74 lines
1.7 KiB
Markdown
74 lines
1.7 KiB
Markdown
# DocTrPP: DocTr++ in PaddlePaddle
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## Introduction
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This is a PaddlePaddle implementation of DocTr++. The original paper is [DocTr++: Deep Unrestricted Document Image Rectification](https://arxiv.org/abs/2304.08796). The original code is [here](https://github.com/fh2019ustc/DocTr-Plus).
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## Requirements
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You need to install the latest version of PaddlePaddle, which is done through this [link](https://www.paddlepaddle.org.cn/).
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## Training
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1. Data Preparation
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To prepare datasets, refer to [doc3D](https://github.com/cvlab-stonybrook/doc3D-dataset).
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2. Training
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```shell
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sh train.sh
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```
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or
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```shell
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export OPENCV_IO_ENABLE_OPENEXR=1
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export CUDA_VISIBLE_DEVICES=0
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python train.py --img-size 288 \
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--name "DocTr++" \
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--batch-size 12 \
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--lr 2.5e-5 \
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--exist-ok \
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--use-vdl
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```
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3. Load Trained Model and Continue Training
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```shell
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export OPENCV_IO_ENABLE_OPENEXR=1
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export CUDA_VISIBLE_DEVICES=0
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python train.py --img-size 288 \
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--name "DocTr++" \
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--batch-size 12 \
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--lr 2.5e-5 \
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--resume "runs/train/DocTr++/weights/last.ckpt" \
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--exist-ok \
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--use-vdl
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```
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## Test and Inference
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Test the dewarp result on a single image:
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```shell
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python predict.py -i "crop/12_2 copy.png" -m runs/train/DocTr++/weights/best.ckpt -o 12.2.png
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```
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## Export to onnx
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```
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pip install paddle2onnx
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python export.py -m ./best.ckpt --format onnx
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```
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## Model Download
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The trained model can be downloaded from [here](https://github.com/GreatV/DocTrPP/releases/download/v0.0.2/best.ckpt).
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