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add hub serving

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dyning 2020-07-09 20:34:42 +08:00
parent 453d21d035
commit f09d1f730b
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0
deploy/ocr_hubserving/ocr_det/__init__.py Normal file
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deploy/ocr_hubserving/ocr_det/config.json Normal file
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{
"modules_info": {
"ocr_det": {
"init_args": {
"version": "1.0.0",
"det_model_dir": "./inference/ch_det_mv3_db/",
"use_gpu": true
},
"predict_args": {
"visualization": false
}
}
}
}

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deploy/ocr_hubserving/ocr_det/module.py Normal file
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# -*- coding:utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import ast
import copy
import math
import os
import time
from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
from paddlehub.common.logger import logger
from paddlehub.module.module import moduleinfo, runnable, serving
from PIL import Image
import cv2
import numpy as np
import paddle.fluid as fluid
import paddlehub as hub
from tools.infer.utility import draw_boxes, base64_to_cv2
from tools.infer.predict_det import TextDetector
class Config(object):
pass
@moduleinfo(
name="ocr_det",
version="1.0.0",
summary="ocr detection service",
author="paddle-dev",
author_email="paddle-dev@baidu.com",
type="cv/text_recognition")
class OCRDet(hub.Module):
def _initialize(self,
det_model_dir="",
det_algorithm="DB",
use_gpu=False
):
"""
initialize with the necessary elements
"""
self.config = Config()
self.config.use_gpu = use_gpu
if use_gpu:
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
print("use gpu: ", use_gpu)
print("CUDA_VISIBLE_DEVICES: ", _places)
except:
raise RuntimeError(
"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
)
self.config.ir_optim = True
self.config.gpu_mem = 8000
#params for text detector
self.config.det_algorithm = det_algorithm
self.config.det_model_dir = det_model_dir
# self.config.det_model_dir = "./inference/det/"
#DB parmas
self.config.det_db_thresh =0.3
self.config.det_db_box_thresh =0.5
self.config.det_db_unclip_ratio =2.0
#EAST parmas
self.config.det_east_score_thresh = 0.8
self.config.det_east_cover_thresh = 0.1
self.config.det_east_nms_thresh = 0.2
def read_images(self, paths=[]):
images = []
for img_path in paths:
assert os.path.isfile(
img_path), "The {} isn't a valid file.".format(img_path)
img = cv2.imread(img_path)
if img is None:
logger.info("error in loading image:{}".format(img_path))
continue
images.append(img)
return images
def det_text(self,
images=[],
paths=[],
det_max_side_len=960,
draw_img_save='ocr_det_result',
visualization=False):
"""
Get the text box in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
use_gpu (bool): Whether to use gpu. Default false.
output_dir (str): The directory to store output images.
visualization (bool): Whether to save image or not.
box_thresh(float): the threshold of the detected text box's confidence
Returns:
res (list): The result of text detection box and save path of images.
"""
if images != [] and isinstance(images, list) and paths == []:
predicted_data = images
elif images == [] and isinstance(paths, list) and paths != []:
predicted_data = self.read_images(paths)
else:
raise TypeError("The input data is inconsistent with expectations.")
assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
self.config.det_max_side_len = det_max_side_len
text_detector = TextDetector(self.config)
all_results = []
for img in predicted_data:
result = {'save_path': ''}
if img is None:
logger.info("error in loading image")
result['data'] = []
all_results.append(result)
continue
dt_boxes, elapse = text_detector(img)
print("Predict time : ", elapse)
result['data'] = dt_boxes.astype(np.int).tolist()
if visualization:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
draw_img = draw_boxes(image, dt_boxes)
draw_img = np.array(draw_img)
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
saved_name = 'ndarray_{}.jpg'.format(time.time())
save_file_path = os.path.join(draw_img_save, saved_name)
cv2.imwrite(save_file_path, draw_img[:, :, ::-1])
print("The visualized image saved in {}".format(save_file_path))
result['save_path'] = save_file_path
all_results.append(result)
return all_results
@serving
def serving_method(self, images, **kwargs):
"""
Run as a service.
"""
images_decode = [base64_to_cv2(image) for image in images]
results = self.det_text(images_decode, **kwargs)
return results
if __name__ == '__main__':
ocr = OCRDet()
image_path = [
'./doc/imgs/11.jpg',
'./doc/imgs/12.jpg',
]
res = ocr.det_text(paths=image_path, visualization=True)
print(res)

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deploy/ocr_hubserving/ocr_rec/__init__.py Normal file
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deploy/ocr_hubserving/ocr_rec/config.json Normal file
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{
"modules_info": {
"ocr_rec": {
"init_args": {
"version": "1.0.0",
"det_model_dir": "./inference/ch_rec_mv3_crnn/",
"use_gpu": true
},
"predict_args": {
}
}
}
}

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deploy/ocr_hubserving/ocr_rec/module.py Normal file
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# -*- coding:utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import ast
import copy
import math
import os
import time
from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
from paddlehub.common.logger import logger
from paddlehub.module.module import moduleinfo, runnable, serving
from PIL import Image
import cv2
import numpy as np
import paddle.fluid as fluid
import paddlehub as hub
from tools.infer.utility import base64_to_cv2
from tools.infer.predict_rec import TextRecognizer
class Config(object):
pass
@moduleinfo(
name="ocr_rec",
version="1.0.0",
summary="ocr recognition service",
author="paddle-dev",
author_email="paddle-dev@baidu.com",
type="cv/text_recognition")
class OCRRec(hub.Module):
def _initialize(self,
rec_model_dir="",
rec_algorithm="CRNN",
rec_char_dict_path="./ppocr/utils/ppocr_keys_v1.txt",
rec_batch_num=30,
use_gpu=False
):
"""
initialize with the necessary elements
"""
self.config = Config()
self.config.use_gpu = use_gpu
if use_gpu:
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
print("use gpu: ", use_gpu)
print("CUDA_VISIBLE_DEVICES: ", _places)
except:
raise RuntimeError(
"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
)
self.config.ir_optim = True
self.config.gpu_mem = 8000
#params for text recognizer
self.config.rec_algorithm = rec_algorithm
self.config.rec_model_dir = rec_model_dir
# self.config.rec_model_dir = "./inference/rec/"
self.config.rec_image_shape = "3, 32, 320"
self.config.rec_char_type = 'ch'
self.config.rec_batch_num = rec_batch_num
self.config.rec_char_dict_path = rec_char_dict_path
self.config.use_space_char = True
def read_images(self, paths=[]):
images = []
for img_path in paths:
assert os.path.isfile(
img_path), "The {} isn't a valid file.".format(img_path)
img = cv2.imread(img_path)
if img is None:
logger.info("error in loading image:{}".format(img_path))
continue
images.append(img)
return images
def rec_text(self,
images=[],
paths=[]):
"""
Get the text box in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
Returns:
res (list): The result of text detection box and save path of images.
"""
if images != [] and isinstance(images, list) and paths == []:
predicted_data = images
elif images == [] and isinstance(paths, list) and paths != []:
predicted_data = self.read_images(paths)
else:
raise TypeError("The input data is inconsistent with expectations.")
assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
text_recognizer = TextRecognizer(self.config)
img_list = []
for img in predicted_data:
if img is None:
continue
img_list.append(img)
try:
rec_res, predict_time = text_recognizer(img_list)
except Exception as e:
print(e)
return []
return rec_res
@serving
def serving_method(self, images, **kwargs):
"""
Run as a service.
"""
images_decode = [base64_to_cv2(image) for image in images]
results = self.det_text(images_decode, **kwargs)
return results
if __name__ == '__main__':
ocr = OCRRec()
image_path = [
'./doc/imgs_words/ch/word_1.jpg',
'./doc/imgs_words/ch/word_2.jpg',
'./doc/imgs_words/ch/word_3.jpg',
]
res = ocr.rec_text(paths=image_path)
print(res)

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deploy/ocr_hubserving/ocr_system/__init__.py Normal file
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deploy/ocr_hubserving/ocr_system/config.json Normal file
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{
"modules_info": {
"ocr_system": {
"init_args": {
"version": "1.0.0",
"det_model_dir": "./inference/ch_det_mv3_db/",
"rec_model_dir": "./inference/ch_rec_mv3_crnn/",
"use_gpu": true
},
"predict_args": {
"visualization": false
}
}
}
}

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deploy/ocr_hubserving/ocr_system/module.py Normal file
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# -*- coding:utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import ast
import copy
import math
import os
import time
from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
from paddlehub.common.logger import logger
from paddlehub.module.module import moduleinfo, runnable, serving
from PIL import Image
import cv2
import numpy as np
import paddle.fluid as fluid
import paddlehub as hub
from tools.infer.utility import draw_ocr, base64_to_cv2
from tools.infer.predict_system import TextSystem
class Config(object):
pass
@moduleinfo(
name="ocr_system",
version="1.0.0",
summary="ocr system service",
author="paddle-dev",
author_email="paddle-dev@baidu.com",
type="cv/text_recognition")
class OCRSystem(hub.Module):
def _initialize(self,
det_model_dir="",
det_algorithm="DB",
rec_model_dir="",
rec_algorithm="CRNN",
rec_char_dict_path="./ppocr/utils/ppocr_keys_v1.txt",
rec_batch_num=30,
use_gpu=False
):
"""
initialize with the necessary elements
"""
self.config = Config()
self.config.use_gpu = use_gpu
if use_gpu:
try:
_places = os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
print("use gpu: ", use_gpu)
print("CUDA_VISIBLE_DEVICES: ", _places)
except:
raise RuntimeError(
"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
)
self.config.ir_optim = True
self.config.gpu_mem = 8000
#params for text detector
self.config.det_algorithm = det_algorithm
self.config.det_model_dir = det_model_dir
# self.config.det_model_dir = "./inference/det/"
#DB parmas
self.config.det_db_thresh =0.3
self.config.det_db_box_thresh =0.5
self.config.det_db_unclip_ratio =2.0
#EAST parmas
self.config.det_east_score_thresh = 0.8
self.config.det_east_cover_thresh = 0.1
self.config.det_east_nms_thresh = 0.2
#params for text recognizer
self.config.rec_algorithm = rec_algorithm
self.config.rec_model_dir = rec_model_dir
# self.config.rec_model_dir = "./inference/rec/"
self.config.rec_image_shape = "3, 32, 320"
self.config.rec_char_type = 'ch'
self.config.rec_batch_num = rec_batch_num
self.config.rec_char_dict_path = rec_char_dict_path
self.config.use_space_char = True
def read_images(self, paths=[]):
images = []
for img_path in paths:
assert os.path.isfile(
img_path), "The {} isn't a valid file.".format(img_path)
img = cv2.imread(img_path)
if img is None:
logger.info("error in loading image:{}".format(img_path))
continue
images.append(img)
return images
def recognize_text(self,
images=[],
paths=[],
det_max_side_len=960,
draw_img_save='ocr_result',
visualization=False,
text_thresh=0.5):
"""
Get the chinese texts in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
use_gpu (bool): Whether to use gpu.
batch_size(int): the program deals once with one
output_dir (str): The directory to store output images.
visualization (bool): Whether to save image or not.
box_thresh(float): the threshold of the detected text box's confidence
text_thresh(float): the threshold of the recognize chinese texts' confidence
Returns:
res (list): The result of chinese texts and save path of images.
"""
if images != [] and isinstance(images, list) and paths == []:
predicted_data = images
elif images == [] and isinstance(paths, list) and paths != []:
predicted_data = self.read_images(paths)
else:
raise TypeError("The input data is inconsistent with expectations.")
assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
self.config.det_max_side_len = det_max_side_len
text_sys = TextSystem(self.config)
cnt = 0
all_results = []
for img in predicted_data:
result = {'save_path': ''}
if img is None:
logger.info("error in loading image")
result['data'] = []
all_results.append(result)
continue
starttime = time.time()
dt_boxes, rec_res = text_sys(img)
elapse = time.time() - starttime
cnt += 1
print("Predict time of image %d: %.3fs" % (cnt, elapse))
dt_num = len(dt_boxes)
rec_res_final = []
for dno in range(dt_num):
text, score = rec_res[dno]
# if the recognized text confidence score is lower than text_thresh, then drop it
if score >= text_thresh:
# text_str = "%s, %.3f" % (text, score)
# print(text_str)
rec_res_final.append(
{
'text': text,
'confidence': float(score),
'text_box_position': dt_boxes[dno].astype(np.int).tolist()
}
)
result['data'] = rec_res_final
if visualization:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
boxes = dt_boxes
txts = [rec_res[i][0] for i in range(len(rec_res))]
scores = [rec_res[i][1] for i in range(len(rec_res))]
draw_img = draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5)
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
saved_name = 'ndarray_{}.jpg'.format(time.time())
save_file_path = os.path.join(draw_img_save, saved_name)
cv2.imwrite(save_file_path, draw_img[:, :, ::-1])
print("The visualized image saved in {}".format(save_file_path))
result['save_path'] = save_file_path
all_results.append(result)
return all_results
@serving
def serving_method(self, images, **kwargs):
"""
Run as a service.
"""
images_decode = [base64_to_cv2(image) for image in images]
results = self.recognize_text(images_decode, **kwargs)
return results
if __name__ == '__main__':
ocr = OCRSystem()
image_path = [
'./doc/imgs/11.jpg',
'./doc/imgs/12.jpg',
]
res = ocr.recognize_text(paths=image_path, visualization=True)
print(res)

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doc/doc_ch/hubserving.md Normal file
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# 服务部署
PaddleOCR提供2种服务部署方式：
- 基于HubServing的部署：已集成到PaddleOCR中（[code](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/deploy/hubserving)），按照本教程使用；
- 基于PaddleServing的部署：详见PaddleServing官网[demo](https://github.com/PaddlePaddle/Serving/tree/develop/python/examples/ocr)，后续也将集成到PaddleOCR。
服务部署目录下包括检测、识别、2阶段串联三种服务包，根据需求选择相应的服务包进行安装和启动。目录如下：
```
deploy/hubserving/
└─ ocr_det 检测模块服务包
└─ ocr_rec 识别模块服务包
└─ ocr_system 检测+识别串联服务包
```
每个服务包下包含3个文件。以2阶段串联服务包为例，目录如下：
```
deploy/hubserving/ocr_system/
└─ __init__.py 空文件
└─ config.json 配置文件，启动服务时作为参数传入
└─ module.py 主模块，包含服务的完整逻辑
```
## 启动服务
以下步骤以检测+识别2阶段串联服务为例，如果只需要检测服务或识别服务，替换相应文件路径即可。
### 1. 安装paddlehub
```pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple```
### 2. 安装服务模块
PaddleOCR提供3种服务模块，根据需要安装所需模块。如：
安装检测服务模块：
```hub install deploy/hubserving/ocr_det/```
或，安装识别服务模块：
```hub install deploy/hubserving/ocr_rec/```
或，安装检测+识别串联服务模块：
```hub install deploy/hubserving/ocr_system/```
### 3. 修改配置文件
在config.json中指定模型路径、是否使用GPU、是否对结果做可视化等参数，如，串联服务ocr_system的配置：
```python
{
"modules_info": {
"ocr_system": {
"init_args": {
"version": "1.0.0",
"det_model_dir": "./inference/det/",
"rec_model_dir": "./inference/rec/",
"use_gpu": true
},
"predict_args": {
"visualization": false
}
}
}
}
```
其中，模型路径对应的模型为```inference模型```。
### 4. 运行启动命令
```hub serving start -m ocr_system --config hubserving/ocr_det/config.json```
这样就完成了一个服务化API的部署，默认端口号为8866。
**NOTE:** 如使用GPU预测(即，config中use_gpu置为true)，则需要在启动服务之前，设置CUDA_VISIBLE_DEVICES环境变量，如：```export CUDA_VISIBLE_DEVICES=0```，否则不用设置。
## 发送预测请求
配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果:
```python
import requests
import json
import cv2
import base64
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
# 发送HTTP请求
data = {'images':[cv2_to_base64(open("./doc/imgs/11.jpg", 'rb').read())]}
headers = {"Content-type": "application/json"}
# url = "http://127.0.0.1:8866/predict/ocr_det"
# url = "http://127.0.0.1:8866/predict/ocr_rec"
url = "http://127.0.0.1:8866/predict/ocr_system"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
# 打印预测结果
print(r.json()["results"])
```
你可能需要根据实际情况修改```url```字符串中的端口号和服务模块名称。
上面所示代码都已写入测试脚本，可直接运行命令：```python tools/test_hubserving.py```
## 自定义修改服务模块
如果需要修改服务逻辑，你一般需要操作以下步骤：
1、 停止服务
```hub serving stop -m ocr_system```
2、 到相应的module.py文件中根据实际需求修改代码
3、 卸载旧服务包
```hub uninstall ocr_system```
4、 安装修改后的新服务包
```hub install deploy/hubserving/ocr_system/```

19
tools/infer/utility.py
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@ -302,6 +302,25 @@ def text_visual(texts, scores, img_h=400, img_w=600, threshold=0.):
return np.array(blank_img)
def base64_to_cv2(b64str):
import base64
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
def draw_boxes(image, boxes, scores=None, drop_score=0.5):
if scores is None:
scores = [1] * len(boxes)
for (box, score) in zip(boxes, scores):
if score < drop_score:
continue
box = np.reshape(np.array(box), [-1, 1, 2]).astype(np.int64)
image = cv2.polylines(np.array(image), [box], True, (255, 0, 0), 2)
return image
if __name__ == '__main__':
test_img = "./doc/test_v2"
predict_txt = "./doc/predict.txt"

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tools/test_hubserving.py Normal file
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#!usr/bin/python
# -*- coding: utf-8 -*-
import requests
import json
import cv2
import base64
import time
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
start = time.time()
# 发送HTTP请求
data = {'images':[cv2_to_base64(open("./doc/imgs/11.jpg", 'rb').read())]}
headers = {"Content-type": "application/json"}
# url = "http://127.0.0.1:8866/predict/ocr_det"
# url = "http://127.0.0.1:8866/predict/ocr_rec"
url = "http://127.0.0.1:8866/predict/ocr_system"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
end = time.time()
# 打印预测结果
print(r.json()["results"])
print("time cost: ", end - start)