diff --git a/deploy/pdserving/README.md b/deploy/pdserving/README.md
index cb5e4bfa014a699daa492523918d2ed42fc6cd28..3aae4781897deff3268e97bd4fe18096f11131d2 100644
--- a/deploy/pdserving/README.md
+++ b/deploy/pdserving/README.md
@@ -37,13 +37,11 @@ PaddleOCR operating environment and Paddle Serving operating environment are nee
Install serving which used to start the service
```
- pip3 install paddle-serving-server==0.5.0 # for CPU
- pip3 install paddle-serving-server-gpu==0.5.0 # for GPU
+ pip3 install paddle-serving-server==0.6.0 # for CPU
+ pip3 install paddle-serving-server-gpu==0.6.0 # for GPU
# Other GPU environments need to confirm the environment and then choose to execute the following commands
- pip3 install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0
- pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
- pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
- pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7
+ pip3 install paddle-serving-server-gpu==0.6.0.post101 # GPU with CUDA10.1 + TensorRT6
+ pip3 install paddle-serving-server-gpu==0.6.0.post11 # GPU with CUDA11 + TensorRT7
```
3. Install the client to send requests to the service
@@ -51,13 +49,13 @@ PaddleOCR operating environment and Paddle Serving operating environment are nee
The python3.7 version is recommended here:
```
- wget https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
+ wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl
```
4. Install serving-app
```
- pip3 install paddle-serving-app==0.3.1
+ pip3 install paddle-serving-app==0.6.0
```
**note:** If you want to install the latest version of PaddleServing, refer to [link](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md).
@@ -197,6 +195,23 @@ The recognition model is the same.
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
+## WINDOWS Users
+
+Windows does not support Pipeline Serving, if we want to lauch paddle serving on Windows, we should use Web Service, for more infomation please refer to [Paddle Serving for Windows Users](https://github.com/PaddlePaddle/Serving/blob/develop/doc/WINDOWS_TUTORIAL.md)
+
+
+1. Start Server
+
+```
+cd win
+python3 ocr_web_server.py
+```
+
+2. Client Send Requests
+
+```
+python3 ocr_web_client.py
+```
## FAQ
diff --git a/deploy/pdserving/README_CN.md b/deploy/pdserving/README_CN.md
index 1e53cb639ce903b7a42840f2e769e63f6894e2ce..5ac609ee1780ac57d8b7ba8b5073e1a6de62d618 100644
--- a/deploy/pdserving/README_CN.md
+++ b/deploy/pdserving/README_CN.md
@@ -36,26 +36,24 @@ PaddleOCR提供2种服务部署方式：
1. 安装serving，用于启动服务
```
- pip3 install paddle-serving-server==0.5.0 # for CPU
- pip3 install paddle-serving-server-gpu==0.5.0 # for GPU
+ pip3 install paddle-serving-server==0.6.0 # for CPU
+ pip3 install paddle-serving-server-gpu==0.6.0 # for GPU
# 其他GPU环境需要确认环境再选择执行如下命令
- pip3 install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0
- pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
- pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
- pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7
+ pip3 install paddle-serving-server-gpu==0.6.0.post101 # GPU with CUDA10.1 + TensorRT6
+ pip3 install paddle-serving-server-gpu==0.6.0.post11 # GPU with CUDA11 + TensorRT7
```
2. 安装client，用于向服务发送请求
在[下载链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)中找到对应python版本的client安装包，这里推荐python3.7版本：
```
- wget https://paddle-serving.bj.bcebos.com/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
+ wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.0.0-cp37-none-any.whl
pip3 install paddle_serving_client-0.0.0-cp37-none-any.whl
```
3. 安装serving-app
```
- pip3 install paddle-serving-app==0.3.1
+ pip3 install paddle-serving-app==0.6.0
```
**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)。
@@ -193,6 +191,23 @@ python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_in
2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
```
+## WINDOWS用户
+
+Windows用户不能使用上述的启动方式，需要使用Web Service，详情参见[Windows平台使用Paddle Serving指导](https://github.com/PaddlePaddle/Serving/blob/develop/doc/WINDOWS_TUTORIAL_CN.md)
+
+
+1. 启动服务端程序
+
+```
+cd win
+python3 ocr_web_server.py
+```
+
+2. 发送服务请求
+
+```
+python3 ocr_web_client.py
+```
diff --git a/deploy/pdserving/web_service.py b/deploy/pdserving/web_service.py
index 3f77f03537bce6df980abd8af83e7ed772e44d98..21db1e1411a8706dbbd9a22ce2ce7db8e16da5ec 100644
--- a/deploy/pdserving/web_service.py
+++ b/deploy/pdserving/web_service.py
@@ -11,10 +11,7 @@
# 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.
-try:
- from paddle_serving_server_gpu.web_service import WebService, Op
-except ImportError:
- from paddle_serving_server.web_service import WebService, Op
+from paddle_serving_server.web_service import WebService, Op
import logging
import numpy as np
diff --git a/deploy/pdserving/win/ocr_reader.py b/deploy/pdserving/win/ocr_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f219784fca79715d09ae9353a32d95e2e427cb6
--- /dev/null
+++ b/deploy/pdserving/win/ocr_reader.py
@@ -0,0 +1,435 @@
+# Copyright (c) 2021 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 cv2
+import copy
+import numpy as np
+import math
+import re
+import sys
+import argparse
+import string
+from copy import deepcopy
+
+
+class DetResizeForTest(object):
+ def __init__(self, **kwargs):
+ super(DetResizeForTest, self).__init__()
+ self.resize_type = 0
+ if 'image_shape' in kwargs:
+ self.image_shape = kwargs['image_shape']
+ self.resize_type = 1
+ elif 'limit_side_len' in kwargs:
+ self.limit_side_len = kwargs['limit_side_len']
+ self.limit_type = kwargs.get('limit_type', 'min')
+ elif 'resize_short' in kwargs:
+ self.limit_side_len = 736
+ self.limit_type = 'min'
+ else:
+ self.resize_type = 2
+ self.resize_long = kwargs.get('resize_long', 960)
+
+ def __call__(self, data):
+ img = deepcopy(data)
+ src_h, src_w, _ = img.shape
+
+ if self.resize_type == 0:
+ img, [ratio_h, ratio_w] = self.resize_image_type0(img)
+ elif self.resize_type == 2:
+ img, [ratio_h, ratio_w] = self.resize_image_type2(img)
+ else:
+ img, [ratio_h, ratio_w] = self.resize_image_type1(img)
+
+ return img
+
+ def resize_image_type1(self, img):
+ resize_h, resize_w = self.image_shape
+ ori_h, ori_w = img.shape[:2] # (h, w, c)
+ ratio_h = float(resize_h) / ori_h
+ ratio_w = float(resize_w) / ori_w
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ return img, [ratio_h, ratio_w]
+
+ def resize_image_type0(self, img):
+ """
+ resize image to a size multiple of 32 which is required by the network
+ args:
+ img(array): array with shape [h, w, c]
+ return(tuple):
+ img, (ratio_h, ratio_w)
+ """
+ limit_side_len = self.limit_side_len
+ h, w, _ = img.shape
+
+ # limit the max side
+ if self.limit_type == 'max':
+ if max(h, w) > limit_side_len:
+ if h > w:
+ ratio = float(limit_side_len) / h
+ else:
+ ratio = float(limit_side_len) / w
+ else:
+ ratio = 1.
+ else:
+ if min(h, w) < limit_side_len:
+ if h < w:
+ ratio = float(limit_side_len) / h
+ else:
+ ratio = float(limit_side_len) / w
+ else:
+ ratio = 1.
+ resize_h = int(h * ratio)
+ resize_w = int(w * ratio)
+
+ resize_h = int(round(resize_h / 32) * 32)
+ resize_w = int(round(resize_w / 32) * 32)
+
+ try:
+ if int(resize_w) <= 0 or int(resize_h) <= 0:
+ return None, (None, None)
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ except:
+ print(img.shape, resize_w, resize_h)
+ sys.exit(0)
+ ratio_h = resize_h / float(h)
+ ratio_w = resize_w / float(w)
+ # return img, np.array([h, w])
+ return img, [ratio_h, ratio_w]
+
+ def resize_image_type2(self, img):
+ h, w, _ = img.shape
+
+ resize_w = w
+ resize_h = h
+
+ # Fix the longer side
+ if resize_h > resize_w:
+ ratio = float(self.resize_long) / resize_h
+ else:
+ ratio = float(self.resize_long) / resize_w
+
+ resize_h = int(resize_h * ratio)
+ resize_w = int(resize_w * ratio)
+
+ max_stride = 128
+ resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+ resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+ img = cv2.resize(img, (int(resize_w), int(resize_h)))
+ ratio_h = resize_h / float(h)
+ ratio_w = resize_w / float(w)
+
+ return img, [ratio_h, ratio_w]
+
+
+class BaseRecLabelDecode(object):
+ """ Convert between text-label and text-index """
+
+ def __init__(self, config):
+ support_character_type = [
+ 'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
+ 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
+ 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
+ 'ne', 'EN'
+ ]
+ character_type = config['character_type']
+ character_dict_path = config['character_dict_path']
+ use_space_char = True
+ assert character_type in support_character_type, "Only {} are supported now but get {}".format(
+ support_character_type, character_type)
+
+ self.beg_str = "sos"
+ self.end_str = "eos"
+
+ if character_type == "en":
+ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+ dict_character = list(self.character_str)
+ elif character_type == "EN_symbol":
+ # same with ASTER setting (use 94 char).
+ self.character_str = string.printable[:-6]
+ dict_character = list(self.character_str)
+ elif character_type in support_character_type:
+ self.character_str = ""
+ assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
+ character_type)
+ with open(character_dict_path, "rb") as fin:
+ lines = fin.readlines()
+ for line in lines:
+ line = line.decode('utf-8').strip("\n").strip("\r\n")
+ self.character_str += line
+ if use_space_char:
+ self.character_str += " "
+ dict_character = list(self.character_str)
+
+ else:
+ raise NotImplementedError
+ self.character_type = character_type
+ dict_character = self.add_special_char(dict_character)
+ self.dict = {}
+ for i, char in enumerate(dict_character):
+ self.dict[char] = i
+ self.character = dict_character
+
+ def add_special_char(self, dict_character):
+ return dict_character
+
+ def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
+ """ convert text-index into text-label. """
+ result_list = []
+ ignored_tokens = self.get_ignored_tokens()
+ batch_size = len(text_index)
+ for batch_idx in range(batch_size):
+ char_list = []
+ conf_list = []
+ for idx in range(len(text_index[batch_idx])):
+ if text_index[batch_idx][idx] in ignored_tokens:
+ continue
+ if is_remove_duplicate:
+ # only for predict
+ if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
+ batch_idx][idx]:
+ continue
+ char_list.append(self.character[int(text_index[batch_idx][
+ idx])])
+ if text_prob is not None:
+ conf_list.append(text_prob[batch_idx][idx])
+ else:
+ conf_list.append(1)
+ text = ''.join(char_list)
+ result_list.append((text, np.mean(conf_list)))
+ return result_list
+
+ def get_ignored_tokens(self):
+ return [0] # for ctc blank
+
+
+class CTCLabelDecode(BaseRecLabelDecode):
+ """ Convert between text-label and text-index """
+
+ def __init__(
+ self,
+ config,
+ #character_dict_path=None,
+ #character_type='ch',
+ #use_space_char=False,
+ **kwargs):
+ super(CTCLabelDecode, self).__init__(config)
+
+ def __call__(self, preds, label=None, *args, **kwargs):
+ preds_idx = preds.argmax(axis=2)
+ preds_prob = preds.max(axis=2)
+ text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
+ if label is None:
+ return text
+ label = self.decode(label)
+ return text, label
+
+ def add_special_char(self, dict_character):
+ dict_character = ['blank'] + dict_character
+ return dict_character
+
+
+class CharacterOps(object):
+ """ Convert between text-label and text-index """
+
+ def __init__(self, config):
+ self.character_type = config['character_type']
+ self.loss_type = config['loss_type']
+ if self.character_type == "en":
+ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
+ dict_character = list(self.character_str)
+ elif self.character_type == "ch":
+ character_dict_path = config['character_dict_path']
+ self.character_str = ""
+ with open(character_dict_path, "rb") as fin:
+ lines = fin.readlines()
+ for line in lines:
+ line = line.decode('utf-8').strip("\n").strip("\r\n")
+ self.character_str += line
+ dict_character = list(self.character_str)
+ elif self.character_type == "en_sensitive":
+ # same with ASTER setting (use 94 char).
+ self.character_str = string.printable[:-6]
+ dict_character = list(self.character_str)
+ else:
+ self.character_str = None
+ assert self.character_str is not None, \
+ "Nonsupport type of the character: {}".format(self.character_str)
+ self.beg_str = "sos"
+ self.end_str = "eos"
+ if self.loss_type == "attention":
+ dict_character = [self.beg_str, self.end_str] + dict_character
+ self.dict = {}
+ for i, char in enumerate(dict_character):
+ self.dict[char] = i
+ self.character = dict_character
+
+ def encode(self, text):
+ """convert text-label into text-index.
+ input:
+ text: text labels of each image. [batch_size]
+
+ output:
+ text: concatenated text index for CTCLoss.
+ [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
+ length: length of each text. [batch_size]
+ """
+ if self.character_type == "en":
+ text = text.lower()
+
+ text_list = []
+ for char in text:
+ if char not in self.dict:
+ continue
+ text_list.append(self.dict[char])
+ text = np.array(text_list)
+ return text
+
+ def decode(self, text_index, is_remove_duplicate=False):
+ """ convert text-index into text-label. """
+ char_list = []
+ char_num = self.get_char_num()
+
+ if self.loss_type == "attention":
+ beg_idx = self.get_beg_end_flag_idx("beg")
+ end_idx = self.get_beg_end_flag_idx("end")
+ ignored_tokens = [beg_idx, end_idx]
+ else:
+ ignored_tokens = [char_num]
+
+ for idx in range(len(text_index)):
+ if text_index[idx] in ignored_tokens:
+ continue
+ if is_remove_duplicate:
+ if idx > 0 and text_index[idx - 1] == text_index[idx]:
+ continue
+ char_list.append(self.character[text_index[idx]])
+ text = ''.join(char_list)
+ return text
+
+ def get_char_num(self):
+ return len(self.character)
+
+ def get_beg_end_flag_idx(self, beg_or_end):
+ if self.loss_type == "attention":
+ if beg_or_end == "beg":
+ idx = np.array(self.dict[self.beg_str])
+ elif beg_or_end == "end":
+ idx = np.array(self.dict[self.end_str])
+ else:
+ assert False, "Unsupport type %s in get_beg_end_flag_idx"\
+ % beg_or_end
+ return idx
+ else:
+ err = "error in get_beg_end_flag_idx when using the loss %s"\
+ % (self.loss_type)
+ assert False, err
+
+
+class OCRReader(object):
+ def __init__(self,
+ algorithm="CRNN",
+ image_shape=[3, 32, 320],
+ char_type="ch",
+ batch_num=1,
+ char_dict_path="./ppocr_keys_v1.txt"):
+ self.rec_image_shape = image_shape
+ self.character_type = char_type
+ self.rec_batch_num = batch_num
+ char_ops_params = {}
+ char_ops_params["character_type"] = char_type
+ char_ops_params["character_dict_path"] = char_dict_path
+ char_ops_params['loss_type'] = 'ctc'
+ self.char_ops = CharacterOps(char_ops_params)
+ self.label_ops = CTCLabelDecode(char_ops_params)
+
+ def resize_norm_img(self, img, max_wh_ratio):
+ imgC, imgH, imgW = self.rec_image_shape
+ if self.character_type == "ch":
+ imgW = int(32 * max_wh_ratio)
+ h = img.shape[0]
+ w = img.shape[1]
+ ratio = w / float(h)
+ if math.ceil(imgH * ratio) > imgW:
+ resized_w = imgW
+ else:
+ resized_w = int(math.ceil(imgH * ratio))
+ resized_image = cv2.resize(img, (resized_w, imgH))
+ resized_image = resized_image.astype('float32')
+ resized_image = resized_image.transpose((2, 0, 1)) / 255
+ resized_image -= 0.5
+ resized_image /= 0.5
+ padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
+
+ padding_im[:, :, 0:resized_w] = resized_image
+ return padding_im
+
+ def preprocess(self, img_list):
+ img_num = len(img_list)
+ norm_img_batch = []
+ max_wh_ratio = 0
+ for ino in range(img_num):
+ h, w = img_list[ino].shape[0:2]
+ wh_ratio = w * 1.0 / h
+ max_wh_ratio = max(max_wh_ratio, wh_ratio)
+
+ for ino in range(img_num):
+ norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
+ norm_img = norm_img[np.newaxis, :]
+ norm_img_batch.append(norm_img)
+ norm_img_batch = np.concatenate(norm_img_batch)
+ norm_img_batch = norm_img_batch.copy()
+
+ return norm_img_batch[0]
+
+ def postprocess_old(self, outputs, with_score=False):
+ rec_res = []
+ rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
+ rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
+ if with_score:
+ predict_lod = outputs["softmax_0.tmp_0.lod"]
+ for rno in range(len(rec_idx_lod) - 1):
+ beg = rec_idx_lod[rno]
+ end = rec_idx_lod[rno + 1]
+ if isinstance(rec_idx_batch, list):
+ rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
+ else: #nd array
+ rec_idx_tmp = rec_idx_batch[beg:end, 0]
+ preds_text = self.char_ops.decode(rec_idx_tmp)
+ if with_score:
+ beg = predict_lod[rno]
+ end = predict_lod[rno + 1]
+ if isinstance(outputs["softmax_0.tmp_0"], list):
+ outputs["softmax_0.tmp_0"] = np.array(outputs[
+ "softmax_0.tmp_0"]).astype(np.float32)
+ probs = outputs["softmax_0.tmp_0"][beg:end, :]
+ ind = np.argmax(probs, axis=1)
+ blank = probs.shape[1]
+ valid_ind = np.where(ind != (blank - 1))[0]
+ score = np.mean(probs[valid_ind, ind[valid_ind]])
+ rec_res.append([preds_text, score])
+ else:
+ rec_res.append([preds_text])
+ return rec_res
+
+ def postprocess(self, outputs, with_score=False):
+ preds = outputs["save_infer_model/scale_0.tmp_1"]
+ try:
+ preds = preds.numpy()
+ except:
+ pass
+ preds_idx = preds.argmax(axis=2)
+ preds_prob = preds.max(axis=2)
+ text = self.label_ops.decode(
+ preds_idx, preds_prob, is_remove_duplicate=True)
+ return text
diff --git a/deploy/pdserving/win/ocr_web_client.py b/deploy/pdserving/win/ocr_web_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2885293168701537fed2d517dbdfd64d3cc9d39
--- /dev/null
+++ b/deploy/pdserving/win/ocr_web_client.py
@@ -0,0 +1,45 @@
+# 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/"
+for idx, img_file in enumerate(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)
+ for i in range(1):
+ data = {"feed": [{"image": image}], "fetch": ["save_infer_model/scale_0.tmp_1"]}
+ r = requests.post(url=url, headers=headers, data=json.dumps(data))
+ print(r.json())
+
+test_img_dir = "../../../doc/imgs/"
+print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))
diff --git a/deploy/pdserving/win/ocr_web_server.py b/deploy/pdserving/win/ocr_web_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f1394fb72acebe58ef5b2a03bf27cd25838c536
--- /dev/null
+++ b/deploy/pdserving/win/ocr_web_server.py
@@ -0,0 +1,114 @@
+# 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
+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
+from ocr_reader import OCRReader
+try:
+ from paddle_serving_server_gpu.web_service import WebService
+except ImportError:
+ from paddle_serving_server.web_service import WebService
+from paddle_serving_app.local_predict import LocalPredictor
+import time
+import re
+import base64
+
+
+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))
+ ])
+ self.det_client = LocalPredictor()
+ if sys.argv[1] == 'gpu':
+ self.det_client.load_model_config(
+ det_model_config, use_gpu=True, gpu_id=1)
+ elif sys.argv[1] == 'cpu':
+ self.det_client.load_model_config(det_model_config)
+ self.ocr_reader = OCRReader(
+ char_dict_path="../../../ppocr/utils/ppocr_keys_v1.txt")
+
+ 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)
+ _, 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={"x": det_img}, fetch=["save_infer_model/scale_0.tmp_1"], batch=True)
+ 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["save_infer_model/scale_0.tmp_1"], [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 = {"x": imgs.copy()}
+ fetch = ["save_infer_model/scale_0.tmp_1"]
+ return feed, fetch, True
+
+ 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("../ppocr_rec_mobile_2.0_serving")
+ocr_service.prepare_server(workdir="workdir", port=9292)
+ocr_service.init_det_debugger(det_model_config="../ppocr_det_mobile_2.0_serving")
+if sys.argv[1] == 'gpu':
+ ocr_service.set_gpus("0")
+ ocr_service.run_debugger_service(gpu=True)
+elif sys.argv[1] == 'cpu':
+ ocr_service.run_debugger_service()
+ocr_service.run_web_service()