
 - 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，使用方法参考[文档](../../deploy/hubserving/readme.md)；
 - 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，按照本教程使用。
 
+* AIStudio演示案例可参考 [基于PaddleServing的OCR服务化部署实战](https://aistudio.baidu.com/aistudio/projectdetail/3630726)
+
 # 基于PaddleServing的服务部署
 
 本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署PP-OCR动态图模型的pipeline在线服务。
@@ -32,8 +34,6 @@ PaddleOCR提供2种服务部署方式：
 
 - 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
 
-  根据环境下载对应的paddlepaddle whl包，推荐安装2.2.2版本
-
 - 准备PaddleServing的运行环境，步骤如下
 
 ```bash
@@ -197,9 +197,24 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
 
 C++ 服务部署在环境搭建和数据准备阶段与 python 相同，区别在于启动服务和客户端发送请求时不同。
 
+| 语言 | 速度 | 二次开发 | 是否需要编译 |
+|-----|-----|---------|------------|
+| C++ | 很快 | 略有难度 | 单模型预测无需编译，多模型串联需要编译 |
+| python | 一般 ｜ 容易 | 单模型/多模型 均无需编译|
+
 1. 准备 Serving 环境
 
-为了提高预测性能，C++ 服务同样提供了多模型串联服务。与python pipeline服务不同，多模型串联的过程中需要将模型前后处理代码写在服务端，因此需要在本地重新编译生成serving。具体可参考官方文档：[如何编译Serving](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Compile_CN.md)
+为了提高预测性能，C++ 服务同样提供了多模型串联服务。与python pipeline服务不同，多模型串联的过程中需要将模型前后处理代码写在服务端，因此需要在本地重新编译生成serving。
+
+首先需要下载Serving代码库, 把OCR文本检测预处理相关代码替换到Serving库中
+```
+git clone https://github.com/PaddlePaddle/Serving
+
+cp -rf general_detection_op.cpp Serving/core/general-server/op
+
+```
+
+具体可参考官方文档：[如何编译Serving](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Compile_CN.md)，注意需要开启 WITH_OPENCV 选项。
 
 完成编译后，注意要安装编译出的三个whl包，并设置SERVING_BIN环境变量。
 
@@ -209,12 +224,25 @@ C++ 服务部署在环境搭建和数据准备阶段与 python 相同，区别
 
     ```
     # 启动服务，运行日志保存在log.txt
-    python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralRecOp --port 9293 &>log.txt &
+    python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralInferOp --port 9293 &>log.txt &
     ```
     成功启动服务后，log.txt中会打印类似如下日志
     ![](./imgs/start_server.png)
 
 3. 发送服务请求：
+
+   由于需要在C++Server部分进行前后处理，为了加速传入C++Server的仅仅是图片的base64编码的字符串，故需要手动修改
+   ppocrv2_det_client/serving_client_conf.prototxt 中 feed_type 字段 和 shape 字段，修改成如下内容：
+   ```
+    feed_var {
+    name: "x"
+    alias_name: "x"
+    is_lod_tensor: false
+    feed_type: 20
+    shape: 1
+    }
+   ```
+   启动客户端
    ```
     python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
    ```

deploy/pdserving/ocr_cpp_client.py

@@ -47,9 +47,7 @@ for img_file in os.listdir(test_img_dir):
     res_list = []
     fetch_map = client.predict(
         feed={"x": image}, fetch=["save_infer_model/scale_0.tmp_1"], batch=True)
-    print("fetrch map:", fetch_map)
     one_batch_res = ocr_reader.postprocess(fetch_map, with_score=True)
     for res in one_batch_res:
-        res_list.append(res[0])
     res = {"res": str(res_list)}
     print(res)

deploy/pdserving/web_service.py

@@ -15,6 +15,7 @@ from paddle_serving_server.web_service import WebService, Op
 
 import logging
 import numpy as np
+import copy
 import cv2
 import base64
 # from paddle_serving_app.reader import OCRReader
@@ -29,14 +30,16 @@ _LOGGER = logging.getLogger()
 class DetOp(Op):
     def init_op(self):
         self.det_preprocess = Sequential([
-            DetResizeForTest(), Div(255),
-            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
+            DetResizeForTest(), 
+            Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+            Transpose(
                 (2, 0, 1))
         ])
         self.filter_func = FilterBoxes(10, 10)
         self.post_func = DBPostProcess({
             "thresh": 0.3,
-            "box_thresh": 0.5,
+            "box_thresh": 0.6,
             "max_candidates": 1000,
             "unclip_ratio": 1.5,
             "min_size": 3
@@ -79,8 +82,10 @@ class RecOp(Op):
         raw_im = input_dict["image"]
         data = np.frombuffer(raw_im, np.uint8)
         im = cv2.imdecode(data, cv2.IMREAD_COLOR)
-        dt_boxes = input_dict["dt_boxes"]
-        dt_boxes = self.sorted_boxes(dt_boxes)
+        self.dt_list = input_dict["dt_boxes"]
+        self.dt_list = self.sorted_boxes(self.dt_list)
+        # deepcopy to save origin dt_boxes
+        dt_boxes = copy.deepcopy(self.dt_list)
         feed_list = []
         img_list = []
         max_wh_ratio = 0
@@ -126,25 +131,29 @@ class RecOp(Op):
                 imgs[id] = norm_img
             feed = {"x": imgs.copy()}
             feed_list.append(feed)
-
         return feed_list, False, None, ""
 
     def postprocess(self, input_dicts, fetch_data, data_id, log_id):
-        res_list = []
+        rec_list = []
+        dt_num = len(self.dt_list)
         if isinstance(fetch_data, dict):
             if len(fetch_data) > 0:
                 rec_batch_res = self.ocr_reader.postprocess(
                     fetch_data, with_score=True)
                 for res in rec_batch_res:
-                    res_list.append(res[0])
+                    rec_list.append(res[0])
         elif isinstance(fetch_data, list):
             for one_batch in fetch_data:
                 one_batch_res = self.ocr_reader.postprocess(
                     one_batch, with_score=True)
                 for res in one_batch_res:
-                    res_list.append(res[0])
-
-        res = {"res": str(res_list)}
+                    rec_list.append(res[0])
+        result_list = []
+        for i in range(dt_num):
+            text = rec_list[i]
+            dt_box = self.dt_list[i]
+            result_list.append([text,dt_box.tolist()])
+        res = {"result": str(result_list)}
         return res, None, ""