基于Paddle框架的PSENet复现

Related tags

Computer VisionPSENet-Paddle
Overview

PSENet-Paddle

基于Paddle框架的PSENet复现

本项目基于paddlepaddle框架复现PSENet，并参加百度第三届论文复现赛，将在2021年5月15日比赛完后提供AIStudio链接～敬请期待

AIStudio链接

参考项目：

whai362-PSENet

环境配置

本项目利用AIstudio平台，采用paddlepaddle: 2.0.2-gpu Version，除此之外你需要通过pip install mmcv editdistance Polygon3 pyclipper或者pip install -r requirement.txt来安装依赖包

数据集

本项目已搭载PSENet比赛指定数据集，你可以在此找到搭载的数据集，包含ICDAR2015 Task4以及Total-Text

工程目录

注意到你需要将submitPSENet重命名为PSENet

/home/aistudio/PSENet
|───data(解压的data.zip)
└───config
└───models
└───dataset
└───eval
└───utils
└───compile.sh
└───__init__.py
└───test.py
└───train.py
└───requirement.txt
└───logo.gif

项目配置**

注意：由于aistudio的docker环境并不适配本项目的编译，所以你需要在本地计算机编译完成后上传编译文件，在本地计算机我才用如下配置，你可以使用gcc --version和g++ --version查看配置

AIStudio Local PC
gcc (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.		 gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
g++ (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
Copyright (C) 2015 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.		 g++ (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions. There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

可以发现AIStudio的g++版本不适配，注意：你需要相同的架构，系统以及python版本，(Ubuntu)linux-x86_64&python3.7

`./compile.sh` or `bash compile.sh` if come out bash: ./compile.sh: Permission denied

或者直接进入指定目录，手动编译

cd /home/aistudio/PSENet/models/post_processing/pse
python setup.py build_ext --inplace

编译完成后你会在/home/aistudio/PSENet/models/post_processing/pse得到build/temp.linux-x86_64-3.7/pse.o文件和pse.cpython-37m-x86_64-linux-gnu.so文件

注意：本项目已经全部配置完成，这一步无需操作

训练

需要注意的是，在paddlepaddle-2.0.2中并不支持字典数据读取，因此我在/home/aistudio/PSENet/utils/data_loader.py利用迭代器重写了DataLoader这拉慢了数据读取的速度，会导致训练速度略慢，例如在使用psenet_r50_ic15_1024_finetune.py训练一个epoch需要512.4秒，另外paddlepaddle2.0.2暂不支持Identity方法，因此我在/home/aistudio/PSENet/models/utils/fuse_conv_bn.py通过继承Paddle.nn.Layer写了Identity类

cd /home/aistudio/PSENet/
python train.py ${CONFIG_FILE}

例如：

cd /home/aistudio/PSENet/
python train.py config/psenet/psenet_r50_ic15_736.py

训练开启时，会生成一个类似/home/aistudio/PSENet/checkpoints/psenet_r50_ic15_1024_finetune的文件夹，里面将保存权重和优化器参数

测试

cd /home/aistudio/PSENet/
python test.py ${CONFIG_FILE} ${CHECKPOINT_FILE}

例如：

cd /home/aistudio/PSENet/
python test.py config/psenet/psenet_r50_ic15_736.py PSENet/PretrainedModel/checkpoint_ic15_736.pdparams

评估

你需要注意的是：测试和评估是递进的，通过测试生成文件后，进行评估

ICDAR 2015

cd /home/aistudio/PSENet/eval
`./eval_ic15.sh` or `bash ./eval_ic15.sh`

你会得到如下类似信息：

Calculated!{"precision": 0.8620689655172413, "recall": 0.7944150216658642, "hmean": 0.826860435980957, "AP": 0}

以下是paddlepaddle预训练模型测试指标

Method Backbone Fine-tuning Scale Config Precision (%) Recall (%) F-measure (%) Model
PSENet ResNet50 N Shorter Side: 736 psenet_r50_ic15_736.py 83.6 74.0 78.5 checkpoint_ic15_736
PSENet ResNet50 N Shorter Side: 1024 psenet_r50_ic15_1024.py 84.4 76.3 80.2 checkpoint_ic15_1024
PSENet ResNet50 Y Shorter Side: 736 psenet_r50_ic15_736_finetune.py 85.3 76.8 80.9 checkpoint_ic15_736_finetune
PSENet ResNet50 Y Shorter Side: 1024 psenet_r50_ic15_1024_finetune.py 86.2 79.4 82.7 checkpoint_ic15_1024_finetune

Total-Text

Text detection

cd /home/aistudio/PSENet/eval
./eval_tt.sh or `bash ./eval_tt.sh`

你会得到如下类似信息：

Precision:_0.8727937336814604_______/Recall:_0.7786751361161512/Hmean:_0.8230524859472805

pb

以下是paddlepaddle预训练模型测试指标

Method Backbone Fine-tuning Config Precision (%) Recall (%) F-measure (%) Model
PSENet ResNet50 N psenet_r50_tt.py 87.3 77.9 82.3 checkpoint_tt
PSENet ResNet50 Y psenet_r50_tt_finetune.py 89.3 79.6 84.2 checkpoint_tt_finetune

速度测试

python test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --report_speed

例如：

cd /home/aistudio/PSENet/
python test.py config/psenet/psenet_r50_ic15_736.py PSENet/PretrainedModel/checkpoint_ic15_736.pdparams --report_speed

你会得到如下类似信息

Testing 283/3000
backbone_time: 0.0152
neck_time: 0.0029
det_head_time: 0.0005
det_pse_time: 0.0660
FPS: 11.8
Testing 284/3000
backbone_time: 0.0152
neck_time: 0.0029
det_head_time: 0.0005
det_pse_time: 0.0660
FPS: 11.8
Testing 285/3000
backbone_time: 0.0152
neck_time: 0.0029
det_head_time: 0.0005
det_pse_time: 0.0660
FPS: 11.8
Testing 286/3000
backbone_time: 0.0152
neck_time: 0.0029
det_head_time: 0.0005
det_pse_time: 0.0660
FPS: 11.8

Citation

@inproceedings{wang2019shape,
  title={Shape robust text detection with progressive scale expansion network},
  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Hou, Wenbo and Lu, Tong and Yu, Gang and Shao, Shuai},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={9336--9345},
  year={2019}
}
Owner
QuanHao Guo
master at UESTC
QuanHao Guo
GitHub Repository
Text layer for bio-image annotation.

napari-text-layer Napari text layer for bio-image annotation. Installation You can install using pip: pip install napari-text-layer Keybindings and m

6 Sep 29, 2022
Awesome Spectral Indices in Python.

Awesome Spectral Indices in Python: Numpy | Pandas | GeoPandas | Xarray | Earth Engine | Planetary Computer | Dask GitHub: https://github.com/davemlz/

David Montero Loaiza 98 Jan 02, 2023
Recognizing cropped text in natural images.

ASTER: Attentional Scene Text Recognizer with Flexible Rectification ASTER is an accurate scene text recognizer with flexible rectification mechanism.

Baoguang Shi 681 Jan 02, 2023
Camera Intrinsic Calibration and Hand-Eye Calibration in Pybullet

This repository is mainly for camera intrinsic calibration and hand-eye calibration. Synthetic experiments are conducted in PyBullet simulator. 1. Tes

CAI Junhao 7 Oct 03, 2022
Isearch (OSINT) 🔎 Face recognition reverse image search on Instagram profile feed photos.

isearch is an OSINT tool on Instagram. Offers a face recognition reverse image search on Instagram profile feed photos.

Malek salem 20 Oct 25, 2022
An interactive interface for using OpenCV's GrabCut algorithm for image segmentation.

Interactive GrabCut An interactive interface for using OpenCV's GrabCut algorithm for image segmentation. Setup Install dependencies: pip install nump

Jason Y. Zhang 16 Oct 10, 2022
A bot that extract text from images using the Tesseract OCR.

Text from image (OCR) @ocr_text_bot A simple bot to extract text from images. Usage What do I need? A AWS key configured locally, see here. NodeJS. I

Weverton Marques 4 Aug 06, 2021
OCR, Object Detection, Number Plate, Real Time

README.md PrePareded anaconda env requirements.txt clova AI → deep text recognition → trained weights (ex, .pth) wpod-net weights (ex, .h5 , .json) ht

Kaven Lee 7 Dec 06, 2022
graph learning code for ogb

The final code for OGB Installation Requirements: ogb=1.3.1 torch=1.7.0 torch-geometric=1.7.0 torch-scatter=2.0.6 torch-sparse=0.6.9 Baseline models T

PierreHao 20 Nov 10, 2022
End-to-end pipeline for real-time scene text detection and recognition.

Real-time-Scene-Text-Detection-and-Recognition-System End-to-end pipeline for real-time scene text detection and recognition. The detection model use

Fangneng Zhan 89 Aug 04, 2022
A set of workflows for corpus building through OCR, post-correction and normalisation

PICCL: Philosophical Integrator of Computational and Corpus Libraries PICCL offers a workflow for corpus building and builds on a variety of tools. Th

Language Machines 41 Dec 27, 2022
Educational application aimed at automating user-defined workflows for the mobile game, "Granblue Fantasy", using a variety of CV technologies in the backend such as OpenCV, PyAutoGUI and EasyOCR and a frontend coded in Typescript.

Granblue Automation using Template Matching (It is like Full Auto, but with Full Customization!) Discord here: https://discord.gg/5Yv4kqjAbm Android v

Steve Tu 71 Dec 30, 2022
Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation

This is the official implementation of "Multi-Oriented Scene Text Detection via Corner Localization and Region Segmentation". For more details, please

Pengyuan Lyu 309 Dec 06, 2022
An OCR evaluation tool

dinglehopper dinglehopper is an OCR evaluation tool and reads ALTO, PAGE and text files. It compares a ground truth (GT) document page with a OCR resu

QURATOR-SPK 40 Dec 20, 2022
Handwriting Recognition System based on a deep Convolutional Recurrent Neural Network architecture

Handwriting Recognition System This repository is the Tensorflow implementation of the Handwriting Recognition System described in Handwriting Recogni

Edgard Chammas 346 Jan 07, 2023
Comparison-of-OCR (KerasOCR, PyTesseract,EasyOCR)

Optical Character Recognition OCR (Optical Character Recognition) is a technology that enables the conversion of document types such as scanned paper

21 Dec 25, 2022
Virtual Zoom Gesture using OpenCV

Virtual_Zoom_Gesture I have created a virtual zoom gesture where we can Zoom in and Zoom out any image and even we can move that image anywhere on the

Mudit Sinha 2 Dec 26, 2021
Textboxes implementation with Tensorflow (python)

tb_tensorflow A python implementation of TextBoxes Dependencies TensorFlow r1.0 OpenCV2 Code from Chaoyue Wang 03/09/2017 Update: 1.Debugging optimize

Jayne Shin (신재인) 20 May 31, 2019
A dataset handling library for computer vision datasets in LOST-fromat

A dataset handling library for computer vision datasets in LOST-fromat

8 Dec 15, 2022
Introduction to image processing, most used and popular functions of OpenCV

👀 OpenCV 101 Introduction to image processing, most used and popular functions of OpenCV go here.

Vusal Ismayilov 3 Jul 02, 2022