diff --git a/deploy/jeston/images/00057937.jpg b/deploy/jeston/images/00057937.jpg deleted file mode 100644 index a35896e78d122c1916db8ff4135068e8d3a0d419..0000000000000000000000000000000000000000 Binary files a/deploy/jeston/images/00057937.jpg and /dev/null differ diff --git a/deploy/jeston/images/det_res_french_0.jpg b/deploy/jeston/images/det_res_french_0.jpg deleted file mode 100644 index 5f0e4886dfa542319034f529c4f08ece7ffd65cf..0000000000000000000000000000000000000000 Binary files a/deploy/jeston/images/det_res_french_0.jpg and /dev/null differ diff --git a/deploy/jeston/readme.md b/deploy/jeston/readme.md deleted file mode 100644 index 1eb0ea44f77b522567100fc6b38a23dab68613af..0000000000000000000000000000000000000000 --- a/deploy/jeston/readme.md +++ /dev/null @@ -1,85 +0,0 @@ - -# Jetson部署PaddleOCR模型 - -本节介绍PaddleOCR在Jetson NX、TX2、nano、AGX等系列硬件的部署。 - - -## 1. 环境准备 - -需要准备一台Jetson开发板，如果需要TensorRT预测，需准备好TensorRT环境，建议使用7.1.3版本的TensorRT； - -1. Jetson安装PaddlePaddle - -PaddlePaddle下载[链接](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python) -请选择适合的您Jetpack版本、cuda版本、trt版本的安装包。 - -安装命令： -```shell -# 安装paddle，以paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl 为例 -pip3 install -U paddlepaddle_gpu-2.3.0rc0-cp36-cp36m-linux_aarch64.whl -``` - - -2. 下载PaddleOCR代码并安装依赖 - -首先 clone PaddleOCR 代码： -``` -git clone https://github.com/PaddlePaddle/PaddleOCR -``` - -然后，安装依赖： -``` -cd PaddleOCR -pip3 install -r requirements.txt -``` - -*注：jetson硬件CPU较差，依赖安装较慢，请耐心等待* - - -## 2. 执行预测 - -从[文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/ppocr_introduction.md#6-%E6%A8%A1%E5%9E%8B%E5%BA%93) 模型库中获取PPOCR模型，下面以PP-OCRv3模型为例，介绍在PPOCR模型在jetson上的使用方式： - -下载并解压PP-OCRv3模型 -``` -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 -``` - -执行文本检测预测： -``` -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 -``` - -执行命令后在终端会打印出预测的信息，并在 `./inference_results/` 下保存可视化结果。 -![](./images/det_res_french_0.jpg) - - -执行文本识别预测： -``` -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" -``` - -执行命令后在终端会打印出预测的信息，输出如下： -``` -[2022/04/28 15:41:45] root INFO: Predicts of ./doc/imgs_words/en/word_2.png:('yourself', 0.98084533) -``` - -执行文本检测+文本识别串联预测： - -``` -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/ --use_gpu=True --rec_image_shape="3,48,320" -``` - -执行命令后在终端会打印出预测的信息，并在 `./inference_results/` 下保存可视化结果。 -![](./images/00057937.jpg) - -开启TRT预测只需要在以上命令基础上设置`--use_tensorrt=True`即可： -``` -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 --use_tensorrt=True --rec_image_shape="3,48,320" -``` - -更多ppocr模型预测请参考[文档](../../doc/doc_ch/models_list.md) diff --git a/deploy/jeston/readme_en.md b/deploy/jeston/readme_en.md deleted file mode 100644 index d499989160e13d6dd6bf1092c890e8ec11681ce4..0000000000000000000000000000000000000000 --- a/deploy/jeston/readme_en.md +++ /dev/null @@ -1,83 +0,0 @@ - -# 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](https://www.paddlepaddle.org.cn/inference/user_guides/download_lib.html#python) -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： -```shell -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](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/ppocr_introduction_en.md#6-model-zoo) 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. -![](./images/det_res_french_0.jpg) - - -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. -![](./images/00057937.jpg) - -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 to[document](../../doc/doc_en/models_list_en.md)