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 index 4f87c0fa5f8ad151d65361702a1955bf0d6d9143..fe5d45f6e0f2475089dfaaebc021a3e194a2cb22 100644 --- a/deploy/cpp_infer/include/args.h +++ b/deploy/cpp_infer/include/args.h @@ -27,7 +27,7 @@ DECLARE_string(precision); DECLARE_bool(benchmark); DECLARE_string(output); DECLARE_string(image_dir); -DECLARE_bool(visualize); +DECLARE_string(type); // detection related DECLARE_string(det_model_dir); DECLARE_int32(max_side_len); @@ -36,11 +36,17 @@ 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); \ No newline at end of file +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..53bb778c18edce6a0d1b7c1b63de0c194f9101dd 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 = 0.5; // 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 24392495348c8d85350e6b34725a33c76f0415f1..7efd4d8f0f4ccb705fc34695bb9843e0b6af5a9b 100644 --- a/deploy/cpp_infer/include/ocr_det.h +++ b/deploy/cpp_infer/include/ocr_det.h @@ -73,7 +73,7 @@ public: // Run predictor void Run(cv::Mat &img, std::vector>> &boxes, - std::vector *times); + std::vector ×); private: std::shared_ptr predictor_; diff --git a/deploy/cpp_infer/include/ocr_rec.h b/deploy/cpp_infer/include/ocr_rec.h index 4052553d967fb365c3fb895c9d5b8145935fd45d..a55d482bd9ffaf27996f71e90a4b64ac80f070ed 100644 --- a/deploy/cpp_infer/include/ocr_rec.h +++ b/deploy/cpp_infer/include/ocr_rec.h @@ -68,7 +68,7 @@ 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_; 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/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 d269cc9e8fdfd68700e32c14816dc3b313bfad23..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| @@ -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 b7687a9f6530976655a99690e9be02a04bbce6d3..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| @@ -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 index 539968eb1a8a0a28ca193b231f2b858601cfc07d..82cfb54ae7f05e549d58d3824504cce9ba7c6329 100644 --- a/deploy/cpp_infer/src/args.cpp +++ b/deploy/cpp_infer/src/args.cpp @@ -25,7 +25,9 @@ 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_bool(visualize, true, "Whether show the detection results."); +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."); @@ -34,12 +36,19 @@ 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."); \ No newline at end of file + "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/src/main.cpp b/deploy/cpp_infer/src/main.cpp index 27c7bde09c7c5dffcfb769d78bf8d992c8a1e3a6..84bf1698a556edcce6bfdce18cda380514b60a23 100644 --- a/deploy/cpp_infer/src/main.cpp +++ b/deploy/cpp_infer/src/main.cpp @@ -11,248 +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 - -#include "auto_log/autolog.h" +#include -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_det_db_score_mode, 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++) { - cout << '['; - for (int m = 0; m < boxes[n].size(); m++) { - cout << '[' << boxes[n][m][0] << ',' << boxes[n][m][1] << "]"; - if (m != boxes[n].size() - 1) { - cout << ','; - } - } - cout << ']'; - if (n != boxes.size() - 1) { - cout << ','; - } - } - 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; - 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_det_db_score_mode, 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; - 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/ " @@ -260,7 +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/ " @@ -268,19 +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); } @@ -293,19 +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); @@ -315,13 +69,26 @@ 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) { + 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 ff7164ca24bec74e1334ed9237dd33d813470c94..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{}; @@ -165,16 +165,15 @@ void DBDetector::Run(cv::Mat &img, 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..3ea6e78d05b0b7aef8e398fc535a739067afbf7a --- /dev/null +++ b/deploy/cpp_infer/src/paddleocr.cpp @@ -0,0 +1,225 @@ +// 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; + 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/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