first commit

deploy/pdserving/rec_web_server.py → deploy/pdserving/clas_local_server.py

@@ -18,60 +18,111 @@ import cv2
 import sys
 import numpy as np
 import os
-from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
-from paddle_serving_app.reader import Div, Normalize, Transpose
-from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
-if sys.argv[1] == 'gpu':
-    from paddle_serving_server_gpu.web_service import WebService
-elif sys.argv[1] == 'cpu':
-    from paddle_serving_server.web_service import WebService
 import time
 import re
 import base64
+from tools.infer.predict_cls import TextClassifier
+import tools.infer.utility as utility
+
+global_args = utility.parse_args()
+if global_args.use_gpu:
+    from paddle_serving_server_gpu.web_service import WebService
+else:
+    from paddle_serving_server.web_service import WebService
+
+
+class TextClassifierHelper(TextClassifier):
+    def __init__(self, args):
+        self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
+        self.cls_batch_num = args.rec_batch_num
+        self.label_list = args.label_list
+        self.cls_thresh = args.cls_thresh
+        self.fetch = [
+            "save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"
+        ]
+
+    def preprocess(self, img_list):
+        args = {}
+        img_num = len(img_list)
+        args["img_list"] = img_list
+        # Calculate the aspect ratio of all text bars
+        width_list = []
+        for img in img_list:
+            width_list.append(img.shape[1] / float(img.shape[0]))
+        # Sorting can speed up the cls process
+        indices = np.argsort(np.array(width_list))
+        args["indices"] = indices
+        cls_res = [['', 0.0]] * img_num
+        batch_num = self.cls_batch_num
+        predict_time = 0
+        beg_img_no, end_img_no = 0, img_num
+        norm_img_batch = []
+        max_wh_ratio = 0
+        for ino in range(beg_img_no, end_img_no):
+            h, w = img_list[indices[ino]].shape[0:2]
+            wh_ratio = w * 1.0 / 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(img_list[indices[ino]])
+            norm_img = norm_img[np.newaxis, :]
+            norm_img_batch.append(norm_img)
+        norm_img_batch = np.concatenate(norm_img_batch)
+        feed = {"image": norm_img_batch.copy()}
+        return feed, self.fetch, args
+
+    def postprocess(self, outputs, args):
+        prob_out = outputs[0]
+        label_out = outputs[1]
+        indices = args["indices"]
+        cls_res = [['', 0.0]] * len(label_out)
+        if len(label_out.shape) != 1:
+            prob_out, label_out = label_out, prob_out
+        for rno in range(len(label_out)):
+            label_idx = label_out[rno]
+            score = prob_out[rno][label_idx]
+            label = self.label_list[label_idx]
+            cls_res[indices[rno]] = [label, score]
+            if '180' in label and score > self.cls_thresh:
+                img_list[indices[rno]] = cv2.rotate(img_list[indices[rno]], 1)
+        return args["img_list"], cls_res
 
 
 class OCRService(WebService):
     def init_rec(self):
         self.ocr_reader = OCRReader()
+        self.text_classifier = TextClassifierHelper(global_args)
 
     def preprocess(self, feed=[], fetch=[]):
-        # TODO: to handle batch rec images
         img_list = []
         for feed_data in feed:
             data = base64.b64decode(feed_data["image"].encode('utf8'))
             data = np.fromstring(data, np.uint8)
             im = cv2.imdecode(data, cv2.IMREAD_COLOR)
             img_list.append(im)
-        feed_list = []
-        max_wh_ratio = 0
-        for i, boximg in enumerate(img_list):
-            h, w = boximg.shape[0:2]
-            wh_ratio = w * 1.0 / h
-            max_wh_ratio = max(max_wh_ratio, wh_ratio)
-        for img in img_list:
-            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
-            feed = {"image": norm_img}
-            feed_list.append(feed)
-        fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
-        return feed_list, fetch
+        feed, fetch, self.tmp_args = self.text_classifier.preprocess(img_list)
+        return feed, fetch
 
     def postprocess(self, feed={}, fetch=[], fetch_map=None):
-        rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
-        res_lst = []
-        for res in rec_res:
-            res_lst.append(res[0])
-        res = {"res": res_lst}
+        outputs = [fetch_map[x] for x in self.text_classifier.fetch]
+        for x in fetch_map.keys():
+            if ".lod" in x:
+                self.tmp_args[x] = fetch_map[x]
+        _, rec_res = self.text_classifier.postprocess(outputs, self.tmp_args)
+        res = {
+            "pred_text": [x[0] for x in rec_res],
+            "score": [str(x[1]) for x in rec_res]
+        }
         return res
 
 
-ocr_service = OCRService(name="ocr")
-ocr_service.load_model_config("ocr_rec_model")
-ocr_service.init_rec()
-if sys.argv[1] == 'gpu':
-    ocr_service.set_gpus("0")
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
-elif sys.argv[1] == 'cpu':
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
-ocr_service.run_rpc_service()
-ocr_service.run_web_service()
+if __name__ == "__main__":
+    ocr_service = OCRService(name="ocr")
+    ocr_service.load_model_config("cls_server")
+    ocr_service.init_rec()
+    if global_args.use_gpu:
+        ocr_service.prepare_server(
+            workdir="workdir", port=9292, device="gpu", gpuid=0)
+    else:
+        ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+    ocr_service.run_debugger_service()
+    ocr_service.run_web_service()

deploy/pdserving/clas_rpc_server.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.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import OCRReader
+import cv2
+import sys
+import numpy as np
+import os
+import time
+import re
+import base64
+from tools.infer.predict_cls import TextClassifier
+import tools.infer.utility as utility
+
+global_args = utility.parse_args()
+if global_args.use_gpu:
+    from paddle_serving_server_gpu.web_service import WebService
+else:
+    from paddle_serving_server.web_service import WebService
+
+
+class TextClassifierHelper(TextClassifier):
+    def __init__(self, args):
+        self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
+        self.cls_batch_num = args.rec_batch_num
+        self.label_list = args.label_list
+        self.cls_thresh = args.cls_thresh
+        self.fetch = [
+            "save_infer_model/scale_0.tmp_0", "save_infer_model/scale_1.tmp_0"
+        ]
+
+    def preprocess(self, img_list):
+        args = {}
+        img_num = len(img_list)
+        args["img_list"] = img_list
+        # Calculate the aspect ratio of all text bars
+        width_list = []
+        for img in img_list:
+            width_list.append(img.shape[1] / float(img.shape[0]))
+        # Sorting can speed up the cls process
+        indices = np.argsort(np.array(width_list))
+        args["indices"] = indices
+        cls_res = [['', 0.0]] * img_num
+        batch_num = self.cls_batch_num
+        predict_time = 0
+        beg_img_no, end_img_no = 0, img_num
+        norm_img_batch = []
+        max_wh_ratio = 0
+        for ino in range(beg_img_no, end_img_no):
+            h, w = img_list[indices[ino]].shape[0:2]
+            wh_ratio = w * 1.0 / 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(img_list[indices[ino]])
+            norm_img = norm_img[np.newaxis, :]
+            norm_img_batch.append(norm_img)
+        norm_img_batch = np.concatenate(norm_img_batch)
+        if img_num > 1:
+            feed = [{
+                "image": norm_img_batch[x]
+            } for x in range(norm_img_batch.shape[0])]
+        else:
+            feed = {"image": norm_img_batch[0]}
+        return feed, self.fetch, args
+
+    def postprocess(self, outputs, args):
+        prob_out = outputs[0]
+        label_out = outputs[1]
+        indices = args["indices"]
+        cls_res = [['', 0.0]] * len(label_out)
+        if len(label_out.shape) != 1:
+            prob_out, label_out = label_out, prob_out
+        for rno in range(len(label_out)):
+            label_idx = label_out[rno]
+            score = prob_out[rno][label_idx]
+            label = self.label_list[label_idx]
+            cls_res[indices[rno]] = [label, score]
+            if '180' in label and score > self.cls_thresh:
+                img_list[indices[rno]] = cv2.rotate(img_list[indices[rno]], 1)
+        return args["img_list"], cls_res
+
+
+class OCRService(WebService):
+    def init_rec(self):
+        self.ocr_reader = OCRReader()
+        self.text_classifier = TextClassifierHelper(global_args)
+
+    def preprocess(self, feed=[], fetch=[]):
+        # TODO: to handle batch rec images
+        img_list = []
+        for feed_data in feed:
+            data = base64.b64decode(feed_data["image"].encode('utf8'))
+            data = np.fromstring(data, np.uint8)
+            im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+            img_list.append(im)
+        feed, fetch, self.tmp_args = self.text_classifier.preprocess(img_list)
+        return feed, fetch
+
+    def postprocess(self, feed={}, fetch=[], fetch_map=None):
+        outputs = [fetch_map[x] for x in self.text_classifier.fetch]
+        for x in fetch_map.keys():
+            if ".lod" in x:
+                self.tmp_args[x] = fetch_map[x]
+        _, rec_res = self.text_classifier.postprocess(outputs, self.tmp_args)
+        res = {
+            "direction": [x[0] for x in rec_res],
+            "score": [str(x[1]) for x in rec_res]
+        }
+        return res
+
+
+if __name__ == "__main__":
+    ocr_service = OCRService(name="ocr")
+    ocr_service.load_model_config(global_args.cls_model_dir)
+    ocr_service.init_rec()
+    if global_args.use_gpu:
+        ocr_service.prepare_server(
+            workdir="workdir", port=9292, device="gpu", gpuid=0)
+    else:
+        ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+    ocr_service.run_rpc_service()
+    ocr_service.run_web_service()

deploy/pdserving/clas_web_client.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.
+# -*- coding: utf-8 -*-
+
+import requests
+import json
+import cv2
+import base64
+import os, sys
+import time
+
+
+def cv2_to_base64(image):
+    #data = cv2.imencode('.jpg', image)[1]
+    return base64.b64encode(image).decode(
+        'utf8')  #data.tostring()).decode('utf8')
+
+
+headers = {"Content-type": "application/json"}
+url = "http://127.0.0.1:9292/ocr/prediction"
+test_img_dir = "../../doc/imgs_words/ch/"
+for img_file in os.listdir(test_img_dir):
+    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+        image_data1 = file.read()
+    image = cv2_to_base64(image_data1)
+    data = {"feed": [{"image": image}], "fetch": ["res"]}
+    r = requests.post(url=url, headers=headers, data=json.dumps(data))
+    print(r.json())
+    break

deploy/pdserving/det_local_server.py

@@ -17,63 +17,91 @@ import cv2
 import sys
 import numpy as np
 import os
-from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, ResizeByFactor
-from paddle_serving_app.reader import Div, Normalize, Transpose
-from paddle_serving_app.reader import DBPostProcess, FilterBoxes
-if sys.argv[1] == 'gpu':
-    from paddle_serving_server_gpu.web_service import WebService
-elif sys.argv[1] == 'cpu':
-    from paddle_serving_server.web_service import WebService
 import time
 import re
 import base64
+from tools.infer.predict_det import TextDetector
+import tools.infer.utility as utility
+
+global_args = utility.parse_args()
+if global_args.use_gpu:
+    from paddle_serving_server_gpu.web_service import WebService
+else:
+    from paddle_serving_server.web_service import WebService
+
+
+class TextDetectorHelper(TextDetector):
+    def __init__(self, args):
+        super(TextDetectorHelper, self).__init__(args)
+        if self.det_algorithm == "SAST":
+            self.fetch = [
+                "bn_f_border4.output.tmp_2", "bn_f_tco4.output.tmp_2",
+                "bn_f_tvo4.output.tmp_2", "sigmoid_0.tmp_0"
+            ]
+        elif self.det_algorithm == "EAST":
+            self.fetch = ["sigmoid_0.tmp_0", "tmp_2"]
+        elif self.det_algorithm == "DB":
+            self.fetch = ["sigmoid_0.tmp_0"]
+
+    def preprocess(self, img):
+        img = img.copy()
+        im, ratio_list = self.preprocess_op(img)
+        if im is None:
+            return None, 0
+        return {
+            "image": im.copy()
+        }, self.fetch, {
+            "ratio_list": [ratio_list],
+            "ori_im": img
+        }
+
+    def postprocess(self, outputs, args):
+        outs_dict = {}
+        if self.det_algorithm == "EAST":
+            outs_dict['f_geo'] = outputs[0]
+            outs_dict['f_score'] = outputs[1]
+        elif self.det_algorithm == 'SAST':
+            outs_dict['f_border'] = outputs[0]
+            outs_dict['f_score'] = outputs[1]
+            outs_dict['f_tco'] = outputs[2]
+            outs_dict['f_tvo'] = outputs[3]
+        else:
+            outs_dict['maps'] = outputs[0]
+        dt_boxes_list = self.postprocess_op(outs_dict, args["ratio_list"])
+        dt_boxes = dt_boxes_list[0]
+        if self.det_algorithm == "SAST" and self.det_sast_polygon:
+            dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes,
+                                                         args["ori_im"].shape)
+        else:
+            dt_boxes = self.filter_tag_det_res(dt_boxes, args["ori_im"].shape)
+        return dt_boxes
 
 
-class OCRService(WebService):
+class DetService(WebService):
     def init_det(self):
-        self.det_preprocess = Sequential([
-            ResizeByFactor(32, 960), 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,
-            "max_candidates": 1000,
-            "unclip_ratio": 1.5,
-            "min_size": 3
-        })
+        self.text_detector = TextDetectorHelper(global_args)
 
     def preprocess(self, feed=[], fetch=[]):
         data = base64.b64decode(feed[0]["image"].encode('utf8'))
         data = np.fromstring(data, np.uint8)
         im = cv2.imdecode(data, cv2.IMREAD_COLOR)
-        self.ori_h, self.ori_w, _ = im.shape
-        det_img = self.det_preprocess(im)
-        _, self.new_h, self.new_w = det_img.shape
-        return {"image": det_img[np.newaxis, :].copy()}, ["concat_1.tmp_0"]
+        feed, fetch, self.tmp_args = self.text_detector.preprocess(im)
+        return feed, fetch
 
     def postprocess(self, feed={}, fetch=[], fetch_map=None):
-        det_out = fetch_map["concat_1.tmp_0"]
-        ratio_list = [
-            float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
-        ]
-        dt_boxes_list = self.post_func(det_out, [ratio_list])
-        dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
-        return {"dt_boxes": dt_boxes.tolist()}
+        outputs = [fetch_map[x] for x in fetch]
+        res = self.text_detector.postprocess(outputs, self.tmp_args)
+        return {"boxes": res.tolist()}
 
 
-ocr_service = OCRService(name="ocr")
-ocr_service.load_model_config("ocr_det_model")
-ocr_service.init_det()
-if sys.argv[1] == 'gpu':
-    ocr_service.set_gpus("0")
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
-    ocr_service.run_debugger_service(gpu=True)
-elif sys.argv[1] == 'cpu':
-    ocr_service.prepare_server(workdir="workdir", port=9292)
+if __name__ == "__main__":
+    ocr_service = DetService(name="ocr")
+    ocr_service.load_model_config("serving_server_dir")
+    ocr_service.init_det()
+    if global_args.use_gpu:
+        ocr_service.prepare_server(
+            workdir="workdir", port=9292, device="gpu", gpuid=0)
+    else:
+        ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
     ocr_service.run_debugger_service()
-ocr_service.init_det()
-ocr_service.run_web_service()
+    ocr_service.run_web_service()

deploy/pdserving/det_web_server.py → deploy/pdserving/det_rpc_server.py

@@ -17,62 +17,91 @@ import cv2
 import sys
 import numpy as np
 import os
-from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, ResizeByFactor
-from paddle_serving_app.reader import Div, Normalize, Transpose
-from paddle_serving_app.reader import DBPostProcess, FilterBoxes
-if sys.argv[1] == 'gpu':
-    from paddle_serving_server_gpu.web_service import WebService
-elif sys.argv[1] == 'cpu':
-    from paddle_serving_server.web_service import WebService
 import time
 import re
 import base64
+from tools.infer.predict_det import TextDetector
+import tools.infer.utility as utility
+
+global_args = utility.parse_args()
+if global_args.use_gpu:
+    from paddle_serving_server_gpu.web_service import WebService
+else:
+    from paddle_serving_server.web_service import WebService
+
+
+class TextDetectorHelper(TextDetector):
+    def __init__(self, args):
+        super(TextDetectorHelper, self).__init__(args)
+        if self.det_algorithm == "SAST":
+            self.fetch = [
+                "bn_f_border4.output.tmp_2", "bn_f_tco4.output.tmp_2",
+                "bn_f_tvo4.output.tmp_2", "sigmoid_0.tmp_0"
+            ]
+        elif self.det_algorithm == "EAST":
+            self.fetch = ["sigmoid_0.tmp_0", "tmp_2"]
+        elif self.det_algorithm == "DB":
+            self.fetch = ["sigmoid_0.tmp_0"]
+
+    def preprocess(self, img):
+        im, ratio_list = self.preprocess_op(img)
+        if im is None:
+            return None, 0
+        return {
+            "image": im[0]
+        }, self.fetch, {
+            "ratio_list": [ratio_list],
+            "ori_im": img
+        }
+
+    def postprocess(self, outputs, args):
+        outs_dict = {}
+        if self.det_algorithm == "EAST":
+            outs_dict['f_geo'] = outputs[0]
+            outs_dict['f_score'] = outputs[1]
+        elif self.det_algorithm == 'SAST':
+            outs_dict['f_border'] = outputs[0]
+            outs_dict['f_score'] = outputs[1]
+            outs_dict['f_tco'] = outputs[2]
+            outs_dict['f_tvo'] = outputs[3]
+        else:
+            outs_dict['maps'] = outputs[0]
+        dt_boxes_list = self.postprocess_op(outs_dict, args["ratio_list"])
+        dt_boxes = dt_boxes_list[0]
+        if self.det_algorithm == "SAST" and self.det_sast_polygon:
+            dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes,
+                                                         args["ori_im"].shape)
+        else:
+            dt_boxes = self.filter_tag_det_res(dt_boxes, args["ori_im"].shape)
+        return dt_boxes
 
 
-class OCRService(WebService):
+class DetService(WebService):
     def init_det(self):
-        self.det_preprocess = Sequential([
-            ResizeByFactor(32, 960), 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,
-            "max_candidates": 1000,
-            "unclip_ratio": 1.5,
-            "min_size": 3
-        })
+        self.text_detector = TextDetectorHelper(global_args)
+        print("init finish")
 
     def preprocess(self, feed=[], fetch=[]):
         data = base64.b64decode(feed[0]["image"].encode('utf8'))
         data = np.fromstring(data, np.uint8)
         im = cv2.imdecode(data, cv2.IMREAD_COLOR)
-        self.ori_h, self.ori_w, _ = im.shape
-        det_img = self.det_preprocess(im)
-        _, self.new_h, self.new_w = det_img.shape
-        print(det_img)
-        return {"image": det_img}, ["concat_1.tmp_0"]
+        feed, fetch, self.tmp_args = self.text_detector.preprocess(im)
+        return feed, fetch
 
     def postprocess(self, feed={}, fetch=[], fetch_map=None):
-        det_out = fetch_map["concat_1.tmp_0"]
-        ratio_list = [
-            float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
-        ]
-        dt_boxes_list = self.post_func(det_out, [ratio_list])
-        dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
-        return {"dt_boxes": dt_boxes.tolist()}
+        outputs = [fetch_map[x] for x in fetch]
+        res = self.text_detector.postprocess(outputs, self.tmp_args)
+        return {"boxes": res.tolist()}
 
 
-ocr_service = OCRService(name="ocr")
-ocr_service.load_model_config("ocr_det_model")
-if sys.argv[1] == 'gpu':
-    ocr_service.set_gpus("0")
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
-elif sys.argv[1] == 'cpu':
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
-ocr_service.init_det()
-ocr_service.run_rpc_service()
-ocr_service.run_web_service()
+if __name__ == "__main__":
+    ocr_service = DetService(name="ocr")
+    ocr_service.load_model_config("serving_server_dir")
+    ocr_service.init_det()
+    if global_args.use_gpu:
+        ocr_service.prepare_server(
+            workdir="workdir", port=9292, device="gpu", gpuid=0)
+    else:
+        ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+    ocr_service.run_rpc_service()
+    ocr_service.run_web_service()

deploy/pdserving/ocr_local_server.py

@@ -18,97 +18,107 @@ import cv2
 import sys
 import numpy as np
 import os
-from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
-from paddle_serving_app.reader import Div, Normalize, Transpose
-from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
-if sys.argv[1] == 'gpu':
-    from paddle_serving_server_gpu.web_service import WebService
-elif sys.argv[1] == 'cpu':
-    from paddle_serving_server.web_service import WebService
-from paddle_serving_app.local_predict import Debugger
 import time
 import re
 import base64
+from clas_local_server import TextClassifierHelper
+from det_local_server import TextDetectorHelper
+from rec_local_server import TextRecognizerHelper
+import tools.infer.utility as utility
+from tools.infer.predict_system import TextSystem, sorted_boxes
+from paddle_serving_app.local_predict import Debugger
+import copy
 
+global_args = utility.parse_args()
+if global_args.use_gpu:
+    from paddle_serving_server_gpu.web_service import WebService
+else:
+    from paddle_serving_server.web_service import WebService
 
-class OCRService(WebService):
-    def init_det_debugger(self, det_model_config):
-        self.det_preprocess = Sequential([
-            ResizeByFactor(32, 960), Div(255),
-            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
-                (2, 0, 1))
-        ])
+
+class TextSystemHelper(TextSystem):
+    def __init__(self, args):
+        self.text_detector = TextDetectorHelper(args)
+        self.text_recognizer = TextRecognizerHelper(args)
+        self.use_angle_cls = args.use_angle_cls
+        if self.use_angle_cls:
+            self.clas_client = Debugger()
+            self.clas_client.load_model_config(
+                "ocr_clas_server", gpu=True, profile=False)
+            self.text_classifier = TextClassifierHelper(args)
         self.det_client = Debugger()
-        if sys.argv[1] == 'gpu':
-            self.det_client.load_model_config(
-                det_model_config, gpu=True, profile=False)
-        elif sys.argv[1] == 'cpu':
-            self.det_client.load_model_config(
-                det_model_config, gpu=False, profile=False)
-        self.ocr_reader = OCRReader()
+        self.det_client.load_model_config(
+            "serving_server_dir", gpu=True, profile=False)
+        self.fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+
+    def preprocess(self, img):
+        feed, fetch, self.tmp_args = self.text_detector.preprocess(img)
+        fetch_map = self.det_client.predict(feed, fetch)
+        print("det fetch_map", fetch_map)
+        outputs = [fetch_map[x] for x in fetch]
+        dt_boxes = self.text_detector.postprocess(outputs, self.tmp_args)
+        if dt_boxes is None:
+            return None, None
+        img_crop_list = []
+        dt_boxes = sorted_boxes(dt_boxes)
+        for bno in range(len(dt_boxes)):
+            tmp_box = copy.deepcopy(dt_boxes[bno])
+            img_crop = self.get_rotate_crop_image(img, tmp_box)
+            img_crop_list.append(img_crop)
+        if self.use_angle_cls:
+            feed, fetch, self.tmp_args = self.text_classifier.preprocess(
+                img_crop_list)
+            fetch_map = self.clas_client.predict(feed, fetch)
+            outputs = [fetch_map[x] for x in self.text_classifier.fetch]
+            for x in fetch_map.keys():
+                if ".lod" in x:
+                    self.tmp_args[x] = fetch_map[x]
+            img_crop_list, _ = self.text_classifier.postprocess(outputs,
+                                                                self.tmp_args)
+        feed, fetch, self.tmp_args = self.text_recognizer.preprocess(
+            img_crop_list)
+        return feed, self.fetch, self.tmp_args
+
+    def postprocess(self, outputs, args):
+        return self.text_recognizer.postprocess(outputs, args)
+
+
+class OCRService(WebService):
+    def init_rec(self):
+        args = utility.parse_args()
+        self.text_system = TextSystemHelper(args)
 
     def preprocess(self, feed=[], fetch=[]):
+        # TODO: to handle batch rec images
+        print("start preprocess")
         data = base64.b64decode(feed[0]["image"].encode('utf8'))
         data = np.fromstring(data, np.uint8)
         im = cv2.imdecode(data, cv2.IMREAD_COLOR)
-        ori_h, ori_w, _ = im.shape
-        det_img = self.det_preprocess(im)
-        _, new_h, new_w = det_img.shape
-        det_img = det_img[np.newaxis, :]
-        det_img = det_img.copy()
-        det_out = self.det_client.predict(
-            feed={"image": det_img}, fetch=["concat_1.tmp_0"])
-        filter_func = FilterBoxes(10, 10)
-        post_func = DBPostProcess({
-            "thresh": 0.3,
-            "box_thresh": 0.5,
-            "max_candidates": 1000,
-            "unclip_ratio": 1.5,
-            "min_size": 3
-        })
-        sorted_boxes = SortedBoxes()
-        ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
-        dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list])
-        dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
-        dt_boxes = sorted_boxes(dt_boxes)
-        get_rotate_crop_image = GetRotateCropImage()
-        img_list = []
-        max_wh_ratio = 0
-        for i, dtbox in enumerate(dt_boxes):
-            boximg = get_rotate_crop_image(im, dt_boxes[i])
-            img_list.append(boximg)
-            h, w = boximg.shape[0:2]
-            wh_ratio = w * 1.0 / h
-            max_wh_ratio = max(max_wh_ratio, wh_ratio)
-        if len(img_list) == 0:
-            return [], []
-        _, w, h = self.ocr_reader.resize_norm_img(img_list[0],
-                                                  max_wh_ratio).shape
-        imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
-        for id, img in enumerate(img_list):
-            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
-            imgs[id] = norm_img
-        feed = {"image": imgs.copy()}
-        fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+        feed, fetch, self.tmp_args = self.text_system.preprocess(im)
+        print("ocr preprocess done")
         return feed, fetch
 
     def postprocess(self, feed={}, fetch=[], fetch_map=None):
-        rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
-        res_lst = []
-        for res in rec_res:
-            res_lst.append(res[0])
-        res = {"res": res_lst}
+        outputs = [fetch_map[x] for x in self.text_system.fetch]
+        for x in fetch_map.keys():
+            if ".lod" in x:
+                self.tmp_args[x] = fetch_map[x]
+        rec_res = self.text_system.postprocess(outputs, self.tmp_args)
+        res = {
+            "pred_text": [x[0] for x in rec_res],
+            "score": [str(x[1]) for x in rec_res]
+        }
         return res
 
 
-ocr_service = OCRService(name="ocr")
-ocr_service.load_model_config("ocr_rec_model")
-ocr_service.init_det_debugger(det_model_config="ocr_det_model")
-if sys.argv[1] == 'gpu':
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
-    ocr_service.run_debugger_service(gpu=True)
-elif sys.argv[1] == 'cpu':
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+if __name__ == "__main__":
+    ocr_service = OCRService(name="ocr")
+    ocr_service.load_model_config("ocr_rec_model")
+    ocr_service.init_rec()
+    if global_args.use_gpu:
+        ocr_service.prepare_server(
+            workdir="workdir", port=9292, device="gpu", gpuid=0)
+    else:
+        ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
     ocr_service.run_debugger_service()
-ocr_service.run_web_service()
+    ocr_service.run_web_service()

deploy/pdserving/ocr_rpc_server.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.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import OCRReader
+import cv2
+import sys
+import numpy as np
+import os
+import time
+import re
+import base64
+from clas_rpc_server import TextClassifierHelper
+from det_rpc_server import TextDetectorHelper
+from rec_rpc_server import TextRecognizerHelper
+import tools.infer.utility as utility
+from tools.infer.predict_system import TextSystem
+import copy
+
+global_args = utility.parse_args()
+if global_args.use_gpu:
+    from paddle_serving_server_gpu.web_service import WebService
+else:
+    from paddle_serving_server.web_service import WebService
+
+
+class TextSystemHelper(TextSystem):
+    def __init__(self, args):
+        self.text_detector = TextDetectorHelper(args)
+        self.text_recognizer = TextRecognizerHelper(args)
+        self.use_angle_cls = args.use_angle_cls
+        if self.use_angle_cls:
+            self.clas_client = Client()
+            self.clas_client.load_client_config(
+                "ocr_clas_client/serving_client_conf.prototxt")
+            self.clas_client.connect(["127.0.0.1:9294"])
+            self.text_classifier = TextClassifierHelper(args)
+        self.det_client = Client()
+        self.det_client.load_client_config(
+            "ocr_det_server/serving_client_conf.prototxt")
+        self.det_client.connect(["127.0.0.1:9293"])
+        self.fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+
+    def preprocess(self, img):
+        feed, fetch, self.tmp_args = self.text_detector.preprocess(img)
+        fetch_map = self.det_client.predict(feed, fetch)
+        outputs = [fetch_map[x] for x in fetch]
+        dt_boxes = self.text_detector.postprocess(outputs, self.tmp_args)
+        if dt_boxes is None:
+            return None, None
+        img_crop_list = []
+        sorted_boxes = SortedBoxes()
+        dt_boxes = sorted_boxes(dt_boxes)
+        for bno in range(len(dt_boxes)):
+            tmp_box = copy.deepcopy(dt_boxes[bno])
+            img_crop = self.get_rotate_crop_image(img, tmp_box)
+            img_crop_list.append(img_crop)
+        if self.use_angle_cls:
+            feed, fetch, self.tmp_args = self.text_classifier.preprocess(
+                img_crop_list)
+            fetch_map = self.clas_client.predict(feed, fetch)
+            outputs = [fetch_map[x] for x in self.text_classifier.fetch]
+            for x in fetch_map.keys():
+                if ".lod" in x:
+                    self.tmp_args[x] = fetch_map[x]
+            img_crop_list, _ = self.text_classifier.postprocess(outputs,
+                                                                self.tmp_args)
+        feed, fetch, self.tmp_args = self.text_recognizer.preprocess(
+            img_crop_list)
+        return feed, self.fetch, self.tmp_args
+
+    def postprocess(self, outputs, args):
+        return self.text_recognizer.postprocess(outputs, args)
+
+
+class OCRService(WebService):
+    def init_rec(self):
+        args = utility.parse_args()
+        self.text_system = TextSystemHelper(args)
+
+    def preprocess(self, feed=[], fetch=[]):
+        # TODO: to handle batch rec images
+        data = base64.b64decode(feed[0]["image"].encode('utf8'))
+        data = np.fromstring(data, np.uint8)
+        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+        feed, fetch, self.tmp_args = self.text_system.preprocess(im)
+        return feed, fetch
+
+    def postprocess(self, feed={}, fetch=[], fetch_map=None):
+        outputs = [fetch_map[x] for x in self.text_system.fetch]
+        for x in fetch_map.keys():
+            if ".lod" in x:
+                self.tmp_args[x] = fetch_map[x]
+        rec_res = self.text_system.postprocess(outputs, self.tmp_args)
+        res = {
+            "pred_text": [x[0] for x in rec_res],
+            "score": [str(x[1]) for x in rec_res]
+        }
+        return res
+
+
+if __name__ == "__main__":
+    ocr_service = OCRService(name="ocr")
+    ocr_service.load_model_config(global_args.rec_model_dir)
+    ocr_service.init_rec()
+    if global_args.use_gpu:
+        ocr_service.prepare_server(
+            workdir="workdir", port=9292, device="gpu", gpuid=0)
+    else:
+        ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+    ocr_service.run_rpc_service()
+    ocr_service.run_web_service()

deploy/pdserving/ocr_web_client.py

@@ -20,11 +20,13 @@ import base64
 import os, sys
 import time
 
+
 def cv2_to_base64(image):
     #data = cv2.imencode('.jpg', image)[1]
     return base64.b64encode(image).decode(
         'utf8')  #data.tostring()).decode('utf8')
 
+
 headers = {"Content-type": "application/json"}
 url = "http://127.0.0.1:9292/ocr/prediction"
 test_img_dir = "../../doc/imgs/"
@@ -34,4 +36,8 @@ for img_file in os.listdir(test_img_dir):
     image = cv2_to_base64(image_data1)
     data = {"feed": [{"image": image}], "fetch": ["res"]}
     r = requests.post(url=url, headers=headers, data=json.dumps(data))
-    print(r.json())
+    print(r)
+    rjson = r.json()
+    print(rjson)
+    #for x in rjson["result"]["pred_text"]:
+    #    print(x)

deploy/pdserving/ocr_web_server.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.
-
-from paddle_serving_client import Client
-from paddle_serving_app.reader import OCRReader
-import cv2
-import sys
-import numpy as np
-import os
-from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
-from paddle_serving_app.reader import Div, Normalize, Transpose
-from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
-if sys.argv[1] == 'gpu':
-    from paddle_serving_server_gpu.web_service import WebService
-elif sys.argv[1] == 'cpu':
-    from paddle_serving_server.web_service import WebService
-import time
-import re
-import base64
-
-
-class OCRService(WebService):
-    def init_det_client(self, det_port, det_client_config):
-        self.det_preprocess = Sequential([
-            ResizeByFactor(32, 960), Div(255),
-            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
-                (2, 0, 1))
-        ])
-        self.det_client = Client()
-        self.det_client.load_client_config(det_client_config)
-        self.det_client.connect(["127.0.0.1:{}".format(det_port)])
-        self.ocr_reader = OCRReader()
-
-    def preprocess(self, feed=[], fetch=[]):
-        data = base64.b64decode(feed[0]["image"].encode('utf8'))
-        data = np.fromstring(data, np.uint8)
-        im = cv2.imdecode(data, cv2.IMREAD_COLOR)
-        ori_h, ori_w, _ = im.shape
-        det_img = self.det_preprocess(im)
-        det_out = self.det_client.predict(
-            feed={"image": det_img}, fetch=["concat_1.tmp_0"])
-        _, new_h, new_w = det_img.shape
-        filter_func = FilterBoxes(10, 10)
-        post_func = DBPostProcess({
-            "thresh": 0.3,
-            "box_thresh": 0.5,
-            "max_candidates": 1000,
-            "unclip_ratio": 1.5,
-            "min_size": 3
-        })
-        sorted_boxes = SortedBoxes()
-        ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
-        dt_boxes_list = post_func(det_out["concat_1.tmp_0"], [ratio_list])
-        dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
-        dt_boxes = sorted_boxes(dt_boxes)
-        get_rotate_crop_image = GetRotateCropImage()
-        feed_list = []
-        img_list = []
-        max_wh_ratio = 0
-        for i, dtbox in enumerate(dt_boxes):
-            boximg = get_rotate_crop_image(im, dt_boxes[i])
-            img_list.append(boximg)
-            h, w = boximg.shape[0:2]
-            wh_ratio = w * 1.0 / h
-            max_wh_ratio = max(max_wh_ratio, wh_ratio)
-        for img in img_list:
-            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
-            feed = {"image": norm_img}
-            feed_list.append(feed)
-        fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
-        return feed_list, fetch
-
-    def postprocess(self, feed={}, fetch=[], fetch_map=None):
-        rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
-        res_lst = []
-        for res in rec_res:
-            res_lst.append(res[0])
-        res = {"res": res_lst}
-        return res
-
-
-ocr_service = OCRService(name="ocr")
-ocr_service.load_model_config("ocr_rec_model")
-if sys.argv[1] == 'gpu':
-    ocr_service.set_gpus("0")
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
-elif sys.argv[1] == 'cpu':
-    ocr_service.prepare_server(workdir="workdir", port=9292)
-ocr_service.init_det_client(
-    det_port=9293,
-    det_client_config="ocr_det_client/serving_client_conf.prototxt")
-ocr_service.run_rpc_service()
-ocr_service.run_web_service()

deploy/pdserving/readme.md

 [English](readme_en.md) | 简体中文
 
 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](../hubserving/readme.md)。	
+- 基于PaddleHub Serving的部署：代码路径为"`./deploy/hubserving`"，使用方法参考[文档](../hubserving/readme.md)。
 - 基于PaddleServing的部署：代码路径为"`./deploy/pdserving`"，按照本教程使用。
 
 # Paddle Serving 服务部署
@@ -11,7 +11,7 @@ PaddleOCR提供2种服务部署方式：
 
 ### 1. 准备环境
 我们先安装Paddle Serving相关组件
-我们推荐用户使用GPU来做Paddle Serving的OCR服务部署 
+我们推荐用户使用GPU来做Paddle Serving的OCR服务部署
 
 **CUDA版本：9.0**
 
@@ -39,7 +39,7 @@ python -m pip install paddle_serving_app paddle_serving_client
 python -m paddle_serving_app.package --get_model ocr_rec
 tar -xzvf ocr_rec.tar.gz
 python -m paddle_serving_app.package --get_model ocr_det
-tar -xzvf ocr_det.tar.gz 
+tar -xzvf ocr_det.tar.gz
 ```
 执行上述命令会下载`db_crnn_mobile`的模型，如果想要下载规模更大的`db_crnn_server`模型，可以在下载预测模型并解压之后。参考[如何从Paddle保存的预测模型转为Paddle Serving格式可部署的模型](https://github.com/PaddlePaddle/Serving/blob/develop/doc/INFERENCE_TO_SERVING_CN.md)。
 
@@ -72,7 +72,7 @@ feed_var_names, fetch_var_names = inference_model_to_serving(
 
 ```
 # cpu，gpu启动二选一，以下是cpu启动
-python -m paddle_serving_server.serve --model ocr_det_model --port 9293 
+python -m paddle_serving_server.serve --model ocr_det_model --port 9293
 python ocr_web_server.py cpu
 # gpu启动
 python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0

deploy/pdserving/readme_en.md

 English | [简体中文](readme.md)
 
-PaddleOCR provides 2 service deployment methods: 
+PaddleOCR provides 2 service deployment methods:
 - Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please refer to the [tutorial](../hubserving/readme_en.md) for usage.
 - Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please follow this tutorial.
 
@@ -37,7 +37,7 @@ You can directly use converted model provided by `paddle_serving_app` for conven
 python -m paddle_serving_app.package --get_model ocr_rec
 tar -xzvf ocr_rec.tar.gz
 python -m paddle_serving_app.package --get_model ocr_det
-tar -xzvf ocr_det.tar.gz 
+tar -xzvf ocr_det.tar.gz
 ```
 Executing the above command will download the `db_crnn_mobile` model, which is in different format with inference model. If you want to use other models for deployment, you can refer to the [tutorial](https://github.com/PaddlePaddle/Serving/blob/develop/doc/INFERENCE_TO_SERVING_CN.md) to convert your inference model to a model which is deployable for Paddle Serving.
 
@@ -71,7 +71,7 @@ Start the standard version or the fast version service according to your actual
 
 ```
 # start with CPU
-python -m paddle_serving_server.serve --model ocr_det_model --port 9293 
+python -m paddle_serving_server.serve --model ocr_det_model --port 9293
 python ocr_web_server.py cpu
 
 # or, with GPU

deploy/pdserving/rec_local_server.py

@@ -18,62 +18,159 @@ import cv2
 import sys
 import numpy as np
 import os
-from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
-from paddle_serving_app.reader import Div, Normalize, Transpose
-from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
-if sys.argv[1] == 'gpu':
-    from paddle_serving_server_gpu.web_service import WebService
-elif sys.argv[1] == 'cpu':
-    from paddle_serving_server.web_service import WebService
 import time
 import re
 import base64
+from tools.infer.predict_rec import TextRecognizer
+import tools.infer.utility as utility
+
+global_args = utility.parse_args()
+if global_args.use_gpu:
+    from paddle_serving_server_gpu.web_service import WebService
+else:
+    from paddle_serving_server.web_service import WebService
+
+
+class TextRecognizerHelper(TextRecognizer):
+    def __init__(self, args):
+        super(TextRecognizerHelper, self).__init__(args)
+        if self.loss_type == "ctc":
+            self.fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+
+    def preprocess(self, img_list):
+        img_num = len(img_list)
+        args = {}
+        # Calculate the aspect ratio of all text bars
+        width_list = []
+        for img in img_list:
+            width_list.append(img.shape[1] / float(img.shape[0]))
+        indices = np.argsort(np.array(width_list))
+        args["indices"] = indices
+        predict_time = 0
+        beg_img_no = 0
+        end_img_no = img_num
+        norm_img_batch = []
+        max_wh_ratio = 0
+        for ino in range(beg_img_no, end_img_no):
+            h, w = img_list[indices[ino]].shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        for ino in range(beg_img_no, end_img_no):
+            if self.loss_type != "srn":
+                norm_img = self.resize_norm_img(img_list[indices[ino]],
+                                                max_wh_ratio)
+                norm_img = norm_img[np.newaxis, :]
+                norm_img_batch.append(norm_img)
+            else:
+                norm_img = self.process_image_srn(img_list[indices[ino]],
+                                                  self.rec_image_shape, 8, 25,
+                                                  self.char_ops)
+                encoder_word_pos_list = []
+                gsrm_word_pos_list = []
+                gsrm_slf_attn_bias1_list = []
+                gsrm_slf_attn_bias2_list = []
+                encoder_word_pos_list.append(norm_img[1])
+                gsrm_word_pos_list.append(norm_img[2])
+                gsrm_slf_attn_bias1_list.append(norm_img[3])
+                gsrm_slf_attn_bias2_list.append(norm_img[4])
+                norm_img_batch.append(norm_img[0])
+        norm_img_batch = np.concatenate(norm_img_batch, axis=0).copy()
+        feed = {"image": norm_img_batch.copy()}
+        return feed, self.fetch, args
+
+    def postprocess(self, outputs, args):
+        if self.loss_type == "ctc":
+            rec_idx_batch = outputs[0]
+            predict_batch = outputs[1]
+            rec_idx_lod = args["ctc_greedy_decoder_0.tmp_0.lod"]
+            predict_lod = args["softmax_0.tmp_0.lod"]
+            indices = args["indices"]
+            print("indices", indices, rec_idx_lod)
+            rec_res = [['', 0.0]] * (len(rec_idx_lod) - 1)
+            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]
+                if len(valid_ind) == 0:
+                    continue
+                score = np.mean(probs[valid_ind, ind[valid_ind]])
+                rec_res[indices[rno]] = [preds_text, score]
+        elif self.loss_type == 'srn':
+            char_num = self.char_ops.get_char_num()
+            preds = rec_idx_batch.reshape(-1)
+            elapse = time.time() - starttime
+            predict_time += elapse
+            total_preds = preds.copy()
+            for ino in range(int(len(rec_idx_batch) / self.text_len)):
+                preds = total_preds[ino * self.text_len:(ino + 1) *
+                                    self.text_len]
+                ind = np.argmax(probs, axis=1)
+                valid_ind = np.where(preds != int(char_num - 1))[0]
+                if len(valid_ind) == 0:
+                    continue
+                score = np.mean(probs[valid_ind, ind[valid_ind]])
+                preds = preds[:valid_ind[-1] + 1]
+                preds_text = self.char_ops.decode(preds)
+                rec_res[indices[ino]] = [preds_text, score]
+        else:
+            for rno in range(len(rec_idx_batch)):
+                end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
+                if len(end_pos) <= 1:
+                    preds = rec_idx_batch[rno, 1:]
+                    score = np.mean(predict_batch[rno, 1:])
+                else:
+                    preds = rec_idx_batch[rno, 1:end_pos[1]]
+                    score = np.mean(predict_batch[rno, 1:end_pos[1]])
+                preds_text = self.char_ops.decode(preds)
+                rec_res[indices[rno]] = [preds_text, score]
+        return rec_res
 
 
 class OCRService(WebService):
     def init_rec(self):
         self.ocr_reader = OCRReader()
+        self.text_recognizer = TextRecognizerHelper(global_args)
 
     def preprocess(self, feed=[], fetch=[]):
+        # TODO: to handle batch rec images
         img_list = []
         for feed_data in feed:
             data = base64.b64decode(feed_data["image"].encode('utf8'))
             data = np.fromstring(data, np.uint8)
             im = cv2.imdecode(data, cv2.IMREAD_COLOR)
             img_list.append(im)
-        max_wh_ratio = 0
-        for i, boximg in enumerate(img_list):
-            h, w = boximg.shape[0:2]
-            wh_ratio = w * 1.0 / h
-            max_wh_ratio = max(max_wh_ratio, wh_ratio)
-        _, w, h = self.ocr_reader.resize_norm_img(img_list[0],
-                                                  max_wh_ratio).shape
-        imgs = np.zeros((len(img_list), 3, w, h)).astype('float32')
-        for i, img in enumerate(img_list):
-            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
-            imgs[i] = norm_img
-        feed = {"image": imgs.copy()}
-        fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+        feed, fetch, self.tmp_args = self.text_recognizer.preprocess(img_list)
         return feed, fetch
 
     def postprocess(self, feed={}, fetch=[], fetch_map=None):
-        rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
-        res_lst = []
-        for res in rec_res:
-            res_lst.append(res[0])
-        res = {"res": res_lst}
+        outputs = [fetch_map[x] for x in self.text_recognizer.fetch]
+        for x in fetch_map.keys():
+            if ".lod" in x:
+                self.tmp_args[x] = fetch_map[x]
+        rec_res = self.text_recognizer.postprocess(outputs, self.tmp_args)
+        print("rec_res", rec_res)
+        res = {
+            "pred_text": [x[0] for x in rec_res],
+            "score": [str(x[1]) for x in rec_res]
+        }
         return res
 
 
-ocr_service = OCRService(name="ocr")
-ocr_service.load_model_config("ocr_rec_model")
-ocr_service.init_rec()
-if sys.argv[1] == 'gpu':
-    ocr_service.set_gpus("0")
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
-    ocr_service.run_debugger_service(gpu=True)
-elif sys.argv[1] == 'cpu':
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+if __name__ == "__main__":
+    ocr_service = OCRService(name="ocr")
+    ocr_service.load_model_config("ocr_rec_model")
+    ocr_service.init_rec()
+    if global_args.use_gpu:
+        ocr_service.prepare_server(
+            workdir="workdir", port=9292, device="gpu", gpuid=0)
+    else:
+        ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
     ocr_service.run_debugger_service()
-ocr_service.run_web_service()
+    ocr_service.run_web_service()

deploy/pdserving/rec_rpc_server.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.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import OCRReader
+import cv2
+import sys
+import numpy as np
+import os
+import time
+import re
+import base64
+from tools.infer.predict_rec import TextRecognizer
+import tools.infer.utility as utility
+
+global_args = utility.parse_args()
+if global_args.use_gpu:
+    from paddle_serving_server_gpu.web_service import WebService
+else:
+    from paddle_serving_server.web_service import WebService
+
+
+class TextRecognizerHelper(TextRecognizer):
+    def __init__(self, args):
+        super(TextRecognizerHelper, self).__init__(args)
+        if self.loss_type == "ctc":
+            self.fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+
+    def preprocess(self, img_list):
+        img_num = len(img_list)
+        args = {}
+        # Calculate the aspect ratio of all text bars
+        width_list = []
+        for img in img_list:
+            width_list.append(img.shape[1] / float(img.shape[0]))
+        indices = np.argsort(np.array(width_list))
+        args["indices"] = indices
+        predict_time = 0
+        beg_img_no = 0
+        end_img_no = img_num
+        norm_img_batch = []
+        max_wh_ratio = 0
+        for ino in range(beg_img_no, end_img_no):
+            h, w = img_list[indices[ino]].shape[0:2]
+            wh_ratio = w * 1.0 / h
+            max_wh_ratio = max(max_wh_ratio, wh_ratio)
+        for ino in range(beg_img_no, end_img_no):
+            if self.loss_type != "srn":
+                norm_img = self.resize_norm_img(img_list[indices[ino]],
+                                                max_wh_ratio)
+                norm_img = norm_img[np.newaxis, :]
+                norm_img_batch.append(norm_img)
+            else:
+                norm_img = self.process_image_srn(img_list[indices[ino]],
+                                                  self.rec_image_shape, 8, 25,
+                                                  self.char_ops)
+                encoder_word_pos_list = []
+                gsrm_word_pos_list = []
+                gsrm_slf_attn_bias1_list = []
+                gsrm_slf_attn_bias2_list = []
+                encoder_word_pos_list.append(norm_img[1])
+                gsrm_word_pos_list.append(norm_img[2])
+                gsrm_slf_attn_bias1_list.append(norm_img[3])
+                gsrm_slf_attn_bias2_list.append(norm_img[4])
+                norm_img_batch.append(norm_img[0])
+
+        norm_img_batch = np.concatenate(norm_img_batch, axis=0)
+        if img_num > 1:
+            feed = [{
+                "image": norm_img_batch[x]
+            } for x in range(norm_img_batch.shape[0])]
+        else:
+            feed = {"image": norm_img_batch[0]}
+        return feed, self.fetch, args
+
+    def postprocess(self, outputs, args):
+        if self.loss_type == "ctc":
+            rec_idx_batch = outputs[0]
+            predict_batch = outputs[1]
+            rec_idx_lod = args["ctc_greedy_decoder_0.tmp_0.lod"]
+            predict_lod = args["softmax_0.tmp_0.lod"]
+            indices = args["indices"]
+            print("indices", indices, rec_idx_lod)
+            rec_res = [['', 0.0]] * (len(rec_idx_lod) - 1)
+            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]
+                if len(valid_ind) == 0:
+                    continue
+                score = np.mean(probs[valid_ind, ind[valid_ind]])
+                rec_res[indices[rno]] = [preds_text, score]
+        elif self.loss_type == 'srn':
+            char_num = self.char_ops.get_char_num()
+            preds = rec_idx_batch.reshape(-1)
+            elapse = time.time() - starttime
+            predict_time += elapse
+            total_preds = preds.copy()
+            for ino in range(int(len(rec_idx_batch) / self.text_len)):
+                preds = total_preds[ino * self.text_len:(ino + 1) *
+                                    self.text_len]
+                ind = np.argmax(probs, axis=1)
+                valid_ind = np.where(preds != int(char_num - 1))[0]
+                if len(valid_ind) == 0:
+                    continue
+                score = np.mean(probs[valid_ind, ind[valid_ind]])
+                preds = preds[:valid_ind[-1] + 1]
+                preds_text = self.char_ops.decode(preds)
+                rec_res[indices[ino]] = [preds_text, score]
+        else:
+            for rno in range(len(rec_idx_batch)):
+                end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
+                if len(end_pos) <= 1:
+                    preds = rec_idx_batch[rno, 1:]
+                    score = np.mean(predict_batch[rno, 1:])
+                else:
+                    preds = rec_idx_batch[rno, 1:end_pos[1]]
+                    score = np.mean(predict_batch[rno, 1:end_pos[1]])
+                preds_text = self.char_ops.decode(preds)
+                rec_res[indices[rno]] = [preds_text, score]
+        return rec_res
+
+
+class OCRService(WebService):
+    def init_rec(self):
+        self.ocr_reader = OCRReader()
+        self.text_recognizer = TextRecognizerHelper(global_args)
+
+    def preprocess(self, feed=[], fetch=[]):
+        # TODO: to handle batch rec images
+        img_list = []
+        for feed_data in feed:
+            data = base64.b64decode(feed_data["image"].encode('utf8'))
+            data = np.fromstring(data, np.uint8)
+            im = cv2.imdecode(data, cv2.IMREAD_COLOR)
+            img_list.append(im)
+        feed, fetch, self.tmp_args = self.text_recognizer.preprocess(img_list)
+        return feed, fetch
+
+    def postprocess(self, feed={}, fetch=[], fetch_map=None):
+        outputs = [fetch_map[x] for x in self.text_recognizer.fetch]
+        for x in fetch_map.keys():
+            if ".lod" in x:
+                self.tmp_args[x] = fetch_map[x]
+        rec_res = self.text_recognizer.postprocess(outputs, self.tmp_args)
+        print("rec_res", rec_res)
+        res = {
+            "pred_text": [x[0] for x in rec_res],
+            "score": [str(x[1]) for x in rec_res]
+        }
+        return res
+
+
+if __name__ == "__main__":
+    ocr_service = OCRService(name="ocr")
+    ocr_service.load_model_config(global_args.rec_model_dir)
+    ocr_service.init_rec()
+    if global_args.use_gpu:
+        ocr_service.prepare_server(
+            workdir="workdir", port=9292, device="gpu", gpuid=0)
+    else:
+        ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
+    ocr_service.run_rpc_service()
+    ocr_service.run_web_service()

deploy/pdserving/rec_web_client.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.
+# -*- coding: utf-8 -*-
+
+import requests
+import json
+import cv2
+import base64
+import os, sys
+import time
+
+
+def cv2_to_base64(image):
+    #data = cv2.imencode('.jpg', image)[1]
+    return base64.b64encode(image).decode(
+        'utf8')  #data.tostring()).decode('utf8')
+
+
+headers = {"Content-type": "application/json"}
+url = "http://127.0.0.1:9292/ocr/prediction"
+test_img_dir = "../../doc/imgs_words/ch/"
+for img_file in os.listdir(test_img_dir):
+    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+        image_data1 = file.read()
+    image = cv2_to_base64(image_data1)
+    data = {"feed": [{"image": image}], "fetch": ["res"]}
+    r = requests.post(url=url, headers=headers, data=json.dumps(data))
+    print(r.json())
+    break

doc/doc_ch/serving_inference.md

+# 使用Paddle Serving预测推理
+
+阅读本文档之前，请先阅读文档 [基于Python预测引擎推理](./inference.md)
+
+同本地执行预测一样，我们需要保存一份可以用于Paddle Serving的模型。
+
+接下来首先介绍如何将训练的模型转换成Paddle Serving模型，然后将依次介绍文本检测、文本识别以及两者串联基于预测引擎推理。
+
+
+
+## 一、训练模型转Serving模型
+
+### 检测模型转Serving模型
+
+下载超轻量级中文检测模型：
+
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db.tar && tar xf ./ch_lite/ch_det_mv3_db.tar -C ./ch_lite/
+```
+
+上述模型是以MobileNetV3为backbone训练的DB算法，将训练好的模型转换成Serving模型只需要运行如下命令：
+
+```
+# -c后面设置训练算法的yml配置文件
+# -o配置可选参数
+# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python tools/export_serving_model.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./ch_lite/det_mv3_db/best_accuracy Global.save_inference_dir=./inference/det_db/
+```
+
+转Serving模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.checkpoints`、`Global.save_inference_dir`参数。 其中`Global.checkpoints`指向训练中保存的模型参数文件，`Global.save_inference_dir`是生成的inference模型要保存的目录。 转换成功后，在`save_inference_dir`目录下有两个文件：
+
+```
+inference/det_db/
+├── serving_client_dir # 客户端配置文件夹
+└── serving_server_dir # 服务端配置文件夹
+
+```
+
+### 识别模型转Serving模型
+
+下载超轻量中文识别模型：
+
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn.tar && tar xf ./ch_lite/ch_rec_mv3_crnn.tar -C ./ch_lite/
+```
+
+识别模型转inference模型与检测的方式相同，如下：
+
+```
+# -c后面设置训练算法的yml配置文件
+# -o配置可选参数
+# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python3 tools/export_serving_model.py -c configs/rec/rec_chinese_lite_train.yml -o Global.checkpoints=./ch_lite/rec_mv3_crnn/best_accuracy \
+        Global.save_inference_dir=./inference/rec_crnn/
+```
+
+**注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
+
+转换成功后，在目录下有两个文件：
+
+```
+/inference/rec_crnn/
+├── serving_client_dir # 客户端配置文件夹
+└── serving_server_dir # 服务端配置文件夹
+```
+
+### 方向分类模型转Serving模型
+
+下载方向分类模型：
+
+```
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_pre.tar && tar xf ./ch_lite/ch_ppocr_mobile-v1.1.cls_pre.tar -C ./ch_lite/
+```
+
+方向分类模型转inference模型与检测的方式相同，如下：
+
+```
+# -c后面设置训练算法的yml配置文件
+# -o配置可选参数
+# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python3 tools/export_serving_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \
+        Global.save_inference_dir=./inference/cls/
+```
+
+转换成功后，在目录下有两个文件：
+
+```
+/inference/cls/
+├── serving_client_dir # 客户端配置文件夹
+└── serving_server_dir # 服务端配置文件夹
+```
+
+在接下来的教程中，我们将给出推理的demo模型下载链接。
+
+```
+wget --no-check-certificate ocr_serving_model_zoo.tar.gz
+tar zxf ocr_serving_model_zoo.tar.gz
+```
+
+
+
+## 二、文本检测模型Serving推理
+
+文本检测模型推理，默认使用DB模型的配置参数。当不使用DB模型时，在推理时，需要通过传入相应的参数进行算法适配，细节参考下文。
+
+与本地预测不同的是，Serving预测需要一个客户端和一个服务端，因此接下来的教程都是两行代码。所有的
+
+### 1. 超轻量中文检测模型推理
+
+超轻量中文检测模型推理，可以执行如下命令启动服务端：
+
+```
+#根据环境只需要启动其中一个就可以
+python det_rpc_server.py --use_serving True #标准版，Linux用户
+python det_local_server.py --use_serving True #快速版，Windows/Linux用户
+```
+
+客户端
+
+```
+python det_web_client.py
+```
+
+
+
+Serving的推测和本地预测不同点在于，客户端发送请求到服务端，服务端需要检测到文字框之后返回框的坐标，此处没有后处理的图片，只能看到坐标值。
+
+### 2. DB文本检测模型推理
+
+首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar))，可以使用如下命令进行转换：
+
+```
+# -c后面设置训练算法的yml配置文件
+# Global.checkpoints参数设置待转换的训练模型地址，不用添加文件后缀.pdmodel，.pdopt或.pdparams。
+# Global.save_inference_dir参数设置转换的模型将保存的地址。
+
+python3 tools/export_serving_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints="./models/det_r50_vd_db/best_accuracy" Global.save_inference_dir="./inference/det_db"
+```
+
+经过转换之后，会在`./inference/det_db` 目录下出现`serving_server_dir`和`serving_client_dir`，然后指定`det_model_dir` 。
+
+## 三、文本识别模型Serving推理
+
+下面将介绍超轻量中文识别模型推理、基于CTC损失的识别模型推理和基于Attention损失的识别模型推理。对于中文文本识别，建议优先选择基于CTC损失的识别模型，实践中也发现基于Attention损失的效果不如基于CTC损失的识别模型。此外，如果训练时修改了文本的字典，请参考下面的自定义文本识别字典的推理。
+
+### 1. 超轻量中文识别模型推理
+
+超轻量中文识别模型推理，可以执行如下命令启动服务端：
+
+```
+#根据环境只需要启动其中一个就可以
+python rec_rpc_server.py --use_serving True #标准版，Linux用户
+python rec_local_server.py --use_serving True #快速版，Windows/Linux用户
+```
+
+客户端
+
+```
+python rec_web_client.py
+```
+
+
+
+执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
+
+```
+{u'result': {u'score': [u'0.89547354'], u'pred_text': ['实力活力']}}
+```
+
+
+
+## 四、方向分类模型推理
+
+下面将介绍方向分类模型推理。
+
+
+
+### 1. 方向分类模型推理
+
+方向分类模型推理， 可以执行如下命令启动服务端：
+
+```
+#根据环境只需要启动其中一个就可以
+python clas_rpc_server.py --use_serving True #标准版，Linux用户
+python clas_local_server.py --use_serving True #快速版，Windows/Linux用户
+```
+
+客户端
+
+```
+python rec_web_client.py
+```
+
+![](../imgs_words/ch/word_4.jpg)
+
+执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下：
+
+```
+{u'result': {u'direction': [u'0'], u'score': [u'0.9999963']}}
+```
+
+
+## 五、文本检测、方向分类和文字识别串联Serving推理
+
+### 1. 超轻量中文OCR模型推理
+
+在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。与本地预测不同的是，为了减少网络传输耗时，可视化识别结果目前不做处理，用户收到的是推理得到的文字字段。
+
+执行如下命令启动服务端：
+
+```
+#标准版，Linux用户
+#GPU用户
+python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0
+python -m paddle_serving_server_gpu.serve --model ocr_cls_model --port 9294 --gpu_id 0
+python ocr_rpc_server.py --use_serving True --use_gpu True
+#CPU用户
+python -m paddle_serving_server.serve --model ocr_det_model --port 9293
+python -m paddle_serving_server.serve --model ocr_cls_model --port 9294
+python ocr_rpc_server.py --use_serving True --use_gpu False
+
+#快速版，Windows/Linux用户
+python ocr_local_server.py --use_serving True
+```
+
+客户端
+
+```
+python rec_web_client.py
+```

tools/export_serving_model.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+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__, '..')))
+
+
+def set_paddle_flags(**kwargs):
+    for key, value in kwargs.items():
+        if os.environ.get(key, None) is None:
+            os.environ[key] = str(value)
+
+
+# NOTE(paddle-dev): All of these flags should be
+# set before `import paddle`. Otherwise, it would
+# not take any effect.
+set_paddle_flags(
+    FLAGS_eager_delete_tensor_gb=0,  # enable GC to save memory
+)
+
+import program
+from paddle import fluid
+from ppocr.utils.utility import initial_logger
+logger = initial_logger()
+from ppocr.utils.save_load import init_model
+from paddle_serving_client.io import save_model
+
+
+def main():
+    startup_prog, eval_program, place, config, _ = program.preprocess()
+
+    feeded_var_names, target_vars, fetches_var_name = program.build_export(
+        config, eval_program, startup_prog)
+    eval_program = eval_program.clone(for_test=True)
+    exe = fluid.Executor(place)
+    exe.run(startup_prog)
+
+    init_model(config, eval_program, exe)
+
+    save_inference_dir = config['Global']['save_inference_dir']
+    if not os.path.exists(save_inference_dir):
+        os.makedirs(save_inference_dir)
+    serving_client_dir = "{}/serving_client_dir".format(save_inference_dir)
+    serving_server_dir = "{}/serving_server_dir".format(save_inference_dir)
+
+    feed_dict = {
+        x: eval_program.global_block().var(x)
+        for x in feeded_var_names
+    }
+    fetch_dict = {x.name: x for x in target_vars}
+    save_model(serving_server_dir, serving_client_dir, feed_dict, fetch_dict,
+               eval_program)
+    print(
+        "paddle serving model saved in {}/serving_server_dir and {}/serving_client_dir".
+        format(save_inference_dir, save_inference_dir))
+    print("save success, output_name_list:", fetches_var_name)
+
+
+if __name__ == '__main__':
+    main()

tools/infer/predict_cls.py

@@ -33,8 +33,9 @@ from paddle import fluid
 
 class TextClassifier(object):
     def __init__(self, args):
-        self.predictor, self.input_tensor, self.output_tensors = \
-            utility.create_predictor(args, mode="cls")
+        if args.use_serving is False:
+            self.predictor, self.input_tensor, self.output_tensors = \
+                utility.create_predictor(args, mode="cls")
         self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
         self.cls_batch_num = args.rec_batch_num
         self.label_list = args.label_list
@@ -103,7 +104,6 @@ class TextClassifier(object):
             label_out = self.output_tensors[1].copy_to_cpu()
             if len(label_out.shape) != 1:
                 prob_out, label_out = label_out, prob_out
-
             elapse = time.time() - starttime
             predict_time += elapse
             for rno in range(len(label_out)):

tools/infer/predict_det.py

@@ -75,9 +75,9 @@ class TextDetector(object):
         else:
             logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
             sys.exit(0)
-
-        self.predictor, self.input_tensor, self.output_tensors =\
-            utility.create_predictor(args, mode="det")
+        if args.use_gpu is False:
+            self.predictor, self.input_tensor, self.output_tensors =\
+                utility.create_predictor(args, mode="det")
 
     def order_points_clockwise(self, pts):
         """

tools/infer/predict_rec.py

@@ -34,8 +34,9 @@ from ppocr.utils.character import CharacterOps
 
 class TextRecognizer(object):
     def __init__(self, args):
-        self.predictor, self.input_tensor, self.output_tensors =\
-            utility.create_predictor(args, mode="rec")
+        if args.use_serving is False:
+            self.predictor, self.input_tensor, self.output_tensors =\
+                utility.create_predictor(args, mode="rec")
         self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
         self.character_type = args.rec_char_type
         self.rec_batch_num = args.rec_batch_num
@@ -320,7 +321,7 @@ def main(args):
         print(e)
         logger.info(
             "ERROR!!!! \n"
-            "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
+            "Please read the FAQ: https://github.com/PaddlePaddle/PaddleOCR#faq \n"
             "If your model has tps module:  "
             "TPS does not support variable shape.\n"
             "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")

tools/infer/utility.py

@@ -37,6 +37,7 @@ def parse_args():
     parser.add_argument("--ir_optim", type=str2bool, default=True)
     parser.add_argument("--use_tensorrt", type=str2bool, default=False)
     parser.add_argument("--gpu_mem", type=int, default=8000)
+    parser.add_argument("--use_serving", type=str2bool, default=False)
 
     # params for text detector
     parser.add_argument("--image_dir", type=str)