未验证 提交 43d7dcc3 编写于 作者: M MissPenguin 提交者: GitHub

Merge pull request #2311 from LDOUBLEV/pdserver_dyg

add pdserver demo to dygraph
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
PaddleOCR提供2种服务部署方式: 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`",按照本教程使用; - 基于PaddleHub Serving的部署:代码路径为"`./deploy/hubserving`",按照本教程使用;
- (coming soon)基于PaddleServing的部署:代码路径为"`./deploy/pdserving`",使用方法参考[文档](../../deploy/pdserving/readme.md) - 基于PaddleServing的部署:代码路径为"`./deploy/pdserving`",使用方法参考[文档](../../deploy/pdserving/README_CN.md)
# 基于PaddleHub Serving的服务部署 # 基于PaddleHub Serving的服务部署
......
...@@ -2,7 +2,7 @@ English | [简体中文](readme.md) ...@@ -2,7 +2,7 @@ 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 follow this tutorial. - Based on **PaddleHub Serving**: Code path is "`./deploy/hubserving`". Please follow this tutorial.
- (coming soon)Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../../deploy/pdserving/readme.md) for usage. - Based on **PaddleServing**: Code path is "`./deploy/pdserving`". Please refer to the [tutorial](../../deploy/pdserving/README.md) for usage.
# Service deployment based on PaddleHub Serving # Service deployment based on PaddleHub Serving
......
# OCR Pipeline WebService
(English|[简体中文](./README_CN.md))
This document will introduce how to use the [PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md) to deploy the PPOCR dynamic graph model as a pipeline online service.
Compared with hubserving deployment, PaddleServing supports high concurrency and efficient communication between the client and the server.
The introduction and tutorial of Paddle Serving service deployment framework reference [document](https://aistudio.baidu.com/aistudio/projectdetail/1550674).
## Contents
- Environmental preparation
- Model conversion
- Paddle Serving pipeline deployment
- FAQ
## Environmental preparation
Need to prepare PaddleOCR operating environment and Paddle Serving operating environment.
1. Prepare PaddleOCR operating environment reference [link](../../doc/doc_ch/installation.md)
2. Prepare the operating environment of PaddleServing, the steps are as follows
Install serving, 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
# 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
```
2. Install the client to send requests to the service
```
pip3 install paddle-serving-client==0.5.0 # for CPU
pip3 install paddle-serving-client-gpu==0.5.0 # for GPU
```
3. Install serving-app
```
pip3 install paddle-serving-app==0.3.0
# fix local_predict to support load dynamic model
# find the install directoory of paddle_serving_app
vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
# replace line 85 of local_predict.py config = AnalysisConfig(model_path) with:
if os.path.exists(os.path.join(model_path, "__params__")):
config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
else:
config = AnalysisConfig(model_path)
```
**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).
## Model conversion
When using PaddleServing for service deployment, you need to convert the saved inference model into a serving model that is easy to deploy.
First, download the [inference model] of PPOCR(https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15)
```
# Download and unzip the OCR text detection model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar
# Download and unzip the OCR text recognition model
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar
# Conversion detection model
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_det_server_2.0_serving/ \
--serving_client ./ppocr_det_server_2.0_client/
# Conversion recognition model
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_rec_server_2.0_serving/ \
--serving_client ./ppocr_rec_server_2.0_client/
```
After the detection model is converted, there will be additional folders of `ppocr_det_server_2.0_serving` and `ppocr_det_server_2.0_client` in the current folder, with the following format:
```
|- ppocr_det_server_2.0_serving/
|- __model__
|- __params__
|- serving_server_conf.prototxt
|- serving_server_conf.stream.prototxt
|- ppocr_det_server_2.0_client
|- serving_client_conf.prototxt
|- serving_client_conf.stream.prototxt
```
The recognition model is the same.
## Paddle Serving pipeline deployment
1. Download the PaddleOCR code, if you have already downloaded it, you can skip this step.
```
git clone https://github.com/PaddlePaddle/PaddleOCR
# Enter the working directory
cd PaddleOCR/deploy/pdserver/
```
The pdserver directory contains the code to start the pipeline service and send prediction requests, including:
```
__init__.py
config.yml # Start the service configuration file
ocr_reader.py # OCR model pre-processing and post-processing code implementation
pipeline_http_client.py # Script to send pipeline prediction request
web_service.py # Start the script of the pipeline server
```
2. Run the following command to start the service.
```
# Start the service and save the running log in log.txt
python3 web_service.py &>log.txt &
```
After the service is successfully started, a log similar to the following will be printed in log.txt
![](./imgs/start_server.png)
3. Send service request
```
python3 pipeline_http_client.py
```
After successfully running, the predicted result of the model will be printed in the cmd window. An example of the result is:
![](./imgs/results.png)
## FAQ
** Q1**: No result return after sending the request
** A1**: Do not set the proxy when starting the service and sending the request. You can close the proxy before starting the service and before sending the request. The command to close the proxy is:
```
unset https_proxy
unset http_proxy
```
# PPOCR 服务化部署
([English](./README.md)|简体中文)
本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署PPOCR
动态图模型的pipeline在线服务。
相比较于hubserving部署,PaddleServing支持客户端和服务端之间 高并发和高效通信,更多有关Paddle Serving服务化部署框架介绍和使用教程参考[文档](https://aistudio.baidu.com/aistudio/projectdetail/1550674)
## 目录
- 环境准备
- 模型转换
- Paddle Serving pipeline部署
- FAQ
## 环境准备
需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。
1. 准备PaddleOCR的运行环境参考[链接](../../doc/doc_ch/installation.md)
2. 准备PaddleServing的运行环境,步骤如下
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
# 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
```
2. 安装client,用于向服务发送请求
```
pip3 install paddle-serving-client==0.5.0 # for CPU
pip3 install paddle-serving-client-gpu==0.5.0 # for GPU
```
3. 安装serving-app
```
pip3 install paddle-serving-app==0.3.0
```
**note:** 安装0.3.0版本的serving-app后,为了能加载动态图模型,需要修改serving_app的源码,具体为:
```
# 找到paddle_serving_app的安装目录,找到并编辑local_predict.py文件
vim /usr/local/lib/python3.7/site-packages/paddle_serving_app/local_predict.py
# 将local_predict.py 的第85行 config = AnalysisConfig(model_path) 替换为:
if os.path.exists(os.path.join(model_path, "__params__")):
config = AnalysisConfig(os.path.join(model_path, "__model__"), os.path.join(model_path, "__params__"))
else:
config = AnalysisConfig(model_path)
```
**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)
## 模型转换
使用PaddleServing做服务化部署时,需要将保存的inference模型转换为serving易于部署的模型。
首先,下载PPOCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15)
```
# 下载并解压 OCR 文本检测模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar
# 下载并解压 OCR 文本识别模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar
# 转换检测模型
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_det_server_2.0_serving/ \
--serving_client ./ppocr_det_server_2.0_client/
# 转换识别模型
python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--serving_server ./ppocr_rec_server_2.0_serving/ \
--serving_client ./ppocr_rec_server_2.0_client/
```
检测模型转换完成后,会在当前文件夹多出`ppocr_det_server_2.0_serving``ppocr_det_server_2.0_client`的文件夹,具备如下格式:
```
|- ppocr_det_server_2.0_serving/
|- __model__
|- __params__
|- serving_server_conf.prototxt
|- serving_server_conf.stream.prototxt
|- ppocr_det_server_2.0_client
|- serving_client_conf.prototxt
|- serving_client_conf.stream.prototxt
```
识别模型同理。
## Paddle Serving pipeline部署
```
# 下载PaddleOCR代码,若已下载可跳过此步骤
git clone https://github.com/PaddlePaddle/PaddleOCR
# 进入到工作目录
cd PaddleOCR/deploy/pdserver/
```
pdserver目录包含启动pipeline服务和发送预测请求的代码,包括:
```
__init__.py
config.yml # 启动服务的配置文件
ocr_reader.py # OCR模型预处理和后处理的代码实现
pipeline_http_client.py # 发送pipeline预测请求的脚本
web_service.py # 启动pipeline服务端的脚本
```
启动服务可运行如下命令
```
# 启动服务,运行日志保存在log.txt
python3 web_service.py &>log.txt &
```
成功启动服务后,log.txt中会打印类似如下日志
![](./imgs/start_server.png)
发送服务请求
```
python3 pipeline_http_client.py
```
成功运行后,模型预测的结果会打印在cmd窗口中,结果示例为:
![](./imgs/results.png)
## FAQ
** Q1**: 发送请求后得不到结果
** A1**: 启动服务和发送请求时不要设置代理,可以在启动服务前和发送请求前关闭代理,关闭代理的命令是:
```
unset https_proxy
unset http_proxy
```
# 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.
#rpc端口, rpc_port和http_port不允许同时为空。当rpc_port为空且http_port不为空时,会自动将rpc_port设置为http_port+1
rpc_port: 18090
#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时,不自动生成http_port
http_port: 9999
#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程,每个进程内构建grpcSever和DAG
##当build_dag_each_worker=False时,框架会设置主线程grpc线程池的max_workers=worker_num
worker_num: 20
#build_dag_each_worker, False,框架在进程内创建一条DAG;True,框架会每个进程内创建多个独立的DAG
build_dag_each_worker: false
dag:
#op资源类型, True, 为线程模型;False,为进程模型
is_thread_op: False
#重试次数
retry: 1
#使用性能分析, True,生成Timeline性能数据,对性能有一定影响;False为不使用
use_profile: False
tracer:
interval_s: 10
op:
det:
#并发数,is_thread_op=True时,为线程并发;否则为进程并发
concurrency: 4
#当op配置没有server_endpoints时,从local_service_conf读取本地服务配置
local_service_conf:
#client类型,包括brpc, grpc和local_predictor.local_predictor不启动Serving服务,进程内预测
client_type: local_predictor
#det模型路径
model_config: /paddle/serving/models/det_serving_server/ #ocr_det_model
#Fetch结果列表,以client_config中fetch_var的alias_name为准
fetch_list: ["save_infer_model/scale_0.tmp_1"]
#计算硬件ID,当devices为""或不写时为CPU预测;当devices为"0", "0,1,2"时为GPU预测,表示使用的GPU卡
devices: "2"
ir_optim: True
rec:
#并发数,is_thread_op=True时,为线程并发;否则为进程并发
concurrency: 1
#超时时间, 单位ms
timeout: -1
#Serving交互重试次数,默认不重试
retry: 1
#当op配置没有server_endpoints时,从local_service_conf读取本地服务配置
local_service_conf:
#client类型,包括brpc, grpc和local_predictor。local_predictor不启动Serving服务,进程内预测
client_type: local_predictor
#rec模型路径
model_config: /paddle/serving/models/rec_serving_server/ #ocr_rec_model
#Fetch结果列表,以client_config中fetch_var的alias_name为准
fetch_list: ["save_infer_model/scale_0.tmp_1"] #["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
#计算硬件ID,当devices为""或不写时为CPU预测;当devices为"0", "0,1,2"时为GPU预测,表示使用的GPU卡
devices: "2"
ir_optim: True
# 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
import paddle
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_long' in kwargs:
self.resize_type = 2
self.resize_long = kwargs.get('resize_long', 960)
else:
self.limit_side_len = 736
self.limit_type = 'min'
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):
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
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
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import requests
import json
import base64
import os
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
url = "http://127.0.0.1:9999/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 = {"key": ["image"], "value": [image]}
r = requests.post(url=url, data=json.dumps(data))
print(r.json())
test_img_dir = "../doc/imgs/"
print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))
# 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.
try:
from paddle_serving_server_gpu.pipeline import PipelineClient
except ImportError:
from paddle_serving_server.pipeline import PipelineClient
import numpy as np
import requests
import json
import cv2
import base64
import os
client = PipelineClient()
client.connect(['127.0.0.1:18090'])
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
test_img_dir = "imgs/"
for img_file in os.listdir(test_img_dir):
with open(os.path.join(test_img_dir, img_file), 'rb') as file:
image_data = file.read()
image = cv2_to_base64(image_data)
for i in range(1):
ret = client.predict(feed_dict={"image": image}, fetch=["res"])
print(ret)
#print(ret)
# 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.
try:
from paddle_serving_server_gpu.web_service import WebService, Op
except ImportError:
from paddle_serving_server.web_service import WebService, Op
import logging
import numpy as np
import cv2
import base64
# from paddle_serving_app.reader import OCRReader
from ocr_reader import OCRReader, DetResizeForTest
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, GetRotateCropImage, SortedBoxes
_LOGGER = logging.getLogger()
class DetOp(Op):
def init_op(self):
self.det_preprocess = Sequential([
DetResizeForTest(), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
(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
})
def preprocess(self, input_dicts, data_id, log_id):
(_, input_dict), = input_dicts.items()
data = base64.b64decode(input_dict["image"].encode('utf8'))
data = np.fromstring(data, np.uint8)
# Note: class variables(self.var) can only be used in process op mode
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
self.im = im
self.ori_h, self.ori_w, _ = im.shape
det_img = self.det_preprocess(self.im)
_, self.new_h, self.new_w = det_img.shape
print("det image shape", det_img.shape)
return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
print("input_dicts: ", input_dicts)
det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
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])
out_dict = {"dt_boxes": dt_boxes, "image": self.im}
print("out dict", out_dict["dt_boxes"])
return out_dict, None, ""
class RecOp(Op):
def init_op(self):
self.ocr_reader = OCRReader(
char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
self.get_rotate_crop_image = GetRotateCropImage()
self.sorted_boxes = SortedBoxes()
def preprocess(self, input_dicts, data_id, log_id):
(_, input_dict), = input_dicts.items()
im = input_dict["image"]
dt_boxes = input_dict["dt_boxes"]
dt_boxes = self.sorted_boxes(dt_boxes)
feed_list = []
img_list = []
max_wh_ratio = 0
for i, dtbox in enumerate(dt_boxes):
boximg = self.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)
_, 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
print("rec image shape", imgs.shape)
feed = {"x": imgs.copy()}
return feed, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True)
res_lst = []
for res in rec_res:
res_lst.append(res[0])
res = {"res": str(res_lst)}
return res, None, ""
class OcrService(WebService):
def get_pipeline_response(self, read_op):
det_op = DetOp(name="det", input_ops=[read_op])
rec_op = RecOp(name="rec", input_ops=[det_op])
return rec_op
uci_service = OcrService(name="ocr")
uci_service.prepare_pipeline_config("config.yml")
uci_service.run_service()
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