AutoPercenty3/test/paddle2onnx/PaddleOCR/deploy/Jetson

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Jetson Deployment for PaddleOCR

This section introduces the deployment of PaddleOCR on Jetson NX, TX2, nano, AGX and other series of hardware.

1. Prepare Environment

You need to prepare a Jetson development hardware. If you need TensorRT, you need to prepare the TensorRT environment. It is recommended to use TensorRT version 7.1.3;

1. Install PaddlePaddle in Jetson

The PaddlePaddle download link Please select the appropriate installation package for your Jetpack version, cuda version, and trt version. Here, we download paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl.

Install PaddlePaddle：

pip3 install -U paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl

2. Download PaddleOCR code and install dependencies

Clone the PaddleOCR code:

git clone https://github.com/PaddlePaddle/PaddleOCR

and install dependencies：

cd PaddleOCR
pip3 install -r requirements.txt

Note: Jetson hardware CPU is poor, dependency installation is slow, please wait patiently

2. Perform prediction

Obtain the PPOCR model from the document model library. The following takes the PP-OCRv3 model as an example to introduce the use of the PPOCR model on Jetson:

Download and unzip the PP-OCRv3 models.

wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
tar xf ch_PP-OCRv3_det_infer.tar
tar xf ch_PP-OCRv3_rec_infer.tar

The text detection inference:

cd PaddleOCR
python3 tools/infer/predict_det.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/  --image_dir=./doc/imgs/french_0.jpg  --use_gpu=True

After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the ./inference_results/ directory.

The text recognition inference:

python3 tools/infer/predict_det.py --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/  --image_dir=./doc/imgs_words/en/word_2.png  --use_gpu=True --rec_image_shape="3,48,320"

After executing the command, the predicted information will be printed on the terminal, and the output is as follows:

[2022/04/28 15:41:45] root INFO: Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.98084533)

The text detection and text recognition inference:

python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/00057937.jpg --use_gpu=True --rec_image_shape="3,48,320"

After executing the command, the predicted information will be printed out in the terminal, and the visualization results will be saved in the ./inference_results/ directory.

To enable TRT prediction, you only need to set --use_tensorrt=True on the basis of the above command:

python3 tools/infer/predict_system.py --det_model_dir=./inference/ch_PP-OCRv2_det_infer/ --rec_model_dir=./inference/ch_PP-OCRv2_rec_infer/ --image_dir=./doc/imgs/  --rec_image_shape="3,48,320" --use_gpu=True --use_tensorrt=True

For more ppocr model predictions, please refer todocument