Refactor the code to keep the logic consistent with the whl package

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);

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<cv::Mat> img_list, std::vector<int> &cls_labels,
+           std::vector<float> &cls_scores, std::vector<double> &times);
 
 private:
   std::shared_ptr<Predictor> 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<float> mean_ = {0.5f, 0.5f, 0.5f};
   std::vector<float> 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
 

deploy/cpp_infer/include/ocr_det.h

@@ -73,7 +73,7 @@ public:
 
   // Run predictor
   void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes,
-           std::vector<double> *times);
+           std::vector<double> &times);
 
 private:
   std::shared_ptr<Predictor> predictor_;

deploy/cpp_infer/include/ocr_rec.h

@@ -68,7 +68,7 @@ public:
   void LoadModel(const std::string &model_dir);
 
   void Run(std::vector<cv::Mat> img_list, std::vector<std::string> &rec_texts,
-           std::vector<float> &rec_text_scores, std::vector<double> *times);
+           std::vector<float> &rec_text_scores, std::vector<double> &times);
 
 private:
   std::shared_ptr<Predictor> predictor_;

deploy/cpp_infer/include/paddleocr.h

+// 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 <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <include/ocr_cls.h>
+#include <include/ocr_det.h>
+#include <include/ocr_rec.h>
+#include <include/preprocess_op.h>
+#include <include/utility.h>
+
+using namespace paddle_infer;
+
+namespace PaddleOCR {
+
+class PaddleOCR {
+public:
+  explicit PaddleOCR();
+  ~PaddleOCR();
+  std::vector<std::vector<OCRPredictResult>>
+  ocr(std::vector<cv::String> 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<OCRPredictResult> &ocr_results,
+           std::vector<double> &times);
+  void rec(std::vector<cv::Mat> img_list,
+           std::vector<OCRPredictResult> &ocr_results,
+           std::vector<double> &times);
+  void cls(std::vector<cv::Mat> img_list,
+           std::vector<OCRPredictResult> &ocr_results,
+           std::vector<double> &times);
+  void log(std::vector<double> &det_times, std::vector<double> &rec_times,
+           std::vector<double> &cls_times, int img_num);
+};
+
+} // namespace PaddleOCR

deploy/cpp_infer/include/utility.h

@@ -32,14 +32,21 @@
 
 namespace PaddleOCR {
 
+struct OCRPredictResult {
+  std::vector<std::vector<int>> box;
+  std::string text;
+  float score = -1.0;
+  float cls_score;
+  int cls_label = -1;
+};
+
 class Utility {
 public:
   static std::vector<std::string> ReadDict(const std::string &path);
 
-  static void
-  VisualizeBboxes(const cv::Mat &srcimg,
-                  const std::vector<std::vector<std::vector<int>>> &boxes,
-                  const std::string &save_path);
+  static void VisualizeBboxes(const cv::Mat &srcimg,
+                              const std::vector<OCRPredictResult> &ocr_result,
+                              const std::string &save_path);
 
   template <class ForwardIterator>
   inline static size_t argmax(ForwardIterator first, ForwardIterator last) {
@@ -55,6 +62,10 @@ public:
   static std::vector<int> argsort(const std::vector<float> &array);
 
   static std::string basename(const std::string &filename);
+
+  static bool PathExists(const std::string &path);
+
+  static void print_result(const std::vector<OCRPredictResult> &ocr_result);
 };
 
 } // namespace PaddleOCR
\ No newline at end of file

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
 ```
 
 <a name="22"></a>
@@ -213,36 +219,71 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 
 运行方式：  
 ```shell
-./build/ppocr <mode> [--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。
 
-<div align="center">
-    <img src="./imgs/cpp_infer_pred_12.png" width="600">
-</div>
+```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
 

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 <mode> [--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.
 
-<div align="center">
-    <img src="./imgs/cpp_infer_pred_12.png" width="600">
-</div>
+```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

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

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 <chrono>
-#include <iomanip>
 #include <iostream>
-#include <ostream>
-#include <sys/stat.h>
 #include <vector>
 
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
 #include <include/args.h>
-#include <include/ocr_cls.h>
-#include <include/ocr_det.h>
-#include <include/ocr_rec.h>
-#include <include/utility.h>
-#include <sys/stat.h>
-
-#include "auto_log/autolog.h"
+#include <include/paddleocr.h>
 
-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::String> cv_all_img_names) {
-  std::vector<double> 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<std::vector<std::vector<int>>> boxes;
-    std::vector<double> 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::String> cv_all_img_names) {
-  std::vector<double> 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<cv::Mat> 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<std::string> rec_texts(img_list.size(), "");
-  std::vector<float> rec_text_scores(img_list.size(), 0);
-  std::vector<double> 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::String> cv_all_img_names) {
-  std::vector<double> time_info_det = {0, 0, 0};
-  std::vector<double> 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<std::vector<std::vector<int>>> boxes;
-    std::vector<double> det_times;
-    std::vector<double> 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<cv::Mat> 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<std::string> rec_texts(img_list.size(), "");
-    std::vector<float> 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<std::vector<OCRPredictResult>> 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;
   }
 }

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<cv::Mat> img_list,
+                     std::vector<int> &cls_labels,
+                     std::vector<float> &cls_scores,
+                     std::vector<double> &times) {
+  std::chrono::duration<float> preprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> inference_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> postprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+
+  int img_num = img_list.size();
   std::vector<int> 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<float> 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<float> softmax_out;
-  std::vector<int64_t> 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<int>());
-
-  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<cv::Mat> 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<float> 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<float> 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<int>());
+    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();

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<std::vector<std::vector<int>>> &boxes,
-                     std::vector<double> *times) {
+                     std::vector<double> &times) {
   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<float> 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<float> 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<float> postprocess_diff =
       postprocess_end - postprocess_start;
-  times->push_back(double(postprocess_diff.count() * 1000));
+  times.push_back(double(postprocess_diff.count() * 1000));
 }
 
 } // namespace PaddleOCR

deploy/cpp_infer/src/ocr_rec.cpp

@@ -19,7 +19,7 @@ namespace PaddleOCR {
 void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
                          std::vector<std::string> &rec_texts,
                          std::vector<float> &rec_text_scores,
-                         std::vector<double> *times) {
+                         std::vector<double> &times) {
   std::chrono::duration<float> preprocess_diff =
       std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
   std::chrono::duration<float> inference_diff =
@@ -38,6 +38,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> 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<cv::Mat> img_list,
       float wh_ratio = w * 1.0 / h;
       max_wh_ratio = max(max_wh_ratio, wh_ratio);
     }
+
     int batch_width = 0;
     std::vector<cv::Mat> norm_img_batch;
     for (int ino = beg_img_no; ino < end_img_no; ino++) {
@@ -59,15 +61,14 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
       batch_width = max(resize_img.cols, batch_width);
     }
 
-    std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
+    std::vector<float> 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<cv::Mat> 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<cv::Mat> 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) {

deploy/cpp_infer/src/paddleocr.cpp

+// 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/args.h>
+#include <include/paddleocr.h>
+
+#include "auto_log/autolog.h"
+#include <numeric>
+#include <sys/stat.h>
+
+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<OCRPredictResult> &ocr_results,
+                    std::vector<double> &times) {
+  std::vector<std::vector<std::vector<int>>> boxes;
+  std::vector<double> 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<cv::Mat> img_list,
+                    std::vector<OCRPredictResult> &ocr_results,
+                    std::vector<double> &times) {
+  std::vector<std::string> rec_texts(img_list.size(), "");
+  std::vector<float> rec_text_scores(img_list.size(), 0);
+  std::vector<double> 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<cv::Mat> img_list,
+                    std::vector<OCRPredictResult> &ocr_results,
+                    std::vector<double> &times) {
+  std::vector<int> cls_labels(img_list.size(), 0);
+  std::vector<float> cls_scores(img_list.size(), 0);
+  std::vector<double> 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<std::vector<OCRPredictResult>>
+PaddleOCR::ocr(std::vector<cv::String> cv_all_img_names, bool det, bool rec,
+               bool cls) {
+  std::vector<double> time_info_det = {0, 0, 0};
+  std::vector<double> time_info_rec = {0, 0, 0};
+  std::vector<double> time_info_cls = {0, 0, 0};
+  std::vector<std::vector<OCRPredictResult>> ocr_results;
+
+  if (!det) {
+    std::vector<OCRPredictResult> ocr_result;
+    // read image
+    std::vector<cv::Mat> 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<OCRPredictResult> 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<OCRPredictResult> 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<cv::Mat> 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<double> &det_times,
+                    std::vector<double> &rec_times,
+                    std::vector<double> &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

deploy/cpp_infer/src/utility.cpp

@@ -38,16 +38,16 @@ std::vector<std::string> Utility::ReadDict(const std::string &path) {
   return m_vec;
 }
 
-void Utility::VisualizeBboxes(
-    const cv::Mat &srcimg,
-    const std::vector<std::vector<std::vector<int>>> &boxes,
-    const std::string &save_path) {
+void Utility::VisualizeBboxes(const cv::Mat &srcimg,
+                              const std::vector<OCRPredictResult> &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<OCRPredictResult> &ocr_result) {
+  for (int i = 0; i < ocr_result.size(); i++) {
+    std::cout << i << "\t";
+    // det
+    std::vector<std::vector<int>> 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