diff --git a/deploy/lite/ocr_db_crnn.cc b/deploy/lite/ocr_db_crnn.cc index 07bec2c6f8fc02936b6c69d472984735c7b541cb..2786d15ad0581bf10f9e8de4b06f94cc5d4027aa 100644 --- a/deploy/lite/ocr_db_crnn.cc +++ b/deploy/lite/ocr_db_crnn.cc @@ -290,7 +290,7 @@ RunDetModel(std::shared_ptr predictor, cv::Mat img, cv::Mat bit_map; cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY); cv::Mat dilation_map; - cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2,2)); + cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2)); cv::dilate(bit_map, dilation_map, dila_ele); auto boxes = BoxesFromBitmap(pred_map, dilation_map, Config); @@ -366,7 +366,8 @@ std::map LoadConfigTxt(std::string config_path) { int main(int argc, char **argv) { if (argc < 5) { std::cerr << "[ERROR] usage: " << argv[0] - << " det_model_file rec_model_file image_path\n"; + << " det_model_file cls_model_file rec_model_file image_path " + "charactor_dict\n"; exit(1); } std::string det_model_file = argv[1]; diff --git a/deploy/lite/readme.md b/deploy/lite/readme.md index bb52a02ea497a0611ea8210b5d5b4a9f9d429828..adedcde1a59da8a548af4a780ce60b1b9a640f0e 100644 --- a/deploy/lite/readme.md +++ b/deploy/lite/readme.md @@ -83,7 +83,7 @@ Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括 |模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本| |-|-|-|-|-|-|-| -|V1.1|超轻量中文OCR 移动端模型|3.0M|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_cls_quant_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|develop| +|V1.1|超轻量中文OCR 移动端模型|3.5M|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_cls_quant_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|develop| |V1.0|轻量级中文OCR 移动端模型|8.6M|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_det_mv3_db_opt.nb)|---|[下载地址](https://paddleocr.bj.bcebos.com/deploy/lite/ch_rec_mv3_crnn_opt.nb)|develop| 注意：V1.1 3.0M 轻量模型是使用PaddleSlim优化后的，需要配合Paddle-Lite最新预测库使用。 @@ -123,18 +123,27 @@ cd build.opt/lite/api/ 下面以PaddleOCR的超轻量中文模型为例，介绍使用编译好的opt文件完成inference模型到Paddle-Lite优化模型的转换。 ``` -# 下载PaddleOCR的超轻量文inference模型，并解压 +# 可以下载PaddleOCR V1.1版本的inference模型，V1.1比1.0效果更好，模型大小更小 +wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar && tar xf ch_ppocr_mobile_v1.1_det_prune_infer.tar +wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar && tar xf ch_ppocr_mobile_v1.1_rec_quant_infer.tar +# 转换V1.1检测模型 +./opt --model_file=./ch_ppocr_mobile_v1.1_det_prune_infer/model --param_file=./ch_ppocr_mobile_v1.1_det_prune_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_det_prune_opt --valid_targets=arm +# 转换V1.1识别模型 +./opt --model_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/model --param_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_rec_quant_opt --valid_targets=arm + + +# 或下载使用PaddleOCR的V1.0超轻量文inference模型，并解压 wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar - -# 转换检测模型 +# 转换V1.0检测模型 ./opt --model_file=./ch_det_mv3_db/model --param_file=./ch_det_mv3_db/params --optimize_out_type=naive_buffer --optimize_out=./ch_det_mv3_db_opt --valid_targets=arm - -# 转换识别模型 +# 转换V1.0识别模型 ./opt --model_file=./ch_rec_mv3_crnn/model --param_file=./ch_rec_mv3_crnn/params --optimize_out_type=naive_buffer --optimize_out=./ch_rec_mv3_crnn_opt --valid_targets=arm ``` -转换成功后，当前目录下会多出`ch_det_mv3_db_opt.nb`, `ch_rec_mv3_crnn_opt.nb`结尾的文件，即是转换成功的模型文件。 +# 转换V1.1检测模型 + +转换成功后，当前目录下会多出`.nb`结尾的文件，即是转换成功的模型文件。 注意：使用paddle-lite部署时，需要使用opt工具优化后的模型。 opt 转换的输入模型是paddle保存的inference模型 @@ -184,15 +193,16 @@ wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar ``` 准备测试图像，以`PaddleOCR/doc/imgs/11.jpg`为例，将测试的图像复制到`demo/cxx/ocr/debug/`文件夹下。 - 准备lite opt工具优化后的模型文件，比如使用`ch_det_mv3_db_opt.nb，ch_rec_mv3_crnn_opt.nb`，模型文件放置在`demo/cxx/ocr/debug/`文件夹下。 + 准备lite opt工具优化后的模型文件，比如使用`ch_ppocr_mobile_v1.1_det_prune_opt.nb，ch_ppocr_mobile_v1.1_rec_quant_opt.nb, ch_ppocr_mobile_cls_quant_opt.nb`，模型文件放置在`demo/cxx/ocr/debug/`文件夹下。 执行完成后，ocr文件夹下将有如下文件格式： ``` demo/cxx/ocr/ |-- debug/ -| |--ch_det_mv3_db_opt.nb 优化后的检测模型文件 -| |--ch_rec_mv3_crnn_opt.nb 优化后的识别模型文件 +| |--ch_ppocr_mobile_v1.1_det_prune_opt.nb 优化后的检测模型文件 +| |--ch_ppocr_mobile_v1.1_rec_quant_opt.nb 优化后的识别模型文件 +| |--ch_ppocr_mobile_cls_quant_opt.nb 优化后的文字方向分类器模型文件 | |--11.jpg 待测试图像 | |--ppocr_keys_v1.txt 字典文件 | |--libpaddle_light_api_shared.so C++预测库文件 @@ -223,7 +233,7 @@ demo/cxx/ocr/ adb shell cd /data/local/tmp/debug export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH - ./ocr_db_crnn ch_det_mv3_db_opt.nb ch_rec_mv3_crnn_opt.nb ./11.jpg ppocr_keys_v1.txt + ./ocr_db_crnn ch_ppocr_mobile_v1.1_det_prune_opt.nb ch_ppocr_mobile_v1.1_rec_quant_opt.nb ch_ppocr_mobile_cls_quant_opt.nb ./11.jpg ppocr_keys_v1.txt ``` 如果对代码做了修改，则需要重新编译并push到手机上。 diff --git a/deploy/lite/readme_en.md b/deploy/lite/readme_en.md index ec7d1422eb82793c3e2958a9b22c5780fb936657..f36d76266f4025fe7f872c2efa695be13ccdd502 100644 --- a/deploy/lite/readme_en.md +++ b/deploy/lite/readme_en.md @@ -66,7 +66,7 @@ If you use PaddleOCR 8.6M OCR model to deploy, you can directly download the opt |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle Lite branch | |-|-|-|-|-|-| -|V1.1|extra-lightweight chinese OCR optimized model|3.0M|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_cls_quant_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|develop| +|V1.1|extra-lightweight chinese OCR optimized model|3.5M|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile_cls_quant_opt.nb)|[Download](https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_opt.nb)|develop| |V1.0|lightweight Chinese OCR optimized model|8.6M|[Download](https://paddleocr.bj.bcebos.com/deploy/lite/ch_det_mv3_db_opt.nb)|---|[Download](https://paddleocr.bj.bcebos.com/deploy/lite/ch_rec_mv3_crnn_opt.nb)|develop| If the model to be deployed is not in the above table, you need to follow the steps below to obtain the optimized model. @@ -86,6 +86,14 @@ The `opt` can optimize the inference model saved by paddle.io.save_inference_mod The usage of opt is as follows： ``` +# V1.1 is better than V1.0. steps for convert V1.1 model to nb file are as follows +wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/det/ch_ppocr_mobile_v1.1_det_prune_infer.tar && tar xf ch_ppocr_mobile_v1.1_det_prune_infer.tar +wget https://paddleocr.bj.bcebos.com/20-09-22/mobile-slim/rec/ch_ppocr_mobile_v1.1_rec_quant_infer.tar && tar xf ch_ppocr_mobile_v1.1_rec_quant_infer.tar + +./opt --model_file=./ch_ppocr_mobile_v1.1_det_prune_infer/model --param_file=./ch_ppocr_mobile_v1.1_det_prune_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_det_prune_opt --valid_targets=arm +./opt --model_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/model --param_file=./ch_ppocr_mobile_v1.1_rec_quant_infer/params --optimize_out=./ch_ppocr_mobile_v1.1_rec_quant_opt --valid_targets=arm + +# or use V1.0 model wget https://paddleocr.bj.bcebos.com/ch_models/ch_det_mv3_db_infer.tar && tar xf ch_det_mv3_db_infer.tar wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar xf ch_rec_mv3_crnn_infer.tar @@ -94,8 +102,7 @@ wget https://paddleocr.bj.bcebos.com/ch_models/ch_rec_mv3_crnn_infer.tar && tar ``` -When the above code command is completed, there will be two more files `ch_det_mv3_db_opt.nb`, -`ch_rec_mv3_crnn_opt.nb` in the current directory, which is the converted model file. +When the above code command is completed, there will be two more files `.nb` in the current directory, which is the converted model file. ## 5. Run optimized model on Phone @@ -154,8 +161,9 @@ The structure of the OCR demo is as follows after the above command is executed: ``` demo/cxx/ocr/ |-- debug/ -| |--ch_det_mv3_db_opt.nb Detection model -| |--ch_rec_mv3_crnn_opt.nb Recognition model +| |--ch_ppocr_mobile_v1.1_det_prune_opt.nb Detection model +| |--ch_ppocr_mobile_v1.1_rec_quant_opt.nb Recognition model +| |--ch_ppocr_mobile_cls_quant_opt.nb Text direction classification model | |--11.jpg Image for OCR | |--ppocr_keys_v1.txt Dictionary file | |--libpaddle_light_api_shared.so C++ .so file @@ -181,7 +189,7 @@ adb shell cd /data/local/tmp/debug export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH # run model -./ocr_db_crnn ch_det_mv3_db_opt.nb ch_rec_mv3_crnn_opt.nb ./11.jpg ppocr_keys_v1.txt + ./ocr_db_crnn ch_ppocr_mobile_v1.1_det_prune_opt.nb ch_ppocr_mobile_v1.1_rec_quant_opt.nb ch_ppocr_mobile_cls_quant_opt.nb ./11.jpg ppocr_keys_v1.txt ``` The outputs are as follows: