Skip to content

S
Serving

PaddlePaddle / Serving
1 年多 前同步成功

通知 186
Star 833
Fork 253
S
Serving
提交 83b87106 编写于 作者: W wangjiawei04
浏览文件
操作

add readme

上级 946b10c1
隐藏空白更改
内联 并排
Showing with 67 addition and 216 deletion
python/examples/ocr/README.md
浏览文件 @ 83b87106
# OCR
(English|[简体中文](./README_CN.md))
## Get Model
```
python -m paddle_serving_app.package --get_model ocr_rec
......@@ -7,29 +9,6 @@ tar -xzvf ocr_rec.tar.gz
python -m paddle_serving_app.package --get_model ocr_det
tar -xzvf ocr_det.tar.gz
```
## RPC Service
### Start Service
For the following two code block, please check your devices and pick one
for GPU device
```
python -m paddle_serving_server_gpu.serve --model ocr_rec_model --port 9292 --gpu_id 0
python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0
```
for CPU device
```
python -m paddle_serving_server.serve --model ocr_rec_model --port 9292
python -m paddle_serving_server.serve --model ocr_det_model --port 9293
```
### Client Prediction
```
python ocr_rpc_client.py
```
## Web Service
### Start Service
......@@ -43,6 +22,7 @@ python ocr_web_server.py
```
python ocr_web_client.py
```
If you want a faster web service, please try Web Debugger Service
## Web Debugger Service
```
......@@ -53,3 +33,14 @@ python ocr_debugger_server.py
```
python ocr_web_clint.py
```
## Benchmark
CPU: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz * 40
GPU: Nvidia Tesla V100 * 1
| engine | client read image(ms) | client-server tras time(ms) | server read image（ms） | det pre(ms) | det infer(ms) | det post(ms) | rec pre(ms) | rec infer(ms) | rec post(ms) | server-client trans time(ms) | server side time consumption(ms) | server side overhead(ms) | total time（ms) |
|------------------------------|----------------|----------------------------|------------------|--------------------|------------------|--------------------|--------------------|------------------|--------------------|--------------------------|--------------------|--------------|---------------|
| Serving web service | 112.02 | 13.41 | 109.97 | 2.82 | 87.76 | 4.29 | 3.98 | 78.51 | 3.66 | 4.12 | 181.02 | 239.52 | 420.54 |
| Serving Debugger web service | 111.68 | 16.42 | 115.27 | 2.93 | 20.63 | 3.97 | 4.48 | 13.84 | 3.60 | 6.91 | 49.45 | 250.28 | 299.73 |
| | | | | | | | | | | | | | |
python/examples/ocr/README_CN.md 0 → 100644
浏览文件 @ 83b87106
# OCR 服务
([English](./README.md)|简体中文)
## 获取模型
```
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
```
### 客户端预测
```
python ocr_rpc_client.py
```
## Web Service服务
### 启动服务
```
python -m paddle_serving_server_gpu.serve --model ocr_det_model --port 9293 --gpu_id 0
python ocr_web_server.py
```
### 启动客户端
```
python ocr_web_client.py
```
如果用户需要更快的执行速度，请尝试Debugger版Web服务
## 启动Debugger版Web服务
```
python ocr_debugger_server.py
```
## 启动客户端
```
python ocr_web_clint.py
```
## 性能指标
CPU: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz * 40
GPU: Nvidia Tesla V100单卡
| engine | 客户端读图(ms) | 客户端发送请求到服务端(ms) | 服务端读图（ms） | 检测预处理耗时(ms) | 检测模型耗时(ms) | 检测后处理耗时(ms) | 识别预处理耗时(ms) | 识别模型耗时(ms) | 识别后处理耗时(ms) | 服务端回传客户端时间(ms) | 服务端整体耗时(ms) | 空跑耗时(ms) | 整体耗时（ms) |
|------------------------------|----------------|----------------------------|------------------|--------------------|------------------|--------------------|--------------------|------------------|--------------------|--------------------------|--------------------|--------------|---------------|
| Serving web service | 112.02 | 13.41 | 109.97 | 2.82 | 87.76 | 4.29 | 3.98 | 78.51 | 3.66 | 4.12 | 181.02 | 239.52 | 420.54 |
| Serving Debugger web service | 111.68 | 16.42 | 115.27 | 2.93 | 20.63 | 3.97 | 4.48 | 13.84 | 3.60 | 6.91 | 49.45 | 250.28 | 299.73 |
| | | | | | | | | | | | | | |
python/examples/ocr/ocr_rpc_client.py 已删除 100644 → 0
浏览文件 @ 946b10c1
# 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, File2Image, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes
import time
import re
def sorted_boxes(dt_boxes):
"""
Sort text boxes in order from top to bottom, left to right
args:
dt_boxes(array):detected text boxes with shape [4, 2]
return:
sorted boxes(array) with shape [4, 2]
"""
num_boxes = dt_boxes.shape[0]
sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
_boxes = list(sorted_boxes)
for i in range(num_boxes - 1):
if abs(_boxes[i+1][0][1] - _boxes[i][0][1]) < 10 and \
(_boxes[i + 1][0][0] < _boxes[i][0][0]):
tmp = _boxes[i]
_boxes[i] = _boxes[i + 1]
_boxes[i + 1] = tmp
return _boxes
def get_rotate_crop_image(img, points):
#img = cv2.imread(img)
img_height, img_width = img.shape[0:2]
left = int(np.min(points[:, 0]))
right = int(np.max(points[:, 0]))
top = int(np.min(points[:, 1]))
bottom = int(np.max(points[:, 1]))
img_crop = img[top:bottom, left:right, :].copy()
points[:, 0] = points[:, 0] - left
points[:, 1] = points[:, 1] - top
img_crop_width = int(np.linalg.norm(points[0] - points[1]))
img_crop_height = int(np.linalg.norm(points[0] - points[3]))
pts_std = np.float32([[0, 0], [img_crop_width, 0], \
[img_crop_width, img_crop_height], [0, img_crop_height]])
M = cv2.getPerspectiveTransform(points, pts_std)
dst_img = cv2.warpPerspective(
img_crop,
M, (img_crop_width, img_crop_height),
borderMode=cv2.BORDER_REPLICATE)
dst_img_height, dst_img_width = dst_img.shape[0:2]
if dst_img_height * 1.0 / dst_img_width >= 1.5:
dst_img = np.rot90(dst_img)
return dst_img
def read_det_box_file(filename):
with open(filename, 'r') as f:
line = f.readline()
a, b, c = int(line.split(' ')[0]), int(line.split(' ')[1]), int(
line.split(' ')[2])
dt_boxes = np.zeros((a, b, c)).astype(np.float32)
line = f.readline()
for i in range(a):
for j in range(b):
line = f.readline()
dt_boxes[i, j, 0], dt_boxes[i, j, 1] = float(
line.split(' ')[0]), float(line.split(' ')[1])
line = f.readline()
def resize_norm_img(img, max_wh_ratio):
import math
imgC, imgH, imgW = 3, 32, 320
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 main():
client1 = Client()
client1.load_client_config("ocr_det_client/serving_client_conf.prototxt")
client1.connect(["127.0.0.1:9293"])
client2 = Client()
client2.load_client_config("ocr_rec_client/serving_client_conf.prototxt")
client2.connect(["127.0.0.1:9292"])
read_image_file = File2Image()
preprocess = Sequential([
ResizeByFactor(32, 960), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
(2, 0, 1))
])
post_func = DBPostProcess({
"thresh": 0.3,
"box_thresh": 0.5,
"max_candidates": 1000,
"unclip_ratio": 1.5,
"min_size": 3
})
filter_func = FilterBoxes(10, 10)
ocr_reader = OCRReader()
files = [
"./imgs/{}".format(f) for f in os.listdir('./imgs')
if re.match(r'[0-9]+.*\.jpg|[0-9]+.*\.png', f)
]
#files = ["2.jpg"]*30
#files = ["rctw/rctw/train/images/image_{}.jpg".format(i) for i in range(500)]
time_all = 0
time_det_all = 0
time_rec_all = 0
for name in files:
#print(name)
im = read_image_file(name)
ori_h, ori_w, _ = im.shape
time1 = time.time()
img = preprocess(im)
_, new_h, new_w = img.shape
ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
#print(new_h, new_w, ori_h, ori_w)
time_before_det = time.time()
outputs = client1.predict(feed={"image": img}, fetch=["concat_1.tmp_0"])
time_after_det = time.time()
time_det_all += (time_after_det - time_before_det)
#print(outputs)
dt_boxes_list = post_func(outputs["concat_1.tmp_0"], [ratio_list])
dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
dt_boxes = sorted_boxes(dt_boxes)
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 = resize_norm_img(img, max_wh_ratio)
#norm_img = norm_img[np.newaxis, :]
feed = {"image": norm_img}
feed_list.append(feed)
#fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
fetch = ["ctc_greedy_decoder_0.tmp_0"]
time_before_rec = time.time()
if len(feed_list) == 0:
continue
fetch_map = client2.predict(feed=feed_list, fetch=fetch)
time_after_rec = time.time()
time_rec_all += (time_after_rec - time_before_rec)
rec_res = ocr_reader.postprocess(fetch_map)
#for res in rec_res:
# print(res[0].encode("utf-8"))
time2 = time.time()
time_all += (time2 - time1)
print("rpc+det time: {}".format(time_all / len(files)))
if __name__ == '__main__':
main()
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或