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Merge pull request #315 from MissPenguin/develop 

move out visulization from hubserving
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dyning 2020-07-13 20:58:26 +08:00 committed by GitHub
parent a5c095e0aa 8e05ffed7e
commit 115893904f
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9 changed files with 168 additions and 120 deletions
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1
deploy/hubserving/ocr_det/config.json
View File

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

38
deploy/hubserving/ocr_det/module.py
View File

@ -19,7 +19,7 @@ import numpy as np
import paddle.fluid as fluid
import paddlehub as hub
from tools.infer.utility import draw_boxes, base64_to_cv2
from tools.infer.utility import base64_to_cv2
from tools.infer.predict_det import TextDetector
@ -68,16 +68,12 @@ class OCRDet(hub.Module):
def predict(self,
images=[],
paths=[],
draw_img_save='ocr_det_result',
visualization=False):
paths=[]):
"""
Get the text box in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
draw_img_save (str): The directory to store output images.
visualization (bool): Whether to save image or not.
Returns:
res (list): The result of text detection box and save path of images.
"""
@ -93,29 +89,21 @@ class OCRDet(hub.Module):
all_results = []
for img in predicted_data:
result = {'save_path': ''}
if img is None:
logger.info("error in loading image")
result['data'] = []
all_results.append(result)
all_results.append([])
continue
dt_boxes, elapse = self.text_detector(img)
print("Predict time : ", elapse)
result['data'] = dt_boxes.astype(np.int).tolist()
logger.info("Predict time : {}".format(elapse))
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)
rec_res_final = []
for dno in range(len(dt_boxes)):
rec_res_final.append(
{
'text_region': dt_boxes[dno].astype(np.int).tolist()
}
)
all_results.append(rec_res_final)
return all_results
@serving
@ -134,5 +122,5 @@ if __name__ == '__main__':
'./doc/imgs/11.jpg',
'./doc/imgs/12.jpg',
]
res = ocr.predict(paths=image_path, visualization=True)
res = ocr.predict(paths=image_path)
print(res)

16
deploy/hubserving/ocr_rec/module.py
View File

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

1
deploy/hubserving/ocr_system/config.json
View File

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

47
deploy/hubserving/ocr_system/module.py
View File

@ -19,7 +19,7 @@ import numpy as np
import paddle.fluid as fluid
import paddlehub as hub
from tools.infer.utility import draw_ocr, base64_to_cv2
from tools.infer.utility import base64_to_cv2
from tools.infer.predict_system import TextSystem
@ -68,18 +68,12 @@ class OCRSystem(hub.Module):
def predict(self,
images=[],
paths=[],
draw_img_save='ocr_result',
visualization=False,
text_thresh=0.5):
paths=[]):
"""
Get the chinese texts in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
draw_img_save (str): The directory to store output images.
visualization (bool): Whether to save image or not.
text_thresh(float): the threshold of the recognize chinese texts' confidence
Returns:
res (list): The result of chinese texts and save path of images.
"""
@ -93,53 +87,30 @@ class OCRSystem(hub.Module):
assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
cnt = 0
all_results = []
for img in predicted_data:
result = {'save_path': ''}
if img is None:
logger.info("error in loading image")
result['data'] = []
all_results.append(result)
all_results.append([])
continue
starttime = time.time()
dt_boxes, rec_res = self.text_sys(img)
elapse = time.time() - starttime
cnt += 1
print("Predict time of image %d: %.3fs" % (cnt, elapse))
logger.info("Predict time: {}".format(elapse))
dt_num = len(dt_boxes)
rec_res_final = []
for dno in range(dt_num):
text, score = rec_res[dno]
# if the recognized text confidence score is lower than text_thresh, then drop it
if score >= text_thresh:
# text_str = "%s, %.3f" % (text, score)
# print(text_str)
rec_res_final.append(
{
'text': text,
'confidence': float(score),
'text_box_position': dt_boxes[dno].astype(np.int).tolist()
'text_region': dt_boxes[dno].astype(np.int).tolist()
}
)
result['data'] = rec_res_final
if visualization:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
boxes = dt_boxes
txts = [rec_res[i][0] for i in range(len(rec_res))]
scores = [rec_res[i][1] for i in range(len(rec_res))]
draw_img = draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5)
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
saved_name = 'ndarray_{}.jpg'.format(time.time())
save_file_path = os.path.join(draw_img_save, saved_name)
cv2.imwrite(save_file_path, draw_img[:, :, ::-1])
print("The visualized image saved in {}".format(save_file_path))
result['save_path'] = save_file_path
all_results.append(result)
all_results.append(rec_res_final)
return all_results
@serving
@ -158,5 +129,5 @@ if __name__ == '__main__':
'./doc/imgs/11.jpg',
'./doc/imgs/12.jpg',
]
res = ocr.predict(paths=image_path, visualization=False)
res = ocr.predict(paths=image_path)
print(res)

46
doc/doc_ch/serving.md
View File

@ -23,8 +23,14 @@ deploy/hubserving/ocr_system/
## 快速启动服务
以下步骤以检测+识别2阶段串联服务为例，如果只需要检测服务或识别服务，替换相应文件路径即可。
### 1. 安装paddlehub
```pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple```
### 1. 准备环境
```shell
# 安装paddlehub
pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
# 设置环境变量
export PYTHONPATH=.
```
### 2. 安装服务模块
PaddleOCR提供3种服务模块，根据需要安装所需模块。如：
@ -75,7 +81,6 @@ $ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \
"use_gpu": true
},
"predict_args": {
"visualization": false
}
}
},
@ -99,32 +104,21 @@ hub serving start -c deploy/hubserving/ocr_system/config.json
```
## 发送预测请求
配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果:
配置好服务端，可使用以下命令发送预测请求，获取预测结果:
```python
import requests
import json
import cv2
import base64
```python tools/test_hubserving.py server_url image_path```
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
需要给脚本传递2个参数：
- **server_url**：服务地址，格式为
`http://[ip_address]:[port]/predict/[module_name]`
例如，如果使用配置文件启动检测、识别、检测+识别2阶段服务，那么发送请求的url将分别是：
`http://127.0.0.1:8866/predict/ocr_det`
`http://127.0.0.1:8867/predict/ocr_rec`
`http://127.0.0.1:8868/predict/ocr_system`
- **image_path**：测试图像路径，可以是单张图片路径，也可以是图像集合目录路径
# 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```
访问示例：
```python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/```
## 自定义修改服务模块
如果需要修改服务逻辑，你一般需要操作以下步骤（以修改`ocr_system`为例）：

4
tools/infer/predict_system.py
View File

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

2
tools/infer/utility.py
View File

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

121
tools/test_hubserving.py
View File

@ -1,25 +1,114 @@
#!usr/bin/python
# -*- coding: utf-8 -*-
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
from ppocr.utils.utility import initial_logger
logger = initial_logger()
import cv2
import numpy as np
import time
from PIL import Image
from ppocr.utils.utility import get_image_file_list
from tools.infer.utility import draw_ocr, draw_boxes
import requests
import json
import cv2
import base64
import time
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
start = time.time()
# 发送HTTP请求
data = {'images':[cv2_to_base64(open("./doc/imgs/11.jpg", 'rb').read())]}
headers = {"Content-type": "application/json"}
# url = "http://127.0.0.1:8866/predict/ocr_det"
# url = "http://127.0.0.1:8866/predict/ocr_rec"
url = "http://127.0.0.1:8866/predict/ocr_system"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
end = time.time()
# 打印预测结果
print(r.json()["results"])
print("time cost: ", end - start)
def draw_server_result(image_file, res):
img = cv2.imread(image_file)
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
if len(res) == 0:
return np.array(image)
keys = res[0].keys()
if 'text_region' not in keys: # for ocr_rec, draw function is invalid
print("draw function is invalid for ocr_rec!")
return None
elif 'text' not in keys: # for ocr_det
print("draw text boxes only!")
boxes = []
for dno in range(len(res)):
boxes.append(res[dno]['text_region'])
boxes = np.array(boxes)
draw_img = draw_boxes(image, boxes)
return draw_img
else: # for ocr_system
print("draw boxes and texts!")
boxes = []
texts = []
scores = []
for dno in range(len(res)):
boxes.append(res[dno]['text_region'])
texts.append(res[dno]['text'])
scores.append(res[dno]['confidence'])
boxes = np.array(boxes)
scores = np.array(scores)
draw_img = draw_ocr(image, boxes, texts, scores, draw_txt=True, drop_score=0.5)
return draw_img
def main(url, image_path):
image_file_list = get_image_file_list(image_path)
is_visualize = False
headers = {"Content-type": "application/json"}
cnt = 0
total_time = 0
for image_file in image_file_list:
img = open(image_file, 'rb').read()
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
# 发送HTTP请求
starttime = time.time()
data = {'images':[cv2_to_base64(img)]}
r = requests.post(url=url, headers=headers, data=json.dumps(data))
elapse = time.time() - starttime
total_time += elapse
print("Predict time of %s: %.3fs" % (image_file, elapse))
res = r.json()["results"][0]
# print(res)
if is_visualize:
draw_img = draw_server_result(image_file, res)
if draw_img is not None:
draw_img_save = "./server_results/"
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
cv2.imwrite(
os.path.join(draw_img_save, os.path.basename(image_file)),
draw_img[:, :, ::-1])
print("The visualized image saved in {}".format(
os.path.join(draw_img_save, os.path.basename(image_file))))
cnt += 1
if cnt % 100 == 0:
print(cnt, "processed")
print("avg time cost: ", float(total_time)/cnt)
if __name__ == '__main__':
if len(sys.argv) != 3:
print("Usage: %s server_url image_path" % sys.argv[0])
else:
server_url = sys.argv[1]
image_path = sys.argv[2]
main(server_url, image_path)