Merge branch 'dygraph' into dygraph

PPOCRLabel/README.md

@@ -86,15 +86,8 @@ PPOCRLabel  # [Normal mode] for [detection + recognition] labeling
 PPOCRLabel --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
 ```
 
-#### 1.2.2 Build and Install the Whl Package Locally
-
-```bash
-cd PaddleOCR/PPOCRLabel
-python3 setup.py bdist_wheel
-pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl
-```
-
-#### 1.2.3 Run PPOCRLabel by Python Script
+#### 1.2.2 Run PPOCRLabel by Python Script
+If you modify the PPOCRLabel file (for example, specifying a new built-in model), it will be more convenient to see the results by running the Python script. If you still want to start with the whl package, you need to uninstall the whl package in the current environment and then recompile it according to the next section.
 
 ```bash
 cd ./PPOCRLabel  # Switch to the PPOCRLabel directory
@@ -104,6 +97,13 @@ python PPOCRLabel.py  # [Normal mode] for [detection + recognition] labeling
 python PPOCRLabel.py --kie True # [KIE mode] for [detection + recognition + keyword extraction] labeling
 ```
 
+#### 1.2.3 Build and Install the Whl Package Locally
+Compile and install a new whl package, where 1.0.2 is the version number, you can specify the new version in 'setup.py'.
+```bash
+cd PaddleOCR/PPOCRLabel
+python3 setup.py bdist_wheel
+pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl
+```
 
 
 ## 2. Usage

PPOCRLabel/README_ch.md

@@ -86,28 +86,26 @@ PPOCRLabel --lang ch --kie True  # 启动 【KIE 模式】，用于打【检测+
 
 > 如果上述安装出现问题，可以参考3.6节 错误提示
 
-#### 1.2.2 本地构建whl包并安装
+#### 1.2.2 通过Python脚本运行PPOCRLabel
+
+如果您对PPOCRLabel文件有所更改（例如指定新的内置模型），通过Python脚本运行会更加方便的看到更改的结果。如果仍然需要通过whl包启动，则需要先卸载当前环境中的whl包，然后参考下节重新编译whl包。
 
 ```bash
-cd PaddleOCR/PPOCRLabel
-python3 setup.py bdist_wheel 
-pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple
+cd ./PPOCRLabel  # 切换到PPOCRLabel目录
+python PPOCRLabel.py --lang ch
 ```
 
-#### 1.2.3 通过Python脚本运行PPOCRLabel
+#### 1.2.3 本地构建whl包并安装
 
-如果您对PPOCRLabel文件有所更改，通过Python脚本运行会更加方面的看到更改的结果
+编译与安装新的whl包，其中1.0.2为版本号，可在 `setup.py` 中指定新版本。
 
 ```bash
-cd ./PPOCRLabel  # 切换到PPOCRLabel目录
-
-# 选择标签模式来启动
-python PPOCRLabel.py --lang ch  # 启动【普通模式】，用于打【检测+识别】场景的标签
-python PPOCRLabel.py --lang ch --kie True  # 启动 【KIE 模式】，用于打【检测+识别+关键字提取】场景的标签
+cd PaddleOCR/PPOCRLabel
+python3 setup.py bdist_wheel 
+pip3 install dist/PPOCRLabel-1.0.2-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple
 ```
 
 
-
 ## 2. 使用
 
 ### 2.1 操作步骤

deploy/cpp_infer/include/args.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 <gflags/gflags.h>
+
+// common args
+DECLARE_bool(use_gpu);
+DECLARE_bool(use_tensorrt);
+DECLARE_int32(gpu_id);
+DECLARE_int32(gpu_mem);
+DECLARE_int32(cpu_threads);
+DECLARE_bool(enable_mkldnn);
+DECLARE_string(precision);
+DECLARE_bool(benchmark);
+DECLARE_string(output);
+DECLARE_string(image_dir);
+DECLARE_string(type);
+// detection related
+DECLARE_string(det_model_dir);
+DECLARE_int32(max_side_len);
+DECLARE_double(det_db_thresh);
+DECLARE_double(det_db_box_thresh);
+DECLARE_double(det_db_unclip_ratio);
+DECLARE_bool(use_dilation);
+DECLARE_string(det_db_score_mode);
+DECLARE_bool(visualize);
+// classification related
+DECLARE_bool(use_angle_cls);
+DECLARE_string(cls_model_dir);
+DECLARE_double(cls_thresh);
+DECLARE_int32(cls_batch_num);
+// recognition related
+DECLARE_string(rec_model_dir);
+DECLARE_int32(rec_batch_num);
+DECLARE_string(rec_char_dict_path);
+// forward related
+DECLARE_bool(det);
+DECLARE_bool(rec);
+DECLARE_bool(cls);

deploy/cpp_infer/include/ocr_cls.h

@@ -42,7 +42,8 @@ public:
                       const int &gpu_id, const int &gpu_mem,
                       const int &cpu_math_library_num_threads,
                       const bool &use_mkldnn, const double &cls_thresh,
-                      const bool &use_tensorrt, const std::string &precision) {
+                      const bool &use_tensorrt, const std::string &precision,
+                      const int &cls_batch_num) {
     this->use_gpu_ = use_gpu;
     this->gpu_id_ = gpu_id;
     this->gpu_mem_ = gpu_mem;
@@ -52,14 +53,17 @@ public:
     this->cls_thresh = cls_thresh;
     this->use_tensorrt_ = use_tensorrt;
     this->precision_ = precision;
+    this->cls_batch_num_ = cls_batch_num;
 
     LoadModel(model_dir);
   }
+  double cls_thresh = 0.9;
 
   // Load Paddle inference model
   void LoadModel(const std::string &model_dir);
 
-  cv::Mat Run(cv::Mat &img);
+  void Run(std::vector<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

@@ -45,8 +45,9 @@ public:
                       const double &det_db_thresh,
                       const double &det_db_box_thresh,
                       const double &det_db_unclip_ratio,
-                      const bool &use_polygon_score, const bool &use_dilation,
-                      const bool &use_tensorrt, const std::string &precision) {
+                      const std::string &det_db_score_mode,
+                      const bool &use_dilation, const bool &use_tensorrt,
+                      const std::string &precision) {
     this->use_gpu_ = use_gpu;
     this->gpu_id_ = gpu_id;
     this->gpu_mem_ = gpu_mem;
@@ -58,7 +59,7 @@ public:
     this->det_db_thresh_ = det_db_thresh;
     this->det_db_box_thresh_ = det_db_box_thresh;
     this->det_db_unclip_ratio_ = det_db_unclip_ratio;
-    this->use_polygon_score_ = use_polygon_score;
+    this->det_db_score_mode_ = det_db_score_mode;
     this->use_dilation_ = use_dilation;
 
     this->use_tensorrt_ = use_tensorrt;
@@ -72,7 +73,7 @@ public:
 
   // Run predictor
   void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes,
-           std::vector<double> *times);
+           std::vector<double> &times);
 
 private:
   std::shared_ptr<Predictor> predictor_;
@@ -88,7 +89,7 @@ private:
   double det_db_thresh_ = 0.3;
   double det_db_box_thresh_ = 0.5;
   double det_db_unclip_ratio_ = 2.0;
-  bool use_polygon_score_ = false;
+  std::string det_db_score_mode_ = "slow";
   bool use_dilation_ = false;
 
   bool visualize_ = true;

deploy/cpp_infer/include/ocr_rec.h

@@ -30,7 +30,6 @@
 #include <numeric>
 
 #include <include/ocr_cls.h>
-#include <include/postprocess_op.h>
 #include <include/preprocess_op.h>
 #include <include/utility.h>
 
@@ -68,7 +67,7 @@ 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_;
@@ -93,9 +92,6 @@ private:
   Normalize normalize_op_;
   PermuteBatch permute_op_;
 
-  // post-process
-  PostProcessor post_processor_;
-
 }; // class CrnnRecognizer
 
 } // namespace PaddleOCR

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/postprocess_op.h

@@ -56,7 +56,7 @@ public:
   std::vector<std::vector<std::vector<int>>>
   BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
                   const float &box_thresh, const float &det_db_unclip_ratio,
-                  const bool &use_polygon_score);
+                  const std::string &det_db_score_mode);
 
   std::vector<std::vector<std::vector<int>>>
   FilterTagDetRes(std::vector<std::vector<std::vector<int>>> boxes,

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|
@@ -267,7 +317,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 |det_db_thresh|float|0.3|用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显|
 |det_db_box_thresh|float|0.5|DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小|
 |det_db_unclip_ratio|float|1.6|表示文本框的紧致程度，越小则文本框更靠近文本|
-|use_polygon_score|bool|false|是否使用多边形框计算bbox score，false表示使用矩形框计算。矩形框计算速度更快，多边形框对弯曲文本区域计算更准确。|
+|det_db_score_mode|string|slow|slow:使用多边形框计算bbox score，fast:使用矩形框计算。矩形框计算速度更快，多边形框对弯曲文本区域计算更准确。|
 |visualize|bool|true|是否对结果进行可视化，为1时，预测结果会保存在`output`字段指定的文件夹下和输入图像同名的图像上。|
 
 - 方向分类器相关
@@ -277,6 +327,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 |use_angle_cls|bool|false|是否使用方向分类器|
 |cls_model_dir|string|-|方向分类器inference model地址|
 |cls_thresh|float|0.9|方向分类器的得分阈值|
+|cls_batch_num|int|1|方向分类器batchsize|
 
 - 识别模型相关
 
@@ -284,15 +335,22 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 | :---: | :---: | :---: | :---: |
 |rec_model_dir|string|-|识别模型inference model地址|
 |rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|字典文件|
+|rec_batch_num|int|6|识别模型batchsize|
 
 
 * PaddleOCR也支持多语言的预测，更多支持的语言和模型可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分，如果希望进行多语言预测，只需将修改`rec_char_dict_path`（字典文件路径）以及`rec_model_dir`（inference模型路径）字段即可。
 
 最终屏幕上会输出检测结果如下。
 
-<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|
@@ -260,7 +304,7 @@ More parameters are as follows,
 |det_db_thresh|float|0.3|Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result|
 |det_db_box_thresh|float|0.5|DB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate|
 |det_db_unclip_ratio|float|1.6|Indicates the compactness of the text box, the smaller the value, the closer the text box to the text|
-|use_polygon_score|bool|false|Whether to use polygon box to calculate bbox score, false means to use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.|
+|det_db_score_mode|string|slow| slow: use polygon box to calculate bbox score, fast: use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.|
 |visualize|bool|true|Whether to visualize the results，when it is set as true, the prediction results will be saved in the folder specified by the `output` field on an image with the same name as the input image.|
 
 - Classifier related parameters
@@ -270,6 +314,7 @@ More parameters are as follows,
 |use_angle_cls|bool|false|Whether to use the direction classifier|
 |cls_model_dir|string|-|Address of direction classifier inference model|
 |cls_thresh|float|0.9|Score threshold of the  direction classifier|
+|cls_batch_num|int|1|batch size of classifier|
 
 - Recognition related parameters
 
@@ -277,15 +322,22 @@ More parameters are as follows,
 | --- | --- | --- | --- |
 |rec_model_dir|string|-|Address of recognition inference model|
 |rec_char_dict_path|string|../../ppocr/utils/ppocr_keys_v1.txt|dictionary file|
+|rec_batch_num|int|6|batch size of recognition|
 
 * Multi-language inference is also supported in PaddleOCR, you can refer to [recognition tutorial](../../doc/doc_en/recognition_en.md) for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of `rec_char_dict_path` and `rec_model_dir`.
 
 
 The detection results will be shown on the screen, which is as follows.
 
-<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

+// 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 <gflags/gflags.h>
+
+// common args
+DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
+DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
+DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
+DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU.");
+DEFINE_int32(cpu_threads, 10, "Num of threads with CPU.");
+DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU.");
+DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8");
+DEFINE_bool(benchmark, false, "Whether use benchmark.");
+DEFINE_string(output, "./output/", "Save benchmark log path.");
+DEFINE_string(image_dir, "", "Dir of input image.");
+DEFINE_string(
+    type, "ocr",
+    "Perform ocr or structure, the value is selected in ['ocr','structure'].");
+// detection related
+DEFINE_string(det_model_dir, "", "Path of det inference model.");
+DEFINE_int32(max_side_len, 960, "max_side_len of input image.");
+DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh.");
+DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh.");
+DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio.");
+DEFINE_bool(use_dilation, false, "Whether use the dilation on output map.");
+DEFINE_string(det_db_score_mode, "slow", "Whether use polygon score.");
+DEFINE_bool(visualize, true, "Whether show the detection results.");
+// classification related
+DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls.");
+DEFINE_string(cls_model_dir, "", "Path of cls inference model.");
+DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
+DEFINE_int32(cls_batch_num, 1, "cls_batch_num.");
+// recognition related
+DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
+DEFINE_int32(rec_batch_num, 6, "rec_batch_num.");
+DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt",
+              "Path of dictionary.");
+
+// ocr forward related
+DEFINE_bool(det, true, "Whether use det in forward.");
+DEFINE_bool(rec, true, "Whether use rec in forward.");
+DEFINE_bool(cls, false, "Whether use cls in forward.");
\ No newline at end of file

deploy/cpp_infer/src/main.cpp

@@ -11,273 +11,19 @@
 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 // See the License for the specific language governing permissions and
 // limitations under the License.
-
-#include "omp.h"
 #include "opencv2/core.hpp"
 #include "opencv2/imgcodecs.hpp"
 #include "opencv2/imgproc.hpp"
-#include <chrono>
-#include <iomanip>
 #include <iostream>
-#include <ostream>
-#include <sys/stat.h>
 #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/utility.h>
-#include <sys/stat.h>
-
-#include "auto_log/autolog.h"
-#include <gflags/gflags.h>
+#include <include/args.h>
+#include <include/paddleocr.h>
 
-DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
-DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
-DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU.");
-DEFINE_int32(cpu_threads, 10, "Num of threads with CPU.");
-DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU.");
-DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
-DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8");
-DEFINE_bool(benchmark, false, "Whether use benchmark.");
-DEFINE_string(output, "./output/", "Save benchmark log path.");
-// detection related
-DEFINE_string(image_dir, "", "Dir of input image.");
-DEFINE_string(det_model_dir, "", "Path of det inference model.");
-DEFINE_int32(max_side_len, 960, "max_side_len of input image.");
-DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh.");
-DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh.");
-DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio.");
-DEFINE_bool(use_polygon_score, false, "Whether use polygon score.");
-DEFINE_bool(use_dilation, false, "Whether use the dilation on output map.");
-DEFINE_bool(visualize, true, "Whether show the detection results.");
-// classification related
-DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls.");
-DEFINE_string(cls_model_dir, "", "Path of cls inference model.");
-DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
-// recognition related
-DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
-DEFINE_int32(rec_batch_num, 6, "rec_batch_num.");
-DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt",
-              "Path of dictionary.");
-
-using namespace std;
-using namespace cv;
 using namespace PaddleOCR;
 
-static bool PathExists(const std::string &path) {
-#ifdef _WIN32
-  struct _stat buffer;
-  return (_stat(path.c_str(), &buffer) == 0);
-#else
-  struct stat buffer;
-  return (stat(path.c_str(), &buffer) == 0);
-#endif // !_WIN32
-}
-
-int main_det(std::vector<cv::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_use_polygon_score, FLAGS_use_dilation,
-                 FLAGS_use_tensorrt, FLAGS_precision);
-
-  if (!PathExists(FLAGS_output)) {
-    mkdir(FLAGS_output.c_str(), 0777);
-  }
-
-  for (int i = 0; i < cv_all_img_names.size(); ++i) {
-    if (!FLAGS_benchmark) {
-      cout << "The predict img: " << cv_all_img_names[i] << endl;
-    }
-
-    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
-    if (!srcimg.data) {
-      std::cerr << "[ERROR] image read failed! image path: "
-                << cv_all_img_names[i] << endl;
-      exit(1);
-    }
-    std::vector<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++) {
-        for (int m = 0; m < boxes[n].size(); m++) {
-          cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' ';
-        }
-      }
-      cout << endl;
-    }
-  }
-
-  if (FLAGS_benchmark) {
-    AutoLogger autolog("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
-                       FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
-                       FLAGS_precision, time_info, cv_all_img_names.size());
-    autolog.report();
-  }
-  return 0;
-}
-
-int main_rec(std::vector<cv::String> cv_all_img_names) {
-  std::vector<double> time_info = {0, 0, 0};
-
-  std::string rec_char_dict_path = FLAGS_rec_char_dict_path;
-  if (FLAGS_benchmark)
-    rec_char_dict_path = FLAGS_rec_char_dict_path.substr(6);
-  cout << "label file: " << rec_char_dict_path << endl;
-
-  CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
-                     FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
-                     rec_char_dict_path, FLAGS_use_tensorrt, FLAGS_precision,
-                     FLAGS_rec_batch_num);
-
-  std::vector<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_use_polygon_score, FLAGS_use_dilation,
-                 FLAGS_use_tensorrt, FLAGS_precision);
-
-  Classifier *cls = nullptr;
-  if (FLAGS_use_angle_cls) {
-    cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
-                         FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
-                         FLAGS_cls_thresh, FLAGS_use_tensorrt, FLAGS_precision);
-  }
-
-  std::string rec_char_dict_path = FLAGS_rec_char_dict_path;
-  if (FLAGS_benchmark)
-    rec_char_dict_path = FLAGS_rec_char_dict_path.substr(6);
-  cout << "label file: " << rec_char_dict_path << endl;
-
-  CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
-                     FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
-                     rec_char_dict_path, FLAGS_use_tensorrt, FLAGS_precision,
-                     FLAGS_rec_batch_num);
-
-  for (int i = 0; i < cv_all_img_names.size(); ++i) {
-    cout << "The predict img: " << cv_all_img_names[i] << endl;
-
-    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
-    if (!srcimg.data) {
-      std::cerr << "[ERROR] image read failed! image path: "
-                << cv_all_img_names[i] << endl;
-      exit(1);
-    }
-    // det
-    std::vector<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/ "
@@ -285,7 +31,7 @@ void check_params(char *mode) {
       exit(1);
     }
   }
-  if (strcmp(mode, "rec") == 0) {
+  if (FLAGS_rec) {
     if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) {
       std::cout << "Usage[rec]: ./ppocr "
                    "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
@@ -293,19 +39,10 @@ void check_params(char *mode) {
       exit(1);
     }
   }
-  if (strcmp(mode, "system") == 0) {
-    if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() ||
-         FLAGS_image_dir.empty()) ||
-        (FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) {
-      std::cout << "Usage[system without angle cls]: ./ppocr "
-                   "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
-                << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
-                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
-      std::cout << "Usage[system with angle cls]: ./ppocr "
-                   "--det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
-                << "--use_angle_cls=true "
-                << "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
-                << "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
+  if (FLAGS_cls && FLAGS_use_angle_cls) {
+    if (FLAGS_cls_model_dir.empty() || FLAGS_image_dir.empty()) {
+      std::cout << "Usage[cls]: ./ppocr "
+                << "--cls_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
                 << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
       exit(1);
     }
@@ -318,19 +55,11 @@ void check_params(char *mode) {
 }
 
 int main(int argc, char **argv) {
-  if (argc <= 1 ||
-      (strcmp(argv[1], "det") != 0 && strcmp(argv[1], "rec") != 0 &&
-       strcmp(argv[1], "system") != 0)) {
-    std::cout << "Please choose one mode of [det, rec, system] !" << std::endl;
-    return -1;
-  }
-  std::cout << "mode: " << argv[1] << endl;
-
   // Parsing command-line
   google::ParseCommandLineFlags(&argc, &argv, true);
-  check_params(argv[1]);
+  check_params();
 
-  if (!PathExists(FLAGS_image_dir)) {
+  if (!Utility::PathExists(FLAGS_image_dir)) {
     std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir
               << endl;
     exit(1);
@@ -340,13 +69,37 @@ int main(int argc, char **argv) {
   cv::glob(FLAGS_image_dir, cv_all_img_names);
   std::cout << "total images num: " << cv_all_img_names.size() << endl;
 
-  if (strcmp(argv[1], "det") == 0) {
-    return main_det(cv_all_img_names);
-  }
-  if (strcmp(argv[1], "rec") == 0) {
-    return main_rec(cv_all_img_names);
-  }
-  if (strcmp(argv[1], "system") == 0) {
-    return main_system(cv_all_img_names);
+  PaddleOCR::PaddleOCR ocr = PaddleOCR::PaddleOCR();
+
+  std::vector<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) {
+    if (FLAGS_benchmark) {
+      cout << cv_all_img_names[i] << '\t';
+      for (int n = 0; n < ocr_results[i].size(); n++) {
+        for (int m = 0; m < ocr_results[i][n].box.size(); m++) {
+          cout << ocr_results[i][n].box[m][0] << ' '
+               << ocr_results[i][n].box[m][1] << ' ';
+        }
+      }
+      cout << endl;
+    } else {
+      cout << cv_all_img_names[i] << "\n";
+      Utility::print_result(ocr_results[i]);
+      if (FLAGS_visualize && FLAGS_det) {
+        cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
+        if (!srcimg.data) {
+          std::cerr << "[ERROR] image read failed! image path: "
+                    << cv_all_img_names[i] << endl;
+          exit(1);
+        }
+        std::string file_name = Utility::basename(cv_all_img_names[i]);
+
+        Utility::VisualizeBboxes(srcimg, ocr_results[i],
+                                 FLAGS_output + "/" + file_name);
+      }
+      cout << "***************************" << endl;
+    }
   }
 }

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{};
 
@@ -161,20 +161,19 @@ void DBDetector::Run(cv::Mat &img,
 
   boxes = post_processor_.BoxesFromBitmap(
       pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
-      this->use_polygon_score_);
+      this->det_db_score_mode_);
 
   boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
   auto postprocess_end = std::chrono::steady_clock::now();
-  std::cout << "Detected boxes num: " << boxes.size() << endl;
 
   std::chrono::duration<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;
+      if (!FLAGS_benchmark) {
+        cout << "predict img: " << cv_all_img_names[i] << endl;
+      }
+
+      cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
+      if (!srcimg.data) {
+        std::cerr << "[ERROR] image read failed! image path: "
+                  << cv_all_img_names[i] << endl;
+        exit(1);
+      }
+      // det
+      this->det(srcimg, ocr_result, time_info_det);
+      // crop image
+      std::vector<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/postprocess_op.cpp

@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <include/clipper.h>
 #include <include/postprocess_op.h>
-#include <include/clipper.cpp>
 
 namespace PaddleOCR {
 
@@ -187,23 +187,22 @@ float PostProcessor::PolygonScoreAcc(std::vector<cv::Point> contour,
   cv::Mat mask;
   mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
 
+  cv::Point *rook_point = new cv::Point[contour.size()];
 
-  cv::Point* rook_point = new cv::Point[contour.size()];
-   
   for (int i = 0; i < contour.size(); ++i) {
     rook_point[i] = cv::Point(int(box_x[i]) - xmin, int(box_y[i]) - ymin);
   }
   const cv::Point *ppt[1] = {rook_point};
   int npt[] = {int(contour.size())};
 
-
   cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
 
   cv::Mat croppedImg;
-  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg);
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
+      .copyTo(croppedImg);
   float score = cv::mean(croppedImg, mask)[0];
 
-  delete []rook_point;
+  delete[] rook_point;
   return score;
 }
 
@@ -247,7 +246,7 @@ float PostProcessor::BoxScoreFast(std::vector<std::vector<float>> box_array,
 
 std::vector<std::vector<std::vector<int>>> PostProcessor::BoxesFromBitmap(
     const cv::Mat pred, const cv::Mat bitmap, const float &box_thresh,
-    const float &det_db_unclip_ratio, const bool &use_polygon_score) {
+    const float &det_db_unclip_ratio, const std::string &det_db_score_mode) {
   const int min_size = 3;
   const int max_candidates = 1000;
 
@@ -281,7 +280,7 @@ std::vector<std::vector<std::vector<int>>> PostProcessor::BoxesFromBitmap(
     }
 
     float score;
-    if (use_polygon_score)
+    if (det_db_score_mode == "slow")
       /* compute using polygon*/
       score = PolygonScoreAcc(contours[_i], pred);
     else