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95 lines
3.5 KiB
Markdown
95 lines
3.5 KiB
Markdown
---
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typora-copy-images-to: images
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comments: true
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---
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# Jetson Deployment for PaddleOCR
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This section introduces the deployment of PaddleOCR on Jetson NX, TX2, nano, AGX and other series of hardware.
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## 1. Prepare Environment
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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;
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### 1. Install PaddlePaddle in Jetson
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The PaddlePaddle download [link](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python)
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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.
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Install PaddlePaddle：
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```bash linenums="1"
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pip3 install -U paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl
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```
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### 2. Download PaddleOCR code and install dependencies
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Clone the PaddleOCR code:
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```bash linenums="1"
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git clone https://github.com/PaddlePaddle/PaddleOCR
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```
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and install dependencies：
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```bash linenums="1"
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cd PaddleOCR
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pip3 install -r requirements.txt
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```
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- Note: Jetson hardware CPU is poor, dependency installation is slow, please wait patiently
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## 2. Perform prediction
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Obtain the PPOCR model from the [document](../model_list.en.md) model library. The following takes the PP-OCRv3 model as an example to introduce the use of the PPOCR model on Jetson:
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Download and unzip the PP-OCRv3 models.
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```bash linenums="1"
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wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
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wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
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tar xf ch_PP-OCRv3_det_infer.tar
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tar xf ch_PP-OCRv3_rec_infer.tar
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```
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The text detection inference:
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```bash linenums="1"
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cd PaddleOCR
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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
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```
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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.
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The text recognition inference:
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```bash linenums="1"
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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"
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```
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After executing the command, the predicted information will be printed on the terminal, and the output is as follows:
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```bash linenums="1"
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[2022/04/28 15:41:45] root INFO: Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.98084533)
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```
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The text detection and text recognition inference:
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```bash linenums="1"
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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"
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```
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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.
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To enable TRT prediction, you only need to set `--use_tensorrt=True` on the basis of the above command:
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```bash linenums="1"
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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
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```
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For more ppocr model predictions, please refer to[document](../model_list.en.md)
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