move out visulization from hubserving

deploy/hubserving/ocr_det/config.json

@@ -6,7 +6,6 @@
                 "use_gpu": true
             },
             "predict_args": {
-                "visualization": false
             }
         }
     },

deploy/hubserving/ocr_det/module.py

@@ -19,7 +19,7 @@ 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.utility import base64_to_cv2
 from tools.infer.predict_det import TextDetector
 
 
@@ -68,16 +68,12 @@ class OCRDet(hub.Module):
 
     def predict(self,
                 images=[],
-                paths=[],
-                draw_img_save='ocr_det_result',
-                visualization=False):
+                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
-            draw_img_save (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.
         """
@@ -93,29 +89,21 @@ class OCRDet(hub.Module):
         
         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)
+                all_results.append([])
                 continue
             dt_boxes, elapse = self.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)
+            logger.info("Predict time : {}".format(elapse))
+
+            rec_res_final = []
+            for dno in range(len(dt_boxes)):
+                rec_res_final.append(
+                    {
+                        'text_region': dt_boxes[dno].astype(np.int).tolist()
+                    }
+                )
+            all_results.append(rec_res_final)
         return all_results
 
     @serving
@@ -134,5 +122,5 @@ if __name__ == '__main__':
         './doc/imgs/11.jpg',
         './doc/imgs/12.jpg',
     ]
-    res = ocr.predict(paths=image_path, visualization=True)
+    res = ocr.predict(paths=image_path)
     print(res)
\ No newline at end of file

deploy/hubserving/ocr_rec/module.py

@@ -92,12 +92,24 @@ class OCRRec(hub.Module):
             if img is None:
                 continue
             img_list.append(img)
+            
+        rec_res_final = []
         try:
             rec_res, predict_time = self.text_recognizer(img_list)
+            for dno in range(len(rec_res)):
+                text, score = rec_res[dno]
+                rec_res_final.append(
+                    {
+                        'text': text,
+                        'confidence': float(score),
+                    }
+                )
         except Exception as e:
             print(e)
-            return []
-        return rec_res
+            return [[]]
+
+        return [rec_res_final]
+
 
     @serving
     def serving_method(self, images, **kwargs):

deploy/hubserving/ocr_system/config.json

@@ -6,7 +6,6 @@
                 "use_gpu": true
             },
             "predict_args": {
-                "visualization": false
             }
         }
     },

deploy/hubserving/ocr_system/module.py

@@ -19,7 +19,7 @@ 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.utility import base64_to_cv2
 from tools.infer.predict_system import TextSystem
 
 
@@ -68,18 +68,12 @@ class OCRSystem(hub.Module):
 
     def predict(self,
                        images=[],
-                       paths=[],
-                       draw_img_save='ocr_result',
-                       visualization=False,
-                       text_thresh=0.5):
+                       paths=[]):
         """
         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
-            draw_img_save (str): The directory to store output images.
-            visualization (bool): Whether to save image or not.
-            text_thresh(float): the threshold of the recognize chinese texts' confidence
         Returns:
             res (list): The result of chinese texts and save path of images.
         """
@@ -93,53 +87,30 @@ class OCRSystem(hub.Module):
 
         assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
 
-        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)
+                all_results.append([])
                 continue
             starttime = time.time()
             dt_boxes, rec_res = self.text_sys(img)
             elapse = time.time() - starttime
-            cnt += 1
-            print("Predict time of image %d: %.3fs" % (cnt, elapse))
+            logger.info("Predict time: {}".format(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()
+                        'text_region': 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)
+            all_results.append(rec_res_final)
         return all_results
 
     @serving
@@ -158,5 +129,5 @@ if __name__ == '__main__':
         './doc/imgs/11.jpg',
         './doc/imgs/12.jpg',
     ]
-    res = ocr.predict(paths=image_path, visualization=False)
+    res = ocr.predict(paths=image_path)
     print(res)
\ No newline at end of file

doc/doc_ch/serving.md

@@ -23,8 +23,14 @@ deploy/hubserving/ocr_system/
 
 ## 快速启动服务
 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```
+### 1. 准备环境
+```shell
+# 安装paddlehub  
+pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+# 设置环境变量  
+export PYTHONPATH=.
+```   
 
 ### 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： 
@@ -75,7 +81,6 @@ $ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \
                 "use_gpu": true
             },
             "predict_args": {
-                "visualization": false
             }
         }
     },
@@ -99,32 +104,21 @@ hub serving start -c deploy/hubserving/ocr_system/config.json
 ```  
 
 ## 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:
+配置好服务端，可使用以下命令发送预测请求，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"])
-```
+```python tools/test_hubserving.py server_url image_path```  
 
-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。  
+需要给脚本传递2个参数：  
+- **server_url**：服务地址，格式为  
+`http://[ip_address]:[port]/predict/[module_name]`  
+例如，如果使用配置文件启动检测、识别、检测+识别2阶段服务，那么发送请求的url将分别是：  
+`http://127.0.0.1:8866/predict/ocr_det`  
+`http://127.0.0.1:8867/predict/ocr_rec`  
+`http://127.0.0.1:8868/predict/ocr_system`  
+- **image_path**：测试图像路径，可以是单张图片路径，也可以是图像集合目录路径  
 
-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```
+访问示例：  
+```python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/```
 
 ## 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（以修改`ocr_system`为例）：  

tools/infer/predict_system.py

@@ -117,16 +117,12 @@ def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     text_sys = TextSystem(args)
     is_visualize = True
-    tackle_img_num = 0
     for image_file in image_file_list:
         img = cv2.imread(image_file)
         if img is None:
             logger.info("error in loading image:{}".format(image_file))
             continue
         starttime = time.time()
-        tackle_img_num += 1
-        if not args.use_gpu and tackle_img_num % 30 == 0:
-            text_sys = TextSystem(args)
         dt_boxes, rec_res = text_sys(img)
         elapse = time.time() - starttime
         print("Predict time of %s: %.3fs" % (image_file, elapse))

tools/infer/utility.py

@@ -91,7 +91,7 @@ def create_predictor(args, mode):
         config.enable_use_gpu(args.gpu_mem, 0)
     else:
         config.disable_gpu()
-        config.enable_mkldnn()
+        # config.enable_mkldnn()
         config.set_cpu_math_library_num_threads(4)
     #config.enable_memory_optim()
     config.disable_glog_info()

tools/test_hubserving.py

-#!usr/bin/python
-# -*- coding: utf-8 -*-
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import sys
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+import cv2
+import numpy as np
+import time
+from PIL import Image
+from ppocr.utils.utility import get_image_file_list
+from tools.infer.utility import draw_ocr, draw_boxes
 
 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)
+
+def draw_server_result(image_file, res):
+    img = cv2.imread(image_file)
+    image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+    if len(res) == 0:
+        return np.array(image)
+    keys = res[0].keys()
+    if 'text_region' not in keys: # for ocr_rec, draw function is invalid 
+        print("draw function is invalid for ocr_rec!")
+        return None
+    elif 'text' not in keys: # for ocr_det
+        print("draw text boxes only!")
+        boxes = []
+        for dno in range(len(res)):
+            boxes.append(res[dno]['text_region'])
+        boxes = np.array(boxes)
+        draw_img = draw_boxes(image, boxes)
+        return draw_img
+    else: # for ocr_system
+        print("draw boxes and texts!")
+        boxes = []
+        texts = []
+        scores = []
+        for dno in range(len(res)):
+            boxes.append(res[dno]['text_region'])
+            texts.append(res[dno]['text'])
+            scores.append(res[dno]['confidence'])
+        boxes = np.array(boxes)
+        scores = np.array(scores)
+        draw_img = draw_ocr(image, boxes, texts, scores, draw_txt=True, drop_score=0.5)       
+        return draw_img
+
+
+def main(url, image_path):
+    image_file_list = get_image_file_list(image_path)
+    is_visualize = False
+    headers = {"Content-type": "application/json"}
+    cnt = 0
+    total_time = 0
+    for image_file in image_file_list:
+        img = open(image_file, 'rb').read()
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+
+        # 发送HTTP请求
+        starttime = time.time()
+        data = {'images':[cv2_to_base64(img)]}
+        r = requests.post(url=url, headers=headers, data=json.dumps(data))
+        elapse = time.time() - starttime
+        total_time += elapse
+        print("Predict time of %s: %.3fs" % (image_file, elapse))
+        res = r.json()["results"][0]
+        # print(res)
+
+        if is_visualize:
+            draw_img = draw_server_result(image_file, res)
+            if draw_img is not None:
+                draw_img_save = "./server_results/"
+                if not os.path.exists(draw_img_save):
+                    os.makedirs(draw_img_save)
+                cv2.imwrite(
+                    os.path.join(draw_img_save, os.path.basename(image_file)),
+                    draw_img[:, :, ::-1])
+                print("The visualized image saved in {}".format(
+                    os.path.join(draw_img_save, os.path.basename(image_file))))
+        cnt += 1
+        if cnt % 100 == 0:
+            print(cnt, "processed")
+    print("avg time cost: ", float(total_time)/cnt)
+
+if __name__  == '__main__':
+    if len(sys.argv) != 3:
+        print("Usage: %s server_url image_path" % sys.argv[0])
+    else:
+        server_url = sys.argv[1]
+        image_path = sys.argv[2]
+        main(server_url, image_path)
\ No newline at end of file