diff --git a/PPOCRLabel/README.md b/PPOCRLabel/README.md index 21db1867aa6b6504595096de56b17f01dbf3e4f6..0a1a7536912b6591517050bfa62260aaef6077cd 100644 --- a/PPOCRLabel/README.md +++ b/PPOCRLabel/README.md @@ -86,15 +86,8 @@ PPOCRLabel # [Normal mode] for [detection + recognition] labeling PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling ``` -#### 1.2.2 Build and Install the Whl Package Locally - -```bash -cd PaddleOCR/PPOCRLabel -python3 setup.py bdist_wheel -pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -``` - -#### 1.2.3 Run PPOCRLabel by Python Script +#### 1.2.2 Run PPOCRLabel by Python Script +If you modify the PPOCRLabel file (for example, specifying a new built-in model), it will be more convenient to see the results by running the Python script. If you still want to start with the whl package, you need to uninstall the whl package in the current environment and then recompile it according to the next section. ```bash cd ./PPOCRLabel # Switch to the PPOCRLabel directory @@ -104,6 +97,13 @@ python PPOCRLabel.py # [Normal mode] for [detection + recognition] labeling python PPOCRLabel.py --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling ``` +#### 1.2.3 Build and Install the Whl Package Locally +Compile and install a new whl package, where 1.0.2 is the version number, you can specify the new version in 'setup.py'. +```bash +cd PaddleOCR/PPOCRLabel +python3 setup.py bdist_wheel +pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl +``` ## 2. Usage diff --git a/PPOCRLabel/README_ch.md b/PPOCRLabel/README_ch.md index 9bf898fd79b6b1642ce20fabda3009708473c354..99c088de83f9cba775733a0473b50596683c47ab 100644 --- a/PPOCRLabel/README_ch.md +++ b/PPOCRLabel/README_ch.md @@ -86,28 +86,26 @@ PPOCRLabel --lang ch --kie True # 启动 【KIE 模式】，用于打【检测+ > 如果上述安装出现问题，可以参考3.6节 错误提示 -#### 1.2.2 本地构建whl包并安装 +#### 1.2.2 通过Python脚本运行PPOCRLabel + +如果您对PPOCRLabel文件有所更改（例如指定新的内置模型），通过Python脚本运行会更加方便的看到更改的结果。如果仍然需要通过whl包启动，则需要先卸载当前环境中的whl包，然后参考下节重新编译whl包。 ```bash -cd PaddleOCR/PPOCRLabel -python3 setup.py bdist_wheel -pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple +cd ./PPOCRLabel # 切换到PPOCRLabel目录 +python PPOCRLabel.py --lang ch ``` -#### 1.2.3 通过Python脚本运行PPOCRLabel +#### 1.2.3 本地构建whl包并安装 -如果您对PPOCRLabel文件有所更改，通过Python脚本运行会更加方面的看到更改的结果 +编译与安装新的whl包，其中1.0.2为版本号，可在 `setup.py` 中指定新版本。 ```bash -cd ./PPOCRLabel # 切换到PPOCRLabel目录 - -# 选择标签模式来启动 -python PPOCRLabel.py --lang ch # 启动【普通模式】，用于打【检测+识别】场景的标签 -python PPOCRLabel.py --lang ch --kie True # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签 +cd PaddleOCR/PPOCRLabel +python3 setup.py bdist_wheel +pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple ``` - ## 2. 使用 ### 2.1 操作步骤 diff --git a/deploy/cpp_infer/imgs/cpp_infer_pred_12.png b/deploy/cpp_infer/imgs/cpp_infer_pred_12.png deleted file mode 100644 index eb5f64e1f6c329f7ae772c50edce7fc8afcb1211..0000000000000000000000000000000000000000 Binary files a/deploy/cpp_infer/imgs/cpp_infer_pred_12.png and /dev/null differ diff --git a/deploy/cpp_infer/include/args.h b/deploy/cpp_infer/include/args.h new file mode 100644 index 0000000000000000000000000000000000000000..fe5d45f6e0f2475089dfaaebc021a3e194a2cb22 --- /dev/null +++ b/deploy/cpp_infer/include/args.h @@ -0,0 +1,52 @@ +// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include + +// common args +DECLARE_bool(use_gpu); +DECLARE_bool(use_tensorrt); +DECLARE_int32(gpu_id); +DECLARE_int32(gpu_mem); +DECLARE_int32(cpu_threads); +DECLARE_bool(enable_mkldnn); +DECLARE_string(precision); +DECLARE_bool(benchmark); +DECLARE_string(output); +DECLARE_string(image_dir); +DECLARE_string(type); +// detection related +DECLARE_string(det_model_dir); +DECLARE_int32(max_side_len); +DECLARE_double(det_db_thresh); +DECLARE_double(det_db_box_thresh); +DECLARE_double(det_db_unclip_ratio); +DECLARE_bool(use_dilation); +DECLARE_string(det_db_score_mode); +DECLARE_bool(visualize); +// classification related +DECLARE_bool(use_angle_cls); +DECLARE_string(cls_model_dir); +DECLARE_double(cls_thresh); +DECLARE_int32(cls_batch_num); +// recognition related +DECLARE_string(rec_model_dir); +DECLARE_int32(rec_batch_num); +DECLARE_string(rec_char_dict_path); +// forward related +DECLARE_bool(det); +DECLARE_bool(rec); +DECLARE_bool(cls); diff --git a/deploy/cpp_infer/include/ocr_cls.h b/deploy/cpp_infer/include/ocr_cls.h index 742e1f8bb0392859ea4bc3a6a4b4410f6b375826..f5429a7c5bc58c2640f042811ad0eed23f29feba 100644 --- a/deploy/cpp_infer/include/ocr_cls.h +++ b/deploy/cpp_infer/include/ocr_cls.h @@ -42,7 +42,8 @@ public: const int &gpu_id, const int &gpu_mem, const int &cpu_math_library_num_threads, const bool &use_mkldnn, const double &cls_thresh, - const bool &use_tensorrt, const std::string &precision) { + const bool &use_tensorrt, const std::string &precision, + const int &cls_batch_num) { this->use_gpu_ = use_gpu; this->gpu_id_ = gpu_id; this->gpu_mem_ = gpu_mem; @@ -52,14 +53,17 @@ public: this->cls_thresh = cls_thresh; this->use_tensorrt_ = use_tensorrt; this->precision_ = precision; + this->cls_batch_num_ = cls_batch_num; LoadModel(model_dir); } + double cls_thresh = 0.9; // Load Paddle inference model void LoadModel(const std::string &model_dir); - cv::Mat Run(cv::Mat &img); + void Run(std::vector img_list, std::vector &cls_labels, + std::vector &cls_scores, std::vector ×); private: std::shared_ptr predictor_; @@ -69,17 +73,17 @@ private: int gpu_mem_ = 4000; int cpu_math_library_num_threads_ = 4; bool use_mkldnn_ = false; - double cls_thresh = 0.5; std::vector mean_ = {0.5f, 0.5f, 0.5f}; std::vector scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f}; bool is_scale_ = true; bool use_tensorrt_ = false; std::string precision_ = "fp32"; + int cls_batch_num_ = 1; // pre-process ClsResizeImg resize_op_; Normalize normalize_op_; - Permute permute_op_; + PermuteBatch permute_op_; }; // class Classifier diff --git a/deploy/cpp_infer/include/ocr_det.h b/deploy/cpp_infer/include/ocr_det.h index 6e4086fbaa6945b9f685e6844b7e701283de2dae..7efd4d8f0f4ccb705fc34695bb9843e0b6af5a9b 100644 --- a/deploy/cpp_infer/include/ocr_det.h +++ b/deploy/cpp_infer/include/ocr_det.h @@ -45,8 +45,9 @@ public: const double &det_db_thresh, const double &det_db_box_thresh, const double &det_db_unclip_ratio, - const bool &use_polygon_score, const bool &use_dilation, - const bool &use_tensorrt, const std::string &precision) { + const std::string &det_db_score_mode, + const bool &use_dilation, const bool &use_tensorrt, + const std::string &precision) { this->use_gpu_ = use_gpu; this->gpu_id_ = gpu_id; this->gpu_mem_ = gpu_mem; @@ -58,7 +59,7 @@ public: this->det_db_thresh_ = det_db_thresh; this->det_db_box_thresh_ = det_db_box_thresh; this->det_db_unclip_ratio_ = det_db_unclip_ratio; - this->use_polygon_score_ = use_polygon_score; + this->det_db_score_mode_ = det_db_score_mode; this->use_dilation_ = use_dilation; this->use_tensorrt_ = use_tensorrt; @@ -72,7 +73,7 @@ public: // Run predictor void Run(cv::Mat &img, std::vector>> &boxes, - std::vector *times); + std::vector ×); private: std::shared_ptr predictor_; @@ -88,7 +89,7 @@ private: double det_db_thresh_ = 0.3; double det_db_box_thresh_ = 0.5; double det_db_unclip_ratio_ = 2.0; - bool use_polygon_score_ = false; + std::string det_db_score_mode_ = "slow"; bool use_dilation_ = false; bool visualize_ = true; diff --git a/deploy/cpp_infer/include/ocr_rec.h b/deploy/cpp_infer/include/ocr_rec.h index 4052553d967fb365c3fb895c9d5b8145935fd45d..f634b6dc8481ad062ced5ef9afef3fb0532d28a6 100644 --- a/deploy/cpp_infer/include/ocr_rec.h +++ b/deploy/cpp_infer/include/ocr_rec.h @@ -30,7 +30,6 @@ #include #include -#include #include #include @@ -68,7 +67,7 @@ public: void LoadModel(const std::string &model_dir); void Run(std::vector img_list, std::vector &rec_texts, - std::vector &rec_text_scores, std::vector *times); + std::vector &rec_text_scores, std::vector ×); private: std::shared_ptr predictor_; @@ -93,9 +92,6 @@ private: Normalize normalize_op_; PermuteBatch permute_op_; - // post-process - PostProcessor post_processor_; - }; // class CrnnRecognizer } // namespace PaddleOCR diff --git a/deploy/cpp_infer/include/paddleocr.h b/deploy/cpp_infer/include/paddleocr.h new file mode 100644 index 0000000000000000000000000000000000000000..499fbee3172abe71fd84ce3c5a18d421d7bc3139 --- /dev/null +++ b/deploy/cpp_infer/include/paddleocr.h @@ -0,0 +1,67 @@ +// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "opencv2/core.hpp" +#include "opencv2/imgcodecs.hpp" +#include "opencv2/imgproc.hpp" +#include "paddle_api.h" +#include "paddle_inference_api.h" +#include +#include +#include +#include +#include + +#include +#include +#include + +#include +#include +#include +#include +#include + +using namespace paddle_infer; + +namespace PaddleOCR { + +class PaddleOCR { +public: + explicit PaddleOCR(); + ~PaddleOCR(); + std::vector> + ocr(std::vector cv_all_img_names, bool det = true, + bool rec = true, bool cls = true); + +private: + DBDetector *detector_ = nullptr; + Classifier *classifier_ = nullptr; + CRNNRecognizer *recognizer_ = nullptr; + + void det(cv::Mat img, std::vector &ocr_results, + std::vector ×); + void rec(std::vector img_list, + std::vector &ocr_results, + std::vector ×); + void cls(std::vector img_list, + std::vector &ocr_results, + std::vector ×); + void log(std::vector &det_times, std::vector &rec_times, + std::vector &cls_times, int img_num); +}; + +} // namespace PaddleOCR diff --git a/deploy/cpp_infer/include/postprocess_op.h b/deploy/cpp_infer/include/postprocess_op.h index b384b79b3041bfcb96f042c6450d3c6e54f00498..4a98b151bdcc53e2ab3fbda1dca55dd9746bd86c 100644 --- a/deploy/cpp_infer/include/postprocess_op.h +++ b/deploy/cpp_infer/include/postprocess_op.h @@ -56,7 +56,7 @@ public: std::vector>> BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap, const float &box_thresh, const float &det_db_unclip_ratio, - const bool &use_polygon_score); + const std::string &det_db_score_mode); std::vector>> FilterTagDetRes(std::vector>> boxes, diff --git a/deploy/cpp_infer/include/utility.h b/deploy/cpp_infer/include/utility.h index f0dddacdac31e979a96648433662c76ccf972ad2..33e995fd02696ab45be92220102faa6d3230ddfe 100644 --- a/deploy/cpp_infer/include/utility.h +++ b/deploy/cpp_infer/include/utility.h @@ -32,14 +32,21 @@ namespace PaddleOCR { +struct OCRPredictResult { + std::vector> box; + std::string text; + float score = -1.0; + float cls_score; + int cls_label = -1; +}; + class Utility { public: static std::vector ReadDict(const std::string &path); - static void - VisualizeBboxes(const cv::Mat &srcimg, - const std::vector>> &boxes, - const std::string &save_path); + static void VisualizeBboxes(const cv::Mat &srcimg, + const std::vector &ocr_result, + const std::string &save_path); template inline static size_t argmax(ForwardIterator first, ForwardIterator last) { @@ -55,6 +62,10 @@ public: static std::vector argsort(const std::vector &array); static std::string basename(const std::string &filename); + + static bool PathExists(const std::string &path); + + static void print_result(const std::vector &ocr_result); }; } // namespace PaddleOCR \ No newline at end of file diff --git a/deploy/cpp_infer/readme.md b/deploy/cpp_infer/readme.md index e7104881027b111de6821af8244ea2a6092fc14b..95b28397d0fca72d28c4f981709754e13ca35e0d 100644 --- a/deploy/cpp_infer/readme.md +++ b/deploy/cpp_infer/readme.md @@ -9,9 +9,12 @@ - [2.1 将模型导出为inference model](#21-将模型导出为inference-model) - [2.2 编译PaddleOCR C++预测demo](#22-编译paddleocr-c预测demo) - [2.3 运行demo](#23-运行demo) - - [1. 只调用检测：](#1-只调用检测) - - [2. 只调用识别：](#2-只调用识别) - - [3. 调用串联：](#3-调用串联) + - [1. 检测+分类+识别：](#1-检测分类识别) + - [2. 检测+识别：](#2-检测识别) + - [3. 检测：](#3-检测) + - [4. 分类+识别：](#4-分类识别) + - [5. 识别：](#5-识别) + - [6. 分类：](#6-分类) - [3. FAQ](#3-faq) # 服务器端C++预测 @@ -181,6 +184,9 @@ inference/ |-- rec_rcnn | |--inference.pdiparams | |--inference.pdmodel +|-- cls +| |--inference.pdiparams +| |--inference.pdmodel ``` @@ -213,36 +219,71 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir 运行方式： ```shell -./build/ppocr [--param1] [--param2] [...] +./build/ppocr [--param1] [--param2] [...] +``` +具体命令如下： + +##### 1. 检测+分类+识别： +```shell +./build/ppocr --det_model_dir=inference/det_db \ + --rec_model_dir=inference/rec_rcnn \ + --cls_model_dir=inference/cls \ + --image_dir=../../doc/imgs/12.jpg \ + --use_angle_cls=true \ + --det=true \ + --rec=true \ + --cls=true \ +``` + +##### 2. 检测+识别： +```shell +./build/ppocr --det_model_dir=inference/det_db \ + --rec_model_dir=inference/rec_rcnn \ + --image_dir=../../doc/imgs/12.jpg \ + --use_angle_cls=false \ + --det=true \ + --rec=true \ + --cls=false \ +``` + +##### 3. 检测： +```shell +./build/ppocr --det_model_dir=inference/det_db \ + --image_dir=../../doc/imgs/12.jpg \ + --det=true \ + --rec=false ``` -其中，`mode`为必选参数，表示选择的功能，取值范围['det', 'rec', 'system']，分别表示调用检测、识别、检测识别串联（包括方向分类器）。具体命令如下： -##### 1. 只调用检测： +##### 4. 分类+识别： ```shell -./build/ppocr det \ - --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \ - --image_dir=../../doc/imgs/12.jpg +./build/ppocr --rec_model_dir=inference/rec_rcnn \ + --cls_model_dir=inference/cls \ + --image_dir=../../doc/imgs_words/ch/word_1.jpg \ + --use_angle_cls=true \ + --det=false \ + --rec=true \ + --cls=true \ ``` -##### 2. 只调用识别： + +##### 5. 识别： ```shell -./build/ppocr rec \ - --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \ - --image_dir=../../doc/imgs_words/ch/ +./build/ppocr --rec_model_dir=inference/rec_rcnn \ + --image_dir=../../doc/imgs_words/ch/word_1.jpg \ + --use_angle_cls=false \ + --det=false \ + --rec=true \ + --cls=false \ ``` -##### 3. 调用串联： + +##### 6. 分类： ```shell -# 不使用方向分类器 -./build/ppocr system \ - --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \ - --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \ - --image_dir=../../doc/imgs/12.jpg -# 使用方向分类器 -./build/ppocr system \ - --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \ +./build/ppocr --cls_model_dir=inference/cls \ + --cls_model_dir=inference/cls \ + --image_dir=../../doc/imgs_words/ch/word_1.jpg \ --use_angle_cls=true \ - --cls_model_dir=inference/ch_ppocr_mobile_v2.0_cls_infer \ - --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \ - --image_dir=../../doc/imgs/12.jpg + --det=false \ + --rec=false \ + --cls=true \ ``` 更多支持的可调节参数解释如下： @@ -258,6 +299,15 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir |enable_mkldnn|bool|true|是否使用mkldnn库| |output|str|./output|可视化结果保存的路径| +- 前向相关 + +|参数名称|类型|默认参数|意义| +| :---: | :---: | :---: | :---: | +|det|bool|true|前向是否执行文字检测| +|rec|bool|true|前向是否执行文字识别| +|cls|bool|false|前向是否执行文字方向分类| + + - 检测模型相关 |参数名称|类型|默认参数|意义| @@ -267,7 +317,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir |det_db_thresh|float|0.3|用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显| |det_db_box_thresh|float|0.5|DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小| |det_db_unclip_ratio|float|1.6|表示文本框的紧致程度，越小则文本框更靠近文本| -|use_polygon_score|bool|false|是否使用多边形框计算bbox score，false表示使用矩形框计算。矩形框计算速度更快，多边形框对弯曲文本区域计算更准确。| +|det_db_score_mode|string|slow|slow:使用多边形框计算bbox score，fast:使用矩形框计算。矩形框计算速度更快，多边形框对弯曲文本区域计算更准确。| |visualize|bool|true|是否对结果进行可视化，为1时，预测结果会保存在`output`字段指定的文件夹下和输入图像同名的图像上。| - 方向分类器相关 @@ -277,6 +327,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir |use_angle_cls|bool|false|是否使用方向分类器| |cls_model_dir|string|-|方向分类器inference model地址| |cls_thresh|float|0.9|方向分类器的得分阈值| +|cls_batch_num|int|1|方向分类器batchsize| - 识别模型相关 @@ -284,15 +335,22 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir | :---: | :---: | :---: | :---: | |rec_model_dir|string|-|识别模型inference model地址| |rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|字典文件| +|rec_batch_num|int|6|识别模型batchsize| * PaddleOCR也支持多语言的预测，更多支持的语言和模型可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分，如果希望进行多语言预测，只需将修改`rec_char_dict_path`（字典文件路径）以及`rec_model_dir`（inference模型路径）字段即可。 最终屏幕上会输出检测结果如下。 -
- -
+```bash +predict img: ../../doc/imgs/12.jpg +../../doc/imgs/12.jpg +0 det boxes: [[79,553],[399,541],[400,573],[80,585]] rec text: 打浦路252935号 rec score: 0.933757 +1 det boxes: [[31,509],[510,488],[511,529],[33,549]] rec text: 绿洲仕格维花园公寓 rec score: 0.951745 +2 det boxes: [[181,456],[395,448],[396,480],[182,488]] rec text: 打浦路15号 rec score: 0.91956 +3 det boxes: [[43,413],[480,391],[481,428],[45,450]] rec text: 上海斯格威铂尔多大酒店 rec score: 0.915914 +The detection visualized image saved in ./output//12.jpg +``` ## 3. FAQ diff --git a/deploy/cpp_infer/readme_en.md b/deploy/cpp_infer/readme_en.md index 61d65095394a9f5b7323bf8eb7324cd1e91b1346..4290fbb0fa03005970a0937d7d9daf5b6ba1995e 100644 --- a/deploy/cpp_infer/readme_en.md +++ b/deploy/cpp_infer/readme_en.md @@ -9,9 +9,12 @@ - [2.1 Export the inference model](#21-export-the-inference-model) - [2.2 Compile PaddleOCR C++ inference demo](#22-compile-paddleocr-c-inference-demo) - [Run the demo](#run-the-demo) - - [1. run det demo:](#1-run-det-demo) - - [2. run rec demo:](#2-run-rec-demo) - - [3. run system demo:](#3-run-system-demo) + - [1. det+cls+rec：](#1-detclsrec) + - [2. det+rec：](#2-detrec) + - [3. det](#3-det) + - [4. cls+rec：](#4-clsrec) + - [5. rec](#5-rec) + - [6. cls](#6-cls) - [3. FAQ](#3-faq) # Server-side C++ Inference @@ -166,6 +169,9 @@ inference/ |-- rec_rcnn | |--inference.pdiparams | |--inference.pdmodel +|-- cls +| |--inference.pdiparams +| |--inference.pdmodel ``` @@ -198,44 +204,72 @@ or the generated Paddle inference library path (`build/paddle_inference_install_ ### Run the demo Execute the built executable file: ```shell -./build/ppocr [--param1] [--param2] [...] +./build/ppocr [--param1] [--param2] [...] ``` -`mode` is a required parameter，and the valid values are - -mode value | Model used ------|------ -det | Detection only -rec | Recognition only -system | End-to-end system Specifically, -##### 1. run det demo: +##### 1. det+cls+rec： +```shell +./build/ppocr --det_model_dir=inference/det_db \ + --rec_model_dir=inference/rec_rcnn \ + --cls_model_dir=inference/cls \ + --image_dir=../../doc/imgs/12.jpg \ + --use_angle_cls=true \ + --det=true \ + --rec=true \ + --cls=true \ +``` + +##### 2. det+rec： ```shell -./build/ppocr det \ - --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \ - --image_dir=../../doc/imgs/12.jpg +./build/ppocr --det_model_dir=inference/det_db \ + --rec_model_dir=inference/rec_rcnn \ + --image_dir=../../doc/imgs/12.jpg \ + --use_angle_cls=false \ + --det=true \ + --rec=true \ + --cls=false \ ``` -##### 2. run rec demo: + +##### 3. det ```shell -./build/ppocr rec \ - --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \ - --image_dir=../../doc/imgs_words/ch/ +./build/ppocr --det_model_dir=inference/det_db \ + --image_dir=../../doc/imgs/12.jpg \ + --det=true \ + --rec=false ``` -##### 3. run system demo: + +##### 4. cls+rec： ```shell -# without text direction classifier -./build/ppocr system \ - --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \ - --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \ - --image_dir=../../doc/imgs/12.jpg -# with text direction classifier -./build/ppocr system \ - --det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \ +./build/ppocr --rec_model_dir=inference/rec_rcnn \ + --cls_model_dir=inference/cls \ + --image_dir=../../doc/imgs_words/ch/word_1.jpg \ --use_angle_cls=true \ - --cls_model_dir=inference/ch_ppocr_mobile_v2.0_cls_infer \ - --rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \ - --image_dir=../../doc/imgs/12.jpg + --det=false \ + --rec=true \ + --cls=true \ +``` + +##### 5. rec +```shell +./build/ppocr --rec_model_dir=inference/rec_rcnn \ + --image_dir=../../doc/imgs_words/ch/word_1.jpg \ + --use_angle_cls=false \ + --det=false \ + --rec=true \ + --cls=false \ +``` + +##### 6. cls +```shell +./build/ppocr --cls_model_dir=inference/cls \ + --cls_model_dir=inference/cls \ + --image_dir=../../doc/imgs_words/ch/word_1.jpg \ + --use_angle_cls=true \ + --det=false \ + --rec=false \ + --cls=true \ ``` More parameters are as follows, @@ -251,6 +285,16 @@ More parameters are as follows, |enable_mkldnn|bool|true|Whether to use mkdlnn library| |output|str|./output|Path where visualization results are saved| + +- forward + +|parameter|data type|default|meaning| +| :---: | :---: | :---: | :---: | +|det|bool|true|前向是否执行文字检测| +|rec|bool|true|前向是否执行文字识别| +|cls|bool|false|前向是否执行文字方向分类| + + - Detection related parameters |parameter|data type|default|meaning| @@ -260,7 +304,7 @@ More parameters are as follows, |det_db_thresh|float|0.3|Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result| |det_db_box_thresh|float|0.5|DB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate| |det_db_unclip_ratio|float|1.6|Indicates the compactness of the text box, the smaller the value, the closer the text box to the text| -|use_polygon_score|bool|false|Whether to use polygon box to calculate bbox score, false means to use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.| +|det_db_score_mode|string|slow| slow: use polygon box to calculate bbox score, fast: use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.| |visualize|bool|true|Whether to visualize the results，when it is set as true, the prediction results will be saved in the folder specified by the `output` field on an image with the same name as the input image.| - Classifier related parameters @@ -270,6 +314,7 @@ More parameters are as follows, |use_angle_cls|bool|false|Whether to use the direction classifier| |cls_model_dir|string|-|Address of direction classifier inference model| |cls_thresh|float|0.9|Score threshold of the direction classifier| +|cls_batch_num|int|1|batch size of classifier| - Recognition related parameters @@ -277,15 +322,22 @@ More parameters are as follows, | --- | --- | --- | --- | |rec_model_dir|string|-|Address of recognition inference model| |rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|dictionary file| +|rec_batch_num|int|6|batch size of recognition| * Multi-language inference is also supported in PaddleOCR, you can refer to [recognition tutorial](../../doc/doc_en/recognition_en.md) for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of `rec_char_dict_path` and `rec_model_dir`. The detection results will be shown on the screen, which is as follows. -
- -
+```bash +predict img: ../../doc/imgs/12.jpg +../../doc/imgs/12.jpg +0 det boxes: [[79,553],[399,541],[400,573],[80,585]] rec text: 打浦路252935号 rec score: 0.933757 +1 det boxes: [[31,509],[510,488],[511,529],[33,549]] rec text: 绿洲仕格维花园公寓 rec score: 0.951745 +2 det boxes: [[181,456],[395,448],[396,480],[182,488]] rec text: 打浦路15号 rec score: 0.91956 +3 det boxes: [[43,413],[480,391],[481,428],[45,450]] rec text: 上海斯格威铂尔多大酒店 rec score: 0.915914 +The detection visualized image saved in ./output//12.jpg +``` ## 3. FAQ diff --git a/deploy/cpp_infer/src/args.cpp b/deploy/cpp_infer/src/args.cpp new file mode 100644 index 0000000000000000000000000000000000000000..82cfb54ae7f05e549d58d3824504cce9ba7c6329 --- /dev/null +++ b/deploy/cpp_infer/src/args.cpp @@ -0,0 +1,54 @@ +// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include + +// common args +DEFINE_bool(use_gpu, false, "Infering with GPU or CPU."); +DEFINE_bool(use_tensorrt, false, "Whether use tensorrt."); +DEFINE_int32(gpu_id, 0, "Device id of GPU to execute."); +DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU."); +DEFINE_int32(cpu_threads, 10, "Num of threads with CPU."); +DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU."); +DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8"); +DEFINE_bool(benchmark, false, "Whether use benchmark."); +DEFINE_string(output, "./output/", "Save benchmark log path."); +DEFINE_string(image_dir, "", "Dir of input image."); +DEFINE_string( + type, "ocr", + "Perform ocr or structure, the value is selected in ['ocr','structure']."); +// detection related +DEFINE_string(det_model_dir, "", "Path of det inference model."); +DEFINE_int32(max_side_len, 960, "max_side_len of input image."); +DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh."); +DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh."); +DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio."); +DEFINE_bool(use_dilation, false, "Whether use the dilation on output map."); +DEFINE_string(det_db_score_mode, "slow", "Whether use polygon score."); +DEFINE_bool(visualize, true, "Whether show the detection results."); +// classification related +DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls."); +DEFINE_string(cls_model_dir, "", "Path of cls inference model."); +DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh."); +DEFINE_int32(cls_batch_num, 1, "cls_batch_num."); +// recognition related +DEFINE_string(rec_model_dir, "", "Path of rec inference model."); +DEFINE_int32(rec_batch_num, 6, "rec_batch_num."); +DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt", + "Path of dictionary."); + +// ocr forward related +DEFINE_bool(det, true, "Whether use det in forward."); +DEFINE_bool(rec, true, "Whether use rec in forward."); +DEFINE_bool(cls, false, "Whether use cls in forward."); \ No newline at end of file diff --git a/deploy/cpp_infer/include/clipper.cpp b/deploy/cpp_infer/src/clipper.cpp similarity index 100% rename from deploy/cpp_infer/include/clipper.cpp rename to deploy/cpp_infer/src/clipper.cpp diff --git a/deploy/cpp_infer/src/main.cpp b/deploy/cpp_infer/src/main.cpp index efc1e50ce929b4f68dff3437faa05b9ac46c2aa0..66ac795f59c8039b046d11819137457c15a5c3ee 100644 --- a/deploy/cpp_infer/src/main.cpp +++ b/deploy/cpp_infer/src/main.cpp @@ -11,273 +11,19 @@ // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. - -#include "omp.h" #include "opencv2/core.hpp" #include "opencv2/imgcodecs.hpp" #include "opencv2/imgproc.hpp" -#include -#include #include -#include -#include #include -#include -#include -#include - -#include -#include -#include -#include -#include - -#include "auto_log/autolog.h" -#include +#include +#include -DEFINE_bool(use_gpu, false, "Infering with GPU or CPU."); -DEFINE_int32(gpu_id, 0, "Device id of GPU to execute."); -DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU."); -DEFINE_int32(cpu_threads, 10, "Num of threads with CPU."); -DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU."); -DEFINE_bool(use_tensorrt, false, "Whether use tensorrt."); -DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8"); -DEFINE_bool(benchmark, false, "Whether use benchmark."); -DEFINE_string(output, "./output/", "Save benchmark log path."); -// detection related -DEFINE_string(image_dir, "", "Dir of input image."); -DEFINE_string(det_model_dir, "", "Path of det inference model."); -DEFINE_int32(max_side_len, 960, "max_side_len of input image."); -DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh."); -DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh."); -DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio."); -DEFINE_bool(use_polygon_score, false, "Whether use polygon score."); -DEFINE_bool(use_dilation, false, "Whether use the dilation on output map."); -DEFINE_bool(visualize, true, "Whether show the detection results."); -// classification related -DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls."); -DEFINE_string(cls_model_dir, "", "Path of cls inference model."); -DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh."); -// recognition related -DEFINE_string(rec_model_dir, "", "Path of rec inference model."); -DEFINE_int32(rec_batch_num, 6, "rec_batch_num."); -DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt", - "Path of dictionary."); - -using namespace std; -using namespace cv; using namespace PaddleOCR; -static bool PathExists(const std::string &path) { -#ifdef _WIN32 - struct _stat buffer; - return (_stat(path.c_str(), &buffer) == 0); -#else - struct stat buffer; - return (stat(path.c_str(), &buffer) == 0); -#endif // !_WIN32 -} - -int main_det(std::vector cv_all_img_names) { - std::vector time_info = {0, 0, 0}; - DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, - FLAGS_max_side_len, FLAGS_det_db_thresh, - FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio, - FLAGS_use_polygon_score, FLAGS_use_dilation, - FLAGS_use_tensorrt, FLAGS_precision); - - if (!PathExists(FLAGS_output)) { - mkdir(FLAGS_output.c_str(), 0777); - } - - for (int i = 0; i < cv_all_img_names.size(); ++i) { - if (!FLAGS_benchmark) { - cout << "The predict img: " << cv_all_img_names[i] << endl; - } - - cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); - if (!srcimg.data) { - std::cerr << "[ERROR] image read failed! image path: " - << cv_all_img_names[i] << endl; - exit(1); - } - std::vector>> boxes; - std::vector det_times; - - det.Run(srcimg, boxes, &det_times); - // visualization - if (FLAGS_visualize) { - std::string file_name = Utility::basename(cv_all_img_names[i]); - Utility::VisualizeBboxes(srcimg, boxes, FLAGS_output + "/" + file_name); - } - time_info[0] += det_times[0]; - time_info[1] += det_times[1]; - time_info[2] += det_times[2]; - - if (FLAGS_benchmark) { - cout << cv_all_img_names[i] << '\t'; - for (int n = 0; n < boxes.size(); n++) { - for (int m = 0; m < boxes[n].size(); m++) { - cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' '; - } - } - cout << endl; - } - } - - if (FLAGS_benchmark) { - AutoLogger autolog("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt, - FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic", - FLAGS_precision, time_info, cv_all_img_names.size()); - autolog.report(); - } - return 0; -} - -int main_rec(std::vector cv_all_img_names) { - std::vector time_info = {0, 0, 0}; - - std::string rec_char_dict_path = FLAGS_rec_char_dict_path; - if (FLAGS_benchmark) - rec_char_dict_path = FLAGS_rec_char_dict_path.substr(6); - cout << "label file: " << rec_char_dict_path << endl; - - CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, - rec_char_dict_path, FLAGS_use_tensorrt, FLAGS_precision, - FLAGS_rec_batch_num); - - std::vector img_list; - for (int i = 0; i < cv_all_img_names.size(); ++i) { - cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); - if (!srcimg.data) { - std::cerr << "[ERROR] image read failed! image path: " - << cv_all_img_names[i] << endl; - exit(1); - } - img_list.push_back(srcimg); - } - std::vector rec_texts(img_list.size(), ""); - std::vector rec_text_scores(img_list.size(), 0); - std::vector rec_times; - rec.Run(img_list, rec_texts, rec_text_scores, &rec_times); - // output rec results - for (int i = 0; i < rec_texts.size(); i++) { - cout << "The predict img: " << cv_all_img_names[i] << "\t" << rec_texts[i] - << "\t" << rec_text_scores[i] << endl; - } - time_info[0] += rec_times[0]; - time_info[1] += rec_times[1]; - time_info[2] += rec_times[2]; - - if (FLAGS_benchmark) { - AutoLogger autolog("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt, - FLAGS_enable_mkldnn, FLAGS_cpu_threads, - FLAGS_rec_batch_num, "dynamic", FLAGS_precision, - time_info, cv_all_img_names.size()); - autolog.report(); - } - return 0; -} - -int main_system(std::vector cv_all_img_names) { - std::vector time_info_det = {0, 0, 0}; - std::vector time_info_rec = {0, 0, 0}; - - if (!PathExists(FLAGS_output)) { - mkdir(FLAGS_output.c_str(), 0777); - } - - DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, - FLAGS_max_side_len, FLAGS_det_db_thresh, - FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio, - FLAGS_use_polygon_score, FLAGS_use_dilation, - FLAGS_use_tensorrt, FLAGS_precision); - - Classifier *cls = nullptr; - if (FLAGS_use_angle_cls) { - cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, - FLAGS_cls_thresh, FLAGS_use_tensorrt, FLAGS_precision); - } - - std::string rec_char_dict_path = FLAGS_rec_char_dict_path; - if (FLAGS_benchmark) - rec_char_dict_path = FLAGS_rec_char_dict_path.substr(6); - cout << "label file: " << rec_char_dict_path << endl; - - CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, - FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn, - rec_char_dict_path, FLAGS_use_tensorrt, FLAGS_precision, - FLAGS_rec_batch_num); - - for (int i = 0; i < cv_all_img_names.size(); ++i) { - cout << "The predict img: " << cv_all_img_names[i] << endl; - - cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); - if (!srcimg.data) { - std::cerr << "[ERROR] image read failed! image path: " - << cv_all_img_names[i] << endl; - exit(1); - } - // det - std::vector>> boxes; - std::vector det_times; - std::vector rec_times; - - det.Run(srcimg, boxes, &det_times); - if (FLAGS_visualize) { - std::string file_name = Utility::basename(cv_all_img_names[i]); - Utility::VisualizeBboxes(srcimg, boxes, FLAGS_output + "/" + file_name); - } - time_info_det[0] += det_times[0]; - time_info_det[1] += det_times[1]; - time_info_det[2] += det_times[2]; - - // rec - std::vector img_list; - for (int j = 0; j < boxes.size(); j++) { - cv::Mat crop_img; - crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]); - if (cls != nullptr) { - crop_img = cls->Run(crop_img); - } - img_list.push_back(crop_img); - } - std::vector rec_texts(img_list.size(), ""); - std::vector rec_text_scores(img_list.size(), 0); - rec.Run(img_list, rec_texts, rec_text_scores, &rec_times); - // output rec results - for (int i = 0; i < rec_texts.size(); i++) { - std::cout << i << "\t" << rec_texts[i] << "\t" << rec_text_scores[i] - << std::endl; - } - time_info_rec[0] += rec_times[0]; - time_info_rec[1] += rec_times[1]; - time_info_rec[2] += rec_times[2]; - } - - if (FLAGS_benchmark) { - AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt, - FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic", - FLAGS_precision, time_info_det, - cv_all_img_names.size()); - AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt, - FLAGS_enable_mkldnn, FLAGS_cpu_threads, - FLAGS_rec_batch_num, "dynamic", FLAGS_precision, - time_info_rec, cv_all_img_names.size()); - autolog_det.report(); - std::cout << endl; - autolog_rec.report(); - } - return 0; -} - -void check_params(char *mode) { - if (strcmp(mode, "det") == 0) { +void check_params() { + if (FLAGS_det) { if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) { std::cout << "Usage[det]: ./ppocr " "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " @@ -285,7 +31,7 @@ void check_params(char *mode) { exit(1); } } - if (strcmp(mode, "rec") == 0) { + if (FLAGS_rec) { if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) { std::cout << "Usage[rec]: ./ppocr " "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " @@ -293,19 +39,10 @@ void check_params(char *mode) { exit(1); } } - if (strcmp(mode, "system") == 0) { - if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() || - FLAGS_image_dir.empty()) || - (FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) { - std::cout << "Usage[system without angle cls]: ./ppocr " - "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " - << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " - << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; - std::cout << "Usage[system with angle cls]: ./ppocr " - "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ " - << "--use_angle_cls=true " - << "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ " - << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " + if (FLAGS_cls && FLAGS_use_angle_cls) { + if (FLAGS_cls_model_dir.empty() || FLAGS_image_dir.empty()) { + std::cout << "Usage[cls]: ./ppocr " + << "--cls_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ " << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl; exit(1); } @@ -318,19 +55,11 @@ void check_params(char *mode) { } int main(int argc, char **argv) { - if (argc <= 1 || - (strcmp(argv[1], "det") != 0 && strcmp(argv[1], "rec") != 0 && - strcmp(argv[1], "system") != 0)) { - std::cout << "Please choose one mode of [det, rec, system] !" << std::endl; - return -1; - } - std::cout << "mode: " << argv[1] << endl; - // Parsing command-line google::ParseCommandLineFlags(&argc, &argv, true); - check_params(argv[1]); + check_params(); - if (!PathExists(FLAGS_image_dir)) { + if (!Utility::PathExists(FLAGS_image_dir)) { std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir << endl; exit(1); @@ -340,13 +69,37 @@ int main(int argc, char **argv) { cv::glob(FLAGS_image_dir, cv_all_img_names); std::cout << "total images num: " << cv_all_img_names.size() << endl; - if (strcmp(argv[1], "det") == 0) { - return main_det(cv_all_img_names); - } - if (strcmp(argv[1], "rec") == 0) { - return main_rec(cv_all_img_names); - } - if (strcmp(argv[1], "system") == 0) { - return main_system(cv_all_img_names); + PaddleOCR::PaddleOCR ocr = PaddleOCR::PaddleOCR(); + + std::vector> ocr_results = + ocr.ocr(cv_all_img_names, FLAGS_det, FLAGS_rec, FLAGS_cls); + + for (int i = 0; i < cv_all_img_names.size(); ++i) { + if (FLAGS_benchmark) { + cout << cv_all_img_names[i] << '\t'; + for (int n = 0; n < ocr_results[i].size(); n++) { + for (int m = 0; m < ocr_results[i][n].box.size(); m++) { + cout << ocr_results[i][n].box[m][0] << ' ' + << ocr_results[i][n].box[m][1] << ' '; + } + } + cout << endl; + } else { + cout << cv_all_img_names[i] << "\n"; + Utility::print_result(ocr_results[i]); + if (FLAGS_visualize && FLAGS_det) { + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " + << cv_all_img_names[i] << endl; + exit(1); + } + std::string file_name = Utility::basename(cv_all_img_names[i]); + + Utility::VisualizeBboxes(srcimg, ocr_results[i], + FLAGS_output + "/" + file_name); + } + cout << "***************************" << endl; + } } } diff --git a/deploy/cpp_infer/src/ocr_cls.cpp b/deploy/cpp_infer/src/ocr_cls.cpp index 3b04b6f8248bb17b9e315ae8b777530840015394..674630bf1e7e04841e027a7320d62af4a453ffc8 100644 --- a/deploy/cpp_infer/src/ocr_cls.cpp +++ b/deploy/cpp_infer/src/ocr_cls.cpp @@ -16,57 +16,84 @@ namespace PaddleOCR { -cv::Mat Classifier::Run(cv::Mat &img) { - cv::Mat src_img; - img.copyTo(src_img); - cv::Mat resize_img; - +void Classifier::Run(std::vector img_list, + std::vector &cls_labels, + std::vector &cls_scores, + std::vector ×) { + std::chrono::duration preprocess_diff = + std::chrono::steady_clock::now() - std::chrono::steady_clock::now(); + std::chrono::duration inference_diff = + std::chrono::steady_clock::now() - std::chrono::steady_clock::now(); + std::chrono::duration postprocess_diff = + std::chrono::steady_clock::now() - std::chrono::steady_clock::now(); + + int img_num = img_list.size(); std::vector cls_image_shape = {3, 48, 192}; - int index = 0; - float wh_ratio = float(img.cols) / float(img.rows); - - this->resize_op_.Run(img, resize_img, this->use_tensorrt_, cls_image_shape); - - this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, - this->is_scale_); - - std::vector input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f); - - this->permute_op_.Run(&resize_img, input.data()); - - // Inference. - auto input_names = this->predictor_->GetInputNames(); - auto input_t = this->predictor_->GetInputHandle(input_names[0]); - input_t->Reshape({1, 3, resize_img.rows, resize_img.cols}); - input_t->CopyFromCpu(input.data()); - this->predictor_->Run(); - - std::vector softmax_out; - std::vector label_out; - auto output_names = this->predictor_->GetOutputNames(); - auto softmax_out_t = this->predictor_->GetOutputHandle(output_names[0]); - auto softmax_shape_out = softmax_out_t->shape(); - - int softmax_out_num = - std::accumulate(softmax_shape_out.begin(), softmax_shape_out.end(), 1, - std::multiplies()); - - softmax_out.resize(softmax_out_num); - - softmax_out_t->CopyToCpu(softmax_out.data()); - - float score = 0; - int label = 0; - for (int i = 0; i < softmax_out_num; i++) { - if (softmax_out[i] > score) { - score = softmax_out[i]; - label = i; + for (int beg_img_no = 0; beg_img_no < img_num; + beg_img_no += this->cls_batch_num_) { + auto preprocess_start = std::chrono::steady_clock::now(); + int end_img_no = min(img_num, beg_img_no + this->cls_batch_num_); + int batch_num = end_img_no - beg_img_no; + // preprocess + std::vector norm_img_batch; + for (int ino = beg_img_no; ino < end_img_no; ino++) { + cv::Mat srcimg; + img_list[ino].copyTo(srcimg); + cv::Mat resize_img; + this->resize_op_.Run(srcimg, resize_img, this->use_tensorrt_, + cls_image_shape); + + this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, + this->is_scale_); + norm_img_batch.push_back(resize_img); } + std::vector input(batch_num * cls_image_shape[0] * + cls_image_shape[1] * cls_image_shape[2], + 0.0f); + this->permute_op_.Run(norm_img_batch, input.data()); + auto preprocess_end = std::chrono::steady_clock::now(); + preprocess_diff += preprocess_end - preprocess_start; + + // inference. + auto input_names = this->predictor_->GetInputNames(); + auto input_t = this->predictor_->GetInputHandle(input_names[0]); + input_t->Reshape({batch_num, cls_image_shape[0], cls_image_shape[1], + cls_image_shape[2]}); + auto inference_start = std::chrono::steady_clock::now(); + input_t->CopyFromCpu(input.data()); + this->predictor_->Run(); + + std::vector predict_batch; + auto output_names = this->predictor_->GetOutputNames(); + auto output_t = this->predictor_->GetOutputHandle(output_names[0]); + auto predict_shape = output_t->shape(); + + int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1, + std::multiplies()); + predict_batch.resize(out_num); + + output_t->CopyToCpu(predict_batch.data()); + auto inference_end = std::chrono::steady_clock::now(); + inference_diff += inference_end - inference_start; + + // postprocess + auto postprocess_start = std::chrono::steady_clock::now(); + for (int batch_idx = 0; batch_idx < predict_shape[0]; batch_idx++) { + int label = int( + Utility::argmax(&predict_batch[batch_idx * predict_shape[1]], + &predict_batch[(batch_idx + 1) * predict_shape[1]])); + float score = float(*std::max_element( + &predict_batch[batch_idx * predict_shape[1]], + &predict_batch[(batch_idx + 1) * predict_shape[1]])); + cls_labels[beg_img_no + batch_idx] = label; + cls_scores[beg_img_no + batch_idx] = score; + } + auto postprocess_end = std::chrono::steady_clock::now(); + postprocess_diff += postprocess_end - postprocess_start; } - if (label % 2 == 1 && score > this->cls_thresh) { - cv::rotate(src_img, src_img, 1); - } - return src_img; + times.push_back(double(preprocess_diff.count() * 1000)); + times.push_back(double(inference_diff.count() * 1000)); + times.push_back(double(postprocess_diff.count() * 1000)); } void Classifier::LoadModel(const std::string &model_dir) { @@ -81,13 +108,10 @@ void Classifier::LoadModel(const std::string &model_dir) { if (this->precision_ == "fp16") { precision = paddle_infer::Config::Precision::kHalf; } - if (this->precision_ == "int8") { + if (this->precision_ == "int8") { precision = paddle_infer::Config::Precision::kInt8; - } - config.EnableTensorRtEngine( - 1 << 20, 10, 3, - precision, - false, false); + } + config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false); } } else { config.DisableGpu(); diff --git a/deploy/cpp_infer/src/ocr_det.cpp b/deploy/cpp_infer/src/ocr_det.cpp index d72dc40cddb0845c370f5ad4bb9b6e2f6fe0bf2f..c08f97b55ab53c934734393af404a53dc77493a9 100644 --- a/deploy/cpp_infer/src/ocr_det.cpp +++ b/deploy/cpp_infer/src/ocr_det.cpp @@ -94,7 +94,7 @@ void DBDetector::LoadModel(const std::string &model_dir) { void DBDetector::Run(cv::Mat &img, std::vector>> &boxes, - std::vector *times) { + std::vector ×) { float ratio_h{}; float ratio_w{}; @@ -161,20 +161,19 @@ void DBDetector::Run(cv::Mat &img, boxes = post_processor_.BoxesFromBitmap( pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_, - this->use_polygon_score_); + this->det_db_score_mode_); boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg); auto postprocess_end = std::chrono::steady_clock::now(); - std::cout << "Detected boxes num: " << boxes.size() << endl; std::chrono::duration preprocess_diff = preprocess_end - preprocess_start; - times->push_back(double(preprocess_diff.count() * 1000)); + times.push_back(double(preprocess_diff.count() * 1000)); std::chrono::duration inference_diff = inference_end - inference_start; - times->push_back(double(inference_diff.count() * 1000)); + times.push_back(double(inference_diff.count() * 1000)); std::chrono::duration postprocess_diff = postprocess_end - postprocess_start; - times->push_back(double(postprocess_diff.count() * 1000)); + times.push_back(double(postprocess_diff.count() * 1000)); } } // namespace PaddleOCR diff --git a/deploy/cpp_infer/src/ocr_rec.cpp b/deploy/cpp_infer/src/ocr_rec.cpp index 4c94e8f3fc966d2a4de8c7aad0e5ef4d4b69c804..54ed3fefcb798033748ad6f9cde20375527b1774 100644 --- a/deploy/cpp_infer/src/ocr_rec.cpp +++ b/deploy/cpp_infer/src/ocr_rec.cpp @@ -19,7 +19,7 @@ namespace PaddleOCR { void CRNNRecognizer::Run(std::vector img_list, std::vector &rec_texts, std::vector &rec_text_scores, - std::vector *times) { + std::vector ×) { std::chrono::duration preprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now(); std::chrono::duration inference_diff = @@ -38,6 +38,7 @@ void CRNNRecognizer::Run(std::vector img_list, beg_img_no += this->rec_batch_num_) { auto preprocess_start = std::chrono::steady_clock::now(); int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_); + int batch_num = end_img_no - beg_img_no; float max_wh_ratio = 0; for (int ino = beg_img_no; ino < end_img_no; ino++) { int h = img_list[indices[ino]].rows; @@ -45,6 +46,7 @@ void CRNNRecognizer::Run(std::vector img_list, float wh_ratio = w * 1.0 / h; max_wh_ratio = max(max_wh_ratio, wh_ratio); } + int batch_width = 0; std::vector norm_img_batch; for (int ino = beg_img_no; ino < end_img_no; ino++) { @@ -59,15 +61,14 @@ void CRNNRecognizer::Run(std::vector img_list, batch_width = max(resize_img.cols, batch_width); } - std::vector input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f); + std::vector input(batch_num * 3 * 32 * batch_width, 0.0f); this->permute_op_.Run(norm_img_batch, input.data()); auto preprocess_end = std::chrono::steady_clock::now(); preprocess_diff += preprocess_end - preprocess_start; - // Inference. auto input_names = this->predictor_->GetInputNames(); auto input_t = this->predictor_->GetInputHandle(input_names[0]); - input_t->Reshape({this->rec_batch_num_, 3, 32, batch_width}); + input_t->Reshape({batch_num, 3, 32, batch_width}); auto inference_start = std::chrono::steady_clock::now(); input_t->CopyFromCpu(input.data()); this->predictor_->Run(); @@ -84,7 +85,6 @@ void CRNNRecognizer::Run(std::vector img_list, output_t->CopyToCpu(predict_batch.data()); auto inference_end = std::chrono::steady_clock::now(); inference_diff += inference_end - inference_start; - // ctc decode auto postprocess_start = std::chrono::steady_clock::now(); for (int m = 0; m < predict_shape[0]; m++) { @@ -120,9 +120,9 @@ void CRNNRecognizer::Run(std::vector img_list, auto postprocess_end = std::chrono::steady_clock::now(); postprocess_diff += postprocess_end - postprocess_start; } - times->push_back(double(preprocess_diff.count() * 1000)); - times->push_back(double(inference_diff.count() * 1000)); - times->push_back(double(postprocess_diff.count() * 1000)); + times.push_back(double(preprocess_diff.count() * 1000)); + times.push_back(double(inference_diff.count() * 1000)); + times.push_back(double(postprocess_diff.count() * 1000)); } void CRNNRecognizer::LoadModel(const std::string &model_dir) { diff --git a/deploy/cpp_infer/src/paddleocr.cpp b/deploy/cpp_infer/src/paddleocr.cpp new file mode 100644 index 0000000000000000000000000000000000000000..861461a01b00269860d74f5ef22bc894920d323b --- /dev/null +++ b/deploy/cpp_infer/src/paddleocr.cpp @@ -0,0 +1,227 @@ +// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include +#include + +#include "auto_log/autolog.h" +#include +#include + +namespace PaddleOCR { + +PaddleOCR::PaddleOCR() { + if (FLAGS_det) { + this->detector_ = new DBDetector( + FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem, + FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_max_side_len, + FLAGS_det_db_thresh, FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio, + FLAGS_det_db_score_mode, FLAGS_use_dilation, FLAGS_use_tensorrt, + FLAGS_precision); + } + + if (FLAGS_cls && FLAGS_use_angle_cls) { + this->classifier_ = new Classifier( + FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem, + FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_cls_thresh, + FLAGS_use_tensorrt, FLAGS_precision, FLAGS_cls_batch_num); + } + if (FLAGS_rec) { + this->recognizer_ = new CRNNRecognizer( + FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem, + FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_rec_char_dict_path, + FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num); + } +}; + +void PaddleOCR::det(cv::Mat img, std::vector &ocr_results, + std::vector ×) { + std::vector>> boxes; + std::vector det_times; + + this->detector_->Run(img, boxes, det_times); + + for (int i = 0; i < boxes.size(); i++) { + OCRPredictResult res; + res.box = boxes[i]; + ocr_results.push_back(res); + } + + times[0] += det_times[0]; + times[1] += det_times[1]; + times[2] += det_times[2]; +} + +void PaddleOCR::rec(std::vector img_list, + std::vector &ocr_results, + std::vector ×) { + std::vector rec_texts(img_list.size(), ""); + std::vector rec_text_scores(img_list.size(), 0); + std::vector rec_times; + this->recognizer_->Run(img_list, rec_texts, rec_text_scores, rec_times); + // output rec results + for (int i = 0; i < rec_texts.size(); i++) { + ocr_results[i].text = rec_texts[i]; + ocr_results[i].score = rec_text_scores[i]; + } + times[0] += rec_times[0]; + times[1] += rec_times[1]; + times[2] += rec_times[2]; +} + +void PaddleOCR::cls(std::vector img_list, + std::vector &ocr_results, + std::vector ×) { + std::vector cls_labels(img_list.size(), 0); + std::vector cls_scores(img_list.size(), 0); + std::vector cls_times; + this->classifier_->Run(img_list, cls_labels, cls_scores, cls_times); + // output cls results + for (int i = 0; i < cls_labels.size(); i++) { + ocr_results[i].cls_label = cls_labels[i]; + ocr_results[i].cls_score = cls_scores[i]; + } + times[0] += cls_times[0]; + times[1] += cls_times[1]; + times[2] += cls_times[2]; +} + +std::vector> +PaddleOCR::ocr(std::vector cv_all_img_names, bool det, bool rec, + bool cls) { + std::vector time_info_det = {0, 0, 0}; + std::vector time_info_rec = {0, 0, 0}; + std::vector time_info_cls = {0, 0, 0}; + std::vector> ocr_results; + + if (!det) { + std::vector ocr_result; + // read image + std::vector img_list; + for (int i = 0; i < cv_all_img_names.size(); ++i) { + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " + << cv_all_img_names[i] << endl; + exit(1); + } + img_list.push_back(srcimg); + OCRPredictResult res; + ocr_result.push_back(res); + } + if (cls && this->classifier_ != nullptr) { + this->cls(img_list, ocr_result, time_info_cls); + for (int i = 0; i < img_list.size(); i++) { + if (ocr_result[i].cls_label % 2 == 1 && + ocr_result[i].cls_score > this->classifier_->cls_thresh) { + cv::rotate(img_list[i], img_list[i], 1); + } + } + } + if (rec) { + this->rec(img_list, ocr_result, time_info_rec); + } + for (int i = 0; i < cv_all_img_names.size(); ++i) { + std::vector ocr_result_tmp; + ocr_result_tmp.push_back(ocr_result[i]); + ocr_results.push_back(ocr_result_tmp); + } + } else { + if (!Utility::PathExists(FLAGS_output) && FLAGS_det) { + mkdir(FLAGS_output.c_str(), 0777); + } + + for (int i = 0; i < cv_all_img_names.size(); ++i) { + std::vector ocr_result; + if (!FLAGS_benchmark) { + cout << "predict img: " << cv_all_img_names[i] << endl; + } + + cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR); + if (!srcimg.data) { + std::cerr << "[ERROR] image read failed! image path: " + << cv_all_img_names[i] << endl; + exit(1); + } + // det + this->det(srcimg, ocr_result, time_info_det); + // crop image + std::vector img_list; + for (int j = 0; j < ocr_result.size(); j++) { + cv::Mat crop_img; + crop_img = Utility::GetRotateCropImage(srcimg, ocr_result[j].box); + img_list.push_back(crop_img); + } + + // cls + if (cls && this->classifier_ != nullptr) { + this->cls(img_list, ocr_result, time_info_cls); + for (int i = 0; i < img_list.size(); i++) { + if (ocr_result[i].cls_label % 2 == 1 && + ocr_result[i].cls_score > this->classifier_->cls_thresh) { + cv::rotate(img_list[i], img_list[i], 1); + } + } + } + // rec + if (rec) { + this->rec(img_list, ocr_result, time_info_rec); + } + ocr_results.push_back(ocr_result); + } + } + if (FLAGS_benchmark) { + this->log(time_info_det, time_info_rec, time_info_cls, + cv_all_img_names.size()); + } + return ocr_results; +} // namespace PaddleOCR + +void PaddleOCR::log(std::vector &det_times, + std::vector &rec_times, + std::vector &cls_times, int img_num) { + if (det_times[0] + det_times[1] + det_times[2] > 0) { + AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt, + FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic", + FLAGS_precision, det_times, img_num); + autolog_det.report(); + } + if (rec_times[0] + rec_times[1] + rec_times[2] > 0) { + AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt, + FLAGS_enable_mkldnn, FLAGS_cpu_threads, + FLAGS_rec_batch_num, "dynamic", FLAGS_precision, + rec_times, img_num); + autolog_rec.report(); + } + if (cls_times[0] + cls_times[1] + cls_times[2] > 0) { + AutoLogger autolog_cls("ocr_cls", FLAGS_use_gpu, FLAGS_use_tensorrt, + FLAGS_enable_mkldnn, FLAGS_cpu_threads, + FLAGS_cls_batch_num, "dynamic", FLAGS_precision, + cls_times, img_num); + autolog_cls.report(); + } +} +PaddleOCR::~PaddleOCR() { + if (this->detector_ != nullptr) { + delete this->detector_; + } + if (this->classifier_ != nullptr) { + delete this->classifier_; + } + if (this->recognizer_ != nullptr) { + delete this->recognizer_; + } +}; + +} // namespace PaddleOCR diff --git a/deploy/cpp_infer/src/postprocess_op.cpp b/deploy/cpp_infer/src/postprocess_op.cpp index c3985572048155cf5aca57c95f1d8a816658ef13..5374fb1a4eba68d8055a52ec91d97c290832aa9d 100644 --- a/deploy/cpp_infer/src/postprocess_op.cpp +++ b/deploy/cpp_infer/src/postprocess_op.cpp @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include #include -#include namespace PaddleOCR { @@ -187,23 +187,22 @@ float PostProcessor::PolygonScoreAcc(std::vector contour, cv::Mat mask; mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1); + cv::Point *rook_point = new cv::Point[contour.size()]; - cv::Point* rook_point = new cv::Point[contour.size()]; - for (int i = 0; i < contour.size(); ++i) { rook_point[i] = cv::Point(int(box_x[i]) - xmin, int(box_y[i]) - ymin); } const cv::Point *ppt[1] = {rook_point}; int npt[] = {int(contour.size())}; - cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1)); cv::Mat croppedImg; - pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg); + pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)) + .copyTo(croppedImg); float score = cv::mean(croppedImg, mask)[0]; - delete []rook_point; + delete[] rook_point; return score; } @@ -247,7 +246,7 @@ float PostProcessor::BoxScoreFast(std::vector> box_array, std::vector>> PostProcessor::BoxesFromBitmap( const cv::Mat pred, const cv::Mat bitmap, const float &box_thresh, - const float &det_db_unclip_ratio, const bool &use_polygon_score) { + const float &det_db_unclip_ratio, const std::string &det_db_score_mode) { const int min_size = 3; const int max_candidates = 1000; @@ -281,7 +280,7 @@ std::vector>> PostProcessor::BoxesFromBitmap( } float score; - if (use_polygon_score) + if (det_db_score_mode == "slow") /* compute using polygon*/ score = PolygonScoreAcc(contours[_i], pred); else diff --git a/deploy/cpp_infer/src/utility.cpp b/deploy/cpp_infer/src/utility.cpp index 034df07804745178368a621936cd1ddabfd3a050..339e992daa1489737ac92b45ca7d13f71a41abc2 100644 --- a/deploy/cpp_infer/src/utility.cpp +++ b/deploy/cpp_infer/src/utility.cpp @@ -38,16 +38,16 @@ std::vector Utility::ReadDict(const std::string &path) { return m_vec; } -void Utility::VisualizeBboxes( - const cv::Mat &srcimg, - const std::vector>> &boxes, - const std::string &save_path) { +void Utility::VisualizeBboxes(const cv::Mat &srcimg, + const std::vector &ocr_result, + const std::string &save_path) { cv::Mat img_vis; srcimg.copyTo(img_vis); - for (int n = 0; n < boxes.size(); n++) { + for (int n = 0; n < ocr_result.size(); n++) { cv::Point rook_points[4]; - for (int m = 0; m < boxes[n].size(); m++) { - rook_points[m] = cv::Point(int(boxes[n][m][0]), int(boxes[n][m][1])); + for (int m = 0; m < ocr_result[n].box.size(); m++) { + rook_points[m] = + cv::Point(int(ocr_result[n].box[m][0]), int(ocr_result[n].box[m][1])); } const cv::Point *ppt[1] = {rook_points}; @@ -196,4 +196,43 @@ std::string Utility::basename(const std::string &filename) { return filename.substr(index + 1, len - index); } +bool Utility::PathExists(const std::string &path) { +#ifdef _WIN32 + struct _stat buffer; + return (_stat(path.c_str(), &buffer) == 0); +#else + struct stat buffer; + return (stat(path.c_str(), &buffer) == 0); +#endif // !_WIN32 +} + +void Utility::print_result(const std::vector &ocr_result) { + for (int i = 0; i < ocr_result.size(); i++) { + std::cout << i << "\t"; + // det + std::vector> boxes = ocr_result[i].box; + if (boxes.size() > 0) { + std::cout << "det boxes: ["; + for (int n = 0; n < boxes.size(); n++) { + std::cout << '[' << boxes[n][0] << ',' << boxes[n][1] << "]"; + if (n != boxes.size() - 1) { + std::cout << ','; + } + } + std::cout << "] "; + } + // rec + if (ocr_result[i].score != -1.0) { + std::cout << "rec text: " << ocr_result[i].text + << " rec score: " << ocr_result[i].score << " "; + } + + // cls + if (ocr_result[i].cls_label != -1) { + std::cout << "cls label: " << ocr_result[i].cls_label + << " cls score: " << ocr_result[i].cls_score; + } + std::cout << std::endl; + } +} } // namespace PaddleOCR \ No newline at end of file diff --git a/deploy/hubserving/ocr_rec/params.py b/deploy/hubserving/ocr_rec/params.py index 70b50dd4d680f744dca5cf1cbe0ebe8f0984d93a..09bdeeb3c62abe3a1d197719b79d4f523ff5e5e1 100644 --- a/deploy/hubserving/ocr_rec/params.py +++ b/deploy/hubserving/ocr_rec/params.py @@ -29,8 +29,7 @@ def read_params(): cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/" cfg.rec_image_shape = "3, 32, 320" - cfg.rec_char_type = 'ch' - cfg.rec_batch_num = 30 + cfg.rec_batch_num = 6 cfg.max_text_length = 25 cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt" diff --git a/deploy/hubserving/ocr_system/params.py b/deploy/hubserving/ocr_system/params.py index 6d74294438cfbc83a8445f994585e7d82ada5f7f..9972a3ded83589e7552b308c59b9dc09a9a4399b 100755 --- a/deploy/hubserving/ocr_system/params.py +++ b/deploy/hubserving/ocr_system/params.py @@ -47,8 +47,7 @@ def read_params(): cfg.rec_model_dir = "./inference/ch_PP-OCRv2_rec_infer/" cfg.rec_image_shape = "3, 32, 320" - cfg.rec_char_type = 'ch' - cfg.rec_batch_num = 30 + cfg.rec_batch_num = 6 cfg.max_text_length = 25 cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt" diff --git a/deploy/hubserving/readme.md b/deploy/hubserving/readme.md index 22699d7122faaab2cdeacad40dff3bbc9f981b03..ab6dbeff749beb5ddb14d116f2d3580ad074d337 100755 --- a/deploy/hubserving/readme.md +++ b/deploy/hubserving/readme.md @@ -188,7 +188,7 @@ hub serving start -c deploy/hubserving/ocr_system/config.json - **output**：可视化结果保存路径，默认为`./hubserving_result` 访问示例： -```python tools/test_hubserving.py --server_url=http://127.0.0.1:8868/predict/ocr_system --image_dir./doc/imgs/ --visualize=false``` +```python tools/test_hubserving.py --server_url=http://127.0.0.1:8868/predict/ocr_system --image_dir=./doc/imgs/ --visualize=false``` ## 4. 返回结果格式说明 返回结果为列表（list），列表中的每一项为词典（dict），词典一共可能包含3种字段，信息如下： diff --git a/deploy/hubserving/readme_en.md b/deploy/hubserving/readme_en.md index b32e6aa822c55771bbebdf49bb81b9c9202279f5..8b99796a257f45d48cf3e0386c741ec798ee23e0 100755 --- a/deploy/hubserving/readme_en.md +++ b/deploy/hubserving/readme_en.md @@ -196,7 +196,7 @@ For example, if using the configuration file to start the text angle classificat **Eg.** ```shell -python tools/test_hubserving.py --server_url=http://127.0.0.1:8868/predict/ocr_system --image_dir./doc/imgs/ --visualize=false` +python tools/test_hubserving.py --server_url=http://127.0.0.1:8868/predict/ocr_system --image_dir=./doc/imgs/ --visualize=false` ``` ## 4. Returned result format diff --git a/deploy/hubserving/structure_table/params.py b/deploy/hubserving/structure_table/params.py index cc1a73687b22e73346addb35e702254ef67ee8db..9632c2f70b794854d191e9f088f3f2e301a5dbb3 100755 --- a/deploy/hubserving/structure_table/params.py +++ b/deploy/hubserving/structure_table/params.py @@ -25,7 +25,6 @@ def read_params(): # params for table structure model cfg.table_max_len = 488 cfg.table_model_dir = './inference/en_ppocr_mobile_v2.0_table_structure_infer/' - cfg.table_char_type = 'en' cfg.table_char_dict_path = './ppocr/utils/dict/table_structure_dict.txt' cfg.show_log = False return cfg diff --git a/deploy/slim/quantization/README.md b/deploy/slim/quantization/README.md index 8d3f779e0028a62d8396601166283f0ee54d43a7..d7c67a3bad4851aab5a27abb695da14314a7282e 100644 --- a/deploy/slim/quantization/README.md +++ b/deploy/slim/quantization/README.md @@ -42,7 +42,7 @@ python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3 # 比如下载提供的训练模型 wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar tar -xf ch_ppocr_mobile_v2.0_det_train.tar -python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_inference_model +python deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.save_model_dir=./output/quant_model ``` 如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。 diff --git a/deploy/slim/quantization/export_model.py b/deploy/slim/quantization/export_model.py index 34cf80f5e5566707a08d15ddeaaa51348dcd9acf..822fd5da4c30a934d0e590ab1067f9f9188213c2 100755 --- a/deploy/slim/quantization/export_model.py +++ b/deploy/slim/quantization/export_model.py @@ -127,11 +127,13 @@ def main(): arch_config = config["Architecture"] if arch_config["algorithm"] in ["Distillation", ]: # distillation model for idx, name in enumerate(model.model_name_list): + model.model_list[idx].eval() sub_model_save_path = os.path.join(save_path, name, "inference") export_single_model(quanter, model.model_list[idx], infer_shape, sub_model_save_path, logger) else: save_path = os.path.join(save_path, "inference") + model.eval() export_single_model(quanter, model, infer_shape, save_path, logger) diff --git a/doc/doc_ch/inference_ppocr.md b/doc/doc_ch/inference_ppocr.md index 3e46f17d3a781839dfe5e632f85aabcd03d0fd17..5fb3811eb40addd506dfa37d257c00a0c2a44258 100644 --- a/doc/doc_ch/inference_ppocr.md +++ b/doc/doc_ch/inference_ppocr.md @@ -3,12 +3,13 @@ 本文介绍针对PP-OCR模型库的Python推理引擎使用方法，内容依次为文本检测、文本识别、方向分类器以及三者串联在CPU、GPU上的预测方法。 -- [1. 文本检测模型推理](#文本检测模型推理) -- [2. 文本识别模型推理](#文本识别模型推理) - - [2.1 超轻量中文识别模型推理](#超轻量中文识别模型推理) - - [2.2 多语言模型的推理](#多语言模型的推理) -- [3. 方向分类模型推理](#方向分类模型推理) -- [4. 文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理) +- [基于Python引擎的PP-OCR模型库推理](#基于python引擎的pp-ocr模型库推理) + - [1. 文本检测模型推理](#1-文本检测模型推理) + - [2. 文本识别模型推理](#2-文本识别模型推理) + - [2.1 超轻量中文识别模型推理](#21-超轻量中文识别模型推理) + - [2.2 多语言模型的推理](#22-多语言模型的推理) + - [3. 方向分类模型推理](#3-方向分类模型推理) + - [4. 文本检测、方向分类和文字识别串联推理](#4-文本检测方向分类和文字识别串联推理) @@ -82,7 +83,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153) 如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别： ``` wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" ``` ![](../imgs_words/korean/1.jpg) diff --git a/doc/doc_ch/quickstart.md b/doc/doc_ch/quickstart.md index a8b4c604a894ad5ff6139820f03f6679c1231ae9..e05861b95ba5d452aa8175bdeec5c50a097d419f 100644 --- a/doc/doc_ch/quickstart.md +++ b/doc/doc_ch/quickstart.md @@ -13,6 +13,7 @@ - [2.2.1 中英文与多语言使用](#221) - [3.小结](#3) + ## 1. 安装 diff --git a/doc/doc_ch/recognition.md b/doc/doc_ch/recognition.md index cf55af29e7b6a0c92022b35746081776451627a0..6cdd547517ebb8888374b22c1b52314da53eebab 100644 --- a/doc/doc_ch/recognition.md +++ b/doc/doc_ch/recognition.md @@ -2,19 +2,20 @@ 本文提供了PaddleOCR文本识别任务的全流程指南，包括数据准备、模型训练、调优、评估、预测，各个阶段的详细说明： -- [1 数据准备](#数据准备) - - [1.1 自定义数据集](#自定义数据集) - - [1.2 数据下载](#数据下载) - - [1.3 字典](#字典) - - [1.4 支持空格](#支持空格) -- [2 启动训练](#启动训练) - - [2.1 数据增强](#数据增强) - - [2.2 通用模型训练](#通用模型训练) - - [2.3 多语言模型训练](#多语言模型训练) - - [2.4 知识蒸馏训练](#知识蒸馏训练) -- [3 评估](#评估) -- [4 预测](#预测) -- [5 转Inference模型测试](#Inference) +- [文字识别](#文字识别) + - [1. 数据准备](#1-数据准备) + - [1.1 自定义数据集](#11-自定义数据集) + - [1.2 数据下载](#12-数据下载) + - [1.3 字典](#13-字典) + - [1.4 添加空格类别](#14-添加空格类别) + - [2. 启动训练](#2-启动训练) + - [2.1 数据增强](#21-数据增强) + - [2.2 通用模型训练](#22-通用模型训练) + - [2.3 多语言模型训练](#23-多语言模型训练) + - [2.4 知识蒸馏训练](#24-知识蒸馏训练) + - [3 评估](#3-评估) + - [4 预测](#4-预测) + - [5. 转Inference模型测试](#5-转inference模型测试) @@ -477,8 +478,8 @@ python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_trai - 自定义模型推理 - 如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径，并且设置 `rec_char_type=ch` + 如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径 ``` - python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="ch" --rec_char_dict_path="your text dict path" + python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_dict_path="your text dict path" ``` diff --git a/doc/doc_ch/serving_inference.md b/doc/doc_ch/serving_inference.md index fea5a24546ddd2141085f56eeb99cdf72577bff3..30ea7ee7c11692ba02e8314036d74a21c2f090e5 100644 --- a/doc/doc_ch/serving_inference.md +++ b/doc/doc_ch/serving_inference.md @@ -98,7 +98,6 @@ def read_params(): cfg.rec_model_dir = "./ocr_rec_server/" # 识别算法模型路径 cfg.rec_image_shape = "3, 32, 320" - cfg.rec_char_type = 'ch' cfg.rec_batch_num = 30 cfg.max_text_length = 25 diff --git a/doc/doc_ch/whl.md b/doc/doc_ch/whl.md index 2d524b83d73d4951939c7e0f108c494ab79a86c6..b2eb4ba17cf70edeaea36b5e54fe976605de850f 100644 --- a/doc/doc_ch/whl.md +++ b/doc/doc_ch/whl.md @@ -401,7 +401,6 @@ im_show.save('result.jpg') | rec_algorithm | 使用的识别算法类型 | CRNN | | rec_model_dir | 识别模型所在文件夹。传参方式有两种，1. None: 自动下载内置模型到 `~/.paddleocr/rec`；2.自己转换好的inference模型路径，模型路径下必须包含model和params文件 | None | | rec_image_shape | 识别算法的输入图片尺寸 | "3,32,320" | -| rec_char_type | 识别算法的字符类型，中英文(ch)、英文(en)、法语(french)、德语(german)、韩语(korean)、日语(japan) | ch | | rec_batch_num | 进行识别时，同时前向的图片数 | 30 | | max_text_length | 识别算法能识别的最大文字长度 | 25 | | rec_char_dict_path | 识别模型字典路径，当rec_model_dir使用方式2传参时需要修改为自己的字典路径 | ./ppocr/utils/ppocr_keys_v1.txt | diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md index a8a96e30f020b98b52bb465140b3463cd88beebb..d1233780d89c175729e835d069db1bcc0bb9273f 100755 --- a/doc/doc_en/inference_en.md +++ b/doc/doc_en/inference_en.md @@ -296,7 +296,7 @@ Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073) - The image resolution used in training is different: the image resolution used in training the above model is [3，32，100], while during our Chinese model training, in order to ensure the recognition effect of long text, the image resolution used in training is [3, 32, 320]. The default shape parameter of the inference stage is the image resolution used in training phase, that is [3, 32, 320]. Therefore, when running inference of the above English model here, you need to set the shape of the recognition image through the parameter `rec_image_shape`. -- Character list: the experiment in the DTRB paper is only for 26 lowercase English characters and 10 numbers, a total of 36 characters. All upper and lower case characters are converted to lower case characters, and characters not in the above list are ignored and considered as spaces. Therefore, no characters dictionary file is used here, but a dictionary is generated by the below command. Therefore, the parameter `rec_char_type` needs to be set during inference, which is specified as "en" in English. +- Character list: the experiment in the DTRB paper is only for 26 lowercase English characters and 10 numbers, a total of 36 characters. All upper and lower case characters are converted to lower case characters, and characters not in the above list are ignored and considered as spaces. Therefore, no characters dictionary file is used here, but a dictionary is generated by the below command. ``` self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz" @@ -320,7 +320,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png ### 3.4 Text Recognition Model Inference Using Custom Characters Dictionary -If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch` +If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path` ``` python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_dict_path="your text dict path" diff --git a/doc/doc_en/inference_ppocr_en.md b/doc/doc_en/inference_ppocr_en.md index 21f4c64d6dc99054a3480a66cd710b5d09461ca1..8dc30d3106048575a9ad722386daf9cb658dd455 100755 --- a/doc/doc_en/inference_ppocr_en.md +++ b/doc/doc_en/inference_ppocr_en.md @@ -4,12 +4,13 @@ This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU. -- [Text Detection Model Inference](#DETECTION_MODEL_INFERENCE) -- [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE) - - [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION) - - [2. Multilingual Model Inference](#MULTILINGUAL_MODEL_INFERENCE) -- [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE) -- [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION) +- [Python Inference for PP-OCR Model Zoo](#python-inference-for-pp-ocr-model-zoo) + - [Text Detection Model Inference](#text-detection-model-inference) + - [Text Recognition Model Inference](#text-recognition-model-inference) + - [1. Lightweight Chinese Recognition Model Inference](#1-lightweight-chinese-recognition-model-inference) + - [2. Multilingual Model Inference](#2-multilingual-model-inference) + - [Angle Classification Model Inference](#angle-classification-model-inference) + - [Text Detection Angle Classification and Recognition Inference Concatenation](#text-detection-angle-classification-and-recognition-inference-concatenation) @@ -82,7 +83,7 @@ You need to specify the visual font path through `--vis_font_path`. There are sm ``` wget wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar -python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" +python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf" ``` ![](../imgs_words/korean/1.jpg) diff --git a/doc/doc_en/quickstart_en.md b/doc/doc_en/quickstart_en.md index e44345a8e65f6efc94f83604590d980e052f2abd..8a9c38069f384dcef06db60f6b1266e6eb116d84 100644 --- a/doc/doc_en/quickstart_en.md +++ b/doc/doc_en/quickstart_en.md @@ -1,18 +1,19 @@ +- [PaddleOCR Quick Start](#paddleocr-quick-start) + - [1. Installation](#1-installation) + - [1.1 Install PaddlePaddle](#11-install-paddlepaddle) + - [1.2 Install PaddleOCR Whl Package](#12-install-paddleocr-whl-package) + - [2. Easy-to-Use](#2-easy-to-use) + - [2.1 Use by Command Line](#21-use-by-command-line) + - [2.1.1 Chinese and English Model](#211-chinese-and-english-model) + - [2.1.2 Multi-language Model](#212-multi-language-model) + - [2.1.3 Layout Analysis](#213-layout-analysis) + - [2.2 Use by Code](#22-use-by-code) + - [2.2.1 Chinese & English Model and Multilingual Model](#221-chinese--english-model-and-multilingual-model) + - [2.2.2 Layout Analysis](#222-layout-analysis) + - [3. Summary](#3-summary) # PaddleOCR Quick Start -+ [1. Installation](#1installation) - + [1.1 Install PaddlePaddle](#11-install-paddlepaddle) - + [1.2 Install PaddleOCR Whl Package](#12-install-paddleocr-whl-package) -* [2. Easy-to-Use](#2-easy-to-use) - + [2.1 Use by Command Line](#21-use-by-command-line) - - [2.1.1 English and Chinese Model](#211-english-and-chinese-model) - - [2.1.2 Multi-language Model](#212-multi-language-model) - - [2.1.3 Layout Analysis](#213-layoutAnalysis) - + [2.2 Use by Code](#22-use-by-code) - - [2.2.1 Chinese & English Model and Multilingual Model](#221-chinese---english-model-and-multilingual-model) - - [2.2.2 Layout Analysis](#222-layoutAnalysis) -* [3. Summary](#3) @@ -196,7 +197,7 @@ paddleocr --image_dir=../doc/table/1.png --type=structure | output | The path where excel and recognition results are saved | ./output/table | | table_max_len | The long side of the image is resized in table structure model | 488 | | table_model_dir | inference model path of table structure model | None | - | table_char_type | dict path of table structure model | ../ppocr/utils/dict/table_structure_dict.txt | + | table_char_dict_path | dict path of table structure model | ../ppocr/utils/dict/table_structure_dict.txt | diff --git a/doc/doc_en/recognition_en.md b/doc/doc_en/recognition_en.md index 20f4b9457b2fd05058bd2b723048f94de92605b6..c3700070b9d01c89cf8189a7af5f13d877114fb2 100644 --- a/doc/doc_en/recognition_en.md +++ b/doc/doc_en/recognition_en.md @@ -470,8 +470,8 @@ inference/det_db/ - Text recognition model Inference using custom characters dictionary - If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch` + If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path` ``` - python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="ch" --rec_char_dict_path="your text dict path" + python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_dict_path="your text dict path" ``` diff --git a/doc/doc_en/whl_en.md b/doc/doc_en/whl_en.md index 2671fbb9f0b5653cff29908a0c40d14a25b2cc58..35b2b1798ad8b566ee87e921e23be84a5ecccf24 100644 --- a/doc/doc_en/whl_en.md +++ b/doc/doc_en/whl_en.md @@ -348,7 +348,6 @@ im_show.save('result.jpg') | rec_algorithm | Type of recognition algorithm selected | CRNN | | rec_model_dir | the text recognition inference model folder. There are two ways to transfer parameters, 1. None: Automatically download the built-in model to `~/.paddleocr/rec`; 2. The path of the inference model converted by yourself, the model and params files must be included in the model path | None | | rec_image_shape | image shape of recognition algorithm | "3,32,320" | -| rec_char_type | Character type of recognition algorithm, Chinese (ch) or English (en) | ch | | rec_batch_num | When performing recognition, the batchsize of forward images | 30 | | max_text_length | The maximum text length that the recognition algorithm can recognize | 25 | | rec_char_dict_path | the alphabet path which needs to be modified to your own path when `rec_model_Name` use mode 2 | ./ppocr/utils/ppocr_keys_v1.txt | diff --git a/doc/joinus.PNG b/doc/joinus.PNG index c3b26cf4814417b646785b42c7fcbaa0350edac6..3881b386cf35b48a33abd73d789a10175b36ff55 100644 Binary files a/doc/joinus.PNG and b/doc/joinus.PNG differ diff --git a/ppocr/modeling/heads/det_fce_head.py b/ppocr/modeling/heads/det_fce_head.py index 5d5e9205f7fc55965c3cfff5d531068ba89a83c3..9503989f58f09a5137f1002f7b90d0942a97d1d6 100644 --- a/ppocr/modeling/heads/det_fce_head.py +++ b/ppocr/modeling/heads/det_fce_head.py @@ -63,7 +63,7 @@ class FCEHead(nn.Layer): weight_attr=ParamAttr( name='cls_weights', initializer=Normal( - mean=paddle.to_tensor(0.), std=paddle.to_tensor(0.01))), + mean=0., std=0.01)), bias_attr=True) self.out_conv_reg = nn.Conv2D( in_channels=self.in_channels, @@ -75,7 +75,7 @@ class FCEHead(nn.Layer): weight_attr=ParamAttr( name='reg_weights', initializer=Normal( - mean=paddle.to_tensor(0.), std=paddle.to_tensor(0.01))), + mean=0., std=0.01)), bias_attr=True) def forward(self, feats, targets=None): diff --git a/ppocr/modeling/heads/rec_ctc_head.py b/ppocr/modeling/heads/rec_ctc_head.py index 35d33d5f56b3b378286565cbfa9755f43343b278..6c1cf0659607186d54dfee6983b135f34d542446 100755 --- a/ppocr/modeling/heads/rec_ctc_head.py +++ b/ppocr/modeling/heads/rec_ctc_head.py @@ -80,7 +80,6 @@ class CTCHead(nn.Layer): result = (x, predicts) else: result = predicts - if not self.training: predicts = F.softmax(predicts, axis=2) result = predicts diff --git a/ppocr/postprocess/rec_postprocess.py b/ppocr/postprocess/rec_postprocess.py index de771acca86a8956b06b366b840aac7e21f835a4..3bc7bcdf9b388bb8da6c656682e2e06a18a0f4fb 100644 --- a/ppocr/postprocess/rec_postprocess.py +++ b/ppocr/postprocess/rec_postprocess.py @@ -89,7 +89,7 @@ class CTCLabelDecode(BaseRecLabelDecode): use_space_char) def __call__(self, preds, label=None, *args, **kwargs): - if isinstance(preds, tuple): + if isinstance(preds, tuple) or isinstance(preds, list): preds = preds[-1] if isinstance(preds, paddle.Tensor): preds = preds.numpy() diff --git a/ppstructure/table/README.md b/ppstructure/table/README.md index 6137cfaef657d70a2b3a2b7eb9c69e364e421d96..65d2cd22b6f18d06fe538ffe1fd243c0c0bfaa3c 100644 --- a/ppstructure/table/README.md +++ b/ppstructure/table/README.md @@ -117,7 +117,7 @@ teds: 93.32 ```python cd PaddleOCR/ppstructure -python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table +python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table ``` After running, the excel sheet of each picture will be saved in the directory specified by the output field diff --git a/ppstructure/table/README_ch.md b/ppstructure/table/README_ch.md index 2f3ed1cd763b5b040fb2704d0f1a84111910605a..c68b8c88b92bdca3f3a6c777f5f4681093fa89f5 100644 --- a/ppstructure/table/README_ch.md +++ b/ppstructure/table/README_ch.md @@ -128,7 +128,7 @@ teds: 93.32 ```python cd PaddleOCR/ppstructure -python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --rec_char_type=EN --det_limit_side_len=736 --det_limit_type=min --output ../output/table +python3 table/predict_table.py --det_model_dir=path/to/det_model_dir --rec_model_dir=path/to/rec_model_dir --table_model_dir=path/to/table_model_dir --image_dir=../doc/table/1.png --rec_char_dict_path=../ppocr/utils/dict/table_dict.txt --table_char_dict_path=../ppocr/utils/dict/table_structure_dict.txt --det_limit_side_len=736 --det_limit_type=min --output ../output/table ``` # Reference diff --git a/ppstructure/table/predict_structure.py b/ppstructure/table/predict_structure.py index fc85327b3a446573259546d84c439f5f8e5b3ac7..0179c614ae4864677576f6073f291282fb772988 100755 --- a/ppstructure/table/predict_structure.py +++ b/ppstructure/table/predict_structure.py @@ -58,7 +58,6 @@ class TableStructurer(object): }] postprocess_params = { 'name': 'TableLabelDecode', - "character_type": args.table_char_type, "character_dict_path": args.table_char_dict_path, } @@ -104,7 +103,9 @@ class TableStructurer(object): res_loc_final.append([left, top, right, bottom]) structure_str_list = structure_str_list[0][:-1] - structure_str_list = ['', '', ''] + structure_str_list + ['
', '', ''] + structure_str_list = [ + '', '', '' + ] + structure_str_list + ['
', '', ''] elapse = time.time() - starttime return (structure_str_list, res_loc_final), elapse diff --git a/ppstructure/utility.py b/ppstructure/utility.py index 10d9f71a7cdfed00b555c46689b2dd3c5aad807c..081a5f6ae3cd4a01bc2d1ba4812f39086e16cfe9 100644 --- a/ppstructure/utility.py +++ b/ppstructure/utility.py @@ -26,7 +26,6 @@ def init_args(): # params for table structure parser.add_argument("--table_max_len", type=int, default=488) parser.add_argument("--table_model_dir", type=str) - parser.add_argument("--table_char_type", type=str, default='en') parser.add_argument( "--table_char_dict_path", type=str, diff --git a/ppstructure/vqa/README.md b/ppstructure/vqa/README.md index 636c8882d2a4cfb69412dfd5568e71bbfb699abb..e3a10671ddb6494eb15073e7ac007aa1e8e6a32a 100644 --- a/ppstructure/vqa/README.md +++ b/ppstructure/vqa/README.md @@ -1,57 +1,55 @@ -# 文档视觉问答（DocVQA） +English | [简体中文](README_ch.md) -- [1. 简介](#1) -- [2. 性能](#2) -- [3. 效果演示](#3) - - [3.1 SER](#31) - - [3.2 RE](#32) -- [4. 安装](#4) - - [4.1 安装依赖](#41) - - [4.2 安装PaddleOCR](#42) -- [5. 使用](#5) - - [5.1 数据和预训练模型准备](#51) - - [5.2 SER](#52) - - [5.3 RE](#53) -- [6. 参考链接](#6) +- [Document Visual Question Answering (Doc-VQA)](#Document-Visual-Question-Answering) + - [1. Introduction](#1-Introduction) + - [2. Performance](#2-performance) + - [3. Effect demo](#3-Effect-demo) + - [3.1 SER](#31-ser) + - [3.2 RE](#32-re) + - [4. Install](#4-Install) + - [4.1 Installation dependencies](#41-Install-dependencies) + - [4.2 Install PaddleOCR](#42-Install-PaddleOCR) + - [5. Usage](#5-Usage) + - [5.1 Data and Model Preparation](#51-Data-and-Model-Preparation) + - [5.2 SER](#52-ser) + - [5.3 RE](#53-re) + - [6. Reference](#6-Reference-Links) +# Document Visual Question Answering +## 1 Introduction - -## 1. 简介 +VQA refers to visual question answering, which mainly asks and answers image content. DOC-VQA is one of the VQA tasks. DOC-VQA mainly asks questions about the text content of text images. -VQA指视觉问答，主要针对图像内容进行提问和回答，DocVQA是VQA任务中的一种，DocVQA主要针对文本图像的文字内容提出问题。 +The DOC-VQA algorithm in PP-Structure is developed based on the PaddleNLP natural language processing algorithm library. -PP-Structure 里的DocVQA算法基于PaddleNLP自然语言处理算法库进行开发。 +The main features are as follows: -主要特性如下： +- Integrate [LayoutXLM](https://arxiv.org/pdf/2104.08836.pdf) model and PP-OCR prediction engine. +- Supports Semantic Entity Recognition (SER) and Relation Extraction (RE) tasks based on multimodal methods. Based on the SER task, the text recognition and classification in the image can be completed; based on the RE task, the relationship extraction of the text content in the image can be completed, such as judging the problem pair (pair). +- Supports custom training for SER tasks and RE tasks. +- Supports end-to-end system prediction and evaluation of OCR+SER. +- Supports end-to-end system prediction of OCR+SER+RE. -- 集成[LayoutXLM](https://arxiv.org/pdf/2104.08836.pdf)模型以及PP-OCR预测引擎。 -- 支持基于多模态方法的语义实体识别 (Semantic Entity Recognition, SER) 以及关系抽取 (Relation Extraction, RE) 任务。基于 SER 任务，可以完成对图像中的文本识别与分类；基于 RE 任务，可以完成对图象中的文本内容的关系提取，如判断问题对(pair)。 -- 支持SER任务和RE任务的自定义训练。 -- 支持OCR+SER的端到端系统预测与评估。 -- 支持OCR+SER+RE的端到端系统预测。 +This project is an open source implementation of [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/pdf/2104.08836.pdf) on Paddle 2.2, +Included fine-tuning code on [XFUND dataset](https://github.com/doc-analysis/XFUND). -本项目是 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/pdf/2104.08836.pdf) 在 Paddle 2.2上的开源实现， -包含了在 [XFUND数据集](https://github.com/doc-analysis/XFUND) 上的微调代码。 +## 2. Performance - -## 2. 性能 +We evaluate the algorithm on the Chinese dataset of [XFUND](https://github.com/doc-analysis/XFUND), and the performance is as follows -我们在 [XFUN](https://github.com/doc-analysis/XFUND) 的中文数据集上对算法进行了评估，性能如下 - -| 模型 | 任务 | hmean | 模型下载地址 | +| Model | Task | hmean | Model download address | |:---:|:---:|:---:| :---:| -| LayoutXLM | SER | 0.9038 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) | -| LayoutXLM | RE | 0.7483 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) | -| LayoutLMv2 | SER | 0.8544 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) -| LayoutLMv2 | RE | 0.6777 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) | -| LayoutLM | SER | 0.7731 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) | +| LayoutXLM | SER | 0.9038 | [link](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) | +| LayoutXLM | RE | 0.7483 | [link](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) | +| LayoutLMv2 | SER | 0.8544 | [link](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) +| LayoutLMv2 | RE | 0.6777 | [link](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) | +| LayoutLM | SER | 0.7731 | [link](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) | - -## 3. 效果演示 +## 3. Effect demo -**注意：** 测试图片来源于XFUN数据集。 +**Note:** The test images are from the XFUND dataset. ### 3.1 SER @@ -59,13 +57,13 @@ PP-Structure 里的DocVQA算法基于PaddleNLP自然语言处理算法库进行 ![](../docs/vqa/result_ser/zh_val_0_ser.jpg) | ![](../docs/vqa/result_ser/zh_val_42_ser.jpg) ---|--- -图中不同颜色的框表示不同的类别，对于XFUN数据集，有`QUESTION`, `ANSWER`, `HEADER` 3种类别 +Boxes with different colors in the figure represent different categories. For the XFUND dataset, there are 3 categories: `QUESTION`, `ANSWER`, `HEADER` -* 深紫色：HEADER -* 浅紫色：QUESTION -* 军绿色：ANSWER +* Dark purple: HEADER +* Light purple: QUESTION +* Army Green: ANSWER -在OCR检测框的左上方也标出了对应的类别和OCR识别结果。 +The corresponding categories and OCR recognition results are also marked on the upper left of the OCR detection frame. ### 3.2 RE @@ -74,183 +72,187 @@ PP-Structure 里的DocVQA算法基于PaddleNLP自然语言处理算法库进行 ---|--- -图中红色框表示问题，蓝色框表示答案，问题和答案之间使用绿色线连接。在OCR检测框的左上方也标出了对应的类别和OCR识别结果。 +The red box in the figure represents the question, the blue box represents the answer, and the question and the answer are connected by a green line. The corresponding categories and OCR recognition results are also marked on the upper left of the OCR detection frame. - -## 4. 安装 +## 4. Install - -### 4.1 安装依赖 +### 4.1 Install dependencies -- **（1) 安装PaddlePaddle** +- **(1) Install PaddlePaddle** ```bash python3 -m pip install --upgrade pip -# GPU安装 +# GPU installation python3 -m pip install "paddlepaddle-gpu>=2.2" -i https://mirror.baidu.com/pypi/simple -# CPU安装 +# CPU installation python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simple -``` -更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 +```` +For more requirements, please refer to the instructions in [Installation Documentation](https://www.paddlepaddle.org.cn/install/quick). - -### 4.2 安装PaddleOCR +### 4.2 Install PaddleOCR -- **（1）pip快速安装PaddleOCR whl包（仅预测）** +- **(1) pip install PaddleOCR whl package quickly (prediction only)** ```bash python3 -m pip install paddleocr -``` +```` -- **（2）下载VQA源码（预测+训练）** +- **(2) Download VQA source code (prediction + training)** ```bash -【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR +[Recommended] git clone https://github.com/PaddlePaddle/PaddleOCR -# 如果因为网络问题无法pull成功，也可选择使用码云上的托管： +# If the pull cannot be successful due to network problems, you can also choose to use the hosting on the code cloud: git clone https://gitee.com/paddlepaddle/PaddleOCR -# 注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。 -``` +# Note: Code cloud hosting code may not be able to synchronize the update of this github project in real time, there is a delay of 3 to 5 days, please use the recommended method first. +```` -- **（3）安装VQA的`requirements`** +- **(3) Install VQA's `requirements`** ```bash python3 -m pip install -r ppstructure/vqa/requirements.txt -``` +```` - -## 5. 使用 +## 5. Usage - -### 5.1 数据和预训练模型准备 +### 5.1 Data and Model Preparation -如果希望直接体验预测过程，可以下载我们提供的预训练模型，跳过训练过程，直接预测即可。 +If you want to experience the prediction process directly, you can download the pre-training model provided by us, skip the training process, and just predict directly. -* 下载处理好的数据集 +* Download the processed dataset -处理好的XFUN中文数据集下载地址：[https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。 +The download address of the processed XFUND Chinese dataset: [https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar). -下载并解压该数据集，解压后将数据集放置在当前目录下。 +Download and unzip the dataset, and place the dataset in the current directory after unzipping. ```shell wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar -``` +```` + +* Convert the dataset -* 转换数据集 +If you need to train other XFUND datasets, you can use the following commands to convert the datasets -若需进行其他XFUN数据集的训练，可使用下面的命令进行数据集的转换 +```bash +python3 ppstructure/vqa/tools/trans_xfun_data.py --ori_gt_path=path/to/json_path --output_path=path/to/save_path +```` +* Download the pretrained models ```bash -python3 ppstructure/vqa/helper/trans_xfun_data.py --ori_gt_path=path/to/json_path --output_path=path/to/save_path -``` +mkdir pretrain && cd pretrain +#download the SER model +wget https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar && tar -xvf ser_LayoutXLM_xfun_zh.tar +#download the RE model +wget https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar && tar -xvf re_LayoutXLM_xfun_zh.tar +cd ../ +```` ### 5.2 SER -启动训练之前，需要修改下面的四个字段 +Before starting training, you need to modify the following four fields -1. `Train.dataset.data_dir`：指向训练集图片存放目录 -2. `Train.dataset.label_file_list`：指向训练集标注文件 -3. `Eval.dataset.data_dir`：指指向验证集图片存放目录 -4. `Eval.dataset.label_file_list`：指向验证集标注文件 +1. `Train.dataset.data_dir`: point to the directory where the training set images are stored +2. `Train.dataset.label_file_list`: point to the training set label file +3. `Eval.dataset.data_dir`: refers to the directory where the validation set images are stored +4. `Eval.dataset.label_file_list`: point to the validation set label file -* 启动训练 +* start training ```shell CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/ser/layoutxlm.yml -``` +```` -最终会打印出`precision`, `recall`, `hmean`等指标。 -在`./output/ser_layoutxlm/`文件夹中会保存训练日志，最优的模型和最新epoch的模型。 +Finally, `precision`, `recall`, `hmean` and other indicators will be printed. +In the `./output/ser_layoutxlm/` folder will save the training log, the optimal model and the model for the latest epoch. -* 恢复训练 +* resume training -恢复训练需要将之前训练好的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。 +To resume training, assign the folder path of the previously trained model to the `Architecture.Backbone.checkpoints` field. ```shell CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir -``` +```` -* 评估 +* evaluate -评估需要将待评估的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。 +Evaluation requires assigning the folder path of the model to be evaluated to the `Architecture.Backbone.checkpoints` field. ```shell CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir -``` -最终会打印出`precision`, `recall`, `hmean`等指标 +```` +Finally, `precision`, `recall`, `hmean` and other indicators will be printed -* 使用`OCR引擎 + SER`串联预测 +* Use `OCR engine + SER` tandem prediction -使用如下命令即可完成`OCR引擎 + SER`的串联预测 +Use the following command to complete the series prediction of `OCR engine + SER`, taking the pretrained SER model as an example: ```shell -CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=ser_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_42.jpg -``` +CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/Global.infer_img=doc/vqa/input/zh_val_42.jpg +```` -最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。 +Finally, the prediction result visualization image and the prediction result text file will be saved in the directory configured by the `config.Global.save_res_path` field. The prediction result text file is named `infer_results.txt`. -* 对`OCR引擎 + SER`预测系统进行端到端评估 +* End-to-end evaluation of `OCR engine + SER` prediction system -首先使用 `tools/infer_vqa_token_ser.py` 脚本完成数据集的预测，然后使用下面的命令进行评估。 +First use the `tools/infer_vqa_token_ser.py` script to complete the prediction of the dataset, then use the following command to evaluate. ```shell export CUDA_VISIBLE_DEVICES=0 -python3 helper/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt -``` +python3 tools/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt +```` ### 5.3 RE -* 启动训练 +* start training -启动训练之前，需要修改下面的四个字段 +Before starting training, you need to modify the following four fields -1. `Train.dataset.data_dir`：指向训练集图片存放目录 -2. `Train.dataset.label_file_list`：指向训练集标注文件 -3. `Eval.dataset.data_dir`：指指向验证集图片存放目录 -4. `Eval.dataset.label_file_list`：指向验证集标注文件 +1. `Train.dataset.data_dir`: point to the directory where the training set images are stored +2. `Train.dataset.label_file_list`: point to the training set label file +3. `Eval.dataset.data_dir`: refers to the directory where the validation set images are stored +4. `Eval.dataset.label_file_list`: point to the validation set label file ```shell CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/re/layoutxlm.yml -``` +```` -最终会打印出`precision`, `recall`, `hmean`等指标。 -在`./output/re_layoutxlm/`文件夹中会保存训练日志，最优的模型和最新epoch的模型。 +Finally, `precision`, `recall`, `hmean` and other indicators will be printed. +In the `./output/re_layoutxlm/` folder will save the training log, the optimal model and the model for the latest epoch. -* 恢复训练 +* resume training -恢复训练需要将之前训练好的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。 +To resume training, assign the folder path of the previously trained model to the `Architecture.Backbone.checkpoints` field. ```shell CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir -``` +```` -* 评估 +* evaluate -评估需要将待评估的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。 +Evaluation requires assigning the folder path of the model to be evaluated to the `Architecture.Backbone.checkpoints` field. ```shell CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir -``` -最终会打印出`precision`, `recall`, `hmean`等指标 +```` +Finally, `precision`, `recall`, `hmean` and other indicators will be printed -* 使用`OCR引擎 + SER + RE`串联预测 +* Use `OCR engine + SER + RE` tandem prediction -使用如下命令即可完成`OCR引擎 + SER + RE`的串联预测 +Use the following command to complete the series prediction of `OCR engine + SER + RE`, taking the pretrained SER and RE models as an example: ```shell export CUDA_VISIBLE_DEVICES=0 -python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=re_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=ser_LayoutXLM_xfun_zh/ -``` +python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm. yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ +```` -最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。 +Finally, the prediction result visualization image and the prediction result text file will be saved in the directory configured by the `config.Global.save_res_path` field. The prediction result text file is named `infer_results.txt`. - -## 6. 参考链接 +## 6. Reference Links - LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf - microsoft/unilm/layoutxlm, https://github.com/microsoft/unilm/tree/master/layoutxlm diff --git a/ppstructure/vqa/README_ch.md b/ppstructure/vqa/README_ch.md new file mode 100644 index 0000000000000000000000000000000000000000..ff513f8f7d603d66a372ce383883f3bcf97a7880 --- /dev/null +++ b/ppstructure/vqa/README_ch.md @@ -0,0 +1,257 @@ +[English](README.md) | 简体中文 + +- [文档视觉问答（DOC-VQA）](#文档视觉问答doc-vqa) + - [1. 简介](#1-简介) + - [2. 性能](#2-性能) + - [3. 效果演示](#3-效果演示) + - [3.1 SER](#31-ser) + - [3.2 RE](#32-re) + - [4. 安装](#4-安装) + - [4.1 安装依赖](#41-安装依赖) + - [4.2 安装PaddleOCR（包含 PP-OCR 和 VQA）](#42-安装paddleocr包含-pp-ocr-和-vqa) + - [5. 使用](#5-使用) + - [5.1 数据和预训练模型准备](#51-数据和预训练模型准备) + - [5.2 SER](#52-ser) + - [5.3 RE](#53-re) + - [6. 参考链接](#6-参考链接) + +# 文档视觉问答（DOC-VQA） + +## 1. 简介 + +VQA指视觉问答，主要针对图像内容进行提问和回答,DOC-VQA是VQA任务中的一种，DOC-VQA主要针对文本图像的文字内容提出问题。 + +PP-Structure 里的 DOC-VQA算法基于PaddleNLP自然语言处理算法库进行开发。 + +主要特性如下： + +- 集成[LayoutXLM](https://arxiv.org/pdf/2104.08836.pdf)模型以及PP-OCR预测引擎。 +- 支持基于多模态方法的语义实体识别 (Semantic Entity Recognition, SER) 以及关系抽取 (Relation Extraction, RE) 任务。基于 SER 任务，可以完成对图像中的文本识别与分类；基于 RE 任务，可以完成对图象中的文本内容的关系提取，如判断问题对(pair)。 +- 支持SER任务和RE任务的自定义训练。 +- 支持OCR+SER的端到端系统预测与评估。 +- 支持OCR+SER+RE的端到端系统预测。 + +本项目是 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/pdf/2104.08836.pdf) 在 Paddle 2.2上的开源实现， +包含了在 [XFUND数据集](https://github.com/doc-analysis/XFUND) 上的微调代码。 + +## 2. 性能 + +我们在 [XFUND](https://github.com/doc-analysis/XFUND) 的中文数据集上对算法进行了评估，性能如下 + +| 模型 | 任务 | hmean | 模型下载地址 | +|:---:|:---:|:---:| :---:| +| LayoutXLM | SER | 0.9038 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar) | +| LayoutXLM | RE | 0.7483 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar) | +| LayoutLMv2 | SER | 0.8544 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLMv2_xfun_zh.tar) +| LayoutLMv2 | RE | 0.6777 | [链接](https://paddleocr.bj.bcebos.com/pplayout/re_LayoutLMv2_xfun_zh.tar) | +| LayoutLM | SER | 0.7731 | [链接](https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutLM_xfun_zh.tar) | + +## 3. 效果演示 + +**注意：** 测试图片来源于XFUND数据集。 + +### 3.1 SER + +![](../../doc/vqa/result_ser/zh_val_0_ser.jpg) | ![](../../doc/vqa/result_ser/zh_val_42_ser.jpg) +---|--- + +图中不同颜色的框表示不同的类别，对于XFUND数据集，有`QUESTION`, `ANSWER`, `HEADER` 3种类别 + +* 深紫色：HEADER +* 浅紫色：QUESTION +* 军绿色：ANSWER + +在OCR检测框的左上方也标出了对应的类别和OCR识别结果。 + +### 3.2 RE + +![](../../doc/vqa/result_re/zh_val_21_re.jpg) | ![](../../doc/vqa/result_re/zh_val_40_re.jpg) +---|--- + + +图中红色框表示问题，蓝色框表示答案，问题和答案之间使用绿色线连接。在OCR检测框的左上方也标出了对应的类别和OCR识别结果。 + +## 4. 安装 + +### 4.1 安装依赖 + +- **（1) 安装PaddlePaddle** + +```bash +python3 -m pip install --upgrade pip + +# GPU安装 +python3 -m pip install "paddlepaddle-gpu>=2.2" -i https://mirror.baidu.com/pypi/simple + +# CPU安装 +python3 -m pip install "paddlepaddle>=2.2" -i https://mirror.baidu.com/pypi/simple + +``` +更多需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。 + +### 4.2 安装PaddleOCR（包含 PP-OCR 和 VQA） + +- **（1）pip快速安装PaddleOCR whl包（仅预测）** + +```bash +python3 -m pip install paddleocr +``` + +- **（2）下载VQA源码（预测+训练）** + +```bash +【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR + +# 如果因为网络问题无法pull成功，也可选择使用码云上的托管： +git clone https://gitee.com/paddlepaddle/PaddleOCR + +# 注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。 +``` + +- **（3）安装VQA的`requirements`** + +```bash +python3 -m pip install -r ppstructure/vqa/requirements.txt +``` + +## 5. 使用 + +### 5.1 数据和预训练模型准备 + +如果希望直接体验预测过程，可以下载我们提供的预训练模型，跳过训练过程，直接预测即可。 + +* 下载处理好的数据集 + +处理好的XFUND中文数据集下载地址：[https://paddleocr.bj.bcebos.com/dataset/XFUND.tar](https://paddleocr.bj.bcebos.com/dataset/XFUND.tar)。 + + +下载并解压该数据集，解压后将数据集放置在当前目录下。 + +```shell +wget https://paddleocr.bj.bcebos.com/dataset/XFUND.tar +``` + +* 转换数据集 + +若需进行其他XFUND数据集的训练，可使用下面的命令进行数据集的转换 + +```bash +python3 ppstructure/vqa/tools/trans_xfun_data.py --ori_gt_path=path/to/json_path --output_path=path/to/save_path +``` + +* 下载预训练模型 +```bash +mkdir pretrain && cd pretrain +#下载SER模型 +wget https://paddleocr.bj.bcebos.com/pplayout/ser_LayoutXLM_xfun_zh.tar && tar -xvf ser_LayoutXLM_xfun_zh.tar +#下载RE模型 +wget https://paddleocr.bj.bcebos.com/pplayout/re_LayoutXLM_xfun_zh.tar && tar -xvf re_LayoutXLM_xfun_zh.tar +cd ../ +``` + +### 5.2 SER + +启动训练之前，需要修改下面的四个字段 + +1. `Train.dataset.data_dir`：指向训练集图片存放目录 +2. `Train.dataset.label_file_list`：指向训练集标注文件 +3. `Eval.dataset.data_dir`：指指向验证集图片存放目录 +4. `Eval.dataset.label_file_list`：指向验证集标注文件 + +* 启动训练 +```shell +CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/ser/layoutxlm.yml +``` + +最终会打印出`precision`, `recall`, `hmean`等指标。 +在`./output/ser_layoutxlm/`文件夹中会保存训练日志，最优的模型和最新epoch的模型。 + +* 恢复训练 + +恢复训练需要将之前训练好的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。 + +```shell +CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir +``` + +* 评估 + +评估需要将待评估的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。 + +```shell +CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir +``` +最终会打印出`precision`, `recall`, `hmean`等指标 + +* 使用`OCR引擎 + SER`串联预测 + +使用如下命令即可完成`OCR引擎 + SER`的串联预测, 以SER预训练模型为例: +```shell +CUDA_VISIBLE_DEVICES=0 python3 tools/infer_vqa_token_ser.py -c configs/vqa/ser/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_42.jpg +``` + +最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。 + +* 对`OCR引擎 + SER`预测系统进行端到端评估 + +首先使用 `tools/infer_vqa_token_ser.py` 脚本完成数据集的预测，然后使用下面的命令进行评估。 + +```shell +export CUDA_VISIBLE_DEVICES=0 +python3 tools/eval_with_label_end2end.py --gt_json_path XFUND/zh_val/xfun_normalize_val.json --pred_json_path output_res/infer_results.txt +``` + +### 5.3 RE + +* 启动训练 + +启动训练之前，需要修改下面的四个字段 + +1. `Train.dataset.data_dir`：指向训练集图片存放目录 +2. `Train.dataset.label_file_list`：指向训练集标注文件 +3. `Eval.dataset.data_dir`：指指向验证集图片存放目录 +4. `Eval.dataset.label_file_list`：指向验证集标注文件 + +```shell +CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/re/layoutxlm.yml +``` + +最终会打印出`precision`, `recall`, `hmean`等指标。 +在`./output/re_layoutxlm/`文件夹中会保存训练日志，最优的模型和最新epoch的模型。 + +* 恢复训练 + +恢复训练需要将之前训练好的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。 + +```shell +CUDA_VISIBLE_DEVICES=0 python3 tools/train.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir +``` + +* 评估 + +评估需要将待评估的模型所在文件夹路径赋值给 `Architecture.Backbone.checkpoints` 字段。 + +```shell +CUDA_VISIBLE_DEVICES=0 python3 tools/eval.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=path/to/model_dir +``` +最终会打印出`precision`, `recall`, `hmean`等指标 + +* 使用`OCR引擎 + SER + RE`串联预测 + +使用如下命令即可完成`OCR引擎 + SER + RE`的串联预测, 以预训练SER和RE模型为例： +```shell +export CUDA_VISIBLE_DEVICES=0 +python3 tools/infer_vqa_token_ser_re.py -c configs/vqa/re/layoutxlm.yml -o Architecture.Backbone.checkpoints=pretrain/re_LayoutXLM_xfun_zh/ Global.infer_img=doc/vqa/input/zh_val_21.jpg -c_ser configs/vqa/ser/layoutxlm.yml -o_ser Architecture.Backbone.checkpoints=pretrain/ser_LayoutXLM_xfun_zh/ +``` + +最终会在`config.Global.save_res_path`字段所配置的目录下保存预测结果可视化图像以及预测结果文本文件，预测结果文本文件名为`infer_results.txt`。 + +## 6. 参考链接 + +- LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding, https://arxiv.org/pdf/2104.08836.pdf +- microsoft/unilm/layoutxlm, https://github.com/microsoft/unilm/tree/master/layoutxlm +- XFUND dataset, https://github.com/doc-analysis/XFUND + +## License + +The content of this project itself is licensed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) diff --git a/ppstructure/vqa/helper/eval_with_label_end2end.py b/ppstructure/vqa/tools/eval_with_label_end2end.py similarity index 100% rename from ppstructure/vqa/helper/eval_with_label_end2end.py rename to ppstructure/vqa/tools/eval_with_label_end2end.py diff --git a/ppstructure/vqa/helper/trans_xfun_data.py b/ppstructure/vqa/tools/trans_xfun_data.py similarity index 100% rename from ppstructure/vqa/helper/trans_xfun_data.py rename to ppstructure/vqa/tools/trans_xfun_data.py diff --git a/test_tipc/configs/ch_PP-OCRv2_det_PACT/train_infer_python.txt b/test_tipc/configs/ch_PP-OCRv2_det_PACT/train_infer_python.txt index e6ed9df937e9b8def00513e3b4ac6c6310b6692c..038fa850614d45dbefe076b866571cead57b8450 100644 --- a/test_tipc/configs/ch_PP-OCRv2_det_PACT/train_infer_python.txt +++ b/test_tipc/configs/ch_PP-OCRv2_det_PACT/train_infer_python.txt @@ -6,7 +6,7 @@ Global.use_gpu:True|True Global.auto_cast:fp32 Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500 Global.save_model_dir:./output/ -Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4 +Train.loader.batch_size_per_card:lite_train_lite_infer=1|whole_train_whole_infer=4 Global.pretrained_model:null train_model_name:latest train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/ diff --git a/test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt b/test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt index 03d749f55765b2ea9e82d538cb4e6fb3d29e0b9f..98c125229d7f968cd3f650c3885ba4edb0de754c 100644 --- a/test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt +++ b/test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt @@ -1,13 +1,13 @@ ===========================train_params=========================== -model_name:PPOCRv2_ocr_rec_pact +model_name:ch_PPOCRv2_rec_PACT python:python3.7 gpu_list:0|0,1 Global.use_gpu:True|True Global.auto_cast:fp32 -Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=300 +Global.epoch_num:lite_train_lite_infer=6|whole_train_whole_infer=300 Global.save_model_dir:./output/ Train.loader.batch_size_per_card:lite_train_lite_infer=16|whole_train_whole_infer=128 -Global.pretrained_model:null +Global.pretrained_model:pretrain_models/ch_PP-OCRv2_rec_train/best_accuracy train_model_name:latest train_infer_img_dir:./inference/rec_inference null:null diff --git a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt b/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt index 0b4139876976f0f1c34619a8fb4e0b3b6c35c9e0..d0ae17ccb55f40ddf65de936ca3cfc06bdd19475 100644 --- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt +++ b/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt @@ -3,7 +3,7 @@ model_name:ocr_det use_opencv:True infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/ infer_quant:False -inference:./deploy/cpp_infer/build/ppocr det +inference:./deploy/cpp_infer/build/ppocr --use_gpu:True|False --enable_mkldnn:True|False --cpu_threads:1|6 @@ -13,4 +13,8 @@ inference:./deploy/cpp_infer/build/ppocr det --det_model_dir: --image_dir:./inference/ch_det_data_50/all-sum-510/ null:null ---benchmark:True \ No newline at end of file +--benchmark:True +--det:True +--rec:False +--cls:False +--use_angle_cls:False \ No newline at end of file diff --git a/test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt b/test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt index 0603fa10a640fd6d7b71582a92b92f026b4d1d51..5634297973bafbdad6c168e369d15520db09aba3 100644 --- a/test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt +++ b/test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt @@ -1,13 +1,13 @@ ===========================train_params=========================== model_name:det_mv3_east_v2.0 python:python3.7 -gpu_list:0 +gpu_list:0|0,1 Global.use_gpu:True|True Global.auto_cast:fp32 Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500 Global.save_model_dir:./output/ Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4 -Global.pretrained_model:null +Global.pretrained_model:./pretrain_models/det_mv3_east_v2.0_train/best_accuracy train_model_name:latest train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/ null:null diff --git a/test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt b/test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt index 32df669f9779f730d78d128d8aceac022ce78616..c22767c60fa8294aa244536b4c04135f7f7ade02 100644 --- a/test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt +++ b/test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt @@ -37,7 +37,7 @@ export2:null train_model:./inference/rec_mv3_tps_bilstm_att_v2.0_train/best_accuracy infer_export:tools/export_model.py -c test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml -o infer_quant:False -inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE" +inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE" --min_subgraph_size=5 --use_gpu:True|False --enable_mkldnn:True|False --cpu_threads:1|6 diff --git a/test_tipc/configs/rec_r31_sar/train_infer_python.txt b/test_tipc/configs/rec_r31_sar/train_infer_python.txt index c5018500f9a58297b30729e9f68b42806a7631e2..1a32a3d507d8923a8b51be726c7624ea2049ae14 100644 --- a/test_tipc/configs/rec_r31_sar/train_infer_python.txt +++ b/test_tipc/configs/rec_r31_sar/train_infer_python.txt @@ -50,4 +50,4 @@ inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict90.t --benchmark:True null:null ===========================infer_benchmark_params========================== -random_infer_input:[{float32,[3,48,48,160]}] +random_infer_input:[{float32,[3,48,160]}] diff --git a/test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt b/test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt index 9cee5d0b7d01eb5ae04c6ae9fef9990d3788a741..55e937881bec1852fade4f99d81a319b8b2c5b67 100644 --- a/test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt +++ b/test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt @@ -37,7 +37,7 @@ export2:null train_model:./inference/rec_r34_vd_tps_bilstm_att_v2.0_train/best_accuracy infer_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml -o infer_quant:False -inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE" +inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE" --min_subgraph_size=5 --use_gpu:True|False --enable_mkldnn:True|False --cpu_threads:1|6 diff --git a/test_tipc/configs/rec_r50_fpn_vd_none_srn/train_infer_python.txt b/test_tipc/configs/rec_r50_fpn_vd_none_srn/train_infer_python.txt index 187c1cc13a72c2d0ba8f7b57c2b9f5b7ba388d79..4877512b689ec87b7b2cd0258a2fac706968322b 100644 --- a/test_tipc/configs/rec_r50_fpn_vd_none_srn/train_infer_python.txt +++ b/test_tipc/configs/rec_r50_fpn_vd_none_srn/train_infer_python.txt @@ -37,7 +37,7 @@ export2:null train_model:./inference/rec_r50_vd_srn_train/best_accuracy infer_export:tools/export_model.py -c test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml -o infer_quant:False -inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="1,64,256" --rec_algorithm="SRN" --use_space_char=False +inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="1,64,256" --rec_algorithm="SRN" --use_space_char=False --min_subgraph_size=3 --use_gpu:True|False --enable_mkldnn:True|False --cpu_threads:1|6 diff --git a/test_tipc/prepare.sh b/test_tipc/prepare.sh index 31433884a69da24fc55dee4f0853e5cee1ea8edb..6a8983009e527b8a59b41c1d9b950e8e3f349ef2 100644 --- a/test_tipc/prepare.sh +++ b/test_tipc/prepare.sh @@ -64,6 +64,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar --no-check-certificate cd ./pretrain_models/ && tar xf ch_ppocr_server_v2.0_det_train.tar && cd ../ fi + if [ ${model_name} == "ch_PPOCRv2_rec" ] || [ ${model_name} == "ch_PPOCRv2_rec_PACT" ]; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf ch_PP-OCRv2_rec_train.tar && cd ../ + fi if [ ${model_name} == "det_r18_db_v2_0" ]; then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/pretrained/ResNet18_vd_pretrained.pdparams --no-check-certificate fi @@ -91,6 +95,10 @@ if [ ${MODE} = "lite_train_lite_infer" ];then wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar --no-check-certificate cd ./pretrain_models/ && tar xf ch_ppocr_mobile_v2.0_rec_train.tar && cd ../ fi + if [ ${model_name} == "det_mv3_east_v2.0" ]; then + wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar --no-check-certificate + cd ./pretrain_models/ && tar xf det_mv3_east_v2.0_train.tar && cd ../ + fi elif [ ${MODE} = "whole_train_whole_infer" ];then wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate diff --git a/test_tipc/test_inference_cpp.sh b/test_tipc/test_inference_cpp.sh index 257200fb1015dce1c2fdf9407f25ca7a34d818b0..9885e3937255658d4aacc5835eba634b74ea12a0 100644 --- a/test_tipc/test_inference_cpp.sh +++ b/test_tipc/test_inference_cpp.sh @@ -2,7 +2,7 @@ source test_tipc/common_func.sh FILENAME=$1 -dataline=$(awk 'NR==1, NR==16{print}' $FILENAME) +dataline=$(awk 'NR==1, NR==20{print}' $FILENAME) # parser params IFS=$'\n' @@ -34,6 +34,14 @@ cpp_infer_key1=$(func_parser_key "${lines[14]}") cpp_infer_value1=$(func_parser_value "${lines[14]}") cpp_benchmark_key=$(func_parser_key "${lines[15]}") cpp_benchmark_value=$(func_parser_value "${lines[15]}") +cpp_det_key=$(func_parser_key "${lines[16]}") +cpp_det_value=$(func_parser_value "${lines[16]}") +cpp_rec_key=$(func_parser_key "${lines[17]}") +cpp_rec_value=$(func_parser_value "${lines[17]}") +cpp_cls_key=$(func_parser_key "${lines[18]}") +cpp_cls_value=$(func_parser_value "${lines[18]}") +cpp_use_angle_cls_key=$(func_parser_key "${lines[19]}") +cpp_use_angle_cls_value=$(func_parser_value "${lines[19]}") LOG_PATH="./test_tipc/output" mkdir -p ${LOG_PATH} @@ -68,7 +76,11 @@ function func_cpp_inference(){ set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}") set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}") set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}") - command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 " + set_det=$(func_set_params "${cpp_det_key}" "${cpp_det_value}") + set_rec=$(func_set_params "${cpp_rec_key}" "${cpp_rec_value}") + set_cls=$(func_set_params "${cpp_cls_key}" "${cpp_cls_value}") + set_use_angle_cls=$(func_set_params "${cpp_use_angle_cls_key}" "${cpp_use_angle_cls_value}") + command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_det} ${set_rec} ${set_cls} ${set_use_angle_cls} ${set_infer_params1} > ${_save_log_path} 2>&1 " eval $command last_status=${PIPESTATUS[0]} eval "cat ${_save_log_path}" @@ -97,7 +109,11 @@ function func_cpp_inference(){ set_precision=$(func_set_params "${cpp_precision_key}" "${precision}") set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}") set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}") - command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 " + set_det=$(func_set_params "${cpp_det_key}" "${cpp_det_value}") + set_rec=$(func_set_params "${cpp_rec_key}" "${cpp_rec_value}") + set_cls=$(func_set_params "${cpp_cls_key}" "${cpp_cls_value}") + set_use_angle_cls=$(func_set_params "${cpp_use_angle_cls_key}" "${cpp_use_angle_cls_value}") + command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_det} ${set_rec} ${set_cls} ${set_use_angle_cls} ${set_infer_params1} > ${_save_log_path} 2>&1 " eval $command last_status=${PIPESTATUS[0]} eval "cat ${_save_log_path}" diff --git a/tools/infer/predict_rec.py b/tools/infer/predict_rec.py index eebb2b3ba4a1489512de3b977ddf9f1ef8f67ec1..c5aacb060060068ec4b0b9432b2fb045aaff0370 100755 --- a/tools/infer/predict_rec.py +++ b/tools/infer/predict_rec.py @@ -119,6 +119,10 @@ class TextRecognizer(object): resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) + if self.rec_algorithm == 'RARE': + if resized_w > self.rec_image_shape[2]: + resized_w = self.rec_image_shape[2] + imgW = self.rec_image_shape[2] resized_image = cv2.resize(img, (resized_w, imgH)) resized_image = resized_image.astype('float32') resized_image = resized_image.transpose((2, 0, 1)) / 255 diff --git a/tools/infer/utility.py b/tools/infer/utility.py index 80abba67b293e3412afa6c1ea8da0291331ef8de..b16aecd496ec291fcbe9c66dccf3ec04bb662034 100644 --- a/tools/infer/utility.py +++ b/tools/infer/utility.py @@ -312,12 +312,26 @@ def create_predictor(args, mode, logger): input_names = predictor.get_input_names() for name in input_names: input_tensor = predictor.get_input_handle(name) - output_names = predictor.get_output_names() - output_tensors = [] + output_tensors = get_output_tensors(args, mode, predictor) + return predictor, input_tensor, output_tensors, config + + +def get_output_tensors(args, mode, predictor): + output_names = predictor.get_output_names() + output_tensors = [] + if mode == "rec" and args.rec_algorithm == "CRNN": + output_name = 'softmax_0.tmp_0' + if output_name in output_names: + return [predictor.get_output_handle(output_name)] + else: + for output_name in output_names: + output_tensor = predictor.get_output_handle(output_name) + output_tensors.append(output_tensor) + else: for output_name in output_names: output_tensor = predictor.get_output_handle(output_name) output_tensors.append(output_tensor) - return predictor, input_tensor, output_tensors, config + return output_tensors def get_infer_gpuid():